1. AI for Scientific Comprehension

1.1 Textual Scientific Comprehension

• Open-retrieval conversational question answering, Qu et al.,
• A non-factoid question-answering taxonomy, Bolotova et al.,
• How Well Do Large Language Models Extract Keywords? A Systematic Evaluation on Scientific Corpora, Mansour et al.,

1.1.1 Semi-Automatic Scientific Comprehension

• Scholarchemqa: Unveiling the power of language models in chemical research question answering, Chen et al.,
• Evaluating and Training Long-Context Large Language Models for Question Answering on Scientific Papers, Hilgert et al.,
• Are plain language summaries more readable than scientific abstracts? Evidence from six biomedical and life sciences journals, Wen et al.,

Human-Guided Scientific Comprehension

• Clam: Selective clarification for ambiguous questions with generative language models, Kuhn et al.,
• Clarify when necessary: Resolving ambiguity through interaction with lms, Zhang et al.,
• Empowering language models with active inquiry for deeper understanding, Pang et al.,
• Iqa-eval: Automatic evaluation of human-model interactive question answering, Li et al.,
• The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search, Yamada et al.,
• Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation, Yang et al.,

Tool-Augmented Scientific Comprehension

• CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding, Wright et al.,
• Scienceqa: A novel resource for question answering on scholarly articles, Saikh et al.,
• Human and technological infrastructures of fact-checking, Juneja et al.,
• Paperqa: Retrieval-augmented generative agent for scientific research, Lala et al.,
• Efficacy analysis of online artificial intelligence fact-checking tools, Hartley et al.,
• Language agents achieve superhuman synthesis of scientific knowledge, Skarlinski et al.,
• Graphusion: a RAG framework for Knowledge Graph Construction with a global perspective, Yang et al.,
• SciAgent: Tool-augmented Language Models for Scientific Reasoning, Ma et al.,
• Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks, Gosmar et al.,
• MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation, Kim et al.,
• Towards reasoning era: A survey of long chain-of-thought for reasoning large language models, Chen et al.,
• Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering, Chu et al.,
• CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models, Zhang et al.,

Self-guided Scientific Comprehension

• Boolq: Exploring the surprising difficulty of natural yes/no questions, Clark et al.,
• SciBERT: A Pretrained Language Model for Scientific Text, Beltagy et al.,
• CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice, Raza et al.,
• Quaser: Question answering with scalable extractive rationalization, Ghoshal et al.,
• Spaceqa: Answering questions about the design of space missions and space craft concepts, Garcia-Silva et al.,
• What if: Generating code to answer simulation questions in chemistry texts, Peretz et al.,
• Biomedlm: A 2.7 b parameter language model trained on biomedical text, Bolton et al.,
• Scifibench: Benchmarking large multimodal models for scientific figure interpretation, Roberts et al.,
• Scholarchemqa: Unveiling the power of language models in chemical research question answering, Chen et al.,
• Mmsci: A dataset for graduate-level multi-discipline multimodal scientific understanding, Li et al.,
• Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models, Li et al.,
• What are the essential factors in crafting effective long context multi-hop instruction datasets? insights and best practices, Chen et al.,
• Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study, Rostam et al.,
• L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?, Tang et al.,
• Toward expert-level medical question answering with large language models, Singhal et al.,
• A comprehensive survey on long context language modeling, Liu et al.,
• A survey on transformer context extension: Approaches and evaluation, Liu et al.,

• Scholarchemqa: Unveiling the power of language models in chemical research question answering, Chen et al.,
• Evaluating and Training Long-Context Large Language Models for Question Answering on Scientific Papers, Hilgert et al.,
• Are plain language summaries more readable than scientific abstracts? Evidence from six biomedical and life sciences journals, Wen et al.,

1.1.2 Full-Automatic Scientific Comprehension

Summarization-guided Automatic Scientific Comprehension

• Straight from the scientist's mouth—plain language summaries promote laypeople's comprehension and knowledge acquisition when reading about individual research findings in psychology, Kerwer et al.,
• Hierarchical attention graph for scientific document summarization in global and local level, Zhao et al.,
• Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?, Fonseca et al.,
• Autonomous LLM-Driven Research—from Data to Human-Verifiable Research Papers, Ifargan et al.,

Self-Questioning & Self-Reflection Automatic Scientific Comprehension

• Large language models can self-improve, Huang et al.,
• Selfcheck: Using llms to zero-shot check their own step-by-step reasoning, Miao et al.,
• Enabling Language Models to Implicitly Learn Self-Improvement, Wang et al.,
• Sciglm: Training scientific language models with self-reflective instruction annotation and tuning, Zhang et al.,
• Generating Multiple Choice Questions from Scientific Literature via Large Language Models, Luo et al.,
• SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation, Wan et al.,
• Recursive introspection: Teaching language model agents how to self-improve, Qu et al.,
• Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models, Song et al.,
• FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights, Yu et al.,
• Introspective Growth: Automatically Advancing LLM Expertise in Technology Judgment, Wu et al.,

• Open-retrieval conversational question answering, Qu et al.,
• A non-factoid question-answering taxonomy, Bolotova et al.,
• How Well Do Large Language Models Extract Keywords? A Systematic Evaluation on Scientific Corpora, Mansour et al.,

1.2 Table & Chart Scientific Comprehension

• How well do large language models understand tables in materials science?, Circi et al.,
• ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models, Newman et al.,
• Sciverse: Unveiling the knowledge comprehension and visual reasoning of lmms on multi-modal scientific problems, Guo et al.,

1.2.1 Table Understanding

• A survey on table-and-text hybridqa: Concepts, methods, challenges and future directions, Wang et al.,
• Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding, Wang et al.,
• Improving demonstration diversity by human-free fusing for text-to-SQL, Wang et al.,
• Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study, Sui et al.,
• Multimodal Table Understanding, Zheng et al.,
• Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding, Ji et al.,
• Tablemaster: A recipe to advance table understanding with language models, Cao et al.,
• A survey of table reasoning with large language models, Zhang et al.,
• The Mighty ToRR: A Benchmark for Table Reasoning and Robustness, Ashury-Tahan et al.,
• Tablebench: A comprehensive and complex benchmark for table question answering, Wu et al.,

1.2.2 Chart Understanding

• Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning, Meng et al.,
• SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers, Pramanick et al.,
• ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning, Masry et al.,
• ChartAssistant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning, Meng et al.,
• SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark, Liang et al.,
• Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models, Li et al.,
• SynChart: Synthesizing Charts from Language Models, Liu et al.,
• NovaChart: A Large-scale Dataset towards Chart Understanding and Generation of Multimodal Large Language Models, Hu et al.,
• ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild, Masry et al.,
• ChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding, Huang et al.,
• Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework, Yang et al.,

• How well do large language models understand tables in materials science?, Circi et al.,
• ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models, Newman et al.,
• Sciverse: Unveiling the knowledge comprehension and visual reasoning of lmms on multi-modal scientific problems, Guo et al.,

2. AI for Academic Survey

• Pre-writing: The stage of discovery in the writing process, Rohman et al.,

2.1 Related Work Retrieval

• Paper recommender systems: a literature survey, Beel et al.,
• A Review on Personalized Academic Paper Recommendation., Li et al.,
• Insights into relevant knowledge extraction techniques: a comprehensive review, Shahid et al.,
• A survey on rag meeting llms: Towards retrieval-augmented large language models, Fan et al.,

Semantic-Guided Retrieval

• Scientific paper recommendation: A survey, Bai et al.,
• SPLADE v2: Sparse lexical and expansion model for information retrieval, Formal et al.,
• Scientific paper recommendation systems: a literature review of recent publications, Kreutz et al.,
• Clinical Trial Retrieval via Multi-grained Similarity Learning, Luo et al.,
• Related Work and Citation Text Generation: A Survey, Li et al.,
• MIR: Methodology Inspiration Retrieval for Scientific Research Problems, Garikaparthi et al.,

Graph-Guided Retrieval

• From who you know to what you read: Augmenting scientific recommendations with implicit social networks, Kang et al.,
• Comlittee: Literature discovery with personal elected author committees, Kang et al.,
• Citationsum: Citation-aware graph contrastive learning for scientific paper summarization, Luo et al.,
• Explaining relationships among research papers, Li et al.,
• KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction, Boylan et al.,
• An academic recommender system on large citation data based on clustering, graph modeling and deep learning, Stergiopoulos et al.,
• ArZiGo: A recommendation system for scientific articles, Pinedo et al.,
• Graphusion: a RAG framework for Knowledge Graph Construction with a global perspective, Yang et al.,
• Taxonomy Tree Generation from Citation Graph, Hu et al.,
• Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model, Ye et al.,
• Docs2KG: A Human-LLM Collaborative Approach to Unified Knowledge Graph Construction from Heterogeneous Documents, Sun et al.,

LLM-Augmented Retrieval

• Paperweaver: Enriching topical paper alerts by contextualizing recommended papers with user-collected papers, Lee et al.,
• Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models, Seabra et al.,
• Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG, Singh et al.,
• PaSa: An LLM Agent for Comprehensive Academic Paper Search, He et al.,
• CuriousLLM: Elevating multi-document question answering with llm-enhanced knowledge graph reasoning, Yang et al.,
• Introducing Deep Research, {OpenAI} et al.,
• LitLLMs, LLMs for Literature Review: Are we there yet?, Agarwal et al.,
• Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation, Liu et al.,
• GPT-4o Search Preview, {OpenAI} et al.,
• WebDancer: Towards Autonomous Information Seeking Agency, Wu et al.,
• Iterative self-incentivization empowers large language models as agentic searchers, Shi et al.,
• Multimodal DeepResearcher: Generating Text-Chart Interleaved Reports From Scratch with Agentic Framework, Yang et al.,
• DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents, Du et al.,
• AcademicBrowse: Benchmarking Academic Browse Ability of LLMs, Zhou et al.,

• Paper recommender systems: a literature survey, Beel et al.,
• A Review on Personalized Academic Paper Recommendation., Li et al.,
• Insights into relevant knowledge extraction techniques: a comprehensive review, Shahid et al.,
• A survey on rag meeting llms: Towards retrieval-augmented large language models, Fan et al.,

2.2 Overview Report Generation

• Towards automated related work summarization, Hoang et al.,

2.2.1 Research Roadmap Mapping

• Hierarchical catalogue generation for literature review: a benchmark, Zhu et al.,
• Assisting in writing wikipedia-like articles from scratch with large language models, Shao et al.,
• Chime: Llm-assisted hierarchical organization of scientific studies for literature review support, Hsu et al.,
• Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature, Katz et al.,
• Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning, Zhuang et al.,
• Artificial intelligence for literature reviews: Opportunities and challenges, Bolanos et al.,
• Taxonomy Tree Generation from Citation Graph, Hu et al.,
• LLMs for Literature Review: Are we there yet?, Agarwal et al.,
• Autosurvey: Large language models can automatically write surveys, Wang et al.,
• SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing, Yan et al.,
• Towards reasoning era: A survey of long chain-of-thought for reasoning large language models, Chen et al.,
• Ai2 Scholar QA: Organized Literature Synthesis with Attribution, Singh et al.,

2.2.2 Section-level Related Work Generation

• Towards automated related work summarization, Hoang et al.,
• Capturing relations between scientific papers: An abstractive model for related work section generation, Chen et al.,
• Target-aware abstractive related work generation with contrastive learning, Chen et al.,
• The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study, Ovelman et al.,
• Related Work and Citation Text Generation: A Survey, Li et al.,
• 376 Using a large language model to create lay summaries of clinical study descriptions, Kaiser et al.,
• Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation, Liu et al.,

Extractive Related Work.

• Towards automated related work summarization, Hoang et al.,
• Automatic generation of related work sections in scientific papers: an optimization approach, Hu et al.,
• Neural related work summarization with a joint context-driven attention mechanism, Wang et al.,
• Automatic generation of related work through summarizing citations, Chen et al.,
• Toc-rwg: Explore the combination of topic model and citation information for automatic related work generation, Wang et al.,
• Automatic Related Work Section Generation by Sentence Extraction and Reordering., Deng et al.,

Generative Related Work.

• Neural related work summarization with a joint context-driven attention mechanism, Wang et al.,
• Automated lay language summarization of biomedical scientific reviews, Guo et al.,
• BACO: A background knowledge-and content-based framework for citing sentence generation, Ge et al.,
• Capturing relations between scientific papers: An abstractive model for related work section generation, Chen et al.,
• Target-aware abstractive related work generation with contrastive learning, Chen et al.,
• Multi-document scientific summarization from a knowledge graph-centric view, Wang et al.,
• Controllable citation sentence generation with language models, Gu et al.,
• Causal intervention for abstractive related work generation, Liu et al.,
• Cited text spans for citation text generation, Li et al.,
• Towards a unified framework for reference retrieval and related work generation, Shi et al.,
• Explaining relationships among research papers, Li et al.,
• Shallow synthesis of knowledge in gpt-generated texts: A case study in automatic related work composition, Martin-Boyle et al.,
• Related work and citation text generation: A survey, Li et al.,
• RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization, Pu et al.,
• Reinforced Subject-Aware Graph Neural Network for Related Work Generation, Yu et al.,
• Disentangling Instructive Information from Ranked Multiple Candidates for Multi-Document Scientific Summarization, Wang et al.,
• Toward Related Work Generation with Structure and Novelty Statement, Nishimura et al.,
• Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization, Pratapa et al.,
• Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization, Achkar et al.,

• Towards automated related work summarization, Hoang et al.,
• Capturing relations between scientific papers: An abstractive model for related work section generation, Chen et al.,
• Target-aware abstractive related work generation with contrastive learning, Chen et al.,
• The use of a large language model to create plain language summaries of evidence reviews in healthcare: A feasibility study, Ovelman et al.,
• Related Work and Citation Text Generation: A Survey, Li et al.,
• 376 Using a large language model to create lay summaries of clinical study descriptions, Kaiser et al.,
• Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation, Liu et al.,

2.2.3 Document-level Survey Generation

• Analyzing the past to prepare for the future: Writing a literature review, Webster et al.,
• Hierarchical catalogue generation for literature review: a benchmark, Zhu et al.,
• Bio-sieve: exploring instruction tuning large language models for systematic review automation, Robinson et al.,
• Litllm: A toolkit for scientific literature review, Agarwal et al.,
• Assisting in writing wikipedia-like articles from scratch with large language models, Shao et al.,
• Artificial intelligence for literature reviews: Opportunities and challenges, Bolanos et al.,
• Language agents achieve superhuman synthesis of scientific knowledge, Skarlinski et al.,
• Instruct Large Language Models to Generate Scientific Literature Survey Step by Step, Lai et al.,
• Openscholar: Synthesizing scientific literature with retrieval-augmented lms, Asai et al.,
• Intelligent summaries: Will Artificial Intelligence mark the finale for biomedical literature reviews?, Galli et al.,
• Autosurvey: Large language models can automatically write surveys, Wang et al.,
• LAG: LLM agents for Leaderboard Auto Generation on Demanding, Wu et al.,
• SurveyX: Academic Survey Automation via Large Language Models, Liang et al.,
• Automating research synthesis with domain-specific large language model fine-tuning, Susnjak et al.,
• SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing, Yan et al.,

• Towards automated related work summarization, Hoang et al.,

• Pre-writing: The stage of discovery in the writing process, Rohman et al.,

3. AI for Scientific Discovery

• Scientific discovery in the age of artificial intelligence, Wang et al.,
• Beyond Benchmarking: Automated Capability Discovery via Model Self-Exploration, Lu et al.,
• AIRUS: a simple workflow for AI-assisted exploration of scientific data, Harris et al.,
• On the Rise of New Mathematical Spaces and Towards AI-Driven Scientific Discovery, Raeini et al.,
• From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models, He et al.,
• AI-Driven Discovery: The Transformative Impact of Machine Learning on Research and Development, Roy et al.,

3.1 Idea Mining

• Can Large Language Models Unlock Novel Scientific Research Ideas?, Kumar et al.,
• Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers, Si et al.,
• LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research, Gu et al.,
• Large language models for causal hypothesis generation in science, Cohrs et al.,
• Futuregen: Llm-rag approach to generate the future work of scientific article, Azher et al.,
• ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition, Liu et al.,
• Sparks of science: Hypothesis generation using structured paper data, O'Neill et al.,
• Spark: A System for Scientifically Creative Idea Generation, Sanyal et al.,
• CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature, Sternlicht et al.,
• Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation, Lin et al.,

3.1.1 Idea Mining from Internal Knowledge

• Ideas are dimes a dozen: Large language models for idea generation in innovation, Girotra et al.,
• Prompting Diverse Ideas: Increasing AI Idea Variance, Meincke et al.,
• Using Large Language Models for Idea Generation in Innovation, Meincke et al.,
• Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers, Si et al.,
• Can Large Language Models Unlock Novel Scientific Research Ideas?, Kumar et al.,
• ECM: A Unified Electronic Circuit Model for Explaining the Emergence of In-Context Learning and Chain-of-Thought in Large Language Model, Chen et al.,
• Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations, Chen et al.,
• Improving Research Idea Generation Through Data: An Empirical Investigation in Social Science, Liu et al.,
• Enhance Innovation by Boosting Idea Generation with Large Language Models, Haarmann et al.,

3.1.2 Idea Mining from External Signal

Idea Mining from External Knowledge

• Literature based discovery: models, methods, and trends, Henry et al.,
• Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network, Krenn et al.,
• A survey of large language models, Zhao et al.,
• Large language models meet nlp: A survey, Qin et al.,
• Position: data-driven discovery with large generative models, Majumder et al.,
• Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models, Gu et al.,
• Interesting scientific idea generation using knowledge graphs and llms: Evaluations with 100 research group leaders, Gu et al.,
• Scimon: Scientific inspiration machines optimized for novelty, Wang et al.,
• Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning, Buehler et al.,
• Literature meets data: A synergistic approach to hypothesis generation, Liu et al.,
• Chain of ideas: Revolutionizing research via novel idea development with llm agents, Li et al.,
• SciPIP: An LLM-based Scientific Paper Idea Proposer, Wang et al.,
• LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research, Gu et al.,
• Learning to Generate Research Idea with Dynamic Control, Li et al.,
• Graph of AI Ideas: Leveraging Knowledge Graphs and LLMs for AI Research Idea Generation, Gao et al.,
• Sparks of science: Hypothesis generation using structured paper data, O'Neill et al.,

Idea Mining from External Environment Feedback

• gpt-researcher, Assafelovic et al.,
• Mlagentbench: Evaluating language agents on machine learning experimentation, Huang et al.,
• Researchagent: Iterative research idea generation over scientific literature with large language models, Baek et al.,
• Augmenting large language models with chemistry tools, M. Bran et al.,
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration, Ni et al.,
• The virtual lab: AI agents design new SARS-CoV-2 nanobodies with experimental validation, Swanson et al.,
• Agent laboratory: Using llm agents as research assistants, Schmidgall et al.,
• LUMI-lab: a Foundation Model-Driven Autonomous Platform Enabling Discovery of New Ionizable Lipid Designs for mRNA Delivery, Cui et al.,
• Towards an AI co-scientist, Gottweis et al.,
• Zochi Technical Report, AI et al.,
• AgentRxiv: Towards Collaborative Autonomous Research, Schmidgall et al.,
• Carl Technical Report, Institute et al.,
• Ideasynth: Iterative research idea development through evolving and composing idea facets with literature-grounded feedback, Pu et al.,

3.1.3 Idea Mining from Team discussion

AI-AI Collaboration

• Large language models for automated open-domain scientific hypotheses discovery, Yang et al.,
• Exploring collaboration mechanisms for llm agents: A social psychology view, Zhang et al.,
• Acceleron: A tool to accelerate research ideation, Nigam et al.,
• Hypothesis generation with large language models, Zhou et al.,
• Researchagent: Iterative research idea generation over scientific literature with large language models, Baek et al.,
• Llm and simulation as bilevel optimizers: A new paradigm to advance physical scientific discovery, Ma et al.,
• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning, Ghafarollahi et al.,
• Two heads are better than one: A multi-agent system has the potential to improve scientific idea generation, Su et al.,
• Chain of ideas: Revolutionizing research via novel idea development with llm agents, Li et al.,
• Nova: An iterative planning and search approach to enhance novelty and diversity of llm generated ideas, Hu et al.,
• The virtual lab: AI agents design new SARS-CoV-2 nanobodies with experimental validation, Swanson et al.,
• AIGS: Generating Science from AI-Powered Automated Falsification, Liu et al.,
• Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses, Yang et al.,
• Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback, Yuan et al.,
• Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming, Lagzian et al.,
• Can Language Models Falsify? Evaluating Algorithmic Reasoning with Counterexample Creation, Sinha et al.,
• PiFlow: Principle-aware Scientific Discovery with Multi-Agent Collaboration, Pu et al.,

Human-AI Collaboration

• An Interactive Co-Pilot for Accelerated Research Ideation, Nigam et al.,
• Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination, Radensky et al.,
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration, Ni et al.,
• IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery, Garikaparthi et al.,
• Human creativity in the age of llms: Randomized experiments on divergent and convergent thinking, Kumar et al.,

• Can Large Language Models Unlock Novel Scientific Research Ideas?, Kumar et al.,
• Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers, Si et al.,
• LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research, Gu et al.,
• Large language models for causal hypothesis generation in science, Cohrs et al.,
• Futuregen: Llm-rag approach to generate the future work of scientific article, Azher et al.,
• ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition, Liu et al.,
• Sparks of science: Hypothesis generation using structured paper data, O'Neill et al.,
• Spark: A System for Scientifically Creative Idea Generation, Sanyal et al.,
• CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature, Sternlicht et al.,
• Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation, Lin et al.,

3.2 Novelty & Significance Assessment

• Does writing with language models reduce content diversity?, Padmakumar et al.,
• Greater variability in judgements of the value of novel ideas, Johnson et al.,
• How AI ideas affect the creativity, diversity, and evolution of human ideas: evidence from a large, dynamic experiment, Ashkinaze et al.,
• A content-based novelty measure for scholarly publications: A proof of concept, Wang et al.,
• Art or artifice? large language models and the false promise of creativity, Chakrabarty et al.,
• How ai processing delays foster creativity: Exploring research question co-creation with an llm-based agent, Liu et al.,
• Homogenization effects of large language models on human creative ideation, Anderson et al.,
• Shared imagination: Llms hallucinate alike, Zhou et al.,
• Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers, Si et al.,
• Supporting Assessment of Novelty of Design Problems Using Concept of Problem SAPPhIRE, Singh et al.,
• Semi-Supervised Classification With Novelty Detection Using Support Vector Machines and Linear Discriminant Analysis, Dove et al.,
• Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the Correspondence between Patent Claim and Prior Art, Ikoma et al.,
• How do Humans and Language Models Reason About Creativity? A Comparative Analysis, Laverghetta Jr et al.,
• Grapheval: A lightweight graph-based llm framework for idea evaluation, Feng et al.,
• SCI-IDEA: Context-Aware Scientific Ideation Using Token and Sentence Embeddings, Keya et al.,
• Enabling ai scientists to recognize innovation: A domain-agnostic algorithm for assessing novelty, Wang et al.,
• SC4ANM: Identifying optimal section combinations for automated novelty prediction in academic papers, Wu et al.,

3.3 Theory Analysis

3.3.1 Scientific Claim Formalization

• LF: a foundational higher-order-logic, Goodsell et al.,
• Natural Language Hypotheses in Scientific Papers and How to Tame Them: Suggested Steps for Formalizing Complex Scientific Claims, Heger et al.,
• Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning, Yan et al.,
• Sciclaimhunt: A large dataset for evidence-based scientific claim verification, Kumar et al.,
• Towards Effective Extraction and Evaluation of Factual Claims, Metropolitansky et al.,
• NSF-SciFy: Mining the NSF Awards Database for Scientific Claims, Rao et al.,
• Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks, Ganguly et al.,
• Valsci: an open-source, self-hostable literature review utility for automated large-batch scientific claim verification using large language models, Edelman et al.,

3.3.2 Scientific Evidence Collection

• MultiVerS: Improving scientific claim verification with weak supervision and full-document context, Wadden et al.,
• Missing counter-evidence renders NLP fact-checking unrealistic for misinformation, Glockner et al.,
• Investigating zero-and few-shot generalization in fact verification, Pan et al.,
• Comparing knowledge sources for open-domain scientific claim verification, Vladika et al.,
• Understanding Fine-grained Distortions in Reports of Scientific Findings, W{\"u}hrl et al.,
• Improving health question answering with reliable and time-aware evidence retrieval, Vladika et al.,
• Zero-shot scientific claim verification using LLMs and citation text, Alvarez et al.,
• Grounding fallacies misrepresenting scientific publications in evidence, Glockner et al.,
• Can foundation models actively gather information in interactive environments to test hypotheses?, Ke et al.,
• LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification, Wang et al.,
• SciClaims: An End-to-End Generative System for Biomedical Claim Analysis, Ortega et al.,

3.3.3 Scientific Verification Analysis

• Proofver: Natural logic theorem proving for fact verification, Krishna et al.,
• The state of human-centered NLP technology for fact-checking, Das et al.,
• aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions, Altuncu et al.,
• FactKG: Fact verification via reasoning on knowledge graphs, Kim et al.,
• Fact-checking complex claims with program-guided reasoning, Pan et al.,
• Prompt to be consistent is better than self-consistent? few-shot and zero-shot fact verification with pre-trained language models, Zeng et al.,
• Unsupervised Pretraining for Fact Verification by Language Model Distillation, Bazaga et al.,
• Towards llm-based fact verification on news claims with a hierarchical step-by-step prompting method, Zhang et al.,
• Characterizing and Verifying Scientific Claims: Qualitative Causal Structure is All You Need, Wu et al.,
• Can Large Language Models Detect Misinformation in Scientific News Reporting?, Cao et al.,
• What makes medical claims (un) verifiable? analyzing entity and relation properties for fact verification, W{\"u}hrl et al.,
• ClaimVer: Explainable claim-level verification and evidence attribution of text through knowledge graphs, Dammu et al.,
• Generating fact checking explanations, Atanasova et al.,
• MAGIC: Multi-Argument Generation with Self-Refinement for Domain Generalization in Automatic Fact-Checking, Kao et al.,
• Robust Claim Verification Through Fact Detection, Jafari et al.,
• Automated justification production for claim veracity in fact checking: A survey on architectures and approaches, Eldifrawi et al.,
• Enhancing natural language inference performance with knowledge graph for COVID-19 automated fact-checking in Indonesian language, Muharram et al.,
• Augmenting the Veracity and Explanations of Complex Fact Checking via Iterative Self-Revision with LLMs, Zhang et al.,
• DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts, Braun et al.,
• TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding, Ku et al.,
• Explainable Biomedical Claim Verification with Large Language Models, Liang et al.,
• Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?, GX-Chen et al.,

3.3.4 Theorem Proving

• Generative language modeling for automated theorem proving, Polu et al.,
• Draft, sketch, and prove: Guiding formal theorem provers with informal proofs, Jiang et al.,
• Hypertree proof search for neural theorem proving, Lample et al.,
• Thor: Wielding hammers to integrate language models and automated theorem provers, Jiang et al.,
• Decomposing the enigma: Subgoal-based demonstration learning for formal theorem proving, Zhao et al.,
• Dt-solver: Automated theorem proving with dynamic-tree sampling guided by proof-level value function, Wang et al.,
• Lego-prover: Neural theorem proving with growing libraries, Wang et al.,
• Baldur: Whole-proof generation and repair with large language models, First et al.,
• Mustard: Mastering uniform synthesis of theorem and proof data, Huang et al.,
• A survey on deep learning for theorem proving, Li et al.,
• Towards large language models as copilots for theorem proving in lean, Song et al.,
• Proving theorems recursively, Wang et al.,
• Deepseek-prover: Advancing theorem proving in llms through large-scale synthetic data, Xin et al.,
• Lean-star: Learning to interleave thinking and proving, Lin et al.,
• Data for mathematical copilots: Better ways of presenting proofs for machine learning, Frieder et al.,
• Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics, Zhu et al.,
• Discovering Symbolic Differential Equations with Symmetry Invariants, Yang et al.,

3.4 Scientific Experiment Conduction

• Toward machine learning optimization of experimental design, Baydin et al.,
• AI-assisted design of experiments at the frontiers of computation: methods and new perspectives, Vischia et al.,
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research, Chen et al.,
• EXP-Bench: Can AI Conduct AI Research Experiments?, Kon et al.,
• AI Scientists Fail Without Strong Implementation Capability, Zhu et al.,

3.4.1 Experiment Design

• Augmenting large language models with chemistry tools, M. Bran et al.,
• Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning, Ghafarollahi et al.,
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration, Ni et al.,
• AI-assisted design of experiments at the frontiers of computation: methods and new perspectives, Vischia et al.,
• LUMI-lab: a Foundation Model-Driven Autonomous Platform Enabling Discovery of New Ionizable Lipid Designs for mRNA Delivery, Cui et al.,
• Towards an AI co-scientist, Gottweis et al.,

Semi-Automatic Experiment Design

• AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers, Huang et al.,
• Augmenting large language models with chemistry tools, M. Bran et al.,
• Meta-Designing Quantum Experiments with Language Models, Arlt et al.,
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration, Ni et al.,
• The application of artificial intelligence-assisted technology in cultural and creative product design, Liang et al.,
• A Human-LLM Note-Taking System with Case-Based Reasoning as Framework for Scientific Discovery, Craig et al.,

Full-Automatic Experiment Design

• Researchagent: Iterative research idea generation over scientific literature with large language models, Baek et al.,
• Biodiscoveryagent: An ai agent for designing genetic perturbation experiments, Roohani et al.,
• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• The virtual lab: AI agents design new SARS-CoV-2 nanobodies with experimental validation, Swanson et al.,
• Large Language Model Assisted Experiment Design with Generative Human-Behavior Agents, Liu et al.,
• Agent laboratory: Using llm agents as research assistants, Schmidgall et al.,
• Carl Technical Report, Institute et al.,
• Zochi Technical Report, AI et al.,
• AgentRxiv: Towards Collaborative Autonomous Research, Schmidgall et al.,
• Robin: A multi-agent system for automating scientific discovery, Ghareeb et al.,

• Augmenting large language models with chemistry tools, M. Bran et al.,
• Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning, Ghafarollahi et al.,
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration, Ni et al.,
• AI-assisted design of experiments at the frontiers of computation: methods and new perspectives, Vischia et al.,
• LUMI-lab: a Foundation Model-Driven Autonomous Platform Enabling Discovery of New Ionizable Lipid Designs for mRNA Delivery, Cui et al.,
• Towards an AI co-scientist, Gottweis et al.,

3.4.2 Pre-Experiment Estimation

Evaluative Prediction

• DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation, Wu et al.,
• Physical formula enhanced multi-task learning for pharmacokinetics prediction, Li et al.,
• MASSW: A new dataset and benchmark tasks for ai-assisted scientific workflows, Zhang et al.,
• Unimatch: Universal matching from atom to task for few-shot drug discovery, Li et al.,
• LUMI-lab: a Foundation Model-Driven Autonomous Platform Enabling Discovery of New Ionizable Lipid Designs for mRNA Delivery, Cui et al.,
• Predicting Empirical AI Research Outcomes with Language Models, Wen et al.,
• Large language models surpass human experts in predicting neuroscience results, Luo et al.,

Exploratory Forecasting

• Automatic chemical design using a data-driven continuous representation of molecules, G{\'o}mez-Bombarelli et al.,
• MolGAN: An implicit generative model for small molecular graphs, De Cao et al.,
• Google DeepMind's AI Dreamed Up 380,000 New Materials. The Next Challenge Is Making Them, Barber et al.,
• Augmenting large language models with chemistry tools, M. Bran et al.,
• MASSW: A new dataset and benchmark tasks for ai-assisted scientific workflows, Zhang et al.,
• The virtual lab: AI agents design new SARS-CoV-2 nanobodies with experimental validation, Swanson et al.,
• Towards an AI co-scientist, Gottweis et al.,
• FlavorDiffusion: Modeling Food-Chemical Interactions with Diffusion, Seo et al.,
• MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated Experimental Feedback, Liu et al.,

3.4.3 Experiment Management

• Transforming science labs into automated factories of discovery, Angelopoulos et al.,
• Development of an Automated Workflow for Screening the Assembly and Host--Guest Behavior of Metal-Organic Cages Towards Accelerated Discovery, Basford et al.,
• AI Driven Experiment Calibration and Control, Britton et al.,
• Agents for self-driving laboratories applied to quantum computing, Cao et al.,
• Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors, Kvapil et al.,
• Artificial intelligence meets laboratory automation in discovery and synthesis of metal--organic frameworks: A review, Zhao et al.,
• Agents for Change: Artificial Intelligent Workflows for Quantitative Clinical Pharmacology and Translational Sciences, Shahin et al.,
• Science acceleration and accessibility with self-driving labs, Canty et al.,
• Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs, Fehlis et al.,
• Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents, Fehlis et al.,
• Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research, Hatakeyama-Sato et al.,

Open-Loop Management

• The future of self-driving laboratories: from human in the loop interactive AI to gamification, Hysmith et al.,
• Self-driving labs are the new AI asset, {Axios} et al.,
• DeepMind and BioNTech build AI lab assistants for scientific research, Times} et al.,
• Autonomous platform for solution processing of electronic polymers, Wang et al.,
• Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida, Zournas et al.,

Close-Loop Management

• Functional genomic hypothesis generation and experimentation by a robot scientist, King et al.,
• Self-driving laboratory for accelerated discovery of thin-film materials, MacLeod et al.,
• Self-driving laboratories for chemistry and materials science, Tom et al.,
• Autonomous platform for solution processing of electronic polymers, Wang et al.,
• Self-driving laboratory platform for many-objective self-optimisation of polymer nanoparticle synthesis with cloud-integrated machine learning and orthogonal online analytics, Knox et al.,

• Transforming science labs into automated factories of discovery, Angelopoulos et al.,
• Development of an Automated Workflow for Screening the Assembly and Host--Guest Behavior of Metal-Organic Cages Towards Accelerated Discovery, Basford et al.,
• AI Driven Experiment Calibration and Control, Britton et al.,
• Agents for self-driving laboratories applied to quantum computing, Cao et al.,
• Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors, Kvapil et al.,
• Artificial intelligence meets laboratory automation in discovery and synthesis of metal--organic frameworks: A review, Zhao et al.,
• Agents for Change: Artificial Intelligent Workflows for Quantitative Clinical Pharmacology and Translational Sciences, Shahin et al.,
• Science acceleration and accessibility with self-driving labs, Canty et al.,
• Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs, Fehlis et al.,
• Uncovering Bottlenecks and Optimizing Scientific Lab Workflows with Cycle Time Reduction Agents, Fehlis et al.,
• Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research, Hatakeyama-Sato et al.,

3.4.4 Experimental Conduction

Automated Machine Learning Experiment Conduction

• AIDE: Human-Level Performance on Data Science Competitions, Dominik et al.,
• Automl-gpt: Automatic machine learning with gpt, Zhang et al.,
• Automl in the age of large language models: Current challenges, future opportunities and risks, Tornede et al.,
• Opendevin: An open platform for ai software developers as generalist agents, Wang et al.,
• Mlr-copilot: Autonomous machine learning research based on large language models agents, Li et al.,
• Autokaggle: A multi-agent framework for autonomous data science competitions, Li et al.,
• Large language models orchestrating structured reasoning achieve kaggle grandmaster level, Grosnit et al.,
• MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?, Zhang et al.,
• AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage, Zhao et al.,
• Variable Extraction for Model Recovery in Scientific Literature, Liu et al.,
• AlphaEvolve: A coding agent for scientific and algorithmic discovery, Novikov et al.,

Real-world Experimental Simulation & Conduction.

• Large language models can self-improve, Huang et al.,
• Mlcopilot: Unleashing the power of large language models in solving machine learning tasks, Zhang et al.,
• Training socially aligned language models in simulated human society, Liu et al.,
• Toolllm: Facilitating large language models to master 16000+ real-world apis, Qin et al.,
• An autonomous laboratory for the accelerated synthesis of novel materials, Szymanski et al.,
• Autonomous chemical research with large language models, Boiko et al.,
• Reflexion: Language agents with verbal reinforcement learning, Shinn et al.,
• Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings, Hao et al.,
• Toolformer: Language models can teach themselves to use tools, Schick et al.,
• scGPT: toward building a foundation model for single-cell multi-omics using generative AI, Cui et al.,
• Large language model agent for hyper-parameter optimization, Liu et al.,
• MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge, Ni et al.,
• Researchagent: Iterative research idea generation over scientific literature with large language models, Baek et al.,
• Automated social science: Language models as scientist and subjects, Manning et al.,
• Crispr-gpt: An llm agent for automated design of gene-editing experiments, Huang et al.,
• Position: LLMs can’t plan, but can help planning in LLM-modulo frameworks, Kambhampati et al.,
• Augmenting large language models with chemistry tools, M. Bran et al.,
• Mlr-copilot: Autonomous machine learning research based on large language models agents, Li et al.,
• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning, Ghafarollahi et al.,
• Wrong-of-thought: An integrated reasoning framework with multi-perspective verification and wrong information, Zhang et al.,
• Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities, Zabaleta et al.,
• An automatic end-to-end chemical synthesis development platform powered by large language models, Ruan et al.,
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration, Ni et al.,
• Towards LLM-Driven Multi-Agent Pipeline for Drug Discovery: Neurodegenerative Diseases Case Study, Solovev et al.,
• From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents, Mou et al.,
• On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Evaluation Framework, Siddiqui et al.,
• PSYCHE: A Multi-faceted Patient Simulation Framework for Evaluation of Psychiatric Assessment Conversational Agents, Lee et al.,
• Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback, Yuan et al.,
• DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective, Peng et al.,
• Simulating cooperative prosocial behavior with multi-agent LLMs: Evidence and mechanisms for AI agents to inform policy decisions, Sreedhar et al.,
• Reinforcing clinical decision support through multi-agent systems and ethical ai governance, Chen et al.,
• OpenFOAMGPT 2.0: end-to-end, trustworthy automation for computational fluid dynamics, Feng et al.,
• Researchcodeagent: An llm multi-agent system for automated codification of research methodologies, Gandhi et al.,
• The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search, Yamada et al.,
• MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation, Zhang et al.,
• Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation, Hu et al.,

3.4.5 Experimental Analysis

Automated Evaluation Metrics

• Eight years of AutoML: categorisation, review and trends, Barbudo et al.,
• Efficient bayesian learning curve extrapolation using prior-data fitted networks, Adriaensen et al.,
• Automated machine learning: past, present and future, Baratchi et al.,

Theoretical Consistency Analysis

• Variable Extraction for Model Recovery in Scientific Literature, Liu et al.,
• AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage, Zhao et al.,

Exploratory Analysis

• HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation, Bian et al.,
• Table meets llm: Can large language models understand structured table data? a benchmark and empirical study, Sui et al.,
• Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning, Xing et al.,
• LLM Based Exploratory Data Analysis Using BigQuery Data Canvas, Chaudhuri et al.,

• Toward machine learning optimization of experimental design, Baydin et al.,
• AI-assisted design of experiments at the frontiers of computation: methods and new perspectives, Vischia et al.,
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research, Chen et al.,
• EXP-Bench: Can AI Conduct AI Research Experiments?, Kon et al.,
• AI Scientists Fail Without Strong Implementation Capability, Zhu et al.,

3.5 Full-Automatic Discovery

• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• Aviary: training language agents on challenging scientific tasks, Narayanan et al.,
• Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback, Yuan et al.,
• Autonomous Microscopy Experiments through Large Language Model Agents, Mandal et al.,
• Agent laboratory: Using llm agents as research assistants, Schmidgall et al.,
• Curie: Toward rigorous and automated scientific experimentation with ai agents, Kon et al.,
• DORA AI Scientist: Multi-agent Virtual Research Team for Scientific Exploration Discovery and Automated Report Generation, Naumov et al.,
• Carl Technical Report, Institute et al.,
• AgentRxiv: Towards Collaborative Autonomous Research, Schmidgall et al.,
• Zochi Technical Report, AI et al.,
• NovelSeek: When Agent Becomes the Scientist--Building Closed-Loop System from Hypothesis to Verification, Team et al.,
• AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists, Li et al.,
• VISION: A modular AI assistant for natural human-instrument interaction at scientific user facilities, Mathur et al.,

• Scientific discovery in the age of artificial intelligence, Wang et al.,
• Beyond Benchmarking: Automated Capability Discovery via Model Self-Exploration, Lu et al.,
• AIRUS: a simple workflow for AI-assisted exploration of scientific data, Harris et al.,
• On the Rise of New Mathematical Spaces and Towards AI-Driven Scientific Discovery, Raeini et al.,
• From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models, He et al.,
• AI-Driven Discovery: The Transformative Impact of Machine Learning on Research and Development, Roy et al.,

4. AI for Academic Writing

• Using artificial intelligence in academic writing and research: An essential productivity tool, Khalifa et al.,
• Human-LLM Coevolution: Evidence from Academic Writing, Geng et al.,
• Large language models penetration in scholarly writing and peer review, Zhou et al.,
• And Plato met ChatGPT: an ethical reflection on the use of chatbots in scientific research writing, with a particular focus on the social sciences, Calderon et al.,

4.1 Semi-Automatic Academic Writing

4.1.1 Assistance During Manuscript Preparation

Title Formulation and Optimization

• Personalized Graph-Based Retrieval for Large Language Models, Au et al.,
• Generating Accurate and Engaging Research Paper Titles Using NLP Techniques, Bikku et al.,
• MoDeST: A dataset for Multi Domain Scientific Title Generation, B{\"o}l{\"u}c{\"u} et al.,
• Can pre-trained language models generate titles for research papers?, Rehman et al.,

Overall Logical Structure Guidance

• LalaEval: A Holistic Human Evaluation Framework for Domain-Specific Large Language Models, Sun et al.,
• LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts, Hashemi et al.,

4.1.2 Assistance During Manuscript Writing

• Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students, Song et al.,
• Human-AI collaboration patterns in AI-assisted academic writing, Nguyen et al.,
• Patterns and Purposes: A Cross-Journal Analysis of AI Tool Usage in Academic Writing, Xu et al.,
• Divergent llm adoption and heterogeneous convergence paths in research writing, Lin et al.,
• Artificial intelligence-assisted academic writing: recommendations for ethical use, Cheng et al.,

Drawing Figures and Charts

• Text2chart: A multi-staged chart generator from natural language text, Rashid et al.,
• ChartReader: A unified framework for chart derendering and comprehension without heuristic rules, Cheng et al.,
• Figgen: Text to scientific figure generation, Rodriguez et al.,
• Automatikz: Text-guided synthesis of scientific vector graphics with tikz, Belouadi et al.,
• Scicapenter: Supporting caption composition for scientific figures with machine-generated captions and ratings, Hsu et al.,
• ChartFormer: A large vision language model for converting chart images into tactile accessible SVGs, Moured et al.,
• Figuring out Figures: Using Textual References to Caption Scientific Figures, Cao et al.,
• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification, Hogan et al.,
• ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?, Zhang et al.,
• Chartcoder: Advancing multimodal large language model for chart-to-code generation, Zhao et al.,
• Understanding How Paper Writers Use AI-Generated Captions in Figure Caption Writing, Yin et al.,
• Multi-LLM Collaborative Caption Generation in Scientific Documents, Kim et al.,
• TikZero: Zero-Shot Text-Guided Graphics Program Synthesis, Belouadi et al.,
• Enhancing Chart-to-Code Generation in Multimodal Large Language Models via Iterative Dual Preference Learning, Zhang et al.,
• StarVector: Generating scalable vector graphics code from images and text, Rodriguez et al.,
• The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search, Yamada et al.,
• How to Create Accurate Scientific Illustrations with AI in 2025, Team et al.,

Formula Transcription

• Towards Semantic Markup of Mathematical Documents via User Interaction, Vre{\v{c}}ar et al.,
• Automated LaTeX Code Generation from Handwritten Math Expressions Using Vision Transformer, Sundararaj et al.,
• LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement, Jiang et al.,

Citation Recommendation & Integration

• Chronological citation recommendation with time preference, Ma et al.,
• When large language models meet citation: A survey, Zhang et al.,
• Directed Criteria Citation Recommendation and Ranking Through Link Prediction, Watson et al.,
• ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation, Roy et al.,
• CiteBART: Learning to Generate Citations for Local Citation Recommendation, {\c{C}}elik et al.,
• Benchmark for Evaluation and Analysis of Citation Recommendation Models, Maharjan et al.,
• PaSa: An LLM Agent for Comprehensive Academic Paper Search, He et al.,
• ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations, Wang et al.,
• How deep do large language models internalize scientific literature and citation practices?, Algaba et al.,
• SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment, Li et al.,
• Towards AI-assisted Academic Writing, Liebling et al.,

• Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students, Song et al.,
• Human-AI collaboration patterns in AI-assisted academic writing, Nguyen et al.,
• Patterns and Purposes: A Cross-Journal Analysis of AI Tool Usage in Academic Writing, Xu et al.,
• Divergent llm adoption and heterogeneous convergence paths in research writing, Lin et al.,
• Artificial intelligence-assisted academic writing: recommendations for ethical use, Cheng et al.,

4.1.3 Assistance After Manuscript Completion

Grammar Correction

• Csed: A chinese semantic error diagnosis corpus, Sun et al.,
• Neural Automated Writing Evaluation with Corrective Feedback, Wang et al.,
• LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction, Wang et al.,
• Improving Grammatical Error Correction via Contextual Data Augmentation, Wang et al.,
• How Paperpal Enhances English Writing Quality and Improves Productivity for Japanese Academics, George et al.,
• Transforming hematological research documentation with large language models: an approach to scientific writing and data analysis, Yang et al.,
• The usage of a transformer based and artificial intelligence driven multidimensional feedback system in english writing instruction, Zheng et al.,

Expression & Logical Revision

• Learning to split and rephrase from Wikipedia edit history, Botha et al.,
• WikiAtomicEdits: A multilingual corpus of Wikipedia edits for modeling language and discourse, Faruqui et al.,
• Diamonds in the rough: Generating fluent sentences from early-stage drafts for academic writing assistance, Ito et al.,
• Text editing by command, Faltings et al.,
• Wordcraft: A human-AI collaborative editor for story writing, Coenen et al.,
• Machine-in-the-loop rewriting for creative image captioning, Padmakumar et al.,
• Read, revise, repeat: A system demonstration for human-in-the-loop iterative text revision, Du et al.,
• Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities, Lee et al.,
• Sparks: Inspiration for science writing using language models, Gero et al.,
• Techniques for supercharging academic writing with generative AI, Lin et al.,
• Overleafcopilot: Empowering academic writing in overleaf with large language models, Wen et al.,
• Augmenting the author: Exploring the potential of AI collaboration in academic writing, Tu et al.,
• Step-Back Profiling: Distilling User History for Personalized Scientific Writing, Tang et al.,
• Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions, Nair et al.,
• Enhancing Chinese Essay Discourse Logic Evaluation Through Optimized Fine-Tuning of Large Language Models, Song et al.,
• Cocoa: Co-Planning and Co-Execution with AI Agents, Feng et al.,
• Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild, Mysore et al.,
• XtraGPT: LLMs for Human-AI Collaboration on Controllable Academic Paper Revision, Chen et al.,
• The usage of a transformer based and artificial intelligence driven multidimensional feedback system in english writing instruction, Zheng et al.,
• Autonomous LLM-Driven Research—from Data to Human-Verifiable Research Papers, Ifargan et al.,

4.2 Full-Automatic Academic Writing

• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• Agent laboratory: Using llm agents as research assistants, Schmidgall et al.,
• ScholaWrite: A Dataset of End-to-End Scholarly Writing Process, Wang et al.,
• Beyond outlining: Heterogeneous recursive planning for adaptive long-form writing with language models, Xiong et al.,
• AgentRxiv: Towards Collaborative Autonomous Research, Schmidgall et al.,
• Zochi Technical Report, AI et al.,
• Carl Technical Report, Institute et al.,
• The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search, Yamada et al.,

• Using artificial intelligence in academic writing and research: An essential productivity tool, Khalifa et al.,
• Human-LLM Coevolution: Evidence from Academic Writing, Geng et al.,
• Large language models penetration in scholarly writing and peer review, Zhou et al.,
• And Plato met ChatGPT: an ethical reflection on the use of chatbots in scientific research writing, with a particular focus on the social sciences, Calderon et al.,

5. AI for Academic Peer Reviewing

• Can we automate scientific reviewing?, Yuan et al.,
• Reviewergpt? an exploratory study on using large language models for paper reviewing, Liu et al.,
• Unveiling the sentinels: Assessing ai performance in cybersecurity peer review, Niu et al.,
• Automated scholarly paper review: Concepts, technologies, and challenges, Lin et al.,
• What Can Natural Language Processing Do for Peer Review?, Kuznetsov et al.,
• Artificial intelligence to support publishing and peer review: A summary and review, Kousha et al.,
• Large language models for automated scholarly paper review: A survey, Zhuang et al.,
• Evaluating the predictive capacity of ChatGPT for academic peer review outcomes across multiple platforms, Thelwall et al.,
• A framework for reviewing the results of automated conversion of structured organic synthesis procedures from the literature, Machi et al.,

5.1 Pre-Review

5.1.1 Desk-Review

• How to Make Peer Review Recommendations and Decisions, Society et al.,
• Helping editors find reviewers, Tedford et al.,
• Snapp: Springer Nature's next-generation peer review system, Nature et al.,
• Matching papers and reviewers at large conferences, Leyton-Brown et al.,
• Streamlining the review process: AI-generated annotations in research manuscripts, D{\'\i}az et al.,
• Artificial intelligence in peer review: enhancing efficiency while preserving integrity, Doskaliuk et al.,
• Enhancing Academic Decision-Making: A Pilot Study of AI-Supported Journal Selection in Higher Education, Farber et al.,

5.1.2 Reviewer Matching

• A framework for optimizing paper matching, Charlin et al.,
• The Toronto paper matching system: an automated paper-reviewer assignment system, Charlin et al.,
• Pistis: A conflict of interest declaration and detection system for peer review management, Wu et al.,
• An automated conflict of interest based greedy approach for conference paper assignment system, Pradhan et al.,
• Matching papers and reviewers at large conferences, Leyton-Brown et al.,
• Autonomous Machine Learning-Based Peer Reviewer Selection System, Aitymbetov et al.,
• Automated Research Review Support Using Machine Learning, Large Language Models, and Natural Language Processing, Pendyala et al.,
• Peer review expert group recommendation: A multi-subject coverage-based approach, Fu et al.,

5.2 In-Review

5.2.1 Peer-Review

Score Prediction

• ALL-IN-ONE: Multi-Task Learning BERT Models for Evaluating Peer Assessments., Jia et al.,
• The quality assist: A technology-assisted peer review based on citation functions to predict the paper quality, Basuki et al.,
• Exploiting labeled and unlabeled data via transformer fine-tuning for peer-review score prediction, Muangkammuen et al.,
• RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance, Couto et al.,

Comment Generation

• Kid-review: knowledge-guided scientific review generation with oracle pre-training, Yuan et al.,
• Gpt4 is slightly helpful for peer-review assistance: A pilot study, Robertson et al.,
• Marg: Multi-agent review generation for scientific papers, D'Arcy et al.,
• Peer review as a multi-turn and long-context dialogue with role-based interactions, Tan et al.,
• Agentreview: Exploring peer review dynamics with llm agents, Jin et al.,
• Can large language models provide useful feedback on research papers? A large-scale empirical analysis, Liang et al.,
• Automated Focused Feedback Generation for Scientific Writing Assistance, Chamoun et al.,
• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• SEAGraph: Unveiling the Whole Story of Paper Review Comments, Yu et al.,
• The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search, Yamada et al.,

Unified Generation

• A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications, Kang et al.,
• Peerassist: leveraging on paper-review interactions to predict peer review decisions, Bharti et al.,
• Marg: Multi-agent review generation for scientific papers, D'Arcy et al.,
• Peer review as a multi-turn and long-context dialogue with role-based interactions, Tan et al.,
• Automated review generation method based on large language models, Wu et al.,
• AI-Driven review systems: evaluating LLMs in scalable and bias-aware academic reviews, Tyser et al.,
• MAMORX: Multi-agent multi-modal scientific review generation with external knowledge, Taechoyotin et al.,
• Cycleresearcher: Improving automated research via automated review, Weng et al.,
• OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews, Idahl et al.,
• The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors, Lee et al.,
• PiCO: Peer Review in LLMs based on Consistency Optimization, Ning et al.,
• Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews, Shin et al.,
• Revieweval: An evaluation framework for ai-generated reviews, Kirtani et al.,
• Automatically Evaluating the Paper Reviewing Capability of Large Language Models, Shin et al.,
• Deepreview: Improving llm-based paper review with human-like deep thinking process, Zhu et al.,
• Reviewagents: Bridging the gap between human and ai-generated paper reviews, Gao et al.,
• Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic Evaluation, Zhang et al.,
• REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning, Taechoyotin et al.,
• TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review, Chang et al.,
• PaperEval: A universal, quantitative, and explainable paper evaluation method powered by a multi-agent system, Huang et al.,

5.2.2 Meta-Review

• Summarizing multiple documents with conversational structure for meta-review generation, Li et al.,
• Meta-review generation with checklist-guided iterative introspection, Zeng et al.,
• When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews, Kumar et al.,
• A sentiment consolidation framework for meta-review generation, Li et al.,
• Prompting LLMs to Compose Meta-Review Drafts from Peer-Review Narratives of Scholarly Manuscripts, Santu et al.,
• Towards automated meta-review generation via an NLP/ML pipeline in different stages of the scholarly peer review process, Kumar et al.,
• Metawriter: Exploring the potential and perils of ai writing support in scientific peer review, Sun et al.,
• GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews, Darrin et al.,
• PeerArg: Argumentative Peer Review with LLMs, Sukpanichnant et al.,
• Bridging Social Psychology and LLM Reasoning: Conflict-Aware Meta-Review Generation via Cognitive Alignment, Chen et al.,
• LLMs as Meta-Reviewers' Assistants: A Case Study, Hossain et al.,

5.3 Post-Review

5.3.1 Influence Analysis

• Popular and/or prestigious? Measures of scholarly esteem, Ding et al.,
• Measuring academic influence: Not all citations are equal, Zhu et al.,
• An overview of microsoft academic service (mas) and applications, Sinha et al.,
• Factors affecting number of citations: a comprehensive review of the literature, Tahamtan et al.,
• Relative citation ratio (RCR): a new metric that uses citation rates to measure influence at the article level, Hutchins et al.,
• HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction, Hao et al.,
• From Words to Worth: Newborn Article Impact Prediction with LLM, Zhao et al.,
• Large language models surpass human experts in predicting neuroscience results, Luo et al.,

5.3.2 Promotion Enhancement

• From complexity to clarity: How AI enhances perceptions of scientists and the public's understanding of science, Markowitz et al.,
• Automatic Evaluation Metrics for Artificially Generated Scientific Research, H{\"o}pner et al.,
• Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation, Park et al.,
• P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark, Sun et al.,

• Can we automate scientific reviewing?, Yuan et al.,
• Reviewergpt? an exploratory study on using large language models for paper reviewing, Liu et al.,
• Unveiling the sentinels: Assessing ai performance in cybersecurity peer review, Niu et al.,
• Automated scholarly paper review: Concepts, technologies, and challenges, Lin et al.,
• What Can Natural Language Processing Do for Peer Review?, Kuznetsov et al.,
• Artificial intelligence to support publishing and peer review: A summary and review, Kousha et al.,
• Large language models for automated scholarly paper review: A survey, Zhuang et al.,
• Evaluating the predictive capacity of ChatGPT for academic peer review outcomes across multiple platforms, Thelwall et al.,
• A framework for reviewing the results of automated conversion of structured organic synthesis procedures from the literature, Machi et al.,

6. Application

6.1 AI for Natural Science Research

6.1.1 AI for Physics Research

• Colloquium: Machine learning in nuclear physics, Boehnlein et al.,
• Toward the end-to-end optimization of particle physics instruments with differentiable programming, Dorigo et al.,
• AI meets physics: a comprehensive survey, Jiao et al.,
• Artificial intelligence for partial differential equations in computational mechanics: A review, Wang et al.,
• When physics meets machine learning: A survey of physics-informed machine learning, Meng et al.,

Physical World Simulation

• Interaction networks for learning about objects, relations and physics, Battaglia et al.,
• End-to-end differentiable physics for learning and control, de Avila Belbute-Peres et al.,
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Raissi et al.,
• Hamiltonian neural networks, Greydanus et al.,
• Lagrangian neural networks, Cranmer et al.,
• Physics-informed neural networks and extensions, Raissi et al.,

Automated Law Discovery

• LLM-SR: Scientific Equation Discovery via Programming with Large Language Models, Shojaee et al.,
• LLM-Feynman: Leveraging Large Language Models for Universal Scientific Formula and Theory Discovery, Song et al.,
• AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge, Fang et al.,
• MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data, Li et al.,
• LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models, Shojaee et al.,
• DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience, Wang et al.,

• Colloquium: Machine learning in nuclear physics, Boehnlein et al.,
• Toward the end-to-end optimization of particle physics instruments with differentiable programming, Dorigo et al.,
• AI meets physics: a comprehensive survey, Jiao et al.,
• Artificial intelligence for partial differential equations in computational mechanics: A review, Wang et al.,
• When physics meets machine learning: A survey of physics-informed machine learning, Meng et al.,

6.1.2 AI for Biology & Medical Research

• Can gpt-4v (ision) serve medical applications? case studies on gpt-4v for multimodal medical diagnosis, Wu et al.,
• Advancing multimodal medical capabilities of Gemini, Yang et al.,
• A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation, Tang et al.,
• Large language models in plant biology, Lam et al.,
• The virtual lab: AI agents design new SARS-CoV-2 nanobodies with experimental validation, Swanson et al.,
• A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy, Jiang et al.,
• Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research, Kapitonova et al.,
• From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine, Buess et al.,
• A survey of llm-based agents in medicine: How far are we from baymax?, Wang et al.,
• Large language model for knowledge synthesis and AI-enhanced biomanufacturing, Li et al.,
• Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects, Othman et al.,
• Large Language Models for Zero-shot Inference of Causal Structures in Biology, Newsham et al.,
• Transforming hematological research documentation with large language models: an approach to scientific writing and data analysis, Yang et al.,
• SpatialAgent: An autonomous AI agent for spatial biology, Wang et al.,
• A Human-LLM Note-Taking System with Case-Based Reasoning as Framework for Scientific Discovery, Craig et al.,
• AI-assisted Drug Re-purposing for Human Liver Fibrosis, Guan et al.,
• Biomni: A General-Purpose Biomedical AI Agent, Huang et al.,
• Autonomous LLM-Driven Research—from Data to Human-Verifiable Research Papers, Ifargan et al.,

Protein Discovery.

• Improved protein structure prediction using potentials from deep learning, Senior et al.,
• Highly accurate protein structure prediction with AlphaFold, Jumper et al.,
• Leveraging biomolecule and natural language through multi-modal learning: A survey, Pei et al.,
• ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning, Ghafarollahi et al.,
• Accurate structure prediction of biomolecular interactions with AlphaFold 3, Abramson et al.,
• Automating exploratory proteomics research via language models, Ding et al.,
• Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles, Ghafarollahi et al.,
• Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning, Lin et al.,

Cell & Gene Modeling.

• GenePT: a simple but effective foundation model for genes and cells built from ChatGPT, Chen et al.,
• Biodiscoveryagent: An ai agent for designing genetic perturbation experiments, Roohani et al.,
• Cellagent: An llm-driven multi-agent framework for automated single-cell data analysis, Xiao et al.,
• Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas, Rood et al.,
• General-purpose pre-trained large cellular models for single-cell transcriptomics, Bian et al.,
• ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentation, Agraz et al.,
• LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs--Evaluation through Synthetic Data Generation, Afonja et al.,
• Autonomous Robotic System with Optical Coherence Tomography Guidance for Vascular Anastomosis, Haworth et al.,
• How to build the virtual cell with artificial intelligence: Priorities and opportunities, Bunne et al.,
• Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction, Maleki et al.,
• NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics, Garijo et al.,
• The rise of agentic AI teammates in medicine, Zou et al.,
• Transformers and genome language models, Consens et al.,

Drug Discovery

• A deep learning approach to antibiotic discovery, Stokes et al.,
• Artificial intelligence to deep learning: machine intelligence approach for drug discovery, Gupta et al.,
• HGTDR: Advancing drug repurposing with heterogeneous graph transformers, Gharizadeh et al.,
• A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation, Tang et al.,
• A data science roadmap for open science organizations engaged in early-stage drug discovery, Edfeldt et al.,
• Drugclip: Contrastive drug-disease interaction for drug repurposing, Lu et al.,
• Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review, Gangwal et al.,
• A foundation model for clinician-centered drug repurposing, Huang et al.,
• Drugagent: Automating ai-aided drug discovery programming through llm multi-agent collaboration, Liu et al.,
• Towards LLM-Driven Multi-Agent Pipeline for Drug Discovery: Neurodegenerative Diseases Case Study, Solovev et al.,
• A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data, Lee et al.,
• Hallucinations Can Improve Large Language Models in Drug Discovery, Yuan et al.,
• RAG-Enhanced Collaborative LLM Agents for Drug Discovery, Lee et al.,
• LUMI-lab: a Foundation Model-Driven Autonomous Platform Enabling Discovery of New Ionizable Lipid Designs for mRNA Delivery, Cui et al.,
• Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects, Othman et al.,
• DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery, Li et al.,
• AI-assisted Drug Re-purposing for Human Liver Fibrosis, Guan et al.,

Clinical Diagnosis

• Large language models encode clinical knowledge, Singhal et al.,
• Can gpt-4v (ision) serve medical applications? case studies on gpt-4v for multimodal medical diagnosis, Wu et al.,
• Advancing clinical decision support: The role of artificial intelligence across six domains, Khalifa et al.,
• Ai hospital: Benchmarking large language models in a multi-agent medical interaction simulator, Fan et al.,
• Agent hospital: A simulacrum of hospital with evolvable medical agents, Li et al.,
• Autonomous Robotic System with Optical Coherence Tomography Guidance for Vascular Anastomosis, Haworth et al.,
• Piors: Personalized intelligent outpatient reception based on large language model with multi-agents medical scenario simulation, Bao et al.,
• Towards an AI co-scientist, Gottweis et al.,
• Generative Artificial Intelligence in Anatomic Pathology, Brodsky et al.,
• Clinicalgpt-r1: Pushing reasoning capability of generalist disease diagnosis with large language model, Lan et al.,
• A Human-LLM Note-Taking System with Case-Based Reasoning as Framework for Scientific Discovery, Craig et al.,
• PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactions, Kyung et al.,
• MedSyn: Enhancing Diagnostics with Human-AI Collaboration, Sayin et al.,

• Can gpt-4v (ision) serve medical applications? case studies on gpt-4v for multimodal medical diagnosis, Wu et al.,
• Advancing multimodal medical capabilities of Gemini, Yang et al.,
• A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation, Tang et al.,
• Large language models in plant biology, Lam et al.,
• The virtual lab: AI agents design new SARS-CoV-2 nanobodies with experimental validation, Swanson et al.,
• A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy, Jiang et al.,
• Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research, Kapitonova et al.,
• From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine, Buess et al.,
• A survey of llm-based agents in medicine: How far are we from baymax?, Wang et al.,
• Large language model for knowledge synthesis and AI-enhanced biomanufacturing, Li et al.,
• Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects, Othman et al.,
• Large Language Models for Zero-shot Inference of Causal Structures in Biology, Newsham et al.,
• Transforming hematological research documentation with large language models: an approach to scientific writing and data analysis, Yang et al.,
• SpatialAgent: An autonomous AI agent for spatial biology, Wang et al.,
• A Human-LLM Note-Taking System with Case-Based Reasoning as Framework for Scientific Discovery, Craig et al.,
• AI-assisted Drug Re-purposing for Human Liver Fibrosis, Guan et al.,
• Biomni: A General-Purpose Biomedical AI Agent, Huang et al.,
• Autonomous LLM-Driven Research—from Data to Human-Verifiable Research Papers, Ifargan et al.,

6.1.3 AI for Chemistry& Materials Research

• Accelerating materials discovery using artificial intelligence, high performance computing and robotics, Pyzer-Knapp et al.,
• Accelerating materials language processing with large language models, Choi et al.,
• Augmenting large language models with chemistry tools, M. Bran et al.,
• Nano & AI: A Nobel Partnership, Chen et al.,
• Simulating 500 million years of evolution with a language model, Hayes et al.,
• AI4Materials: Transforming the Landscape of Materials Science and Enigneering, Jiang et al.,
• Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry, Mroz et al.,
• Empowering Generalist Material Intelligence with Large Language Models, Yuan et al.,
• From Literature to Lab: Hardware-Independent Autonomous Chemical Synthesis with Reinforcement Learning, Wu et al.,

Automatic Analysis

• Graph networks as a universal machine learning framework for molecules and crystals, Chen et al.,
• An autonomous laboratory for the accelerated synthesis of novel materials, Szymanski et al.,
• Accelerating the Discovery of Abiotic Vesicles with AI-Guided Automated Experimentation, Ekosso et al.,
• Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery, Li et al.,
• High-throughput robotic collection, imaging, and machine learning analysis of salt patterns: composition and concentration from dried droplet photos, Batista et al.,
• Adaptive representation of molecules and materials in Bayesian optimization, Rajabi-Kochi et al.,
• FlavorDiffusion: Modeling Food-Chemical Interactions with Diffusion, Seo et al.,

Automatic Discovery

• Chatgpt-Assisted Rational Design for Iterative Performance Optimization of Perovskite Solar Cells, Zhang et al.,
• Machine learning for molecular and materials science, Butler et al.,
• Scaling deep learning for materials discovery, Merchant et al.,
• Experimental discovery of novel ammonia synthesis catalysts via active learning, Jayarathna et al.,
• A sober look at LLMs for material discovery: Are they actually good for Bayesian optimization over molecules?, Kristiadi et al.,
• BatGPT-Chem: A Foundation Large Model For Chemical Engineering, Yang et al.,
• AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers, Huang et al.,
• Real-time experiment-theory closed-loop interaction for autonomous materials science, Liang et al.,
• Autonomous mobile robots for exploratory synthetic chemistry, Dai et al.,
• Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation, Dangayach et al.,
• ORGANA: a robotic assistant for automated chemistry experimentation and characterization, Darvish et al.,
• Adaptive AI decision interface for autonomous electronic material discovery, Dai et al.,

Full Human-AI Collaboration Process Management

• Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist, Zhu et al.,
• ChemReasoner: Heuristic search over a large language model's knowledge space using quantum-chemical feedback, Sprueill et al.,
• Efficient evolutionary search over chemical space with large language models, Wang et al.,
• MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration, Ni et al.,
• Autonomous Microscopy Experiments through Large Language Model Agents, Mandal et al.,
• Automated Retrosynthesis Planning of Macromolecules Using Large Language Models and Knowledge Graphs, Ma et al.,
• A multiagent-driven robotic ai chemist enabling autonomous chemical research on demand, Song et al.,
• Agentic Assistant for Material Scientists, Feng et al.,
• Physics-informed, dual-objective optimization of high-entropy-alloy nanozymes by a robotic AI chemist, Luo et al.,
• Intelligent, Personalized Scientific Assistant via Large Language Models for Solid-State Battery Research, Leng et al.,
• Prim: Principle-inspired material discovery through multi-agent collaboration, Lai et al.,

• Accelerating materials discovery using artificial intelligence, high performance computing and robotics, Pyzer-Knapp et al.,
• Accelerating materials language processing with large language models, Choi et al.,
• Augmenting large language models with chemistry tools, M. Bran et al.,
• Nano & AI: A Nobel Partnership, Chen et al.,
• Simulating 500 million years of evolution with a language model, Hayes et al.,
• AI4Materials: Transforming the Landscape of Materials Science and Enigneering, Jiang et al.,
• Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry, Mroz et al.,
• Empowering Generalist Material Intelligence with Large Language Models, Yuan et al.,
• From Literature to Lab: Hardware-Independent Autonomous Chemical Synthesis with Reinforcement Learning, Wu et al.,

6.2 AI for Applied Science and Engineering Research

6.2.1 AI for Robotics and Control Research

• The AI CUDA engineer: Agentic CUDA kernel discovery, optimization and composition, Lange et al.,
• Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review, Lee et al.,

Autonomous Design & Optimization

• Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting, Mieszczanek et al.,
• Closed-loop transfer enables artificial intelligence to yield chemical knowledge, Angello et al.,
• Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation, Bu et al.,
• Real-time experiment-theory closed-loop interaction for autonomous materials science, Liang et al.,
• AI-Driven Robotics for Free-Space Optics, Uddin et al.,

End-to-End Vision-Based Control

• End-to-end training of deep visuomotor policies, Levine et al.,
• Domain randomization for transferring deep neural networks from simulation to the real world, Tobin et al.,
• Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection, Levine et al.,
• Scalable deep reinforcement learning for vision-based robotic manipulation, Kalashnikov et al.,

Sim-to-Real Robustness & Safety

• Real-world humanoid locomotion with reinforcement learning, Radosavovic et al.,
• Improving generalization of robot locomotion policies via Sharpness-Aware Reinforcement Learning, Bochem et al.,
• Robustness Evaluation of Offline Reinforcement Learning for Robot Control Against Action Perturbations, Ayabe et al.,
• Zero-shot Sim-to-Real Transfer for Reinforcement Learning-based Visual Servoing of Soft Continuum Arms, Yang et al.,
• Guided by Guardrails: Control Barrier Functions as Safety Instructors for Robotic Learning, Guerrier et al.,

Multi-Task & Multi-Agent Control Frameworks

• Value Iteration for Learning Concurrently Executable Robotic Control Tasks, Tahmid et al.,
• NovelSeek: When Agent Becomes the Scientist--Building Closed-Loop System from Hypothesis to Verification, Team et al.,

• The AI CUDA engineer: Agentic CUDA kernel discovery, optimization and composition, Lange et al.,
• Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review, Lee et al.,

6.2.2 AI for Software Engineering

Code Generation

• Evaluating large language models trained on code, Chen et al.,
• Codegen: An open large language model for code with multi-turn program synthesis, Nijkamp et al.,
• Starcoder: may the source be with you!, Li et al.,
• Code llama: Open foundation models for code, Roziere et al.,
• DeepSeek-Coder: When the Large Language Model Meets Programming--The Rise of Code Intelligence, Guo et al.,
• Starcoder 2 and the stack v2: The next generation, Lozhkov et al.,
• MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios, Huang et al.,
• Seed-Coder: Let the Code Model Curate Data for Itself, Zhang et al.,

End-to-End Software Development

• Application of large language models to software engineering tasks: Opportunities, risks, and implications, Ozkaya et al.,
• Chatdev: Communicative agents for software development, Qian et al.,
• Large language models for software engineering: Survey and open problems, Fan et al.,
• Experiential co-learning of software-developing agents, Qian et al.,
• Repoexec: Evaluate code generation with a repository-level executable benchmark, Le Hai et al.,
• SWE-bench: Can Language Models Resolve Real-world Github Issues?, Jimenez et al.,
• Hyperagent: Generalist software engineering agents to solve coding tasks at scale, Phan et al.,
• Explainable automated debugging via large language model-driven scientific debugging, Kang et al.,

6.3 AI for Social Science Research

6.3.1 AI for Sociology Research

• Ethnography and Machine Learning: Synergies and New Directions, Li et al.,
• Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence, Karjus et al.,
• Agent-Enhanced Large Language Models for Researching Political Institutions, Loffredo et al.,
• Reimagining urban science: Scaling causal inference with large language models, Xia et al.,

AI-Assisted Experimental and Interview Studies.

• Automated social science: Language models as scientist and subjects, Manning et al.,
• Step Further Towards Automated Social Science: An AI-Powered Interview Platform, Liu et al.,

Large-Scale Simulation of Social Phenomena.

• RAISE: A New Method to Develop Experimental Stimuli for Advertising Research with Image Generative Artificial Intelligence, Zamudio et al.,
• Cultural evolution in populations of Large Language Models, Perez et al.,
• Economic Anthropology in the Era of Generative Artificial Intelligence, Sheldon et al.,
• Malinowski in the Age of AI: Can large language models create a text game based on an anthropological classic?, Hoffmann et al.,
• AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making, Huang et al.,
• ResearchTown: Simulator of Human Research Community, Yu et al.,
• Simulating cooperative prosocial behavior with multi-agent LLMs: Evidence and mechanisms for AI agents to inform policy decisions, Sreedhar et al.,
• Predicting Field Experiments with Large Language Models, Chen et al.,
• Language Models Surface the Unwritten Code of Science and Society, Bao et al.,

Potential Risks Discussion.

• Automated social science: Language models as scientist and subjects, Manning et al.,
• ChatGPT as research scientist: probing GPT’s capabilities as a research librarian, research ethicist, data generator, and data predictor, Lehr et al.,
• Predicting Results of Social Science Experiments Using Large Language Models, Luke et al.,

• Ethnography and Machine Learning: Synergies and New Directions, Li et al.,
• Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence, Karjus et al.,
• Agent-Enhanced Large Language Models for Researching Political Institutions, Loffredo et al.,
• Reimagining urban science: Scaling causal inference with large language models, Xia et al.,

6.3.2 AI for Psychology Research

• Automating psychological hypothesis generation with AI: when large language models meet causal graph, Tong et al.,
• Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits, Li et al.,

Experiment Workflow Automation and Simulation.

• Using cognitive psychology to understand GPT-3, Binz et al.,
• Can AI language models replace human participants?, Dillion et al.,
• The emergence of economic rationality of GPT, Chen et al.,
• AI-experiments in education: An AI-driven randomized controlled trial for higher education research, Cingillioglu et al.,
• RAISE: A New Method to Develop Experimental Stimuli for Advertising Research with Image Generative Artificial Intelligence, Zamudio et al.,
• Frontiers: Can Large Language Models Capture Human Preferences?, Goli et al.,
• Testing theory of mind in large language models and humans, Strachan et al.,
• Do large language models show decision heuristics similar to humans? A case study using GPT-3.5., Suri et al.,
• Towards a client-centered assessment of llm therapists by client simulation, Wang et al.,
• Interactive agents: Simulating counselor-client psychological counseling via role-playing llm-to-llm interactions, Qiu et al.,
• Can AI Replace Human Subjects? A Large-Scale Replication of Psychological Experiments with LLMs, Cui et al.,

Human-AI Trust and Safety Design.

• MMSD2. 0: Towards a reliable multi-modal sarcasm detection system, Qin et al.,
• Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust, Li et al.,
• From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents, Chandra et al.,

Psychological Interventions.

• Using cognitive psychology to understand GPT-3, Binz et al.,
• Can AI language models replace human participants?, Dillion et al.,
• Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT, Hagendorff et al.,
• Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study, Deiner et al.,
• Crafting clarity: Leveraging large language models to decode consumer reviews, Praveen et al.,
• ChatGPT for Textual Analysis? How to Use Generative LLMs in Accounting Research, de Kok et al.,
• The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems, Spytska et al.,
• Randomized trial of a generative ai chatbot for mental health treatment, Heinz et al.,
• Large language models as mental health resources: Patterns of use in the united states, Rousmaniere et al.,
• Large Language Models Pass the Turing Test, Jones et al.,
• Experiential Narratives in Marketing: A Comparison of Generative AI and Human Content, Wen et al.,

• Automating psychological hypothesis generation with AI: when large language models meet causal graph, Tong et al.,
• Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits, Li et al.,

7. Future and Frontiers

7.1 Interdisciplinary AI Models

• Artificial intelligence in cancer research: learning at different levels of data granularity, Cirillo et al.,
• Generating full length wikipedia biographies: The impact of gender bias on the retrieval-based generation of women biographies, Fan et al.,
• Contrastive knowledge integrated graph neural networks for Chinese medical text classification, Lan et al.,
• Heterogeneous federated learning: State-of-the-art and research challenges, Ye et al.,
• A comprehensive survey of cross-domain policy transfer for embodied agents, Niu et al.,
• Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models, Gu et al.,
• A survey of trustworthy representation learning across domains, Zhu et al.,
• BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science, Lin et al.,
• Knowledge transfer for cross-domain reinforcement learning: a systematic review, Serrano et al.,
• Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning, Buehler et al.,
• Heterogeneous data integration: Challenges and opportunities, Putrama et al.,
• A comprehensive survey of foundation models in medicine, Khan et al.,
• Foundation models and intelligent decision-making: Progress, challenges, and perspectives, Huang et al.,

7.2 Ethics and Safety in AI4Research

• Causal learning for socially responsible AI, Cheng et al.,
• Artificial intelligence and ethics: a comprehensive review of bias mitigation, transparency, and accountability in AI Systems, Mensah et al.,
• Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies, Ferrara et al.,
• AXOLOTL: fairness through assisted self-debiasing of large language model outputs, Ebrahimi et al.,
• Policy advice and best practices on bias and fairness in AI, Alvarez et al.,
• Automated Peer-Reviewer Assignment can be Manipulated to Secure Reviews from Colluders, Hsieh et al.,
• Mitigating bias in artificial intelligence: Fair data generation via causal models for transparent and explainable decision-making, Gonz{\'a}lez-Sendino et al.,
• Enhancing peer review efficiency: A mixed-methods analysis of artificial intelligence-assisted reviewer selection across academic disciplines, Farber et al.,
• Beyond principlism: practical strategies for ethical AI use in research practices, Lin et al.,
• SciTrust: Evaluating the Trustworthiness of Large Language Models for Science, Herron et al.,
• Are we there yet? revealing the risks of utilizing large language models in scholarly peer review, Ye et al.,
• Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions, Raghunathan et al.,
• How human--AI feedback loops alter human perceptual, emotional and social judgements, Glickman et al.,
• The hidden dimensions of llm alignment: A multi-dimensional safety analysis, Pan et al.,
• Responsible AI in biotechnology: balancing discovery, innovation and biosecurity risks, Wheeler et al.,
• All that glitters is not novel: Plagiarism in ai generated research, Gupta et al.,
• Detecting llm-written peer reviews, Rao et al.,
• Ethical and bias considerations in artificial intelligence/machine learning, Hanna et al.,
• Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology, Rosbach et al.,
• Considering the Ethics of Large Machine Learning Models in the Chemical Sciences, Spotte-Smith et al.,
• Generative artificial intelligence for academic research: evidence from guidance issued for researchers by higher education institutions in the United States, Ganguly et al.,
• Artificial intelligence and dichotomania, McShane et al.,
• The Plagiarism Singularity Conjecture, Ranga et al.,
• Toward Reliable Biomedical Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models, Xiong et al.,
• BiasFilter: An Inference-Time Debiasing Framework for Large Language Models, Cheng et al.,
• SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents, Zhu et al.,
• OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models, Sun et al.,

7.3 AI for Collaborative Research

• A hybrid approach to privacy-preserving federated learning, Truex et al.,
• A review of applications in federated learning, Li et al.,
• A survey on federated learning, Zhang et al.,
• A systematic review of federated learning: Challenges, aggregation methods, and development tools, Guendouzi et al.,
• Federated learning and data privacy: A review of challenges and opportunities, Myakala et al.,
• Designing collaborative intelligence systems for employee-AI service co-production, Blaurock et al.,
• Collaborative Intelligence: A scoping review of current applications, Schleiger et al.,
• Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation, Holter et al.,
• The ai scientist: Towards fully automated open-ended scientific discovery, Lu et al.,
• Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review, Gomez et al.,
• Text2world: Benchmarking large language models for symbolic world model generation, Hu et al.,
• Distributed cross-learning for equitable federated models-privacy-preserving prediction on data from five California hospitals, Kuo et al.,
• Multi-agent risks from advanced ai, Hammond et al.,
• Simulating cooperative prosocial behavior with multi-agent LLMs: Evidence and mechanisms for AI agents to inform policy decisions, Sreedhar et al.,
• Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs, Fehlis et al.,
• 34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery, Zimmermann et al.,
• DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery, Li et al.,
• The role of agentic ai in shaping a smart future: A systematic review, Hosseini et al.,

7.4 Explainability and Transparency of AI4Research

• On gradient-like explanation under a black-box setting: when black-box explanations become as good as white-box, Cai et al.,
• Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review, Frasca et al.,
• Towards uncovering how large language model works: An explainability perspective, Zhao et al.,
• Mechanistic Interpretability for AI Safety--A Review, Bereska et al.,
• A practical review of mechanistic interpretability for transformer-based language models, Rai et al.,
• Interpreting black-box models: a review on explainable artificial intelligence, Hassija et al.,
• Unlocking the capabilities of thought: A reasoning boundary framework to quantify and optimize chain-of-thought, Chen et al.,
• Explainable AI reloaded: Challenging the xai status quo in the era of large language models, Ehsan et al.,
• Beyond principlism: practical strategies for ethical AI use in research practices, Lin et al.,
• ECM: A Unified Electronic Circuit Model for Explaining the Emergence of In-Context Learning and Chain-of-Thought in Large Language Model, Chen et al.,
• RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning, Chen et al.,

7.5 AI for Dynamic and Real‑Time Optimized Scientific Experimentation

• Tree-planner: Efficient close-loop task planning with large language models, Hu et al.,
• Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept, Lo et al.,
• Self-driving laboratories for chemistry and materials science, Tom et al.,
• Hiagent: Hierarchical working memory management for solving long-horizon agent tasks with large language model, Hu et al.,
• Real-time experiment-theory closed-loop interaction for autonomous materials science, Liang et al.,
• AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery, Desai et al.,
• Adaptive AI decision interface for autonomous electronic material discovery, Dai et al.,
• Science acceleration and accessibility with self-driving labs, Canty et al.,

7.6 Multimodal Integration in AI4Research

• Look, read and enrich-learning from scientific figures and their captions, Gomez-Perez et al.,
• Uniter: Universal image-text representation learning, Chen et al.,
• T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering, Wang et al.,
• Figcaps-hf: A figure-to-caption generative framework and benchmark with human feedback, Singh et al.,
• M ³ CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought, Chen et al.,
• Every Part Matters: Integrity Verification of Scientific Figures Based on Multimodal Large Language Models, Shi et al.,
• S3 agent: Unlocking the power of VLLM for zero-shot multi-modal sarcasm detection, Wang et al.,
• Vlm4bio: A benchmark dataset to evaluate pretrained vision-language models for trait discovery from biological images, Maruf et al.,
• Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs, Wang et al.,
• What factors affect multi-modal in-context learning? an in-depth exploration, Qin et al.,
• Bigdocs: An open dataset for training multimodal models on document and code tasks, Rodriguez et al.,
• InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback, Zhao et al.,
• MERMaid: Universal multimodal mining of chemical reactions from PDFs using vision-language models, Leong et al.,
• Comt: A novel benchmark for chain of multi-modal thought on large vision-language models, Cheng et al.,
• Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought, Cheng et al.,
• HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights, Gokdemir et al.,

7.7 Multilingual Integration in AI4Research

• Languages are still a major barrier to global science, Amano et al.,
• Unsupervised cross-lingual representation learning at scale, Conneau et al.,
• SimAlign: High quality word alignments without parallel training data using static and contextualized embeddings, Sabet et al.,
• Ten tips for overcoming language barriers in science, Amano et al.,
• Improving low-resource languages in pre-trained multilingual language models, Hangya et al.,
• Hit-scir at mmnlu-22: Consistency regularization for multilingual spoken language understanding, Zheng et al.,
• Crosslingual capabilities and knowledge barriers in multilingual large language models, Chua et al.,
• AutoCAP: Towards automatic cross-lingual alignment planning for zero-shot chain-of-thought, Zhang et al.,
• Rule-based, neural and LLM back-translation: Comparative insights from a variant of Ladin, Frontull et al.,
• A survey of multilingual large language models, Qin et al.,
• A smack of all neighbouring languages: How multilingual is scholarly communication?, Pradier et al.,
• X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System, Wang et al.,

8. Related Materials

• AI-powered platform for scientific discovery, Trifonov et al.,
• Hypothesis generation with large language models, Zhou et al.,
• Artificial intelligence and scientific discovery: A model of prioritized search, Agrawal et al.,
• A comprehensive survey of scientific large language models and their applications in scientific discovery, Zhang et al.,
• Artificial intelligence for literature reviews: Opportunities and challenges, Bolanos et al.,
• Creativity in AI: Progresses and Challenges, Ismayilzada et al.,
• LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions, Liao et al.,
• Towards scientific discovery with generative ai: Progress, opportunities, and challenges, Reddy et al.,
• LLM4SR: A Survey on Large Language Models for Scientific Research, Luo et al.,
• Large language models for automated scholarly paper review: A survey, Zhuang et al.,
• Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models, Barman et al.,
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation, Eger et al.,
• Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing?, Yu et al.,
• A review of llm-assisted ideation, Li et al.,
• Towards scientific intelligence: A survey of llm-based scientific agents, Ren et al.,
• Agentichypothesis: A survey on hypothesis generation using llm systems, Bazgir et al.,
• Agentic ai for scientific discovery: A survey of progress, challenges, and future directions, Gridach et al.,
• A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models, Alkan et al.,
• Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery, Zhang et al.,
• Scientific hypothesis generation and validation: Methods, datasets, and future directions, Kulkarni et al.,
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research, Chen et al.,
• Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation, Huang et al.,
• Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards, Kim et al.,
• From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery, Zheng et al.,
• AI Scientists Fail Without Strong Implementation Capability, Zhu et al.,

9. Resources

9.1 AI for Scientific Comprehension

9.1.1 Textual Scientific Comprehension

• Pubmedqa: A dataset for biomedical research question answering, Jin et al.,
• Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering, Pal et al.,
• CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice, Raza et al.,
• Scienceqa: A novel resource for question answering on scholarly articles, Saikh et al.,
• Clam: Selective clarification for ambiguous questions with generative language models, Kuhn et al.,
• BioASQ-QA: A manually curated corpus for Biomedical Question Answering, Krithara et al.,
• The sciqa scientific question answering benchmark for scholarly knowledge, Auer et al.,
• Theoremqa: A theorem-driven question answering dataset, Chen et al.,
• Scibench: Evaluating college-level scientific problem-solving abilities of large language models, Wang et al.,
• What if: Generating code to answer simulation questions in chemistry texts, Peretz et al.,
• Enabling Language Models to Implicitly Learn Self-Improvement, Wang et al.,
• Paperqa: Retrieval-augmented generative agent for scientific research, L{\'a}la et al.,
• Sciglm: Training scientific language models with self-reflective instruction annotation and tuning, Zhang et al.,
• Generating Multiple Choice Questions from Scientific Literature via Large Language Models, Luo et al.,
• Biomedlm: A 2.7 b parameter language model trained on biomedical text, Bolton et al.,
• SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation, Wan et al.,
• M ³ CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought, Chen et al.,
• Scifibench: Benchmarking large multimodal models for scientific figure interpretation, Roberts et al.,
• Sciknoweval: Evaluating multi-level scientific knowledge of large language models, Feng et al.,
• BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science, Lin et al.,
• Scholarchemqa: Unveiling the power of language models in chemical research question answering, Chen et al.,
• Mmsci: A dataset for graduate-level multi-discipline multimodal scientific understanding, Li et al.,
• SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers, Pramanick et al.,
• Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models, Li et al.,
• SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark, Liang et al.,
• Language agents achieve superhuman synthesis of scientific knowledge, Skarlinski et al.,
• Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study, Rostam et al.,
• Graphusion: a RAG framework for Knowledge Graph Construction with a global perspective, Yang et al.,
• M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models, Li et al.,
• SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers, Singh et al.,
• SciAgent: Tool-augmented Language Models for Scientific Reasoning, Ma et al.,
• SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature, Wadden et al.,
• PaSa: An LLM Agent for Comprehensive Academic Paper Search, He et al.,
• BioMaze: Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoning, Zhao et al.,
• AutoPaperBench: An MLLM-Based Framework for Automatic Generation of Paper Understanding Evaluation Benchmarks, Kim et al.,
• FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights, Yu et al.,
• SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models, Yu et al.,
• EarthSE: A Benchmark Evaluating Earth Scientific Exploration Capability for Large Language Models, Xu et al.,
• Scaling Physical Reasoning with the PHYSICS Dataset, Zheng et al.,

9.1.2 Table & Chart Scientific Comprehension

• ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning, Masry et al.,
• Chartx & chartvlm: A versatile benchmark and foundation model for complicated chart reasoning, Xia et al.,
• Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study, Sui et al.,
• NovaChart: A Large-scale Dataset towards Chart Understanding and Generation of Multimodal Large Language Models, Hu et al.,
• CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs, Wang et al.,
• The Mighty ToRR: A Benchmark for Table Reasoning and Robustness, Ashury-Tahan et al.,
• Tablebench: A comprehensive and complex benchmark for table question answering, Wu et al.,

9.2 AI for Academic Survey

• Ms2: Multi-document summarization of medical studies, DeYoung et al.,
• Generating (factual?) narrative summaries of rcts: Experiments with neural multi-document summarization, Wallace et al.,
• Overview of MSLR2022: A shared task on multi-document summarization for literature reviews, Wang et al.,
• Generating a structured summary of numerous academic papers: Dataset and method, Liu et al.,
• SciReviewGen: a large-scale dataset for automatic literature review generation, Kasanishi et al.,
• SurveySum: A Dataset for Summarizing Multiple Scientific Articles into a Survey Section, Fernandes et al.,
• OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining, Zhang et al.,
• OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources, Docekal et al.,
• Autosurvey: Large language models can automatically write surveys, Wang et al.,
• SurveyX: Academic Survey Automation via Large Language Models, Liang et al.,
• SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing, Yan et al.,
• Browsecomp: A simple yet challenging benchmark for browsing agents, Wei et al.,
• LLM times MapReduce-V2: Entropy-Driven Convolutional Test-Time Scaling for Generating Long-Form Articles from Extremely Long Resources, Wang et al.,
• AcademicBrowse: Benchmarking Academic Browse Ability of LLMs, Zhou et al.,

9.3 AI for Scientific Discovery

Idea Mining

• OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining, Zhang et al.,
• Can Large Language Models Unlock Novel Scientific Research Ideas?, Kumar et al.,
• LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context, Ruan et al.,
• Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses, Yang et al.,
• Learning to Generate Research Idea with Dynamic Control, Li et al.,
• Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations, Chen et al.,
• ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition, Liu et al.,
• Ai idea bench 2025: Ai research idea generation benchmark, Qiu et al.,
• Sparks of science: Hypothesis generation using structured paper data, O'Neill et al.,
• Spark: A System for Scientifically Creative Idea Generation, Sanyal et al.,
• Improving Research Idea Generation Through Data: An Empirical Investigation in Social Science, Liu et al.,
• CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature, Sternlicht et al.,

Novelty & Significant Assessment

• Blade: Benchmarking language model agents for data-driven science, Gu et al.,
• Empowering AI as Autonomous Researchers: Evaluating LLMs in Generating Novel Research Ideas through Automated Metrics, Dasgupta et al.,
• LLMs Tackle Meta-Analysis: Automating Scientific Hypothesis Generation with Statistical Rigor, Lin et al.,
• A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models, Tan et al.,
• Hypobench: Towards systematic and principled benchmarking for hypothesis generation, Liu et al.,
• Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications, Lin et al.,
• Harnessing Large Language Models for Scientific Novelty Detection, Liu et al.,

Theory Analysis

• Minif2f: a cross-system benchmark for formal olympiad-level mathematics, Zheng et al.,
• FactKG: Fact verification via reasoning on knowledge graphs, Kim et al.,
• Investigating zero-and few-shot generalization in fact verification, Pan et al.,
• Fimo: A challenge formal dataset for automated theorem proving, Liu et al.,
• Can Large Language Models Detect Misinformation in Scientific News Reporting?, Cao et al.,
• Mustard: Mastering uniform synthesis of theorem and proof data, Huang et al.,
• MAGIC: Multi-Argument Generation with Self-Refinement for Domain Generalization in Automatic Fact-Checking, Kao et al.,
• Zero-shot scientific claim verification using LLMs and citation text, Alvarez et al.,
• Grounding fallacies misrepresenting scientific publications in evidence, Glockner et al.,
• Augmenting the Veracity and Explanations of Complex Fact Checking via Iterative Self-Revision with LLMs, Zhang et al.,
• DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts, Braun et al.,
• TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding, Ku et al.,
• BioDSA-1K: Benchmarking Data Science Agents for Biomedical Research, Wang et al.,

Experiment Design

• Benchmarking compound activity prediction for real-world drug discovery applications, Tian et al.,
• A bioactivity foundation model using pairwise meta-learning, Feng et al.,
• BioProBench: Comprehensive Dataset and Benchmark in Biological Protocol Understanding and Reasoning, Liu et al.,
• LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation, Zhang et al.,

Experiment Conduction

• Mlagentbench: Evaluating language agents on machine learning experimentation, Huang et al.,
• Infiagent-dabench: Evaluating agents on data analysis tasks, Hu et al.,
• DSBench: How Far Are Data Science Agents to Becoming Data Science Experts?, Jing et al.,
• Mle-bench: Evaluating machine learning agents on machine learning engineering, Chan et al.,
• Mlgym: A new framework and benchmark for advancing ai research agents, Nathani et al.,
• MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?, Zhang et al.,
• Scireplicate-bench: Benchmarking llms in agent-driven algorithmic reproduction from research papers, Xiang et al.,
• Can AI Agents Design and Implement Drug Discovery Pipelines?, Smbatyan et al.,
• EXP-Bench: Can AI Conduct AI Research Experiments?, Kon et al.,
• Scienceboard: Evaluating multimodal autonomous agents in realistic scientific workflows, Sun et al.,
• AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage, Zhao et al.,
• MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research, Chen et al.,
• Autobio: A simulation and benchmark for robotic automation in digital biology laboratory, Lan et al.,
• ResearchCodeBench: Benchmarking LLMs on Implementing Novel Machine Learning Research Code, Hua et al.,

Experimental Analysis

• Microvqa: A multimodal reasoning benchmark for microscopy-based scientific research, Burgess et al.,

Full Automatic Discovery

• Ds-agent: Automated data science by empowering large language models with case-based reasoning, Guo et al.,
• Discoverybench: Towards data-driven discovery with large language models, Majumder et al.,
• Blade: Benchmarking language model agents for data-driven science, Gu et al.,
• Scienceagentbench: Toward rigorous assessment of language agents for data-driven scientific discovery, Chen et al.,
• DISCOVERYWORLD: A virtual environment for developing and evaluating automated scientific discovery agents, Jansen et al.,
• Curie: Toward rigorous and automated scientific experimentation with ai agents, Kon et al.,
• A vision for auto research with llm agents, Liu et al.,
• Can AI Agents Design and Implement Drug Discovery Pipelines?, Smbatyan et al.,
• Llm-srbench: A new benchmark for scientific equation discovery with large language models, Shojaee et al.,
• Towards llm agents for earth observation, Kao et al.,
• Benchmarking AI scientists in omics data-driven biological research, Luo et al.,

• ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition, Liu et al.,

9.4 AI for Academic Writing

9.4.1 Semi-Automatic Academic Writing

Assistance During Manuscript Preparation.

• LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts, Hashemi et al.,
• MoDeST: A dataset for Multi Domain Scientific Title Generation, Bölücü et al.,

Assistance During Manuscript Writing

• CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding, Wright et al.,
• Figgen: Text to scientific figure generation, Rodriguez et al.,
• Scicapenter: Supporting caption composition for scientific figures with machine-generated captions and ratings, Hsu et al.,
• Figuring out Figures: Using Textual References to Caption Scientific Figures, Cao et al.,
• CiteBART: Learning to Generate Citations for Local Citation Recommendation, {\c{C}}elik et al.,
• TikZero: Zero-Shot Text-Guided Graphics Program Synthesis, Belouadi et al.,
• Futuregen: Llm-rag approach to generate the future work of scientific article, Azher et al.,
• ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations, Wang et al.,
• XtraGPT: LLMs for Human-AI Collaboration on Controllable Academic Paper Revision, Chen et al.,

Assistance After Manuscript Completion.

• WikiAtomicEdits: A multilingual corpus of Wikipedia edits for modeling language and discourse, Faruqui et al.,
• Learning to split and rephrase from Wikipedia edit history, Botha et al.,
• Diamonds in the rough: Generating fluent sentences from early-stage drafts for academic writing assistance, Ito et al.,
• Neural Automated Writing Evaluation with Corrective Feedback, Wang et al.,
• AAAR-1.0: Assessing AI's Potential to Assist Research, Lou et al.,
• Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers, Pang et al.,
• The usage of a transformer based and artificial intelligence driven multidimensional feedback system in english writing instruction, Zheng et al.,

9.5 AI for Academic Peer Reviewing

• A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications, Kang et al.,
• Citetracked: A longitudinal dataset of peer reviews and citations, Plank et al.,
• COMPARE: a taxonomy and dataset of comparison discussions in peer reviews, Singh et al.,
• Peer review analyze: A novel benchmark resource for computational analysis of peer reviews, Ghosal et al.,
• Reviewergpt? an exploratory study on using large language models for paper reviewing, Liu et al.,
• NLPeer: A Unified Resource for the Computational Study of Peer Review, Dycke et al.,
• Moprd: A multidisciplinary open peer review dataset, Lin et al.,
• The Open Review-Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High-Energy Physics, Szumega et al.,
• Pre: A peer review based large language model evaluator, Chu et al.,
• Is LLM a reliable reviewer? A comprehensive evaluation of LLM on automatic paper reviewing tasks, Zhou et al.,
• PolitePEER: does peer review hurt? A dataset to gauge politeness intensity in the peer reviews, Bharti et al.,
• RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance, Couto et al.,
• Peer review as a multi-turn and long-context dialogue with role-based interactions, Tan et al.,
• MASSW: A new dataset and benchmark tasks for ai-assisted scientific workflows, Zhang et al.,
• Scientific opinion summarization: Paper meta-review generation dataset, methods, and evaluation, Zeng et al.,
• Can large language models provide useful feedback on research papers? A large-scale empirical analysis, Liang et al.,
• An Analysis of Tasks and Datasets in Peer Reviewing, Staudinger et al.,
• PeerArg: Argumentative Peer Review with LLMs, Sukpanichnant et al.,
• Enhancing peer review efficiency: A mixed-methods analysis of artificial intelligence-assisted reviewer selection across academic disciplines, Farber et al.,
• Automatic Large Language Model Evaluation via Peer Review, Chu et al.,
• AAAR-1.0: Assessing AI's Potential to Assist Research, Lou et al.,
• Is your paper being reviewed by an llm? investigating ai text detectability in peer review, Yu et al.,
• WithdrarXiv: A Large-Scale Dataset for Retraction Study, Rao et al.,
• OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews, Idahl et al.,
• Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews, Shin et al.,
• PeerQA: A Scientific Question Answering Dataset from Peer Reviews, Baumg{\"a}rtner et al.,
• Revieweval: An evaluation framework for ai-generated reviews, Kirtani et al.,
• LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews, Purkayastha et al.,
• When AI co-scientists fail: SPOT-a benchmark for automated verification of scientific research, Son et al.,
• Re ²: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions, Zhang et al.,
• PaperEval: A universal, quantitative, and explainable paper evaluation method powered by a multi-agent system, Huang et al.,