Papers with scientific discovery
AbductionRules: Training Transformers to Explain Unexpected Inputs (2022.findings-acl)
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| Challenge: | AbductionRules is a set of natural language datasets designed to train and test generalisable abduction over natural-language knowledge bases. |
| Approach: | They propose to train and test generalisable abduction over natural-language knowledge bases by using natural language datasets to fine tune pre-trained Transformers. |
| Outcome: | The proposed models learned generalisable abduction techniques but also exploited the structure of the datasets. |
Learning to Ideate for Machine Learning Engineering Agents (2026.eacl-short)
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Yunxiang Zhang, Kang Zhou, Zhichao Xu, Kiran Ramnath, Yun Zhou, Sangmin Woo, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. |
| Approach: | They propose a framework that separates ideation from implementation that allows an implementation agent to request strategic help from a dedicated Ideator. |
| Outcome: | The proposed framework outperforms implementation-only agent baselines on MLE-Bench and can be trained with reinforcement learning to generate more effective ideas. |
IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery (2025.acl-demo)
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| Challenge: | Recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, but none incorporates human-in-the-loop (HITL) integration. |
| Approach: | They propose an open-source platform to enable researchers to leverage LLM-assisted scientific ideation. |
| Outcome: | The proposed system empowers researchers with greater control throughout ideation process. |
On the Emergence and Test-Time Use of Structural Information in Large Language Models (2026.acl-long)
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| Challenge: | a controlled environment is required to study how language models learn structural information from observational data. |
| Approach: | They propose a natural language dataset based on linguistic structural transformations to study how language models learn abstract structures and utilize the learnt structural information at test-time. |
| Outcome: | The proposed model can generate new knowledge outside the training corpus in a controlled environment. |
From Generalist to Specialist: A Survey of Large Language Models for Chemistry (2025.coling-main)
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| Challenge: | Existing studies on pretraining of LLMs on extensive web-based texts are insufficient for advanced scientific discovery, especially in chemistry. |
| Approach: | They outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs and conceptualize chemistry LLM agents using chemistry tools. |
| Outcome: | The proposed models are based on domain-specific chemistry knowledge and multi-modal information and are capable of accelerating scientific research. |
To be or not to be an Integer? Encoding Variables for Mathematical Text (2022.findings-acl)
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| Challenge: | a number of natural language inference models are limited in interpreting mathematical knowledge written in Natural Language . a variable's meaning is determined exclusively by its defining type, i.e., its context . |
| Approach: | They propose a method that can create context-based representations for variables . they propose 'variable slot' approach which can be used to model variables based on their meaning . |
| Outcome: | The proposed model can be used to represent variables in natural language . it can be applied to a task of variable typing and create context-based representations for variables . |
MolXPT: Wrapping Molecules with Text for Generative Pre-training (2023.acl-short)
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| Challenge: | Experimental results show that Generative pre-trained Transformers (GPT) have great success in natural language processing. |
| Approach: | They propose a unified language model of text and molecules pre-trained on SMILES wrapped by text. |
| Outcome: | The proposed model outperforms strong baselines of molecular property prediction on MoleculeNet and performs comparably to the best model in text-molecule translation while using less than half of its parameters. |
Retrieve to Explain: Evidence-driven Predictions for Explainable Drug Target Identification (2025.acl-long)
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| Challenge: | Existing language models lack the capability to quantitatively and faithfully compare answer plausibility in terms of supporting evidence. |
| Approach: | They propose a retrieval-based model that scores and ranks all possible answers to a research question based on evidence retrieved from a document corpus. |
| Outcome: | The proposed model scores and ranks all possible answers to a scientific question based on evidence retrieved from a document corpus . |
Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science (2025.emnlp-main)
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| Challenge: | Existing systems generate hypothesis, run experiments, analyze data, and write or review papers, but they are costly and impractical. |
| Approach: | They propose a challenge dataset for determining the feasibility of hypotheses framed as claims and a temporally-filtered claim verification task using backtesting to test the validity of claims. |
| Outcome: | The proposed model performs well on retrieval augmented generation and code generation while performing 50% of the task. |
Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding (2026.acl-long)
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Sensen Gao, Shanshan Zhao, Xu Jiang, Lunhao Duan, Yong Xien Chng, Qing-Guo Chen, Weihua Luo, Kaifu Zhang, Jia-Wang Bian, Mingming Gong
| Challenge: | Document understanding is critical for applications from financial analysis to scientific discovery. |
| Approach: | They propose a taxonomy based on domain, retrieval modality, and granularity and review advances involving graph structures and agentic frameworks. |
| Outcome: | The proposed model enables holistic retrieval and reasoning across all modalities, unlocking comprehensive document intelligence. |
Graphine: A Dataset for Graph-aware Terminology Definition Generation (2021.emnlp-main)
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| Challenge: | Lack of large-scale terminology definition dataset hinders definition generation . lack of precise terminology definitions poses great challenges in scientific communication . |
| Approach: | They propose a large-scale terminology definition dataset Graphine that exploits the graph structure of terminologies to generate graph-aware text generation models. |
| Outcome: | The proposed model outperforms existing models by exploiting graph structure of terminologies. |
Agent Laboratory: Using LLM Agents as Research Assistants (2025.findings-emnlp)
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Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, Emad Barsoum
| Challenge: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Approach: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Outcome: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision (2021.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a fundamental step in scientific literature analysis to build AI-driven systems for molecular discovery, synthetic strategy designing, and manufacturing. |
| Approach: | They propose an ontology-guided method for fine-grained named entity recognition (NER) it leverages the chemistry type ontologies to generate distant labels with flexible KB-matching . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods with a .25 absolute F1 improvement. |
EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery (2026.acl-long)
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| Challenge: | Existing methods for large language models (LLMs) face challenges in the design of research workflows and multi-role collaboration mechanisms. |
| Approach: | They propose a multi-agent scientific collaboration framework which integrates bio-inspired evolution with knowledge graph modeling. |
| Outcome: | EvoSci outperforms baselines in peer-review and ranking evaluations on real-world research topics. |
InfAL: Inference Time Adversarial Learning for Improving Research Ideation (2025.findings-emnlp)
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| Challenge: | Advancements in Large Language Models (LLMs) have opened new opportunities for scientific discovery by assisting researchers in generating novel hypotheses and ideas. |
| Approach: | They propose an inference time adversarial learning approach that optimizes the utilization of LLMs’ parametric knowledge without additional model training. |
| Outcome: | The proposed approach optimizes the utilization of LLMs’ parametric knowledge without requiring additional model training, making adversarial learning efficient and context-driven. |
Improving Chemical Understanding of LLMs via SMILES Parsing (2025.emnlp-main)
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| Challenge: | Molecular string representations such as SMILES and SELFIES are becoming a standard format for applying large language models (LLMs) however, molecular strings follow complex syntactic rules for encoding molecules, which LLMs struggle to interpret. |
| Approach: | They propose a framework that parses SMILES into clean and deterministic tasks to promote graph-level molecular comprehension. |
| Outcome: | The proposed framework improves structural comprehension and competes with the baseline on the Mol-Instructions benchmark. |
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding (2023.findings-emnlp)
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| Challenge: | Pre-trained language models (LMs) have shown effectiveness in literature understanding tasks, especially when tuned via contrastive learning. |
| Approach: | They propose a multi-task contrastive learning framework that enables common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. |
| Outcome: | The proposed framework outperforms state-of-the-art pre-trained language models on a comprehensive dataset. |
ReviewRL: Towards Automated Scientific Review with RL (2025.emnlp-main)
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Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou
| Challenge: | Existing automated review systems struggle with factual accuracy, rating consistency, and analytical depth. |
| Approach: | They propose a framework for generating comprehensive and factually grounded scientific paper reviews using supervised fine-tuning and reinforcement learning. |
| Outcome: | The proposed framework outperforms existing methods on ICLR 2025 papers. |
ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis (2026.findings-acl)
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| Challenge: | Existing approaches to climate research are limited to simple Q A tasks . a lack of data and computational expertise has created bottlenecks . |
| Approach: | They propose a general-purpose autonomous framework to perform end-to-end climate research tasks across diverse climate sub-fields. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks in rigorousness and practicality. |
Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang
| Challenge: | Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models. |
| Approach: | This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy . |
| Outcome: | The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications. |
InductionBench: LLMs Fail in the Simplest Complexity Class (2025.acl-long)
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| Challenge: | Existing benchmarks focus on deductive reasoning, largely overlooking inductive reasoning. |
| Approach: | They propose a benchmark to evaluate the inductive reasoning ability of large language models. |
| Outcome: | The proposed benchmark demonstrates that even the most advanced modelw struggle to master the simplest complexity classes within the subregular hierarchy of functions. |
MIR: Methodology Inspiration Retrieval for Scientific Research Problems (2025.acl-long)
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Aniketh Garikaparthi, Manasi Patwardhan, Aditya Sanjiv Kanade, Aman Hassan, Lovekesh Vig, Arman Cohan
| Challenge: | Existing methods for generating ideas rely on grounding the discovery process within the literature, but their effectiveness varies significantly with the quality and nature of the retrieved literature. |
| Approach: | They construct a methodological inspiration retrieval task using a citation-based methodology adjacency graph and embed an "intuitive prior'' into dense retrievers. |
| Outcome: | The proposed method achieves significant gains in Recall@3 and mAP over strong baselines. |
Datasets for Scientific Literature Understanding: A Survey (2026.findings-acl)
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| Challenge: | Empowering machines to understand scientific literature is crucial for accelerating scientific discovery and advancing the AI for Science paradigm. |
| Approach: | They propose a systematic taxonomy that organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
| Outcome: | The proposed taxonomy organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
Scaling Unverifiable Rewards: A Case Study on Visual Insights (2026.findings-acl)
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| Challenge: | Existing methods to scale complex, open-ended tasks with unverifiable rewards are not scalable to multi-stage pipelines. |
| Approach: | They propose a process-based refinement framework that scales inference across stages of a multi-agent pipeline, instead of refining a single output over time. |
| Outcome: | The proposed framework scales inference across stages of a multi-agent pipeline, instead of refining a single output over time as in prior work. |
Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series? (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) and Multimodal LLMs (MLLMs) show strong performance in complex reasoning tasks, but their ability to extract symbolic laws from time series data remains underexplored. |
| Approach: | They propose a benchmark to assess symbolic reasoning over real-world time series across three tasks: multivariate symbolic regression, Boolean network inference, and causal discovery. |
| Outcome: | The proposed framework integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system. |
BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning (2026.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. |
| Approach: | They propose a framework that anchors probabilistic planning in a deterministic Finite State Machine (FSM) they propose reducing token consumption by 6 through symbolic abstraction to address context bottlenecks . |
| Outcome: | The proposed framework achieves 95.6% physical compliance, compared to 21.0% for ReAct, in the extended BioProBench benchmark. |