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FirstResearch introduces a structured framework for LLM scientific discovery agents that generates a Research Question Certificate containing primitive definitions, assumptions, mechanism, falsifiable hypothesis, and failure update rules, making the proposed research question inspectable before execution. Preliminary evaluations using LLM judges show that the certificate-centered approach outperforms baseline systems in audibility and score.
EO-Agents presents a three-agent LLM pipeline for generating Earth observation hypotheses, leveraging a NASA knowledge graph and graph neural network to rank candidate dataset pairings, with LLM agents filtering, generating, and evaluating structured research hypotheses.
DiscoPER is an autonomous framework leveraging large language models and dynamic code generation for open-ended scientific research, using second-order meta-reflection to synthesize discoveries and statistical testing for rigor. Evaluated on a multimodal ecological benchmark, it outperforms baselines in recovering known patterns.
This paper proposes an evolutionary framework inspired by parallel tempering that uses multi-temperature sampling and information exchange to improve the diversity and quality of scientific hypotheses generated by large language models, demonstrated across molecular, equation, and algorithm discovery.
This paper introduces conditional hypothesis generation, a framework that incorporates researcher-specified covariates to steer LLM-based text analysis toward discovering meaningful subgroup differences while addressing confounds like stratum imbalance and sign reversal.
HypoAgent is an agentic framework for interactive abductive hypothesis generation over knowledge graphs, integrating three agents to handle evolving user intents and fine-grained diagnosis, achieving state-of-the-art performance.
LLM-AutoSciLab is a closed-loop framework that uses LLMs to iteratively generate hypotheses, select informative experiments, and refine mechanisms, achieving superior accuracy and sample efficiency on physics and biology benchmarks over prior static methods.
The paper introduces the Multi-Persona Debate System (MPDS), a literature-grounded framework that uses LLMs, persona induction, and structured multi-agent debate to automate the generation of scientific hypotheses, with evaluations in battery materials research showing improved hypothesis quality and cross-perspective integration.
Two AI-powered science assistants, Google's Co-Scientist and FutureHouse's Robin, can generate hypotheses and analyze biological data for drug retargeting, helping researchers find non-obvious connections across scientific fields.
Google DeepMind introduces Co-Scientist, a system that uses a multi-agent 'idea tournament' to generate, debate, and evaluate research hypotheses for open challenges.
This paper proposes a scalable supervised fine-tuning method for training language models to propose research hypotheses across disciplines. It has been accepted by ICML 2026 and the code is open source.
DeepMind's Co-Scientist AI system helped researchers at the University of Edinburgh generate a novel, experimentally verified hypothesis linking the NLRP3 inflammasome to the mechanism of drug resmetirom in MASH liver disease, potentially enabling targeted combination therapies.
Google DeepMind introduces Co-Scientist, a multi-agent AI system built with Gemini that generates, debates, and evolves scientific hypotheses to accelerate research, published in Nature.
Google DeepMind's Co-Scientist is a multi-agent AI system that acts as a virtual team of scientists to search literature, generate hypotheses, and design experiments, compressing months of research into days and already yielding new scientific discoveries.