The paper introduces DiscoveryRSI, a framework that recursively learns to generate valuable scientific hypotheses by building hypothesis trees through multi‑agent evolutionary search, compiling investigations into executable tasks, and refining the process via credit optimization, replay, and rediscovery. It also presents DFM‑Bench, a benchmark for assessing hypothesis quality across multiple dimensions. Experiments in four life‑science domains show that the system improves hypothesis quality, particularly in falsifiability and data resolvability, and outperforms other large‑language‑model agents in expert evaluations.
arXiv:2609.33357v2 Announce Type: replace Abstract: Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially red...
arXiv:2606. 11851v1 Announce Type: new Abstract: Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions.
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-end...
arXiv:2608.31076v1 Announce Type: cross Abstract: Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experi...
arXiv:2606. 11926v1 Announce Type: cross Abstract: Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction.
Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the resulting lessons into later attempts.
arXiv:2609.15938v1 Announce Type: new Abstract: Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems com...
The paper introduces an experimental model‑class revision framework that jointly proposes structural edits to a hypothesis space and diagnostic experiments to test those edits. By coupling a class‑level distinguishability objective with anytime‑valid sequential evidence, the method only revises the model class after the current one is rejected. On 400 controlled dynamical environments, the approach achieves 89.5% exact recovery with 32 experiments, outperforming baselines and transferring well to unseen mechanisms, library insufficiency detection, and other benchmark tasks.
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.
arXiv:2607. 05682v1 Announce Type: new Abstract: LLM systems for scientific discovery increasingly assist with ideation, literature synthesis, experiment planning, and report generation, but the first research question they propose can remain difficult to audit: it may sound plausible without exposing the mechanism, falsifier, or assumption that a scientist should inspect.
arXiv:2609.07611v1 Announce Type: new Abstract: Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Exi...
arXiv:2607. 01131v1 Announce Type: cross Abstract: Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation.