NatureBench evaluates ten frontier AI coding agents on 90 tasks from Nature papers under web-search-disabled conditions and finds the strongest agent surpasses published SOTA on only 17.8% of tasks, succeeding mainly by translating problems into familiar supervised learning setups.
E.et al.A multi-agent system for automating scientific discovery.Nature(2026)
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7verdicts
UNVERDICTED 7representative citing papers
Speak-to-Objective is a modular agentic pipeline that translates spoken or written commands into fully differentiable objective functions for optofluidic microparticle assembly using LLMs, inverse solvers, and experimental platforms.
A multi-LLM council scores predictive processing papers on an expert ontology, maps results in 3D hypothesis space, and introduces a dispersion metric showing greater spread in global versus local oddball paradigms.
Human-AI collaboration expanded a meta-idea on rational approximation into sign-embedding quantum algorithms for matrix problems, with humans retaining final judgment on routes and refinements.
My Chemical Harness performs evolutionary molecular design by searching over validated synthetic routes with LLMs restricted to high-level preferences, outperforming baselines on an sEH proxy task across multiple metrics.
Coordinated AI agents improve scientific inference from partial evidence in cross-domain tasks when single sources are incomplete, as demonstrated by AUROC gains in vector-borne disease and exoplanet benchmarks but tied performance in others.
The paper proposes the Cybersecurity AI Scientist as a modular multi-agent architecture for automating cybersecurity research, distinguished by its focus on non-stationary threats and anchored in a four-zeros risk-trust-incident-energy frame.
citing papers explorer
-
Agentic Language-to-Objective Synthesis for Optofluidic Assembly
Speak-to-Objective is a modular agentic pipeline that translates spoken or written commands into fully differentiable objective functions for optofluidic microparticle assembly using LLMs, inverse solvers, and experimental platforms.