DiscoverPhysics is a new benchmark with 22 on-demand N-body simulated worlds where LLM agents design experiments to infer non-standard physics, evaluated via held-out trajectory MSE and LLM-judged explanation quality.
Biodiscoveryagent: An ai agent for designing genetic perturbation experiments
7 Pith papers cite this work, alongside 16 external citations. Polarity classification is still indexing.
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OPD-Evolver uses on-policy self-distillation in fast interaction and slow attribution loops to build agents with holistic memory competence, outperforming prior systems by up to 11.5% and allowing a 9B model to compete with much larger ones.
IR-Agent is a multi-agent LLM framework that emulates expert IR spectral analysis procedures to improve molecular structure elucidation accuracy and adaptability.
A principled reward design for tool selection and application in RL-trained LLMs delivers 17% gains over base models and 15% over SFT across benchmarks.
CLIO agent applies calibrated deference in closed-loop AORFB negolyte design, achieving 90 mV redox potential gain with restored reversibility after hypothesis-driven redesign from phosphonate to sulfonate.
A review paper that surveys AI uses across the food innovation pipeline for sustainable proteins and identifies four strategic priorities for the emerging field.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
citing papers explorer
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DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking
DiscoverPhysics is a new benchmark with 22 on-demand N-body simulated worlds where LLM agents design experiments to infer non-standard physics, evaluated via held-out trajectory MSE and LLM-judged explanation quality.
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OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation
OPD-Evolver uses on-policy self-distillation in fast interaction and slow attribution loops to build agents with holistic memory competence, outperforming prior systems by up to 11.5% and allowing a 9B model to compete with much larger ones.
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IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra
IR-Agent is a multi-agent LLM framework that emulates expert IR spectral analysis procedures to improve molecular structure elucidation accuracy and adaptability.
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ToolRL: Reward is All Tool Learning Needs
A principled reward design for tool selection and application in RL-trained LLMs delivers 17% gains over base models and 15% over SFT across benchmarks.
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Closed-Loop Molecular Design with Calibrated Deference
CLIO agent applies calibrated deference in closed-loop AORFB negolyte design, achieving 90 mV redox potential gain with restored reversibility after hypothesis-driven redesign from phosphonate to sulfonate.
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Artificial Intelligence for Food Innovation
A review paper that surveys AI uses across the food innovation pipeline for sustainable proteins and identifies four strategic priorities for the emerging field.
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Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.