Collider-Bench is a new benchmark showing that current LLM agents cannot reliably reproduce LHC analyses at the level of a physicist-in-the-loop.
Frontierscience: Evaluating ai’s ability to perform expert-level scien- tific reasoning
9 Pith papers cite this work. Polarity classification is still indexing.
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CITE certifies that a prespecified answer is the unique mode of an LLM response distribution with anytime-valid error control under arbitrary data-driven stopping and without prior knowledge of the answer set.
A training recipe for tool-integrated reasoning models achieves state-of-the-art open-source results on math benchmarks such as 96.7% and 99.2% on AIME 2025 at 4B and 30B scales by balancing tool-use trajectories and optimizing for pass@k during SFT before stable RLVR.
SciResearcher automates creation of diverse scientific reasoning tasks from academic evidence to train an 8B model that sets new SOTA at 19.46% on HLE-Bio/Chem-Gold and gains 13-15% on SuperGPQA-Hard-Biology and TRQA-Literature.
LABBench2 is a more challenging benchmark than LAB-Bench for assessing AI performance on biology research tasks, with frontier models showing accuracy drops of 26-46% across subtasks.
A 30B model trained via reverse-perplexity SFT, two-stage RL, and test-time scaling achieves gold-medal-level results on IMO 2025 and IPhO 2024/2025.
EvoMaster is a self-evolving agent framework that achieves state-of-the-art results on scientific benchmarks by enabling iterative hypothesis refinement and knowledge accumulation across domains.
COMPOSITE-STEM is a new benchmark of 70 expert-curated STEM tasks where frontier AI agents score at most 21% using flexible exact-match and rubric-based grading.
A summary of expert opinions on AI's impact on the research-education environment in physics from a KITP discussion session.
citing papers explorer
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Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
Collider-Bench is a new benchmark showing that current LLM agents cannot reliably reproduce LHC analyses at the level of a physicist-in-the-loop.
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CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
CITE certifies that a prespecified answer is the unique mode of an LLM response distribution with anytime-valid error control under arbitrary data-driven stopping and without prior knowledge of the answer set.
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Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
A training recipe for tool-integrated reasoning models achieves state-of-the-art open-source results on math benchmarks such as 96.7% and 99.2% on AIME 2025 at 4B and 30B scales by balancing tool-use trajectories and optimizing for pass@k during SFT before stable RLVR.
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SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning
SciResearcher automates creation of diverse scientific reasoning tasks from academic evidence to train an 8B model that sets new SOTA at 19.46% on HLE-Bio/Chem-Gold and gains 13-15% on SuperGPQA-Hard-Biology and TRQA-Literature.
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LABBench2: An Improved Benchmark for AI Systems Performing Biology Research
LABBench2 is a more challenging benchmark than LAB-Bench for assessing AI performance on biology research tasks, with frontier models showing accuracy drops of 26-46% across subtasks.
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Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
A 30B model trained via reverse-perplexity SFT, two-stage RL, and test-time scaling achieves gold-medal-level results on IMO 2025 and IPhO 2024/2025.
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EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
EvoMaster is a self-evolving agent framework that achieves state-of-the-art results on scientific benchmarks by enabling iterative hypothesis refinement and knowledge accumulation across domains.
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COMPOSITE-Stem
COMPOSITE-STEM is a new benchmark of 70 expert-curated STEM tasks where frontier AI agents score at most 21% using flexible exact-match and rubric-based grading.
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AI and the Research-Education Environment of Physics
A summary of expert opinions on AI's impact on the research-education environment in physics from a KITP discussion session.