Publications

Shows that mid-training language models on their own self-generated data improves downstream reinforcement learning, yielding stronger reasoning than RL alone.

Decouples exploration from generation at inference time so repeated sampling produces genuinely diverse candidate solutions, improving pass@50 by ~21.6% over standard repeated sampling.

A challenging, realistic benchmark of diverse real-world open-source software for evaluating LLM agents on automatically compiling projects, plus OSS-Build-Agent, a strong baseline with an enhanced build-instruction retrieval module.

Shows that as LLMs get better at reasoning they become more susceptible to novel jailbreaks. Introduces ACE and LACE — attacks that encode malicious queries with custom and layered ciphers — and CipherBench to measure cipher-decoding ability.

A GRPO-based interactive training method where a teacher model gives corrective feedback on a student model’s rollouts, instilling self-reflective reasoning without distillation.

Combines multi-agent simulation with an evolutionary algorithm for query rewriting: multiple LLM agents act as simulated shoppers, and their averaged scores form a dynamic reward that iteratively refines the query — improving fitness by ~22%.

A diagnostic benchmark spanning eight domains that evaluates LLMs on six dimensions of reasoning about actions and change, including new ramification constraints for indirect effects. State-of-the-art models struggle across all dimensions, especially ramifications.

A contamination-free, time-sensitive QA benchmark built on synthetic facts, forcing models to do genuine temporal reasoning rather than recalling pre-training knowledge.

Goal-driven, constraint-guided LLM agents that generate scientific hypotheses for materials discovery and design.

Systematically constructs contexts that break common assumptions and shows that, while models reason well over assumption-following contexts, performance drops by up to 20% when those assumptions are broken.