INSPIRE is an Internalize-Then-Improve approach combining Reference-Guided Student Internalization (RGSI), which produces high-quality preference candidates under the policy model's own distribution, with a stage-wise rubric preference training strategy that decomposes learning into method-oriented and correctness-oriented stages.
Shuai Wang, Jiayi Kuang, Yinghui Li et al.· 0 citations
Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.
Jiayi Kuang, Yinghui Li, Yun-Ze Song et al.· 0 citations
This work presents PhyAgentOS, a runtime foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services, and distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.
Yang Liu, Weixing Chen, Xinshuai Song et al.· 2 citations