Practical static analysis of large systems such as the Linux kernel often depends on information that is difficult to capture precisely and on identifying the parts of a large codebase where precise reasoning is needed. This dissertation studies how LLMs can complement static analysis through two roles: specification r...
Hao-Nan Li· Companion Proceedings of the...· 0 citations
Skills equip LLM agents with professional knowledge and guidance to complete long-horizon and complex tasks. Although skills have been widely adopted in recent agent paradigms and harnesses, how to synthesize reliable training data and how to train agents for skill use remain underexplored. In this work, we propose Ski...
Renxi Wang, M. Hee, Fajri Koto et al.· 0 citations
K2-V2 is introduced, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and knowledge retrieval from general LLMs, to empower the community with a capable, reasoning-centric foundation.
K2 Team Zhengzhong Liu, Liping Tang, Ling-Hao Jin et al.· 0 citations
This paper introduces Magenta, a training-free agentic pipeline that, given only a natural-language problem, produces an answer, expresses it as a Lean 4 statement, and constructs a machine-checked proof, which achieves 100% accuracy across all evaluated olympiad benchmarks.
Joshua Ong Jun Leang, Hao-Nan Li, Zheng-Yang Zhao et al.· 0 citations
The results show that improving IF in LRMs can significantly enhance privacy, suggesting a promising direction for future privacy-aware LRMs, and introduces an SFT dataset that teaches models to follow general instructions throughout their reasoning process.
Haritz Puerto, Haonan Li, Xudong Han et al.· 0 citations
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