5 papers · 1 filter
AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Yang Yue, Xuancheng Zhu, Yuyang Ma +7
The automated design of agentic systems offers a promising pathway for scaling large language models (LLMs) beyond single-agent reasoning. While prior work has advanced task perfor…
Can LLM Agents Sustain Long-Horizon Organizational Dynamics?
Xuancheng Zhu, Yang Yue, Shuaibing Wan +4
Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organizations, where goals must p…
Klear-AgentForge: Forging Agentic Intelligence through Posttraining Scaling
Qi Wang, Hongzhi Zhang, Jia Fu +12
Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In thi…
Leanabell-Prover: Posttraining Scaling in Formal Reasoning
Jingyuan Zhang, Qi Wang, Xingguang Ji +6
Recent advances in automated theorem proving (ATP) through LLMs have highlighted the potential of formal reasoning with Lean 4 codes. However, ATP has not yet be revolutionized by…
Leanabell-Prover-V2: Verifier-integrated Reasoning for Formal Theorem Proving via Reinforcement Learning
Xingguang Ji, Yahui Liu, Qi Wang +7
We introduce our Leanabell-Prover-V2, a 7B large language models (LLMs) that can produce formal theorem proofs in Lean 4, with verifier-integrated Long Chain-of-Thoughts (CoT). Fol…