16 papers
WFM: Wiki Foundation Model for Complex Agentic Reasoning
Junnan Dong, Linhao Luo, Senlei Zhang +9
Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented generation. While graphs have show…
From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities
Jiayi Kuang, Yinghui Li, Yunze Song +11
Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks evaluate onl…
RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents
Qiang Liu, Taian Guo, Ruizhi Qiao +1
Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks. However, in long-horizon, multi-turn tasks char…
Skills Know Their Neighbors: Cluster-Contrastive Capability Pages for Skill Retrieval
Zifei Wang, Wei Wen, Qiang Ji +1
As skill libraries grow, large language model agents must retrieve reusable skills from candidates that often share the same topic and vocabulary but implement different capabiliti…
Training-Free Hashing-Based Attention via Binary Principal Components
Daohai Yu, Zhanpeng Zeng, Keyu Chen +6
Long-context large language models (LLMs) are increasingly deployed in real-world applications, yet self-attention remains a major efficiency bottleneck -- especially during decodi…
Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints
Juntao Wu, Wei Wen, Xianting Huang +4
Evaluating the exhaustive search capabilities of large language models (LLMs) is plagued by a fundamental paradox: verifying completeness requires complete ground truth, yet high-e…