6 papers
Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values
Haonan Dong, Qiguan Feng, Kehan Jiang +3
Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly drawn growing research attentio…
NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons
Haonan Dong, Kehan Jiang, Haoran Ye +3
Large Reasoning Models (LRMs) have recently achieved remarkable success in complex reasoning tasks. However, closer scrutiny reveals persistent failure modes compromising performan…
FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models
Kehan Jiang, Haonan Dong, Zhaolu Kang +2
Recent Large Reasoning Models (LRMs) like DeepSeek-R1 have demonstrated remarkable success in complex reasoning tasks, exhibiting human-like patterns in exploring multiple alternat…
Meta Context Engineering via Agentic Skill Evolution
Haoran Ye, Xuning He, Vincent Arak +2
The operational efficacy of large language models relies heavily on their inference-time context. This has established Context Engineering (CE) as a formal discipline for optimizin…
AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
Haonan Dong, Wenhao Zhu, Guojie Song +1
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method validated across NLP and CV domains. However, LoRA faces an inherent low-rank bottlenec…
Meta-R1: Empowering Large Reasoning Models with Metacognition
Haonan Dong, Haoran Ye, Wenhao Zhu +2
Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex tasks, exhibiting emergent, human-like thinking patterns. Despite their advances, we identify a fundame…