most citedCoEvolve: Training LLM Agents via Agent-Data Mutual Evolution

1 citations · 1 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CV2026

LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks

Ziyu Ma, Hailang Huang, Shun Zou +5

Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, exis…

cs.CV2026

Towards High-Resolution Visual Perception via Hierarchical Entity Exploration

Ziyu Ma, Shidong Yang, Yuxiang Ji +5

High-resolution (HR) image perception remains a key challenge in multimodal large language models (MLLMs), as fine-grained details are often lost when the image is processed as a w…

cs.CV2026

Towards Memory-Efficient Autoregressive Video Generation via Instance-Specific Parametric Absorption

Xiaomeng Fu, Jia Li, Yiming Hu +5

Autoregressive (AR) streaming models have emerged as a powerful paradigm for long video generation. However, the linearly growing Key-Value (KV) cache poses a significant bottlenec…

cs.CL2026

BlockPilot: Instance-Adaptive Policy Learning for Diffusion-based Speculative Decoding

Hao Zhang, Yiming Hu, Yong Wang +3

Speculative decoding accelerates inference by using a lightweight draft model to generate candidate tokens in parallel, and are then verified by the target model, enabling lossless…

cs.CL20261 cited

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution

Shidong Yang, Ziyu Ma, Tongwen Huang +3

Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent's evolving behavior and leads to poor coverage of comp…

cs.AI2026

SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

Ziyu Ma, Shidong Yang, Yuxiang Ji +5

Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar w…