works on

From the 1 of 13 linked papers with an AI index.

collaborators

13 papers

cs.AI2026

AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions

Zhiyao Cui, Qianyi Wang, Haoyang Yan +26

Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a s…

cs.AI2026

SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation

Zelin Tan, Yiqun Zhang, Hao Li +11

Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge. However, providing skills alone does not guarantee that curr…

cs.CL2026

Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

Lei Bai, Zongsheng Cao, Yang Chen +50

The paper introduces Agents-A1, a 35B mixture-of-experts agent model that attains trillion-parameter-level performance by extending the length of reasoning horizons and integrating…

cs.CL2026

Self-Harness: Harnesses That Improve Themselves

Hangfan Zhang, Shao Zhang, Kangcong Li +5

The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit d…

cs.CV2026

CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels

Mengke Li, Haiquan Ling, Lihao Chen +3

Learning from real-world data is frequently hindered by the compound challenge of long-tailed class distributions and noisy annotations. Existing methods partially address these is…

cs.CV2026

How Many Visual Tokens Do Multimodal Language Models Need? Scaling Visual Token Pruning with F^3A

YiJie Huang, Yiqun Zhang, Zhuoyue Jia +7

Vision-language models improve perception by feeding increasingly long visual token sequences into language backbones, but the resulting inference cost raises a basic scaling quest…