From the 1 of 7 linked papers with an AI index.
2 citations · 2 across the 5 of their papers we have counts for
7 papers
LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception
Binyan Xu, Haitao Li, Kehuan Zhang
The paper demonstrates that large language model agents can better manage their working memory when given an interface that reveals the size, age, and usage of each memory block, i…
OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning
Ziyou Hu, Zhengliang Shi, Minghang Zhu +5
Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both training and inference. However, exist…
APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL
Bowen Cao, Weibin Liao, Yushi Sun +3
Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The primary limitation lies in their reli…
From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?
Binyan Xu, Dong Fang, Haitao Li +1
Multi-agent systems (MAS) for structured data-science tasks externalize analytical control through workflows spanning stages, tools, shared state, verification, and repair. Distill…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +339
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
Enhancing LLM-Based Agents via Global Planning and Hierarchical Execution
Junjie Chen, Haitao Li, Jingli Yang +2
Intelligent agent systems based on Large Language Models (LLMs) have shown great potential in real-world applications. However, existing agent frameworks still face critical limita…