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cs.AI2026

Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents

Qi Liu, Yiqun Chen, Zidan Chen +6

Search agents now answer questions that take dozens of searches to settle, yet how such an agent reads a page has drawn far less attention than how it finds one. Nearly all of them…

cs.AI2026

UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems

Yiqun Chen, Wei Yang, Erhan Zhang +14

LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely op…

cs.AI2026

OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search

Erhan Zhang, Yiqun Chen, Zechun Niu +6

Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforcement learning with verifiable rewa…

cs.AI2026

TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens

Jianpeng Cheng, Xian Wu, Jiangfan Zhang +10

Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this paradigm, a generative model produc…

cs.AI2026

Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models

Wei Wu, Liyi Chen, Congxi Xiao +7

Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their chain-of-thought. However, this par…

cs.AI2026

Knowledge-Graph Paths as Intermediate Supervision for Self-Evolving Search Agents

Huyu Wu, Jun Liu, Xiaochi Wei +3

Self-evolving search agents reduce reliance on human-written training questions by generating and solving their own search tasks. We build on Search Self-Play (SSP), a representati…