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#search agents

WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

Intermediate

Zelai Xu, Zhexuan Xu et al.Feb 4arXiv

WideSeek-R1 teaches a small 4B-parameter language model to act like a well-run team: one leader plans, many helpers work in parallel, and everyone learns together with reinforcement learning.

#width scaling#multi-agent reinforcement learning#orchestration

Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning

Intermediate

Yansong Ning, Jun Fang et al.Feb 4arXiv

Agent-Omit teaches AI agents to skip unneeded thinking and old observations, cutting tokens while keeping accuracy high.

#LLM agents#reinforcement learning#agentic RL

PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR

Intermediate

James Burgess, Jan N. Hansen et al.Jan 26arXiv

This paper teaches a language-model agent to look up facts in millions of scientific paper summaries and answer clear, single-answer questions.

#RLVR#search agents#PaperSearchQA

Adaptation of Agentic AI

Intermediate

Pengcheng Jiang, Jiacheng Lin et al.Dec 18arXiv

This paper organizes how AI agents learn and improve into one simple map with four roads: A1, A2, T1, and T2.

#agentic AI#adaptation#A1 A2 T1 T2