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New Co-ReAct framework guides AI agents with step-level rubrics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Co-ReAct, a new framework that uses step-level rubrics to guide ReAct-style AI agents during inference. This approach aims to improve the decision-making process for search-intensive, multi-step reasoning tasks, which often suffer from shallow or redundant trajectories. Co-ReAct injects rubrics into the agent's context at each step to guide evidence seeking, reasoning, and self-evaluation, leading to consistent improvements on benchmarks like DeepResearchBench and SQA-CS-V2. AI

IMPACT Enhances AI agent performance in complex reasoning tasks by providing step-by-step guidance.

RANK_REASON The cluster contains an academic paper detailing a new AI framework and its performance on benchmarks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Co-ReAct framework guides AI agents with step-level rubrics

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The cluster contains an academic paper detailing a new AI framework and its performance on benchmarks.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jiazheng Kang, Bowen Zhang, Zixin Song, Jiangwang Chen, Xiao Yang, Da Zhu, Guanjun Jiang ·

    Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents

    arXiv:2605.23590v1 Announce Type: new Abstract: ReAct-style agents for search-intensive, multi-step reasoning tasks rely largely on their own internal judgment to decide what evidence to seek, which reasoning or action step to take next, and when to stop, often producing shallow,…

  2. arXiv cs.AI TIER_1 English(EN) · Guanjun Jiang ·

    Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents

    ReAct-style agents for search-intensive, multi-step reasoning tasks rely largely on their own internal judgment to decide what evidence to seek, which reasoning or action step to take next, and when to stop, often producing shallow, redundant, or poorly targeted trajectories. Pri…

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