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New PRInTS model enhances AI agents' long-horizon information seeking

Researchers have developed PRInTS, a new generative reward model designed to improve AI agents' ability to seek information over long periods. Unlike previous models that offered binary judgments on short tasks, PRInTS provides dense, multi-dimensional scoring for each step, considering factors like tool interpretation and output informativeness. It also compresses long contexts into summaries while retaining essential information for evaluation. Experiments on benchmarks like FRAMES and GAIA show that PRInTS significantly enhances information-seeking capabilities in various agents, even outperforming larger, frontier models. AI

IMPACT Enhances AI agent capabilities in complex, multi-step information gathering, potentially improving performance in tasks requiring extensive tool use and reasoning.

RANK_REASON This is a research paper describing a new model and its evaluation on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jaewoo Lee, Archiki Prasad, Justin Chih-Yao Chen, Zaid Khan, Elias Stengel-Eskin, Mohit Bansal ·

    PRInTS: Reward Modeling for Long-Horizon Information Seeking

    arXiv:2511.19314v2 Announce Type: replace Abstract: Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories. However, such multi-step information-seeking tasks remain challenging for agen…