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SearchJev model boosts search agent speed and accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SearchJev, a novel System-1 model designed to enhance the efficiency and reliability of search agents. This model separates quick decision-making from complex reasoning, scoring potential search actions without generating autoregressive output. SearchJev utilizes a new technique called Soft-Label Learning for Calibrated Decisions (SLCD) to improve confidence in its judgments, even with uncertain supervision. When integrated into a dual-system agent, SearchJev significantly speeds up search processes and improves answer accuracy compared to traditional autoregressive models. AI

IMPACT Enhances search agent efficiency and decision reliability, potentially speeding up information retrieval tasks.

RANK_REASON The cluster contains an academic paper detailing a new model and benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

SearchJev model boosts search agent speed and accuracy

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The cluster contains an academic paper detailing a new model and benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Congfeng Cao, Lipeng Zuo, Konstantinos Papakostas, Qiwei Xu, Songwei Xu, Qianru Meng, Lun Zhou, Zhaochun Ren, Yougang Lyu, Xiaohui Yan ·

    SearchJev: A Calibrated System-1 Model for Search Agents

    arXiv:2610.05107v2 Announce Type: replace-cross Abstract: Search agents repeatedly make short decisions about relevance, evidence sufficiency, and search actions. Using generative language models for these decisions introduces latency and unreliable confidence. We present SearchJ…

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