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New RITE benchmark tests LLM agent resilience to silent errors

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced RITE, a new benchmark designed to evaluate the resilience of large language model agents to silent errors in e-commerce workflows. These silent errors, which occur when a tool returns a plausible but incorrect value without explicit failure, can propagate through multi-step processes and halve task success rates. The RITE benchmark, built from industrial workflows and production logs, injects semantic errors into fixed workflow prefixes to test agent performance. Findings indicate that silent errors are more detrimental than explicit failures, with recognition, verification, and evidence integration posing successive bottlenecks for agent resilience. A proposed 'State Checker' intervention aims to improve this resilience. AI

IMPACT Highlights the challenge of silent errors in LLM agents, potentially impacting the reliability of AI in e-commerce and other complex task domains.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for evaluating AI systems. [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 →

New RITE benchmark tests LLM agent resilience to silent errors

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The cluster describes a new academic paper introducing a benchmark for evaluating AI systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wenshuo Zhang, Yuquan Lin, Yushi Sun, Yishi Lin, Rui Sheng, Peng He, Han Gao, Wang Jikai, Yifeng Rong ·

    RITE: Evaluating End-to-End Resilience to Silent Tool Errors in E-Commerce Agentic Systems

    arXiv:2610.27777v1 Announce Type: new Abstract: Tool-using LLM agents increasingly answer e-commerce queries through multi-step tool interactions. The expected output of a tool call can shift between agent development and execution, allowing a plausible but incorrect value to be …

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