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EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence | AI Learning Platform

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EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence

Qijin Zhang, Hanzhen Wang, Yuehan Zhao et al.1 min readarXivOct 22, 2026

Key Takeaway for Students

Tool-using language model agents rely on environmental facts to decide whether an action is permitted. When a tool response omits a safety-relevant fact, an agent may carry out a prohibited action even though the user request is benign and every returned statement is true. We introduce \bench{}, an executable benchmark for evaluating unsafe execution, safety boundaries, and evi

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Career Insight

high demand

Why it matters

This paper addresses a critical safety challenge for LLM agents: their potential to execute unsafe actions when environmental evidence is incomplete. For ML engineers, understanding these vulnerabilities and how to evaluate them is crucial for deploying robust and safe AI systems. The benchmark provides a standardized way to measure and improve agent reliability in real-world, uncertain environments.

Skills to learn

Designing and implementing LLM agent architectures with tool-use capabilitiesDeveloping safety guardrails and fact-checking mechanisms for LLM agentsUtilizing and extending benchmarks for LLM agent evaluation (e.g., EpiSEM-Bench)Implementing evidence recovery strategies in agentic workflowsDebugging and analyzing agent failures related to missing or ambiguous information

Interview questions

Describe the challenges of ensuring safety in tool-using LLM agents, particularly when external information sources are incomplete or misleading. How would you design an agent to mitigate these risks?
Given a scenario where an LLM agent needs to make a safety-critical decision based on tool outputs, what strategies could you employ to detect and handle missing or ambiguous evidence from the tools?
Design a system architecture for an LLM agent that integrates multiple external tools and prioritizes safety. How would you incorporate a 'safety boundary' mechanism and an 'evidence recovery' module into this design, and what metrics would you use to evaluate its effectiveness?

Relevant for ~20 months · generated by the enrichment pipeline