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Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems

Summary

This paper introduces Partial Evidence Bench, a deterministic benchmark for measuring when enterprise AI agents produce seemingly complete answers despite missing critical evidence due to authorization constraints. It covers three scenario families with 72 tasks and evaluates four aspects. Results show silent filtering is catastrophically unsafe, while explicit fail-and-report behavior eliminates unsafe completeness.

SourcearXiv AIAuthor: Krti Tallam
Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems
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[2605.05379] Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems

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Computer Science > Artificial Intelligence

arXiv:2605.05379 (cs)

[Submitted on 6 May 2026]

Title:Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems

Authors:Krti Tallam

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Abstract:Enterprise agents increasingly operate inside scoped retrieval systems, delegated workflows, and policy-constrained evidence environments. In these settings, access control can be enforced correctly while the system still produces an answer that appears complete even though material evidence lies outside the caller's authorization boundary. This paper introduces Partial Evidence Bench, a deterministic benchmark for measuring that failure mode. The benchmark ships three scenario families -- due diligence, compliance audit, and security incident response -- with 72 tasks total, ACL-partitioned corpora, oracle complete answers, oracle authorized-view answers, oracle completeness judgments, and structured gap-report oracles. It evaluates systems along four surfaces: answer correctness, completeness awareness, gap-report quality, and unsafe completeness behavior. Checked-in baselines show that silent filtering is catastrophically unsafe across all shipped families, while explicit fail-and-report behavior eliminates unsafe completeness without collapsing the task into trivial abstention. Preliminary real-model runs show model-dependent and scenario-sensitive differences in whether systems overclaim completeness, conservatively underclaim, or report incompleteness in an enterprise-usable form. The benchmark's broader contribution is to make a governance-critical agent failure measurable without human judges or contamination-prone static corpora.

Comments: Benchmark paper with deterministic synthetic corpora, 14 pages, 6 tables

Subjects:

Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Emerging Technologies (cs.ET)

Cite as: arXiv:2605.05379 [cs.AI]

(or arXiv:2605.05379v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.05379

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Krti Tallam [view email] [v1] Wed, 6 May 2026 19:01:29 UTC (17 KB)

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Key points and analysis

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Key points

  • Enterprise AI agents often give apparently complete answers while missing evidence due to authorization limits.
  • Partial Evidence Bench measures this failure mode with 72 tasks across three scenarios.
  • Silent filtering is catastrophically unsafe across all scenarios.
  • Explicit fail-and-report behavior eliminates unsafe completeness without collapsing into trivial abstention.

Highlights and analysis are generated automatically and may contain errors. Check the original source.