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Partial Evidence Bench for Agentic Systems
๐กNew benchmark exposes unsafe completeness claims in enterprise AI agents
โก 30-Second TL;DR
What Changed
72 tasks in three enterprise scenarios: due diligence, compliance audit, security incident response
Why It Matters
This benchmark quantifies critical governance failures in enterprise agents without human judges, promoting safer designs. It reveals model- and scenario-specific over/under claiming of completeness, essential for policy-constrained AI deployments.
What To Do Next
Download arXiv:2605.05379 and run Partial Evidence Bench on your enterprise agent workflows.
Who should care:Researchers & Academics
Key Points
- โข72 tasks in three enterprise scenarios: due diligence, compliance audit, security incident response
- โขEvaluates correctness, completeness awareness, gap-report quality, unsafe completeness
- โขACL-partitioned corpora with oracle complete/authorized answers and gap-reports
- โขBaselines show silent filtering catastrophically unsafe; fail-and-report safer
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขThe benchmark utilizes a 'Partial Evidence' framework to specifically address the 'hallucination of completeness' problem, where agents assume missing data implies a negative result rather than an unknown state.
- โขIt introduces a novel metric, 'Unsafe Completeness Rate' (UCR), which quantifies how often an agent provides a definitive answer despite lacking the necessary authorized access to the full corpus.
- โขThe dataset architecture employs a 'Ground Truth Oracle' that distinguishes between 'Authorized-Complete' answers and 'Gap-Report' requirements, forcing agents to explicitly acknowledge information boundaries.
๐ Competitor Analysisโธ Show
| Feature | Partial Evidence Bench | AgentBench | GAIA Benchmark |
|---|---|---|---|
| Focus | Authorization-limited evidence | General agent capabilities | General AI assistants |
| Scenario Type | Enterprise/Compliance | Diverse/General | Real-world/Multimodal |
| Failure Mode Analysis | High (Silent Filtering) | Low | Low |
| Access Control Modeling | Yes (ACL-partitioned) | No | No |
๐ ๏ธ Technical Deep Dive
- โขCorpus Architecture: Uses a hierarchical ACL (Access Control List) structure where documents are tagged with sensitivity levels (Public, Internal, Restricted, Confidential).
- โขEvaluation Protocol: Employs a dual-path scoring system: 1) Correctness of retrieved/synthesized info, 2) Validity of 'Gap-Reporting' when the agent encounters an ACL-restricted document.
- โขOracle Mechanism: Uses a high-parameter LLM (e.g., GPT-4o or Claude 3.5 Opus) as the ground-truth judge to compare agent outputs against a 'Full-Access' reference corpus.
- โขTask Generation: Tasks are synthetically generated via a template-based approach that injects 'missing evidence' scenarios into the prompt, forcing the agent to choose between hallucination and reporting a gap.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
Enterprise RAG systems will shift from 'silent retrieval' to 'explicit uncertainty reporting'.
The benchmark's findings on the dangers of silent filtering will likely force a standard requirement for agents to cite the scope of their search permissions.
ACL-aware evaluation will become a standard component of LLM safety certifications.
As agents gain autonomy in enterprise environments, the ability to respect data boundaries will be prioritized over raw reasoning performance.
โณ Timeline
2025-11
Initial development of ACL-partitioned synthetic enterprise datasets.
2026-02
Internal pilot testing of the oracle-based gap-reporting evaluation framework.
2026-05
Public release of the Partial Evidence Bench on ArXiv.
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Original source: ArXiv AI โ