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ARXIV:2604.12177 · AGENTS · SUBMITTED 15 APR · 17:00 UTC · FRESHNESS STALE
ARXIV:2604.12177AGENTSSUBMITTED 15 APR · 17:00 UTCFRESHNESS STALEJie Wu · Ming Gong · arXiv
A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines.
Opportunity summary
Pain A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines. We call this failure mode policy-invisible violations: cases in which compliance depends on entity attributes, contextual state,…
LLM-based agents can execute actions that are syntactically valid, user-sanctioned, and semantically appropriate, yet still violate organizational policy because the facts needed for correct policy judgment are hidden at decision time. We call this…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To demonstrate what world-state-grounded enforcement can achieve under favorable conditions, we introduce Sentinel, an enforcement framework based on counterfactual graph simulation. Code availability is…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines.
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10.48550/arXiv.2604.12177A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines.
Abstract
LLM-based agents can execute actions that are syntactically valid, user-sanctioned, and semantically appropriate, yet still violate organizational policy because the facts needed for correct policy judgment are hidden at decision time. We call this failure mode policy-invisible violations: cases in which compliance depends on entity attributes, contextual state, or session history absent from the agent's visible context. We present PhantomPolicy, a benchmark spanning eight violation categories with balanced violation and safe-control cases, in which all tool responses contain clean business data without policy metadata. We manually review all 600 model traces produced by five frontier models and evaluate them using human-reviewed trace labels. Manual review changes 32 labels (5.3%) relative to the original case-level annotations, confirming the need for trace-level human review. To demonstrate what world-state-grounded enforcement can achieve under favorable conditions, we introduce Sentinel, an enforcement framework based on counterfactual graph simulation. Sentinel treats every agent action as a proposed mutation to an organizational knowledge graph, performs speculative execution to materialize the post-action world state, and verifies graph-structural invariants to decide Allow/Block/Clarify. Against human-reviewed trace labels, Sentinel substantially outperforms a content-only DLP baseline (68.8% vs. 93.0% accuracy) while maintaining high precision, though it still leaves room for improvement on certain violation categories. These results demonstrate what becomes achievable once policy-relevant world state is made available to the enforcement layer.
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unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines. We call this failure mode policy-invisible violations: cases in which compliance depends on entity attributes, contextual state...
METHOD
LLM-based agents can execute actions that are syntactically valid, user-sanctioned, and semantically appropriate, yet still violate organizational policy because the facts needed for correct policy judgment are hidden at decision time. We call this failure mode policy-invisible...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To demonstrate what world-state-grounded enforcement can achieve under favorable conditions, we introduce Sentinel, an enforcement framework based on counterfactual graph simulation. Code availability is...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines. We call this failure mode policy-invisible violations: cases in which compliance depends on entity attributes, contextual state, or session history absent from the agent's visible context.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM-based agents can execute actions that are syntactically valid, user-sanctioned, and semantically appropriate, yet still violate organizational policy because the facts needed for correct policy judgment are hidden at decision time. We call this failure mode policy-invisible violations: cases in which compliance depends on entity attributes, contextual state, or session history absent from the agent's visible context.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To demonstrate what world-state-grounded enforcement can achieve under favorable conditions, we introduce Sentinel, an enforcement framework based on counterfactual graph simulation. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for LLM agents that enforces organizational policies by simulating world-state changes, significantly outperforming content-only baselines.
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Commercial read
7.0/10 public viability
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reason
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Technical feasibility
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ARTIFACTS
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DEFENSIBILITY
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