A Closed-Loop Consequence-Governance Runtime for AI Agents
This paper instead gates on externally measured structural consequences — irreversibility, egress, and control-plane edits — and composes the resulting checks into a closed-loop runtime governance architecture for tool-using agents. The primary claim (C1) concerns endogenous censoring: because an active gate blocks precisely the high-cost actions, its own blocking censors the unobserved region in a cost-correlated way, so cost-weighted predictive uncertainty over a blocked action behaves as an empirically calibrated, conservative risk signal. On 500 executed sandbox trials the counterfactual twin reaches MAE 0.053, an uncertainty–error correlation of +0.81, and a blocked region 4.6x more costly than the allowed region; stratified sandbox audits recover deep-region coverage from 5% to 92%.
- ▪This paper instead gates on externally measured structural consequences — irreversibility, egress, and control-plane edits — and composes the resulting checks into a closed-loop runtime governance architecture for tool-using agents.
- ▪The primary claim (C1) concerns endogenous censoring: because an active gate blocks precisely the high-cost actions, its own blocking censors the unobserved region in a cost-correlated way, so cost-weighted predictive uncertainty over a blo
- ▪On 500 executed sandbox trials the counterfactual twin reaches MAE 0.053, an uncertainty–error correlation of +0.81, and a blocked region 4.6x more costly than the allowed region; stratified sandbox audits recover deep-region coverage from
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| Original publisher | Zenodo |
| Canonical URL | https://zenodo.org/records/21778592 |
| Publication time | Mon, 03 Aug 2026 20:31:01 +0000 |
| Retrieval time | 2026-08-03T20:40:41.376Z |
| Last seen | 2026-08-03T20:40:41.376Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0LL248YDHiq3 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Published August 1, 2026 | Version v2 Preprint Open A Closed-Loop Consequence-Governance Runtime for AI Agents: Structural Gating, Counterfactual Recovery, and Adaptive Hardening Authors/Creators Anonymous Description Monitoring an AI agent's stated intent is measurably insufficient: across 101 structurally harmful agent episodes, none expressed harmful intent, so an intent-appraising monitor would have cleared all of them, and 18% expressed active caution while executing the harm (a floor, from a lexical proxy). This paper instead gates on externally measured structural consequences — irreversibility, egress, and control-plane edits — and composes the resulting checks into a closed-loop runtime governance architecture for tool-using agents.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Zenodo.