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ARXIV:2602.14606 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.14606AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments.
Opportunity summary
Pain A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments. Existing safety approaches emphasize alignment, interpretability, or action-level filtering.
Autonomous agentic systems are increasingly deployed in regulated, high-stakes domains where decisions may be irreversible and institutionally constrained. Existing safety approaches emphasize alignment, interpretability, or action-level filtering.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity.
Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments.
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Paper Pack
10.48550/arXiv.2602.14606A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments.
Abstract
Autonomous agentic systems are increasingly deployed in regulated, high-stakes domains where decisions may be irreversible and institutionally constrained. Existing safety approaches emphasize alignment, interpretability, or action-level filtering. We argue that these mechanisms are necessary but insufficient because they do not directly govern selection power: the authority to determine which options are generated, surfaced, and framed for decision. We propose a governance architecture that separates cognition, selection, and action into distinct domains and models autonomy as a vector of sovereignty. Cognitive autonomy remains unconstrained, while selection and action autonomy are bounded through mechanically enforced primitives operating outside the agent's optimization space. The architecture integrates external candidate generation (CEFL), a governed reducer, commit-reveal entropy isolation, rationale validation, and fail-loud circuit breakers. We evaluate the system across multiple regulated financial scenarios under adversarial stress targeting variance manipulation, threshold gaming, framing skew, ordering effects, and entropy probing. Metrics quantify selection concentration, narrative diversity, governance activation cost, and failure visibility. Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity. Although probabilistic concentration remains, the architecture measurably bounds selection authority relative to conventional scalar pipelines. This work reframes governance as bounded causal power rather than internal intent alignment, offering a foundation for deploying autonomous agents where silent failure is unacceptable.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 3.0
PROBLEM
A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments. Existing safety approaches emphasize alignment, interpretability, or action-level filtering.
METHOD
Autonomous agentic systems are increasingly deployed in regulated, high-stakes domains where decisions may be irreversible and institutionally constrained. Existing safety approaches emphasize alignment, interpretability, or action-level filtering.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments. Existing safety approaches emphasize alignment, interpretability, or action-level filtering.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Autonomous agentic systems are increasingly deployed in regulated, high-stakes domains where decisions may be irreversible and institutionally constrained. Existing safety approaches emphasize alignment, interpretability, or action-level filtering.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity.
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 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A new governance architecture for autonomous agents that bounds decision authority while preserving cognitive reasoning, suitable for high-stakes regulated environments.
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Adoption evidence
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Commercial read
3.0/10 public viability
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proof status
unverified
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next verification path
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