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De-Ying Yu

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#machine learning Preprint Oct 2026

KedgeFlow: A Programmable Control Plane for Enterprise AI Agents

Enterprise AI agents can execute altered versions of approved actions, concurrently reuse the same permission, or retry operations whose responses were lost. These failures arise when approval and resource execution lack a shared contract. KedgeFlow specifies language-independent contracts for trusted controllers to au...

Jun-Fei He, De-Ying Yu · 0 citations
Preprint Sep 2026

Stored Is Not Supported: Typed Provenance and Assertion Guardrails for Persistent AI Agents

Persistent AI agents construct autobiographical state through reflection, retrieval, and consolidation. Persistence changes availability, not epistemic standing: stored or retrieved material is not thereby supported. Untrusted inputs, prompt injections, and model inferences can therefore enter persistent state and late...

Jun-Fei He, De-Ying Yu · 1 citation
Preprint Aug 2026

Beyond Memory: A Transactional Continuity Kernel for Long-Lived AI Agents

The Continuity Kernel (CK), an activation contract that decouples off-commit candidate evaluation from atomic state activation from atomic state activation is presented, defining continuity as an unbroken, authorized lineage of accepted branch heads.

Jun-Fei He, De-Ying Yu · 5 citations · ⚡1
#artificial intelligence Preprint Sep 2026

The Cognitive Continuity Test: Verifying Governed State Transitions in Persistent AI Agents

Persistent AI agents revise beliefs, consolidate memory, and replace execution substrates. Similar successor states can accompany differently authorized transition claims, while legitimate development can change state substantially. We introduce the Cognitive Continuity Test (CCT), a policy-relative contract for verify...

Jun-Fei He, De-Ying Yu · 0 citations
#artificial intelligence Preprint Sep 2026

Before Agents Act: Assurance-Aware Semantic Scheduling for Evidence Acquisition in Distributed Systems

Assurance-Aware Semantic Scheduling (AAS) combines integer-program selection, dispatch-aware temporal scheduling, bounded diagnostic expansion, and receipt-aware repair to formulate evidence acquisition as joint witness selection and scheduling under quorum, diversity, freshness, deadline, and resource constraints.

Jun-Fei He, De-Ying Yu · 1 citation
#artificial intelligence Preprint Aug 2026

The Situated Identity Test: Distinguishing Persistent Cognitive Identity from Persona Imitation

Large language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals a fundamental attribution problem: looking the part does not mean having lived the life. Two individuals can share identical public profiles--the same age, hometown, o...

Jun-Fei He, De-Ying Yu · 1 citation
Preprint Aug 2026

Agent-Native Telemetry: Verifiable State-Delta Evidence for Autonomous Operations

This paper introduces agent-native telemetry, an operational evidence architecture for autonomous machine operators founded on verifiable state deltas rather than human prose, and proves an information-preservation lower bound and formalize a ledger-relative verified negative theorem for provable event non-occurrence.

Jun-Fei He, De-Ying Yu · 0 citations
#artificial intelligence Preprint Jul 2026

When AI Agents Commit: Cognitive Serializability Across Data, Evidence, Policy, and Authority

TCT combines immutable versioned executable definitions, registry-derived authority plans, sealed envelopes, guard-first commit transactions, post-seal envelope- and witness-bound grants, co-committed receipts, idempotent grant finalization, and receipt-driven epistemic reconciliation.

Jun-Fei He, De-Ying Yu · 1 citation
#artificial intelligence Preprint Sep 2026

Cognitive Admission Control: Risk-Conditioned Assurance for Consequential Actions in Agentic Distributed Systems

This work formalizes the admission calculus and the assumptions connecting it to mediated execution, and establishes tested implementation behaviors and local costs, not production failure rates or comparisons of language-model capability.

Jun-Fei He, De-Ying Yu · 2 citations

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