SearcharxivSearch

arXiv subjects

Marcelo Fernandez

Publications and source records attributed to Marcelo Fernandez.

4 recordsLinked to original sources

Atomic Decision Boundaries: A Structural Requirement for Guaranteeing Execution-Time Admissibility in Autonomous Systems

Autonomous systems increasingly execute actions that directly modify shared state, creating an urgent need for precise control over which transitions are permitted to occur. Existing governance mechanisms evaluate policies prior to execution or reconstruct behavior post hoc, but do not enforce admissibility at the exact moment a state transition is committed. We introduce the atomic decision boundary, a structural property of admission control systems in which the decision and the resulting state transition are jointly determined as a single indivisible step in the labeled transition system (LTS) model of execution. We distinguish two classes: atomic systems, where evaluation and transition are coupled within a single LTS step, and split evaluation systems, where they are separate transitions interleaved by environmental actions. The separation introduces an architectural gap -- the decision is evaluated in one system state; the transition fires in a potentially different one -- that no policy, regardless of sophistication, can close from within a split architecture. Under realistic concurrent environments, we prove via a constructive counterexample trace that no construction can make a split system equivalent to an atomic system with respect to admissibility. Three corollaries follow: impossibility of execution-time guarantees in split systems, insufficiency of external state enrichment, and admissibility as an execution-time rather than evaluation-time property. We further formalize the Escalate outcome -- absent from classical TOCTOU analyses -- proving that it transfers rather than eliminates the atomicity requirement: resolution is safe if and only if it is itself atomic. We classify RBAC, ABAC, OPA, Cedar, and AWS IAM as split systems and ACP as atomic, providing a structural taxonomy of existing governance mechanisms. Admissibility is a property of execution, not evaluation.

cs.LO

From Admission to Invariants: Measuring Deviation in Delegated Agent Systems

Autonomous agent systems are governed by enforcement mechanisms that flag hard constraint violations at runtime. The Agent Control Protocol identifies a structural limit of such systems: a correctly-functioning enforcement engine can enter a regime in which behavioral drift is invisible to it, because the enforcement signal operates below the layer where deviation is measurable. We show that enforcement-based governance is structurally unable to determine whether an agent behavior remains within the admissible behavior space A0 established at admission time. Our central result, the Non-Identifiability Theorem, proves that A0 is not in the sigma-algebra generated by the enforcement signal g under the Local Observability Assumption, which every practical enforcement system satisfies. The impossibility arises from a fundamental mismatch: g evaluates actions locally against a point-wise rule set, while A0 encodes global, trajectory-level behavioral properties set at admission time. An agent can therefore drift -- systematically shifting its behavioral distribution away from admission-time expectations -- while every individual action remains within the permitted action space. We define the Invariant Measurement Layer (IML), which bypasses this limitation by retaining direct access to the generative model of A0, restoring observability precisely in the region where enforcement is structurally blind. We prove an information-theoretic impossibility for enforcement-based monitoring and show IML detects admission-time drift with provably finite detection delay. Validated across four settings: three drift scenarios (300 and 1000 steps), a live n8n webhook pipeline, and a LangGraph StateGraph agent -- enforcement triggers zero violations while IML detects each drift type within 9-258 steps of drift onset.

cs.AI

Agent Control Protocol: Admission Control for Agent Actions

Autonomous agents can produce harmful behavioral patterns from individually valid requests -- a threat class per-request policy evaluation cannot address, because stateless engines evaluate each request in isolation. We present ACP, a temporal admission control protocol enforcing behavioral properties over execution traces via static risk scoring combined with stateful signals (anomaly accumulation, cooldown) through a LedgerQuerier abstraction. ACP blocks execution based on deterministic, history-aware risk scoring -- not anomaly detection. Under a 500-request workload where every request is individually valid (RS=35), a stateless engine approves all 500; ACP limits autonomous execution to 2 out of 500 (0.4%), escalating after 3 actions and denying after 11. We identify a state-mixing vulnerability in ACP-RISK-2.0 (cross-context false denials) and introduce ACP-RISK-3.0, scoping anomaly signals to PatternKey(agentID, capability, resource). Decision evaluation: 739-832 ns (p50); throughput 1,720,000 req/s. Safety and liveness model-checked via TLA+ (11 invariants + 4 temporal properties, 0 violations) across 4,294,930,695 distinct states. We formalize deviation collapse -- enforcement active but never exercised due to upstream constraints -- and introduce Boundary Activation Rate (BAR) as its detection mechanism. An adversary suppressing BAR to 0.00 is detected via DeltaBAR before collapse (BAR_C=1.00). N coordinated agents accumulate risk independently; coordination window CW_appr=2N with zero deviation: activity scales linearly, preventing superlinear amplification. ACP is Paper 1 of a 6-paper Agent Governance Series: P0 -- atomic decision boundaries; P2 -- behavioral drift detection (IML); P3/4 -- governance structure, fair allocation, and irreducibility; P5 -- runtime execution validity (RAM, arXiv:2604.22898); P6 -- operationalization of RAM.

cs.CR

A Production Oriented Approach for Vandalism Detection in Wikidata - The Buffaloberry Vandalism Detector at WSDM Cup 2017

Wikidata is a free and open knowledge base from the Wikimedia Foundation, that not only acts as a central storage of structured data for other projects of the organization, but also for a growing array of information systems, including search engines. Like Wikipedia, Wikidata's content can be created and edited by anyone; which is the main source of its strength, but also allows for malicious users to vandalize it, risking the spreading of misinformation through all the systems that rely on it as a source of structured facts. Our task at the WSDM Cup 2017 was to come up with a fast and reliable prediction system that narrows down suspicious edits for human revision. Elaborating on previous works by Heindorf et al. we were able to outperform all other contestants, while incorporating new interesting features, unifying the programming language used to only Python and refactoring the feature extractor into a simpler and more compact code base.

cs.IR