arXiv subjects

Arun Pratap Bhardwaj

Publications and source records attributed to Arun Pratap Bhardwaj.

3 recordsLinked to original sources

arXiv2026

SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents

We present SuperLocalMemory 4.0, a governed, local-first memory operating system for AI agents, unifying multi-channel retrieval under reciprocal-rank fusion, bi-temporal recall, multi-scope isolation, role-based access, verified erasure, and a hash-chained audit trail. A reliability spine governs the primary write path: generation-fenced admission, verifiable memory transactions with per-projection apply, verify, compensate and erase owners, and hash-checkable completion manifests. Eleven fault-injection scenarios, each repeated 200 times, upheld 2,199 of 2,200 scoped component properties. This version leads with a negative result. Ten mechanisms here were implemented, reachable on a live call path, and ineffective at their final connection. Implemented, reachable and effective are three different questions, and the third requires an oracle independent of the mechanism under test. We contribute two mechanical invariants that supply one: a prior-distance assertion over Bayesian learners, and a join-liveness assertion over schema-guarded paths that reports where a guard's missing data resides. A three-arm ablation varying only the recall session-identifier namespace moves no posterior with the defect present and every instantiated arm with it absent, while a negative control that writes every ticket but supplies no engagement settles nothing. We withdraw the previous version's governed write-envelope overhead figure: the two paths it differenced are not comparable. Timing the envelope in place gives an 11.0 ms governed write of which the envelope is 70.6 percent, but the generation fence costs 1.9 microseconds and the obligation ledger 42 microseconds. The cost is durability, not governance.

cs.AI↗

arXiv2026

Bounded Loops: Pre-Run Spend Bounds, Proved Termination, and Verified Completion for Agent Harnesses

In mainstream agent frameworks, a step ends when the agent's own output says it has finished. Durable-execution platforms bound retries and time, but their checker conventionally lives in the same codebase as the work: a discipline the deployment is trusted to keep, not a property the harness enforces. We state what an agent harness must guarantee, prove it, and build the instrument that measures whether a harness delivers it. A bounded loop is a worker, an independent gate the worker cannot write to, and a declared budget; a bounded-loop graph composes them with a repair relation that lets a downstream failure re-run a finished upstream node. Three guarantees follow. It finishes: termination holds under repair, with the worst-case attempt total in closed form, if the repair budget is global not per node. It does not drift: no node reaches DONE without a gate verdict in an append-only hash-chained ledger, proved from control flow, since repair leaves no topological order to induct along. It does not overspend: the ceiling is enforced inside an attempt, not between attempts. Gates are measured against a two-tier held-out mutant corpus. We characterise two classes that let a sound-looking check pass anything: vacuity, satisfied by the absence of the thing checked, and self-attestation, where the subject supplies the value the check is applied to. On a 69-loop catalogue the instrument found 47 vacuous gates in shipped, reviewed code. Against the repaired gates it reports no false accepts over 209 destroying mutants ($α\le 1.8\%$, Wilson 95%); that figure is saturation, not quality: freezing the gates and applying a fresh operator family recovers a 23.3% false-accept rate where the exhausted corpus reported none. A rate belongs to a specific gate; the apparatus, not our number, is the contribution. Engine, catalogue and corpus are Apache-2.0.

cs.SE↗

arXiv2026

Agent Behavioral Contracts II: Certifying Compositional Reliability Without Assuming Independence

Compositional reliability bounds for multi-agent systems multiply component reliabilities, a step licensed by a conditional-independence assumption that is routinely stated and rarely tested. We test it. Two instances of one model, in a two-agent handoff, co-fail on 90.0% of the missions on which either fails (log OR 6.66, 95% CI [6.38, 7.00]; phi 0.916), in a preregistered evaluation of 18,000 missions scored by deterministic code with no LLM judge. Substituting a different model reduces the association in six of six contrasts; substituting a different vendor, model already different, does not -- a registered hypothesis reported as a null. The error is signed and runs against the operator: positive dependence inflates joint failure above the independence product, so redundancy is over-credited exactly when components share a model. The assumption-free alternative is often vacuous, and fitting a dependence model is worse: we prove a bootstrap bound on a fitted model's functional loses coverage of the truth as n grows, the identification gap being O(1) while the bootstrap haircut is O(n^{-1/2}). More data makes such a certificate worse, with no visible symptom. We give a finite-sample certificate assuming no dependence structure: a linear program over the joint, over a Bonferroni-Clopper-Pearson box around measured co-execution moments. It is sound, sharp for the information supplied, and monotone in the moment family. Enriching ten moment functionals to fourteen narrows the identified interval by 85.7% and lifts the certified floor from 0.2455 to 0.4116. A companion anytime-valid certificate holds type-I error at 0.0471 under optional stopping. Common dependence statistics are marginal-bounded and can reverse an apparent ordering of conditions when the compared agents fail at different rates. Contracts, scoring code, analysis scripts, and the preregistration are released.

cs.AI↗