arXiv subjects

Jiten Oswal

Publications and source records attributed to Jiten Oswal.

4 recordsLinked to original sources

arXiv2026

Five Primitives for Governing Autonomous AI Agents at Runtime

Enterprise deployments of autonomous AI agents inherit a control model built for human users and long-lived services, and the fit fails in three specific ways: agent principals are ephemeral, appearing and vanishing faster than provisioning; their actions are selected by a model rather than programmed, so the set of things they may attempt is not known in advance; and the population is discovered rather than provisioned, because anyone who can call an API can create one. We argue that governing such agents is a runtime problem -- not a model-alignment problem and not a build-time problem -- and we derive five primitives from the questions that must be answered before an action takes effect and after it has: discovery, identity, governance, attestation, and supply chain. For each we state what fails if it is absent and why the others cannot structurally supply it. We describe an implementation in which an agent's action is mediated against policy before it takes effect, authorised against a per-tenant action vocabulary, and recorded in a hash-linked signed ledger a third party can verify with the vendor out of the loop. We report what the architecture costs: the enforcement point sits on the request's critical path, identity requires a sidecar per workload, and fail-closed mediation converts availability incidents into denial. We are explicit about implementation status: four primitives are built and running in private pilots, and the fifth is built as separate tooling and not yet integrated into the request path. We keep it in the set deliberately: a five-part decomposition that exactly matches what its authors happened to build is not a taxonomy but a description of a codebase.

cs.AI↗

arXiv2026

Separating Disclosure from Authorization: Field-Tier Minimization for Agent Action Mediation

A system that authorizes an action must see enough of it to decide, and a system that attests to its decision must record enough to be audited. Both pressures push raw action parameters -- recipients, payment memos, record identifiers -- into an append-only ledger that cannot delete them. We show the two are separable. We classify each parameter field, not each action class, into three tiers: fields a policy may legitimately match on, which cross raw; fields that are policy-relevant but identifying, which cross only as projections such as an email domain or a templated route shape; and fields with no legitimate policy use, which never leave the workload. The central property is that the ledger's commitment is a canonical digest of the full, unminimized parameters, computed before minimization runs. The commitment is therefore independent of the tier table: reclassifying a field changes what is disclosed without invalidating a historical entry, reopening a hash, or altering what an offline verifier checks. Tier table, policy schema and wire schema are generated from one per-action declaration, so the deciding and recording parties cannot hold different rules. We then address a question the architecture forces: which party should compute each attested fact? We argue it is settled by which party could lie about it undetectably, and derive three answers within one request -- the client computes the parameter digest, being the only party holding the data; it is structurally prevented from naming the definition that governed it, since that would write a false statement into a signed ledger; and it attests which tier table it applied, so divergence is detectable. We give a leakage analysis of each projection, report an incident in which a first-cut projection preserved the identifier it was written to remove, and state the residual trust the design does not eliminate.

cs.CR↗

arXiv2025

PERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learning

In hierarchal order of molecular geometry, we compare the performances of Geometric Quantum Machine Learning models. Two molecular datasets are considered: the simplistic linear shaped LiH-molecule and the trigonal pyramidal molecule NH3. Both accuracy and generalizability metrics are considered. A classical equivariant model is used as a baseline for the performance comparison. The comparative performance of Quantum Machine Learning models with no symmetry equivariance, rotational and permutational equivariance, and graph embedded permutational equivariance is investigated. The performance differentials and the molecular geometry in question reveals the criteria for choice of models for generalizability. Graph embedding of features is shown to be an effective pathway to greater trainability for geometric datasets. Permutational symmetric embedding is found to be the most generalizable quantum Machine Learning model for geometric learning.

cs.LG↗

arXiv2025

HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means

We contrast a minimalistic implementation of quantum k-means algorithm to classical k-means algorithm. With classical simulation results, we demonstrate a quantum performance, on and above par, with the classical k-means algorithm. We present benchmarks of its accuracy for test cases of both well-known and experimental datasets. Despite extensive research into quantum k-means algorithms, our approach reveals previously unexplored methodological improvements. The encoding step can be minimalistic with classical data imported into quantum states more directly than existing approaches. The proposed quantum-inspired algorithm performs better in terms of accuracy and Adjusted Rand Index (ARI) with respect to the bare classical k-means algorithm. By investigating multiple encoding strategies, we provide nuanced insights into quantum computational clustering techniques.

quant-ph↗