Varun Pratap Bhardwaj
Research

And proved it with math.

Ten public arXiv preprints powering the Qualixar platform — behavioral contracts, supply chain security, agent memory, stochastic testing, and multi-agent orchestration.

Preprints on arXiv — not peer reviewed. Every benchmark reproduces; code and protocols ship with the paper.

10
arXiv preprints
4
on agent memory
7
arXiv categories
2026
all published
arXiv:2609.27871August 2026
cs.SEcs.AI
Bounded Loops: Pre-Run Spend Bounds, Proved Termination, and Verified Completion for Agent Harnesses

Varun Pratap Bhardwaj, Garima Singh, Arun Pratap Bhardwaj

→ The instrument found 47 vacuous gates — checks satisfied by the absence of the thing they check — in shipped, reviewed code

In mainstream agent frameworks a step ends when the agent's own output says it has finished. This paper states what a harness must instead guarantee — that it finishes, does not drift, and does not overspend — and proves each one: termination in closed form under a global repair budget, no DONE without a gate verdict in an append-only hash-chained ledger, and a spend ceiling enforced inside an attempt rather than between attempts. It then builds the instrument that measures whether a harness delivers it, characterising two ways a sound-looking check passes anything: vacuity and self-attestation. Engine, catalogue and corpus are Apache-2.0.

arXiv:2608.12895August 2026
cs.AIcs.MA
Agent Behavioral Contracts II: Certifying Compositional Reliability Without Assuming Independence

Varun Pratap Bhardwaj, Garima Singh, Arun Pratap Bhardwaj

→ Two instances of the same model failed together on 90% of missions where either failed — independence is not a safe assumption

Multi-agent reliability analysis routinely assumes component failures are conditionally independent, and almost never tests it. This paper tests it and finds strong positive dependence, which inflates joint failure rates well beyond what independence-based bounds predict. It replaces the assumption with a certificate that assumes no dependence structure at all — a linear program over the joint distribution, bounded by a Bonferroni–Clopper–Pearson box — and proves that fitting a dependence model instead degrades coverage as sample size grows.

arXiv:2608.08253August 2026
cs.AIcs.IR
SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents

Varun Pratap Bhardwaj, Garima Singh, Arun Pratap Bhardwaj

→ Governed memory with scoped component evidence and disclosed negative results

The revised preprint studies governed local-first memory and distinguishes implementation from verified effectiveness. Component evidence is protocol-scoped; current package behavior requires separate checks.

arXiv:2604.06392April 2026
cs.AIcs.MAcs.SE
Qualixar OS: A Universal Operating System for AI Agent Orchestration

Varun Pratap Bhardwaj

→ One universal port for AI agents — 12 topologies, POMDP routing, four-tier degradation

Every agent framework reinvents orchestration, and none of them interoperate. Qualixar OS defines a universal substrate — a topology catalogue, POMDP-based routing between agents, and a four-tier degradation ladder that keeps a system answering when parts of it fail.

arXiv:2604.04514April 2026
cs.AIcs.CLcs.IR
SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems

Varun Pratap Bhardwaj

→ Forgetting as a feature — biologically-inspired decay, cognitive quantization, multi-channel retrieval, zero LLM calls

A memory system that never forgets degrades: old facts crowd out current ones and retrieval quality falls. V3.3 borrows the brain's answer — decay curves, consolidation, and quantized cognitive states — and retrieves across six independent channels fused by reranking, without a single LLM call in the hot path.

arXiv:2603.14588March 2026
cs.AIcs.IRcs.LG
SuperLocalMemory V3: Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory

Varun Pratap Bhardwaj

→ 74.8% on LoCoMo with zero cloud calls — the highest local-first score reported

Retrieval quality is usually bought with LLM calls. V3 buys it with geometry instead: memories live on a statistical manifold where relevance is a distance, so ranking is computed rather than generated. The result is a benchmark score competitive with cloud systems, produced entirely on-device.

arXiv:2603.02601March 2026
cs.AIcs.SE
AgentAssay: Token-Efficient Regression Testing for Non-Deterministic AI Agent Workflows

Varun Pratap Bhardwaj

→ Regression testing that treats non-determinism as the subject, not the obstacle — 5–20x cost reduction

You cannot diff an agent's output against a golden file, because the output legitimately differs every run. AgentAssay tests the distribution instead of the sample: stochastic verdicts over repeated runs, with a sampling strategy that reaches a decision in a fraction of the tokens a naive repeat-and-compare would burn.

arXiv:2603.00195February 2026
cs.CRcs.AIcs.SE
Formal Analysis and Supply Chain Security for Agentic AI Skills

Varun Pratap Bhardwaj

→ Agent skills execute with your agent's full privilege and nobody checks them — 675 tests, 8 novel contributions

An agent skill is arbitrary code that runs with the agent's own authority, installed from a registry with no review gate. This paper gives the threat model a formal treatment, then builds static analysis, provenance verification and SBOM generation across 22 agent frameworks so a skill can be judged before it executes.

arXiv:2602.22302February 2026
cs.AIcs.MAcs.SE
Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents

Varun Pratap Bhardwaj

→ Design-by-contract for agents — invariants that hold on every execution path. 1,980 sessions across 7 models

Prompts express intent; they do not enforce it. This paper adapts design-by-contract to autonomous agents: preconditions, postconditions and invariants stated formally, then checked at runtime, so a violation is caught at the step that caused it rather than three tool calls downstream.

arXiv:2603.02240February 2026
cs.AIcs.CR
SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning

Varun Pratap Bhardwaj

→ Shared memory is an attack surface — Bayesian trust scoring that degrades a poisoned writer instead of trusting it

When several agents write to one memory store, a single compromised agent can poison what every other agent later recalls. This paper models writer trust as a Bayesian posterior updated by downstream outcomes, so a source that keeps producing facts which fail verification loses influence over retrieval — without any of the data leaving the machine.

Research Areas

What I investigate.

Agent Behavioral Contracts

Formal specification and runtime enforcement of agent behavior — invariants that hold under all execution paths, and certificates that hold without assuming failures are independent.

AI Agent Safety & Reliability

Preventing drift, hallucination, and unauthorized actions in autonomous multi-agent systems at production scale.

Supply Chain Security

Detecting, analyzing, and blocking malicious skills before they execute inside agent pipelines.

Formal Methods for AI

Mathematical verification of stochastic, non-deterministic agent systems using contract theory and type-level invariants.

Stochastic Agent Testing

Token-efficient regression frameworks that embrace non-determinism rather than suppressing it.

Eastern Epistemology as Verification

Sāṅkhya and Advaita pramāṇa systems as formal foundations for AI reliability auditing and agent-contract design.

Profiles & Identifiers