Peer-citable research on AI agent memory systems and supply chain security — from the Qualixar research initiative.
Varun Pratap Bhardwaj, Independent Researcher, 2026
DOI: 10.5281/zenodo.19435120
Introduces five contributions: (1) FRQAD — a Fisher-Rao quantization-aware distance achieving 100% precision on mixed-precision embeddings; (2) Ebbinghaus adaptive forgetting with lifecycle-aware quantization (6.7× discriminative power); (3) 7-channel cognitive retrieval achieving 70.4% on LoCoMo in zero-LLM Mode A; (4) memory parameterization via soft prompts; (5) zero-friction auto-cognitive pipeline. Trust-weighted forgetting modulates decay by source reliability. Third paper in the SLM trilogy.
Varun Pratap Bhardwaj, Independent Researcher, 2026
DOI: 10.5281/zenodo.19038659
Introduces three mathematical techniques to agent memory: Fisher-Rao geodesic distance for retrieval, sheaf cohomology for consistency verification, and Riemannian Langevin dynamics for lifecycle management. Mode A Retrieval achieves 74.8% on LoCoMo without cloud dependency — the highest local-first score reported. Mode C reaches 87.7%. A pure zero-LLM configuration scores 60.4%. Scale experiments demonstrate that mathematical layers provide increasing advantage as memory count grows, addressing a critical gap in production deployments.
Varun Pratap Bhardwaj, 2026
DOI: 10.5281/zenodo.18709670
Presents the foundational local-first memory architecture for AI agents with formal Bayesian trust scoring that defends against OWASP ASI06 memory poisoning — without cloud dependencies or LLM inference calls. V3 builds upon this architecture.
Varun Pratap Bhardwaj, 2026
DOI: 10.5281/zenodo.18787663
Addresses security vulnerabilities in AI skill ecosystems with formal verification — achieving F1=96.95% with 100% precision and 0% false positives on a 540-skill benchmark.
Varun Pratap Bhardwaj, 2026
DOI: 10.5281/zenodo.18842011
Introduces behavioral fingerprinting, adaptive budget optimization, and trace-first offline analysis for testing AI agents — delivering statistical confidence at 83% less cost than fixed-N trial approaches.
Varun Pratap Bhardwaj, 2026
DOI: 10.5281/zenodo.18775393
We present Agent Behavioral Contracts (ABC), a formal framework that combines design-by-contract principles with stochastic process theory to provide runtime behavioral guarantees for autonomous AI agents. ABC introduces a four-component contract structure {P, I, G, R} with mathematical drift bounds via Lyapunov stability analysis, achieving Θ=0.9541 aggregate compliance across 200 benchmark scenarios.
Varun Pratap Bhardwaj, Independent Researcher, 2026
DOI: 10.5281/zenodo.19454219
Presents Qualixar OS, a universal agent operating system with model-agnostic orchestration, multi-transport support (stdio, SSE, HTTP), tool calling with automatic schema discovery, and production-grade agent lifecycle management. Designed as a universal connector for AI agents across any IDE, runtime, or transport.
@article{bhardwaj2026slmv33,
title = {SuperLocalMemory V3.3: The Living Brain --- Biologically-Inspired
Forgetting, Cognitive Quantization, and Multi-Channel Retrieval
for Zero-LLM Agent Memory Systems},
author = {Bhardwaj, Varun Pratap},
year = {2026},
eprint = {2604.04514},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
doi = {10.5281/zenodo.19435120},
url = {https://arxiv.org/abs/2604.04514}
}
@article{bhardwaj2026slmv3,
title = {SuperLocalMemory V3: Information-Geometric Foundations
for Zero-LLM Enterprise Agent Memory},
author = {Bhardwaj, Varun Pratap},
year = {2026},
eprint = {2603.14588},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
doi = {10.5281/zenodo.19038659},
url = {https://arxiv.org/abs/2603.14588}
}
@article{bhardwaj2026superlocalmemory,
title = {SuperLocalMemory: Privacy-Preserving Multi-Agent Memory
with Bayesian Trust Defense Against Memory Poisoning},
author = {Bhardwaj, Varun Pratap},
year = {2026},
eprint = {2603.02240},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2603.02240}
}
@article{bhardwaj2026skillfortify,
title = {SkillFortify: Formal Analysis and Supply Chain Security
for Agentic AI Skills},
author = {Bhardwaj, Varun Pratap},
year = {2026},
eprint = {2603.00195},
archivePrefix = {arXiv},
primaryClass = {cs.CR},
url = {https://arxiv.org/abs/2603.00195}
}
@article{bhardwaj2026agentassay,
title = {AgentAssay: Token-Efficient Regression Testing for
Non-Deterministic AI Agent Workflows},
author = {Bhardwaj, Varun Pratap},
year = {2026},
eprint = {2603.02601},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2603.02601}
}
@article{bhardwaj2026qualixaros,
title = {Qualixar OS: A Universal Agent Operating System},
author = {Bhardwaj, Varun Pratap},
year = {2026},
eprint = {2604.06392},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
doi = {10.5281/zenodo.19454219},
url = {https://arxiv.org/abs/2604.06392}
}
@article{bhardwaj2026abc,
title = {Agent Behavioral Contracts: Formal Specification and Runtime
Enforcement for Reliable Autonomous AI Agents},
author = {Bhardwaj, Varun Pratap},
journal = {arXiv preprint arXiv:2602.22302},
year = {2026},
url = {https://arxiv.org/abs/2602.22302},
doi = {10.5281/zenodo.18775393}
}
Whether you are working on memory systems, agent reliability, or AI security, we are open to collaboration.