Open Access Research

Four public research papers. Methods open to scrutiny.

SuperLocalMemory 4.0 is publicly available on arXiv with a companion Zenodo archive, alongside three prior arXiv preprints. These are preprints, not venue-reviewed publications, and product-release claims require separate release evidence.

4
Public research records
Zenodo + arXiv
Open-access archives
2026
Latest preprint
00
arXiv:2608.08253 Zenodo 21853302 2026 August 2026 · V4.0

SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents

Varun Pratap Bhardwaj, Garima Singh, and Arun Pratap Bhardwaj

This SuperLocalMemory 4.0 preprint describes a governed, local-first memory control plane for AI agents: policy-authorized writes, durable projection obligations, completion manifests, multi-scope memory, auditable operations, and the CLI, MCP, dashboard, and integration surfaces around the runtime.

Release evidence
  • Reliability evidence reports 2,200 of 2,200 deterministic repetitions across eleven scoped fault-injection and mechanism scenarios.
  • Claims are scoped to component and mechanism measurements; they are not a generic end-to-end retrieval-accuracy benchmark.
  • The public preprint is available on arXiv:2608.08253, with Zenodo as the companion citable archive.
Public paper
Read the V4 paper on arXiv Companion archive on Zenodo Citation DOI: 10.5281/zenodo.21853302
01
arXiv:2603.14588 2026 March 2026

Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory

Varun Pratap Bhardwaj

This preprint introduces information-geometric principles for agent memory and reports the published V3 LoCoMo evidence carried into V4.0. In the current product pipeline, dense candidate generation still uses cosine similarity while Fisher-derived terms inform later scoring and lifecycle behavior.

Key Contributions
  • Fisher-informed scoring layered onto candidate retrieval
  • Research evaluation of information-geometric retrieval and lifecycle techniques
  • 74.8% Mode A Retrieval across 10 conversations / 1,276 questions, with GPT-4.1-mini answer synthesis disclosed
  • 87.7% Mode C on Conv-30 / 81 questions, with cloud embeddings and GPT-4.1-mini disclosed
Full Paper
Read on arXiv 
@article{bhardwaj2026informationgeometric,
  title={Information-Geometric Foundations for Zero-LLM
    Enterprise Agent Memory},
  author={Bhardwaj, Varun Pratap},
  journal={arXiv preprint arXiv:2603.14588},
  year={2026}
}
02
arXiv:2603.02240 2026 March 2026

Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning

Varun Pratap Bhardwaj

This preprint evaluates privacy, Bayesian trust, search, graph, and multi-agent defenses against memory poisoning. It is not a lifecycle, compression, or million-memory capacity proof for the current release.

Key Contributions
  • Bayesian trust defense against low-trust memory writes
  • Privacy and multi-agent threat model
  • Historical search and graph experiments
  • Versioned research evidence, separate from current release proof
Full Paper
Read on arXiv 
@article{bhardwaj2026privacypreserving,
  title={Privacy-Preserving Multi-Agent Memory with Bayesian
    Trust Defense Against Memory Poisoning},
  author={Bhardwaj, Varun Pratap},
  journal={arXiv preprint arXiv:2603.02240},
  year={2026}
}
03
arXiv:2604.04514 2026 April 2026

The Living Brain: Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval

Varun Pratap Bhardwaj

This preprint reports historical experiments on biologically inspired forgetting, cognitive quantization, and multi-channel retrieval. It does not prove a generic prompt-compression ratio or current V4.0 capacity.

Key Contributions
  • Biologically inspired forgetting experiments
  • Cognitive quantization research
  • Historical multi-channel retrieval evaluation
  • Release behavior requires separate current-artifact verification
Full Paper
Read on arXiv 
@article{bhardwaj2026livingbrain,
  title={The Living Brain: Biologically-Inspired Forgetting,
    Cognitive Quantization, and Multi-Channel Retrieval},
  author={Bhardwaj, Varun Pratap},
  journal={arXiv preprint arXiv:2604.04514},
  year={2026}
}
Evidence
LoCoMo · arXiv:2603.14588 Published V3 protocol carried into V4.0

Protocol-scoped LoCoMo results

The architecture evaluated in the V3 paper is carried into V4.0. These results retain their original models, answer-construction, question count, and conversation scope; they are not relabeled as a newly rerun 3.7 package benchmark.

  • 60.4% — Mode A Raw: 10 conversations, 1,276 questions, local embeddings/retrieval and zero-LLM answer construction.
  • 74.8% — Mode A Retrieval: 10 conversations, 1,276 questions, local retrieval with GPT-4.1-mini answer synthesis.
  • 87.7% — Mode C: Conv-30, 81 questions, text-embedding-3-large plus GPT-4.1-mini answer generation and judge.
  • +12.7pp — information-geometric layers across six conversations: 71.7% with mathematics versus 58.9% without.
Category detail

Mode A Retrieval: 72.0% single-hop, 70.3% multi-hop, 80.0% temporal, and 85.0% open-domain. Mode C reports 64.0%, 100.0%, and 86.0% respectively for single-hop, multi-hop, and open-domain in its Conv-30 scope.

SuperLocalMemory 4.0 — Zenodo 21853302 2603.14588 — V3 SLM: Information-Geometric Agent Memory 2603.02240 — Privacy-Preserving Multi-Agent Memory 2604.04514 — The Living Brain
BibTeX

Cite this work

The SuperLocalMemory 4.0 preprint is publicly archived on Zenodo; three prior papers are open access on arXiv. The current release citation is below.

Zenodo 21853302 — SuperLocalMemory 4.0 Preprint 
@misc{bhardwaj2026slmv4,
  title={SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents},
  author={Bhardwaj, Varun Pratap and Singh, Garima and Bhardwaj, Arun Pratap},
  year={2026},
  doi={10.5281/zenodo.21853302},
  url={https://zenodo.org/records/21853302}
}
Methodology

Why mathematics?

SLM combines inspectable heuristics with mathematical components. Component properties do not guarantee end-to-end recall, so the release keeps research and product evidence separate.

01 · The problem with cosine

Cosine and confidence answer different questions.

Cosine similarity is used for dense candidate generation. Stored-memory confidence is a separate assertion field. Later score enhancement may combine additional signals, but neither value is answer probability.

02 · Fisher-Rao respects probability

Component math has a bounded role.

Fisher-derived terms can inform later scoring when required state is available. Their mathematical properties do not establish retrieval correctness, calibration, or safety for the complete system.

See the mathematics and runtime boundary →
03 · Protocol disclosure matters

Scope before score.

The papers are public arXiv preprints, not venue-reviewed publications. Historical results are kept separate from current V4.0 release claims and must disclose answer construction, sample scope, and cloud assistance.

Read the preprint →

Public preprints. SuperLocalMemory source under AGPL-3.0-or-later.

Every listed preprint is freely available on arXiv. The SuperLocalMemory source and packages are published on GitHub under AGPL-3.0-or-later. Optional models, providers, and third-party components retain their own licenses and terms.

GitHub All Papers on arXiv