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[Submitted on 8 Sep 2026] Title:Nous: Learning and Certifying Memory Decisions Before Source Calibration View a PDF of the paper titled Nous: Learning and Certifying Memory Decisions Before Source Calibration, by Pranav Singh View PDF HTML (experimental) Abstract:Belief-based agent memory needs reliable decisions about current state, yet its evidence may be noisy, copied, or stale. Must a memory calibrate its sources before it can improve its decisions? We separate learning, calibration, and revision certification. On one four-model hidden Markov family, learning an unknown Bayes decision requires Theta(l^-2) records and certifying its improvement over an informative incumbent takes O(l^-2) fresh records from the same observation law, while fixed-precision source estimation requires Theta(l^-4) as persistence l vanishes. Thus learning and certifying useful decisions can require quadratically fewer records than source calibration. A broader model class retains the decision rate and source lower bound. Under an unknown identity-plus-background report channel, we characterize the sharp identified interval for policy improvement and derive a finite-sample certificate using observable witness regions, without pure-class anchors. A robustness extension tolerates bounded history-dependent misspecification and conditional copying; split-trained witnesses apply to arbitrary history spaces with explicit power conditions. We integrate policy-bound receipts with Nous Dimensions and test 45,000 held-out mutable-state histories and 9,000 episodes in three external MiniGrid memory environments with an introduced noisy-report interface. The new certificate accepts 9/9 improvements over a constant incumbent and 4/9 over last-write-wins, versus none for the earlier certificate in MiniGrid. Strong established inference baselines remain competitive or better. The result is a statistical account of when memory decisions can be learned and justified without recovering source reliability, not a universally superior memory algorithm. Comments: 19 pages, 3 figures; code and reproducibility package available at this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2610.00094 [cs.LG] (or arXiv:2610.00094v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.00094 arXiv-issued DOI via DataCite Submission history From: Pranav Singh [view email] [v1] Tue, 8 Sep 2026 09:01:50 UTC (78 KB) Full-text links: Access Paper: View a PDF of the paper titled Nous: Learning and Certifying Memory Decisions Before Source Calibration, by Pranav Singh View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-10 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)