翻訳待ち：Memory Is Communication: The Frontier Between Remembering and Signaling

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

--> [Submitted on 17 Aug 2026] Title:Memory Is Communication: The Frontier Between Remembering and Signaling View a PDF of the paper titled Memory Is Communication: The Frontier Between Remembering and Signaling, by Yashar Talebirad and 3 other authors View PDF HTML (experimental) Abstract:A bounded agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history can reduce later communication, while a peer message can supply what memory lacks. Under limits on both resources, how should an agent allocate its information budget? Given a fixed task and decision rule, the memory and message rate pairs attaining a performance threshold form an achievable region under specified rules for using history and peer observations. We call its efficient boundary the remembering--signaling frontier. Across conditions where history permits the same maximum reduction in task loss, we hypothesize that a bounded agent will need less peer communication when it obtains a larger loss reduction from history. In preliminary referential games, target repetition coincided with shorter successful messages, while predictability from a hidden cyclic rule did not shorten them. Experiments varying memory and message rates can estimate the frontier and test this prediction across cooperative tasks. Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Theory (cs.IT); Multiagent Systems (cs.MA) Cite as: arXiv:2608.17053 [cs.AI] (or arXiv:2608.17053v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.17053 arXiv-issued DOI via DataCite (pending registration) Submission history From: Yashar Talebirad [view email] [v1] Mon, 17 Aug 2026 18:57:59 UTC (10 KB) Full-text links: Access Paper: View a PDF of the paper titled Memory Is Communication: The Frontier Between Remembering and Signaling, by Yashar Talebirad and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs cs.CL cs.IT cs.MA math math.IT 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?) 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?)