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This paper investigates emergent strategic behavior in LLMs through a controlled experiment: an autonomous multi-agent \"Guess My Number\" game. In this game, one LLM agent (Player 1) selects a secret integer between 1 and 100, while another LLM agent (Player 2) attempts to guess it by asking questions and receiving \"Too High\" or \"Too Low\" feedback. A third LLM agent (Host) orchestrates the game, ensuring full autonomy. Our experiments, utilizing various LLMs (including GPT-4.1, Gemini, and Claude variants) via the Roo framework, reveal distinct strategic patterns. Player 2 agents consistently adopted an optimal binary search strategy, often verbalizing their reasoning. More strikingly, Player 1 agents frequently selected secret numbers (e.g., 42, 73) that are known to require the maximum number of guesses for a binary search in the given range, suggesting an emergent counter-strategy or rudimentary opponent modeling. These findings, observed across multiple game instances and different LLM models, highlight the capacity of LLMs for sophisticated, autonomous strategic interaction. This study provides concrete evidence of LLMs' ability to develop and deploy complex strategies in dynamic, goal-oriented environments, offering insights into their potential as reasoning agents. 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Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today’s object detection technique as a revolution driven by deep learning, then, back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This article extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century’s time (from the 1990s to 2022). 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This short overview paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting as well as boosting’s relationship to support-vector machines. Some examples of recent applications of boosting are also described.", "publicationTitle": "", "publisher": "", "place": "", "date": "1999", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Zotero", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "SEW3CDK5" ], "relations": {}, "dateAdded": "2026-03-09T11:48:37Z", "dateModified": "2026-03-09T11:51:34Z" } }, { "key": "GQ9ME8KX", "version": 12189, "library": { "type": "group", "id": 195193, "name": "Sensorimotor Theory of Consciousness", "links": { "alternate": { "href": "https://www.zotero.org/groups/sensorimotor_theory_of_consciousness", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/195193/items/GQ9ME8KX", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/sensorimotor_theory_of_consciousness/items/GQ9ME8KX", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/195193/items/H6L39S4J", "type": "application/json" } }, "meta": { "createdByUser": { "id": 18899, "username": "johnkevinoregan", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/johnkevinoregan", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "GQ9ME8KX", "version": 12189, "parentItem": "H6L39S4J", "itemType": "note", "note": "

Freund, Y., & Schapire, R. E. (n.d.). A Short Introduction to Boosting.

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Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September, 1999. (In Japanese, translation by Naoki Abe.)

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Its aim is to exploit to the full those aspects of computational learning that are known to offer powerful solutions in the acquisition and maintenance of robust knowledge bases. The architecture makes explicit the requirements on the basic computational tasks that are to be performed and is designed to make this computationally tractable even for very large databases. The main claims are that (i) the basic learning and deduction tasks are provably tractable and (ii) tractable learning offers viable approaches to a range of issues that have been previously identified as problematic for artificial intelligence systems that are programmed. Among the issues that learning offers to resolve are robustness to inconsistencies, robustness to incomplete information and resolving among alternatives. Attribute-efficient learning algorithms, which allow learning from few examples in large dimensional systems, are fundamental to the approach. 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