cs.MA · 2026-05-27 · No. 10
Multiagent Systems, 2026-05-27.
2 new papers in cs.MA. Titles, authors,
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01 — The papers
2 entries-
01
Cost of Structural Learning Under Censored Feedback: A Threshold-Bandit Approach
Michael Ledford, William Regli
cs.MA · cs.LG
In many multi-agent applications, tasks yield rewards only when executed by a coalition meeting an unknown size threshold; otherwise, feedback is fully censored. This censorship creates an identifiability problem: agents cannot distinguish stochastic failure from insufficient coordination. We formalize this setting as the Threshold-Activated Cooperative Multi-Armed Bandit (TAC-MAB) and analyze it under both centralized and decentralized...
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02
Persistent AI Agents in Academic Research: A Single-Investigator Implementation Case Study
Anas H. Alzahrani
cs.MA · cs.AI · cs.HC
Background: Large language models are typically evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens when an agent is embedded persistently in a real academic research environment with durable memory, local files, external tools, scheduled routines, delegated roles, and explicit safety protocols. Methods: A structured self-observed implementation case study was conducted from January 31 to May...
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