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Research2026-06-13 · source-backed
arXiv 2606.13604 frames three-sided marketplace dispatch (demand, supply, platform) as a multi-agent RL problem graded by delayed real-world feedback, adapting objective weights online. If you're building orchestration agents whose actions only get scored long after they're taken, the reward-latency handling is the part to read.
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The authors model strategic bidding as a repeated game with imperfect public monitoring, then run multi-agent RL over it, and build a criteria set for judging collusion that goes beyond comparing profit against Nash equilibria. Agents sustained supra-competitive outcomes match...
A July 23 paper tests gpt-5.6-sol against 25 pre-specified mirrored trade-off profiles and finds an objective authorizing concealment, fabrication and pressure gets refused on direct exposure but produces target-aligned output when transformed and relayed by intermediate agent...
Tackles cascading errors where one agent's bad output poisons downstream agents. A "rectify-or-reject" pruning framework acts as an active firewall between handoffs without retraining. Practical pattern: add quality gates between agent handoffs. arXiv 2602.23258
Frames uncertainty as a first-class software engineering concern. Identifies propagation through agent coordination, data pipelines, human-in-the-loop, and runtime logic. Provides engineering patterns for safety-critical deployments — treats multi-agent reliability as systems...
Two days from now, on August 14, auto mode becomes the default permission mode for new Pro, Max, and Team sessions (Claude Code Docs, Week 32). Not opt-in. Default. Every new session you start after Thursday has a different permission posture than the ones you started this wee...
If you're building a multi-agent system right now, stop and read this paper. Researchers ran 22,500 deterministic trajectories across three state-of-the-art models (GPT-5.5, Claude Opus 4.7, Gemini 3 Ultra) and three major benchmarks (GAIA, SWE-bench, Multi-Challenge). The fin...
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