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Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
Announce Type: new Abstract: We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) that combines state-augmented policy learning with distributed consensus over dual variables. Our method targets systems where agents have separable dynamics but must coordinate to satisfy global resource constraints, a setting in which, as we demonstrate empirically, independent learning fails to produce feasible solutions because agents cannot determine appropriate...
A Global Convergence Analysis of Consensus ALADIN for Convex Optimization
arXiv:2606.08112v1 Announce Type: new Abstract: Distributed optimization problems are pervasive in machine learning and optimal control. In this paper, we study smooth strongly convex distributed consensus optimization problems.
Fast TetraBFT: Optimizing Latency Where It Matters
Announce Type: new Abstract: Unauthenticated Byzantine consensus protocols achieve optimal failure resilience while relying only on authenticated point-to-point channels, not authenticated messages. They are an attractive building block for blockchains that do not mandate symmetric trust assumptions as well as for future post-quantum settings. We consider unauthenticated Byzantine consensus in partially synchronous networks and focus on optimizing its good-case latency - the worst-case time...
Anthropic CEO, who warns of AI-led mass layoffs, calls them a necessity
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From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
arXiv:2603.03292v3 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields. While Retrieval-Augmented Generation (RAG) mitigates these issues, existing methods rely on noisy token-level signals and lack the multi-round refinement required for complex reasoning. In this paper, we propose MA-RAG (Multi-Round Agentic...
CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
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MViewRouter: Internalizing Geometric Equivariance via Multi-view Alternating Attention for Combinatorial Routing
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Knowledge Index of Noah's Ark
arXiv:2606.05104v2 Announce Type: replace Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets. We introduce KINA, an 899-item benchmark across 261 fine-grained disciplines, with two formal results. First, we cast representativeness as a coverage-style objective over expert-elicited anchors...
Knowledge Index of Noah's Ark
Announce Type: new Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets. We introduce KINA, an 899-item benchmark across 261 fine-grained disciplines, with two formal results. First, we cast representativeness as a coverage-style objective over expert-elicited anchors and operationalize...