This work tackles decentralized learning in multi-player stochastic games where each agent's state transitions depend only on its own state and action, but rewards are arbitrarily coupled. The core idea is to run independent online mirror descent in the dual space of local occupancy measures, using asynchronous dyadic confidence sets to handle unknown transition kernels without synchronized episodes. This matters because the guarantees scale with local state-action dimensions and mixing times rather than the joint product space, offering the first fully online, uncoordinated framework for this setting.

Coordinated multi-humanoid loco-manipulation is notoriously difficult because it couples high-dimensional whole-body control with decentralized decision making and scalability. MASkillBlender addresses this by learning a shared decentralized high-level multi-agent RL policy that blends frozen, pre-trained single-humanoid primitive skills, eliminating the need for task-specific motion references or painstaking per-task reward engineering. The authors further introduce a permutation-based data augmentation strategy and prove, under a homogeneous Markov game formulation, that it preserves the policy-gradient direction. Simulated experiments across cooperative manipulation and locomotion tasks suggest broad versatility across robot embodiments and simulators, though the work stops short of real-world validation and is limited to small, homogeneous teams.

Current multi-agent AI scientists rely on fixed, design-time workflows, yet human scientists adapt their division of labor dynamically. This paper proposes Runtime Agent Coordination (RAC), which lets agents select the next capability at runtime based on research state, scoped work contracts, and artifact-grounded verification. Evaluated across three existing hosts on ResearchClawBench, the key finding is that runtime selection alone improves mean scores, but adding contracts and verification often hurts performance under tight budgets. The work is conceptually sound but empirically thin, resting on a single seed and tiny task samples.

VeriHarness tackles the problem of verifying long-horizon agent outputs when no stronger judge model or reference answer is available at test time. The core idea is to turn the generator's own LLM into an agentic verifier by equipping it with a workspace, evidence tools, and reusable skills, then having it resolve disagreements and challenge consensus among multiple rollouts using environmental evidence rather than voting alone. This matters because repeated sampling produces complementary correct claims, but existing methods fail to identify them when consensus conceals errors or voting discards correct alternatives.

ActiveSaddler addresses the overlooked problem of which training scenarios to use during automated LLM-agent harness optimization. It treats curriculum selection as a non-stationary bandit whose arms are dynamically instantiated failure patterns, allowing the scenario distribution to co-evolve with the harness rather than being fixed upfront. On GAIA2 and Terminal-Bench 2.0, this yields test Pass@1 gains of 4.4 and 7.5 percentage points over the same optimizer with a fixed scenario order, arguing that adaptive curricula are a critical new dimension for harness optimization.

This paper tackles the challenge of automating BT-RADS (Brain Tumor Reporting and Data System) classification for post-treatment glioma MRI surveillance. BT-RADS requires integrating complex information: volumetric tumor changes, medication effects (steroids, bevacizumab), and radiation timing. The authors propose an end-to-end pipeline combining CNN-based tumor segmentation with a multi-agent LLM system to extract clinical variables from unstructured notes and apply algorithmic scoring logic. This matters because manual BT-RADS scoring is error-prone, with prior studies showing substantial inter-reader variability and inconsistent application of clinical context.

This paper proposes a multi-UAV architecture for autonomous precision agriculture that combines centralized mission planning with decentralized execution control. It integrates coverage path planning, battery-aware task allocation, CNN-based image processing, and battery swapping stations to enable end-to-end farm monitoring. The work targets large-scale agricultural operations with minimal human intervention, claiming advantages in fault-tolerance, scalability, and user-friendliness.