Long-Bo Huang

Diffusion models can now achieve one-step, efficient action generation for online multi-agent coordination, shattering previous scalability barriers.

Decades of hand-crafted heuristics for counting and sampling constrained binary matrices can be replaced by a single amortized GFlowNet that achieves a 99.8% median effective sample fraction across unseen margins.

This work shows that the frozen agent LLM already carries the routing signal in its own forward passes, and that two linear maps suffice to read it out with no skill text in the context, and shows that Gavel (Glance And Verdict from a frozen LLM) reads it in two steps.

GPU-CFR is proposed, a compiler and runtime built on observation that for a fixed game, everything about a CFR iteration except the numerical values is known before the first iteration runs, and beats every CPU and GPU baseline on the mid-to-large games of the suite without changing the update rule.

Skill selection in LLMs can be optimized to achieve a 0.73 task success rate while using 28% fewer tokens than existing methods.

Reward function design just got a major upgrade—MLREF achieves 25.2% better performance by reusing and evolving reward components across iterations.

Traditional ICM strategies miss critical dynamics, leading to an average \$9,036 value error—SCO redefines tournament strategy by integrating continuation values for superior outcomes.

AV-AIVAT enables agent evaluations to stop as soon as the evidence is sufficient, achieving a staggering 74x reduction in game requirements while maintaining statistical validity.

Unleashing an LLM's inner creativity or laser-sharp logic is now as simple as turning a knob, thanks to a new distribution-matching method that avoids heuristic rewards.