Independent learning achieves approximate Nash equilibria in partially observable Markov potential games with decoupled dynamics and near-polynomial complexity via finite history windows.
DiPhon defines graphon diffusion via a Jacobi SDE for scalable graph generation, matching first moments exactly and preserving topology across sizes without retraining.
Dithered randomized Hadamard quantization is unbiased and achieves mean squared error asymptotically matching dense random rotations at O(d log d) cost.
MyoChallenge 2025 benchmarks musculoskeletal sports control via simulated table tennis and soccer tasks, advancing agile motor algorithms across 70 teams.
Standard pass@k scaling laws suffer statistical shortcomings, so a beta-binomial framework and dynamic sampling strategy more accurately predict rare LLM capabilities and risks from limited data.
Neural LoFi frames deep training as iterative spectral low-degree filtering, predicting layer-wise feature selection, concept emergence, and compositional depth via low-degree correlation dynamics.
EverAnimate restores drifted latent flows via persistent memory and restorative matching, improving long human animation quality and identity consistency over minutes.
Mixed-policy LLM reasoning gains stem from buggy baselines; fixing optimizer and loss bugs makes standard SFT-then-RL outperform them by up to 22 points.
Verification of facts is consistently learned before generation, resists continual learning better, and leaves models verifying both old and new answers after updates.
PICID introduces a modular infrastructure that formalizes reproducible PHM evaluation pipelines and enables fair cross-task comparisons across diagnostics and prognostics.
SYNTH is an open-source synthetic dataset derived from Wikipedia that collapses pre-, mid-, and post-training into one stage, training competitive small models with 10-140x fewer tokens and higher factual precision than web-crawled data.
EyeVLM benchmarks vision-language models on gaze following and social gaze prediction, finding they lack precise gaze understanding despite training improvements.