ACT*ONOMY introduces a three-level taxonomy of 10 actions and 46 subactions for describing autonomous agent behavior at runtime, plus an open repository and automated analysis pipeline that compares behavioral profiles and surfaces failure patterns.
GeoWind2Plan predicts mission-time 3D urban wind via neural operators to enable energy-efficient UAV planning in seconds, reducing energy by up to 12.7% versus wind-agnostic paths.
DynaTokens teaches dynamics to frozen camera-controlled video models via scene-specific learnable tokens, improving simultaneous dynamics and camera control over full fine-tuning.
Winfree Oscillatory Neural Network applies generalized synchronization dynamics to vision and reasoning tasks, scaling to ImageNet-1K and achieving 80.1% Maze-hard accuracy with 1% of prior parameters.
Low-bit KV cache quantization silently collapses LLM safety alignment via geometric subspace vulnerability, and per-channel reduction diagnostics recover up to 97% of lost refusals.
Strategic risk aversion acts as an inductive bias for generalizable collaboration, yielding robust multi-agent policies with reduced free-riding and stronger equilibrium outcomes alongside unseen partners.
RLVR trains a 30B LLM buyer via verifiable economic rewards to negotiate, revealing four-phase strategic evolution and outperforming much larger frontier models in surplus extraction.
Online discrete diffusion adaptation for molecular optimization finds acquisition, reward shaping, and debiasing complementarily boost reward, with replay and validity control stabilizing exploration to outperform offline and search baselines.
Strategic decision-focused learning predicts exogenous states for multi-agent games where better accuracy can reduce equilibrium payoffs, requiring strategic-aware predictors.
T2Mo generates dynamic 3D shapes via feed-forward conditioning on 3D trajectories and text, with shape-grounded trajectory embeddings handling arbitrary trajectory densities to improve motion fidelity.