DiffATS uses orthogonal Procrustes-aligned Tucker primitives to enable direct diffusion modeling of high-resolution spatiotemporal fields, achieving 3.9x to 210x compression without pretrained autoencoders.
RAPTOR is a ridge-regularized logistic probe that uses validation-tuned L2 penalties to extract accurate, stable concept vectors with low training cost for activation steering. It matches or exceeds baseline accuracy and stability across instruction-tuned LLMs, with theoretical analysis via CGMT exp
A unified framework combines unsupervised pretraining and supervised neural knowledge graph learning, with a nonasymptotic risk bound showing unlabeled data reduces downstream prediction error.
GraDE uses a graph diffusion estimator to score subgraph typicality and discovers large-scale neural architecture motifs with up to 30x higher median frequency than sampling methods.
Live Music Diffusion Models modify diffusion inference with block-wise KV caching to surpass discrete autoregressive efficiency, enabling stable alignment via ARC-Forcing and real-time interactive generation on consumer hardware.
TEMPO trains LLMs via mode-separated reinforcement learning to eliminate post-cutoff knowledge leakage in temporal backtesting, cutting leakage to 0.6-3.7% while improving task performance up to 13%.
HiLight trains a lightweight actor via reinforcement learning to insert highlight tags around pivotal evidence spans in frozen LLM contexts, boosting reasoning without altering inputs or requiring evidence labels.
A framework generates population-aligned personas from social media via quality filtering, importance sampling, and task-specific adaptation, reducing bias in LLM social simulations.
The paper defines truthful multiclass calibration errors for linear label properties, proves they preserve Blackwell informativeness ordering, and show they stabilize model rankings across bin choices.
OASIS stabilizes dual-normalized attention-residual architectures via null routing and token-to-depth null coupling, reducing activation outliers by 81.75% and improving low-bit quantized reasoning by 42.11%.
A model-based bootstrap for controlled Markov chain transitions yields consistent estimators and valid confidence intervals for offline policy evaluation and optimal recovery.
Cat-DPO applies per-category adaptive safety margins to direct preference optimization, improving aggregate safety and reducing worst-category harm gaps across models.
MAGIC-Video unifies episodic, semantic, and visual content via a multimodal memory graph and narrative chain for agentic ultra-long video reasoning, outperforming prior agentic systems by up to 10.1 points.