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Diffusion-based world models enable high-quality interactive environment generation but suffer from substantial inference overhead due to repeated Transformer evaluations during denoising. Existing caching methods mainly exploit temporal redundancy at the feature or token level, leaving the underlying mathematical stru...
Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every decoder layer, even when token-specific visual computation is unnecessary at many depths. We introduce Patch-to-Prune (P2P), inspired by Mechanistic Interpretability, a tr...
Hour-scale audio-visual question answering is constrained by a context dilemma: dense whole-recording encoding rapidly exhausts context limits, whereas uniform temporal compression severely dilutes fine-grained acoustic and visual evidence. We introduce LEAP, a framework where the model retrieves its own evidence witho...
P2Flow is introduced, a phoneme-aware progressive flow matching (FM) framework designed for extreme SSR with three main strategies, which leverages phonetic information to reconstruct missing spectral components and employs a progressive architectural design that hierarchically restores distinct frequency regions.
A three-stage fine-tuning curriculum applied to Qwen3-27B is described that is designed to progressively specialize the model for the C-to-Rust (C2Rust) translation task, and the resulting model is evaluated using the agentic, static-analysis-guided verification framework of SACTOR.
Hydra is presented, a common-schema, phase-aware workload characterization framework for LLM inference on edge SoCs that enables reproducible, phase-aware characterization of edge LLM inference.
Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction. While LLMs display in-context learning capabilities, the mechanisms with which they accomplish time-series prediction remain unclear. Specifically, whether they tru...
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