Joint Subspace Sparse Attention for Efficient Long-Context Decoding
Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely...
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Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely...
Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed ...
Large language models (LLMs) increasingly tackle complex real-world tasks in specialized domains. However, identifyin...
Most vision-language-action models represent future motion as fixed-rate action chunks, tying temporal resolution and...
Large language model (LLM) agents accomplish complex tasks through multi-turn interactions with external tools. Howev...
Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their o...
Autoregressive generation underlies real-time, causal video synthesis and is the backbone of video world models. Toda...
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate sample...
Current video object removal methods suffer from prohibitive latency (minutes per video) and non-causal processing th...
Knowledge-based Visual Question Answering (VQA) requires reasoning that extends beyond visible image content, demandi...
AI coding agents now write much of the code that ships, yet whether that code is secure remains unclear. We introduce...
World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video ...