Improved Large Language Diffusion Models
Modern large language models are predominantly trained with autoregressive factorization and causal attention. We pre...
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Modern large language models are predominantly trained with autoregressive factorization and causal attention. We pre...
Existing low-bit KV-cache quantizers often treat each cached key as a flat vector. Under RoPE, however, a key's contr...
The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. ...
Open domain subject-driven text-to-video (S2V) generation has drawn significant interest in academia and industry. Op...
We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality train...
Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LL...
Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a da...
While Video Virtual Try-on (VVT) has achieved remarkable progress in synthesizing realistic garment overlays on dynam...
We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single succ...
Fine-grained visual reasoning requires multimodal large language models (MLLMs) to identify task-relevant visual evid...
Generating a coherent multi-shot video requires structured cross-shot memory. Subject appearance, scene context, and ...
As LLM agents increasingly select tools autonomously, their choices among tools with different privileges become safe...