TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving
TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving Original: Shuhuai Li et al., "TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving"…
Posts
All posts on technology, daily life, and thoughts.
118 pages
1 /
20
Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs
The problem: re-reading the image at every step Diffusion language models that answer questions from images do not complete a response in one pass. While filling in the…
On the SPOT: Enhancing Speculative Decoding for Diffusion Language Models via Commit-Order Prediction
Diffusion language models must decide 'what' and 'where' at the same time Diffusion language models generate sentences by repeatedly filling masked tokens. To decode…
Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data
Introduction In settings where the batch size is close to 1, such as conversational services, the latency of a mixture-of-experts language model comes mostly from reading…
Extreme Compression of Per-Layer Embeddings via Cross-Layer Latent Vector Quantization
Recent language models keep increasing the amount of information packed into a single vocabulary entry. Per-layer embeddings (PLE), first introduced in Gemma 3n, carried…
Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
Problem: Why It's Hard to Unify Understanding and Generation in One Representation There have been many attempts to merge image understanding and image generation into a…