推論系統

推論 runtime、量化、 serving、KV cache、成本與效能。

推薦系統

NVIDIA Rewrites KV and Beam Search for Its Generative Recommendation Stack, Making 5K-Context Inference 2.27× Faster

NVIDIA has assembled HSTU, dynamic embeddings, and Semantic ID-based generative recommendation into an executable PyTorch reference stack. It rewrites the caching and decoding paths for recommendation workloads characterized by long histories, generation of only a few tokens, and extremely large beam widths, though the results are currently limited to NVIDIA hardware and synthetic configurations.

代理工具

AI4AI-Bench Tasks Agents With Rewriting Ten Types of Training Algorithms; Best System Averages Just 0.250

AI4AI-Bench evaluates hyperparameter tuning separately from genuine changes to learning methods, requiring coding agents to submit training code that can be rerun from scratch. Across 290 trials involving six systems, more than 40% underperformed the original algorithms, while increased reasoning effort mainly made agents more willing to modify core training mechanisms.

推論系統

FormalTCS Tests the Research Workflow With 175 Lean Problems; Automated Formalization Scores Just 11.5 at Best

FormalTCS extracts research-grade problems from top theoretical computer science papers published over the past two years, measuring models stage by stage as they turn natural-language claims into machine-verifiable proofs. The best model achieves a Pass@8 of 28.6 when proving already-formalized theorems, but translating natural language into correct Lean theorem statements remains the primary bottleneck.

推論系統

Daedalus-150M Replaces Two-Thirds of Attention Layers With Convolutions, Accelerating CPU Decoding by 1.76× at 2K Context

Daedalus-150M is designed specifically for single-user, 4-bit CPU inference, allowing most network layers to avoid repeatedly reading an ever-growing KV cache. Preliminary results indicate that its speed advantage increases with context length, but the model is limited to 2K context, and its performance figures still await independent reproduction across different hardware.