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  1. 271

    The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction

    The paper presents Edge0, a streaming MoE inference engine that predicts the next layer's routing one token ahead, allowing expert weights to be fetched from SSD while compute proceeds. This enables a 35 B‑parameter MoE to run on a single 24 GB machine at ~20 tokens/s using only ~3 GiB of active memory and with near‑teacher accuracy.

    Hugging Face Daily Papersarxiv.org1 minpaper
  2. 275

    Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling

    Increasing the number of LLM candidates (N) improves reasoning accuracy, but the way those candidates are generated (batch size vs sequential calls) dramatically affects latency, GPU‑hours, and energy. On A100 GPUs, eight serial 1‑candidate calls consume ~5× more energy and take ~6× longer than a single 8‑candidate batched call, even though total candidate count is identical. The authors recommen…

    Hugging Face Daily Papersarxiv.org2 minpaper
  3. 276

    HuRo: Robotizing Human Videos for Scalable VLA Pretraining

    The paper introduces a pipeline that converts heterogeneous human videos into robot‑aligned observations and actions, creating the 630K‑episode HuRo dataset. Pretraining vision‑language‑action (VLA) policies on this data boosts real‑world manipulation success from ~51% to ~80% and improves out‑of‑distribution robustness.

    Hugging Face Daily Papersarxiv.org1 minpaper
  4. 277

    UFO: Chain-of-Evaluation for Omni-Condition Alignment in Multi-Modal Image Generation

    UFO introduces an Atomized Chain‑of‑Evaluation (AEU) framework that breaks omni‑condition alignment in multi‑modal image generation into a sequential set of fine‑grained checks, achieving a 15.25 % boost in correlation with human judgments. The authors also release UFO‑Bench, a benchmark for testing how well models satisfy combined textual and visual conditions.

    Hugging Face Daily Papersarxiv.org1 minpaper
  5. 279

    onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction

    onPanda is an interactive annotation tool that lets humans correct LLM outputs token‑by‑token, then resumes generation from the corrected prefix. In a controlled study it cut median annotation time by 52% and the authors release a token‑level correction dataset (Panda‑CVL) for on‑policy fine‑tuning.

    Hugging Face Daily Papersarxiv.org1 minpaper
  6. 288

    RSA-896

    Stephen A. Weis factored the RSA-896 challenge number using Claude on September 19, 2026. The post provides the prime factors p and q for the 896-bit number.

    Hacker News front pagesaweis.net1 minHN22590
  7. 297

    How do Traffic Signals Work (2019)

    A high‑level overview of how traffic signals are designed and operated: basic phases, timing rules, actuated detection, coordination between adjacent lights, and emerging adaptive‑control systems that use centralized data and ML. The piece stays at the level of civil‑engineering concepts and does not dive into implementation details relevant to software engineers.

    Hacker News front pagepractical.engineering9 minHN8963
  8. 298

    Grounded Action Model: 3D Grounding as a Foundation for Robotics

    The Grounded Action Model (GAM) adds explicit 3D metric grounding to robot foundation models via a shared object‑centric representation, improving robustness to scene changes. Experiments on RoboTwin 2.0, LIBERO‑PRO, and real robots show state‑of‑the‑art success rates, especially under visual shift and long‑horizon tasks.

    Hugging Face Daily Papersarxiv.org1 minpaper