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WEEKLY TOP 10 · 2026-09-21 → 2026-09-27 · FINAL

Hot papers · 2026-W39

Ranked by Hugging Face upvotes among papers with a takeaway, grouped by the ISO week they were listed on HF Daily. A week is frozen once complete.

  1. #01▲2402026-09-19

    OmniEdu: Open Foundation Models for Learning and Teaching

    OmniEdu is an open family of K-12 education foundation models covering problem solving, curriculum grounding, diagnosis and tutoring.

    LLMs

  2. #02▲2202026-09-21

    RRSI: Regularized Recursive Self-Improvement of Agent Harnesses

    Adds regularisation to recursive self-improvement of agent harnesses so gains survive out-of-distribution tests.

    AgentsBenchmarks & data

  3. #03▲1842026-09-12

    Realtime-Venus: A full-duplex interaction system with asynchronous delegation

    Realtime-Venus is a full-duplex audio-visual interaction system that listens, watches and talks while delegating tasks asynchronously.

    MultimodalAgents

  4. #04▲1672026-09-23

    Training Object Permanence in World Models

    Builds 150 cognitive-science tasks to test and train object permanence in video world models.

    Visual generationReasoning

  5. #05▲1632026-09-22

    The Tasteful Agent: Measuring and Improving Taste in Long-Horizon Tasks

    Taste-Bench measures an agent's 'taste' for long-horizon decisions and explores ways to improve it.

    AgentsBenchmarks & data

  6. #06▲1612026-09-21

    WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory

    WorldCrafter adds a camera-queryable implicit 3D memory to a video world model to keep long explorations consistent.

    Visual generation

  7. #07▲1522026-09-14

    EvoOntology: A Self-Evolving Ontology Layer for Data Agents

    EvoOntology provides data agents with a self-evolving ontology layer served over MCP to bridge the gap to heterogeneous data.

    Agents

  8. #08▲1382026-09-18

    CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

    CodeMidas turns implemented functionality in codebases into executable coding RL environments using only source code.

    Code & SWEReinforcement learningAgents

  9. #09▲1032026-09-22

    SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue

    SpeakerMem-R1 builds speaker-centred dual-track memory for multi-party dialogue, tracking who said what about whom.

    AgentsReinforcement learning

  10. #10▲892026-09-19

    Transferring the Intelligence of VLMs to Robotic Control

    RoboDawn exposes robot control as simple discrete commands to an agentic VLM, testing whether digital intelligence transfers to the physical world.

    Robotics & embodied AIMultimodal

Data updated: Latest AI papers(UTC+8)