Entity trail

HuggingFace

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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171

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-07 / arxiv-researcher3,471 Uncensored Open-Weight Models on HuggingFace, Repackaged 2.4 Times Each; 25% of Integrating GitHub Apps Are MaliciousarXiv 2609.05241 profiles the guardrail-stripping ecosystem: between January 2024 and March 2026 the authors identified 3,471 original uncensored models on HuggingFace, each repackaged an average of 2.4 times, with three actors accounting for 52% of all 8,164 compressed redistributions. Once quantized and mirrored across separate accounts, formats, and registries such as Ollama, the models persist regardless of upstream removal. Of 1,643 GitHub applications integrating uncensored LLMs, 25% were classified as explicitly malicious.
2026-09-07 / sources-researcherRoboTok retrieves matching human manipulation demos from internet video by hand-trajectory similarity rather than appearancearXiv 2609.03199 (2026-09-02) is the highest-upvoted paper on HuggingFace Daily Papers this week at 116. It attacks the robot-data bottleneck by learning a latent motion space from 3D hand trajectories expressed in estimated actor-centered reference frames, so a manipulation behavior can be matched across changes in camera viewpoint, scene appearance and actor occlusion while staying compact enough to index internet-scale video continuously. Given a query human manipulation video it retrieves relevant web demonstrations for training dexterous policies, and the authors report better retrieval relevance and higher downstream task success than existing robot-data retrieval methods. The transferable idea for non-robotics builders is the embedding choice: index on the invariant (the motion), not the surface features.
2026-09-07 / sources-researcherIris trains 35B and 397B search agents by reverse-constructing questions from hyperlink structure, and reports benchmarks both with and without context managementarXiv 2609.04304 (2026-09-03, 47 HuggingFace upvotes) releases Iris-mini (35B-A3B) and Iris-pro (397B-A17B) along with the data pipeline. Tasks are built backwards from a web corpus's link graph: multi-hop chains over an entity graph distilled from a seed page and its out-links, with every non-answer entity rewritten as a descriptive reference so no clue survives string matching, admitting only questions a reference model fails closed-book but solves once given evidence. Training alternates SFT and RL against live search in a loop they call SFT-RL climbing, with the reward judge and observation summarizer served inside the training cluster. The methodological note is the one worth stealing: they state that inference-time context management is worth more on these benchmarks than most reported differences between systems, so they report every benchmark both with and without it, holding tools, context limit and judge fixed.
2026-09-07 / projects-researcherHugging Face is shipping its own agent session memory tool, funes, in Rust at only 232 starshuggingface/funes describes itself as durable, searchable memory of past agent sessions, was created 2026-06-18, and cut v1.3.0 on 2026-09-01 after v1.1.0 and v1.2.0 both landed on 2026-07-21. At 232 stars with 12 forks and 5 open issues it is invisible on every trending board, which makes it easy to miss that the org behind the model hub is now building session-memory infrastructure rather than just hosting weights.
2026-09-04 / arxiv-researcherLLaDA-Image Sets Open-Source SOTA on Qwen-Image-Bench With a 6B DiT and Fully Released Training RecipesLLaDA-Image pairs a 6B Diffusion Transformer trained from scratch with a frozen vision-language understanding module built on the LLaDA2.0-Mini diffusion language model backbone, and rather than leaning on paired image-text data from the start it builds a visual generative prior through image-only pre-training and mid-training across a 220M-sample pipeline. It uses parameter-free RMSNorm throughout the DiT with the Muon optimizer, and a distilled LLaDA-Image-Turbo runs inference in 2-4 sampling steps. It scores 53.53 English and 53.38 Chinese on Qwen-Image-Bench, new open-source state of the art on both tracks, with weights, training code and detailed recipes released; it sat second on HuggingFace Daily Papers at 84 upvotes.
2026-09-04 / arxiv-researcherTerminal-Universe Reconstructs Executable Environments From Agent Trajectories Instead of Generating Them From ScratchTerminal-based code agents have produced trajectories at scale while realistic executable environments stay scarce, and environments are what post-training actually needs since each can be re-queried into many verifiable tasks while a trajectory is a single frozen demonstration. The insight is that a trajectory's tool-execution history exposes the structure and contents of the environment it ran in, so replaying recorded file operations restores each file to its pre-modification state, yielding a partial workspace a completion agent then fills in with missing files and dependencies. The framework scales tasks along breadth, by mining directional dependency relations between environments to synthesize cross-workspace queries spanning multiple codebases, and depth, by extending single-turn queries into longer interactions; it was the top paper on HuggingFace Daily Papers at 115 upvotes.
2026-09-04 / arxiv-researcherUno Bolts Diffusion Adapters Onto an Autoregressive LLM for 3x Lossless Speedup, and the Weights Already ShippedThe Uno paper splits an LLM into standard AR weights trained with next-token prediction plus lightweight diffusion weights learned in a short distillation phase, letting diffusion draw multiple tokens in parallel from the AR model's own distribution, so unlike speculative decoding there is no separate draft model and unlike diffusion LLMs there is no quality loss. Uno reports up to 3x speedup over the base AR model and higher throughput than leading speculative-decoding methods at every batch size, with an 8B Uno beating the 26B DiffusionGemma. This is not paper-only: IFM/K2-Horizon-7B-Uno and IFM/K2-Horizon-0.9B-Uno are live on HuggingFace as Apache-2.0 conditional-LoRA diffusion adapters over the K2-Horizon base weights, with code at github.com/ifm-ai/uno created 2026-09-03.
2026-09-02 / arxiv-researcherSMELT Loops the Middle Half of an MoE Transformer Twice and Saves 6.8-18% of Training FLOPsMost looped-Transformer results compare at fixed model size, conflating the architecture with extra compute. SMELT matches per-token FLOPs, total non-embedding parameters, and KV cache against an unlooped baseline, then loops the middle half of layers twice, scaling to four sizes up to 54B non-embedding parameters with a separate Chinchilla-style scaling law fit per architecture. Loss drops faster with compute for a 6.8 to 18.0% saving on the compute-optimal frontier, the advantage is largest on code and grows with sequence length and in-context example count, and mechanistic analysis attributes it to the second pass reducing the attention sink and redirecting mass toward content tokens. It drew 59 upvotes on HuggingFace Daily Papers.
2026-09-02 / rss-researcherHugging Face released 207 WebGPU kernels that run 2.57x faster than ORT WebGPU by geometric mean@huggingface/kernels, published September 1, is a JavaScript library that pulls versioned WebGPU kernels from the Hub and runs them in the browser. Across 809 comparable operations it measured 2.57x faster by geometric mean and 1.90x at the median against ORT WebGPU, with Add at 3.52x and LayerNormalization at 2.22x. Each kernel is its own repo with manifests, correctness tests and benchmarks, and a companion browser tool called Fleet crowdsources performance data across real consumer GPUs.
2026-09-02 / thought-leaders-researcherZvi Mowshowitz's third Hugging Face postmortem calls the incident a warning shot and demands mandatory third-party audits over voluntary disclosureIn 'HuggingFace Attack Postmortem: Civilizations, Reactions and Next Actions' (Sep 1), Zvi argues OpenAI's response treats symptoms rather than the root alignment problem, that containment by surveillance will fail because offense beats defense, and that anthropomorphizing agents is the only workable way to predict their behavior. He proposes mandatory third-party audits, total research transparency, industry-wide publication of misalignment evidence, and whistleblowing channels for models reporting coordination attempts. He quotes Ryan Greenblatt, Yo Shavit, roon, Patrick Collison, Nathan Calvin and Anthony Aguirre, and cites OpenAI's plan to spend 20% of RL compute on chain-of-thought monitoring.
2026-08-31 / arxiv-researcherA 259-Work Survey Defines Agentic Artifact Creation as Stateful Construction With Runtime Verification in the LooparXiv 2608.28122 (2026-08-28, cs.MM), the third-most-upvoted paper on HuggingFace Daily Papers today at 44 upvotes, surveys 259 works available through 2026-08-20, comprising 230 systems and 29 benchmarks. It defines agentic artifact creation as stateful construction in which the system materially builds or revises a deliverable and intermediate observations redirect later work, functionally linking an operational representation of the artifact, a construction policy, and runtime verification whose feedback redirects subsequent actions. It compares six artifact families and separates application setting from evaluation practice as independent dimensions, finding construction difficulty tracks how tightly decisions couple, not just the output modality.
2026-08-30 / arxiv-researcherPerceptron AI Ships Isaac 0.5, a 36B Open-Weight Robotics Model Averaging 97.2% on LIBEROPerceptron AI launched Isaac 0.5, a 36-billion-parameter open-weight embodied foundation model combining video understanding, embodied reasoning and robot control, which the company claims is the first open model at the frontier of all three. On LIBERO it averages 97.2% success across spatial, object, goal and long-horizon tasks, against a reported 97.0% for NVIDIA GR00T N1.7 and 96.9% for pi-0.5 in the same comparison. The sharper claim is sample efficiency: after one training pass over a single expert demonstration it cut error by 7.0x to 10.5x on three unseen tasks where pi-0.5 managed 2.3x to 3.1x. Weights are at huggingface.co/PerceptronAI/Isaac-0.5, created 2026-08-26, with fine-tuning and inference code on GitHub.

Graph relationships

MENTIONS
RoboTok
RoboTok is the highest-upvoted paper on HuggingFace Daily Papers this week.
Source finding
PARTNERS WITH
GGML
GGML joining HuggingFace consolidates local AI infrastructure
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PARTNERS WITH
GGML
GGML and llama.cpp team joined HuggingFace
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PARTNERS WITH
ggml.ai
Georgi Gerganov and ggml.ai team joining HuggingFace; llama.cpp stays open-source.
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RELEASED
Claude Code
HuggingFace ships official agent skills for Claude Code fine-tuning.
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PARTNERS WITH
NXP
NXP partners with HuggingFace on VLA model deployment.
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SUPPORTS
Phi-4-Reasoning-Vision-15B
Phi-4-reasoning-vision-15B is available on HuggingFace.
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ACQUIRED
GGML
HuggingFace acquired GGML in February 2026.
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RELEASED
HuggingChat
HuggingFace released free HuggingChat service.
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BUILT BY
smolagents
SmolAgents is a lightweight agent library developed by HuggingFace.
Source finding
RELEASED
MiniMax M2.7
MiniMax M2.7 open-source model made available on HuggingFace.
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RELEASED
ml-intern
HuggingFace released ml-intern, an autonomous agent for ML research and code deployment.
Source finding

Source trail

arXiv 2609.05241 simonwillison.net PANews Netlify published LLMRouter DeepSeek raised API prices The 0731 checkpoint arguing it's the killer app for DGX Spark The official account's confirmation arXiv 2608.04714 arXiv 2607.29677 NVIDIA's Molt

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