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Hugging Face Blog

Build a Robot Pick-and-Place Simulation with SimReady Assets

This walkthrough demonstrates how to create a beverage can pick-and-place task using the UR17G robot arm, Robotiq 2F-140 gripper, and a beverage can in Isaac Sim. The process involves downloading SimReady assets from SimReady Central on Hugging Face, assembling the robot and gripper, validating joint and physics settings, and using an AI agent to generate and run control code for the task. The simulation tests grasp accuracy, object stability during movement, and placement precision, with validation reports confirming compliance with SimReady profiles. The workflow supports reuse of validated assets and encourages developers to contribute their own SimReady-compatible robotics content.

Hugging Face Blog

XYBench: Helping with the problem behind the question

XYBench, a benchmark of 8,115 real-world questions spanning programming and everyday tasks, tests AI assistants' ability to recognize and respond to users' misconceptions—such as asking how to thicken frosting when the real issue is warm cupcakes. Developed with researchers from Carnegie Mellon, NVIDIA, and Johns Hopkins, XYBench measures whether models identify underlying goals (X), address them effectively, and steer users away from flawed approaches (Y). Evaluations of models like Claude Sonnet 4.6, GPT-5, Gemini 3.1 Pro, Qwen3.5-9B, and DeepSeek-V4 Pro showed they frequently focused on the stated request rather than the actual goal, with limited success in misconception identification. Human reference answers outperformed all models, highlighting a gap in pragmatic reasoning. The benchmark uses automated and human evaluation to assess pragmatic solution, emphasis, and misconception identification, offering a tool for researchers to improve AI assistants’ ability to provide helpful advice even when users ask the wrong question.

SiliconANGLE AI

4 insights from Block Builderlab Launch Event 2026: AI agents join the team as working colleagues

At Block Builderlab Launch Event 2026, AI agents moved out of single-player chat windows and into shared workspaces. There they work beside people as named, auditable members of the team. Block Inc. built these tools to run itself as an intelligence company, and it is now offering them to other businesses, building on Buzz, its […] The post 4 insights from Block Builderlab Launch Event 2026: AI agents join the team as working colleagues appeared first on SiliconANGLE.

Hugging Face Blog

LLMON: An LLM-Native Markup Language, now in Granite 4.1 3B and 8B

IBM has introduced LLMON (LLM Object Notation), a new markup language designed to explicitly communicate structure and semantics to large language models. Built to address issues like prompt injection attacks and brittle behavior in smaller models, LLMON uses reserved special-token delimiters to mark semantically coherent components such as instructions, data, and requirements, enabling precise binding between them. Two experimental preview models—granite-4.1-3b-LLMON-preview and granite-4.1-8b-LLMON-preview—are now available, alongside a GitHub repository with conversion tools, workflows, and examples. LLMON enhances robustness by separating instruction from data, reducing indirect prompt injection success rates nearly to zero while maintaining performance comparable to standard Granite 4.1 Instruct models. It also supports custom schemas via LoRA fine-tuning, as demonstrated in ScienceWorld and tool-calling agent use cases. The approach is distinct from JSON, which lacks model-friendly structure, and complements existing chat templates by adding semantic clarity across roles.

arXiv cs.CL

LLMs and the Homogenization of Teaching

arXiv:2611.14828v1 Announce Type: new Abstract: Large language models (LLMs) are known to lack diversity in open-ended text generation, a limitation that is especially consequential in education, where homogeneity can narrow the kinds of thinking and disciplinary work students are invited to do. We study diversity in two educational generation tasks: math items and essay feedback. We compare frontier and open-weight models with human-authored reference distributions using lexical, semantic, and domain-grounded measures. For math, we examine the mathematical activities students are asked to perform; for feedback, we examine the revision work requested and the rhetorical strategies used to communicate it. We evaluate direct prompting, two diversity prompting methods, and post-training. Across both tasks, directly prompted model outputs are markedly less diverse than human-authored content--78% higher on lexical (Self-BLEU) and 51% higher on semantic (embedding) similarity--and represent a narrower range of instructional forms. Diversity prompting and post-training increase variation largely through surface features without broadening the instructional repertoire. Models inherit and amplify the most normative routines of human instruction. Overcoming mode collapse in education will require richer instructional data and investment in teachers' creative capacity and professional judgment.

Hugging Face 官方博客

The Definitive Fine-Tuning Guide for 3D Generation Models

This blog post provides scripts and Docker containers for fine-tuning TRELLIS.2, a 3D generation model, using LoRA adapters on footwear data from the Google Scanned Objects (GSO) dataset. The approach focuses on improving geometric accuracy by training only the sparse-structure and shape flow diffusion transformers while freezing the texture stage. The process includes preprocessing with Blender to generate 16 views per object, converting meshes into latents, and training with Hugging Face Jobs or local Docker. Results show significant improvements in F-score, chamfer distance, and normal consistency on held-out test objects, demonstrating effective adaptation for specific 3D object categories.

Hugging Face Blog

Pay for Hugging Face Hub with your Google Cloud Account

Hugging Face has launched integration with the Google Cloud Marketplace, allowing organizations to link their Hugging Face accounts to Google Cloud for consolidated billing. This enables teams to manage Hugging Face usage—such as GPU workloads, model deployment, and inference services—through their existing Google Cloud billing account, eliminating the need for separate vendor onboarding or credit card payments. The integration supports all paid services, including Jobs, Inference Endpoints, and additional storage, with charges appearing on the Google Cloud invoice. Admins can subscribe via the Google Cloud Marketplace, link their organization, and await approval before usage is billed through Google Cloud. Once approved, the Google Cloud account becomes the active payment method, and usage can be monitored in both Hugging Face’s billing settings and Google Cloud’s billing reports.

Hugging Face 官方博客

Your Experts Have Partners. There's HOPE for Keeping Them Together

HOPE, a second-order expert pruning method for Mixture-of-Experts (MoE) language models, accounts for pairwise expert interactions by optimizing a quadratic program based on an interaction matrix derived from calibration data. Unlike first-order methods that prune experts independently, HOPE preserves cooperative expert groups, leading to better performance—especially at high pruning rates like 50%. On benchmarks such as SWE-Bench Pro, HOPE outperforms state-of-the-art first-order methods like REAP by up to +6.1%, maintaining model quality while significantly reducing memory footprint. The method requires only a single calibration pass and efficient quadratic programming, with negligible overhead. It has been validated on Qwen3.5 and GLM-4.5 models, showing consistent improvements across diverse tasks and calibration sets.

arXiv cs.CL

Why Pay for Long Reasoning When Rewrite Do Trick? Causal Effects of Reasoning History Compaction on Future Generation and Agent Cost

arXiv:2611.03852v1 Announce Type: new Abstract: Reasoning substantially improves language models' ability to solve difficult problems, but long reasoning traces make these gains expensive. Efficient reasoning should express useful analysis in fewer tokens by reducing repetition and text that adds little value to solving the problem. Progress in proprietary models motivates this goal, yet their hidden scratchpads limit direct study of how it is achieved. We investigate whether open-weight models can generate less verbose reasoning by changing the form of their own earlier thoughts, without retraining. Our intervention rewrites prior reasoning into compact state records before pending tool results are revealed, then compares continuations from original and compact histories. Across the tested models, accepted compact histories usually shorten future reasoning; a coding case study also shows less repeated verification after a shared candidate solution. In software-agent deployments, compaction can reduce total spending, although task quality and rewrite-inclusive time depend on the target and compressor. These findings identify reasoning history as one way to reduce future reasoning-token use and motivate model-specific compaction policies evaluated for both efficiency and retained capability.

NVIDIA Blog

Several leading financial institutions in Europe have adopted open-source large models to build proprietary intelligent systems.

At the GTC Berlin conference, European financial institutions including Revolut, Starling Bank, Lloyds, and Santander announced that they are building production‑grade AI capabilities based on NVIDIA’s accelerated computing platform and open‑source weight models—such as PRAGMA, the Nemotron series, and the Kumo family—covering core use cases like trading analytics, speech recognition, fraud detection, and data pipeline acceleration, while emphasizing data sovereignty, cost control, and compliant deployment.

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