The Polynomial That Fixed 30 Years of Cloth Simulation
The clipping bug has lived in every 3D simulation pipeline for three decades. Here is exactly why it happens, how the math breaks, and how swapping one equation fixes it; as well as the python code to see it for yourself!
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Choosing the Right Vector Database for RAG and AI Applications
Modern AI applications rely on understanding meaning rather than matching keywords. As large language models, semantic search, and RAG systems have become mainstream, vector databases have emerged as critical infrastructure for storing and retrieving high-dimensional embeddings at scale. Choosing th...
The following article originally appeared on Paolo Perrone’s The AI Engineer Substack and is being reposted here with the author’s permission. Your team picks LangGraph for a customer support chatbot. Three weeks in, you’ve got 14 nodes in a state graph, a custom checkpointer writing to Redis, and r...
Google Research Adds Agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries
Google Research details an agentic RAG framework in Gemini Enterprise Agent Platform. A Sufficient Context Agent re-searches until multi-hop, multi-source queries have enough grounding to answer. The framework raises factuality accuracy up to 34% versus standard RAG.
The post Google Research Adds Ag...
Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios
arXiv:2606.06546v1 Announce Type: new
Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know. Existing benchmarks emphasize domain-general correctness or depend on manually designed rubrics that scale poorly to long-tail pedagogi...
FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
arXiv:2606.06547v1 Announce Type: new
Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being written. We reveal that Post-Training Quantization (PTQ) error easily fl...
MacArena: Benchmarking Computer Use Agents on an Online macOS Environment
arXiv:2606.06560v1 Announce Type: new
Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidly, driven in part by standardized online evaluation benchmarks such as OSWorld, which serve both as e...
arXiv:2606.06518v1 Announce Type: new
Abstract: Sudoku is a representative constraint satisfaction problem that requires global structural reasoning under strict discrete constraints. The existing works of solving Sudoku mainly focus on two dominant approaches, i.e., traditional heuristic and deep ...
SafeGene: Reusable Adapters for Transferable Safety Alignment
arXiv:2606.06519v1 Announce Type: new
Abstract: Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vulnerable to malicious prompts, even when the training data is not intentionally harmful. This create...
Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
arXiv:2606.06523v1 Announce Type: new
Abstract: Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recent advances in LLMs' agentic capabilities, most agent systems still lack formal methods for specifyi...
CrowdMath: A Dataset of Crowdsourced Mathematical Research Discussions
arXiv:2606.06526v1 Announce Type: new
Abstract: Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step solutions, or complete proofs. They do not capture collaborative open-p...
NVIDIA and LG Group Build an AI Factory to Advance Physical AI, Mobility and AI Infrastructure
NVIDIA and LG Group are building an AI factory to accelerate LG Group’s next wave of AI-driven businesses, spanning robotics, autonomous driving, data center technologies and GPU cloud services. The AI factory will provide LG Group with accelerated computing infrastructure to train, simulate, valida...
Introducing the Third Generation of Apple’s Foundation Models
Our next generation of Apple Intelligence is centered around our users, integrated deeply into our operating systems, and powered by a bold new architecture with privacy at its core.
At the heart of this architecture is our third generation of Apple Foundation Models (AFM), a family of five foundati...
NVIDIA and Doosan Group Collaborate to Advance Physical AI and AI Factory Infrastructure
NVIDIA and Doosan Group are expanding their collaboration to advance new opportunities across physical AI, robotics and AI factory infrastructure, spanning Doosan Robotics, Doosan Bobcat, Doosan Enerbility and Doosan Corporation Electro-Materials BG. The collaboration will bring together NVIDIA’s fu...
Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation
In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve how a small language model solves multi-step arithmetic word problems. We start from a weak seed prompt, build a deterministic benchmark, and define a structured evaluator that returns actionable feedback. A multi-co...