2026-07-25

Models are improving quickly, but the cheaper options are often good enough.

Opus 5 will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases.

Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NOOA takes a simpler approach: an agent is a Python object. Its methods are the actions the model can take, fields are its state, docstrings are its prompts, and its type annotations are contracts. A method whose code body consists of "..." is completed at runtime by an

We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent tasks across four work domains - Code, Web, Office, and Security. Rather than adapting public issue text, every task is reverse-engineered from a real commit, pull request, or business scenario and re

Apertus 1.5 is a family of 8B and 70B parameter language models designed to advance the state of multilingual, multimodal, fully open, and transparent AI. The models support a wide range of languages, handle contexts of up to 262,144 tokens, and it uses only fully open training data whilst delivering performance comparable to other models of similar size. The released models are the result of continued pretraining of Apertus 1.0, adding a multimodal mix of 4T tokens to the 8B model and 2T tokens

Reinforcement learning for large language models (LLMs) typically relies on trust-region masks to stabilize off-policy updates. The dominant PPO-style approach uses the sampled-token importance ratio for two criteria: a proximity criterion, which asks whether the policy has moved too far from the behavior policy, and a direction criterion, which asks whether the update pushes it farther away. Recent work DPPO improves the proximity criterion by replacing PPO's ratio-based test with a probability

audio.cpp again :) Release 0.4 is out. The headline this time is new high-quality TTS coverage plus GGUF becoming a first-class across the project. What’s new: Added Higgs Audio v3 TTS 4B, Fish Audio S2 Pro, Voxtral Realtime ASR and two community models OuteTTS TTS and VieNeu-TTS-v3 audio.cpp now support 35 model families. All released model families now support GGUF. Ready-to-use GGUF packages are now available, and Q8 is starting to show real speed and memory wins on several routes. Check the

Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but typically do not accelerate inference. This paper explores the middle ground of statistically-lossless compression through three complementary notions of losslessness for quantized LLMs. First, task-lossless compression preserves zero-shot b

The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.

On this episode of Uncanny Valley, we dive into accusations that China’s Moonshot AI stole from Anthropic, and how the US Army needs to cut back on AI use.

The AI “startups” worth billions of dollars are raising alarm bells about Chinese AI. The smaller players have a totally different take.

Anthropic's latest model offers top-tier performance at a much cheaper cost.

arXiv:2607.20452v1 Announce Type: new Abstract: Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments. This paper presents AINTMA (Agentic Intelligent Test Management Architecture), a multi-agent agentic AI system that transforms traditional test management into an autonomous quality intelligence ecosystem. AINTMA deploys six specialized AI agents (Test Discovery, Risk Assessment, Reinforcement Learning

arXiv:2607.20466v1 Announce Type: new Abstract: Rigorous benchmarks have driven progress in autonomous GPU kernel performance optimization by establishing a shared target to hillclimb on, but no equivalent exists for TPUs. We present JAXBench, a TPU-native benchmark suite for AI-generated kernel optimization on Google Cloud TPUs. JAXBench comprises 50 JAX workloads that are both relevant and provide headroom for optimization. We extract 17 production ML operators from architectures in the public

As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction. Yet genuine interaction is inherently dynamic: users rarely specify their intent upfront, instead disclosing, revising, and reshaping it as the conversation unfolds. Despite this, LLMs are still predominantly evaluated or trained in single-turn, fully-specified settings, leaving open a fundamental question: how well do LLMs track and act on user inten

Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings. We present WorldWeaver (W^2), a streaming multi-agent video diffusion model that augments rollout with cross-agent world state registe

Introducing FLUX 3. One multi-modal model for Image, Video, Audio and Action-Prediction. Creations are truer to life in every kind of style. Blog Post :

Hey r/LocalLLaMA ! We are back and we have something really amazing today. Our big 5M samples Reasoning Corpus dataset. This dataset features 5 million rows of: - repo_id --> where it's from - tok_len --> how many tokens it is in total - user --> the user promot - thought_trace --> the exact chain-of-thought of the model - assistant --> the final AI models' answer - ChatML --> the user, thought_trace and assistant in ChatML format All samples are within a 5k sequence length to make it fit perfec

Google's AlphaFold can help ID what parts of a gene editing protein enable mistakes.

Chinese AI lab Moonshot’s open model Kimi went viral this week for reasons that had less to do with the model itself and more to do with how the U.S. AI industry reacted to it. Meanwhile, an unreleased OpenAI model wandered outside its test environment and ended up connected to a real security breach at Hugging Face — a reminder […]

arXiv:2607.20469v1 Announce Type: new Abstract: Large language models (LLMs) handle many tasks with one set of parameters, but under KV-cached inference it is unclear what task-general structure, if any, is used at decode time rather than during prefill. We propose DecodeShare, a protocol that identifies a low-dimensional subspace consistently shared across tasks in decode-time hidden states, and then tests its causal role by removing that subspace only during decoding. In our experiments, distu

Article URL: Comments URL: Points: 49 # Comments: 38

In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. From configuring the GPU environment to comparing high-detail tiled Gundam inference and faster Base modes, you'll learn how to process dense layouts, tables, and cross-page content in a reproducible, end-to-end pipeline. The post How to Build an End-to-End OCR Pipeline with Baidu’s Unlimited-OCR for High-Resolution Images and Multi-Page PDF Parsing appeared first on Mar

Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently. Yet, today's best systems depend on depth sensors, multi-camera rigs, or pre-built maps, limiting the hardware they support and increasing deployment cost. We introduce Robostral Navigate, an 8B vision-language model built around this scalability objective. The model consumes only a stream of monocular RGB images - the most ubiquitous sensor acr

in the openai copyright case, a court ordered every chatgpt output log preserved, including chats people had deleted. some users tried to intervene to protect their own conversations. the court ruled they were non-parties. they had no standing over things they personally typed. two weeks ago the publishers filed for sanctions, alleging openai deleted billions of logs anyway and spent two years telling the court it couldn't search its own systems when it already could. openai denies it. the whole

Forgive me naiveness, I’m a little lost. Why must a model be jack of all trades? Why can’t we have a model expert in a single thing?

A U.S. citizen has asked a court to throw out the government's claim that he gave over a passcode to border authorities that wiped his phone's data, opening up fresh questions about a person's constitutional rights at the U.S. border.

Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning often operates in abstract spatial latents that are difficult to supervise and weakly aligned with explicit image-space detections. To address this, we introduce ReferTrack, a referring-then-tracking para

"Here’s a more natural, flowing version of that section..."

Rule would allow states to decide how much—if any—public input there can be.

arXiv:2607.20470v1 Announce Type: new Abstract: Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes a considerable annotation cost, and a lack of optimization methods for tailoring LLMs to specific tasks. To address the above issues, we propose a \textbf{Plan}ning framework for constructing \textbf{E}xtractive-based LLMs called \textbf{PlanE}, which includes data decomp

Even if China was distilling from US models (assuming all accusations are true), nothing about it makes it illegal. It is like saying you distilled knowledge from your professor in colleges and now he can sue you to shut down your careers for IP thefts. Never mind that you paid the tuitions to be taught and the professor is supposed to teach what you want to learn. IP theft happens if China somehow stole the entire model architecture, the weights and just fine tune it then release under their ow

is a tiny native macOS menu-bar app for local SSH port forwards. It has no external dependencies and runs natively. Comments URL: Points: 44 # Comments: 15

Google's selfie videos can be used for account access, AI Avatars, and age verification.

Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in representation learning, partly because these factors are tightly coupled in natural videos and difficult to supervise separately. Yet recovering it is important for learning robust motion representations that separate meaningful object dynamics from camera-induced variation. We

Due to encouragement to move my local huggingface project, it is now available on Github! Let me know your thoughts! Repo: