2026-08-06

Google is making some significant AI leadership changes, including a major shift for Google DeepMind leader Demis Hassabis. Hassabis will become the chair of Google DeepMind and the chief scientist at Alphabet, CEO Sundar Pichai announced on Wednesday. Hassabis will continue to lead Alphabet's Isomorphic Labs, which aims to use AI to develop drugs. Koray […]

Self-evolving agents increasingly convert interaction histories into reusable skills that persist beyond individual tasks. While prior work studies memory and retrieval poisoning, such attacks only affect agents when poisoned records are retrieved as context. We uncover a new and more fundamental risk: poisoned experiences can be transformed by the agent itself into durable behavioral artifacts. We present SkillJack, the first attack that exploits the experience-to-skill pipeline of self-evolvin

I released LLM 0.32 this morning, the most significant new version of LLM since the initial launch of the project. The new version includes support for visible reasoning traces, server-side provider tools, redesigned content-addressable SQLite logs, new models, and new features enabled by the OpenAI Responses API. I also released a new version of the llm-anthropic plugin with substantial updates of its own. Headline features for LLM CLI users Running LLM against reasoning models now displays the

Tool-Integrated Reasoning (TIR) enables LLMs to solve complex tasks through iterative tool interactions. However, existing reinforcement learning methods often rely on trajectory-level supervision, limiting fine-grained credit assignment in long-horizon TIR scenarios. On-policy self-distillation offers denser signals through teacher branches with privileged context, but existing approaches typically derive such context from ground-truth answers or retrieved skills, which may not reflect the stat

Large Language Model (LLM) agents have seen rapid adoption in software engineering. As agents take a greater role in the actual generation of code, they are making larger changes, spanning tens to hundreds of lines. This makes manual review of agent results increasingly infeasible, leading developers to turn to explanations to understand enacted changes. Despite this, there are no benchmarks that evaluate the trustworthiness of agent-generated explanations. To bridge this gap, we propose Explain

Release: llm-anthropic 0.26 Includes new features enabled by LLM 0.32 : New models: claude-fable-5 , claude-sonnet-5 , and claude-opus-5 . #75 , #76 Added server-side tools for WebSearch , WebFetch , CodeExecution , and AnthropicMCP , available through LLM's -T interface or Python tools= . The previous -o web_search* options have been removed in favor of -T WebSearch . #79 Upgraded to llm>=0.32 . Reasoning, tool calls, tool results, and server-side tool results now stream as typed events. Reason

Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities. To bridge this gap, we introduce ContinualSkillBench, a dynamic evaluation framework for in-context continual skill learning. It covers five representative domains, each containing 100 interconnected subtasks ordered by increasing difficult

Cursor Research has open-sourced Mixture-of-Kittens (MoK), the MoE training megakernel behind its Composer models. MoK fuses all mixture-of-experts communication and computation into a single deterministic kernel, and runs up to 2.37x faster than the strongest public baseline on GB300 NVL72 racks. It requires Blackwell SM100 or SM103 GPUs, which puts it out of reach for anyone without NVL72 capacity. The post Cursor Open-Sources Mixture-of-Kittens (MoK): A Deterministic MoE Training Megakernel f

Jeff Dean and other high-profile Google executives have founded Discovery Loop, a startup that will seek AI-powered breakthroughs in everything from drug discovery to chip design.

Security researcher James Kettle tried to push the limit of AI’s hacking abilities—and discovered how effective it can be when combined with human expertise.

Researchers identified more than 50 ads on Meta properties that violated the company's policies on child sexual abuse material.

Google's AI brain drain continues.

A bug in how Apple implements its Private Relay feature, which in theory masks users’ IP addresses from the sites they visit, can reveal users’ real IP addresses.

Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling, yet the scaling behavior of Mixture-of-Experts (MoE) dLLMs remains poorly understood. We systematically characterize how optimization hyperparameters, compute allocation, and architecture scale for MoE dLLMs, identifying quantitative differences from scaling trends previously reported for AR models. Specifically, for optimization, the optimal nominal batch size grows faster, while the optimal learnin

Adapting Large Language Models (LLMs) to specialized domains often incurs an alignment tax, as fine-tuning on domain-specific tasks can cause catastrophic forgetting and substantially degrade performance on general tasks. We propose MemSFT, which mitigates the alignment tax by decoupling domain specialization from backbone parameter updates through a plug-and-play parametric memory. The memory is trained to imitate the behavior of a non-parametric retriever operating over domain data, thereby me

CopilotKit has published the Channels SDK, an MIT licensed library that runs an existing AG-UI agent inside Slack and Microsoft Teams. Version 0.5.0 ships five platform adapters and a documented runtime contract. This breakdown covers the verified deployment paths, the baseline requirements, and the one dependency that is easy to miss The post CopilotKit Open Sources Channels SDK: An MIT Licensed Library That Runs Any AG-UI Agent Inside Slack And Microsoft Teams appeared first on MarkTechPost .

Questions & Responses(in BOLD ) below. Favorite question(s) moved to end of the thread with combined responses(removed duplicates). Be optimistic folks. I'm sure we're getting other models too apart from 27B. And 27B gonna make massive noise on release. (Based on their responses) Tweet thread : you guys skipped 27b and 122b last time, can we expect those this time around? Also i can't seem to find crit pit score in the cards. For sure! We’re actually releasing a 27B model very soon. Stay tuned.

Move beyond traditional text-based parsing with PixelRAG, an end-to-end system that treats web pages and PDFs as images. This tutorial explores the complete pipeline—from rendering and tiling to multimodal embedding and hybrid search—enabling developers to build high-performance, visual document retrieval systems The post Pixel-Native RAG: A Practical Guide to Visual Document Indexing appeared first on MarkTechPost .

Large language model (LLM) agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominantly adopt independent evaluation. Consequently, the behavior of self-evolving agents in realistic streaming settings, where agents adapt to diverse and complex task streams, remains poorly understood. To address this gap, we introduce AgentStream, a unified framework that evaluates self-evolving agents spanning diverse evolution components by organ

I honestly didn’t expect AI to blur the line between actual competence and just looking competent this much. Few days ago I got a PR to review and the code looks like it was put through every AI tool under the sun from start to finish. There’s literally no way the guy who submitted it wrote it himself, or even pieced it together from smaller parts. The task requires a pretty deep understanding of the hardware and the codebase, and I know his work because I’ve reviewed his PRs plenty of times bef

Release: llm 0.32 See my detailed blog post about this release . Tags: llm

Open source agentic development workspace Discussion | Link

A handful of high-level personnel changes are coming to DeepMind.

兔展智能发布RabbitVis，让AI真正走完设计全流程

Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly redundant visual and audio token sequences incurs substantial computational overhead, demanding aggressive token compression for efficient deployment. Existing methods often degrade at low token budgets: pre-LLM compression may discard structurally important and globally distributed evidence, whereas inner-LLM compression often underexploits query-

On-policy distillation, in which a teacher corrects samples that the student itself generates, presupposes that the two models speak the same language: identical VAE latents, matching architectures, and a common timestep grid. We ask what happens when none of this holds, as when the strongest teacher available and the student one wishes to deploy come from different model families, and find that the standard recipes have no answer: teacher latents cannot serve as targets in a foreign coordinate

A quiet day lets us highlight a Cursor launch and an engineering debate

As you all know the model is 2.69B parameters with a 128K context window and purpose-built for multi-step agent workflows. What you are seeing is the Q4_K_M GGUF running on my own inference engine built from scratch. The TUI is my own device probe suite running through ADB (Android Debug Bridge) The whole engine is only 450kb and supports other models arch (Qwen, Gemma, Bonsai etc…) Currently trying to push it at ~30 tok/s

I speed up the generation part of the demo in case you get bored 😄 I also tested another long-form generation, and the VRAM usage looks stable. The demo is about a minute long, and I posted it on X. This started as a random idea and somehow turned into a full detour from working on the next audio.cpp release. The model was uploaded to the audio.cpp HF repo. I will upload the xcframework later, and then push the code to a branch after release 0.6.

Pretty much the title, I have tested a few models with a 6gb gpu and couldn’t get anything resembling llm competing with chat gpt, Gemini or Claude. I was wondering if a buying a new 16gb gpu would make a substantial difference. I wouldn’t want to buy all that just to get something worse than gpt3. Ps I know 6gb is really not a lot of vram but it was so bad that I don’t think quadrupling it would make it better .

Single-image feed-forward 3D Gaussian Splatting (3DGS) aims to directly generate a renderable 3D scene representation from one input image, avoiding the cost of multi-view capture and per-scene optimization. However, existing methods are often constrained by a pixel-aligned representation, where Gaussians are predicted from fixed image-grid locations. Such pixel-aligned primitives can produce promising nearby-view renderings, but they remain weakly coupled to underlying scene surfaces and strugg

Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed. To resolve them, we propose Knowledge-Geometry Decoupling (KGD). For what to learn, conventional next-token prediction treats adjacency as dependency and may encode spurious transitions across unrelated sessions. We introduce Beha

The company is also planning to retire its public API.

NVIDIA released Alpamayo 2 Super, a 34B vision-language-action model for autonomous driving, under OpenMDW-1.1 — a permissive license covering fine-tuning, derivatives and commercial redistribution. It pairs a 32B Cosmos 3 Super Reasoner backbone with a 2.3B diffusion action decoder, scores 79.2 on LingoQA, and emits trajectories, Chain-of-Causation traces, meta-actions, auto-labels and grounded VQA from a single pass. The post NVIDIA Releases Alpamayo 2 Super: A 34B Open Vision-Language-Action

July AI recap header

A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation. Existing video-memory systems primarily support question-conditioned recall, whereas proactive assistants typically use separate memory and control mechanisms. We introduce GROVE, a training-free framework that supports both behaviors with one memory grown causally from a continuous video stream. GROVE retains fine-grained perceptual evidence and incremen

Make private company knowledge usable for AI Discussion | Link

I trained a small MLP to memorize the classic Bad Apple animation, ~2.7 billion pixels of video compressed into 790k parameters (3.2 MB float32, 1.6 MB float16). The network takes a 3D coordinate (t, y, x)- frame index and pixel position- and outputs a grayscale value between 0 and 1. To "play" the video, you can evaluate the function over the full grid. The "video" is stored implicitly in 5 linear layers of sine activations (Sitzmann et al.'s SIREN) with 512 hidden units, ω₀ = 30, and sigmoid o

the programmable wallet for the agentic Internet Discussion | Link

Hello there my good people of r/self -hosted! Dawarich got some new stuff since my last post, so here I (Evgenii) am, ready to tell all about it! Make yourself comfortable and prepare for awesome stuff :) By the way, Dawarich — is your free (now and forever) open source self-hostable alternative to Google Timeline. And hey — we just reached 10k stars on Github! Yay! Here's a small bonus for you! --- Our links: Github: Website: iOS app: Android app: --- First of all, tons of bugs were fixed. Righ