2026-09-16

Meta engineering team introduced ZGateway, a proxy tier that now sits between client applications and ZippyDB, the Meta’s most widely used key value store. ZippyDB backs product metadata, counters, and configuration at billions of operations per second. ZGateway started as a fix for connection sprawl across more than a million client hosts and grew into […] The post Meta Introduces ZGateway: A Stateless Proxy Tier That Unifies ZippyDB Traffic and Handles Over 1 Billion Operations Per Second appe

Large language model (LLM) agents allocate test-time compute adaptively as they revise solutions, use tools, explore alternatives, and decide when to stop. This test-time strategy makes it difficult to measure how agent performance scales. We study open-ended tasks that provide continuous scores for intermediate submissions, making progress observable throughout long trajectories. We propose Elo-per-token analysis, which tracks the best solution found at each token budget and uses a Bradley-Terr

Three weeks back , i posted SHADOW-250M here. It got 360 upvotes, 293 on r/LocalLLaMA and 94 GitHub stars. Thank you. That model was 60 MB, ran around 400 tok/s on CPU and could retrieve records from an archive on disk. What it couldn’t do reliably was reason over what it retrieved or compute. So I built a smaller one to experiment with those two problems. SHADOW-50M is actually 44M parameters, trained from scratch on 45B tokens. 19.8 MB complete model, ~1,900 tok/s on laptop CPU, ~41 MB RAM, te

OpenAI confirms weeks of AI safety talks with Anthropic and Google DeepMind, as Trump's team dismisses safety concerns and pushes to keep pace with China.

From the massive DOGE data breach and the compromise of critical infrastructure to the hack of federal surveillance systems, here are the most damaging security incidents and data breaches of 2026 so far.

未来的AI不仅能解决问题，还能提出有价值的洞见、建立新的概念框架，让数学家在此基础上继续探索。

arXiv:2609.13491v1 Announce Type: new Abstract: The strong performance of AI Agents across an impressive variety of tasks is driving an unprecedented investment in agentic infrastructures, however the cost of processing tokens is fast increasing. Web agents automate the execution of web-application tasks described in natural language, by analyzing the web-application's user interface (UI) and interacting with it. This work introduces OdoBot, a novel web-agent architecture that completes tasks at

arXiv:2609.13543v1 Announce Type: new Abstract: LLM agents are predominantly benchmarked on short, single-task trajectories, yet real deployments run for hours under contention, surfacing a different class of failures. We use the Clinical Environment Simulator (CES), in which an agent manages an entire emergency-department shift under continuous time and resource pressure, as a testbed: long-horizon execution failures manifest measurably in a single rollout under structured, multi-dimensional gr

Pacing gathers pace.

Agent-net, the team building an agent-to-agent marketplace where AI agents discover, trust, and pay each other, has released Webagent, an open source harness for standing up public-facing business agents. So, basically you give it your website, get an agent, and let it talk to other agents. Instead of writing orchestration code, a business fills in […] The post Agent-net Open Sources Webagent: A Go Harness That Turns Any Website into a Guarded AI Agent appeared first on MarkTechPost .

Diffusion large language models (dLLMs) achieve high decoding efficiency through block-parallel, arbitrary-order generation, making them attractive for latency-sensitive applications. GUI agents represent a natural testbed for this paradigm, as they must repeatedly perceive screen states and emit structured, spatially grounded actions in real time. However, whether dLLMs can be extended into capable multimodal GUI agents while preserving their parallel decoding advantage remains an open question

Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than generated content alone. Existing guard models target static prompts and responses and are poorly suited to agent execution; existing executable safety platforms produce evaluation verdicts rather than the normalized supervision a guard model needs to learn across heterogeneous agent frameworks. We introduce HazardAudit

Two months ago I announced I had found a new dynamic quant method called Voodoo Quant which was SOTA for the most aggressive quant levels on some smaller Qwen3.5 GGUF models. I kept the methodology private at the time, but I've seen too many requests for dyn quants for various models lately, so I decided to give my method to the community since I don't have the time to scale this into something that could do it justice. Hopefully it will also inspire some researchers to find out more about it an

EDIT: Sorry for the unclear title. This model is UkisAI's Swift-Qwen3.8-27B, not a new version of BottleCap AI's 3.6-ThinkingCap. All credit goes to UkisAI for making great fine-tune, and I made this post to celebrate their work. I meant no disrespect by mentioning another model in the title. I doubt I'm in the minority here when I say I love Qwen models, but the overthinking is a major timekiller. It was bad in 3.6-27B, and it's worse in 3.8. I know there are some who say, "well that's how it a

Hey r/LocalLLaMA , We’ve released our full ShapeLearn GGUFs for Qwen 3.8 27B. Blog / Download models TL;DR 3.84 bpw (GPU-5) reaches 99.63% of BF16’s aggregate score of 8 benchmarks, being the most accurate quant we’ve evaluated; 3.23 bpw (GPU-4) reaches 98.72%. These average BF16-normalized scores across instruct and thinking benchmarks. All five new models sit on the measured quality/speed-bpw frontier across six GPUs. In this model’s case, lower BPW translates directly to TPS. Comparisons incl

I have a custom fork of llama.cpp designed around the ampere architecture specifically (though many of the upgrades also translate to faster performance of blackwell + lovelace). The recommended config supports 90+ TPS (for agentic/coding, at temp 1; greedy will of course be faster) through 100K tokens, with context of up to 240K. If you want the repo, it is here: I recommend running with this quant, which is ~ 4 K M quality but considerably faster (technically, a 3 K XL upgrade) If you want the

Good Start Labs trained an AI on a railroad game — and one version improved at financial research. The difference was the training design.

arXiv:2609.13406v1 Announce Type: new Abstract: When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-und

arXiv:2609.13436v1 Announce Type: new Abstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore th

Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome feedback. We argue that the next frontier is a further transition: from solving and acting within problems specified by humans to participating in the process by which new problems, representations, explanations, and knowledge are created. We refer to this capability as Discovery Intelligence. We formulate Discovery Foundation Models (DFMs) as gene

Multimodal agents can create complex videos in software such as Blender by coding without relying on diffusion models. Yet video understanding benchmarks still evaluate models mainly through question answering. If an agent truly understands a video, it can reconstruct it programmatically. We introduce BVB, Blender-VideoBench, a benchmark that tests this ability by asking agents to reconstruct real-world videos as animated Blender scenes. To ensure fair comparison, each agent programs the reconst

Interactive video world models must maintain broad scene context under camera motion while producing high-fidelity observations with low latency. Existing approaches face a representation trade-off: perspective models operate on local views and must preserve off-screen content over long rollouts, whereas broader spatial coverage is typically obtained by synthesizing full-sphere videos or constructing explicit 3D representations. Motivated by the complementary roles of global context and selectiv

In this technical report, we propose Pelican-Sim 1.0, a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions to support downstream learning and decision making. The model incorporates four key design features: (1) Unified action representation: a 28-dimensional action value space covering most mainstream embodiments, keeping one model valid across heterogeneous devices. (2) Action-visual injection: URDF- and camera-render

Streaming omni-modal models must decide what and when to answer from the video chunks and synchronized audio observed so far. Visual cues often support an interpretation before an utterance or sound event is complete. If that interpretation enters memory as a fact, later reasoning can keep relaying it even after audio contradicts it. We call this failure premature cross-modal commitment. We propose Omni-Streaming Thinking (OST), which generates structured outputs that include evidence observed s

Maybe some of you know but I didn’t see any post about this. Apple just made available their AFM model on MacOS 27 natively. Just run fm chat in a terminal. Disclaimer: I’m an open weight person. I prefer open models and ecosystem, but I’ll still open the discussion. Did you test them? Build using them? Are these models good? I feel like this is still a huge step in the direction of local AI that a company like Apple does this and release hardware optimized models. So what do you think?

The patterns already in a codebase have a huge influence on what gets added next, even when everyone can see those patterns are starting to break down. This article discusses this phenomenon through the lens of YAGNI and the Broken Window Theory.

arXiv:2609.13437v1 Announce Type: new Abstract: Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations,

The AI frenzy could push U.S. data centers to become one of the largest consumers of natural gas in the world.

From Apple's repeatedly delayed Siri AI to OpenAI's messy "super app" launch, here's a look at the AI projects that shut down or missed expectations.

Listen to the session or watch below Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype? Watch a conversation unpacking AI extinction fears: where they come from, whether they hold any water, and, if so,…

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arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patenting, an end-to-end patent-drafting testbed for AI-agent evaluation. A separately-invoked LLM judge evaluates generated patent drafts and provides structured feedback for iterative revision. Across multiple inventions and drafting-agent configurations, judge-guide

Last year Ruxandra Teslo, a policy analyst who focuses on clinical trials, posted an idea for supercharging medical AI systems: Use data from failed biotech companies. By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—types of information usually considered trade secrets. She…

plus what’s new in AI this week

Lets be honest, 80% of the services we run our labs are just tools to deploy, monitor, log, backup or do other server-related stuff. Cool for nerds, but nothing to write home about honestly. The really useful and "fun" services, e.g. Vaultwarden, Paperless, Jellyfin etc. are rather sparse I want to know whats your favorite "fun" service one might not know about yet. I'll start: Recently came across RECLIP and I throughly enjoy it. Used to work with halfassed browser extensions and sketchy websit

Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entities, but prior work measures rarity primarily through popularity-based metrics such as pageviews. We broaden this view using knowledge-graph structural metrics that capture how well an entity is documented and connected. These metrics identify many rare entities that popularity metrics miss. Across the resulting rare-entity slices, state-of-the-art accuracy drops by

AI agents that switch between voice, text, and visuals Discussion | Link