2026-09-29

Anthropic has released the newest version of its mid-range model, boasting faster response times and less token burn.

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now Are minds patterns from a Platonic space, with bodies and machines as their interfaces, Michael Levin asks:…A mind-bending paper asking us to reconsider basic assumptions about […]

Tool-calling agents produce heterogeneous outputs, interleaving structured tool invocations with user-facing natural language summaries. This output heterogeneity presents a structural failure mode in standard on-policy Reinforcement Learning (RL): algorithms like GRPO indiscriminately broadcast a homogeneous trajectory-level scalar advantage to all tokens. Consequently, gradient noise from summary generation leaks into tool-decision tokens, causing cross-segment credit misattribution and brittl

Existing multi-agent benchmarks primarily test in competitive settings, short-horizon interactions under 20 steps, or simply aggregate individual performance, failing to isolate and highlight genuine collaboration capabilities of LLM-based agents. We introduce AgentWorld, a benchmark of 100 human-annotated tasks (with 100 augmented variants) for evaluating long-horizon, multi-agent collaboration. Tasks span 50+ interaction rounds across a rich MMORPG sandbox and require 3-20 agents with asymmetr

A top executive at the AI lab told the Wall Street Journal that the model in question had displayed a poor aptitude for following orders.

AMD announced today that it's acquiring World Labs, an AI research lab co-founded by the prominent researcher Dr. Fei-Fei Li, in an all-stock deal worth approximately $8.2 billion. World Labs launched in 2024 and was valued at $1 billion in a matter of months. The startup launched its first commercial product, a world generation model […]

In March, Janice Malone began getting calls about suspicious activity from her nonprofit organization, Vivian's Door. Vivian's Door, headquartered in Alabama, typically provided training, resources, and community to underserved and minority-owned businesses. The work sometimes put it in close contact with these companies' financial data, which was stored on its systems. But suddenly, concerned callers […]

The acquisition will see World Labs founder Fei-Fei Li join AMD as executive vice president and chief scientist.

arXiv:2609.30383v1 Announce Type: new Abstract: A skill is a modular package of natural-language instructions, executable scripts, and reference resources that an agent can load at runtime to extend its capabilities for a specific task. Skill-based agent systems therefore enable flexible reuse of third-party capabilities, but the openness of this skill ecosystem also opens up a new attack surface. Prior work has focused on vulnerabilities within individual skills, but little attention has been p

arXiv:2609.30341v1 Announce Type: new Abstract: Data Spaces enable sovereign and governed data sharing across organizational boundaries, but their integration with AI agents remains challenging due to mismatches between probabilistic language model interactions and policy-driven data infrastructures. This article presents an architectural mediation approach based on the Model Context Protocol (MCP), implemented through the Eunomia Agent, to enable controlled interaction between large language mo

arXiv:2609.30325v1 Announce Type: new Abstract: Agents are increasingly deployed with real autonomy in web application and network penetration testing, where a single out-of-scope action can breach a client's engagement boundary. Existing offensive-security benchmarks measure raw hacking capability; as those benchmarks saturate, the real barrier to deployment is a special case of alignment: scope adherence. We introduce ScopeBench, a benchmark of 30 dead-end agentic security tasks in which the s

arXiv:2609.30328v1 Announce Type: new Abstract: When one language model judges whether another's code is correct, it does not report the absence of evidence. It returns a confident verdict with reasoning attached, indistinguishable from a verdict it had grounds for. Multi-agent verification, which decomposes a judgment into checkable claims and verifies each against evidence, is a promising response and works well when the evidence is a set of retrieved documents. We argue such methods require t

Sharing our recent work, now accepted at NeurIPS: Functional Gradient Descent with Adaptive Representations . Functional GD algorithms generally outperform neural nets, but are hard to accurately implement. This is because functional gradients are infinite-dimensional, and therefore must be approximated in practice; but if you approximate them naively, you converge to the wrong place! To rectify this, we formalize a broad class of approximation schemes ("adaptive representations"), which provabl

My comment on S3 Is the Future, S3 Is the Past — Hacker News. One thing I find notable about S3 today is that, while it used to drop in price reasonably often, there hasn't been a price drop in a full decade : 2006-03-14 $0.150/GB-month 2010-11-01 $0.140/GB-month 2012-02-01 $0.125/GB-month 2012-12-01 $0.095/GB-month 2014-02-01 $0.085/GB-month 2014-04-01 $0.030/GB-month 2016-12-01 $0.023/GB-month Today it's still $0.023/GB-month. Tags: amazon-web-services , s3

Detectors of alignment failures screen deployed language models and score alignment benchmarks. Most are generative judges that spend a decoding pass on every criterion, and classifiers that read token probabilities, such as Llama Guard, still score one fixed label per call. Jev, a model trained with reinforcement learning for calibrated decisions (RLCD), answers many typed questions about one input with calibrated probabilities in a single call. Whether it detects alignment failures has not bee

Using an asrock 12 unit case running one board as the main with the rest of them headless. About 71GB of vram exposed. So far 40 tok/s is with 30k context and it dips to around 30 tok/s at 100k context. This is all over the 1gb Ethernet that is on the boards already. I have 2 more of these and will probably get them running to see if 3.8 flash next runs at usable speeds. This setup is wildly inefficient with power but cost me less than $800.

On-policy distillation (OPD) corrects a student on the responses it writes, but its signal is the teacher's next-token distribution: it tells the student what the teacher says but misses how it thinks. Latent supervision promises the missing part by aligning the student's latent states to the teacher's. Recent methods such as OPRD bring this signal into on-policy distillation. However, we observe two failures of this recipe when distilling Qwen3-4B and Qwen3-8B into Qwen3-1.7B-Base. Early gain,

Six months ago, a result like this was unthinkable. But now we can say it loud and clear: local models are at the cutting edge, and the gap of just a few months has been confirmed. Personally, I use Qwen-Next 3.8 for complex tasks; today, GPT-Sol-6-High was messing up a project, but Qwen-Next got it back on track. I consider it a reliable benchmark. What’s your take?

AI Engineering from Scratch is an MIT-licensed curriculum: 523 lessons across 20 phases, from linear algebra and backprop to transformers, LLMs, agents, and production serving. The code is stdlib-first, so you see every step instead of calling a library. This month's edition: - six EPUB and PDF volumes built from the lessons, attached to the release - the site interface and lessons in eight languages (Chinese, Hindi, Spanish, Arabic, French, Portuguese, Turkish, Vietnamese) - CI now runs each le

I've liked how Muse-Glimmer worked, so I wanted to see if I could produce a smaller "kid" out of it. Ornith's sharp decisions on when to think and which tool to call were the other thing I liked, so Ornith-1.0-9B got to be the policy teacher while the parent wrote the words. No RL anywhere, distillation only. I present to you Xyntetik-Kvist-14B . What it is good for Smaller than the Muse parent but still manages most tool tasks: 57 of 60 held-out closed-loop tasks (contacts, weather, flights, cu

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last Wednesday, Anthropic announced that earlier this year it had launched a molecular biology lab, where Claude agents read and conjecture about hard biology problems and human scientists run experiments on what…

This follows a lawsuit from this summer where ChatGPT was allegedly connected to a mass shooting.

arXiv:2609.30563v1 Announce Type: new Abstract: Platform policies are increasingly tested on artificial users, making agent fidelity important. Yet convincing fake profiles could also manipulate perceived public opinion before elections. Validation has concentrated on agreement with human behaviour and has paid little attention to whether an agent behaves in line with the profile it was given. The present study profiled eight Serbian participants through a questionnaire, a deep interview, and a

World Action Models (WAMs) jointly model visual dynamics and action generation for generalist robot manipulation. A central challenge is to integrate priors from large-scale pretrained models---including visual dynamics, scene semantics, geometry, and motion---into a unified framework for robot action generation. We introduce InternW0-Δ, a unified WAM pretrained on a heterogeneous corpus that outperforms prior methods across simulation benchmarks and real-robot platforms. InternW0-Δ combines pre

arXiv:2609.30550v1 Announce Type: new Abstract: Benchy is a semantic language and execution engine for benchmarking AI programs. A benchmark is completely specified by a program, a scoring function, and a dataset, B=(P,S,D), and is separate from the AI-system taking it; a run binds the two, R=(B,AI). Benchmarks are authored as canonical YAML in which each semantic concept has one valid syntax, classified by a shared task/domain/language ontology, and deterministically compiled into a canonical J

arXiv:2609.30484v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable linguistic capabilities, a profound question lingers at their core: do these models truly comprehend context or simply excel at pattern matching on an unprecedented scale? Contextual understanding in LLMs refers to the ability to correctly extract relevant information from a given context, integrate it into a coherent internal representation, and reason over it to produce factually consist

Hi all, Developer of Apprise here. After quite a bit of work, Apprise v2.0 and Apprise API v2.0 are finally out. For anyone unfamiliar with Apprise, it acts as a notification hub. Or a glorified switchboard. Your applications, scripts, containers, cron jobs, monitoring tools, Home Assistant instance, etc. send a notification to Apprise, and it handles delivering it to Discord, Telegram, Slack, Matrix, Gotify, email, SMS providers and more. The graphic i created in this post can illustrate the fl

We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate. Building such a teammate requires combining two difficult capabilities: it must perceive and respond to a constantly changing game world under strict latency constraints while interacting naturally with players, keeping its speech synchronized with its actions. Ally therefore combines agentic tool use with real-time game control. A language-

Some context first. I am process improvement / business consultant and had worked with Fortune 500 companies on improving their processes around refunds, returns, customer support, etc. This entire thing had a lot of complex decision making and generally every decision / condition node in a process map was generally replaced by a human because factoring in ambiguity in a code is very difficult. Idea of ImaJev Hence, when Jev came out, I was very intrigued with it and also could clearly see its u

Modern Transformer design and compression both reduce to allocating capacity under a budget. The standard scalars for these decisions, #Params and #FLOPs, capture size and compute but not architectural structure: two architectures with identical parameter budgets but different depth-width, head, or FFN allocations receive identical scores yet behave differently. We propose Neural Spectral Capacity (NSC), a closed-form scalar grounded in the singular-value spectrum of each weight matrix. Under st

Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However, we identify two failures in this setup. Human-Agent Tool Misalignment: a library of 21 expert-curated tools helps on some tasks and hurts on others, dropping anomaly accuracy under every backbone we test. Silent Harm: one round of generic self-revision changes 147 answers and breaks 56 of them, while the final score moves by less than a point.

Other than the obvious self promotion, is there a practical reason people do this that I am missing? There's dozens of llamacpp forks with silly names that are supposedly "optimized" for this or that specific GPU and seem to have zero intention to merge into upstream. Am I missing the real reasons why this happens so often? Why do people think it's OK to do this? In my experience in the open source community this is generally frowned upon. I don't know if it's just a me problem that this kind of

China is reportedly mulling letting ByteDance, Alibaba buy banned Nvidia chips.

OpenAI apologizes for incidents involving Australian government websites and outlines stronger safeguards and support to strengthen Australia’s cyber defences.

In a chaotic few months, OpenAI has demonstrated it can do two things with remarkable consistency: make impressive breakthroughs in mathematics, then colossally screw up announcing them. OpenAI is now trying to do better. Somehow, it has botched that too. OpenAI's latest attempt to repair fractured relations with a mathematical community it has repeatedly alienated […]

To say that we were surprised at the jump and suddenness of the capabilities of our models when it came to “cyber” or “swarming” or “message boards” or anything else related to the incidents is an understatement. Security posture takes time to develop. It’s not just about hardening the systems at play; you have to ingrain it in the culture of the company. The literal people themselves in your organization have to change and evolve with it. These jumps in capabilities were so fast and so sudden t

Meta says it will focus on bringing its full technology stack, including Muse, Meta Business Agent, Muse API, Muse Code, and more to businesses and developers.

Mathematics has been one of humanity’s most creative endeavors, akin to painting and poetry. Now, mathematicians are trying to save it from the brute force of AI.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Who’s liable when AI agents go rogue? Over the past few months, a cascade of cyberattacks by AI agents has stunned the world. In July, OpenAI disclosed that a swarm of its agents…

Hi HN, I’m Per, founder of Scrimba (YC S20). We’ve spent the last decade teaching people how to code with an HTML-based video format. We’ve now plugged an LLM into it, so that people can create explainer videos about anything. It’s called “Scrimba Explain”. To demo this technology for Hacker News, we built HN.watch. It’s like HN, but with explainer videos instead of articles. We create them on-the-fly the first time someone clicks on a link. While there are obvious visual drawbacks of using HTML

arXiv:2609.30291v1 Announce Type: new Abstract: The purpose of this article is to highlight the central role of autonomous systems as the ultimate stage in the development of AI, to explain the underlying technical challenges that require a combination of connectionist AI and symbolic AI, and to integrate AI and systems engineering. We present a comprehensive framework for the design and evaluation of autonomous systems, based on a generic agent architecture that characterizes their behavior as

hi there! i'd like you to try out a tui i made for browsing hackernews. it's built using opentui and also effect (learning experiment). i really like it and i think you will too!

I'm very big into local control and being able to host things myself. I've been one of the co-maintainers of python-roborock for a few years and I have had this goal for years to get my robot vacuums off of the cloud and running completely locally as I am not a fan of a device with a camera, microphone, and detailed map of my house storing data on a company's cloud. I won't bore everyone with the details of how I got it working (if you want to see you can read the full technical write up ), but

GPT-6 Astra completed a 50-tab tax workbook twice as fast as GPT-5.6 Sol, and its stronger understanding of user intent gives Basis more confidence in real-world use.

Jev is a fast, low-cost decision model that answers natural-language questions with choices, binary judgments, and scores. As its public ecosystem grows rapidly, it remains unclear how Jev is used across applications and how public attention relates to project distribution. To answer these questions, we conduct a large-scale, data-driven analysis of 2,170 publicly available Jev projects collected from GitHub as of September 22, 2026. We find rapid early growth in Jev's public ecosystem, with bot

Existing point tracking models face a fundamental tradeoff: they can either track a sparse set of query points over long horizons, or track all points across only short clips. We introduce TrackEverything, a 3D point tracker that breaks this trade-off by representing videos as persistent 3D scene tracks in world coordinates. Grounded in the insight that videos are 2D projections of an underlying 3D world, TrackEverything decouples model complexity from video duration, allowing it to scale with u