2026-09-19

Look like next grok build moment

arXiv:2609.19519v1 Announce Type: new Abstract: Language-model agents are increasingly asked to carry out work spanning days or weeks, such as an operations remediation or a research programme. Such a task outlives any context window, any process and any interval at which a person can attend. In this paper, we argue that a long-horizon agent must run continually without forgetting before it can learn continually. This ability lies in the harness around the model rather than in the model itself.

Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motivates self on-policy distillation (OPD) to supply dense token-level supervision from a self-teacher with privileged task skills, letting a skill-free student internalize them. This recipe, however, is undermined by two findings in agentic tasks: privileged information alone does not always make a teacher reliable, and the benefit of teacher supervision is stage-dependent. We theref

Recently unsealed court documents in the New York Times' case against OpenAI and Microsoft are pretty damning. The companies' own documentation warned that it was starting a "doom loop" that would damage the web, characterized its scraping of data to train its models as the "largest theft of labor in human history," and that it […]

We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify termination-token mismatch between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama, and Gemma, the two models can place their stopping probability on different EOS tokens, even when their declared stopping sets are identical. This mismatch can suppress the student's preferred termina

SynthID can cause models to follow harmful instructions they would otherwise refuse.

Meet Scry, a 500 TB NVMe internet index in ClickHouse that you can run ~arbitrary readonly SQL and some of Datalog over, and I handle the problem of resource-contention with congestion-based micro-auction pricing. When there's capacity, the service is free for non-commercial use. --- Hello. It's 2026, we're training simulated fruit fly brains to play Beat Saber, do we still have to be stuck with internet (re)search as fn: natural language -> black box we can't do anything about -> ranked_list/su

World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF). Extending joint-embedding predictive architectures, OPF decomposes latent targets into complementary factors, learns them through dedicated pa

arXiv:2609.19244v1 Announce Type: new Abstract: Conversational LLM agents increasingly rely on Web search, yet the end-to-end lifecycle of agentic search remains poorly understood. We present the first study of Web search across four major conversational platforms (ChatGPT, Claude, Grok, and DeepSeek), combining real-world user interactions (invivo) with controlled experiments using the same platform's models by their APIs (invitro). We investigate the quality of agentic decisions to invoke Web

arXiv:2609.19465v1 Announce Type: new Abstract: Compositional reasoning is critical for real-world problem solving: since training data is necessarily limited, models must generalize by composing learned skills in new ways. While post-training methods such as reinforcement learning (RL) have substantially improved the reasoning abilities of language models (LMs), their effects on compositional reasoning remain less well understood. We propose a dependency-graph framework to formalize composition

arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as protocols (e.g., MCP, A2A) ease tool/agent connectivity, each framework embeds an implicit runtime for state, memory, budgets, and guardrails, making behavior non-portable and governance brittle. It mirrors computing before operating systems, when every program re-implemented basic services. This pos

arXiv:2609.19472v1 Announce Type: new Abstract: Autonomous systems increasingly rely on Large Language Models (LLMs) yet the safety infrastructure surrounding these models introduces latency and compute overhead. This limits utility in resource-constrained, time-critical deployments. Existing external guardrail models remain blind to the model's internal workings, creating a fundamental assurance gap. We ask: does the model already know when the content is harmful? We extract activations from LL

arXiv:2609.19515v1 Announce Type: new Abstract: Verifier-based selection improves LLM performance by generating multiple candidate solutions and using a verifier to select the most promising one. However, existing methods typically treat verification only as a ranking step and discard its feedback once a fixed candidate pool has been evaluated. In this paper, we ask whether verification can also improve the candidate set itself. To this end, we introduce LLM-as-an-Improver and propose Verify--Re

MiniMax has open-sourced the terminal version of MiniMax Code: How can developers verify the content that encoding proxies read, send, and store? This is a topic that has been widely discussed recently. Open sourcing the agent doesn’t automatically answer every privacy or security question, but it gives the community something concrete to inspect. The repository includes: interactive TUI and headless execution code editing, shell commands, diffs, and test verification permission controls and san

“It’s important for service members to understand the uncertainty inherent to LLMs," a GovAI research scholar warns.

Everyone in the world-models space is sitting on a pile of cash and a ton of buzz, but good luck getting anyone — from the founders to their own data suppliers — to tell you what they're actually building.

Anthropic’s CEO says that safety hinges on understanding how AI “thinks.” So far the evidence is disturbing.

Being a computer scientist who refuses to find anything about LLMs interesting right now is a bit like being a geneticist who refuses to find anything interesting about the recently opened Jurassic Park. Tags: llms , ai , generative-ai

The Federal Register website briefly used an open source Chinese AI search tool.

Executives from OpenAI and Microsoft were reportedly worried about ChatGPT training that scraped millions of news articles.

从一次成功到一万次稳定执行，具身智能还缺什么？

Simple discovery of the provider's default schemas is possible without any credentials (since they are built into the binary). If you want it to fetch customizations of your specific instance, give it credentials to your org. The repo has more details. Comments URL: Points: 32 # Comments: 17

arXiv:2609.19513v1 Announce Type: new Abstract: High-quality pre-training data is a critical bottleneck for educational and STEM-specific language models targeting edge AI and on-device deployment where token budgets are tightly constrained. While major organizations train ever-larger models on private corpora, the open ecosystem lacks STEM-focused synthetic datasets that deliver high per-token learning value efficiently for small models. To address this gap, we introduce QVAC Genesis III, a 191

Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text and risky webpages separately, obscuring how target recovery affects downstream evidence acquisition. We introduce RiskChainBench, pairing 3,600 synthetic token-text restoration inputs from 600 source

Multiplayer space for your AI coding agents and teams Discussion | Link

Looking at jev launch website and demo video on x.com…. it seems like it’s a very intelligent classifier with custom prompt and custom criteria instruction reading capabilities. It can do well defined narrow and well defined task Me, following NLP since good old days of word embedding and BERT,,, be like asking…. Isn’t that BERT? yeah i know BERT need fine tuning to adapt to custom domain, but can Jev be like generalised form of BERT?

Hey community, Jovan from UkisAI here, We're the team behind Swift Qwen3.8 27B, the Qwen model with token usage and overthinking error improvements Our estimate is that we can make a great improvement to Bonsai 2, as our testing indicates that it suffers greatly from overthinking loops and in general high token usage impacting it's performance. My ask for you is: Is a Swifted version of Bonsai 2 something you guys would enjoy? If yes, what size is the most relevant. 1-bit, 2-bit or both? Thank y

do you want some omni? here is omni for you 1. 🧭 Overview This repository hosts two checkpoints of the Realtime-Venus system: Realtime-Venus-Omni ( Realtime-Venus-Omni/ ): the 9B audio-visual interaction model. It continuously watches and listens, decides whether and when to respond, and generates text and speech on a shared causal timeline. Adapted from MiniCPM-o 4.5, it supports proactive interaction, semantic interruption handling, and training-free long-video memory. Realtime-Venus-Audio ( R

Hey r/LocalLLaMA , Prism-LM recently released its Bonsai 2 QAT models based on Qwen3.8, and they quickly gained traction. In our evaluation, the models strike a strong balance between throughput and quality, reaching roughly 91.5% on our composite benchmark . We wanted to see how they compare under the same methodology we use for the rest of our Qwen3.8 evaluations, so we ran Bonsai 2 through our own benchmark suite. One important clarification: these are our evaluation results, not Prism’s repo

CNN's sources claim the US nearly attacked a Chinese ship it believed was carrying nuclear weapon components.

Even if big AI companies agree to a pause, ensuring that nobody tries to sneak ahead could prove tricky.

AI leaders have been promising that AI is the key to curing human disease. Anthropic researchers have also been warning that AI might kill us all.

arXiv:2609.19387v1 Announce Type: new Abstract: Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot establish why. An agent that improves an accelerator may be reasoning about the machine, or may be searching competently over knobs whose meaning it never recovers -- and only the first transfers to the next architecture. Existing evaluations cannot tell the two apart, because they vary the agent while h

In a new lawsuit, the labels argue Suno found a roundabout way to train its v6 models on unlicensed music.

California Gov. Gavin Newsom (D) is positioning the state to take the lead on AI oversight, including the potential to mandate a "kill switch" for frontier models, with a new executive order issued Friday. Newsom's order directs the state to convene a group of experts that will deliver recommendations within two months on how to […]

Google is refocusing its CC AI agent on household coordination, letting families share emails, schedules, and tasks so the AI can manage calendars, fill out forms, make shopping lists, plan meals, and more.

Using your deep knowledge, wide knowledge, taste, and agency

Article URL: Comments URL: Points: 95 # Comments: 45

Anthropic now has its own bio research lab in San Francisco.

A week after an Anthropic researcher’s doomsday warning rattled the AI world, the company’s CEO Dario Amodei has outlined his plan to “pace the frontier” of AI development. The proposal leans on independent safety evaluators and coordination between AI labs in democratic countries, and it’s already picked up some industry support, along with some pointed pushback from Nvidia’s Jensen Huang. Watch […]

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. Could AI really kill us all? Your questions, answered On Wednesday, MIT Technology Review hosted a live Roundtables event that asked the question many seem to be asking right now: could…

In recent weeks, leaders of some of the biggest AI companies have warned that the very tech they are developing is dangerous. Last weekend, Anthropic CEO Dario Amodei argued that AI carries serious risk and that progress should be slowed. OpenAI CEO Sam Altman responded on X: “I agree with Dario that we need to pace the…

Scaleout deploys decentralized AI-driven learning to military bases and drones.

Building an AI prototype is easy, but operating autonomous agents at scale requires production-grade tooling. Salesforce Agentforce bridges the gap from "vibe coding" to enterprise reliability by combining synthetic stress-testing, real-time optimization, dynamic agentic UIs, and deterministic guardrails—as proven by Southwest Airlines' 7x ROI. The post Salesforce Agentforce: Bridging the Enterprise AI Gap from ‘Vibe Coding’ to Battle-Tested Orchestration appeared first on MarkTechPost .

Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unified architecture raises a fundamental question: Can multimodal alignment improve the model's world reasoning, and what new evaluation paradigms do omni-modal inputs enable? To investigate this questio

Multiple family members can share data to help the agent make plans and complete tasks.

arXiv:2609.19182v1 Announce Type: new Abstract: Benchmarks are central to how progress in large language models (LLMs) is assessed and communicated. Yet model rankings alone reveal little about how evaluation requirements themselves are changing. The expanding variety of benchmarks offers another perspective: what researchers expect LLMs to do, and what they count as successful performance. We systematically map 14,767 papers introducing or updating evaluation resources from arXiv submissions be

Baseten launched a new safety infrastructure standard alongside its Base Labs research arm on Wednesday, partnering with Hugging Face and Goodfire AI to build safety evaluation and monitoring infrastructure for open-weight models. The announcement lands amid debate for the safety of open-weight models — which can be made dangerous by removing their safeguards through a rising technique known as abliteration . The scale of the problem is massive: Hugging Face, which hosts open source AI models, c

LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social situations from subjective user narratives, and (ii) social properties, such as others' intentions, typically lack verifiable ground truth. To address these challenges, we introduce Fuse, a multi-agent simulation framework for studying user-mediated social reasoning. In Fuse, a target a

We're partnering with Accenture on independent evaluation of frontier AI. This is an important step toward the commitment, made in our CEO’s essay “We Must Pace the Frontier,” to embed evaluators within Anthropic. The partnership will be led by Faculty, Accenture’s specialist AI business, and will include evaluating and red-teaming models, conducting alignment assessments, and testing model safeguards. Accenture helps businesses and governments deploy AI across many industries. Their understandi

Just had a meeting this week where this was uttered because of the development of AI. I still think the language choice matters a lot based on if Im doing embedded or web development work and for how we want to hire developers in the future. Maybe Im backwards.

bonsai claim 98.2% intelligent retained, but their own documents show Ternary Bonsai 2 27B reaches 52.8 and 60.8, respectively, compared with 69.7 and 80.6 for Qwen3.5-27B, retaining roughly three quarters of the full-precision performance on both benchmarks. that qwen3.5 is a typo cause these are qwen3.8 numbers, altho qwen3.5 numbers are bonsai_2/q3.5 52.8 / 41.6 = 126.9% bonsai_2/q3.5 60.8 / 72.4 = 84.0% Long-context and coding performance. This release also delivers on the roadmap set out in

My understanding so far: You take an LLM and use it without thinking (That's what openjev does?) You leave out the text generation in the end and take the confidence score in the matrix before that phase That's it. Right? They gave it a mysterious marketing name.

TypeSafe announced Jev on Tuesday: you give it data plus typed questions (yes/no, pick-one, 0–N scale) and it returns a probability for every option, crazy fast. I signed up and then refreshed my inbox. A lot. Meanwhile Matt Mastracci opened vLLM PR #57250 , which does the same trick on DiffusionGemma with a single denoising step. The model basically fills in a multiple-choice bubble sheet. My "quick look" turned into three straight days, and now there's OpenJev : an open-source server with Jev'