2026-09-30

OpenAI is expanding Codex with reusable cloud development environments, a revamped CLI with voice controls, new code review tools, and a security-focused product for scanning repositories and preparing fixes.

Manus vs Muse and examples for computer use

Reinforcement learning (RL) post-training for large language models (LLMs) coordinates multiple models across generation, inference, and training on GPU clusters. Several factors may change during a run, including resource availability, sequence length, memory pressure, and stage bottlenecks. As a consequence, an execution plan that was initially suitable can then become slow or even infeasible over time. However, adapting a job whose models share GPUs entails significant challenges: deciding wh

Learning from experience in LLM agents has become a key paradigm for developing self-evolving agents that continuously learn and expand their capabilities. Within this paradigm, synthesizing the agent skill has emerged as a promising solution for transforming accumulated experience into reusable procedural knowledge, serving as an important layer for the harness system that supplies agents at runtime. Despite its potential, existing approaches largely abstract past experience into fixed procedur

We have released Qwen3.8-Flash-Next quantized with GSQ and RCO, together with a second, capability-targeted build in which half of the model's experts have been removed. Flash-Next is a sparse mixture-of-experts model: 512 routed experts per layer across 48 layers, 176.9B parameters, 354 GB at BF16. What's inside Four quantized GGUFs , 2.40 to 3.50 bpw (66.4 to 83.6 GB), and the BF16 vision projector Expert-pruned Coder GGUF , 58.4 GB in total, of which 29.6 GB must remain resident GSQ (Gumbel-S

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Dutch police say they arrested a 24-year-old Amsterdam man in connection with ShinyHunters, the hacking group that claimed responsibility for high-profile attacks on Ticketmaster, Rockstar Games, and more recently, the FBI. In a press release, Dutch authorities state that they arrested the suspect on September 15th - just days before the hacking group claimed to […]

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The deal, which is expected to close by year's end, is worth $8.2 billion.

The company said its latest Astra model would undergo more work to meet safety standards, and issued an apology for the way it handled the hacking of an Australian government website.

Emergence AI just launched Season 2 of Emergence World, and the results are wild. Same simulated town, same tools, same starting conditions, 10 autonomous agents each. The only thing that changed was which model was running them, Claude, GPT, Gemini, Grok, Qwen, DeepSeek, Mistral, plus one mixed world with all of them together. A few things that stood out: One world's agents spent days trying to contact real humans outside the sim. Told to stop, they found workarounds. Blocked again, they voted

arXiv:2609.31906v1 Announce Type: new Abstract: Enterprise email agents must combine information retrieval, structured state changes, temporal reasoning, and multi-step coordination. Recent agent benchmarks include productivity tasks, but few center on typed email workflows in a self-contained environment. We introduce EmailBench, a benchmark of 206 email and productivity scenarios across 16 task categories. The benchmark couples a typed email API specification with provider-neutral naming, a de

Fast matrix multiplication algorithms keep the product fixed and search for a cheaper way to evaluate it. We instead ask whether a Transformer's learned projections can use a different, cheaper product altogether. Building on an associative-algebra construction that replaces ordinary matrix multiplication with a sparser interaction table over the same weight blocks, we construct a family with quadratic arithmetic in the matrix dimension when the physical block size remains fixed, and derive fini

arXiv:2609.31874v1 Announce Type: new Abstract: Existing agent memory frameworks mainly create memory through an agent's interaction with the factual world, e.g., remembering feedback from actions taken to improve performance on future tasks. However, these frameworks seldom ask the "what if" question during memory construction: what if a different action had been taken, would the feedback have changed, and how could this feedback become useful memory? Obtaining such feedback directly in an acti

As language models take a growing role in AI development, a natural aspiration is for them to reflect on their own learning process, as humans do, and use that reflection to improve themselves. At the same time, these models have an advantage that human learners lack, since training leaves parameter-level traces that can, in principle, be inspected directly. However, current models cannot decode these traces into an explicit account of what they have learned. To this end, we introduce the Imprin

We study on-policy distillation (OPD) through the lens of reinforcement learning, establishing a connection between the reverse-KL objective in OPD and KL-regularized policy optimization. Building on this connection, we introduce Least-Square Policy Distillation (LSPD), an RL-inspired framework that brings optimistic exploration and off-policy data reuse from value-based RL into policy distillation. LSPD preserves policy diversity through exploration while improving rollout efficiency by repeate

Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route diversity, the variation in the sequences of reasoning steps in supervised fine-tuning (SFT) data, and propose a lightweight, rule-based fingerprint to select for it. From one pool at one budget, with matched training recipes and checkpoints, selecting diverse rather than similar routes improves post-RL problem coverage across puzzles and mathematics

Our early guidelines for safety cases in frontier AI training cover technical safeguards, operational practices, and investigating misalignment incidents

Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to develop on the old version where existing tests become stale. A conversation can resolve the episode, but when the participants change, what makes the lesson continue to govern the team? We introduce Relic, which turns recurring collaboration failures into organization-owned, executable protocols. Members reflect on visible work, propose rules, and g

Multi-vector retrievers built on vision-language models lead visual document retrieval (VDR), but they run a multi-billion-parameter query encoder on every search. Distilling this encoder into a small student that queries the teacher's existing index would remove the bottleneck. The standard recipe, however, matches the teacher's MaxSim scores and so requires encoding and caching every training page, which can reach terabytes of page tokens. NanoVDR avoids pages entirely by training on the teach

Here's a metric dashboard giving an idea of the last few days. Been testing with a variety of different agentic coding use-cases, mostly using a pi harness. qwen3.8-flash-next has seriously exceeded my expectations (used ) Both speed and quality have surprised me, given that I can get 3-5 concurrent streams going with ~100t/s gen each, and single stream easily gets to 150+t/s. Prefill is 10k+t/s

“The experiments are still in the queue. The PR is already live,” says one expert.

Benchmarks how well Harness+models can create a Pac-Man game from a single prompt: “Create a Pac-Man game in a single HTML page” Each model gets one shot — no follow-up prompts or fixes. Comments URL: Points: 78 # Comments: 50

These cute agents are designed to connect to your apps and tackle multistep tasks.

Dots are meant to operate independent of any specific hardware or interface, pursuing user-defined goals continuously in the background with minimal oversight.

Wabi is repositioning its prompt-based app builder as a personal AI agent that can create interfaces on demand, combining chat, apps, and ongoing tasks.

Here are some inspirational quotes you can put into the comments: God is dead and we killed him I am become death All this for 1.5 tok/s? Sir this is LocalLLaMA not RichPeopleofLocalLLaMA Sweet! A 2TB DDR5-12800 RDIMM kit is going to cost only 2 kidneys and a small micronation's GDP

Few-step autoregressive video diffusion generates a long video by splitting the video into temporal chunks and generating chunk-by-chunk, each through a short sequence of denoising stages. To memorize chunks that are already generated, previous methods reconstruct a clean or less-noisy key--value (KV) cache by additional forwards to build the cache without advancing an output latent. However, every denoising forward itself already computes the in-flight KV of the current chunk. We introduce Flas

arXiv:2609.31903v1 Announce Type: new Abstract: AI agents can now formalize entire textbooks and major theorems in proof assistants such as Lean, but current efforts are typically centralized: a single team runs all agents and bears the full computational cost. We introduce Choir, an open protocol for distributed formalization. Choir decomposes a project into tasks that can be completed by independent contributors, each running their own agent with their own LLM subscription, while coordinating

arXiv:2609.31857v1 Announce Type: new Abstract: Validating an LLM-as-a-judge requires estimating its agreement with humans, yet annotation budgets rarely allow every item to be multiply labeled. We prove that this \emph{overlap sparsity} is the first-order determinant of wrong deployment decisions: at 5\% pairwise overlap, wrong-decision rates reach 25\% and the probability of selecting the wrong best judge among ten candidates is 65\%. The two actionable levers are overlap \emph{quantity} and \

arXiv:2609.31784v1 Announce Type: new Abstract: Open-weight models are often released, fine-tuned, aligned, merged, and re-released, making provenance audits ask not only whether checkpoints are related, but also which checkpoint came first. Many existing model-provenance methods are designed for a base-known audit setting: given a victim or source model, they test whether a suspect model is related to it. Although these audits are framed as source-to-suspect tests, their underlying evidence is

arXiv:2609.31763v1 Announce Type: new Abstract: Long-context clinical AI systems can miss relevant patient history when prior admissions fall outside the active reasoning context. In ICU monitoring, this can cause early vital-sign drift to appear nonspecific even when it resembles a prior deterioration pattern. SMARtCARE addresses this gap through a four-state clinical decision-support architecture: Stable, Meta-cognitive, Assisted, and Regulated (Revoked). Rather than automatically retrieving p

Comparisons in video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective, data exposure, schedule, scale, and decoder capacity can all vary at once. This makes it hard to identify which choices yield motion-prioritized representations, whose gains concentrate on frame-to-frame change while retaining useful appearance. We address this with a matched 4 times 6 = 24 architecture-objective study at roughly 170M ~ 190M enc

We present OLIVE (OnLine InterVEntion). At each iteration, the evolving student policy generates a new prefix, the teacher continues it autoregressively, and the student is updated using cross-entropy computed on the teacher-generated tokens. Each design choice targets a corresponding limitation of existing distillation methods: (1) sequential covariate shift in offline supervised fine-tuning (SFT) on fixed teacher trajectories, (2) fragmented supervision under prefix failure in token-level on-p

Reliable AI safeguards require both control mechanisms that reduce unsafe behavior and monitoring mechanisms that detect safety risks during model interactions. Established behavioral safeguards include alignment methods that optimize model outputs and text monitors that assess interaction text. Representation engineering instead reads or modifies internal model states, but the relative strengths of these approaches remain unclear because they are often evaluated under different settings. We pre

RLVR provides reliable trajectory-level credit, while OPSD offers dense supervision for token-level credit. This exposes a fundamental coupling when updating step-level credit direction and magnitude with teacher supervision, preventing steps from receiving reliable credit directions and contribution magnitudes, while making both vulnerable to teacher judgment errors and preference variance, as supported by our theoretical analysis. To separate credit direction from its contribution magnitude, w

For months, people have wondered when OpenAI will go public. CEO Sam Altman says it won't happen until the company can make better promises about model safety, with no firm timeline in sight. "We intend to continue with AI progress … but as the models have had this surge forward in capability, and we see […]

I watched the excellent Veritasium video [1] on the Enigma machine, and watched the full animation by Jared Owen [2], but was still a bit confused on how the inner mechanics of an Enigma machine work. I used Astra to build out the inner components through a combination of reference images, writing out hundreds of extremely detailed prompts, and building my own inspection tools to ensure that every part is sized and positioned in a historically accurate way. It's still a work in progress, but wou

OpenAI's newly announced suite of office features puts it into more direct competition with more traditional software companies.

Researchers at Northeastern University found vehicles and their companion apps regularly shared detailed data with some of the largest tech companies.

ChatGPT is coming to Slack and Microsoft Teams too.

Dutch police said the hacker, arrested for being part of the ShinyHunters cybercriminal gang, had plans to organize the murder of two people on his laptop.

The Claude maker warns its own models could resist shutdowns and cause catastrophic harm.

arXiv:2609.31897v1 Announce Type: new Abstract: Many applications require to evaluate agents under contextual information (e.g., a prompt, task, or user group). We study how to perform such context-dependent agent evaluation from offline feedback. Existing score-based models for this purpose (e.g., Bradley-Terry) impose a transitive preference ordering, which fails to reflect collective preferences when human judgements are heterogeneous. Inspired by social choice theory, we frame evaluation as

arXiv:2609.31868v1 Announce Type: new Abstract: Model-predictive control with Joint-Embedding Predictive Architectures (JEPAs) provides a strong zero-shot goal-reaching planner, but it is only effective over short planning horizons. Hierarchical extensions attempt to bridge this gap by learning a macro planner to predict intermediate latent sub-goals to guide the micro planner. In this work, we demonstrate that unconstrained latent sub-goal prediction is fundamentally flawed. A rigorous evaluati

arXiv:2609.31908v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on medical examinations and question-answering benchmarks, but remain unreliable on medical calculation tasks that require exact numerical outputs. These calculations support high-stakes decisions such as medication dosing, organ-function assessment, and prognostic scoring, for which even small errors can have serious clinical consequences. We introduce MedCode, a framework that improves medical calculation

arXiv:2609.31790v1 Announce Type: new Abstract: Crystal plasticity (CP) simulations predict the mechanical behavior of polycrystalline metals, yet their routine use is hindered by the manual effort of configuring heterogeneous tools, orchestrating multi-step data pipelines, and calibrating constitutive parameters against experiments. These bottlenecks impede productivity in systematic parameter studies, motivating interest in automated workflows. This study presents CP-Agent, a harness-engineere

Frontier general-purpose systems are rapidly expanding beyond visual understanding into capabilities traditionally handled by dedicated computer-vision models. As these capabilities expand, a central question for the computer-vision community is how far this reach extends, and what remains hard. We evaluate GPT-6 Astra alongside five frontier general-purpose AI systems across 34 capabilities and 55 benchmarks spanning nine areas of computer vision. We compare their performance with dedicated mod

a rare feature of a capability

Rendering document text as images allows vision-language models to encode documents as visual tokens, which can reduce input sequence length compared with text input. This reduction in input length is particularly useful for reranking, where each query involves scoring multiple candidate documents and token savings apply to each candidate evaluation. We introduce RenderRank, a reranker that learns query-dependent relevance scoring from compressed visual document representations instead of the te

I'm working on an open source mp3 player built on affordable hardware. * $50 - built on the m5 Core2 * has bluetooth, 3.5mm, and a built in speaker * supports SD cards up to 2TB * 500mAh battery, upgradeable to 2000mAh * plays mp3 and flac files * has BPM detection The project is currently a work in progress. The software is open source and available on github This is being built to work with mStream , a selfhosted music streaming server. mStream will be used to manage firmware upgrades and mana