Speculative decoding accelerates rollout generation, which dominates the cost of reinforcement learning (RL) post-training. Online co-training can further increase the draft's accuracy, yielding greater speedups. However, scaling this approach to co-training on large models with long contexts poses two obstacles: (1) branch attention is unsupported by standard causal context-parallel (CP) implementations, and (2) target features span across pipeline-parallel (PP) stages. We address both with an
I have several years of software experience, following best practices, design patterns, KISS, DRY, BDD, OOP, etc. My code is actually very readable since I worked on many open source projects and heard praise overall, and always had the time for quality control and refactoring. The truth is I haven't worked at a normal company since AI hit so I'm a bit detached currently from the industry. For the last 1 year I've been using the Pro subscription for 20$ on Codex and Claude on my projects, but I
I have been a dwarfstar fan for awhile and I really liked glm 5.3 flash but needed it to be materially faster to feel good using it. In the screenshot you can see the outcome of using the model with a claude code harness at ~200k depth, with many tool calls and averaging over 38tps output. Yes, I put 60tps in the headline and you will get that if you ask it to write SQL. Main ds4 was single-stream serially decoding GLM-5.3-Flash at about 59 percent of the M3 Ultra's measured memory bandwidth. We
US urges AI firms to ID, then secretly switch, Chinese users to less-capable models.
Linear attention is increasingly used in frontier language models for efficient long-context inference and constant-memory decoding. Its fixed-size recurrent memory, however, requires an online decision at each token: what to write and how strongly to overwrite existing associations before knowing which information future queries will require. Delta-rule models learn this strength from the current token embedding but do not track confidence in the memory estimate, preventing each write from adap
Visual generation is evolving from generative models used through a single invocation into agentic control processes that can plan, select tools, inspect intermediate synthesized outputs, revise failures, and reuse prior experience. In most existing systems, the controller is an LLM or VLM, while visual generation models serve as tools or executors. However, existing work lacks a consistent criterion for determining when a generation system becomes agentic. Planning depth, tool use, multi-role c
arXiv:2609.05441v1 Announce Type: new Abstract: Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dialogue history, not whether remembered facts change what a tool-using agent does. We present MERIT (Memory Evaluation for Realistic Instrumented Tasks), a benchmark and harness that measures the marginal utility of memory for task-executing agents under explicit cost accounting. MERIT provides episodi
Today, Meta has introduced Muse, a personal AI agent that takes actions rather than just answering questions. Muse can send emails, book travel, negotiate bills, and pursue long term goals. It keeps working after you close the app and returns only when it needs approval. The bigger story for AI devs is architectural. Each user […] The post Meta Introduces Muse, a Personal AI Agent That Runs on Its Own Dedicated Secure Cloud Computer appeared first on MarkTechPost .
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, and finally training it. Meanwhile, across diverse settings and modalities, much of machine learning has shifted to the paradigm of foundation models: networks pretrained once at scale and applied to new tasks without fine-tuning. Causal foundation models (CFMs)
arXiv:2609.05511v1 Announce Type: new Abstract: Web agents need to navigate visually rich, long-horizon interfaces that change across sites, yet most previous agents still learn each task in isolation and discard the procedural knowledge they accumulate. Recent skill-augmented frameworks take an important first step, but they treat the skill library as a flat or two-tier prompt-side cache and offer no principled mechanism for compressing redundancy or composing skills recursively. We introduce \
We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production across two systems with one design lineage: DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets for 21 days, February to March 2026) and the DXAP live alpha fleet (500 to 599 user-created agents all-history, 91 to 117 concurrently active, trading Hyperliquid perpetuals, June to August 2026). The record spans roughly six months, 7.5M sing
A shared workspace for Claude, Codex, and your team Discussion | Link
A few posts tagged with "new model" present models that are finetunes. My opinion : I'd rather have the "new model" tag reserved for new "major" releases, like a new Qwen model, Deepseek V4 -> Deepseek V4.1, etc., that involved a new pretrain or intensive post-training (in opposition to a small finetune). Otherwise, maybe prepend "[Finetune]" to the title to indicate that the new model is "less of a big news", a use a "new finetune" tag, to differentiate between the two kinds of new models. I re
I have a successful app I built myself with a solid user base. I had been working on a new version via Claude on and off for about six months In work we use LLMs exclusively. Nobody writes code anymore. It's all hands off and we have a high level understanding of how things work but no more than that. When it comes to my side project, I was adding features at breakneck speed with Claude but I realized I have no clue how the new code works or what it changes or breaks. I spent many years of my li
Contract gig, mid-size fintech. Their security team decided any control panel counts as unnecessary attack surface. So no cPanel, no Plesk, nothing with a web UI on prod. Servers get configured by hand over SSH, using a shared root account for the whole team. I run BeAdmin on my own boxes at home. But that's a different world here. Two people editing the same nginx.conf in one week wiped out three days of one guy's changes, and nobody noticed until a client site went down. How common is this? Wh
Paul Christiano, an influential AI researcher focused on alignment, is joining the OpenAI Foundation as a member of its board.
At Wednesday's iPhone Duo launch event, Apple announced a handful of new Siri AI Audio Intelligence features, including Siri Recap, Live Rewind, Sound Recognition, and Music Recognition. Alongside its announcement, Apple released a document laying out how it plans to balance AI "ambient listening" and users' privacy. It says the raw audio from the new […]
Massachusetts has become the third state in as many months to slap new restrictions on data center development.
The legal AI startup and VC darling has nearly doubled its valuation in nine months.
Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However, visual information being present in a latent state does not imply that the model actually relies on that state when producing its answer, especially when alternative image-conditioned paths remain available. We introduce Causal Visual Recurrent Reasoning (CVRR), which preserves pretrained visual competence while making recurrent computation the re
As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuning methods (e.g., RLHF, DPO) for behavioral control. However, existing work typically validates steering on isolated behaviors, leaving it unclear whether steering vectors encode coherent semantic structure or merely exploit behavior-specific shortcuts. We investigate whether the latent geometry of LLM steering vectors
arXiv:2609.05513v1 Announce Type: new Abstract: Web agents have achieved significant success in automating complex internet tasks but deploying them in real-world environments requires continuous online adaptation. Given that deploying powerful proprietary models remains commercially cost-prohibitive, practitioners must rely on lightweight local models that evolve post-deployment via online teaching from a stronger teacher. However, standard interactive feedback imposes prohibitive costs. We sho
Representing a 3D scene as multi-view images allows 2D VLMs to reason in 3D by reusing priors from pre-training, sidestepping the scarcity of annotated 3D data. However, it produces thousands of redundant visual tokens whose cost grows with every view. Existing visual token pruners fall into two families, each limited in the 3D multi-view setting. Learned importance methods rank tokens by attention or encoder features; because redundancy here is fundamentally spatial, they keep near-duplicate to
arXiv:2609.05439v1 Announce Type: new Abstract: Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provide actionable feedback for model improvement. We propose CriticGen, a fine-grained, generation-aware evaluation framework that turns evaluation into actionable control for answer improvement. CriticGen first generates sample-specific evaluation dimensions and scoring criteria under high-level catego
arXiv:2609.05505v1 Announce Type: new Abstract: Systematic reviews require sustained human judgment across thousands of records, yet existing evaluations of large language models (LLMs) typically examine review stages in isolation. We introduce SciLitBench, a multi-stage benchmark spanning title and abstract screening, full-text screening, and schema-guided data extraction, with 42,981 retrieved records, 1,012 full texts, and annotations for 888 included papers. Across 22 open-weight LLMs from s
蚂蚁集团百灵首个金融增强开放模型 Ling-3.0-flash-Fin发布。
Google's AI map of every possible human DNA mutation Discussion | Link
I wrote recently about how the collection of good, fruitful open problems is now being mined in a non-renewable fashion, leading to the potential scenario of these problems becoming scarce. [...] We have now seen that even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential. The incentives may now be pointing in the direction of no longer sharing any promising research
I can close an eye on using LLMs for research only. These days I feel I am the only one that hasn’t changed their way of writing software at all. I’d honestly switch careers rather than manage agents. But I’m currently out of work (contract ended) and wondering if there is still a sane place to work, or is it truly time to pivot to another career.
Give your AI agent guides that show users where to click Discussion | Link
The City Attorney’s Office has asked Meta to explain how the harmful ads repeatedly ran on Facebook and Instagram. The company claims the ads are not under the city’s jurisdiction.
Apple says its new watches won’t save raw audio, but features that can transcribe recent speech and summarize ambient conversations raise new questions about consent, privacy, and how people behave when they know they could always be recorded.
Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretrained bidirectional video diffusion models into causal AR students through Distribution Matching Distillation (DMD), but the generated videos often suffer from over-saturation and over-smoothing issues, resulting in limited visual quality and realism. The key contributing factor is the mode-seeking behavior of the reverse KL objective in DMD, which can cause the studen
Kepler Computing claims a new approach to chip design—and a proprietary material—can help end the supply bottlenecks that have sent memory prices surging.
In the absence of federal protections from AI, unions are stepping up.
Paul Christiano joins the OpenAI Foundation Board and its Safety and Security Committee, bringing experience in AI alignment, safety, and standards.
OpenAI’s latest mathematical milestone has quickly become mired in controversy. Today, the company announced that its agents have solved one of the Millennium Prize Problems, some of the most important open problems in mathematics. Under normal circumstances, that solution would be a huge feather in OpenAI’s cap. But the announcement has been overshadowed by accusations…
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. What OpenAI’s latest controversy tells us about the future of math OpenAI says its agents have solved one of the most important open problems in mathematics. Under normal circumstances, that would…
Chris Lehane argues that stronger AI capabilities require stronger safety evidence, shared standards, and durable policy action while the policy window remains open.
arXiv:2609.05448v1 Announce Type: new Abstract: Structured post-training pruning of transformers requires selecting complete functional units whose suppression causes limited degradation. We formulate structured-unit selection for language and vision transformers as a damage-aware multi-armed bandit problem under a fixed candidate-evaluation budget. Attention heads and MLP channel groups are temporarily masked on calibration batches. Paired damage is the masked loss minus the base loss on the sa
Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing datasets and benchmarks mainly evaluate task completion under predefined settings, offering limited insight into model reasoning under increasing spatial and procedural complexity. We introduce RoboSPA (Robot Spatial-Procedural Assessment), a large-scale robotic manipulation dataset and benchmark for diagnosing embodied reasoning in VLA models. RoboSPA focuses on two
arXiv:2609.05527v1 Announce Type: new Abstract: Wherever a coding agent works under engineer supervision, or a clinical model assists a radiologist, the deployment question is whether to keep the human-AI workflow or replace it with the human alone or the agent alone. The human-AI workflow is worth keeping only if it beats both of those alternatives. Yet once it is deployed, neither alternative outcome is observed: recovering one means replaying the task under that alternative, and every replay
Hi r/machinelearning . Nice to meet you! My name is Chris Piech and I'm a professor at Stanford University in the AI lab. I built a class called Probability for AI: pai.stanford.edu. It starts Oct 9th and applications are due end of Sept. Its (hopefully) cool for a few reasons: The plan is to have one volunteer teacher for every 10 students! Apps have been open for a week and over 1,000+ folks have applied to teach. So we might actually be able to make this pretty big. I have built a lot of fun