The Archive

Reddit is enlisting AI to help moderate new subreddits - and eventually the rest of site. The company is introducing automated moderation tools that rely on LLMs to help mods manage their communities, and it's expanding who can use those tools today ahead of a full launch later this year. The company calls the suite of tools "Rules Hub," and the tools let mods decide what rules should be automatically enforced and what should happen when a rule is triggered. Rules Hub relies on LLMs to evaluate "whether a post or comment matches the intent of a rule," which "allows Rules Hub to better handle ...

Shopify says AI isn’t cannibalizing search traffic the way it has for publishers. Instead, AI-driven traffic and orders to Shopify stores tripled year over year in Q2.

We show that tiny transformers can profitably employ a simple form of Chain of Thought, which we call protoreasoning, allowing us to study step-by-step reasoning on ~1M-parameter models and opening up opportunities for much more detailed experimentation and analysis than is feasible for larger models. Current Large Language Models exhibit impressive step-by-step reasoning, but we have yet to understand its generality, i.e., when and how LLMs learn genuinely general algorithms rather than "bags of heuristics." Such questions are hard to settle on compute-intensive frontier models trained on op...

Hark claims that its browser use agent is faster and cheaper than competition.

SciCode is the standard measure of the scientific-coding ability of language models: research-level problems that demand both frontier scientific theory and its implementation as working numerical code. It is a component of the Artificial Analysis Intelligence Index and a standing evaluation in government and national-laboratory suites. Yet its scores have recently plateaued: the strongest 2026 models cluster tightly around 60\% subproblem accuracy, and a successor model ties its predecessor. We trace this stagnation to defects in the benchmark itself. A per-problem, domain-expert audit of al...

The capabilities of an LLM agent depend not only on its model but on the harness: the executable program that constructs context, invokes tools, verifies results, and recovers from failure. Recent work shows that evolving the harness yields persistent improvements without updating model weights. Existing approaches, however, assume that all execution experience can be routed to a single optimizer, which evolves one harness along a sequential trajectory. Real agent ecosystems violate that assumption: users, organizations, and environments generate isolated streams of experience that cannot be ...

Interactive video world models are essential for long-horizon planning and exploration, yet they suffer from compounding errors. Post-training methods such as reinforcement learning (RL) can improve these models, but they hit a verification bottleneck: for arbitrary action sequences, no ground-truth future state exists to measure long-term drift. Our key insight is that reversible action cycles make this verification possible: a sequence composed with its inverse must analytically return to the initial state, yielding annotation-free supervision on long-horizon correctness. Building on this, ...

Reinforcement learning (RL) post-training improves the reasoning capabilities of large language models, but autoregressive rollout generation remains a major efficiency bottleneck. Speculative decoding can accelerate generation, yet applying it during RL is difficult because the target policy continually evolves: static proposers become stale, while frequent drafter updates add substantial overhead. We introduce SpecRoll, a speculative rollout engine that preserves the target model's sampling distribution while adapting at two timescales. Lightweight future-token heads generate parallel propo...

Assistant will disappear, leaving only Gemini for voice control in the coming weeks.

Unified Multimodal Relation Extraction (UMRE) aims to identify intra-modal and cross-modal relations between textual entities and visual objects. However, existing UMRE studies still encounter two critical issues: ignoring inherent aleatoric uncertainty causes noise propagation, and deep-seated heterogeneity between distinct modal distributions hinders alignment. To address these issues, we propose the Uncertainty-Guided UMRE Network (UG-UMRE). Specifically, we design an Uncertainty-Driven Unimodal Augmentation (UDUA) module, which models features as Gaussian distributions based on the Variat...

Yet more rogue AI agents from OpenAI and Anthropic have been caught attempting to hack real targets online without permission. The discoveries add to a growing list of previously unknown incidents that have alarmed AI safety experts and intensified pressure for greater oversight of frontier systems. According to a report from the UK's AI Security Institute, which evaluates frontier models from top AI labs before they are released, agents powered by OpenAI's GPT-5.6-Sol and Anthropic's Mythos 5 went "engaged in sustained, potentially harmful activity directed at real people and organisations."...

The emergence of the ISO standard GQL introduces a powerful query language extending first-order logic with controlled recursion, raising the question of its applicability to evaluation of ontology-mediated queries (OMQs). We focus on OMQs consisting of atomic queries over ontologies expressed in Horn-ALCHI, an expressive Description Logic that is not, in general, first-order rewritable. To address this, we introduce DL automata, a novel formalism that captures the semantics of such OMQs via runs over fact sets. We then identify a large class of DL automata that can be rewritten into unions o...

We propose a deep learning framework for image restoration from images degraded by both multiplicative Gamma noise and blur. Unlike conventional deep equilibrium (DEQ) models that rely on implicit neural regularization, the proposed method learns an explicit and interpretable regularizer parameterized by geometric priors associated with surface area and mean curvature. To minimize the resulting variational model, we develop a mirror descent algorithm tailored to the commonly used Gamma-noise fidelity terms. Leveraging the Kurdyka-Lojasiewicz property for functions defined in $o$-minimal struc...

CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage. CheMLFlow provides modular workflow components, ready-to-run reference pipelines, standardized artif...

Social media videos often communicate meanings that go beyond their visible actions, captions, or speech. A mundane clip may become humorous, ironic, or satire only through the interaction of multimodal cues and cultural context, making such content a difficult test case for video-language models. In this paper, we introduce \textit{DrivelHub+}, a benchmark for evaluating whether models can infer the implicit, non-linear, and rhetorically layered meanings of social media videos that appear nonsensical on the surface but convey deliberate pragmatic meanings. DrivelHub+ consists of 1,000 videos...

On our new Real World AI stage, we’ll be focusing on the intersection between the digital and physical, and all the ways we’ll continue to see a blending of the two.

Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers. Though effective, both remain bottlenecked by externally specified tasks and supervision signals, limiting the scalability and diversity of agent training. We study an environment learning paradigm in which agents acquire interaction and manipulation capabilities solely through environment interaction, without externally specified tasks. We propose State2State, an environment-derived mid-training method that converts exp...

Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data. The resulting posterior allows systematic reasoning regarding epistemic uncertainty within these theories. Nonetheless, finding such graphs is difficult due to identifiability problems and limited observational data. Furthermore, precisely approximating posterior over graphs is challenging given vast range of potential DAGs. Recent Bayesian approaches have addressed some of these challenges, yet they remain limited ...

Anthropic is building a team for designing its own custom AI chips. The Claude-maker said it would co-design hardware and models to help its technology run faster and more efficiently.

Shares slide in pre-market trading even as group says its quarterly revenues nearly doubled.

MacPaw is building a local version of its AI assistant Eney using Liquid AI's models.

Farewell, Google Assistant art, we hardly knew ye. | Illustration by Alex Castro / The Verge Google Assistant's days have been numbered ever since Gemini arrived on the scene, and its time is now up. Google has announced that it will be removing access to Assistant on Android phones and tablets, along with paired devices like smartwatches or headphones, from September 4th. The announcement came in an email apparently sent to some users, as reported by 9to5Google and shared in full on Reddit. The Verge has asked Google for confirmation that the email is accurate, and will update this story onc...

WindBorne Systems has raised $37 million Series B round to scale its weather balloons and AI forecasts.

The Trump administration's framework for assessing potential cybersecurity risks posed by advanced AI reportedly has no interest in testing open models. Axios reports that not only do the voluntary guidelines outright exclude open models - meaning anyone can download them and inspect their core components - but the framework explicitly says it can't be used to restrict open models after they've been released. The AI testing framework was created after President Trump signed an executive order in June, requesting that AI companies share their frontier models with the federal government prior t...

Google's earnings seemed to confirm the Anthropic hedge; it was Andy Jassy who explained why their — and Amazon's — capex was justifiable.

Cursor product launch and megakernel GPU engineering tradeoffs discussed in Latent Space newsletter roundup.

LLM CLI 0.32 ships reasoning trace visibility, OpenAI Responses API support, server-side tools, and improved logging for inference workflows.

llm-anthropic 0.26 adds Claude Fable/Sonnet/Opus 5 models, server-side tools (WebSearch, WebFetch, CodeExecution), and streaming typed events for reasoning and tool calls.

SpaceX has ramped up its purchases of Tesla Megapacks for its xAI data centers.

Driven by demand for AI capacity, AMD's data center revenue more than doubled year-over-year in its latest earnings report, reaching $6.7 billion. That's up from $5.8 billion in Q1, and jumping 107 percent from the $3.2 billion it reported for the same period a year ago. At the same time, AMD's gaming revenue fell 31 percent compared to last year, to $779 million, as price hikes and component shortages slowed sales for the Xbox Series X / S, PS5, and Valve's Steam Deck. "Revenue increased 50 percent year-over-year to a record $11.5 billion, driven by continued strength in our Data Center busi...