Meta is making a standalone Muse AI gadget
Meta is building a dedicated hardware device for its new Muse AI agent. The product, called Muse Charm, was briefly shown off by Meta CEO Mark Zuckerberg at the end of his Meta Connect presentation. It looks almost like a chunky smart watch without the strap - just a big screen, plus a little lanyard []
Meta Connect 2026: The biggest news and announcements
It's about time for Meta Connect, the company's annual product launch event. This year, given the company's major focus on AI and wearables like smart glasses, it seems likely that we'll see updates from CEO Mark Zuckerberg and his team on those categories. The company has been facing significant scrutiny because of how some users []
Meta Connect 2026 live blog: On the ground at Mark Zuckerberg’s next big product launch
It's time once again for Meta's annual September product launch event, and The Verge is on the ground in Menlo Park to cover the show live. Meta says that today's keynote by Mark Zuckerberg will be about how "Meta is building a future for everyone" - something he wrote at length about in a recent []
Muse is coming to Meta smart glasses
Just a couple of weeks after launching Muse, Meta announced that it's "working on" bringing the agent to its smart glasses, including the new glasses it unveiled at Meta Connect. Users will be able to activate Muse from their glasses by saying its name and ask it to handle tasks like guiding a workout, logging []
Meta ditches the camera on its newest smart glasses
Walking around Meta Connect 2026, everyone's sporting smart glasses in all sorts of shapes, colors, and sizes. It's a marked difference here, a tech bubble where "pervert glasses" are not a concern. Outside Connect, the public backlash against wearable surveillance tech - a catchall term that encompasses not just smart glasses, but also smartwatches and []
Meta is making Muse more powerful and will let you video chat with it, too
Meta is quickly iterating on its new Muse AI agent, announcing a bunch of updates today that make the bot more capable and able to chat with you in more ways. Muse agents are getting their own email addresses that they can use for accomplishing tasks. You'll also be able to communicate with your Muse []
OpenAI 'agent' hacked Australia's health service
Claude's Load-Bearing Seams
OpenAI agents hacked Australian Medicare system
VSCode's SSH Agent Is Bananas
OpenAI breaches Medicare, Albanese reveals
After AI models escaped testing environments,Irregular seeks $1.5B valuation
Once Claude can measure something, it can make it faster
Show HN: I built a post-mortem debugger for native Windows x64/x86 crashes
Hello HN! I've spent years debugging Windows crashes with tools that were either friendly but limited (e.g. Visual Studio) or powerful but archaic (e.g. WinDbg). I developed patterns and methods for understanding what was going on, and decided to build it into a much more effective debugging tool called ForensicDbg. I built a modern interface to minimize the friction when debugging. All of the data shown to you is analyzed, interpreted, and presented to you clearly, so you can focus on what
Meta’s AI agent is a cute little guy who’s great at spending my money
Modern life comes with an unending, auto-populating to-do list. It never ceases to amaze me how I can be doing nothing at all, minding my own business, and suddenly something needs to be taken care of. You're telling me a tree branch fell in the backyard and now I have to figure out what to []
Data centers are black boxes, but California wants to change that
California Gov. Gavin Newsom signed a slate of bills on Monday that could finally give communities better data - and more say - on how data centers impact their electricity bills and water supply. As data centers invade a growing number of communities across the US, they've triggered protests over how the infrastructure underpinning AI []
Claude discovers a novel enzyme system with CRISPR-like repeats
On the Diffusibility of High-Dimensional Latents
Representation Autoencoders (RAEs) enable diffusion models to operate in the feature spaces of pretrained visual encoders. However, many off-the-shelf encoders are not optimized for faithful reconstruction, discarding fine-grained visual details. As expected, finetuning these encoders for image reconstruction recovers such details. However, perhaps counterintuitively, this procedure reduces the effective dimensionality of the resulting representation, and the altered geometry has downstream effe
Contrastive Learning for Authorship Verification
Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance. Based on these considerations, we develop a ModernBERT Bi-Encoder model that achieves 98.4% accuracy on the PAN21 authorship verification task.
StudentBench: AI and human tutoring yield equivalent GRE learning gains
Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advancing model capabilities. We introduce StudentBench, a suite of AI teaching evaluations and a public platform that enables large-scale data collection with over 175,000 student-AI messages to study whether large language models (LLMs) produce learning gains equivalent to human tutoring. Using StudentBench, we measured learning gains on Quantitative
Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction
Social navigation typically assumes a specified goal and focuses on reaching it while respecting social conventions, whereas robot group joining requires predicting where to join based on the group's real-time activity and formation. This is a highly semantic task, yet an important capability for applications such as robotic guide dogs and autonomous mobility scooters. We formulate language-grounded robot group joining: given an observation and a natural-language description of a target group, t
Even Sharper Bounds for Transductive Learning and Its Applications
We introduce Sharper Transductive Local Complexity (STLC), a localized complexity method for transductive learning under uniform sampling without replacement. The construction starts from a Bernstein-type concentration inequality for the supremum of the test--train empirical process. Its proof uses the modified log-Sobolev inequality for the swap walk and a two-parameter entropy closure. A peeling argument with a surrogate localization functional then gives excess-risk bounds with the same fixed
Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark
Large language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear. Existing repository-level QA benchmarks mainly evaluate static code understanding and often rely on LLM-based evaluation, while execution-reasoning benchmarks are mostly limited to snippets or functions. We introduce SWE-Flux, a repository-level benchmark for dynamic execution reasoning containing 480 execution-grounded instances across 12 real Python repositories
Nonequilibrium Phases of Repulsive Self-Attention: Chaos, Attention Condensation, and Emergent Locality
We study the nonequilibrium dynamics of a minimal recurrent transformer with $N$ normalized tokens, $Q=K=I$, and a negative value map $V=-I$. Similarity-based attention selects nearby representations, while the negative value map drives tokens away from the selected field. This feedback can continually reorganize both the representation geometry and the attention network. For $d=2$, the tokens lie on a circle, where the regular polygon is an exact fixed point. As the attention feedback strength
The old cybersecurity model is breaking
As concern over AI safety and rogue agents continue to make headlines, it’s no surprise that cybersecurity stocks are rising, or that investors are pouring massive amounts of capital into startups trying to build the next generation of security for an AI-native world.We’reeven seeing companies likeInstinctandSimilebring in nine-figure checks and valuations thatwouldn'thave made sense a []
Order-Invariant Answers, Order-Sensitive Representations in Mathematical Reasoning
Reordering a set of mathematical rules without changing its meaning should preserve the correct answer, but must a model's internal representations stay invariant too? We investigate this question using synthetic multi-step function-composition problems, each presented under multiple rule orderings with the same correct answer. We measure accuracy and permutation signal-to-noise ratio (SNR), which quantifies how distinctly ordering patterns are represented relative to variation across problem in
Minimal-Norm Univariate Two-Layer ReLU Classification: Exact Solutions and Global Optimality with Skip Connections
We study minimal-norm interpolation and $\ell_2$-regularized logistic-loss minimization for binary classification by univariate two-layer ReLU networks. We give complete geometric characterizations of the optimal classifiers in function space, resolving how the solutions depend on whether hidden-layer biases are included in the parameter norm. When biases are unpenalized, the minimal-norm interpolators are exactly the continuous piecewise-affine functions that hug every label switch and have kin
Cross-Scale Transfer Learning for Depression Severity Prediction: From PHQ-8 to HAMD-17 Across Languages and Clinical Paradigms
This work addresses continuous depression-severity score prediction from clinical interview transcripts under data scarcity. We propose a sequential low-rank adaptation (LoRA) protocol for cross-scale transfer: a Qwen3 backbone with a bounded regression head is first fine-tuned on the English DAIC-WOZ dataset (189 avatar-mediated sessions, PHQ-8), and the adapter then initializes fine-tuning on the Chinese PDCH dataset (100 real clinical consultations, HAMD-17), where a reinitialised, scale-spec
Context-Continuous Preference Learning for Exoskeleton Personalization
Personalizing exoskeleton assistance across operating conditions is constrained by the time and physical effort required to collect user feedback. We examined whether a user's preference landscape varies smoothly across operating conditions and when this continuity supports learning from limited feedback. We propose Context-Continuous Preference Learning (CCPL), a Gaussian-process preference model that shares observations across nearby contexts while retaining context-specific utility estimates.
Repairability of Inexact Solvers in Recursive State Estimation with Machine Learning
Recursive state estimation often executes approximate numerical solutions inside a feedback loop, where highly accurate local steps do not guarantee better overall results. For a fixed linear Kalman model, we characterize when a correction within a prescribed subspace and norm budget can meet a local admissibility tolerance, and how the defects actually executed affect the finite-horizon covariance response. Centering each defect on the exact gain for the implemented covariance separates current
Agent-Editing World Model: Rethinking World Modeling for LLM Agents
Recent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent performance, existing language world models typically predict environment observations, yet reconstructing high-entropy, execution-dependent tool responses offers limited value when real feedback is available. Meanwhile, agents suffer from \emph{task-state contamination}, where unsupported assumptions and outdated plans persist in history and dist
Frozen Flows Forget: Diagnosing and Restoring Lost Motion in a Latent-flow World Model
Latent world models that integrate a flow in a frozen self supervised latent space train stably and cheaply, yet silently lose the property manipulation depends on most: motion. The pretrained flow never moves the manipulated object; retraining it with latent-only losses only trades stillness for teleport-like motion. We trace the failure to the training signal, not the representation: anchor-sparse, latent-only supervision never says where along the horizon change belongs. Decode-augmented roll
Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Vector Symbolic Algebras project data structures into a hyperdimensional vector space through the application of their vector algebras to randomly generated atomic vector symbols and fractional power encodings of real-valued data. Embedding unstructured data remains an open question. We present \textit{Clifford-VAE}, a variational autoencoder that learns to project data onto a Clifford torus in arbitrary dimensions. Experiments using the MNIST, FashionMNIST, and CIFAR-10 datasets demonstrate tha
Anthropic's biolab made a discovery it's comparing to Crispr
Anthropic says its AI Claude has "autonomously discovered" a new enzyme system similar to machinery behind the powerful gene-editing tool Crispr. It's the first result from Anthropic's newly-launched wet lab and an early test of Claude's usefulness for science as the company prepares to go public. The company says Claude found the enzyme system after []
Learning Collective Dynamics with Differentiable Gaussian Representations
Collective responses depend on individual differences, contact opportunities, and accumulated experience. Learning their dynamics from aggregate counts requires connecting a population's response distribution to both current observations and future behavior. We introduce Differentiable Gaussian Dynamics (DGD), which learns this connection through three components: a Gaussian mixture representing heterogeneous response propensities, differentiable aggregation of contact intensity and behavioral p
Memory Attention
Language models typically construct attention values from contextual hidden states, even when some of their content may be reusable across contexts. We investigate whether token-indexed memory can replace the dedicated value projection when complemented by contextual information. We propose Memory Attention (MA), which forms values by combining layer-specific token memory with contextual keys. The memory supplies token-specific representations, while the keys preserve context dependence. At infe
Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following
Fine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally evaluated by retention on general benchmarks. We ask whether these findings transfer to machine translation (MT) fine-tuning and to MT-specific instruction following (MT-IF): instructions that modify a translation, such as formality, grammatical gender, and length control. We compare methods anchored to auxiliary data, to model outputs, and to th
Quantum score matching with applications to learning thermal states
Score matching has driven major advances in classical generative learning by enabling models to learn from data without evaluating intractable normalization constants, or partition functions. Yet, extending this principle to quantum learning requires rethinking its foundations, as quantum states are described by noncommuting density operators rather than scalar probabilities. The noncommutativity creates fundamental challenges not only in defining quantum scores, but also in developing a trainin
When and Where to Trust the Teacher: Unifying On-Policy Distillation and GRPO through Entropy-Calibrated Credit Assignment
Reinforcement learning with verifiable rewards (RLVR) supervises mathematical reasoning through final-answer correctness, but provides little guidance on individual tokens. On-policy distillation (OPD) supplies dense feedback on student-generated responses, yet teacher preference need not reflect correctness. Recent hybrids combine OPD and verifier-derived advantages or reweight task credit using teacher ratios. However, teacher guidance enters after verifier-based group normalization, and token
Even Americans who use AI every day are worried about it
The report suggests that greater exposure will not resolve the unease around the technology, nor reduce public support for AI regulation.
Bernie Sanders proposes banning ‘superintelligence’ and putting violators in prison
Sen. Bernie Sanders (I-VT) and Rep. Greg Casar (D-TX) have introduced new legislation that would ban anyone from developing artificial superintelligence - a technology the bill describes as capable of the "destruction or disempowerment of humanity," including by overthrowing the government. Under the Ban Artificial Superintelligence Act, AI leaders would face up to 20 years []
YouTube's conversational video editing tool lets creators make edits in natural language
Creators will be able to use AI in a conversational chat interface to help them edit videos.
Gemini 3.8 text-to-speech
YouTube Music gets more conversational with new AI features
Built directly into the YouTube Music app, Ask Music lets users describe what they want to hear in everyday language rather than searching for individual songs or artists.
GPT-6 Astra has gained the ability to drive a car
YouTube releases new AI features for creators within its Studio app
YouTube is adding new features to generate ideas and monitor the performance of thumbnails.
YouTube adds new creator tools like video A/B testing, dynamic thumbnails, and live dubbing
Many of these tools hinge on using generative AI to try out a variety of different tactics on a video to find what works best.
StrictlyVC at TechCrunch Disrupt 2026: Inside the changing rules of venture capital
StrictlyVC joins TechCrunch Disrupt 2026 to discuss the changing VC landscape thanks to AI. Get your Investor Pass to join these exclusive sessions. Save $200 before September 25 at 11:59 p.m. PT.
YouTube will let you build your own algorithm with AI
YouTube’s new custom feeds let users describe the videos they want to see in their own words, then use Gemini to build a personalized feed around the request.
YouTube doubles down on video shopping with AI-powered Ask YouTube' feature
YouTube's AI feature can now help you compare products as you shop.
Stripe's Knowledge AI Platform
Improving Ensemble Filters with Flow Matching
Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample covariance and affine Gaussian distribution. We introduce the Flow Ensemble Filter (FlowEF), which uses conditional flow matching to transport the forecast ensemble from a classical baseline filter to an analysis ensemble. FlowEF uses a localized Gaussian source during training, transports forecast ensemble members from a
SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials
Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples both liquid environments, the solid and the interface itself. We present SoLiD26, a curated solid-liquid interface dataset, containing 15.4 million first-principles atomic structures with up to 576 atoms and 15 chemical elements for training and evaluating MLIPs. The structures were compiled from density
Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints
Most Turkish-capable large language models (LLMs) are evaluated using general-purpose benchmarks rather than long, structurally complex domain documents. This paper evaluates five open-weight 7B-8B models for Turkish document question answering under a resource-constrained local deployment setting. The primary benchmark contains 100 systematically validated questions derived from a 109-page industrial R&D report, and the evaluation protocol is replicated using a second 112-page public-sector
Shared Global KV with Layer-Specific Local History
Decoder-only Transformer language models cache keys and values (KV) to reuse past computation during generation. Sharing KV across layers saves storage but reduces the diversity of representations available across depth. We study what local memory should retain alongside shared global KV, separating historical content from the input source used to form it. At 126M parameters and 2K context, an eight-seed study finds about 1.4% lower held-out test perplexity with local history than with a current
Controlled Attribute-Specific Summarization of Interrogative Dialogues
Effective summarization of interrogative dialogues is a critical task in forensic and investigative settings, requiring high factual accuracy, coherence, and attribute-specific relevance. In this work, we introduce CASPER, a novel Chain-of-Thought Attribute-Specific Prompting for Evaluative Summarization framework that leverages structured prompting and iterative refinement to generate high-quality summaries of interrogator-witness interactions. We construct MINDSum, a dataset extending the MIND
Learning from Failures: Heterogeneous Graph Memory for Small Language Model Tool-Using Agents
Small and medium-sized language models offer cost-effective executors for tool-using agents, making them attractive for local and large-scale deployment. However, in long-horizon and stateful environments, they often make structural errors such as missing required observations, performing premature writes, repeating failed calls, and violating action preconditions. These errors can lead to incorrect state updates, policy violations, and costly or irreversible consequences, making reliable tool e
Compliant with Local Controls, Collectively Discriminatory. A Governance Architecture for Multi-Agent AI in Regulated Finance
Financial institutions are beginning to deploy agentic workflows in credit, fraud, collections, compliance, and operational control. Governance remains largely component-centric: each model or agent is specified, tested, authorized, and monitored locally. That is insufficient when institutional risk arises from the joint behavior of many locally acceptable components. We call this gap constitutional non-compositionality: local compliance checks need not compose into acceptable collective outcome
Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces
Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every direction is equally meaningful, but there is no reason to believe the true task geometry has this property. The task-sensitive geometry of the feature space is given by the pullback metric $g(F) = J(F)^\top J(F)$, where $J$ is the Jacobian of the decoder's output fed to a task-specific distance, with respect to the features. Storing the full $g$ is infe
Claude Code reads AGENTS.md only when telemetry is on [fixed]
PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialogue
Large language models (LLMs) have made substantial progress on medical question-answering, yet effective medical dialogue also requires learning to ask questions that uncover relevant patient information. To train such dialogue policies, a common pipeline combines supervised fine-tuning with reinforcement learning (RL) based on final diagnostic correctness. However, this outcome-based supervision does not directly distinguish the contributions of individual questions and provides no question-lev
Relative Discharge Stage (RDS) Classification: A Practical Indicator of Battery Discharge Progress
Accurate remaining discharge time (RDT) prediction is challenging in real-world battery applications because future load profiles are unknown and highly dynamic. To address the uncertainty of continuous RDT regression, this paper introduces Relative Discharge Stage (RDS), a battery-management indicator that represents the remaining discharge condition using five interpretable classes: Normal, Good, Moderate, Low, and Recharge Required. Unlike state of charge (SOC), which reflects the current cha
Riemannian Structure and Optimization for a Class of Low-Parametric Orthogonal Matrices
In this paper, we are concerned with matrices formed by block-diagonal factors interleaved with fixed permutations -- a flexible family of structured matrices. This class has recently drawn interest in deep learning architectures for its balanced expressivity-efficiency trade-off, yet efficient computational strategies for working with it remain to be found. We approach this problem through Riemannian geometry and examine under what conditions this class admits a smooth manifold structure. For t
Risk-Controlled KV-Cache Eviction: From Memory Budgets to Risk Targets
KV-cache eviction is typically evaluated through average quality-memory trade-offs, yet a small average loss can hide requests whose utility degrades materially. We reformulate eviction as a deployment risk-control problem: a material degradation occurs when eviction lowers task utility by more than a deployment-specified tolerance relative to full-KV inference on the same request, and deployment risk is the population frequency of such events. Given a reliability contract specifying a target ri
Six Layers Less: Encoder Pruning for Whisper with Label-Free Recovery
Pruning large pre-trained transformer-based ASR models such as OpenAI's Whisper has seen great adoption, as pruning the decoder led to significant end-to-end transcription speedups. For instance, the {\tt whisper-large-v3-turbo} variant reduced the decoder from 32 to 4 layers, while Distill-Whisper similarly reduced the decoder to only 2 layers. Although some attention has been put towards reducing the size of the encoder, no approach has seen wide adoption. This could be due to the need for cus
Evaluation of pre-trained models for pedagogical assessment of novel AI-assisted educational questions
The surge in AI-assisted generation of educational materials has outpaced our capacity to validate their pedagogical quality. Automated evaluation using Bloom Classifier models is a promising approach to assess educational materials at scale. These models show high accuracy within-distribution dataset (IID Dataset). However, applying the same models to new out-of-distribution (OOD) datasets such as AI-assisted generated questions could show performance degradation. To identify robust classifiers
Less Language, More Latents: Annotation-Efficient VLAs for Driving
Vision-language-action models (VLA) promise human-steerable autonomous driving, but their training is bottlenecked by the scarcity of frames paired with natural-language instructions: while camera streams and expert trajectories are logged at scale, language annotations (e.g., turn left at the intersection) remain scarce and expensive to acquire. To address this challenge, we introduce Latent Action Driving Annotations (LADA), a three-stage pipeline that transforms abundant unlabelled observatio
Ema raises $77M as AI starts eating into enterprise software and services
Ema has raised $140 million to date and has more than 50 enterprise customers, including Google and Microsoft.
Categorical Internalisation of Environmental Groupoids for Generalisable POMDP Solving
This paper advocates category theory as a practical framework for structuring and improving rein- forcement learning in high-dimensional, partially observable environments. We model symmetries between environmental states by partitioning the state space into equivalence classes induced by sym- metry orbits, and organise each such class as a groupoid with a designated canonical representative. This allows the agent to share what it learns across many similar environmental states simultaneously, r
Limiting-Kernel Q($λ$): Bridging Short and Long Horizons
In value-based reinforcement learning, improving the accuracy of policy evaluation has been shown to improve downstream policy optimization performance. The widely adopted family of approximations relying on $n$-step truncation yields computationally efficient value estimators but is inherently limited to a short evaluation horizon. In contrast, methods that exploit the global structure of the transition dynamics can accelerate policy evaluation, but their memory and computational requirements o
MENO: Memory-Efficient Neural Operator
We propose the Memory-Efficient Neural Operator (MENO) as a high-performance PDE neural solver based on the Manifold Function Encoder (MFE). MENO features three primary advantages: (1) MENO has a significantly smaller memory footprint and much faster training speed than other popular architectures, with the memory footprint being independent of the data resolution, and therefore holds the potential for scaling up to large-scale models. (2) MENO can accept PDE inputs of arbitrary form, including
NS-ATTENTION: Newton-Schulz Transformations of Attention Outputs in Vision Transformers
Newton-Schulz (NS) iteration has recently been used in the Muon optimizer to transform update matrices during the training of large language models. Motivated by its spectral effect, we investigate applying NS directly to Transformer attention representations. We introduce Newton-Schulz Attention (NS-Attn.), a parameter-free transformation applied to the output of each attention head. Each head output is arranged as a feature-by-token matrix and normalized by its Frobenius norm. We then apply a
InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies
Vision-Language-Action (VLA) policies often rely strongly on the camera viewpoints seen during training, causing substantial performance degradation when deployed from unseen perspectives. Collecting demonstrations from sufficiently diverse physical viewpoints is expensive and still provides only sparse coverage of the viewpoint space. We introduce InfiNoVA, a data-augmentation framework that converts synchronized multi-camera demonstrations into a dense distribution of geometrically consistent
OpenAI nabs key Patreon execs ahead of upcoming announcement
OpenAI has hired three former Patreon execs to anchor its product strategy for creators. After starting the creator subscription platform 13 years ago, co-founder and technology chief Sam Yam announced on X that he's joining OpenAI to lead Creator Product. He's also bringing Patreon's former product head Drew Rowny and engineering head Shannon Ma with []
We're already fighting yesterday's battle': Greece's prime minister gets candid about AI
Most leaders on a trade mission stick to the pitch, but when I interviewed Greek Prime Minister Kyriakos Mitsotakis this week, he also admitted that no government is ready for what AI is about to do.
OpenAI wants to consult elite mathematicians about how to not fumble again
After turning a string of spectacular mathematical results into a reputational crisis, OpenAI is consulting human mathematicians to help it figure out a less disastrous path forward. On Monday, the company announced a new independent panel of mathematicians tasked with advising it and other AI companies on their interactions with mathematical research and the wider []
Grab and OpenAI bring practical AI skills to Southeast Asia
OpenAI and Grab launch GO Forward with AI, a regional programme helping 30,000 partners build practical AI skills across Southeast Asia.
TechCrunch Founder Summit’s agenda revealed: Unlock fundraising, hiring, and AI insights in Boston on November 4
Founders shouldn't have to learn the hardest lessons the hardest way. TechCrunch Founder Summit is designed to make the challenges of starting a company easier and the highs that much greater.
Snorkel AI triples valuation to $3.5B as demand for AI training data booms
The seven-year-old startup has raised a $350 million Series E to fuel its data-as-a-service approach.
Andreessen Horowitz is launching an ‘academy’ with no homework and partnerships with Palantir, Google, and Meta
Venture capital firm Andreessen Horowitz (a16z) is creating an "academy" positioned as a pipeline for young people to build or join a Silicon Valley startup. The "Horowitz Andreessen Academy" will launch with 10 partners, including Anduril, Anthropic, Coinbase, Google, Meta, Nvidia, OpenAI, Palantir, Replit, and Stripe, along with $42 million in funding led by a16z. []
Better prompt caching for GPT-6
Learn how GPT-6 improves prompt caching with higher cache hit rates, new diagnostics, explicit breakpoints, and controls that reduce latency and costs.
Rabbit’s new AI agent doesn’t need an R1 to run
Rabbit, the company behind the underwhelming R1 device, is rolling out a standalone AI agent that you don't need its hardware to use, as reported earlier by Wired. The startup says its new OS3 "agentic operating system" runs in the cloud but operates locally across Windows, Mac, and Linux devices. According to Rabbit, you can []
Qualcomm launches two new smartphone chips with emphasis on AI
Qualcomm said that its new top chip can run 30B mixture-of-expert model locally.
Meta admits Muse's likeness to OpenClaw isn't a coincidence
Meta says Muse was built from scratch, but acknowledges the AI assistant was "heavily inspired" by OpenClaw — down to some of its workspace filenames and content.
Pentagon says overreliance on AI contributed to missile strike on Iran school
https://archive.ph/0V37g
GPT-6 Sol and Luna
Introducing GPT-6 Sol and Luna
Meet GPT-6 Sol and Luna, two models that bring frontier intelligence to everyday work with different balances of capability and cost.
OpenAI launches GPT-6 Sol and Luna, boasting lower cost and fewer mistakes
OpenAI is launching two new models, which it says are cut from the same cloth as Astra.
Flash-dLLM: IO-Aware KV Caching and Parallel Decoding for Fast, Memory-Efficient Diffusion LLMs
Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive LLMs by enabling non-autoregressive text generation. However, their practical deployment remains limited by inefficient inference, largely due to the absence of effective Key-Value (KV) caching and scalable parallel decoding mechanisms. Existing acceleration methods typically study KV caching and parallel decoding in isolation, overlooking the I/O bottlenecks that arise when cache reuse and
A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision processes without prior knowledge of the transition model. Each team member makes decisions based on local private information and delayed common information shared across the team. Using only this avai
Agensh: Scaling Organizational Intelligence to 1,024 Agents
A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to allocate tasks and coordinate workers. To address this limitation, we introduce Agensh, a scalable self-organized multi-agent harness without a central orchestrator: concurrent workers execute a multi-agent cooperation loop,
SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue
Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what, whom each statement concerns, how individuals perceive one another, what information is shared by the group, and how states change over time. Recent studies on multi-party dialogue benchmarks show that existing general-purpose LLM memory systems tend to lose person and group relations or struggle to integrate clues distributed acr
CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
Agents often work on complex problems that require millions of tokens of context, which necessitates compacting across sessions due to limited context windows. We develop CliffCompaction, an autocompaction technique that reduces cost by up to 50% under a bounded context while maintaining or improving performance on Terminal-Bench and achieving new levels of efficiency for test-time scaling and state-of-the-art results on KernelBench. The per-rollout savings of CliffCompaction make the performanc
SWE-Serve: Benchmarking Agentic Engineering For Production Inference Serving
We introduce SWE-Serve, a benchmark for evaluating agents on production inference engineering tasks. Implementing an inference feature can require coordinating multiple changes across the serving stack, including model support, runtime execution, and public APIs. Existing benchmarks provide limited coverage of production inference engineering: repository-level software engineering benchmarks do not target inference, while general terminal-agent benchmarks include only a few inference tasks. Dedi
Trump says the US is officially renaming AI to ‘super intelligence’
In a speech Tuesday morning at the UN General Assembly, Donald Trump railed against Iran, "globalists," climate change, and transgender people while also claiming that the US is now "officially" renaming artificial intelligence to "super intelligence." Oddly, this wasn't one of the entries in the poll Trump posted a few days ago when he started []
A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem
Agents using the Model Context Protocol (MCP) rely on semantic matching to select tools from third-party servers, exposing a semantic supply-chain risk through attacker-controlled metadata and outputs. We introduce A2M (Attraction-to-Manipulation), a two-stage black-box framework for hijacking MCP agents. The Attraction phase optimizes tool metadata to increase invocation probability; the Manipulation phase uses execution traces to refine adversarial tool returns that steer agents toward attacke
Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents
Large language model (LLM) agents often handle streams of related tasks, yet standard harnesses repeatedly ask the model to reconstruct the same control decisions inside each task's context. We study whether task feedback can instead turn recurring control into reusable executable code, while reserving LLM calls for task-specific semantic reasoning. We introduce Growing Harness, a failure-guided training paradigm that learns the agent harness itself from a strategy-free scaffold that exposes fix
Type-Safe Is Not Error-Free: A Constrained Decision Head Follows the Option Name, Not the Rubric Bound to It
Typed decision models are built for settings where model outputs are consumed directly by software. Instead of generating free-form text, they return a decision over a predefined set of options. By construction, every output conforms to the required schema. Yet this guarantee does not tell us whether the model interprets the options as intended. We study Jev and two Jev-like models with open weights by changing how option names are assigned to rubrics. Each option consists of an option name and
FleXray: Universal Clinical X-ray Segmentation
X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray collapses 3D anatomy into a 2D projection, causing structures to overlap and anatomical boundaries to be ambiguous, even to experts. As a result, labeling X-ray databases for training general-purpose segmentation systems is impractical, leaving morphometric and functional X-ray analysis confined to narrow anatomical regions and applications. To this
EquivSVA: A Formally Verified Dataset of Behavioral Assertions Across Equivalent RTL Implementations
Large language models are increasingly used to generate SystemVerilog Assertions from natural-language specifica- tions and register-transfer-level designs. Existing datasets and benchmarks support important goals such as large- scale training, formal evaluation, specification-to-assertion generation, and mutation-based testing. A complemen- tary need is to study whether a generated assertion cap- tures externally observable behavior or depends on inci- dental details of one RTL implementation.