The End of AI Frontiers
Microsoft Researcher John Langford Exits. AI Frontiers Group Declared Finished
Show HN: We compressed a 31B video+audio diffusion model to 14B, 6 steps, INT8
The Components of Agent Memory
Big Tech lost big in the 2026 election
On Tuesday, millions of American voters went to the polls and voted overwhelmingly for Democrats and against AI. The fight against Big Tech is about to come roaring back, and the fight over data centers is looking even harder for the folks trying to build them. On this episode of The Vergecast, Nilay and David []
AI is the new dating dealbreaker
When Irene moved to San Francisco last year, she started using dating apps as a way to meet new people, get to know her new city, and connect with others who care about the things she does. After the 25-year-old analyst downloaded Hinge and started swiping, she noticed a trend. "Hinge has a prompt where []
How Akai and Moog are embracing vibe-coded mods
inMusic, the parent company of Akai, Moog, Native Instruments, and more, has decided to embrace the modding community. In an interview with The Verge, inMusic CEO Jack O'Donnell said, "We're going to provide a toolbox to the community, we're going to provide the inner workings of, say, an MPC or Sample so that people can []
Ramp data shows AI spending stalling just as Anthropic nears its IPO
AI usage keeps climbing, but spending has stalled. Price cuts and smarter cost controls among the biggest spenders could challenge investor assumptions about AI hypergrowth.
Amazon will let you text Alexa your shopping list
Amazon is adding a new feature to Alexa for Shopping that allows customers to purchase items by texting the AI shopping assistant. Users can describe the products they're looking to buy and/or provide a photograph of the item, and Alexa for Shopping will search for matching results in Amazon listings, text back with recommendations, and []
Gemini Omni 1.2 Flash
Protesters break into Microsoft data center site in Amsterdam
Protesters have broken into a construction site of a new Microsoft data center in Amsterdam this morning. Members of the Geef Tegengas group helped organize the protest against the construction of three 85-meter data center towers in the outskirts of Amsterdam. Reuters reports that around 100 people made it into the construction site, with some []
App Store 2026 in numbers: AI, pricing, abandoned apps
Global stocks tumble as AI panic grips markets after Democrats' midterm success
Palo Alto Networks in talks to buy AI agent security startup Clutch for $500M
Clutch, backed by Lightspeed and SignalFire, was last valued at $85 million, according to a source.
I struggle to believe that AI R&D is ingenious
Closed AI models dominate the price-performance frontier
OpenAI is stepping back from NDAs for data center projects
Following this week's midterms where voters resoundingly voiced their opposition to data center expansion, OpenAI today announced new commitments to communities where it is working on data center projects, including that it won't use non-disclosure agreements (NDAs) with public officials. Many data center projects have come under scrutiny for relying on NDAs to hide discussions []
Kodivo
Learn coding with AI and PHP through swipeable cards Discussion | Link
Airbnb is working on an AI travel agent
Airbnb plans to roll out an AI agent in the first half of 2027 that will help users plan and book travel, the company said in its Q3 2026 earnings shareholder letter released today. "Today's AI agents are good at simple, routine tasks, like making a restaurant reservation or scheduling an appointment," Airbnb said. "But []
Cuban: Facebook knowingly runs AI deepfake scam ads; docs say $16B fraud revenue
Muse has a voice mode now
Meta now lets you have voice conversations with your Muse AI agent in real time, CEO Mark Zuckerberg announced today. The feature has been rolled out to me, and I gave it a spin on the iOS app while writing this article. As you chat with your Muse agent, the avatar at the top of []
Airbnb says it's launching an AI travel agent in the first half of 2027
Airbnb says its upcoming AI agent will help travelers plan trips, compare options, manage bookings, and get assistance throughout their stay.
Perplexity launches its own take on AI agents for work
Perplexity is taking on OpenAI and Anthropic with its own work-focused AI agents. The startup's new always-on "Teammates" can take on multiple roles within a workspace, while pulling existing projects, skills, and memory from the Perplexity tools you already use. Teammates integrate with Perplexity Computer, an AI system that can deploy agents on your device []
Claude Code sends your email address to websites
Serve Robotics launches new business to become the AWS for robots
Serve Robotics announced alonsgside its third-quarter earnings that it will launch a new business line called Physical AI Services to offer customers its connectivity and data tools.
Meta says US users can now video chat with their Muse AI avatar
Meta's AI agent can now take calls as a customizable animated avatar, ahead of the launch of a Tamagotchi-like wearable designed to bring Muse to life.
Meta warns that election backlash against data centers could make AI expansion harder
Meta warns that growing political opposition to data centers could threaten future AI investments, as communities push back against massive infrastructure projects.
OpenAI released a new cybersecurity model. Is that enough?
OpenAI's newest cybersecurity model comes out as AI labs face scrutiny of their work to protect digital infrastructure.
User Embeddings are Superpositions of Interpretable Behavioral Modes
User embeddings are central components of modern recommendation, personalization, and behavioral modeling systems. Though these embeddings can compactly encode rich behavioral information, they are inherently dense and opaque, limiting interpretability. To address this gap, we argue that user embeddings can be understood as sparse superpositions of interpretable behavioral modes. This structure is directly predicted by generative models of user behavior for linear embeddings and extends approxim
SwarmEvolve: Latent Agent Swarms for Evolutionary Discovery and Optimization
LLM-guided evolutionary search uses AI agents to improve programs for mathematical and computational problems by iteratively refining solutions and combining high-scoring methods. However, the process is often slow because the LLM generates a long textual chain of thought for each round and must repeat this generation many times to maintain population diversity. We present SwarmEvolve, a framework in which hundreds of low-cost agents per round work in swarms, reason in latent space without decod
Scientist World: Grow a Research World for AI-Scientists
AI-Scientists arrived, but a world for them to grow in did not. Human scientists take theirs for granted $-$ centuries of open problems and shared ideas, waiting in the scientific literature. To AI-Scientists, that world is readable but not runnable, so we build Scientist World for them: every research paper becomes an executable environment. Our autonomous pipeline extracts each paper's open-ended research question and reproduces its proposed method as the baseline; an AI-Scientist entering the
XStack: A Benchmark for ML Repo Migration
ML code should run where researchers choose, not where it was first written. Today, machine learning code mainly lives in two ecosystems, PyTorch on NVIDIA GPUs and JAX on Google TPUs. Migrating repositories between them means building and testing across frameworks and hardware. This migration is (1) a practical need, since it unlocks different hardware for researchers, and (2) a natural benchmark: it involves different frameworks and hardware, covers whole repositories, and is long-horizon and
Same Behavior, Different Mechanism: What Finite Experiments Can Certify About Integrated Information
Interpretability, AI auditing and assessments of machine consciousness increasingly rest on mechanism-level quantities: functionals of a system's causal transition law that describe how it computes, not what it outputs. Experiments see that law only through finite, partial and noisy interventions. We ask when such a quantity is identifiable from an experiment's access, continuous in the mechanism and certifiable from finite data, and answer exactly for a deliberately demanding case - the integra
Plainmail
Real email on every domain you own, for $5 a month flat Discussion | Link
Towards interpreting any residual stream direction by scaling activation inversion
To interpret a direction in a language model's residual stream, we can look for text that strongly activates it. But when such text is found by scanning a corpus, it is limited to what that corpus contains. We develop Maximally-Activating Example Metamodels (MAEMs), language models which map residual stream directions from a subject model to text exemplars that strongly activate those directions. Our objective is to maximise the cosine similarity of a supplied residual stream direction with the
Adversarial Training with Large Step Sizes: Implicit Bias, Optimization, and Sharpness
Adversarial Training (AT) is an important method for improving model robustness. We study the dynamics of deterministic Gradient Descent (GD) in AT. Most existing works in this direction focus on the small or vanishing step sizes regime, where GD resembles a continuous-time flow. However, such step sizes can make training inefficient and are rarely used in practice. In this paper, we study the AT dynamics of GD with large constant step sizes. We first study GD in adversarial linear logistic regr
Unpacking the Compute Efficiency of Strong-to-Weak OPD against GRPO
On-policy distillation (OPD) trains a student on its own rollouts under a frozen teacher's per-token supervision. Does this dense signal reduce the training FLOPs needed to reach a target accuracy compared with reinforcement learning? We systematically compare vanilla reverse-KL OPD with Group Relative Policy Optimization (GRPO) across task difficulty, student and teacher size, domain, and optimization choices, measuring held-out accuracy against training FLOPs. With an external post-trained tea
ReproBridge: Building Reproducibility into ML Research Artifacts
Reproducing machine learning experiments requires reconstructing procedures from information scattered across papers, released code, and supporting artifacts. For coding agents, reproduction success can therefore depend on their ability to fill in missing details, rather than on the released artifacts alone. We ask whether preserving recovered procedures and decisions improves subsequent independent reproduction. We define a standardized artifact design comprising a refined codebase, environment
A Hidden Markov Preference Model for Online Prediction of Adversary Actions
A defender anticipating an attacker needs a probability distribution over the attacker's next action from information available before that action occurs. Two structures complicate this: the attacker's behavioral regime persists and can switch within an episode, and each action combines an operation with state-dependent arguments whose feasible values change over time. We capture both with a hidden Markov model whose latent state is the active regime and whose regime-conditioned action law is de
Label-Wording Augmentation in a Typed-Decision Encoder: Gains on Unfamiliar Words, Not on Abbreviations or Codes
Typed-decision models score options that the caller supplies at request time, so their accuracy depends on label wording that the model's developer does not control. We compare two releases of a 307M-parameter multilingual cross-encoder, Ru-Jev, trained on thirteen Russian-language tasks; the later release adds an augmentation that presents labels in several surface forms. Holding questions fixed and varying only how eighteen routing labels are worded, we evaluate ten label vocabularies, produce
Understanding AI from the Inside: The State of Mechanistic Interpretability for AI Safety
Neural networks are likely to be among the most consequential artefacts in human history. As these systems become more capable and autonomous, their potential to cause societal harm also grows, making AI safety one of the defining challenges of our time. Yet we continue to build increasingly powerful systems without fully understanding how they work, how they will behave in unfamiliar situations, or how to reliably control them. Mechanistic interpretability offers a path towards understanding th
MedGraph: Maintaining Patient State with Graph Memory for Long-Term Medical Dialogue
LLM-based medical assistants that follow a patient across visits need a memory that keeps the patient's clinical state correct as it changes. We examine the errors of five memory systems on longitudinal medical dialogue and find three recurring causes: the retrieved evidence gives an outdated state, includes another person's health facts, or lacks a safety fact the answer depends on. We present MedGraph, a graph memory built for these three cases. Construction extracts dated facts from each sess
The Power of Meta Access in the Software of the Future --- Demonstrated on LLM Chat UIs and Chat AI
Chat remains the default surface for many large language model (LLM) tasks, even as agentic tools such as OpenCode excel at coding and automation. Yet the model sees the current dialogue but not the workspace around it. We explore meta access---giving the assistant a structured, permissioned view of the interface itself and propose a ladder of levels: feature tutoring; metadata, settings, and preference access; retrieval over summaries and messages, confirmed mutation, and full orchestration. Me
Failure Prediction and Classification in Vision-Language-Action Models Via Calibrated Latent Probing
Vision-Language-Action models (VLAs) enable general-purpose embodied intelligence, but their performance is brittle under distribution shift, where unanticipated failures can damage equipment or endanger people. Deploying VLAs in real-world systems therefore requires a runtime monitor that is timely, calibrated, and informative while being efficient enough to run on the edge. Existing methods fail to fulfill these requirements. We present VLA-Tug, a lightweight probe over the latent layers of a
Data-driven discovery of intrinsic Lie point symmetries of differential equation systems
Discovering continuous symmetries directly from differential-equation data can reveal geometric structure for model reduction and scientific machine learning without symbolic determining equations or a closed-form representation of the governing system. We formulate intrinsic Lie point-symmetry discovery as an operator-theoretic problem on sampled jet-space data. Admissible generators are identified from a sampled determining operator and separated from redundant, solution-dependent, and geometr
On the limitations of molecular representations
Molecular representations such as fingerprints and learned embeddings play a central role in cheminformatics and small-molecule machine learning. They serve as the primary input for a diverse array of tasks, including clustering, dataset splitting, and virtual screening, yet their evaluation is often limited to downstream model performance on narrow benchmark tasks. We carried out a systematic investigation of the quality of molecular representations, finding that none of the state-of-the-art mo
Benchmarks as Software: Terminal-Bench and the Case for Continuous Benchmarks
Coding agents are among the most economically consequential deployments of LLMs to date, making reliable evaluation increasingly important. Yet even benchmarks that are carefully validated at release can become stale. As models improve, they can discover valid solutions that existing verifiers reject, while changes to evaluation infrastructure can invalidate assumptions embedded in tasks and tests. We call the resulting accumulation of defects benchmark maintenance debt: a growing gap between be
Spectral and Subspace Dynamics of LoRA in Supervised Fine-Tuning and Reinforcement Learning
Low-rank adaptation (LoRA) is commonly used in both supervised fine-tuning (SFT) and reinforcement learning (RL), often under similar optimizer settings. Yet, how the different dynamics of the two stages manifest in the low-rank increment that LoRA optimizes remains unclear. Tracking the singular spectrum and subspaces of LoRA updates on mathematical reasoning tasks, we find that RL concentrates the spectrum at every learning rate, while SFT expands it at moderate learning rates; moreover, RL ro
The Gold Rush in AI4Math, II: What are signatures of AI-assisted Proofs?
The growing use of AI in mathematics raises a question: can we detect AI involvement in proofs, separately from AI assistance with writing? Historical arXiv papers, recent papers disclosing substantive AI use, and manuscripts released by OpenAI provide a rich empirical basis for studying this question. However, several challenges remain: lack of matched case-control pairs; potential incompleteness of disclosure; and lack of ready-to-use features designed to capture signals of AI involvement in p
OpenAI president Greg Brockman says company could be IPO ready in 2027
Speaking at a Wall Street Journal tech conference on Wednesday, the executive touted how the company had been gaining ground in the enterprise market, where rival Anthropic had already successfully made inroads.
The ex-Secret Service agent who flipped a hacker is now raising a second $250M cybersecurity fund
Stephen Ward, a former Secret Service agent and Home Depot CISO, is preparing a second fund of roughly $250 million for Brightmind Partners.
Claude can now analyze your spending with its new Personal Finance tool
With Personal Finance in Claude, users can connect their bank and card accounts through Plaid and ask questions about their finances.
Kivo
Free on device dictation for any Mac app and AI agent Discussion | Link
Claude AI can plug directly into your bank account
Anthropic has a new pitch for managing your personal finances: let Claude do it instead, with a new Personal Finance feature that connects it to your bank and card accounts via the Plaid bank bridging platform. You can ask the chatbot "where did my money go this month," for example, and it'll create an overview []
Launch HN: Rivet (YC W23) – Scale and Orchestrate Agents on S3
Hey HN, this is Nathan and Nicholas from Rivet ( https://rivet.dev ) – a fast, high-density, and scalable orchestrator for agentic workloads using the actor model. Rather than running a heavyweight sandbox per agent, each agent gets a lightweight actor backed by S3. Actors provide a long-lived process for long agent turns, in-memory state for context, a WebSocket server for multiplayer, durable SQLite storage backed by S3, and hibernation when idle. It's a similar model to Cloudfl
OpenAI says its new cybersecurity model is better at not hacking other companies
OpenAI is launching GPT-6 Cyber, the latest version of its cybersecurity AI model, which it says is better at finding vulnerabilities and less likely to go rogue and hack other platforms in the process. OpenAI says that during testing, GPT-6 Cyber went out of bounds less than 1 percent of the time, down from around []
Axiom Math wants to keep AI slop out of mathematics
As AI floods math with unchecked proofs, Axiom Math is betting on verification. The startup’s AI agents just formally verified the Stacks Project, one of the field’s biggest libraries.
Show HN: Stroq – a local audit of what your coding agent did and why
Building a Multi-Agent System from Scratch: Architecture Patterns
Dropbox acquires Tigris Data for $350M to bolster its AI infrastructure
The deal is expected to support the development of DBX Agent Cloud, a new storage platform aimed at developers building AI models and agents.
ElevenLabs now lets users publish AI-generated music directly on SoundCloud
ElevenLabs said more than 25 million tracks have been generated using its music models.
You can post AI music straight to SoundCloud from ElevenLabs
After partnering with Moises earlier this year, SoundCloud is now adding integration with another AI music platform, ElevenLabs. Now, rather than having to export and download your outputs from ElevenLabs and then upload them to SoundCloud, you can publish them directly. As part of the partnership, SoundCloud Artist Pro subscribers are also getting three months []
Aina's smart ring has a screen, a joystick and a mic so you can talk to AI agents
Aina's ring can also take calls and help you dictate notes on your Mac.
The hidden supply chain costs of the AI boom
Today, I’m talking with Willy Shih, an economist, a professor of management practice at Harvard Business School, and the author of the upcoming book, Where Does My Stuff Come From? The Powerful Economics Behind Global Supply Chains. Willy is one of my all-time favorite Decoder guests — his way of looking at the world through []
Box taps Nvidia’s Nemotron for new agent-first content retrieval system
The system can supposedly answer queries four times faster, with 20% fewer errors and 90% fewer tokens used.
Grimes is going to star in an AI movie that’s coming to theaters
The AI-generated sci-fi film Gods Don't Give Gifts has added a bit of star power: Pop star Grimes is going to appear in the movie. Billed as "the first AI film to come to theatres," Gods Don't Give Gifts is a notable test case for the commercial potential of AI films, and it releases on []
A YC startup is putting $1M behind five unsolved materials science problems for AI
Discovered Materials, a months-old YC startup that uses AI to find chip materials, is offering $200,000 for each solution to five open materials science problems.
Big Tech’s big AI buildout will not end until someone stops it
After four of the biggest competitors in the AI race held quarterly earnings calls late last month, two things are clear: Big Tech is not done spending record amounts on the AI data center buildout, and the AI data center buildout is not any closer to yielding its promised returns. A third thing is also []
Thunderduck Inc.
Modular Data Warehouse for Agentic Data Teams Discussion | Link
Two-stage residual learning for calibrated uncertainty in solar irradiance forecasts
Accurate forecasting of renewable generation requires not only point predictions but also reliable estimates of the uncertainty attached to them, particularly at extended horizons. Machine learning models are increasingly used to predict solar output, but they typically report a single value and capture the associated uncertainty poorly. The standard remedy is to train a model to predict the parameters of an assumed probability distribution directly. Here we develop an alternative two-stage meth
IMUDINO: Self-distilled Foundation Model for Inertial Sensing
We present IMUDINO, the first foundation model (FM) for inertial sensing to achieve competitive performance across diverse sensing tasks including human activity recognition (HAR), health, motion capture, and odometry. Prior inertial FMs often focus on one subfield of inertial sensing and sometimes underperform state-of-the-art supervised methods (e.g., in HAR), making their scope considerably narrower than FMs for vision and natural language. Moreover, they often require fixed input length and
Typed Knowledge over Constructive Institutions
Type theories designed for mathematics or program verification need only that a proposition holds; proof irrelevance in conversion erases, at the kernel level, how a claim was established. A persistent knowledge store, by contrast, must retain that record: who established a claim, by which derivation or external tool, and under which assumptions. This volume presents the theory of Eigenius---a store of typed knowledge over constructive institutions---and develops its metatheory from that require
Truncated backpropagation through time induces a two-sided gradient turnpike property
Truncated backpropagation through time reduces the computational burden of training recurrent models by restricting gradient computations to shorter temporal contexts. This induces two distinct boundary effects: missing past information perturbs the forward state evolution, while truncating the backward recursion omits future information. This motivates decoupling the interval over which gradient contributions are averaged for the parameter update from the surrounding temporal context over which
Spectral symmetry fingerprints in equivariant representations
Symmetry governs the organization of physical structures, while symmetry breaking reveals their transformations. In geometric deep learning, symmetry imposes rank constraints on equivariant representations, limiting the expressive capacity of equivariant graph neural networks (GNNs). Here we show that these constraints can instead be exploited: the singular-value spectra of equivariant representations provide a unified fingerprint of structural symmetry. We introduce spectral symmetry fingerprin
PSGAgent: Enhancing MLLMs' Dynamic Visual Perception for Panoptic Scene Graph Generation via Agentic Reasoning
Panoptic Scene Graph generation (PSG) requires holistic yet fine-grained reasoning over complex relations among objects. However, current Multimodal Large Language Models (MLLMs) applied to PSG suffer from a perception bottleneck: they function as static observers limited by fixed-resolution views. As a result, models often overlook critical relations involving small or peripheral objects and struggle to differentiate ambiguous predicates in complex scenes. To address these limitations, we propo
FreePath: Neural Mean Free Path for Image Dehazing
Single image dehazing requires recovering scene radiance from visual responses that are weakened and veiled by participating media. Existing methods either invert explicit scattering variables, which can be unstable under complex scenes, or learn direct restoration mappings, which often discard the physical organization of haze formation. We present \emph{FreePath}, a neural mean free path framework for image dehazing. Instead of estimating atmospheric parameters, FreePath represents dehazing as
Rigorously Certified Neural-Network Computation of Nonlinear Water Waves at Near-Machine Precision
We present a computational framework that combines machine-learning-based discovery with rigorous computer-assisted numerical certification and formal verification for strongly nonlinear partial differential equations. We demonstrate the approach for periodic gravity-capillary traveling water waves. Physics-informed neural networks are used as a search mechanism, with the wave profile, wave speed and, in continuation experiments, surface tension learned directly from the governing equations with
CacheGraft: Enhancing Retrieval-Augmented Generation via Latent-Space Document Enrichment
Recent work in machine learning has demonstrated that transformer-based language model agents can communicate state not only through text, but also directly in latent space via key-value (KV) caches. In this paper, we argue that such cache-to-cache translation can provide utility beyond the original motivating application of real-time agent communication and can instead be utilized for retrieval-augmented generation (RAG). Building on the Cache-to-Cache (C2C) fusion framework, our proposed syste
WeLM-Image: A Unified Image Generation Model towards Visual Intelligence
We present \textbf{WeLM-Image}, a unified image foundation model that integrates multimodal understanding into text-to-image generation, instruction-guided editing, and multi-reference synthesis. Designed for single-GPU deployment in WeChat's large-scale consumer applications, the model processes text, reference images, and target latents as one interleaved sequence. A Mixture-of-Transformers (MoT) backbone combines shared attention with independent understanding and generation parameters to lim
Adversarial Multiplicity: Cherry-Picked Models and the Limits of Disclosure
Predictive multiplicity -- where similarly accurate models disagree on individual predictions -- can be leveraged to optimize secondary metrics (e.g., fairness) without loss in predictive performance. However, exploiting multiplicity opens the opportunity for motivated parties (or coding agents gaming their objectives) to steer predictions toward their own ends, such as in litigation or consumer disputes with automated decision-making tools. In these adversarial multiplicity settings, we disting
When Scores Conflict with Preferences: Pre-Alignment Diagnosis and Adaptive Correction of Constraint Drift in DPO
Direct Preference Optimization (DPO) is increasingly used in settings where pairwise preferences coexist with pointwise criteria such as response length, answer correctness, or safety scores. When the two disagree, standard DPO treats pairs whose preferred output scores worse on the pointwise criterion (conflict pairs) in the same way as the remaining (matching) pairs. We characterize a systematic failure mode, constraint drift: for sufficiently small updates, conflict pairs tend to increase con
EnvOpt: Automated Optimization for Agent Environments
An LLM agent's cost and success depend on its model, its harness, and its environment. In addition to the widely studied directions of parameter tuning and harness optimization, we propose the third direction: optimization over stationary environments. Agents are often deployed in rich environments shared by many tasks, such as a company's Slack workspace or a complex collection of data sources, documents or tickets. We call these environments stationary environments. We introduce EnvOpt, a meth
Test-Time Self-Play: Scaling without a Verifier
Test-time scaling methods spend additional compute at deployment to improve a large language model's performance on a hard target problem. Most methods rely on a verifier that scores candidate solutions for correctness, using it to select among or refine the model's outputs. In many deployment regimes, however, a strong verifier on the target problem is unavailable. We introduce Test-Time Self-Play (TTSP), an approach to test-time scaling using asymmetric self-play without reliance on a verifier
Asymptotic-Preserving Tensor Neural Networks for 3D High-Field Kinetic Equations with Applications to Uncertainty Quantification
Kinetic equations in the high-field regime describe particle transport dominated by electric-field acceleration and collisional relaxation. These equations are widely used in simulations of plasmas and semiconductor devices. Typical models include the Vlasov--Poisson--Fokker--Planck (VPFP) and Boltzmann--Poisson systems. Their high dimensionality and multiscale nature pose two of the major challenges to accurate and efficient numerical solution. To address these challenges, we propose an asympto
Acoustic Anomaly Detection for Early Identification of Drill Jamming
In the context of Industry 4.0, predictive maintenance is essential to minimize unplanned downtime and ensure operational safety. In machining processes, drill jamming is a critical failure that compromises both workpiece quality and tool integrity. This work proposes an acoustic anomaly detection system using ultrasonic sensors to identify pre-failure patterns in Computer Numerical Control (CNC) machining. We release a public dataset of time- and frequency-domain acoustic features from 17 drill
From What Is Used to What Is Missing: Rethinking Dynamic Rank in LoRA
Parameter-efficient fine-tuning with LoRA is governed by rank, which controls parameter budget and locally accessible task updates. Existing dynamic-rank methods mainly focus on capacity allocation, leaving unresolved whether the current adaptation space is sufficient for the task. We characterize the first-order information boundary of standard factor gradients, showing that their unobservable subspace coincides with the normal space of the low-rank manifold. Thus, factor gradients alone cannot
AtlasULP: Domain-aware Universal Link Prediction via Relation Atlas
Link prediction (LP) is a widely applied task in graph learning. Conventional LP approaches typically follow a dataset-specific paradigm, requiring independent training for each graph and incurring high computational and maintenance costs in large-scale applications. Motivated by these limitations, recent work explores Universal Link Prediction (ULP), aiming to enable training-free inference on arbitrary unseen graphs. However, existing methods primarily rely on subgraph sampling strategies to c
Selective Retrieval Routing for Multi-Violation Knowledge Graph Repair
Knowledge graphs often contain missing or inconsistent facts that undermine the applications built on them. Schema validation detects such violations but does not identify which of several valid repairs recovers the intended graph. External corpora can supply the evidence needed to identify the intended repair, but retrieving from them is costly and not always successful. We propose a framework for selective retrieval in iterative graph repair. An LLM repair agent explores and edits the graph, w
Last-Iterate Convergence of Stochastic Gradient Methods for Unbounded Problems
Last-iterate convergence guarantees for stochastic gradient methods have attracted significant attention as practical implementations typically return the last iterate instead of an average of the iterates. In this paper, we study two approaches for analyzing the last-iterate convergence of proximal and projected stochastic gradient methods without bounded domain or bounded variance assumptions, where black-box variational inference serves as a running example. Under two related assumptions rela
Pressure
Need a second opinion? Have two agents debate your topic Discussion | Link
Apple is reportedly cutting budgets for its internal AI usage
The company is said to be significantly dropping the amount it's spending on AI for its engineering teams.
The Blue Wave Has Tech Bros Scrambling for Data Centers in Space
After Democrats won big in Texas and other AI infrastructure hot spots on Tuesday, data center enthusiasts looked for comfort in low-Earth orbit.
AI PACs spent millions trying to shape Congress — here's how they fared
AI industry PACs spent millions influencing the 2026 midterms, with mixed results. Political groups are already looking toward the next election cycle.
Qualcomm is betting big on data centers as smartphone sales sink
With smartphone sales tanking by around 20 percent compared to 2025, Qualcomm is looking to data centers to help boost its bottom line. During its Q4 2026 earnings call, the company laid out its plans to make a splash in the AI data center business, through partnerships with companies like Amazon and Meta announced earlier []
Nothing CEO says company's mysterious AI-first device is dropping next month
While it's not yet clear what kind of product the smartphone startup is cooking up, Nothing's CEO said the product is designed to leverage AI for productivity gains.
Almost no one uses AI agents to book their Uber, company says
During its busiest week last quarter, Uber said only four people booked a ride using an AI agent.
17 better ways Elon Musk could have wasted $446 million
One of the basic problems of spending too much time on social media is that you are cooking your brain by reading posts from a pack of attention-seeking lunatics. If your social media of choice is the site formerly known as Twitter, and it is your main source of contact with the outside world, you []
OpenAI is already trying to shape the 2028 election
OpenAI’s Chris Lehane wants AI to define the 2028 election, proposing a new social contract that could benefit Americans while also advancing the AI giant’s own business interests.
Meta acquires AI coding startup Conductor
The startup allows software engineers to deploy teams of agents to work on various aspects of a project simultaneously.
My therapist thinks I should chuck the Amazon Bee Frontier out a window
This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. "How does that make you feel?" I get asked this a lot in therapy. Plenty of life events, big and []