2026-11-03 的 AI 资讯

Apple may try to increase revenue by doing…something with third-party AI apps

Apple hinted at ways that it could spur revenue through third-party "opportunities" during its Monday night earnings call.

Y Combinator’s Garry Tan uses AI agents so much, he spends up to $200K a year on tokens

Y Combinator Garry Tan has jokingly admitted that he's suffered from "cyber psychosis” and barely sleeps sometimes because he’s so excited to be working with his AI agents. On a new episode of Walter Isaacson’s podcast, The Builders, Tan put a price tag on his passion.

Stolen AI API keys generated nearly $1M in charges for a single company

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Developers are downgrading their AI on purpose – and it's working

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Scaling Git at OpenAI

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Trajectory raises $45M to make AI model training accessible to nontechnical experts

The one-year-old startup is now valued at $300 million.

Anthropic's growth just flattened

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The SEC Should Block Anthropic's IPO to Protect the Public

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LLMs are not superhuman at reading and writing neural network weights

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Anthropic warns that it’s very close to RSI capability, aka self-building AI

RSI is the point at which AI grows so good at training itself that it spawns a dramatic acceleration of AI capabilities. To the AI doomers, RSI represents the cataclysm from which there is no return.

Oracle files to sell $20B in new stock, bonds to finance AI buildout

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Web host Squarespace lays off 19% of its employees

The website builder said that the job losses are part of a broader restructuring of its organization.

New York Times and other outlets sue web browser Brave over alleged content scraping

The New York Times is suing privacy-focused web browser company Brave for allegedly scraping the outlet’s copyrighted content and unlawfully distributing it to its own customers, like Anthropic and Perplexity.

Anthropic clamps down on Claude’s AI video game clones

Anthropic has updated Claude Code's system prompt to prevent the AI tool from being used to re-create existing video games, as reported by Tom's Hardware. Retro gaming is currently being overrun with AI-made decompilations, recompilations, and ports, but while Anthropic will still let people use Claude Code to make games, it doesn't want people to […]

Egoless Mathematics in the AI Era

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Agentic backdoors via pretraining poisoning

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Astra can read and write weights to improve Qwen

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OpenAI pitches data center power plants as a way to protect nearby areas

In an announcement on Monday, OpenAI says it will pilot its "Grid Protectors Program" at an upcoming data center in Effingham County, Georgia, where the company promises to free up to 1,000 megawatts of power during extreme weather or emergencies, enough to power air conditioners in around 700,000 homes. The announcement comes as opposition to […]

Rewarding defenders with bigger bug bounties

OpenAI is raising Security Bug Bounty rewards and introducing report quality, loyalty, and priority campaign bonuses for independent security researchers.

Preparing our next security sprint to harden OpenAI

OpenAI is preparing its next internal security sprint with Cathedral Systems to test and strengthen its defenses using AI.

The New York Times sues Brave web browser for AI scraping and bypassing paywalls

The New York Times and a group of other publications have filed lawsuits against the company that makes the Brave web browser over alleged unlawful scraping to train AI and also letting users bypass their paywalls, as reported by Reuters. According to a complaint filed by the New York Daily News, the Chicago Tribune, and […]

Launch HN: Momentic (YC W24) – Agents that catch bugs by using your app

Hey HN, we’re Wei-Wei and Jeff. We’re building Momentic, QA agents that autonomously plan, explore, and bug-bash controls and user flows in your web, iOS, Android, and (soon) desktop apps to catch bugs. Here’s a demo: https://youtu.be/BFsM-Se1Fh8 We’re both software engineers that spent our careers on dev tooling and infra across companies like Robinhood, Retool, WeWork, and Qualtrics. Testing was

Anthropic's internal AI use, at retail prices, now exceeds its employee pay

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42AI comes out of stealth with $62.5M and a world model that can ‘predict human behavior’

Here's a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?

Elon Musk throttles Grok Bot limits due to ‘unsustainably high’ costs

Musk recently referred to Grok Bot as having "the fastest growth of a product that I've ever seen in my life."

LongCat-2.5: Native Multimodality for Agentic Tasks

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Introducing the Grid Protectors Program

OpenAI’s Grid Protectors Program will work with utilities to help future compute campuses support grid reliability and protect consumer electricity costs.

Omen AI adds liquid know-how in $16 million Aquatic Labs acquisition

Two startups tackling the data center water problem are joining forces in a $16 million deal.

Jobless tech workers are being left out of San Francisco's AI boom

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2 days till TechCrunch Founder Summit: Boston founders don’t need a Silicon Valley address to build something big

TechCrunch Founder Summit happens on November 4 in Boston for a founder-focused agenda and impactful connections. Save up to $100 or get discounts with group passes.

OpenAI is finally merging ChatGPT’s Chat and Work tabs

OpenAI said that ChatGPT and Codex will draw usage from the same allocation pool after this change.

LifeVal

Introducing LifeVal, a benchmark measuring how well AI helps people complete everyday tasks, from finding childcare to managing finances and government paperwork.

Qodo builds context into AI code review with Codex

Qodo brings company knowledge and coding standards into Codex, helping developers catch P0 issues across repositories before opening a pull request.

OpenAI appoints Dan Driscoll as Senior Counselor for National Security Modernization

OpenAI appoints Dan Driscoll to advise on responsible AI for national security modernization, cyber resilience, and collaboration with U.S. allies.

OpenAI might rebrand its data centers as ‘AI compute campuses’

Data centers are deeply unpopular. But what if we called them something else?

When Modernization Breaks Working Code: A Study of LLMs on Legacy PHP

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AI can make tech CEOs’ lives easier. What about everyone else?

On Equity, we discuss why tech CEO statements can seem so tone deaf.

Data centers are now a midterm issue, even in Brooklyn

At a Brooklyn midterm party five days before Election Day, every conversation, from a council member’s fireside chat to a debate about the Democratic Party, kept circling back to AI and data centers.

Apple CEO says the company doesn’t ‘want to ship AI for AI’s sake’

Apple's new CEO defended the company's current position in the AI landscape.

I built an AI Tamagotchi before Meta could

Mark Zuckerberg's Meta made AI look so stinking cute by giving it a squishy little face - and this holiday season, he wants to sell you a Muse Charm so you can carry your jolly AI helper around like a Tamagotchi. But I won't be buying one, because we have Muse Charm at home. It […]

Evidence-grounded large language models guide perovskite solar cell design and optimization

Using large language models to interpret experimental evidence, design discriminating experiments and formulate well-supported mechanistic explanations remains a central challenge in materials research. Here we establish PSM-Bench, an 800-question benchmark developed by perovskite researchers to evaluate scientific reasoning in perovskite photovoltaics. We also introduce OpenPerov, an expert-infor

TopoQuant: Topological Framework to Characterize Behavioral Shifts in Quantized Large Language Models

Post-training quantization (PTQ) reduces the com putational cost of large language models (LLMs), but it can introduce internal structural and behavioral changes that are not captured by traditional metrics such as accuracy and per plexity. We present TopoQuant, a topological framework that uses topological data analysis (TDA) to quantify structural drift in self-attention weights and relate it to

Autonomous Analysis of Thermoelectric Materials Big Data by Multi-Agentic AI and Construction of a Knowledge Base and Specialized AI

Extracting knowledge from large databases of experimental materials data requires repeated analyses and reusable records of their choices and results. We developed Multi-Agentic AI (MAA), a multi-agent system in which AI agents with distinct roles analyze experimental thermoelectric data under predefined instructions and quality checks, saving analysis records traceable to their sources. In four e

XLA from the Trenches: Correctness on TPU Workloads at Scale

Accelerating modern foundational models has pushed domain-specific compilers toward increasingly complex, multi-tiered optimization pipelines. However, this heavy optimization focus introduces delicate tradeoffs between peak execution performance and correctness. Subtle codegen defects can trigger numerical degradation and memory or buffer corruption, invalidating multi-million-dollar training run

Log-Driven Semantic Tuning for Mixed Integer Programming Solvers

Automated configuration of mixed-integer programming (MIP) solvers largely relies on black-box optimization. Each trial requires an expensive solver run and provides only a scalar outcome, so tuning can require many such evaluations. Human experts take a different approach. They inspect solver logs, identify performance bottlenecks, and adjust the parameters that address them. We propose Log-Drive

Discovering Models for Experimental Quantum Systems with Agentic AI

A central obstacle in realizing color center cavity-QED-based quantum technologies is inhomogeneity, or disorder, among individual emitters. As the number of emitters increases, so does the complexity of the experimental data, making it difficult to quantify the present physical mechanisms of disorder. Here we introduce an artificial intelligence-driven model discovery framework in which a large l

LM6-CastBench: A Geometry-Aware Simulation Dataset for Transient Thermal Prediction in LM6 Sand Casting

Transient thermal fields during solidification connect casting process conditions and cooling behaviour to the formation of defect-prone regions in aluminum cast ings. Repeatedly solving this thermal problem across geometries and process conditions is computationally expensive, motivating data-driven surrogate models. However, existing machine-learning studies on casting thermal behaviour rely on

Enhancing Language Model Reasoning with Structured Multi-Level Modeling

Inference-time scaling enhances a model's reasoning by extending its chain-of-thought (CoT). However, existing approaches typically rely on a single policy trained with outcome-reward reinforcement learning (RL), which often suffers from long-horizon plan failures where the implicit plan drifts from valid strategies, especially for small LMs with limited capacity. To address this, we propose Multi

Learning from an Imitable Teacher: Competition Solution for the MetaFine Challenge

The MetaFine Open Competition (RoboFineMani workshop, CoRL 2026) evaluates fine-grained manipulation beyond binary success. Its five simulated tasks, from grasping a bottle by an instructed part to inserting letter-shaped pegs with 4 mm clearance, are scored on perception under lighting and camera changes, instruction understanding, and behavior. This report presents Team Friday's solution, which

ECG2Gene: Deep Learning-Based Germline Tumor Susceptibility Gene Risk Assessment Using Electrocardiograms

Electrocardiograms (ECGs) may contain physiological information related to hereditary cancer susceptibility, but its relationship with future cancer risk remains unclear. Here, we present ECG2Gene, a two-stage deep-learning framework that derives digital phenotypes related to germline tumor susceptibility from routine 12-lead ECGs and predicts future cancer risk. In Stage 1, carrier status for pat

Roz: A Taste Test for World Model Latents

State of the art latent dynamics world models achieve impressive performance for robot planning. But do they actually encode the physics of the world? In this paper, we show that popular world models today achieve strong results on tasks that reduce to image similarity but forget key details about objects in the scene like mass, deformation, friction, and bounciness. We achieve this by defining pr

Trail: Accurate Local Smart Home Control Using Decision Models

Natural-language driven smart home control remains challenging: cloud-hosted Large Language Models (LLMs) raise privacy concerns, while local Small Language Models (SLMs) can generate invalid actions or misinterpret requests. We introduce Trail (Typed Registry-Anchored Inference for Language commands in Smart Homes), a fully local framework that replaces open-ended command generation with hierarch

Words Before Pixels: Selective Modality Routing for Vision-Language Model Unlearning

Vision-language model (VLM) unlearning is often treated as an objective-design problem: given multimodal forget data, the goal is to optimize a loss that removes undesirable behavior while preserving retained capabilities. We argue that this view overlooks an equally important data-centric question: how should each forget example be represented before optimization? Existing methods typically keep

Sparse Autoencoder Attribution Graphs Are Decided at the Margin

Attribution graphs over the features of sparse autoencoders (SAEs) are used to predict what an ablation does and are compared across training seeds. A TopK SAE keeps the largest preactivations at each token, and a feature's margin is its preactivation's distance to the cutoff. On five public TopK SAE pairs from three Pythia models, zeroing one feature moves a next layer feature across the cutoff i

Conversational Interaction for LLM-Based Clinical Text-to-SQL: A User Study with Healthcare Professionals

Conversational text-to-SQL interfaces could give healthcare professionals natural-language access to electronic health record (EHR) data, but evidence for their real-world usability remains limited: prior evaluations have relied largely on curated benchmarks with fully specified questions or on simulated users. We report a user study in which 14 physicians who regularly work with EHR data used bot

Editing the Goal: Per-Token SAE Steering and Its Depth Limits in $π_{0.5}$

Sparse autoencoders (SAEs) have recovered interpretable, causally relevant features in robot foundation models (RFMs) such as $π_{0.5}$ (arXiv:2603.19183, arXiv:2603.19233). SAE features on individual instruction tokens are less interpretable than timestep-pooled ones (arXiv:2603.19183), and whether they can redirect a robot's goal remains untested. We show that replacing the SAE features of a sin

Multi-Estimate Elicitation Does Not Uniformly Debias Language Model Judgments

Does eliciting five weighted estimates within a single response change how a language model responds to information? Across anchoring, hindsight, base-rate, conjunction, and status-quo/default tasks, we compare direct numerical answers with a multi-estimate response format, contrasting source framings, explicitly random cues, and probability-consistency constraints. We evaluate three completed con

X-World 1.6: Building Controllable Virtual Cities with World Models

Deploying generative world models for end-to-end closed-loop evaluation and policy learning requires precise scenario control, consistent observations, and sustained generation quality during extended interactions. This report presents \emph{X-World~1.6}, advancing X-World~1.0 toward a more reliable and versatile driving simulator. The upgraded base model offers finer-grained control over traffic

Do Orthogonality Constraints Help Language-Model Training Beyond Sphere Optimization?

A growing body of work reports benefits from orthogonality constraints in fine-tuning large language models (LLMs). These results raise an open question: do orthogonality constraints also help LLM pre-training? We conduct a systematic empirical study of decoder-only LLM pre-training with models ranging from 360M to 3.1B parameters, trained on nearly 200B tokens. We examine fourteen variants of ort

Saidzoda-Tajik-E5-Large: Fine-Tuning a 560M Embedder for Tajik Retrieval

Finding the passage that answers a question is the first step of most practical systems built on documents, and for Tajik we found no openly described model for it. We present Saidzoda-Tajik-E5-Large, a Tajik text embedder built from multilingual-e5-large-instruct (560M parameters). We asked our own Tajik language model to write questions for passages from Tajik laws, tax documents, Wikipedia and