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The most talked-about robotics and AI stories right now — ranked by coverage, authority, and freshness.
Blackstone’s Jas Khaira says fears over AI-buildout debt are overblown
Blackstone’s AI head on why the PE giant isn't panicked by market jitters.
Meta wants to pay for its broad AI buildout with a newly created class of stock
The social media giant is planning a new stock strategy to help power its broad investments in AI models and services.
Nscale adds former OpenAI exec Fidji Simo to its board ahead of potential IPO
The No. 2 exec at OpenAI also led Instacart through its IPO in 2023.
Tesla says customers can add their own vehicles to the robotaxi fleet next year — will it happen?
For years, Elon Musk has been making big promises to Tesla owners about unsupervised autonomous driving and the ability to add their own vehicles to the company's robotaxi fleet. During today's third-quarter earnings call, the company finally gave itself a deadline. According to Tesla VP for Autonomous Driving Ashok Elluswamy, Tesla owners will be able to add their own vehicles to the company's robotaxi fleet in the second quarter of 2027. Here's his exact quote (emphasis ours): Like I mentioned earlier, the software for Cybercabs and Model Y is the same. And pretty soon we'll be able to release the same software as part of the V15 relea … Read the full story at The Verge.
Volvo XC40 PHEV is back with a new look, better sensors, and Gemini AI
It's been about three years since Volvo discontinued its plug-in hybrid XC40. The mild hybrid version remains on sale in the US and elsewhere, alongside the electric EX40. Today, Volvo announced it was bringing back the PHEV version and giving it a major facelift. When it arrives at dealerships early next year, the new XC40 will have updated exterior styling, a gut-renovated interior featuring Google's Gemini AI assistant, enlarged infotainment screen, and a new sensor-and-software safety suite. As a hybrid, the XC40 serves as a bridge for customers who want better gas mileage but aren't ready for the pure EV lifestyle. Volvo itself needs t … Read the full story at The Verge.
Data centers could erase decades of air quality gains
A rush of new data centers could bring air pollution roaring back to levels not seen since the 2000s in Texas, which has become ground zero for the physical infrastructure behind generative AI. New and proposed data centers are demanding a tremendous amount of electricity. How developers and utilities meet that demand - with gas turbines or renewable energy - will determine just how dirty things will get. So far, though, things are looking grim. Tech companies and data center developers are devising new ways to get enough electricity, including building out their own power sources. But if all planned data center projects were to plug into … Read the full story at The Verge.
Anthropic's Public Benefit Charter
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GLM-5.3-Flash will likely handle 45% of your AI workloads
A week ago, a mystery model called Ox Alpha showed up on OpenRouter — one more entrant among more than 400 models, with roughly 10 new ones launching every week. What made it stand out wasn t just the free price tag; it was quietly good. Hobbyists and indie developers noticed fast, pushing several trillion tokens through it daily, with community estimates for the week ranging from single digits to over 20 trillion.AI enthusiasts spent the next six days doing forensics and speculating who could have built it, and who could have the infrastructure to serve that many tokens for free. First the guess was a U.S. lab: the long-awaited Gemini, or Anthropic shipping a good-enough middle tier, or Elon sitting on so much capacity he dropped Ox Alpha (note the naming). People ran tokenizer traces and networking analysis. A real Sherlock Holmes mystery week.On August 26, Z.ai put its name on it. Ox Alpha was GLM-5.3-Flash. They d been running it on public traffic on purpose, but the real surprise
Rust off Rails: The opinionated web stack for the AI era
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Compromised LiteLLM 1.107.0 on PyPI Installs a Credential Stealer and Backdoor
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Salesforce just put its entire CRM inside Claude — and says you’ll never need its app again
Salesforce and Anthropic announced Tuesday a sweeping expansion of their partnership, called Claudeforce, that pushes the world s largest customer relationship management platform directly inside Claude — a tacit acknowledgment that the future of enterprise software may not involve enterprise software s own screens at all.The centerpiece of the announcement is Salesforce in Claude, a plugin for Anthropic s Claude CoWork that ships with 37 pre-built sales skills — covering meeting preparation, deal health reviews, and pipeline analysis — and lets sellers query, update, and act on live CRM data without ever opening Salesforce itself. The product is available to select pilot customers today, with an open beta planned for September and additional skills for other business functions beginning to launch in the third quarter.Marc Benioff, Salesforce s chair and CEO, and Dario Amodei, Anthropic s CEO, were scheduled to appear on television together Tuesday afternoon, hours before Salesforce re
AGCO to deploy Agility Digit humanoid in Kansas factory
The Agility pilot, which is expected to begin in 2027, will begin at an AGCO production facility for agricultural equipment. The post AGCO to deploy Agility Digit humanoid in Kansas factory appeared first on The Robot Report.
When Information is Worth the Risk: Behavioral Valuation for Hazardous Robotic Exploration
arXiv:2609.10726v1 Announce Type: new Abstract: Hazardous robotic exploration requires robots to map spatial risks, such as unsafe terrain, radiation, fire, mines, or structural damage, while operating where collecting information can itself cause failure. A highly informative path may expose the robot to hazards, terminate execution, and prevent future observations. Hazardous exploration therefore requires deciding not only where uncertainty is largest, but when reducing it is worth the risk. This paper introduces a valuation-layer view of this problem. We keep the belief update, sensor model, physical risk model, and finite-horizon informative planner fixed, and change only the scalar objective used to rank feasible paths. Within this framework, we introduce a risk-augmented Behavioral Information objective based on Prelec probability weighting, yielding an interpretable family of conservative-to-aggressive information-risk valuations. Theoretically, we show that valuation parameters
Adaptive Entangled Game Modules in Artificial General Intelligence
arXiv:2609.09226v1 Announce Type: new Abstract: We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. This framework captures a broad range of human intelligence behaviors with analytical mechanisms and offers an indirect method to examine the Liu-Chen-Ao (LCA) hypothesis of nonlocal entangled nerve fibers in the brain through collective trader behaviors. Our empirical analysis of Chinese intraday stock market data demonstrates that adaptive entangled game modes explain 82-94% (89% overall) of observed decision patterns, a sharp contrast to the predictions of neoclassical finance based on independent rational agents. Moreover, 2-12% of behaviors show adaption to intraday news, events, and environments, characterized by dual equilibrium states and abrupt reference point shifts, while purely independent modes occur i
OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows
arXiv:2609.09203v1 Announce Type: new Abstract: Existing benchmarks for autonomous AI scientists evaluate only final outputs---generated code, hypotheses, or papers---yet discard the reasoning process by which those outputs were obtained. This makes it impossible to audit scientific methodology, diagnose failure modes, or distinguish systematic reasoning from fortunate guessing. We present \textbf{OpenDiscoveryTrace}, a public dataset of 558 complete AI scientific agent trajectories that captures how models reason, not just what they produce. Each trajectory records a structured 9-field-per-step trace---including thoughts, tool calls, observations, errors, revision triggers, and self-reported confidence---as models execute 124 scientific tasks spanning drug discovery, materials science, genomics, and scientific literature analysis. The dataset covers seven models: three frontier models (GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro; 124 trajectories each, fully balanced across domains a
M3-Former: Multimodal Transformer with Mixture-of-Experts for Long-Term Vessel Trajectory Prediction
arXiv:2609.10559v1 Announce Type: new Abstract: To address the challenges of behavioral multimodality, limited semantic utilization, and long-term error accumulation in vessel trajectory prediction, this paper proposes M3-Former, a multimodal trajectory prediction framework enhanced by large language models (LLMs). The proposed framework incorporates vessel static attributes and navigational intent as semantic priors for long-term trajectory modeling. Specifically, a unified multimodal representation space is constructed, in which static semantic information is encoded by a pre-trained LLM and aligned with dynamic trajectory features through self-attention. To jointly capture global route planning and local motion variations, a dual-granularity Mixture-of-Experts (MoE) architecture is introduced, where sequence-level experts model global navigation trends and token-level experts refine fine-grained maneuvering behaviors. In addition, a Steering-Weighted Cross-Entropy loss is designed t
Halo: Improving forecast accuracy through heteroscedastic estimation
arXiv:2609.10589v1 Announce Type: new Abstract: Heteroscedastic forecasting, where a network estimates a scale parameter alongside a location parameter, is normally motivated by uncertainty quantification. This paper shows it also improves the point estimate, in contrast to reported negative results for heteroscedastic estimation outside time series. Halo is a modification that reuses an existing deep forecaster's architecture, giving it a second output for the scale of its implied distribution and training it under the matching negative log likelihood. Adapting three state-of-the-art models --- a transformer, a graph network paired with a variational autoencoder, and a single-layer convolutional network --- under both Gaussian and Laplacian losses demonstrates the phenomenon. On the five electricity price markets of a standard forecasting benchmark, Halo improves MSE and MAE in 28 of 30 model-market-metric comparisons, cutting average MSE by 2.6% to 16.5% and average MAE by 1.7% to 11
GPT-6 Built My 3D Game. Rodin Made the Parts Usable.
The first version of my 3D racing game had a small, humiliating bug: the cars moved, but their wheels did not.Continue reading on Medium »
OpenShell: Nvidia's Open-Source Answer to Rogue AI Agents at OpenAI and Anthropic
After rogue agents hit Hugging Face and Medicare, Nvidia s open-source OpenShell runtime puts the fence outside the agentContinue reading on Towards Deep Learning »
I Used AI to Analyze My YouTube Shorts — Here’s What I Learned About Retention
I used to think the secret to getting more views on YouTube Shorts was simple:Continue reading on Medium »