The Archive

The next humanoid robot might not have a head. It might not have legs. It might even sit on a wheeled base and fold down like a deck chair. But, as Genesis AI puts it, "humanoid robots don't need to look human." That explains the look of Eno, the new robot from the French startup backed by former Google CEO Eric Schmidt. Genesis says Eno is designed "around human capability" rather than human appearance and is intended as a fully "general-purpose" robot rather than a machine built around a single task, like folding laundry. One part is still very human though: its hands, which the company say...

We have a new top open model in the world!

Tool: <click-to-play> — a still that plays A progressive enchantment Web Component that turns this markup: <click-to-play> <a href="URL to GIF"> <img src="URL to first frame" alt="..."> </a> </click-to-play> Into a still frame with a click to play button which loads the GIF on demand. For when you don't want big GIFs to be loaded unless people want to play them. Tags: gif , javascript , progressive-enhancement , web-components

NetNewsWire Status I find this inspiring. Brent Simmons retired a year ago, and his retirement project is making one piece of software really, really good - free from any commercial pressure. The software is NetNewsWire, first released in 2002 and made open source in 2018. I've been using it on Mac and iPhone for several years now and I'm finding it indispensable. Via Lobste.rs Tags: brent-simmons , netnewswire , open-source

How Cohere is ensuring that every tenant gets their fair share of compute.

Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.

Anthropic's popularity with business users is growing so well that the latest beef with the government might actually boost it, data from Ramp suggests.

Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live... Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live camera and microphone streams, multimodal AI models, enterprise data, tool use, deployment infrastructure, and device-specific runtimes. NVIDIA XR AI is designed to address this challenge by providing a reusable foundation for… Source

NAACP lawsuit says xAI uses gas turbines without permits for Grok data center.

Release: datasette 1.0a34 Quoting the release notes: The big feature in this alpha is tools to insert, edit and delete rows within the Datasette interface. These features are available on table pages, and edit and delete are also available as action items on the row page. The inspiration for this feature - which is long overdue - was Datasette Agent . I added SQL write support to that the other day which highlighted how absurd it was that you could insert and edit ties via the chat interface but not in the regular Datasette UI! Tags: projects , datasette , annotated-release-notes

UK government partners with Google DeepMind to build a new AI-powered prototype aimed at faster housing decisions.

Move originally planned for Monday would have heavily increased power users' costs.

Every swipe, transfer, and payment on a modern financial network encodes a pattern of human behavior. Transaction data is one of the richest signals an... Every swipe, transfer, and payment on a modern financial network encodes a pattern of human behavior. Transaction data is one of the richest signals an enterprise owns. Yet most production use cases for such tabular data still depend on hand-engineered features and rule sets that are brittle, expensive to maintain, and blind to the sequential structure inside a customer history. Source

Pentagon also claims 1.5 million personnel are using generative AI tools.

Google has released Android 17 and Wear OS 7, introducing new multitasking features, parental controls, security tools, and smartwatch upgrades. The launch is also accompanied by a Pixel Drop that brings Google’s latest AI models to its devices.

Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we propose VERITAS, a generator-verifier framework for generalist robot policies for inference-time policy steering and self-improvement. We use a pre-trained generalist robot policy as a ``generator'' and pair it with a gradient-free ``visual verifier'' that evaluates actions at inference time. This framework enables inference-time steering that improves policy performance without additional training. We demonstrate tha...

Scaling model size, specifically depth and width, has driven significant progress in transformer-based language models. However, most architectures maintain a constant width across all layers, allocating a fixed parameter and computation budget evenly despite different layers potentially playing distinct computational roles. In this work, we empirically investigate nonuniform capacity allocation across network depth by proposing a $\times$-shaped > <former consistently outperforms parameter-matched uniform baselines on language modeling loss. By reducing the average layer width, this architec...

Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist with reproducibility, but they are difficult to scale due to their reliance on substantial manual effort for data curation and evaluation. We introduce ReproRepo, a scalable framework for reproducibility evaluation that leverages human-raised GitHub issues as naturally occurring supervision on realistic reproduction blockers. We instantiate ReproRepo on 1,149 recent machine learning papers from major conferences and e...

In learning theory, the sign rank of a binary concept class captures the smallest dimension in which it can be represented by points and halfspaces. Despite tremendous interest, lower bounds on sign rank are notoriously difficult to come by. Two recent approaches to the problem establish lower bounds on sign rank by measures that are easier to analyze: the $\mathbb{Z}_2$-index and the list replicability number. We order these measures, showing that the $\mathbb{Z}_2$-index is upper-bounded by a linear function of the list replicability number. As a main consequence, we obtain a strong separat...

Zero-Shot Object-Goal Navigation (ZS-OGN) requires embodied agents to explore and locate target objects without any prior training. To this end, recent methods leverage foundation models. But they typically rely on static priors and lack adaptation, which leads to repeated errors and costly trial and error. In this paper, we propose a self-evolving ZS-OGN framework that enables continuous test-time improvement. Specifically, we build an agentic rule memory by extracting actionable knowledge from past trajectories. Then, we propose a retrieval strategy based on upper confidence bound, selectin...

Accurate mechanical properties (or materials) Young's modulus ($E$), Poisson's ratio ($ν$) and density ($ρ$) are essential for reliable physics simulation of digital worlds, but most 3D assets lack this information. We propose AdaVoMP, a method for predicting accurate dense spatially-varying ($E$, $ν$, $ρ$) for input 3D objects across representations, improving the resolution, accuracy, and memory efficiency over the state-of-the-art. The foundation of our technique is a sparse and adaptive voxel structure SAV that efficiently represents both the input 3D shape and the material field output. ...

With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained via Reinforcement Learning (RL). These agents employ neurosymbolic approaches such as behavior trees with learning-enabled components (LECs) to learn, reason, adapt, and implement security rules while maintaining critical operations. However, these autonomous networks are partially observable systems, i.e., the cyber-attacker's (red agent's) actions are not observable, making it difficult for the defender to predict red actions, learn red policies, or a...

We introduce Darshana Graph, a corpus of over 125,000 text records spanning classical Hindu, Buddhist, and Jain philosophical traditions, drawn from public-domain and openly licensed translations of sources including the Bhagavad Gita, Brahma Sutras, principal Upanishads, the Pali Canon, and core Jain texts. Its distinctive contribution lies in a structurally unique subset of roughly 8,500 Hindu and Jain records in which the same root verse or sutra is aligned across eighteen historical commentators representing five schools of Vedanta and other darshanas, enabling direct comparison of how in...

We study finite-horizon queue peaks in generalized switches, a standard stochastic-network model in which many queues share constrained service resources. Arrivals may be dependent, time-varying, and adapted to the past; the standing load condition is uniform interior slack, meaning the conditional mean arrival vector stays in a fixed contraction of the capacity region. We show that this slack reshapes the finite-time peak law for drift-minimizing scheduling policies such as MaxWeight. The square-root envelope that is sharp without slack persists only up to a geometry-dependent threshold; bey...

Knowledge distillation transfers a teacher's competence to a small student but is brittle in the small-student regime: forcing the student to imitate logits from a much larger teacher concentrates it on the teacher's sharpest modes, hurting generalization on benchmark families beyond the training corpus. Reinforcement learning (RL) avoids logit imitation by training on the student's own rollouts. However, on questions where every rollout fails-yielding zero advantage and being silently discarded-injecting a stronger teacher's response into the policy gradient breaks the on-policy assumption a...

Dataset distillation (DD) has emerged as a prominent approach in data centric machine learning, aiming to synthesize compact training sets for efficient training by compressing the information in large datasets into a small number of synthetic samples. However, DD methods are often evaluated under inconsistent evaluation protocols, ranging from standard ERM to single/multi-teacher supervision, making it difficult to isolate the effectiveness of distilled data from evaluation. Moreover, many prior methods claim that DD outperforms data pruning approaches such as coreset selection (CS), based o...

Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding errors. We resolve this by introducing Looped World Models (LoopWM), which are the first looped architectures for world modelling. Our method iteratively refines latent environment states through a parameter-shared transformer block. This yield up to 100x parameter efficiency over conventional approaches with adaptive computation that automatically scales depth to match the complexity of each prediction step. Orthogonal ...

Looped architectures provide an inductive bias toward learning step-by-step procedures for tasks that require compositional reasoning. The number of effective layers reached by looping determines the quality of the solution these models find. Like deep architectures, looped architectures are prone to a signal propagation problem induced by depth as the halting decision is postponed. In this paper, we address this signal propagation issue using pre-norm layers and residual scaling. Building on these architectural modifications, we propose FPRM, a Transformer-based Fixed-Point Reasoning Model t...