Many recurring text functions are easy to describe but difficult to implement with rules, while calling a large remote model for every input introduces repeated cost, latency, and dependency on a provider. We present compile by training, which turns a natural-language specification into a reusable neural function. At compile time, teacher models generate task-specific examples that are used to tra
497 points, 324 comments
Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and uses machine learning to predict SLA risk and prioritize work.
Business intelligence dashboards can fail silently, showing blank, stale, or wrong data even when every infrastructure monitor reports healthy. Learn how an AWS team built an automated, AI-powered content validation solution on Amazon Bedrock that scans hundreds of dashboards and alerts owners, cutting mean time to detection from days to under an hour.
Learn how a global interdealer broker built an automated architecture documentation pipeline on Amazon Bedrock AgentCore that analyzes .NET code bases, generates architecture diagrams, and maintains searchable documentation through Amazon Bedrock Knowledge Bases and AWS CodePipeline.
Learn how University Startups and its AWS partner g/d/n/a scaled Trinity, a conversational AI solution for students with disabilities, into a serverless multi-agent architecture on Amazon Bedrock that produces IDEA-aligned transition plans for school districts across the US.
Official technical announcement and publication from Google DeepMind covering Proactive cyber defense for governments and enterprises.
Official technical announcement and publication from Google DeepMind covering Introducing Gemini 3.8 Flash and 3.8 Flash Cyber.
<div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">AI models have clearly proven their ability to discover and exploit vulnerabilities without much, if any, human assistance. To help defenders gain the advantage with AI, we built the Mantis harness to automate the discovery, triage, reproduction, and patching of software vulnerabilities. </span></p> <p><span style="ve
AI cost optimization goes beyond model selection. Discover how context engineering in Microsoft Foundry helps lower AI costs by improving knowledge retrieval, tool selection, memory, and agent performance at scale. The post The Economics of Agent Optimization: Context engineering for enterprise AI agents appeared first on Microsoft Azure Blog .
940 points, 532 comments
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
This paper documents the acoustic-radio fusion in Techtile (ARFT) dataset, a synchronized measurement campaign for distributed wireless sensing and positioning in the Techtile testbed. Ultrasonic and radio frequency (RF) signals are simultaneously transmitted and captured at multiple positions in a 2D spatial grid inside the Techtile testbed. Each acquisition cycle corresponds to one rover stop, o
143 points, 24 comments
359 points, 166 comments
Official technical announcement and publication from Hugging Face covering Real-Time Intelligence with IBM Time Series Models on Confluent.
411 points, 176 comments
ATV Big Air Tour uses ChatGPT Work to speed up marketing, merchandising, and more. It even turned merchandise photos into an inventory website in 15 minutes.
Nokia analyzes 50M+ lines of code in two weeks with Cursor
Run cloud agents on machines you manage
Official ElevenLabs technical update and publication covering Ashley Kramer joins ElevenLabs as Chief Revenue Officer.
167 points, 106 comments
Light amplification via Raman scattering in plasma has been severely constrained by stringent phase matching conditions and the need for plasma uniformity. To overcome these limitations, we propose a forward Raman amplification scheme that employs a positively chirped seed pulse co-propagating with a pump pulse in a nonuniform plasma with an upramp density profile. We demonstrate that the phase de
We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what [...] Read More... The post An Organizati
The rapid proliferation of large language models (LLMs) and the growing diversity of their applications presents a unique optimization opportunity: selecting the right model for the task, while optimizing for speed, cost, and quality at a per-task level. However, inference endpoints can vary widely in quality, price, latency, context support, tool use, domain expertise, and reasoning behavior. Thi
As ambitious space telescope concepts such as ultra-lightweight planar diffractive optical elements (DOEs) emerge, validating the performance remains a major computational challenge. Conventional Fourier propagation algorithms were observed to fail at meter-class apertures due to severe memory limits caused by rigid grid-sampling requirements, and the scaled-down proxy models used for reflector te
Official Harvey AI technical update and publication covering How We Rebuilt Playbook Review as a Multi-Agent System.
Official Decagon technical update and publication covering Introducing Campaign Composer: Proactive, compliant outreach.
Official Index Ventures technical update and publication covering Scott Morton Revel.
Official Harvey AI technical update and publication covering How We Rebuilt Playbook Review as a Multi-Agent System.
Two questions come up in almost every enterprise evaluation: where exactly does the inference run, and what do you keep? Data residency pins your Text-to-Speech, Speech-to-Text and Speech-to-Speech sessions to our EU servers, or to our US servers, enforced at the server and reported on every response, so you can assert on it instead of trusting a hostname. Zero Data Retention decides whether the t
Official Harvey AI technical update and publication covering How AI is Changing Data Room Due Diligence in M&A.
Official Artificial Analysis technical update and publication covering Google has released Gemini 3.8 Flash, its fourth Flash model in under four months.
How small AI models can make a big impact for enterprises
Official Harvey AI technical update and publication covering How AI is Changing Data Room Due Diligence in M&A.
Sep 2, 2026
Official Stanford (HAI) technical update and publication covering Marlowe (opens in new tab).
Official Artificial Analysis technical update and publication covering Muse Spark 1.3: Meta reaches the frontier.
Official technical announcement and publication from Hugging Face covering BenchMIRT: What are LLM benchmarks actually measuring?.
The senior lecturer, already director of the degree program, will now oversee all aspects of the center’s activities and operations.
Learn how enterprise voice AI works and use seven call-readiness tests to evaluate hearing, latency, action, guardrails, and handoff.
Streaming video understanding is a critical capability for real-world applications, including embodied intelligence, autonomous driving, industrial monitoring, surveillance and early warning, and wearable assistants. However, processing continuous video streams with multimodal large language models (MLLMs) is computationally expensive. Existing efforts have explored reducing streaming overhead thr
We introduce the Graph Machine (GM), an architecture that maintains an O(n)-sized state and accesses it through sparse, dynamic routing. Unlike methods with fixed-size states or sparse but static routing, GM preserves O(n) complexity in its sparse layers without restricting the potentially accessible state size to O(1). Instead, GM uses edges - pointer-like objects updated differentiably by a refe
Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a single pass of a frontier model over an agentic benchmark can cost hundreds to thousands of dollars, a price paid repeatedly across iterative development cycles. Prior efforts, centered on benchmark distillation, reduce the number of evaluation tasks but leave the cost of executing each re
Language models spend most of their attention on a small fraction of context, yet they read the entire KV cache to find the few tokens that matter. If the user asks about a previous detail in a 1M-token conversation, global attention layers must scan the full context to generate each token of the reply. A prominent approach mitigates this cost by pre-selecting relevant tokens via lightweight proxy
On-policy distillation (OPD) accelerates post-training by providing dense token-level supervision from a frozen teacher on the student's own rollouts. Vanilla OPD applies this supervision uniformly across prompts, without checking whether the teacher is reliable for each prompt. Because reverse KL is mode-seeking, a confidently wrong teacher can induce a strong yet misleading update. Distributiona
LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in many important domains like game playing and robotics, the strongest available agents are not language models. Integrating non-language agents with LLMs would require verbalization: compressing their rich continuous representations into sparse textual s
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domain-specific know-how outside the agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. That knowledg
Faithfully translating research papers into repository-level implementations remains challenging because papers often describe methods at a high level, leave implementation assumptions implicit, and require generated repositories to preserve method logic, evaluation protocols, and cross-file consistency. Despite recent advances in paper-to-code agents, their intermediate outputs are often presente
Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently, end-to-end models such as VibeVoice-ASR have unified the two tasks within a single model. However, existing unified models still mainly support offline recognition, making it difficult to meet the low-latency requirements of real-time voice assistants and agents. To tackle this issue, we
Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only the net effect of these changes on correctness, not how often predictions change; the Top-1 Prediction Change Rate (TPCR) instead measures this frequency. We propose Calibrator-Output Repair for Top-1 Decision Preservation (CORD), the first post-fit
Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier. Existing memory mechanisms: retrieval banks, learned compressors, recurrent states must decide what to keep from the past before knowing what a future decision will require. This was motivated by the assumption that minute-scale history is too la
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these systems lack a unified account of coordination, memory improvement, and the role of external verification. We model orchestrator-worker interaction as a bilevel coordination game: under bounded coupling, the workers' loc
Many NLP tasks, such as summarization and extractive question answering, reduce to retrieving relevant content from documents under two constraints: coverage, retaining enough pertinent information to achieve some goal, and conciseness, removing as much irrelevant information as possible. Conformal prediction methods have been used to guarantee coverage, and must be optimized for conciseness throu
Joint audio-video generation models have made substantial progress in visual quality and audio-visual synchronization. However, they still provide limited control over when shot transitions occur and dialogue is spoken. This limitation constrains their application in script-driven content creation, where timing errors can undermine narrative coherence and the viewing experience. Current joint gene
Speech brain-computer interfaces (speech BCIs) translate neural activity into language, offering a path towards restoring speech for people with paralysis and, more broadly, enabling new forms of natural human-computer interaction. Despite this promise, the field lacks a common measure of progress because systems use different datasets, recording methods, types of speech, and vocabularies, so thei
Competitive programming has become a key test of large language model reasoning, with international competitions such as IOI and ICPC representing its most challenging settings. We present an end-to-end specialization pipeline combining large-scale problem curation, synthetic reasoning traces, supervised fine-tuning (SFT), and reinforcement learning (RL). Using 22,000 curated problems, we train Ne
Model compression techniques such as pruning and quantization facilitate the efficient deployment and acceleration of Large Language Models (LLMs). However, recent studies show that weight sparsification methods, such as SparseGPT, can amplify existing biases in models, with outputs varying significantly depending on persona cues in the prompt. In this paper, we introduce Debias-SparseGPT, a post-
Robot learning increasingly depends on broad and diverse demonstrations, yet collecting robot data remains expensive and poorly suited to covering the long tail of real-world tasks. To address this bottleneck, we introduce RoboTok, an internet-scale data engine that, given a query human manipulation video, retrieves manipulation-relevant human demonstrations from web videos for training dexterous
We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approved analytical program that returns both results and evidence. We show that this restriction can remain expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed m
We introduce SolarWM, a fully open foundation for building interactive video world models from data preparation through long-horizon inference. Training across heterogeneous data sources and video backbones is challenging: datasets differ in temporal scale, camera geometry, visual quality, motion, and captioning styles, while video generators use distinct representations and architectures. Naive d
Visual fluency in generated video does not imply physical reliability, and a scalar quality score alone is incapable of indicating the obligation a clip violates or the moment it fails. We present VeriPhy, an auditable physical-verification system in which a text-only planner compiles the prompt into typed physical obligations and a statically validated execution plan before any frame is observed.
We study the dynamics of Stochastic Gradient Descent (SGD), which is known to steer deep neural networks toward invariant sets that correspond to simpler subnetworks. How this steering unfolds over time remains poorly understood. We answer this by modeling the stochastic gradient flow (SGF) as a percolation process, in which architectural symmetries force subnetworks to merge in discrete simultane
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance on coarse outcome rewards leads to limited guidance on intermediate reasoning processes. Existing approaches such as process reward modeling and on-policy distillation introduce additional constraints, such as reliance on a specialized reward model
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, and finally training it. Meanwhile, across diverse settings and modalities, much of machine learning has shifted to the paradigm of foundation models: networks pr
Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which examples are selected and how they are modified. Influence functions (IF) estimate behavioral changes under infinitesimal reweighting, yet IF-selected examples often show limited advantages over random selection under conventional weight-based interventions
Claude Fable 5.1 is now available on Amazon Bedrock and Claude Platform on AWS. This post covers Claude Fable 5.1's improvements, the Enterprise Frontier Safeguards for keeping your data in a cloud environment you control, and how to start building with the model on Amazon Bedrock.
251 points, 183 comments
Climate & Sustainability
580 points, 657 comments
191 points, 46 comments
Official technical announcement and publication from Google DeepMind covering Introducing agentic video understanding with Gemini.
Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply.
When Atos set out to upskill 400 engineers in agentic AI, hands-on learning was the missing ingredient. Over three days, engineers built multi-agent systems on AWS through an AI League event. This post explains why Atos chose the format, what engineers built and learned, and what other enterprises should consider.
As generative AI adoption scales, cost governance becomes a top challenge. Learn how Jamf built real-time, per-user spend enforcement for Amazon Bedrock using IAM Customer Managed Policies, an Amazon Athena cost view, and a serverless AWS Lambda loop that applies tiered model limits in near-real-time without disrupting active sessions.
Amazon Quick proof-of-concept projects often stall when security teams review the production plan. This post walks through designing dashboards, Spaces, knowledge bases, agents, and Flows with security controls that hold as you scale: dataset shaping, agent isolation, document classification, and approval gates.
Official CMU (Carnegie Mellon AI) technical update and publication covering PAIR Helps Students Find Their Place in AI Research.
t54 built x402-secure, a trust layer on Amazon Bedrock AgentCore payments that scores every endpoint before an autonomous agent pays it. See how session budgets, credential isolation, and a deterministic trust gate have governed more than 20 million agent-initiated transactions with no human in the loop.
Learn how ZS built a security-hardened Amazon SageMaker platform that balances developer agility with healthcare-grade governance, serving 1,000+ daily active users across 200+ SageMaker domains.
Boomi Scribe is an AI-powered agent on AWS that automatically generates documentation for enterprise integration workflows. Learn how Boomi uses Amazon Bedrock, Amazon SageMaker AI, Amazon S3, Amazon DynamoDB, and AWS Lambda to parse integration DAGs, generate detailed documentation, and compare component versions at scale.
251 points, 353 comments
This paper addresses the joint reduction of the peak-to-average power ratio (PAPR) and out-of-band emissions (OOBE) in affine frequency division multiplexing (AFDM) systems by selecting the pre-chirp parameter c2. While existing approaches typically optimize either PAPR or OOBE independently, the proposed method jointly considers both metrics. To this end, a weighted cost function combining PAPR a
Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, with stronger safeguards for release.
ChatGPT can now connect to trusted healthcare data, helping clinicians securely access patient context, medical research, and more.
The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being.
Standard continuous gravitational-wave searches with pulsar timing arrays (PTAs) neglect cosmological redshift, restricting their applicability to the local Universe. We introduce a redshift-aware PTA framework and apply it to the Parkes PTA Data Release 3, deriving the first direct constraints on supermassive binary black holes (SMBBHs) at the cosmic dawn. Evaluating our limits across a broad red
See how Gilbert + Tobin combines CEO-led commitment, rigorous governance, and human accountability to scale ChatGPT Enterprise and Codex across the firm.
Official ETH Zürich technical update and publication covering Robotic help in high-risk twin pregnancies.
Official ETH Zürich technical update and publication covering Apertus partners with Proton’s Lumo AI assistant.
Official ETH Zürich technical update and publication covering Low-carbon concrete put to the climate test.
Official ETH Zürich technical update and publication covering The ideal setting for sensitive experiments.
Official ETH Zürich technical update and publication covering Clean sweep for cutting-edge research.
Official ETH Zürich technical update and publication covering ETH Zurich and the SETI Institute join forces to search for the origin of life.
Official Artificial Analysis technical update and publication covering Claude Fable 5.1 tops the Artificial Analysis Intelligence Index.
Official ETH Zürich technical update and publication covering Strong interest in studying at ETH Zurich.
Official ETH Zürich technical update and publication covering Atomic resolution.
Official technical announcement and publication from Hugging Face covering Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI.
Official ETH Zürich technical update and publication covering ETH Zurich to host a new IBM quantum computer.
Official ETH Zürich technical update and publication covering Novel Particle Beam Could Challenge Einstein’s Theory of Gravity.
Official ETH Zürich technical update and publication covering The architect who builds brands.
6542 articles sourced historically · 100 per page