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How to secure edge AI in customer-owned environments

Microsoft outlines security architecture guidance for edge AI, urging runtime attestation, artifact provenance, and deterministic mediation of model actions.

Microsoft details how edge AI shifts trust responsibilities to customers operating their own infrastructure, where prompt injection, model tampering, and malicious firmware updates can occur alongside model weights, credentials, and physical-system access. The guidance recommends verifying runtimes with attestation, verifying AI artifacts with provenance, and constraining model actions through a deterministic mediator outside the model. It also covers new exposure surfaces from MCP, multi-agent systems, and computer-use agents running in disconnected or hostile edge environments.

Microsoft Security Blog · 13d agoAI safety & security

One Extension Could Hijack AI Assistants Across Chrome, Comet, Edge, Opera Neon and Claude

Researchers showed a single browser extension could hijack AI agents in Chrome, Edge, Comet, Opera Neon and Claude in Chrome, earning $20,000 in bounties.

Forever Security demonstrated that a browser extension with two common permissions could seize the trusted page controlling built-in AI assistants in five Chromium-based products and drive the agent, read local files, or access the camera. Chrome's flaw was fixed as CVE-2026-0628 (CVSS 8.8) in Chrome 143.0.7499.192, and Microsoft fixed CVE-2026-55945 (CVSS 4.2) in Edge 150.0.4078.48. Perplexity Comet was the worst case: a hijacked agent could read any file, leak browsing history, take screenshots, and act as the user via an unsecured test subdomain. All attacks require a malicious extension already installed; no in-the-wild exploitation or KEV listing was reported as of September 16, 2026.

The Hacker Newsupdated · 1d agofirst · 1d agoAI safety & security 3 sourcesCVE-2026-0628CVE-2026-55945

Edge Case launches Guardian, an AI platform for tracking risk across autonomous systems

Edge Case launched Guardian, an AI platform creating a Digital Safety Twin to continuously track risk across autonomous and defense systems.

Edge Case launched Guardian, an AI-driven platform that fuses safety analysis, engineering data, and operational signals into a continuous Digital Safety Twin. It targets advanced autonomous platforms in commercial and defense markets, with six customer agreements signed at launch. The platform tracks hazards, mitigations, and source-linked evidence across the development lifecycle, shifting safety from one-time review to continuous capability.

Help Net Security · 16d agoAI tools & infra

TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation

TransNormal-2 improves monocular surface-normal estimation by fixing VAE edge degradation with geometry-aware losses and refinement, matching MoGe-2 with 1.4% of annotations.

TransNormal-2 is a FLUX.2-based rectified-flow framework for monocular surface-normal estimation with single-step deterministic inference. The authors quantify that VAE 8x spatial compression introduces 1.3-8.5 degrees of mean angular error even on ground-truth normals, with edge error up to 2.8x the global error. The method adds geometry-aware pixel-space losses and an RGB-guided Geometric Refinement Module to correct boundary-localized decoding errors. It matches or exceeds MoGe-2 on all eight reported metrics using only 1.4% as many task-specific annotations, and cuts transparent-object MAE by 4.2 degrees on ClearGrasp and 3.1 degrees on ClearPose.

Hugging Face daily papers · 12d agoAI research

Small AI models let drones autonomously identify and attack battlefield targets

NATO-backed startup Scaleout Systems uses federated learning and small edge AI models to let military drones autonomously identify, prioritize, and attack battlefield targets.

Scaleout Systems, founded in 2018 by Uppsala University researchers, joined NATO's DIANA accelerator in 2025 and works on the Federated Aerial Intelligence for Recon project adapting machine learning for drone and forward-base edge hardware. Under the ALMA project led by BAE Systems Bofors, a demonstration showed a kamikaze drone autonomously detecting, prioritizing, and striking an armored engineering vehicle using onboard AI. Federated learning lets local nodes retrain models from battlefield sensor data and share updates without transmitting raw data; the company also tested this at a Swedish Air Force base in Uppsala.

Ars Technica · AI · 15h agoAI industry

Rapidly scaling online storage to serve over 1 billion ChatGPT users

OpenAI's Habitat online storage platform now handles over 70 million requests per second and 500 PB of data for 1 billion users.

OpenAI details the evolution of Habitat, its online storage platform backing ChatGPT and other products, which began in mid-2024 as a Python client-side library over Azure Cosmos DB. Habitat now processes more than 70 million requests per second, serves over 500 petabytes of data across nearly 40 geographic regions, and supports over 1 billion users weekly. By mid-2025 the client library approach became brittle, so OpenAI moved Habitat into a standalone service to centralize deployments, observability, and multi-tenancy reliability. This is part one of a two-part series; a future post will cover read optimization and scaling the Azure Cosmos DB partnership.

OpenAI News · 7d agoAI tools & infra1

Can Edge-Deployable Vision-Language Models Identify Species?

Evaluation of 2-8B VLMs (Qwen3-VL, Gemma3) against BioCLIP on camera-trap species ID shows all models degrade sharply on field imagery.

The study tests whether edge-deployable 2-8B vision-language models carry genuine taxonomic knowledge, comparing Qwen3-VL 2B/4B/8B and Gemma3 4B against the 300M specialist BioCLIP on a 96-species task across clean iNaturalist photos and six LILA.science camera-trap collections. All models degrade 9.6-26.6 percentage points on field imagery, and BioCLIP outperforms every VLM by 33.2-59.2 points on an expanded 200-image sample, suggesting specialized data rather than scale drives the gap. Under open-set prompting, 5.9-9.6% of responses are syntactically valid but taxonomically nonexistent species names, with fabrication rankings replicating across evaluation sets.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

Graph Machine: Towards Better Pretraining via Edges

Researchers propose Graph Machine, an O(n)-state sparse architecture that replaces 75% of Qwen3-0.6B dense layers with only slight loss change.

The paper introduces the Graph Machine (GM), an architecture that maintains an O(n)-sized state accessed through sparse, dynamic routing via pointer-like edges updated differentiably by a referral mechanism resembling pointer chasing. The authors replaced 75% of dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrained from scratch on 15.7B tokens. Retrieving 2 of 4,096 tokens per KV head in each sparse layer degrades loss only slightly, while retrieving 4 marginally improves loss over the dense baseline.

Hugging Face daily papers · 16d agoAI research

Voters mostly don’t like AI and data centers, but neither party seems to have an edge

NYT/Siena poll of 1,503 likely voters finds 61% oppose AI data center construction, yet the issue ranks below 1% among midterm priorities.

A New York Times/Siena University poll of 1,503 likely voters conducted in early September found 61% oppose constructing data centers to power AI, with only 14% strongly supportive. Opposition drivers include environment/water usage (32%), local community impact (21%), and general distrust of AI (18%); 56% of opponents favor limits while 38% want a total ban. Trump 2024 voters split nearly evenly (49% support vs 45% oppose) while Harris voters opposed at 74%. Despite the sentiment, AI and data centers registered under 1% as a top midterm issue for most demographics, and neither party holds a clear trust advantage (42% Republicans vs 40% Democrats).

The Verge · AI · 2d agoAI policy

Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction

MarkTechPost tutorial implements a hierarchical NeRF in JAX using jax3d volume-rendering primitives for novel-view synthesis and 3D reconstruction.

The tutorial builds an end-to-end hierarchical Neural Radiance Field using JAX, Flax, Optax, and jax3d's volume-rendering functions (sample_along_rays, volume_rendering, sample_piecewise_constant_pdf). It implements positional encoding, skip connections, separate coarse and fine networks, and view-direction conditioning with hierarchical importance sampling. Training uses JAX JIT compilation, Adam optimization, exponential learning-rate decay, and gradient clipping. Evaluation covers PSNR, depth and opacity visualization, 360-degree rendering, and marching-cubes geometry extraction.

MarkTechPost · 4d agoAI research

Multi-Step Tool-Calling over Korean Open Public APIs: A Benchmark and a Data-Synthesis Recipe

Researchers introduce KOPA-Bench, a 145-task Korean public API tool-calling benchmark, and EDGE, an execution-grounded data synthesis method.

An arXiv paper presents KOPA-Bench, a benchmark of 145 real-world tasks chaining multiple tool-calls across live Korean government APIs, motivated by data-sovereignty requirements for on-premise open-source LLM agents. It also introduces EDGE, an execution-grounded dynamic graph that keeps only tool-output-to-input links verified by live API calls before synthesizing executable multi-step trajectories. A 9B model fine-tuned with GRPO on the resulting dataset nearly matches its untuned 27B family sibling on KOPA-Bench and improves on the BFCL benchmark.

arXiv cs.AI / cs.LG / cs.CL · 13d agoAI research1

NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut

NVIDIA's Vera Rubin NVL72 debuts in MLPerf Inference v6.1 with up to 3.7x higher throughput than GB300 NVL72 and 99% scaling efficiency at 288 GPUs.

In its first MLPerf Inference preview submission, NVIDIA's Vera Rubin NVL72 achieved up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and 2.5x on DeepSeek-R1. A 288-GPU GB300 NVL72 submission across four racks reached 99% scaling efficiency on the DeepSeek-R1 offline benchmark. Software optimizations delivered up to 1.6x gains over v6.0, leveraging TensorRT-LLM, vLLM, Dynamo, disaggregated serving, and NVFP4 precision.

NVIDIA Blog · 1d agoAI industry

Quoting Paul Ford

Simon Willimon quotes Paul Ford arguing AI can write good software but cutting-edge work still demands human collaboration, craft, and judgment.

Simon Willimon highlights a passage from Paul Ford's essay 'A.I. Was Supposed to Give Us New Killer Apps. What Happened?'. Ford argues that while AI can write very good software, it also makes it easy to do someone else's job badly, which partly explains why many AI-driven projects fail. The quote reflects a broader industry reassessment of AI coding tools after initial fears that developer roles were obsolete.

Simon Willison · 5d agoAI industry1

What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets

Six-month record of 7.5M LLM trading agent invocations shows volatility-blind sizing, minimal upside capture, and no directional edge across two fleets.

The study records autonomous LLM trading agents in production across DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets) and the DXAP fleet (500-599 agents on Hyperliquid perpetuals), spanning roughly six months, 7.5M single-model invocations and about 300K onchain actions. A risk slider explains leverage (+0.425 per level), median leverage is 5.0x in every volatility sextile, and one posture-slider cell holds 62% of liquidations. Agents capture little upside: 43.2% of positions saw +300 bps favorable excursion within 24h yet 49.3% of those closed negative, while the DXAP fleet trails a matched retail benchmark (41% vs 50% roundtrip win rate). A paired-replay league of frontier models finds decision quality statistically indistinguishable at this horizon.

Hugging Face daily papers · 14d agoAI research

The Attention Triangle in Audio-Video Models

Researchers analyze the 'attention triangle' in audio-video diffusion models, showing bias-driven cross-attention routing causes semantic leakage and proposing inference-time interventions that improve grounding.

A study probes the three cross-attention edges linking text, audio, and video streams in audio-video diffusion models. It finds the audio-video edge is bidirectional and shaped by parameter-encoded biases, so prompts in tension with learned priors can be overridden, producing visually canonical but incorrect outputs. Attention-derived signals are used as diagnostics and to guide inference-time interventions that improve cross-modal semantic grounding while preserving generation quality.

Hugging Face daily papers · 15d agoAI research

macOS 27 Golden Gate – Review

Ars Technica reviews macOS 27 Golden Gate, highlighting an unavoidable Apple Intelligence upgrade, new AFM 3 Core models, and dropped Intel Mac support.

macOS 27 Golden Gate delivers the first significant Apple Intelligence upgrade two years after launch, and the toggle to disable the AI features or delete downloaded models is gone. Apple Intelligence runs on a new AFM 3 Core model built in collaboration with Google, while the more capable AFM 3 Core Advanced requires an M3 chip and at least 12GB of RAM. The release drops all Intel Mac support, requiring Apple Silicon, with Sequoia security updates expected to end in fall 2027 and Tahoe's in 2028.

Hacker News · AI · 1d agoAI industry 2 sourcesHN 45↑ · 28 comments

Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPost · 3d agoAI research1

Beijing Hits Back at Anthropic CEO’s Call to Curb China’s AI Development

China's Foreign Ministry rejected Anthropic CEO Dario Amodei's call to curb Chinese AI development as 'Cold War playbook' fearmongering.

China's Ministry of Foreign Affairs pushed back against Anthropic CEO Dario Amodei's essay warning that a Chinese AI lead would endanger the US, with spokesperson Guo Jiakun saying fearmongering and vicious competition disrupt global AI governance. Amodei urged continued restrictions on cutting-edge AI chip sales to China and predicted AI agents could take over the internet within 6-12 months without a global slowdown. The exchange precedes a planned September 24 Trump-Xi meeting covering AI governance, and follows a joint FBI/NSA/CISA advisory alleging Chinese developers distilled capabilities from Claude and GPT.

SecurityWeek · 3d agoAI policy

NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing

NVIDIA open-sourced OSMO, a Kubernetes-native YAML orchestrator running physical-AI training, simulation, and robot testing across mixed GPU tiers.

OSMO (Apache-2.0, latest release 6.3.1) lets teams describe training, simulation, and hardware-in-the-loop pipelines in a single YAML and routes tasks across datacenter GPUs (GB200), workstation RTX hardware, and edge devices like Jetson AGX Thor. It ships Helm charts and containers on NGC, uses the KAI Scheduler with NVLink topology-aware placement, and includes RBAC, OAuth2, and TLS termination. NVIDIA says it is battle-tested on GR00T, Isaac Lab, Isaac Sim, and Isaac ROS, and integrates with Claude Code, OpenAI Codex, and Cursor agents.

MarkTechPost · 4d agoAI tools & infra

Trump and Mike Johnson think the AI industry is overreacting

Trump and House Speaker Mike Johnson reject AI executives' calls to slow development, warning restrictions could let China win the AI race.

Anthropic CEO Dario Amodei published an open letter urging labs to 'pace the frontier' and slow AI development, drawing public support from OpenAI's Sam Altman, Elon Musk, and Alphabet's Demis Hassabis. Trump told the Financial Times the US leads China in AI and that 'whoever wins AI, wins.' House Speaker Mike Johnson told CNN that rushing AI regulation could itself be a 'national security threat' and urged against panic-driven congressional action.

The Verge · AI · 4d agoAI policy3

Fly Language Model (FLM) Wires the Full Fruit Fly Connectome Into a Frozen 1.2B LLM, and Its Own Controls Show the Wiring Does Not Help

Researchers wire the full fruit fly connectome (166,700 nodes) into a frozen LiquidAI LFM2.5-1.2B LLM, but controls show no fly-specific benefit.

The Fly Language Model (FLM) couples the complete MaleCNS v1.0 fruit fly connectome (166,700 nodes, 25,582,938 edges) to a frozen LiquidAI LFM2.5-1.2B-Instruct backbone, training only a 278,528-parameter readout (~0.0238% of backbone parameters). The fly readout improved NLL by 0.0222 nats/token (perplexity 3.98 to 3.90) on 32 SmolTalk dialogues, but a direct-input control without the graph beat it in all three seeds. Relabeling node identities removes the gain and the recurrence contracts state differences by 0.6 per token, so the connectome adds no long-range memory. The MIT-licensed code runs locally on Python 3.12, but study artifacts remain private, limiting independent reproducibility.

MarkTechPost · 5d agoAI research1

I spent $4,000 on a robot dog from China

Hands-on review finds the $4,017 Unitree Go2 Pro robot dog affordable but impractical, as Unitree reaches a $34 billion valuation after its IPO.

Ars Technica reviewed the Unitree Go2 Pro quadruped, purchased for $4,017, finding it astonishingly cheap but of limited practical use; it collapsed from battery drain and heat (84°C internal temperature) on an uphill walk at 87°F. Unitree democratized quadruped research, sells humanoid robots from $13,500, and debuted on the Shanghai stock exchange on August 19 with shares rising over fivefold on day one, valuing the company at $34 billion. Its robots now face legal restrictions in the United States, and it competes with Boston Dynamics, whose Spot starts around $75,000.

Ars Technica · AI · 6d agoAI industry

Cognition launches new SWE-2 model, Rivaling Fable 5.1 and GPT-Astra

Cognition released SWE-2, a coding model post-trained from Kimi K3 that scores 50.0% on FrontierCode 1.1 Main, near Fable 5.1 at 64% lower cost.

Cognition introduced SWE-2, its most advanced coding model, post-trained from the 2.8T-parameter Kimi K3 base model. It achieves 50.0% on FrontierCode 1.1 Main, 73.0% on DeepSWE 1.1, and 92.8% on Terminal-Bench 2.1, beating Grok 4.6 and SWE-1.7 while matching Fable 5.1 and GPT-5.6 Sol at a fraction of the price. The company says it scaled reinforcement learning to the multi-trillion-parameter regime for the first time, using Pareto-informed cost penalties that train all reasoning-effort levels in a single run, tripled RL environments, and NVFP4/FP8 quantization-aware training. SWE-2 is available today in Devin Desktop and CLI, with rollout on Devin Web and Fusion.

Hacker News · AI · 7d agoModel releaseHN 58↑ · 15 comments2

Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems

An empirical study shows distributed Kolmogorov-Arnold Network training reaches 74.7% parallel efficiency at 8 A100 GPUs, with overheads driven by All-Reduce choices.

The study evaluates data-parallel Kolmogorov-Arnold Network (KAN) training on the FinisTerrae III supercomputer using up to 8 NVIDIA A100 GPUs across 4 nodes with PyTorch Distributed Data Parallel. Strong scaling yields 5.97x speedup and 74.7% parallel efficiency at 8 GPUs, comparable to conventional deep learning workloads, while communication overhead ranges from 1.3% to 6.1%, driven mainly by All-Reduce algorithm selection and inter-node latency rather than KAN's edge-wise gradient structure. Weak scaling shows an initial single-to-multi-GPU throughput drop followed by stability, and the parameter-to-memory ratio improves with model size even as training time scales unfavorably. The authors provide GPU topology and model-size deployment guidelines for KAN training.

arXiv cs.AI / cs.LG / cs.CL · 10d agoAI research1

Embedded Graph Flows for Categorical Graph Generation

Researchers propose Embedded Graph Flows, a generative model with learned categorical embeddings that beats DiGress and GruM on molecular graph benchmarks.

Embedded Graph Flows (EGF) learns continuous embeddings for node and unordered-edge categories and transports Gaussian noise toward these endpoints using a permutation-equivariant graph transformer. On QM9 it achieves the best result on all four reported metrics, with a Fréchet ChemNet Distance of 0.150 versus 0.717 for DiGress and 0.812 for GruM. On ZINC250k it retains the lowest NSPDK MMD, indicating close agreement with local substructures of reference molecules. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CL · 13d agoAI research1

Facilitating AI integration with simplicity at scale

Jabil's SAP IT director says simplifying integration across 100+ sites in 30+ countries with SAP Integration Suite created the data backbone for AI.

In an MIT Technology Review Business Lab podcast produced in partnership with SAP, Jabil SAP IT director Harish Manohar described consolidating fragmented tools across more than 100 sites in over 30 countries using SAP Integration Suite. The manufacturer, with 140,000-plus employees and more than 400 top-brand customers, says a standardized data backbone enables real-time supply chain visibility and is a prerequisite for scaling predictive, AI-driven planning and forecasting. The company frames simplification-first modernization as a competitive advantage tied to measurable business value and operational resilience.

MIT Technology Review · AI · 16d agoAI industry1

Senior engineers are spending their week cleaning up AI-generated code

New Relic study finds AI-generated code doubles critical runtime issues, with senior engineers losing a third of their week to fixes.

A New Relic survey of U.S. technology leaders reports AI now writes the majority of shipped code, with senior SRE and DevOps engineers spending up to a third of their week triaging and refactoring it. A large majority of organizations had at least one AI-related production failure in the past six months, and roughly three in ten saw newly introduced security vulnerabilities. AI-generated code showed nearly twice as many critical runtime issues as peer-reviewed human-authored code, with gaps concentrated in edge cases, concurrency, deprecated APIs, and complex state changes. Most teams now prompt AI tools to embed logs and traces directly into generated code.

Help Net Security · 25d agoAI industry1

Salesforce Agentforce: Bridging the Enterprise AI Gap from ‘Vibe Coding’ to Battle-Tested Orchestration

Salesforce pitches Agentforce as an enterprise agent platform with testing, observability, and deterministic gating; Southwest Airlines reports $6M annual savings and 45% autonomous resolution.

Salesforce positions Agentforce as an enterprise agent harness built on Data Cloud and Customer 360, exposing external endpoints via the Model Context Protocol and offering Agentforce Testing Center for synthetic stress-testing, headless CI/CD regressions, Agent Optimizer for live prompt tuning, and deterministic gating to prevent unvalidated actions like payments. Southwest Airlines deployed Agentforce across its Help Center and mobile app starting November 2025, reporting a 45% autonomous resolution rate across more than 2 million interactions, 7x ROI, $6 million in projected annual savings, and a +900% jump in customer satisfaction metrics. The article frames the platform as competing with other enterprise agent orchestration offerings.

MarkTechPost · 6h agoAI industry1

[AINews] not much happened today

Latent Space AI news digest covers Anthropic's Claude Code Projects, Google's managed agent APIs, TypeSafe's Jev classifier, and OpenAI's Astra for Law launch.

The 9/16-9/17/2026 AI news roundup highlights Anthropic's Claude Code Projects enabling one conversation to spawn parallel cloud sessions, and Google's Gemini managed agents adding a Credentials API, Files API, and claims of 30% lower costs. It also covers TypeSafe's Jev, a fast constrained-output classifier being used for routing, judgment, and structured decisions, with open reproductions such as openjev-s on Qwen3.6-35B-A3B. OpenAI launched Astra for Law with 26 partner-built and 47 community plugins via Trusted Access, with reports it beats generic GPT-6 Astra plus web search on Vals' legal benchmark. Research items include DeepMind's Stellar Colosseum multi-agent math harness (Codeforces 4263, 71.0% on TCS-Bench) and NVIDIA-associated Agora using Git commits as shared memory.

Latent Space · 7h agoAI industry 2 sources

Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights

SportSAGE uses a semantic action graph schema to ground agent-generated sports highlights and let viewers query, navigate, and verify narratives.

The semantic action graph represents a sports match as performer, action, recipient, moment, and state nodes connected by role, temporal, and outcome edges, with a closed vocabulary and frame-addressable moments. Instantiated in SportSAGE, a design probe pairing a four-module agentic highlight pipeline with a graph interface, it was evaluated with 12 soccer fans. Participants were satisfied with generated highlight quality and used the interface to search, navigate, and interpret match highlights, suggesting one small human-readable schema can ground both agent generation and human interpretation.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

Accelerating Sharded Data Parallelism at Scale with Federated Learning

Hybrid FL+FSDP and FL+HSDP algorithms cut communication overhead in sharded data parallelism, accelerating Llama3.1 8B pre-training on 512 A100 GPUs by up to 8x.

The paper introduces FL+FSDP and FL+HSDP, hybrid algorithms that interleave sharded data parallelism with FedAvg-style federated aggregations to decouple large DP deployments into loosely-coupled federation groups. Formal communication-cost analysis and experiments on multi-tier GPU interconnects demonstrate scalability and flexibility. A Llama3.1 8B pre-training run on 512 A100 GPUs achieves up to 8.04x faster data processing and 4.48 lower evaluation perplexity than sharded DP baselines under identical hyperparameters.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compression

DeepSeek-V4.1 Flash is a 552B-parameter multimodal MoE model with 1M-token context achieving 4x KV cache compression for long-horizon agent workloads.

A detailed analysis of the DeepSeek-V4.1 Flash technical report describes a 552B-parameter multimodal mixture-of-experts model supporting contexts up to 1 million tokens. Its Causal Encoder-Decoder (CED) architecture activates 8B parameters during prefill and 16B during decode, and reportedly delivers about 420 tokens/s. Joint optimization of architecture (CSA2 cross-layer compression), FP4 KV cache precision, and deployment strategy cuts runtime KV cache to roughly 1/4 and persistent KV cache to about 1/8 of DeepSeek-V4-Flash at the same sequence length, targeting storage and bandwidth bottlenecks in long-horizon agent serving. The author notes all DeepSeek-V4 Pro models were taken offline following the release.

Hacker News · AI · 1d agoModel release 2 sourcesHN 41↑ · 3 comments1

The AI data center e-waste problem is huge — and getting bigger

A Basel Action Network report projects AI data center e-waste could reach 395-617 million metric tons by 2050, far exceeding prior estimates.

The nonprofit Basel Action Network (BAN) published a report arguing AI e-waste has been vastly underestimated because it counts all data center infrastructure, not just servers and GPUs. BAN projects 8.6-13.1 million metric tons of AI-related equipment retired annually, totaling 395-617 million metric tons between 2025 and 2050, based on roughly 70,000 tons per gigawatt and a projected 219GW of capacity by 2030. Less than a quarter of the 68.3 million tons of e-waste generated yearly worldwide is formally collected and recycled, with informal disposal exposing workers and children to toxins like lead and chromium.

The Verge · AI · 1d agoAI industry

MAGS: Multi-agent Auto-formalization Guarantees Safety for Agentic Outputs

MAGS uses multi-agent auto-formalization with Dafny to generate code with machine-checked safety guarantees, achieving 100% success across 220 tasks.

MAGS is a unified multi-agent framework that formalizes and freezes human-audited APIs and safety requirements, translates generated code into Dafny as a verification-aware intermediate representation, repairs violations using verifier feedback, and compiles verified programs back into executable code. Across 220 examples, including 100 CUDA kernels, 100 terminal scripts, and 20 robotic-arm tasks, it achieved a 100% success rate producing programs with non-trivial formal safety guarantees. Independent safety and functional evaluations showed strong performance, while revealing failures when auto-formalized semantics do not fully capture the target behavior.

arXiv cs.CR · 1d agoAI research

Former Infosys chief’s AI startup nabs another $53M

Hang Ten Systems, founded by ex-Infosys CEO Vishal Sikka, added $53 million to its seed round, bringing total funding to $85 million.

The new round was led by Temasek's early-stage platform Xora with Mayfield participating, closing five weeks after the initial $32 million seed. Founded in May 2026, the Palo Alto startup advises enterprises with over $10 billion in annual revenue on AI strategy and builds production software using its in-house Hobie framework of reusable AI skills. It works with 21 major enterprises including Fresenius Kabi, Saudi Aramco, and Siemens Energy, and plans to expand engineering, consulting, and sales teams.

TechCrunch · AI · 2d agoAI industry

Self-improving AI should slow down, von der Leyen tells EU lawmakers

EU Commission President von der Leyen urges frontier labs to slow self-improving AI, citing hacking risks, and announces Canada and UK partnerships on AI security.

European Commission President Ursula von der Leyen used her State of the Union address to call for slowing self-recursive frontier AI, warning that models in development will enable hacking at previously unimagined levels. She announced joint work with Canada and the UK on model evaluation, verification, early warning, and AI security, and proposed widening the CETA trade agreement into an alliance covering AI, quantum technology, and cyber and economic security. She defended the EU AI Act as central to guardrails, promised initiatives for health, transport, agrifood, manufacturing, and defense in November, and backed an EU Kids Act barring social media for children under 13.

Help Net Security · 2d agoAI policy

Mistral X Mozilla: Private, Multilingual AI Browsing

Mistral and Mozilla partnered to power Firefox's Smart Window AI browsing assistant in France and North America, with zero data retention.

Mozilla's Firefox Smart Window (beta) AI browsing assistant is now powered by Mistral models for users in France and North America, with the UK and Germany expected later this year. Conversations are not saved on Mozilla's servers by default, and Mistral agreed to zero data retention. Both companies frame the partnership as advancing open-source, privacy-first, and regionally fine-tuned AI, with models trained on regional languages, dialects, and cultural context.

Hacker News · securityupdated · 1d agofirst · 2d agoAI industry 2 sourcesHN 35↑ · 8 comments2

University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK

University of Manchester retrained NVIDIA Earth-2 CorrDiff and StormCast on Isambard-AI to forecast UK air pollution at 2-3 km resolution.

University of Manchester researchers led by professor David Topping adapted NVIDIA's Earth-2 generative AI frameworks to forecast air pollution across the UK. Earth-2 CorrDiff was retrained in two days on a single eight-GPU node of Isambard-AI (5,448 GH200 Grace Hopper Superchips, 21 exaflops) using a year of hourly simulated pollution data, producing a UK-wide model at 2-3 square kilometer resolution. The team added Earth-2 StormCast for time-dependent forecasts that ingest real air quality observations, and demonstrated the workflow runs on the DGX Spark desktop AI system. Open-source training data and workflows are planned so other countries and cities can build similar pollution models.

NVIDIA Blog · 2d agoAI industry

Agora: Git as Shared Memory for Collective AutoResearch

Agora records multi-agent research as an append-only Git DAG; 13 LLM workers ran nearly 12 days on a weight-transfer problem.

Agora stores every result, hypothesis, and verification as an immutable commit in a Git-stored DAG, with a derived index exposing the frontier and verification status of claims. In a nearly 12-day run, 13 language-model workers with no assigned tasks or central planner published 1,703 contributions on initializing a frozen 119.6M-parameter attention-SSM hybrid from 141 donor models. They improved the evaluator from 3.39 to 1.899 bits per byte, closing 62% of the gap to a trained GPT-2 124M, with 165 independent reproductions posted and none failing.

Hugging Face daily papers · 2d agoAI research

Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging

LP-BTS uses graph proposal policies, learned critics, and budgeted PUCT search to plan mobile charging across dynamic action spaces up to 2,813 stops.

LP-BTS is a learning-guided planning architecture for one-to-many mobile charging, where N=250 sensors induce roughly 1,125 initial candidate charging stops. A graph proposal policy concentrates candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares simulated futures, letting a single frozen checkpoint cover action universes from 736 to 2,813 stops. On a sealed 30-scenario confirmatory bank it attains the highest observed survival (0.4545) and alive-AUC (0.8031), though its +0.0066 survival edge over the strongest engineered comparator is statistically unresolved.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research