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Re: Retrospective by 'gpg.fail' authors

GnuPG's Werner Koch says gpg.fail samples only crash GnuPG via DER-as-printf format string in --debug x509; RCE claim remains unproven.

Werner Koch replied to the gpg.fail retrospective, noting that GnuPG versions above 2.2 produce garbled stderr or crash when the project's certificates are used with --debug x509 because DER data is passed as a printf format string. Testing the certificates from the researchers' Git repo yielded only a segfault, not demonstrated code execution. Koch states how remote code execution would be achieved is unclear and asks for a real reproducer.

oss-securityupdated 05:03 UTC · 8h agofirst Sep 15, 15:24 UTC · 21h agoVulnerability 6 sources

NVIDIA security advisory (AV26-849)

Canada's Cyber Centre relayed NVIDIA security advisories covering vulnerabilities in NemoClaw, OpenShell, Unified Fabric Manager, and DGX Spark.

The Canadian Centre for Cyber Security issued bulletin AV26-849 flagging NVIDIA vulnerabilities across NemoClaw and OpenShell (multiple versions), NVIDIA Unified Fabric Manager (multiple versions and models), and DGX Spark (versions prior to 1.110.13). The advisory links to NVIDIA security bulletins and urges users and administrators to apply updates as they become available. No exploitation details or CVE identifiers are enumerated in the advisory text.

Canadian Centre for Cyber SecurityAug 26, 14:17 UTC · 20d agoAdvisory

Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies

NVIDIA details a three-computer robotaxi platform that Uber, Lyft, May Mobility, Mercedes-Benz and others are adopting to scale autonomous fleets.

NVIDIA says every major commercial robotaxi program runs on its stack, spanning training (DGX with Alpamayo VLA models), simulation and validation (Omniverse, Cosmos, AlpaSim on RTX PRO), and in-vehicle compute (DRIVE Hyperion 10 with dual DRIVE AGX Thor chips). Adding meta-action and chain-of-thought reasoning data to a VLA model reduced minimum average displacement error by 43%, from 2.08 to 1.18. Uber plans NVIDIA DRIVE Hyperion-based fleets across 28 cities by 2028, partnering with Autobrains, Avride, Lucid, May Mobility, Mercedes-Benz, Momenta, Nissan, Nuro, Pony.ai, Stellantis, Waabi, Wayve, WeRide and Zoox. DRIVE Hyperion 10 combines 14 cameras, nine radars, three lidars and 12 ultrasonics with redundant compute and NVIDIA Halos safety validation.

NVIDIA BlogSep 10, 16:00 UTC · 5d agoAI industry

Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026

NVIDIA announces local AI push at IFA 2026 with faster llama.cpp/vLLM inference, PAIR routing tool, and October RTX Spark PCs.

At IFA 2026, NVIDIA announced simplified local AI support for agents in Hermes Agent, OpenClaw, and Perplexity Portable Computer, plus new llama.cpp and vLLM optimizations delivering up to 1.9x faster local inference. NVIDIA also unveiled PAIR, a Personal AI Router for distributing inference across a local network's PCs, and compact RTX Spark Windows PCs from Lenovo and Acer arriving in October. The post recaps recent local-capable model releases including Nemotron 3.5 Lightning (30B), Qwen3.8-Flash-Next and Qwen3.8-27B, DeepSeek v4 Flash (284B MoE, 13B active), Meta Muse Glimmer (30B), Z.ai GLM-5.3-Flash, LTX 2.5, and MiniMax-H3 with the FastH3 distilled variant.

NVIDIA BlogSep 3, 16:00 UTC · 12d agoAI industry

Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX

Perplexity launches Portable Computer local AI agent on Windows for NVIDIA RTX PCs with 24GB+ VRAM, keeping sensitive work on-device.

Perplexity released Portable Computer, a local version of its agentic Perplexity Computer, in its Windows app for NVIDIA GeForce RTX PCs and RTX PRO Workstations with 24GB or more VRAM. It runs a locally post-trained model such as Qwen 3.8 27B optimized for NVIDIA RTX GPUs, handling multistep tasks and file analysis on-device with a SPACE sandbox and built-in browser. Connectors cover Outlook, OneDrive, Word, Google Drive, Gmail, Slack, and GitHub, and the agent can escalate to cloud models only with user permission.

NVIDIA BlogSep 14, 15:00 UTC · 1d agoAI industry

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon WillisonSep 4, 23:59 UTC · 11d agoAI research

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 Blog05:00 UTC · 8h agoAI industry

Building a Linux GPU Driver for the M4 Mac Mini in One Month

Two developers built a fully OpenGL ES 3.0 compliant Linux GPU driver for the M4 Mac Mini in one month via clean-room reverse engineering.

Niklas and the author reverse engineered Apple's AGX GPU firmware ABI and user-space components in about a month, a process that normally takes years, producing an OpenGL ES 3.0 conformant driver fast enough to run Minecraft at 200fps on an M4 Mac Mini. The work was done transparently using hypervisor traces without examining Apple binaries, following clean-room practices, and included a custom shader compiler, command stream builder, and a full Linux kernel driver for the firmware ABI. The A18 Pro firmware ABI proved significantly more complex than the M1's, with 1.5x as many structs and twice as many pointers. All experiments and provenance evidence were published in public agx-re repositories.

Hacker News · AISep 15, 19:30 UTC · 17h agoResearch HN 40↑ · 5 comments

GeForce NOW Gives Gamers More Ways to Play at Gamescom 2026

NVIDIA announced Gamescom 2026 GeForce NOW updates: DLSS 4.5 controls, new Steam device support, GOG single sign-on, Firefox access, and more cloud games.

At Gamescom 2026, NVIDIA announced expanded support for its GeForce NOW cloud gaming service, including new Steam devices, GOG single sign-on, Firefox browser access, and more big PC games coming to the cloud. The company also introduced new NVIDIA DLSS 4.5 technology controls that let members fine-tune gameplay. The announcements are consumer gaming product news rather than security or research developments.

NVIDIA BlogAug 27, 13:00 UTC · 20d agoAI industry

With Groq 3 LPX in Full Production, NVIDIA Extends Vera Rubin Inference for Agents

NVIDIA puts Groq 3 LPX into full production and extends Vera Rubin NVL72 rack-scale systems for fast token generation in agentic AI inference.

NVIDIA announced that Groq 3 LPX is in full production as part of an extension of the Vera Rubin NVL72 rack-scale platform aimed at agentic AI inference. The announcement frames the next era of inference as full-stack AI factory co-design across chips, networking, and systems rather than a single component breakthrough. The focus is improving token generation speed for agent workloads.

NVIDIA BlogAug 24, 15:00 UTC · 22d agoAI industry

[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6

OpenAI unveiled Jalapeno custom inference chip claiming 1.5-1.9x better perf-per-watt than NVIDIA GB200/GB300, deploying in-house by year-end.

At the 37th Hot Chips conference, OpenAI published first benchmark details for its custom Jalapeno inference chip, claiming 1.5-1.9x more work per watt, 1.7-3.6x lower end-to-end latency, and 2.1-4.1x higher interactive-workload performance versus NVIDIA GB200/GB300, with the 700W-rated part staying at or below 550W in tests. Deployment into OpenAI's own infrastructure begins by year-end, with Gen 2 deep in development and Gen 3 underway. OpenAI also said GPT-Astra and Codex helped write low-level kernels, reportedly 1.5-1.8x faster than human-expert code for selected attention and MoE blocks. Cerebras CS-5, Groq 3 LPX and Apple M6 were also featured at the conference.

Latent SpaceAug 27, 01:31 UTC · 20d agoAI industry

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

Introduces VEX-Bench, 75 expert-labeled real-world cases testing whether LLM agents can assess supply chain vulnerability exploitability; frontier models reach about 80% F1.

VEX-Bench is the first benchmark evaluating LLM agents on assessing whether upstream dependency vulnerabilities are exploitable in downstream projects, with 75 real-world expert-labeled cases across Python, Java, and Go mined from GitHub. Nine models across three agent harnesses were evaluated; GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, but only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. The gap highlights the difficulty of moving beyond binary exploitability calls to explaining exploitability reasons, unlike prior benchmarks targeting zero-day settings.

arXiv cs.CRSep 7, 22:52 UTC · 8d agoResearch1

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent SpaceSep 15, 04:50 UTC · 1d agoAI safety & security

From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production

NVIDIA detailed DSX power-management results: Lambda gained 24% token throughput at fixed power, and an AI factory auto-shed 1MW via Emerald AI's grid program.

NVIDIA says Lambda's first validation of DSX MaxLPS on HGX B200 servers ran 19 nodes within a 16-node power budget, lifting cluster token throughput 24% (roughly 4M to 5M tokens/second) and improving performance per watt by 23%. NVIDIA projects DSX MaxLPS can enable up to 40% more GPU capacity for Vera Rubin NVL72 factories within the same megawatt budget. Emerald AI's Conductor platform, running at NVIDIA's Eos factory with Silicon Valley Power, responded to over 200 utility demand signals, automatically dropping power from 4MW to 3MW without interrupting priority workloads. The first dedicated DSX Flex commercial deployment is planned at a 96-megawatt Manassas, Virginia facility.

NVIDIA BlogSep 15, 16:55 UTC · 20h agoAI industry

[AINews] OpenAI to reach AGI bar by end-2026

OpenAI chief scientist Jakub Pachocki says unreleased Astra model meets the 'Automated AI Research Intern' goal; Altman expects internal AGI declaration by December 2026.

OpenAI chief scientist Jakub Pachocki says the unreleased Astra model fulfills the September 2026 'Automated AI Research Intern' target. Sam Altman told TIME he expects OpenAI to declare AGI achieved internally by December 2026. The roundup also covers Zhipu's GLM-5.3-Flash (320B total parameters, 18B active, 1M context), Google's Gemini Omni 1.1 Flash video model topping the Text-to-Video Arena, and the $399 open-source Microduck biped robot from Pollen Robotics and Hugging Face.

Latent SpaceAug 28, 07:12 UTC · 19d agoAI industry

Speculative Decoding in vLLM on AMD GPUs

vLLM benchmarks speculative decoding on AMD Instinct MI300X and MI355X GPUs across five drafting methods including EAGLE-3 and native MTP.

The vLLM project documents draft-and-verify speculative decoding support for AMD GPUs via ROCm, comparing native MTP, Gemma 4 MTP, EAGLE-3, DFlash, and DSpark drafting approaches. Output-token throughput effects varied with drafting method, proposal length, model family, draft checkpoint, workload, and acceptance behavior. The post also covers how to enable each method plus practical tuning and observability considerations.

Hacker News · AISep 7, 09:26 UTC · 9d agoAI tools & infra HN 141↑ · 52 comments1

d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment

d-Matrix will integrate its Raptor inference XPUs with NVIDIA NVLink Fusion, MGX racks and Spectrum-X networking for rack-scale AI factory deployment.

Inference chipmaker d-Matrix announced adoption of NVIDIA NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA's scale-up and scale-out networking, MGX rack architecture, and broader AI factory platform. NVIDIA claims 3x lower XPU-to-XPU latency than off-the-shelf Ethernet and 3 TB/s per-XPU all-to-all bandwidth via sixth-generation NVLink. d-Matrix plans to integrate Vera CPUs, ConnectX-9 SuperNICs, BlueField-4 DPUs and Spectrum-X Ethernet, with racks able to work alongside Vera Rubin NVL72 GPU systems. Other NVLink Fusion partners include AWS, Arm, Intel, Fujitsu, Marvell, MediaTek, Samsung and Cadence.

NVIDIA BlogSep 10, 13:00 UTC · 6d agoAI industry

When LLM Decompilers Recompile More and Preserve Less

Researchers show LLM decompiler outputs can recompile yet diverge behaviorally, proposing the Decompile-Diverge fuzzing oracle to catch hidden changes.

The paper demonstrates that LLM-based decompilers can produce code that recompiles and passes all shipped tests yet diverges on other legitimate inputs—4.9% overall and up to 13% for one system—and can make disclosed vulnerabilities vanish without a visible crash. Across 300 real GitHub functions and 287 CVE-grounded functions, a refinement LLM lifted Ghidra's build rate from 75% to 90% while Matched rate fell from 74% to 62%, with up to one tenth of vulnerabilities showing Crash Absence. Decompile-Diverge detects these gaps by synthesizing drivers, growing fuzzing corpora from the reference, and rerunning decompiled code on identical inputs.

arXiv cs.CRSep 4, 17:16 UTC · 11d agoResearch

Risky Bulletin: Academics find source code overlaps between Geedge and China's Great Firewall

Academics linked Chinese vendor Geedge Networks' Tiangou Secure Gateway source code to one of the Great Firewall's three traffic filtering capabilities.

US researchers presenting at USENIX Security reconstructed Geedge Networks' Tiangou Secure Gateway firmware from over 100,000 leaked files, including Git repositories with commit history, and matched its filtering behavior to sections of China's Great Firewall. They found only 1 of 3 characterized DNS injectors matched Geedge code, noted the system relies on memory-unsafe C components and copied third-party code, and said its bugs could aid future circumvention tools. Geedge also exports censorship tools to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The newsletter additionally rounds up multiple breaches.

Risky Business NewsAug 21, 02:55 UTC · 26d agoResearch2

Re: AI slops from Eve

Jeroen Roovers links a header-less LLM-generated advisory to a similar fake llama.cpp GGUF parser advisory from May 2026.

In the 'AI slops from Eve' oss-security thread, Jeroen Roovers asks whether a current LLM-generated message without a Date header comes from the same source as a May 15, 2026 posting titled 'Security Advisory: Multiple Vulnerabilities in llama.cpp GGUF Format Parsers'. Both fake advisories share the missing Date header trait. The exchange highlights growing LLM-generated noise on security mailing lists.

oss-securityupdated Sep 13, 15:08 UTC · 2d agofirst Sep 13, 14:51 UTC · 2d agoIndustry 12 sources

Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed

Perplexity details its GPU embedding serving stack (Ivy, Tulip, ROSE), which reuses LLM prefill/decode kernels, CUDA graphs, and LazyTensors to cut launch overhead.

Perplexity engineers published a deep dive on the serving infrastructure behind pplx-embed, used across Perplexity Search and its API platform. The stack comprises Ivy (Rust HTTP gateway), Tulip (gRPC scheduling and batching), and ROSE (Runtime-Optimized Serving Engine), which reuses LLM prefill and decode kernels rather than running a separate embedding engine. Optimizations include whole-model CUDA graphs with lazy capture and a LazyTensor abstraction that overlaps CPU batch preparation with in-flight GPU work. Benchmarks are reported against vLLM v0.22.0 in BF16, with FlashAttention 4 generally fastest but FlashInfer 3 winning on Qwen-based models at very long sequence lengths.

MarkTechPostSep 6, 03:20 UTC · 10d agoAI tools & infra1

Miles v0.1: Production-Level Post-Training

Radix Ark open-sources Miles v0.1, a full-stack RL post-training framework demonstrated with asynchronous agentic RL on GLM-5.2 744B-A40B across 64 GB300 GPUs.

Miles v0.1 is a full-stack, open-source system for frontier-scale reinforcement-learning post-training, built on slime with rollout engines on SGLang and trainers supporting NVIDIA Megatron-LM and PyTorch FSDP backends plus three weight-synchronization transports. It supports full-parameter RL, LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends to diffusion models. The end-to-end case study ran fully asynchronous agentic RL on GLM-5.2 744B-A40B for terminal-use coding tasks on 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps. The code is open-sourced on GitHub.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI tools & infra

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 papersSep 3, 20:00 UTC · 12d agoAI research

Leading Publishers Bring Blockbuster PC Games and Technology to NVIDIA RTX Spark

NVIDIA announces EA, Embark, and Ubisoft titles plus anti-cheat and visual upgrades for RTX Spark ahead of launch at Gamescom.

NVIDIA is promoting RTX Spark at Gamescom in Cologne, Germany, with Electronic Arts, Embark, and Ubisoft bringing blockbuster PC titles to the platform ahead of its launch. The announcement covers new game support, anti-cheat technologies, and increased visual quality. The piece is a product-marketing update rather than security research or AI safety news.

NVIDIA BlogAug 25, 15:30 UTC · 21d agoAI industry

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AISep 8, 16:59 UTC · 7d agoAI tools & infra HN 41↑ · 9 comments

DeepZero: Open-source hunting for vulnerable Windows drivers

DeepZero, a new open-source engine, automates discovery of exploitable Windows kernel drivers for BYOVD attacks using Ghidra, Semgrep, and an LLM.

DeepZero is a free, open-source Python pipeline orchestrator that automates hunting for exploitable Windows kernel drivers relevant to BYOVD (bring your own vulnerable driver) attacks. Its seven-stage YAML pipeline parses PE headers, filters for kernel-mode drivers with IOCTL surfaces, excludes drivers listed on loldrivers.io, then runs headless Ghidra decompilation, Semgrep scanning, and an LLM-based exploitability assessment. The maintainer reports multiple verified vulnerabilities in the Snappy Driver Installer corpus, some still in the disclosure process, and notes findings involving plug-and-play-created device objects may need physical hardware to confirm.

Help Net Security05:30 UTC · 7h agoTools

You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs

Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.

Conditioning an instruction-tuned LLM on continuous valence-arousal targets yields gain of only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, far below faithful control of 1.0. The authors attribute undershoot to neutral-heavy preference corpora like EmoBank and candidate pools lacking extreme affect, leaving DPO without extreme exemplars. Uniform target coverage with a hotter candidate pool raises valence gain to 0.40 on Llama-3.1-8B and 0.44 on Qwen3-8B, with modest in-distribution cost; arousal gains remain unstable across seeds.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:47 UTC · 8d agoAI research

LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

LimiX-2 scales Contextual Mechanism Networks pretrained via context-conditional masked modeling, beating tabular foundation models on TabArena, TALENT, and BCCO benchmarks.

LimiX-2 is a new tabular model in the LimiX family, developed through model and data scaling guided by previously established scaling laws under the Contextual Mechanism Networks (CMNs) paradigm. It is pretrained with Context-Conditional Masked Modeling (CCMM) on synthetic datasets generated by structural causal models spanning diverse graph structures, functional mechanisms, and observation processes. It outperforms dataset-specific models and tabular foundation models on TabArena, TALENT, and BCCO, and its feature attention encodes direct causal relationships, enabling accurate causal skeleton recovery.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:30 UTC · 19h agoAI research1

Arm Mali G2-Ultra NX GPU: desktop-class mobile gameplay with AI-native graphics

Arm unveiled Mali G2-Ultra NX, its first AI-native mobile GPU with in-shader neural acceleration, third-gen ray tracing, and up to 24% higher benchmark performance.

Arm announced the Mali G2-Ultra NX, the first AI-native Mali GPU, integrating neural accelerators directly into shader cores alongside a new execution engine and third-generation hardware ray tracing. It introduces Neural Super Sampling (NSS), Neural Frame Rate Upscaling (NFRU), and Neural Super Sampling and Denoising (NSSD); the Neural Dawn demo with Sumo Digital showed up to 4x performance efficiency and 70% lower external memory traffic versus native rendering. Arm claims up to 24% higher benchmark performance, 13% lower DRAM traffic on ray tracing benchmarks, and up to 120 FPS with NFRU. Over 14 billion Mali GPUs have shipped to date.

Hacker News · AISep 8, 04:06 UTC · 8d agoAI industry HN 71↑ · 53 comments1

DataFlex-RL: An Evaluation Platform for RLVR Data Policies

DataFlex-RL benchmark of 13 RLVR data policies on Qwen2.5-7B finds none reproducibly beats uniform sampling under matched GRPO training.

DataFlex-RL is an evaluation platform comparing rollout-selection, reweighting, and mixture data policies for RLVR under a common GRPO recipe. Across 13 configurations and 12 matched seeds with Qwen2.5-7B-Base on 12 math, logic, and science benchmarks, uniform GRPO improved domain-balanced accuracy by 7.76 points, but no alternative policy achieved a statistically significant improvement. A corrected 12-seed Llama-3.1-8B-Base extension found no consistent winner, and math-heavy evaluation summaries were negatively correlated (-0.33) with domain-balanced summaries.

Hugging Face daily papersSep 4, 20:00 UTC · 11d agoAI research

N4D Mesh Controller: New infrastructure, a UPX-packed agent labeled "go-titan," and how to hunt for it

Datadog researchers detonated a new N4D Mesh Controller sample in microVMs, revealing rotated infrastructure, a UPX-packed go-titan agent, and persistence behavior.

Datadog Security Research executed a newer N4D Mesh Controller sample in isolated microVMs, uncovering rotated command infrastructure and a UPX-packed agent labeled go-titan. Runtime evidence showed MCP tool abuse, multi-service scanning, and persistence mechanisms. The report provides hunting guidance for defenders tracking this infrastructure.

Datadog Security LabsAug 20, 00:00 UTC · 27d agoMalware

nex-agi/Nex-N2.5-mini — new model trending #30 on Hugging Face

Nex-AGI releases Nex-N2.5 agentic model family (mini, Pro, Max) with a 1.6-trillion-parameter MoE Max, open weights, and hosted access via OpenRouter.

Nex-AGI launched Nex-N2.5, a family of agentic models in mini, Pro, and Max sizes, with the Max version built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation and the company's first complete post-training effort at trillion-parameter scale. The models target long-horizon computer use, web browsing, and visually grounded agentic tasks, with expanded agent training environments. Reported benchmarks include Max scoring 86.1 on Terminal-Bench 2.1 and 65.7 on SWE-Bench Pro, trailing Claude Opus 5. Weights are being released openly on Hugging Face and ModelScope, with hosted access through OpenRouter.

Hugging Face trending modelsSep 8, 05:08 UTC · 8d agoModel release1

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

PlannerForge unifies scenario-based testing of autonomous driving motion planners in one LLM-agent framework, outperforming prior baselines.

PlannerForge is an LLM-agent framework that covers the full scenario-based testing pipeline for autonomous driving systems, spanning scenario generation, selection, modification, routing, planner testing, plus new enhancement and benchmarking stages. In evaluations with 10 off-the-shelf LLMs, best-per-task scores range from 0.88 to 1.00, and open-source 20-35B backends such as Qwen3.6:35B match commercial APIs on most tasks. End-to-end chaining retains 83% (commercial) and 78% (open) of seed queries, beats Scenario Factory 2.0 on executable generation, and cost-tuning lifts planner success from 50.4% to 70.2% while cutting collisions from 19.0% to 8.4%.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI research

VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification

VDiff-Bench, a 1,756-question benchmark, shows multimodal LLMs struggle with fine-grained image-difference identification, scoring as low as 8.7% on low-level changes.

VDiff-Bench is a multiple-choice benchmark of 1,756 four-way questions over image pairs covering 10 change categories including position, motion, color, texture, OCR/text and illumination, with curated hard negatives. Evaluation of 11 state-of-the-art open- and closed-source MLLMs shows fine-grained visual comparison remains brittle: 7-8B-scale open-source models score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture. Notably, Grok 4.3 shows a sharp performance drop on noise and texture differences, falling behind large open-source models like Kimi K2.5 and K3.

Hugging Face daily papersSep 4, 20:00 UTC · 11d agoAI research