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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 · AI · 1d agoResearchHN 40↑ · 5 comments

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 · 13h agoAI industry 2 sourcesHN 45↑ · 28 comments

Safe Meta-Reinforcement Learning via Information Space Reachability

Safe meta-RL framework reasons about safety in information space, learning a safety value function used for safety filtering and constrained policy optimization.

The paper proposes safe meta-RL that reasons about safety in information space, capturing both physical state and the agent's belief over the underlying task. A safety value function measures the probability of avoiding unsafe regions indefinitely and satisfies a self-consistency condition and Bellman equation, making it learnable via meta-RL. The resulting algorithm uses the learned function for safety filtering and constrained policy optimization, with effectiveness demonstrated on meta-RL benchmarks.

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

Jev Ultrafast: A browser agent with a dynamic, indexed action space

browser-use released Jev Ultrafast, an open-source browser agent that uses a dynamic, indexed action space for web automation.

Hacker News is linking to the browser-use Jev Ultrafast GitHub repository, described as a browser agent with a dynamic, indexed action space. The submission reached 40 points and 4 comments. The source text contains only submission metadata, so architecture and benchmark details are not available.

Hacker News · AI · 6h agoAI tools & infraHN 40↑ · 4 comments

Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models

RS-MFBO couples global sensitivity analysis with fidelity-augmented Gaussian processes to slash costly high-fidelity simulation runs in industrial flowsheet optimization.

The paper presents RS-MFBO, a reduced-space multi-fidelity Bayesian optimization framework for high-dimensional, expensive black-box functions. It integrates Global Sensitivity Analysis for dimensionality reduction with a fidelity-augmented Gaussian process and a cost-aware acquisition strategy featuring cooldown and promotion mechanisms. Validation on a plasmid DNA bioprocess (SuperPro Designer) and a green fuel synthesis plant (Aspen HYSYS) shows substantial reductions in high-fidelity evaluations while remaining competitive with single-fidelity baselines.

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

Disentangling Representation Evolution in Transformers through Directional Decomposition

Decomposes transformer representation updates into parallel and perpendicular components, linking geometry to editing robustness and better pretraining.

The paper decomposes learned transformer updates into parallel and perpendicular components relative to the hidden state, finding substantial parallel components beyond the residual identity path across pretrained models. Targeted edits reveal exclude-self value-space parallel manipulation is more robust than residual-space or perpendicular alternatives, and perpendicular error separates compression methods more clearly. Applying full-aggregate parallel suppression during from-scratch pretraining lowers validation loss and improves downstream averages, with the value-space variant strongest. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research 2 sources1

Building the materials foundation for AI

Syensqo's CTO says AI pushes semiconductors and data centers to physical limits, driving advanced materials demand and AI-accelerated materials discovery.

MIT Technology Review's Business Lab podcast, produced in partnership with Syensqo, features CTO Mike Finelli discussing how AI workloads push semiconductors and data centers to physical limits in performance, thermal management, and reliability. Syensqo develops high-voltage data center materials, semiconductor sealing materials, and immersion cooling fluids, while using AI agents to digitally synthesize millions of molecular combinations and predict performance before lab testing. Finelli describes a reinforcing cycle where AI improves materials that in turn enable better AI infrastructure.

MIT Technology Review · AI · 20h agoAI industry1

[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)

Latent Space AI news roundup: Steve Yegge shuts down Gas Town, Databricks reports 60% higher coding spend on GPT-6 Astra, OpenAI launches misalignment disclosure framework.

Latent Space's AI News digest for September 15-16, 2026 leads with Steve Yegge shutting down his Gas Town orchestrator despite spending thousands monthly on coding-agent subscriptions. Databricks rolled out GPT-6 Astra to roughly 3,500 engineers, reporting superior long-horizon performance over Opus 5 and Sol 5.6 but a ~60% increase in coding spend. OpenAI published a formal framework for disclosing model misalignment incidents with six case reports, while Microsoft and Google Research released safety papers on 'capability laundering' and the Fuse motive-inference benchmark. Xiaomi shared live RL training telemetry for MiMo-V2.6, estimated at $493k/day for the 1T-class Pro run.

Latent Space · 2h agoAI industry 12 sources

A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings

Proposes the Sparse Landmark Embedding kernel, guaranteeing PSD kernels for arbitrary distances like geodesic and Wasserstein without CND requirements.

The paper introduces the Sparse Landmark Embedding (SLE) kernel, which embeds inputs via compactly supported bump functions at all |D| training points so any standard PSD kernel applies, removing the Hilbertian (CND) distance requirement that fails on manifolds and distribution spaces. Compact support controls sparsity, keeping kernel matrices well-conditioned despite high dimensionality. The authors prove PSD, sparsity, stability, and universal approximation guarantees, and show SLE matches or exceeds domain-specific baselines using geodesic and Wasserstein distances on accuracy and uncertainty quantification.

arXiv cs.AI / cs.LG / cs.CL · 16h agoAI research

SK Hynix reportedly in talks with Intel to build memory chips in US

SK Hynix is reportedly negotiating with Intel to manufacture memory chips in the US, possibly leasing space at Intel's Ohio fab.

Reuters reports SK Hynix and Intel have discussed SK Hynix producing RAM in the US for the first time, including leasing space at Intel's planned Ohio factory or forming a joint venture that could include cloud-service providers; SK Hynix says nothing is finalized. The company is already building a $3.8 billion AI chip packaging and research facility in West Lafayette, Indiana, with mass production expected to begin in 2029, amid surging HBM demand from AI data centers. The potential deal could face a South Korean government review over transfers of strategically important chip technology, and follows Intel's 2020 sale of its NAND flash business to SK Hynix for $9 billion.

TechCrunch · AI · 20h agoAI industry

Why you should work on AI for AI Research — Richard Socher of Recursive

Richard Socher's new lab Recursive, backed by $4.65B seed, targets AI systems that automate AI research itself.

Latent Space interviews Richard Socher, founder of You.com and AIX Ventures, about his new venture Recursive, which raised a $4.65 billion seed round to build the 'Eureka Machine' — a superintelligence for automating invention and AI research. Early claimed results include an AI research system outperforming humans and their agents on optimization tasks within two days, and NVIDIA GPU kernel improvements discovered without CUDA experts. Discussion spans reward hacking, constitutional AI critique, AI regulation, open-source models as geopolitical soft power, and hard-takeoff constraints.

Latent Space · 2d agoAI industry1

Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection

Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.

Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.

Palo Alto Unit 42 · 2d agoResearch

Stanford Researchers Release Paper2Agent: Turning Research Papers Into AI Agents That Reproduce Results and Run on New Data

Stanford researchers released Paper2Agent, a Nature-published pipeline that turns research papers into MCP servers agents can execute.

A Stanford team led by Jiacheng Miao and James Zou published Paper2Agent in Nature on 16 September 2026. Built on Claude Code's agent SDK, it converts a paper and its codebase into a Model Context Protocol server with validated tools, resources, and prompts. In benchmarks, the AlphaGenome agent built 22 tools in about 45 minutes for US$14, scored 100% on 15 novel queries versus 78.7% for Claude Code with repository access, and cut median runtime 1.9x. In scale tests, 74 of 100 bioRxiv papers were converted and 593 of 599 proposed tools passed validation.

MarkTechPost · 11h agoAI research1

Fast Learning Rates for Physics-Informed Kernel Methods

Theoretical analysis proves finite-sample learning rates for physics-informed kernel estimators, showing differential observations can improve rates from n^-1/4 to n^-1/2.

The paper analyzes a physics-informed kernel estimator combining n value observations and m differential observations for a linear differential operator D, asking how much differential information improves prediction. The authors prove finite-sample bounds, supported by simulations, revealing a two-regime structure: when m is limited the rate depends jointly on n and m, and when m exceeds a problem-dependent threshold the rate saturates to the oracle rate. Examples in Sobolev spaces, including partial Laplacian constraints on the torus and gradient observations on bounded domains, illustrate improvements from the nonparametric n^-1/4 rate to the parametric n^-1/2 rate, plus physically consistent rates in a stronger norm.

arXiv cs.AI / cs.LG / cs.CL · 17h agoAI research

Locus: A Framework for Exploring and Optimizing Point Addition Hardware for Zero-Knowledge Proofs

Locus framework automates ASIC and FPGA point-addition designs for elliptic curves, achieving 2.71x speedups and 3.11x area reductions for ZKPs.

Locus is a framework that automatically generates ASIC and FPGA implementations of elliptic curve point addition (PADD) for supported equation forms, enabling exploration of over 1,000 design points. On a 12nm technology node, its designs achieve a 2.71x geomean speedup and 3.11x geomean area reduction versus prior ASICs, plus 34.67x geomean speedup over CPU. Integrated into a prior ZKP accelerator at iso-area, it yields a 3.15x geomean speedup on end-to-end proof generation. The framework is open source on GitHub.

arXiv cs.CR · 17h agoResearch

harshatheg/Qwen-2.5-1B-RLCD — new model trending #30 on Hugging Face

A community MLX inference engine evaluates constrained JSON schema fields in parallel on Apple Silicon, reporting 5.6-7.0x latency speedups with guaranteed schema validity.

The repository harshatheg/Qwen-2.5-1B-RLCD appeared at #30 on Hugging Face trending, but its content describes Parallel Constrained Decoding, an MLX-based inference engine for structured extraction and classification on Apple Silicon Macs. Benchmarked with mlx-community/Qwen2.5-1.5B-Instruct-4bit on an M4 Max, it reports 5.6x-7.0x latency reductions (e.g., 1,900 ms to 270 ms for a 28-field support triage task) with 100% syntactic validity and calibrated field-level probabilities. The engine prefills a single KV-cache, broadcasts it across all schema fields, and slices logits to valid candidate tokens for enum fields with up to 255 choices.

Hugging Face trending models · 1d agoAI tools & infra

FLAT: Resampling Image and Text into 1D Flexible-Length Aligned Transmodal Tokens for Retrieval and Generation

FLAT jointly trains a multimodal encoder with text-to-image and image-to-text decoders, producing flexible-length tokens that hit 83.1 GenEval on T2I after fine-tuning.

FLAT (Flexible-Length Aligned Transmodal representations) is a pre-training framework that jointly optimizes a shared multimodal encoder with T2I and I2T decoders, combining contrastive alignment with bidirectional cross-modal generative objectives. It maps visual and textual inputs into a unified continuous 1D sequence space and uses nested dropout over prefix-K tokens for dynamic output lengths. A single pre-training stage supports cross-modal retrieval and generation (71.1 GenEval), with task-specific fine-tuning reaching 83.1 GenEval on T2I, 40.5 BLEU-4 and 138.6 CIDEr on MS-COCO captioning, and strong Recall@5 on MS-COCO and Flickr30K.

Hugging Face daily papers · 2d agoAI research1

Flock Once Touted Its Cameras as ‘Made in the USA.’ Now It’s Not So Clearnew

WIRED investigation finds Flock Safety stopped marketing cameras as Made in USA, with imports from Taiwan's Primax Electronics facilities in Thailand.

WIRED reviewed US import records showing Flock has increasingly imported commodity cameras and radar components from Primax Electronics in Thailand. Earlier marketing claimed cameras were manufactured in the United States, with assembly in Atlanta and later contract facilities run by East West Manufacturing and Plexus. Flock says its products are National Defense Authorization Act compliant amid activist backlash, local government pullouts, and possible congressional legislation on license plate readers.

WIRED · Security · 46m agoIndustry

The AI security question leaders should be asking instead

Gremlin security officer Frederic Bull argues AI has eroded the attacker-defender skill asymmetry while least-privilege controls remain essential for securing AI agents.

In a Help Net Security interview, Gremlin Security Officer Frederic Bull says AI has narrowed the expertise gap between attackers and defenders, enabling faster exploit discovery even by less-skilled actors. His team processed roughly nine times more vulnerabilities in the past year with unchanged staffing using LLM-based tooling, cutting time-to-remediate by about 5%. He argues least privilege, session-based RBAC via OIDC/OBO, and human-in-the-loop oversight remain the bedrock defenses for AI agents, and that hiring should favor engineers able to catch confidently wrong AI output.

Help Net Security · 4h agoIndustry

Iceland-based Treble raises $18 million for its voice simulation platform

Iceland-based startup Treble raised an $18 million Series A extension to expand its acoustic simulation and synthetic data platform for voice AI companies.

Treble, founded in 2020 by acoustic engineers Finnur Pind and Jesper Pedersen, raised $18 million in a Series A extension led by Paladin Capital Group, bringing total funding above $40 million. The company builds physics-based acoustic simulation for synthetic speech data generation, voice AI model evaluation, and virtual prototyping of headphones, speakers, and smart glasses. Customers include Amazon and Logitech, and it partnered with Hugging Face earlier this year on a benchmark for speech recognition models under realistic conditions.

TechCrunch · AI · 4h agoAI industry

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 · 8h agoModel releaseHN 41↑ · 3 comments

Objective vs. Search: Decomposing What Makes a Good Tokeniser

New tokeniser study shows search procedure, not optimisation objective, drives bits-per-byte performance across model sizes, vocabulary sizes, and multilingual settings.

The paper disentangles BPE and UnigramLM along two axes: optimisation objective (compression vs log-likelihood) and search procedure (bottom-up merging vs top-down pruning). Two new algorithms, BottomUpLL and TopDownComp, complete the 2x2 design space, and trained language models are evaluated on bits-per-byte and BLiMP across model sizes, vocabulary sizes, and English-only vs multilingual domains. Bottom-up tokenisers consistently achieve lower bits-per-byte in most settings, while BLiMP shows no consistent relationship with design choice.

arXiv cs.AI / cs.LG / cs.CL · 15h agoAI research

Big Tech’s AI safety rift signals disruption and disparity for enterprises

Diverging AI safety stances among major labs will make frontier model access less predictable, pushing enterprises toward routing layers and independent validation.

A public rift among leading AI labs over safety approaches - Meta's Zuckerberg backing neutral evaluators, Dario Amodei urging a slower pace, and Sam Altman calling for collaboration on standards - is creating operational challenges for enterprise IT. Analysts from Gartner and others say divergent vendor release schedules, access tiers, and regional restrictions will make frontier model access less predictable, effectively treating frontier AI as a managed supply with pricing premiums. Recommendations include routing layers between applications and providers, contractual deprecation terms, and independent validation of models before production use.

CSO Online · 17h agoAI industry

Hamming Ideals and Grobner Bases for ISD-like Syndrome Decoding

Researchers combine Grobner bases with Information Set Decoding for syndrome decoding, testing feasibility against Classic McEliece NIST Category 1 parameters.

The paper proposes GBDecode, an ISD-like decoding algorithm that fixes only a subset of an information set and solves the resulting multivariate nonlinear systems via MultiSolve, which replaces one Grobner basis computation with many computations on simpler systems. Hamming weight constraints are reformulated using elementary symmetric functions and Lucas' identity factorizations to bound equation degree. Experiments on random binary linear codes use parameters matching the NIST Security Category 1 set of the Classic McEliece cryptosystem, assessing practical feasibility rather than breaking the scheme.

arXiv cs.CR · 17h agoResearch

Epsilon-Nash Equilibria in History-Dependent SA-MDPs

Researchers give the first algorithm for computing epsilon-approximate history-dependent equilibria in state-adversarial Markov decision processes with observation-perturbing adversaries.

The paper studies state-adversarial Markov decision processes (SA-MDPs) where an adversary knowing the true state perturbs observations within state-dependent proximity sets each step. The authors prove universal history-dependent equilibrium policies do not exist and reduce SA-MDPs to a strategically equivalent constrained zero-sum one-sided partially observable stochastic game, enabling the first algorithmic route to epsilon-approximations of initial-state dependent equilibria. The algorithm is validated on small analytically verifiable games and scales to larger benchmarks, including Atari Freeway rollouts with a 12-period-ahead horizon.

arXiv cs.CR · 18h agoAI safety & security

Robots are waiting for a ChatGPT moment: Nvidia’s Les Karpas explains why at TechCrunch Disrupt 2026

NVIDIA Inception's Les Karpas will discuss at TechCrunch Disrupt 2026 why robotics lacks a ChatGPT moment, citing missing internet-scale physical AI datasets.

NVIDIA Inception's Global Head of Physical AI, Les Karpas, will speak on the Real World AI Stage at TechCrunch Disrupt 2026, held October 13-15 at San Francisco's Moscone West. His core argument is that general-purpose robots lack an internet-wide dataset for physical AI, unlike language models from OpenAI and Anthropic. Founders from Shield AI, Colossal Biosciences, FieldAI, and Foxglove will join related sessions.

TechCrunch · AI · 18h agoAI industry

EU chief wants joint response to cyberattacks, sabotage

EU Commission President von der Leyen proposed an Emergency Security Protocol letting any member state convene joint responses to cyberattacks and sabotage.

In her State of the Union address, Ursula von der Leyen proposed an Emergency Security Protocol, modeled on NATO Article 4, allowing a single EU member state to summon all 27 governments to coordinate responses to cyberattacks, sabotage, arson, and drone incursions. The proposal is part of a broader European security strategy that includes a European Security Council with partners such as the UK, Canada, Norway, and Ukraine, plus a European Instrument for Strategic Enablers supporting cyber and defense capabilities. She cited rising incidents in Denmark, Lithuania, Poland, and an attempted drone attack in Leipzig, but did not specify whether unanimous decision-making rules would change.

The Record · 20h agoPolicy & legal

Reimagining advertising with AI

OpenAI launches ChatGPT advertising features including Sponsored Agents, AI ad creation in Ads Manager, and integrations with HubSpot and Shopify.

OpenAI is testing Sponsored Agents in the United States, letting users converse with clearly labeled business-sponsored agents after clicking ads in ChatGPT. Advertisers can create, update, and analyze campaigns via natural-language prompts in ChatGPT with an Ads Manager plugin, plus AI-suggested copy and imagery in Ads Manager. HubSpot becomes the first CRM partner and Shopify the first ecommerce partner, with the Shopify app expanding internationally on September 23.

OpenAI News · 20h agoAI industry

The Illusion of Local Privacy: Confidentiality Boundary Failures in Consumer LLM Serving Systems

Researchers show local LLM serving systems leak prompts via memory residue, plaintext persistence, a llama.cpp tenant-isolation flaw, and timing oracles.

A study of consumer local-LLM serving systems identifies four boundaries where prompt confidentiality fails: model loading, runtime memory, wrapper persistence, and the serving interface. Using the LLAnalyzer framework across four open-weight model families and two deployment platforms, the authors recover plaintext prompts from allocator-managed memory after inference and show wrappers extend prompt lifetime. They also uncover a previously undocumented llama.cpp authorization flaw letting one authenticated client restore another tenant's saved conversation state, succeeding in 200/200 trials, plus a remote timing oracle via shared prompt-prefix caching that works over WAN.

arXiv cs.CR · 21h agoAI safety & security

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 · 22h agoAI policy

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent Space · 22h agoModel release1

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 Security · 1d agoTools

Nums AI Releases Causilo: A Tabular Foundation Model That Tops TabArena Among Single Models

Nums AI released Causilo, an Apache-2.0 tabular foundation model achieving the highest single-model Elo (1794) on TabArena for classification and regression.

Nums AI released Causilo 1.0.1, a pretrained in-context learning tabular foundation model for classification (up to 10 classes) and regression, with Apache-2.0 code and research-only weights on Hugging Face. It achieved the highest single-model TabArena Elo of 1792.9 overall, beating TabFM (1764.4) and EXAONE Tabular (1758.8), and a maintainer re-run placed it 3rd of 88 including system entries. It also ranked first by CRPS, R² and RMSE on ScoringBench across 101 datasets, and was fastest on fit and predict versus TabICLv2 and TabPFN-3 on an H100 GPU at 8.15 GiB memory. The model was pretrained only on synthetic data, uses cross-attention to keep cost linear in feature count, and version 1.0.1 adds quantile outputs via 999 native quantiles.

MarkTechPost · 1d agoModel release

Saving Jet Fuel

Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.

A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.

Hacker News · AI · 1d agoAI tools & infraHN 45↑ · 19 comments

Can Skills Learned in Games Transfer to Real-World Work?

Good Start Labs trains models in strategy games like 1830 and Diplomacy, showing terminal-agent training transfers to financial research benchmarks.

Good Start Labs, spun out of Every with $3.6M from General Catalyst and Inovia, trains AI models in verifiable strategy games. A 30B model trained as a multi-turn terminal agent in 1830: The Game of Railroads and Robber Barons improved Finance-Agent benchmark performance, while single-turn QA training did not transfer. The founders also co-authored COS-PLAY, a paper on co-evolving LLM decision and skill-bank agents for long-horizon tasks.

Latent Space · 1d agoAI research

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 · 1d agoAI research

Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening

Researchers identify Value Flattening in PPO critics for LLM RL and propose SP^3O sparse value supervision, improving Qwen3-Base training.

The paper uncovers Value Flattening, a failure mode where PPO critic predictions stay flat while true state values estimated from Monte Carlo continuations change sharply, worsening as state spaces grow. The authors attribute it to an implicit variance penalty in the critic loss and redundant updates from temporally correlated states. They propose SP^3O, which supervises value loss on only a few well-separated states per response, consistently improving policies trained on Qwen3-Base across model sizes and evaluation suites.

Hugging Face daily papers · 1d agoAI research

AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories

At AI Infra Summit, NVIDIA showcased Vera Rubin and DSX gains up to 1.4x tokens per megawatt, plus Annapurna, d-Matrix, and Pinterest partnerships.

Ian Buck's AI Infra Summit keynote before 8,000+ attendees emphasized validated agentic tokens per megawatt as the emerging AI infrastructure metric. Announcements include Amazon's Annapurna Labs collaborating on NVHBM custom high-bandwidth memory, d-Matrix integrating NVLink Fusion with Raptor XPUs, and Pinterest using Blackwell plus Dynamo inference software for conversational visual discovery. Lambda reported 23% better performance per watt with DSX MaxLPS on Blackwell servers, running 19 nodes on a 16-node power budget. NVIDIA says DSX MaxLPS combined with Groq 3 LPX on Vera Rubin NVL72 targets up to 35X token throughput per megawatt versus GB200 NVL72 for 2-trillion-plus-parameter models.

NVIDIA Blog · 1d agoAI industry

RobResilience: Implementing and Evaluating a Resilience Framework for Cyber-Physical Embodied Systems

RobResilience implements a runtime resilience framework for robots in Webots/ROS2, evaluating tolerable disruption, degradation, and mitigation feasibility across eight attack scenarios.

The paper implements a formal resilience framework for embodied cyber-physical systems using a PR2 robot and ROS2 in a Webots simulation. At runtime it evaluates three predicates — tolerable disruption (δ), tolerable degradation (γ), and mitigation feasibility (μ) — over a compromised device set derived from IDS confidence scores, triggering mitigation strategies when resilience is lost. Eight attack scenarios systematically covering the full predicate state space confirm runtime behavior matches theoretical definitions. The work addresses 'graceful failure paralysis,' where autonomous systems cannot distinguish safe degraded states from catastrophic hazards during attacks.

arXiv cs.CR · 1d agoResearch