Search: “quantum machine learning”

Dogged Russia-based botnet dismantled after 23-year run

Law enforcement, CrowdStrike and Shadowserver dismantled the 23-year-old Sality P2P botnet that infected more than 11 million devices.

Sality, a Russia-based peer-to-peer botnet active for 23 years and infecting over 11 million devices, was dismantled by law enforcement working with CrowdStrike and the Shadowserver Foundation. CrowdStrike poisoned the botnet's peer list so infected machines permanently disappeared from the operator's view, while domains were seized in a coordinated effort involving the FBI, Justice Department, Europol and authorities from Bulgaria, Hungary and Romania. The financially motivated operation enabled cryptocurrency theft, DDoS attacks and other cyberattacks, and Europol said the effort dates back to 2017; the operators were not named.

CyberScoop·Sep 2, 17:03 UTC · 14d agoMalware

Certifying Adversarial Robustness of Quantum Classifiers under Known-Readout Query Access

Framework certifies adversarial robustness of quantum classifiers using only measurement statistics and finite-shot outcomes, demonstrated on IBM Quantum hardware.

The paper introduces a measurement-only certification framework for adversarial robustness of quantum classifiers under known-readout query access, requiring no tomography, parameters, or gradients. It returns a lower bound ruling out untargeted errors within a radius and an attack-independent upper bound witnessing an adversarial state, both estimable with finite-sample guarantees. Evaluations show the lower bound tracks exact optima on tractable instances while the upper bound stays informative when standard attacks fail. The method was validated on IBM Quantum hardware using 40 executions of two 8-qubit quantum neural networks.

arXiv cs.CR·Sep 10, 14:45 UTC · 6d agoResearch

Unit 42 warns AI has shifted balance of power from defenders to attackers

Unit 42 says agentic AI has shifted attacker advantage, investigating an incident where one attacker exploited 50 enterprise applications in under 10 hours.

Palo Alto Networks Unit 42 leaders said early waves of agentic AI-enabled attacks are breaking in the wild and that frontier model capabilities have shifted the balance of power from defenders to attackers. The team is actively investigating an attack on a customer where an attacker used an agentic framework to exploit 50 applications and other weaknesses across the enterprise in less than 10 hours, work they estimate would have taken at least 10 days pre-AI. Unit 42 says AI already touches the entire attack chain, including malware development, social engineering, and ransomware negotiations. The warning follows April's Project Glasswing initiative formed with Anthropic around its Mythos model.

CyberScoop·Aug 27, 18:31 UTC · 19d agoAI safety & security in the wild1

Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers

Quantum IQP circuit features lift logistic-regression credit-default F1 from 0.462 to 0.517, beating Kernel PCA at an equal feature budget.

Using the UCI Default of Credit Card Clients dataset and five-fold cross-validation, an 8-qubit IQP circuit adds 16 features that raise Logistic Regression F1 from 0.462 to 0.517 (+0.055, p < 0.0001). Kernel PCA, the best classical non-linear alternative, reaches only 0.493 at the same feature count, with the gap surviving Benjamini-Hochberg correction across 12 tests (p = 0.00007). Only the linear classifier benefits, pointing to a linear-expressivity mechanism. Feature selection matters: Random Forest importance-guided selection reaches F1 = 0.523 while maximally uncorrelated features drop to 0.496.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:41 UTC · 7d agoAI research

NIST wants to overhaul its vulnerability database for the AI age

NIST issued a Federal Register RFI seeking public input on overhauling the National Vulnerability Database for AI-scale, machine-consumable security data.

NIST published a request for information arguing the National Vulnerability Database must adapt as LLMs increasingly find and exploit vulnerabilities at machine scale. The RFI seeks input on integrating automation into vulnerability reporting, faster dissemination to defenders, and transparency and auditability in AI-driven decisions. It follows the White House-backed Gold Eagle clearinghouse at Treasury and the VINCE program with Carnegie Mellon's Software Engineering Institute for AI-discovered vulnerability reports.

CyberScoop·Aug 11, 15:36 UTC · Aug 11, 2026Policy & legal

Observational Indistinguishability and Integrity Blind Regions in Hybrid Quantum-Classical Workflows

Framework formalizes integrity blind regions in hybrid quantum-classical workflows, validated across 3,600 label interventions with conformal detection rules.

The paper presents a claim-relative evidence and reference framework for integrity of hybrid quantum-classical workflows, distinguishing structural blind regions caused by observational indistinguishability from finite-batch statistical misses. Experiments over 3,600 label interventions show exact label-path invariance for feature and prediction views. The geometry-aligned construction detects 343 of 2,700 conclusion-changing interventions using the conformal rule and 1,183 of 2,700 with the uncorrected union, with executed conformal clean false-action rates of 0.048-0.059.

arXiv cs.CR·Sep 15, 13:16 UTC · 1d agoResearch

Trump turns to private sector in offensive hacking operations memo

Trump signed a national security memorandum authorizing vetted private companies to conduct offensive cyber operations against transnational criminal organizations under federal oversight.

The memorandum creates a federal coordination center program authorizing 'Participating Companies' to conduct Cyber Surveillance Operations and Cyber Effects Operations against foreign cyber-enabled transnational criminal organizations. Participating firms must contract with the Justice Department or Department of Homeland Security, undergo vetting, and comply with existing laws including the Computer Fraud and Abuse Act. The White House cited sustained fraud and cyber-enabled campaigns from TCOs as justification, building on a March fraud-focused executive order. Experts including Veracode co-founder Chris Wysopal called it a major shift in U.S. cyber policy, though it stops short of broader 'hack back' proposals.

CyberScoop·Aug 13, 11:48 UTC · Aug 13, 2026Policy & legal1

Evaluating Verified Autonomy in Quantum Engineering

Quantum-Harbor lab and QIQCBench (49 tasks) expose wide performance gaps across 17 frontier agentic systems in verified quantum engineering.

Researchers built Quantum-Harbor, a virtual laboratory providing a controlled execution environment where scientific AI agents interacting with quantum systems can have both actions and conclusions directly verified. QIQCBench contributes 49 expert-authored tasks spanning calibration and control, error correction and compilation, and sensing and networking. Across 17 frontier agentic systems, verified performance varied widely, exposing a substantial gap between demonstrated capability and reliable autonomous operation.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:46 UTC · 1d agoAI research1

Kimwolf botnet rebuilt to survive takedowns, researchers say

Palo Alto Unit 42 reports the Kimwolf/Aisuru botnet now uses HTTP/2 Chrome-impersonation floods and Ethereum blockchain-based command resolution to survive takedowns.

Palo Alto Networks' Unit 42 says a new Kimwolf/Aisuru botnet version active since February conducts DDoS floods over HTTP/2 with full Chrome browser fingerprints, making attack traffic hard to distinguish from real users. The malware resolves command servers via the Ethereum Name Service using five shuffled Ethereum endpoints, with a Tor hidden service fallback, so authorities cannot seize a domain or serve a takedown order. The botnet, powered mostly by hijacked Android TV boxes and IoT devices, previously had servers seized and an alleged operator arrested; new C2 infrastructure traces to a single network in Saint Petersburg, Russia. It is unclear whether the same developers built the new version.

CyberScoop·Aug 12, 00:13 UTC · Aug 12, 2026Malware in the wild1

Searching for New Physics with Reinforcement Learning

Researchers apply reinforcement learning to identify SMEFT operators explaining particle physics anomalies, reproducing and improving known CDF W-mass results.

The paper introduces a reinforcement learning method to search the large Standard Model Effective Field Theory (SMEFT) operator space for explanations of measurement anomalies. It was validated on the CDF W-mass anomaly, reproducing and improving known results, then applied to a harder multi-anomaly scenario. RL efficiently navigates complex loop-level operator correlations that bias human-driven phenomenological analysis.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:07 UTC · 7d agoAI research

Week in review: Records allegedly stolen from Azure tenants, Medusa ransomware hits 500+ orgs

Week in review: Medusa ransomware hit 500+ orgs per CISA, millions of Azure tenant records allegedly stolen, SafePal and French tax authority breaches disclosed.

Help Net Security's weekly roundup covers the FBI, CISA, and HHS joint advisory update reporting Medusa ransomware has breached more than 500 organizations since June 2021, and threat actor TheHatman's claim of millions of employee records stolen from Azure tenants of Fortune 500 firms including McDonald's, Vodafone, Kyndryl, and Tata Consultancy Services, per Hudson Rock. It also covers the SafePal breach affecting 39,798 customers, France's DGFiP breach exposing data on 678,000 individuals, and UT San delaying its fall semester after a cyberattack. Security items include critical unauthenticated GitLab flaw CVE-2026-19478, an actively exploited patched macOS Screen Sharing flaw deploying a cryptominer, US charges against 17 Mabna Institute Iranian hackers over 31TB of stolen academic data, and Google Mandiant's AI agents finding 100+ high-severity vulnerabilities.

Help Net Security·Aug 23, 00:00 UTC · 24d agoData breach in the wildCVE-2026-19478

Python Now Has a Post-Quantum Encryption Library

Schneier on Security·Aug 10, 11:02 UTC · Aug 10, 2026Industry1

CISA and NIST Release Technical Checklist for Safeguarding Identity Tokens From Theft and Misuse

CISA and NIST published NIST IR 8587, final guidance for protecting identity tokens from forgery, theft, replay, and signing-key compromise.

NIST Interagency Report 8587 (September 15, 2026) expands the IA-13 'Identity Providers and Authorization Servers' control from NIST SP 800-53 R5.1.1, guiding federal agencies and cloud providers on SSO, identity federation, and machine-to-machine authentication. It requires hardware-backed signing-key storage for moderate-impact systems, 90-day key rotation for high-impact systems, token lifetimes under one hour, and sender-constrained mechanisms such as mutual TLS and DPoP. The report cites incidents including forged SAML assertions that exposed over 60,000 emails from a federal agency. It also extends guidance to agentic AI systems using signed tokens and urges post-quantum cryptography migration planning.

Cyber Security News·Sep 15, 17:04 UTC · 1d agoAdvisory1

Hardware Fingerprinting FTQC via Quantum Decoder Timing

Quantum decoder timing on IBM Heron processors forms a side channel enabling device fingerprinting with 89% accuracy and workload inference.

The work demonstrates that wall-clock syndrome-decoding times on fault-tolerant quantum computers constitute a novel hardware side channel. Using per-shot decoder timings from three IBM Heron processors collected over 68 days, a passive observer can reconstruct detector-firing distributions, estimate logical error rate, infer code distance, and fingerprint the specific device with up to 89% accuracy versus 33% for random guessing. Noisy simulation based on Google's 105-qubit Willow processor distinguishes nine surface-code patches at 81% accuracy, showing the channel persists across vendors and code families.

arXiv cs.CR·Sep 10, 19:24 UTC · 5d agoResearch

Chinese espionage groups swarm to exploit triple-link chain of zero-days

At least four China-aligned espionage groups chained three zero-days in Chromium browsers and Windows ALPC for espionage since late August.

Proofpoint observed at least four state-aligned threat groups, starting with TA412/Violet Typhoon/APT31 on August 28, chaining three zero-days in the 'BlueMoon' exploit chain targeting Chrome, Chromium-based browsers and Microsoft Windows. The chain includes RCE flaws in Chromium's JavaScript engine (CVE-2026-85046, CVE-2026-87491) and a Windows Advanced Local Procedure Call privilege-escalation zero-day (CVE-2026-85880), enabling sandbox code execution, sandbox escape and system privileges. APT31 delivered the chain via phishing links to NGOs, mining and commodity trading firms in the US, installing a browser extension disguised as Google Gemini to surveil activity and steal credentials. Other groups (UNK_LateNight, UNK_DoubleCheck, UNK_QuietRacket) targeted US aerospace, Vietnamese manufacturing, and Indonesian and Singaporean organizations; fewer than 20 victims were directly observed but the true count is likely higher.

CyberScoop·updated 17:13 UTC · 44m ago·first Sep 9, 21:17 UTC · 6d agoExploit / PoC in the wild 19 sourcesCVE-2026-85046CVE-2026-87491CVE-2026-858802· 1 read

LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams

QuantumEvo uses an LLM to evolve BDD variable-ordering heuristics, achieving a 70.9% tie-or-win rate on quantum circuit cost versus baseline methods.

The QuantumEvo framework uses an LLM as a heuristic generator for quantum-cost-aware BDD variable ordering in reversible circuit synthesis, searching over heuristics initialized from multiple families and selecting them by downstream quantum circuit cost. The discovered heuristic HGA-QE modifies the sifting step inside a genetic algorithm and achieves a 70.9% tie-or-win rate against the per-function best baseline, with strict wins on 13.5% of functions. Advantages are clearer on benchmark suites not used for heuristic discovery.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:25 UTC · 12d agoAI research1

Despite federal warnings, thousands of U.S. industrial controllers used in water systems remain exposed online

CyberScoop·Aug 6, 19:10 UTC · Aug 6, 2026Exploit / PoC in the wildCVE-2017-16740

How GPT-5.6 Sol helps run quantum computing experiments

OpenAI describes an MIT researcher using GPT-5.6 Sol with Codex to autonomously run and calibrate quantum computing experiments.

OpenAI published a case study showing how an MIT researcher uses GPT-5.6 Sol together with Codex to autonomously run quantum computing experiments, analyze results, and calibrate qubits. The post is a product application story rather than a new benchmark, paper, or model release.

OpenAI News·Sep 8, 17:00 UTC · 8d agoAI industry1

Why transparent AI agents matter more than you think

CyberScoop·Aug 10, 14:23 UTC · Aug 10, 2026Exploit / PoC in the wild1

Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models

A seven-person independent team trained open-weight agentic cyber models via a data-centric post-training framework, ranking 10th on CyberGym and first at comparable scale.

The paper presents Feyospace-v1, a data-centric post-training framework combining five systems: Choulea (hidden reasoning analysis), SkyReal (teacher-sampling cost reduction), Hongzwang (bypassing teacher API restrictions), PSBreakup (restoring capabilities weakened by model merging), and Kreator (converting expert interventions into trainable reasoning). The data engine builds resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments, retaining only execution-verified and evidence-audited trajectories, yielding 164,269 trajectories for long-context supervised fine-tuning. Three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a 63.24% verified success rate, ranks 10th on the official CyberGym leaderboard, and all three checkpoints rank 1st among models at comparable parameter scales.

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

AI is getting better at election facts, but voters shouldn’t rely on it

CyberScoop·Aug 5, 09:00 UTC · Aug 5, 2026Exploit / PoC in the wild

HyQuant: Hybrid-Precision Quantization for LLM Attention

HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.

HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.

Hugging Face daily papers·Aug 27, 20:00 UTC · 19d agoAI tools & infra1

A California county wants to hire Tina Peters to help run its elections

Shasta County, California plans to hire Tina Peters, convicted of stealing voting system software, as assistant registrar of voters.

Shasta County registrar of voters Clint Curtis said he plans to hire former Mesa County clerk Tina Peters as assistant registrar after Colorado Governor Jared Polis commuted her nine-year sentence for seven felonies, including identity theft, breaking into an election office, and stealing voting system software. Senators Alex Padilla and Adam Schiff asked California Secretary of State Shirley Weber to provide maximum oversight to prevent Peters from improperly accessing ballots, voting systems, or data of over 100,000 registered voters. The county board of supervisors recently censured Curtis after investigations found he was verbally abusive or physically threatening toward staff.

CyberScoop·Aug 19, 23:05 UTC · 27d agoPolicy & legal

Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning

Review connects control theory, optimal transport, probabilistic inference, thermodynamics, and machine learning via free-energy optimization under constraints.

The review unifies five fields: control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning. The common conceptual thread is optimization of free-energy-like functionals under dynamical or statistical constraints. Selected applications are presented in reinforcement learning, variational inference, and generative modeling. The tutorial-style text assumes no prior familiarity and begins from physics principles.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:16 UTC · 2d agoAI research

Large Language Models Develop Belief State Geometry In-Context

Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.

Researchers prompted six open-source LLMs with data from 40 hidden Markov models selected for non-trivial belief structure and probed residual-stream activations for belief states (posteriors over hidden states). Belief states were linearly decodable with peak R² values of 0.83–0.99 across HMM/LLM combinations, spanning early to late layers. Patching and steering the probe-identified subspace preserved downstream prediction quality while control interventions degraded performance substantially, establishing functional relevance. The results provide representation-level evidence that in-context learning approximates optimal Bayesian prediction over a context-inferred generative model.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:17 UTC · 1d agoAI research

EFI Pairs Without One-Way Puzzles: Oracle Separations from Communication Complexity

Theorists build a classical oracle where one-way puzzles fail yet EFI pairs survive, separating two candidate minimal assumptions of quantum cryptography.

The paper constructs a single classical oracle relative to which one-way puzzles do not exist, even with an unbounded verifier, while an EFI pair survives every classical-query distinguisher holding advice, making one superposition query at the end. Security is proven by reducing adversary knowledge to communication complexity for Vector-in-Subspace, with the superposition query bounded using random matrix theory. Relative to the oracle, quantum polynomial time offers no advantage on tasks with classical inputs and outputs and there is no proof of quantumness, separating the leading minimal assumptions of quantum cryptography.

arXiv cs.CR·Sep 10, 17:54 UTC · 6d agoResearch

Quenched Ensemble Sampling

Quenched Ensemble Sampling generalizes nested sampling's hard energy constraint to repulsive potentials, traversing first-order phase transitions where tempering fails.

Quenched Ensemble Sampling generalizes nested sampling's hard energy constraint into a family of repulsive potentials at the energy boundary, preserving monotone energy descent while making the constrained target amenable to scalable gradient-based kernels. On synthetic phase-transition models it estimates marginal likelihood and draws posterior samples across first-order transitions where popular alternatives such as tempering fail. Applications include marginal likelihood estimation for Bayesian neural network architecture comparison and partition function estimation in a high-dimensional continuous lattice field theory.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:16 UTC · 2d agoAI research1

Post-quantum cryptography adoption and the national security implications

Opinion analysis argues quantum computing favors nation-state espionage, leaving lagging post-quantum adoption at critical infrastructure exposed to harvest-now-decrypt-later attacks.

A CSO Online opinion piece argues that the extreme cost and infrastructure requirements of quantum computers will concentrate cryptographically relevant quantum capability among nation-states and a few corporations, favoring espionage and economic espionage over criminal monetization. It highlights harvest-now-decrypt-later risk to long-lived secrets, expert warnings such as Filippo Valsorda's, and diverging CRQC timelines with Google suggesting possibly 2029. The piece predicts governments, banks, and targeted sectors will adopt PQC first while utilities, small hospitals, and local government lag, widening exploitable gaps in critical infrastructure reminiscent of Salt Typhoon-style access.

CSO Online·Sep 9, 09:00 UTC · 7d agoIndustry

Operational Roles of QRNG-Derived Quantum Entropy in Bitcoin Proof-of-Work Architectures

arXiv study finds quantum-random entropy adds no Bitcoin PoW success advantage but helps assurance in fault and provenance scenarios.

The paper shows replacing classical entropy with QRNG output does not change honest Bitcoin proof-of-work success probability when candidate headers remain distinct. It introduces a reproducible benchmark measuring an entropy-efficiency factor and a reboot-diversity index, finding QRNG value only in assurance-oriented scenarios involving correlated restart faults, namespace reuse, and entropy provenance. Validation is simulation-based, with hardware-in-the-loop testing identified as future work.

arXiv cs.CR·Sep 4, 12:47 UTC · 12d agoResearch

SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions

SQS unifies weight pruning and low-bit quantization via Bayesian variational learning, compressing Llama3.2 and Qwen2.5 at higher rates with comparable accuracy.

SQS introduces a unified Bayesian variational framework performing simultaneous pruning and low-bit quantization, using a spike-and-slab prior for sparsity and Gaussian Mixture Models to model quantized weights. The authors derive an efficient approximation for the intractable objective and provide a consistency result for the variational approach. Experiments on ResNet, BERT-base, Llama3.2, and Qwen2.5 show higher compression rates than prior baselines with comparable performance drops.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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·Sep 1, 20:00 UTC · 14d agoAI research

Optimizing Credential Blast Radius Through Trust Boundaries and Delegation Under Post-Quantum Authentication Costs

Academic paper models credential blast radius optimization across trust domains under post-quantum latency costs, cutting expected impact by up to 36%.

The paper formulates the joint selection of trust domains and credential-derivation structures under policy and latency constraints as an NP-hard optimization problem, showing the scalarized two-domain direct-issuance case reduces to a weighted minimum cut. In 195 of 230 exhaustive synthetic comparisons, joint optimization produced lower credential blast radius than choosing boundaries first, especially under chained delegation. A trace-derived replay using measured post-quantum authentication costs found the best design reduced expected impact by up to 36% relative to a single domain within the latency budget.

arXiv cs.CR·Sep 3, 23:42 UTC · 12d agoResearch

🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

Caltech professor Anima Anandkumar discusses Neural Operators and FourCastNet for physics modeling, arguing inductive biases beat pure token scaling.

Anima Anandkumar, Bren Professor at Caltech and co-founder of Accelerated Understanding, describes Fourier Neural Operators that learn in frequency and spherical-harmonic domains to model weather, fusion, and fluid or heat flow. Her team built FourCastNet 3, a global weather model competitive with physics-based simulations that runs on consumer-grade GPUs. She also introduced TorchLean, a framework for writing PyTorch-style networks inside the Lean proof assistant for formal verification, and was appointed to the United Nations Scientific Advisory Board. She argues physical domains resist scaling due to tiny datasets and context lengths in the hundreds of billions, so progress comes from built-in structure and physical priors.

Latent Space·Aug 26, 15:15 UTC · 21d agoAI research1

An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics

Open post-training pipeline turns Nemotron 3 Ultra checkpoints into an IMO 2026 gold-medal system, scoring 30/42 without formal provers or external tools.

Starting from Nemotron 3 Ultra, researchers trained two specialist checkpoints using supervised fine-tuning and reinforcement learning for natural-language olympiad proof generation. Three checkpoints power an iterative generate-verify-refine search plus a separate high-compute selection stage, operating entirely in natural language with no formal prover, external tools, or internet access. The system scored 30 of 42 points at IMO 2026, reaching the gold-medal threshold. The release includes the post-trained checkpoints, training data, training and inference code, submitted solutions, and Nemotron-IMO-Bench with 200 novel olympiad-level problems.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoModel release

GAUGE: A Formal Framework for Measuring Cryptographic Security under Heterogeneous Adversary Cost Models

GAUGE frames cryptographic security as profiles over adversary cost models, certifying a ranking reversal between ML-KEM-512 and AES-128 from a 4–5% memory pricing shift.

GAUGE represents cryptographic security as a function over admissible adversary cost models (a security profile), proves profiles are piecewise-linear and concave, and establishes a rating trilemma when two profiles cross. A polynomial-time linear-programming procedure certifies whether the ranking of two schemes is robust, reverses under admissible models, or is genuinely incomparable. Applied to NIST post-quantum standards, the framework certifies a ML-KEM-512 versus AES-128 ranking reversal from a 4–5% shift in memory pricing and measures lattice-sieving cost drift of 9.79 bits per year over eight years. A hybrid X25519 + ML-KEM-768 handshake reduces combined-break probability twenty-fold at a 2.3 kilobyte cost.

arXiv cs.CR·Sep 15, 14:55 UTC · 1d agoResearch

Implementing a White-Box Undetectable Backdoor for Random Fourier Features

Researchers implement Goldwasser's CLWE-based undetectable backdoor for Random Fourier Features models in numpy/scipy, confirming practical realizability with no detectable differences from clean models.

The paper provides an end-to-end implementation of the Goldwasser et al. white-box undetectable backdoor for models trained with the Random Fourier Features algorithm, using only numpy and scipy. It derives two samplers for the core GP_d(b_k) distribution: a rejection-sampling proxy and an exact closed-form sampler verified against its analytic form. Statistical indistinguishability tests covering weight-space and functional black-box comparisons found no detectable difference between backdoored and clean models across sparsity ratios. The underlying lattice hardness reduction was not reproduced, and the work demonstrates the threat is realizable with commodity scientific-computing tools rather than specialized cryptographic infrastructure.

arXiv cs.CR·Sep 14, 22:18 UTC · 1d agoResearch

A Generalization of Amari's Bayesian Duality

Paper generalizes Amari's Bayesian duality by connecting it to a convex duality of Bayes' rule.

The authors revisit Amari's less-known work on Bayesian duality from information geometry. They connect Bayesian duality to a convex duality formulation of Bayes' rule and present a generalization of it. The paper is purely theoretical and discusses relevance for modern AI, with no experiments or model releases.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:51 UTC · 8d agoAI research

Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original

Multiverse Computing details quantization-aware healing, producing a 4-bit compressed model that reportedly outperforms its full-precision original.

A Hugging Face blog post by Multiverse Computing's CAI team introduces quantization-aware healing for compressed models. The post claims the resulting 4-bit model outperforms the original full-precision model. No additional details or benchmarks were available in the provided text.

Hugging Face Blog·Aug 25, 11:39 UTC · 22d agoAI research

Likelihood-free inference with nuisance parameters through normalizing flows

Researchers decompose normalizing flows to derive near-pivotal statistics for likelihood-free inference with nuisance parameters, recovering the t-test and beating Welch limits.

A new paper decomposes neural-network normalizing flows to uncover pivotal statistics in the presence of nuisance parameters using only a sample generator from the distribution of interest. The statistic is near-pivotal in the sense of minimum average KL-divergence of its p-values and can incorporate prior knowledge of group invariances such as translation and scale. Experiments show it recovers the one-sample t-test almost exactly, outperforms the Welch test on worst-case size over a constrained variance-ratio range, and delivers higher power and much faster runtime than profile likelihood-ratio techniques on small-to-moderate samples.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:58 UTC · 6d agoAI research

ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending models·Aug 28, 18:38 UTC · 18d agoModel release1