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Learning 3D Editing without Paired Supervision via Generative Prior Distillation

New framework distills 2D editing and VLM priors into a feed-forward 3D editing model without paired 3D training data.

The method, PriorEdit3D, learns feed-forward instruction-guided 3D editing by distilling knowledge from foundation models instead of using ground-truth 3D pairs. Through a differentiable rendering pipeline it supervises a 2D visual prior from an image editing model at the main view and a Vision-Language Model semantic prior at novel views for instruction fidelity and identity preservation. A 3D-aware Distribution Matching regularization constrains outputs to the manifold of realistic 3D assets defined by a pretrained image-to-3D teacher. Experiments report superior instruction fidelity and cross-view consistency over state-of-the-art baselines, with code released on GitHub.

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

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 · 13d agoAI industry

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.CRSep 10, 17:54 UTC · 6d agoResearch

12 Best Application Control & Allowlisting Tools Compared (2026): Features & Pricing

GBHackers compares twelve application allowlisting tools for 2026, naming ThreatLocker and Airlock Digital leaders and Microsoft WDAC the free native option.

GBHackers published an editorial comparison of twelve application control and allowlisting tools for 2026, assessing control depth, manageability, and pricing models. It ranks ThreatLocker and Airlock Digital as leading dedicated allowlisting options, positions Microsoft WDAC/AppLocker as the free native choice for Windows estates, and highlights CyberArk and BeyondTrust for coupling control with privilege management.

GBHackersupdated 09:52 UTC · 9h agofirst Sep 10, 08:24 UTC · 6d agoIndustry 14 sources1

ECHO: A Matched-Contrast Benchmark for Context-Sensitive Turn-Taking in Full-Duplex Dialogue

ECHO benchmark pairs identical-overlap Chinese dialogue examples with contrasting contexts, revealing most full-duplex systems bias toward yielding the floor.

ECHO is a paired diagnostic benchmark for Chinese full-duplex turn-taking that matches examples with identical overlap transcripts but contrasting preceding multi-turn contexts, one requiring Yield and the other Keep. It also includes off-talk examples for diagnosing unnecessary yielding and introduces pair accuracy, which grants no credit to constant-action policies. Experiments across multiple full-duplex systems show most exhibit a pronounced Yield bias, performing substantially better on interruptions than backchannels. The benchmark and metadata will be publicly released.

arXiv cs.AI / cs.LG / cs.CLSep 15, 15:58 UTC · 1d agoAI research

Top 10 Best Cloud Workload Protection (CWPP) Solutions in 2026

Editorial scorecard ranks 2026 cloud workload protection platforms, placing Prisma Cloud first, Sysdig second, and CrowdStrike third on detection quality.

A research-based scorecard rates ten cloud workload protection (CWPP) platforms across runtime depth, container/Kubernetes support, coverage breadth, cloud context, and value. Palo Alto Prisma Cloud leads at 8.9, followed by Sysdig at 8.8 and CrowdStrike at 8.6, with Aqua Security tying CrowdStrike at 8.6. The piece argues agent-versus-agentless is a false choice, with leaders now pairing agentless visibility with eBPF-based runtime sensors. Scores are editorial assessments, not lab benchmarks.

Cyber Security NewsSep 11, 07:15 UTC · 5d agoIndustry1

From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge

Interpretability study traces how Qwen, Llama, and Gemma route query information and internal knowledge across layers when answering questions.

Researchers used layerwise interventions on hidden states to separate query-routing signals from target knowledge in language models, testing Qwen, Llama, and Gemma on country-continent questions with varied answer types. A pair-conditioned request direction strengthens before interventions alter downstream knowledge, opening a causal window while answer-supporting content is still forming. Trajectories differ by model: Gemma shows a partially overlapping mid-layer routing profile, while Llama has no sustained routing-effect window under the same gates.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:39 UTC · 6d agoAI research1

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

A retrospective study found GPT-4 over-flagged emergency department revisit cases while an LLM knowledge-graph screener achieved 83-100% positive predictive value.

In an exploratory retrospective study of 99 emergency department diagnosis pairs from a multihospital health system, clinicians and GPT-4 independently judged whether revisit pairs warranted further assessment. GPT-4 responses correlated poorly with clinicians, flagging 94% of pairs for follow-up, 4.4-13.3 times more than clinicians, though prompt engineering was minimal. An algorithm leveraging an LLM-populated knowledge graph (KGA) achieved 83-100% positive predictive value against at least one clinician rater, suggesting LLM-based screening could broaden revisit quality review without substantially increasing reviewer workload.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:37 UTC · 7d agoAI research1

Rare Not Random Using Token Efficiency for Secrets Scanning

Researcher proposes token efficiency (string length divided by BPE token count) as a better post-regex filter than entropy for secrets scanning, validated on CredData.

The post explores whether Byte-Pair Encoding tokenization can replace Shannon entropy as the primary filter for candidate secrets captured by regex in tools like Gitleaks. It defines 'token efficiency' as string length divided by token count under the cl100k_base tokenizer; secret-like strings such as GitHub tokens tokenize into many small tokens and score low, while natural text scores high. Evaluating labeled secrets from the CredData dataset shows a usable separation, with roughly 2.5 suggested as a minimum cutoff versus Gitleaks' 3.5 entropy threshold. The technique is positioned as a post-regex filtering step rather than a standalone detector.

Lobsters · securitySep 12, 06:42 UTC · 4d agoResearch

ResidualAuth: What Authorization State Must Language Agents Preserve under Revocable Delegation?

Formalizes residual authorization state language agents must preserve under revocable delegation; token-budget summaries mostly fail while hard gates stop unauthorized effects.

The paper shows two authorization histories with identical current permissions can require opposite decisions after the same direct-edge revocation, formalizing the needed information as residual authorization state. Exponentially many future-distinct states can share one transitive closure, with exact or tight asymptotic bounds on the state an exact monitor requires. Across four open-weight models, fixed 256-token summaries solved at most 2 of 16 paired episodes while authenticated current-query reads solved 15-16 of 16. A hard effect gate reduced eight observed unauthorized effects to zero without changing preceding attempts.

arXiv cs.CRSep 8, 00:05 UTC · 8d agoResearch

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

Hugging Face daily papersSep 4, 20:00 UTC · 11d agoAI research1

Cisco releases open-source toolkit for verifying AI model lineage

Help Net SecurityApr 30, 00:00 UTC · Apr 30, 2026Model release1

Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead

Audit of 254 SWE-bench submissions finds top coding-agent entries statistically inseparable, so small leaderboard gaps no longer establish rank.

The paper audits 254 SWE-bench submissions across four splits without running models. On Verified, the top two entries each resolve 396 of 500 instances, and exact paired McNemar tests separate none of the 29 adjacent top-thirty pairs at alpha=0.05. Within-model scaffold score ranges reach 29.8 percentage points, versus an 8.8-point spread among the top thirty. The authors release a five-step audit protocol and recommend reporting comparison-set-specific resolution and model-scaffold provenance.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:29 UTC · 1d agoAI research

11 Best CSPM Tools Compared (2026): Features & Pricing

CSPM comparison ranks Wiz first for agentless attack-path analysis; notes Ermetic absorbed into Tenable and Lacework into Fortinet FortiCNAPP.

An editorial comparison of eleven CSPM tools ranks Wiz as the agentless attack-path momentum leader, Prisma Cloud as the breadth benchmark, and Orca as the agentless SideScanning pioneer. It highlights consolidation: Ermetic now powers Tenable Cloud Security and Lacework became Fortinet's FortiCNAPP. The guide recommends starting with free tiers from Defender for Cloud, Prowler, and native cloud tools before buying.

GBHackersSep 15, 06:37 UTC · 1d agoIndustry1

Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize

ByteDance Seed's HarnessDev benchmark finds LLM-built agent harnesses trail human engineering on code and search, with only 34 of 64 revisions generalizing.

Researchers from ByteDance Seed, SUTD, Georgia Tech, M-A-P, and TokenWave.AI introduce HarnessDev, a benchmark that evaluates the runnable agent harness an LLM writes rather than its answers, using Creation and Evolution stages across SWE-bench Pro, Terminal-Bench 2.1, MLE-bench, EQ-Bench3, and BrowseComp (2,207 instances). Six creator models including Opus 4.8, GPT-5.5, Gemini 3.1 Pro, DeepSeek V4 Pro, Qwen 3.7 Max, and Seed 2.0 Pro were tested; Opus 4.8 posted the best average of 67.8 versus an 86.2 human-engineered reference. Self-built harnesses beat references on writing and ML experimentation but lag badly on code and search, and quality proved executor-specific: Opus 4.8 fell from 69.3 to 33.0 on SWE-bench Pro when the executor was switched to Gemini. Evolution gains were small and noisy: of 64 adjacent changes, feedback and held-out scores agreed only 34 times (53.1%), and much generated state and memory code never executed.

MarkTechPostSep 11, 22:01 UTC · 4d agoAI research 2 sources

Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation

Study shows specialists trained on question-answer pairs implicitly select latent reasoning trajectories, and tuning choices control the precision-generalization trade-off in distillation.

The work demonstrates that specialist optimization implicitly selects from a latent trajectory space when specialists are trained only on question-answer pairs without explicit reasoning supervision. Using student distillation as an agnostic probe across 27 specialist-student pairings, specialization-generalization profiles correlate exceptionally strongly. Explicitly controlling the specialist's distributional drift systematically shifts both teacher and distilled student along a controllable trade-off between domain precision and general-capability retention across chemistry, physics, and multilingual settings, even across divergent model families.

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

A First-Principles Evaluation of Graph-Based Network Intrusion Detection Systems

GIDS-Eval framework reveals evaluation gaps in graph-based network intrusion detection; two crafted edges fully evade three detector-dataset pairs.

Researchers introduce GIDS-Eval, a framework decomposing graph-based network intrusion detection systems into six interchangeable stages to enable controlled comparisons. Surveying nine GIDS and reimplementing five, they find two crafted edges achieve full evasion against three of eight detector-dataset pairs, snapshot windows alone cause a mean 38.3% relative swing in average precision, and none of 18 replayed detector-dataset pairs can alert as events arrive. Their encoder-free GIDS-Lite control ranks first by AP on two of four datasets at up to 575x lower runtime.

arXiv cs.CRSep 10, 22:38 UTC · 5d agoResearch1

Hunting Vulnerabilities Using Frontier Models

Okta used frontier AI models GPT-5.5 Cyber and Mythos via OpenAI and Anthropic programs to scan millions of code lines for vulnerabilities.

Okta describes using frontier AI models, including GPT-5.5 Cyber Preview (TAC) and Mythos Preview, through OpenAI's Daybreak Cyber Partner Program and Anthropic's Project Glasswing to hunt vulnerabilities across its product codebase. The team built a custom Python orchestrator with strong isolation, vendor-agnostic model support, and four distinct scanning pipelines executed as isolated Codex or Claude Code sessions with progressive context loading to reduce context bloat. Human experts and AI agents worked both autonomously and in paired hunts, and Okta reports the best results when humans and agents taught each other.

Okta SecuritySep 8, 00:00 UTC · 8d agoResearch

Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

A distillation framework compresses LLM reasoning into a 15.5M-parameter trade-up recommendation model reaching AUC 0.941 with product-type test-time training.

The paper targets trade-up recommendation, which identifies higher-quality alternatives that preserve customer purchase intent. A retrieval-augmented few-shot LLM teacher generates labels and rationales that supervise a compact embedding-pair classifier; at inference the 15.5M-parameter student uses only two precomputed 768-dimensional embeddings with no LLM calls. On 8,352 annotated pairs, label-only training scored AUC 0.912, reasoning distillation reached 0.924, and product-type test-time training lifted it to 0.941 with average precision 0.940. The distilled student is roughly 5,000x faster and 10,000x cheaper than direct LLM inference on a 100K-pair proxy catalog.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:08 UTC · 12d agoAI research

PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization

PLC-DPO corrects noisy preference labels during DPO by routing each pair as clean, flip, or tie using calibrated policy-reference margins.

PLC-DPO addresses reversed, weak, or ambiguous preference labels in Direct Preference Optimization by using the calibrated policy-reference margin as online evidence to route each pair's training signal as clean, flip, or tie. This actively corrects supervision direction and strength rather than only filtering suspicious examples. Across 57 dataset-model-benchmark cells, PLC-DPO achieves the best mean win rate of 60.5 versus 55.5 for the next-best method. Injected-noise and tie stress tests plus human disagreement analysis show the routing stays stable and distinguishes flipped from weakly directional pairs.

Hugging Face daily papersAug 30, 20:00 UTC · 16d agoAI research

Apple might make servers again to cash in on the AI rush

Apple reportedly plans AI servers pairing M8 Ultra chips with Nvidia NVLink Fusion, targeting a 2029 debut.

According to The Information, Apple is considering re-entering the server market it left in 2011 with the Xserve retirement, betting on its ARM-based M processors amid surging AI compute demand. The servers may run two or four M8 Ultra chips and could incorporate Nvidia's NVLink Fusion interconnect, and Apple recently turned to Nvidia chips for its revamped Siri servers. A product would likely debut around 2029, and plans could still change; Mac Mini and Mac Studio popularity with AI developers has already caused shortages.

The Verge · AIupdated 18:16 UTC · 52m agofirst 17:20 UTC · 1h agoAI industry 2 sources

Apple Reference Image: A New Approach for Verified Photography

Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.

Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.

Hacker News · security02:07 UTC · 17h agoResearch HN 25↑ · 18 comments

Meta’s new One subscriptions put a price on social media and AI

Meta's new One subscription tiers pair app perks with more Meta AI usage, from $2.99 single apps to $499 monthly business Max plan.

The Verge reports Meta's Meta One subscription bundles are now globally available, following the launch of its Muse AI assistant. Individual bundles include Core at $7.99/month and Premium at $19.99/month, combining Instagram Plus, WhatsApp Plus, and Facebook Plus with expanded Meta AI media generation including Muse images and Instagram's Restyle, cheaper than $11/month for all three standalone subscriptions. Creator and business plans span Essential ($14.99/month) to Expert ($149/month) and Max ($499/month), adding verification badges, impersonation protection, and Meta Business Agent capacity.

The Verge · AISep 15, 15:00 UTC · 1d agoAI industry

When LLM judges agree, should we believe them?

Amazon ICML paper uses Ising models to correct correlated LLM-judge votes, beating accuracy-weighted panels by 9-14%.

Amazon Science describes an ICML paper, "Dependence-aware label aggregation for LLM-as-a-judge via Ising models," addressing how correlated judge outputs inflate majority-vote confidence. The unsupervised method models pairwise dependence between judges, learning both reliability and similarity without human reference labels. Tested on relevance, toxicity, and summarization tasks with 10 judge models at temperature zero, it outperformed accuracy-weighted voting by 9% to 14%.

Hacker News · AISep 14, 16:29 UTC · 2d agoAI research HN 41↑ · 23 comments

WhatsApp Restricted Chat locks a conversation to your primary phone

WhatsApp is developing Restricted Chat, a per-chat setting blocking conversation sync to linked devices, defeating third-party clients and AI agents.

Restricted Chat, spotted in Android beta version 2.26.36.5 but not yet enabled, keeps a chosen conversation exclusively on the primary phone, out of reach of WhatsApp Web and linked secondary devices. It extends Advanced Chat Privacy controls: media is not auto-saved to gallery, export is blocked, and messages cannot be handed to Meta AI, with system messages notifying participants of changes. WhatsApp has not announced a release date.

Help Net SecuritySep 14, 08:08 UTC · 2d agoIndustry

ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs

ModaLens image-swap audit shows report availability cuts MedGemma-27B image sensitivity on MIMIC-CXR from 20.94% to 4.26% answer changes.

ModaLens is a paired image-swap audit measuring how report availability affects image sensitivity in report-conditioned medical VLMs. On MedGemma-27B across 3,199 paired MIMIC-CXR cases from 293 patients (14 questions per case), generated answers changed on 4.26% of image-swap trials with the report versus 20.94% without it, a 16.7-point paired difference (95% CI 15.6-17.7). The original prompt with a lowercase first-token readout gave 4.70% versus 17.07%, and the direction replicated in two further model lineages. Labels derived from reports limit conclusions about visual correctness; code, prompts, and run records are publicly released.

Hugging Face daily papersSep 13, 20:00 UTC · 2d agoAI research

DynSHAP: Towards Explainable Dynamic Survival Analysis

DynSHAP extends SHAP explainability to dynamic survival analysis, treating time-feature pairs as Shapley players for longitudinal clinical predictions.

DynSHAP adapts marginal SHAP estimators to dynamic survival analysis by treating time-feature pairs as players in the Shapley game, handling longitudinal irregular inputs and functional survival outputs. Temporal DynSHAP learns linear feature dependencies over time and addresses them with conditional sampling. On synthetic data with ground-truth attributions it recovers temporally dependent features more accurately than marginal estimators, and it produces faithful attributions on two real-world clinical datasets across two DSA architectures.

arXiv cs.AI / cs.LG / cs.CLSep 11, 16:39 UTC · 5d agoAI research

Top 10 Best CNAPP (Cloud-Native Application Protection) Platforms in 2026

GBHackers ranks 10 CNAPP platforms for 2026, naming Wiz, Prisma Cloud, and Microsoft Defender for Cloud as category leaders.

The guide describes CNAPP as the umbrella combining CSPM, CWPP, CIEM, and DSPM, arguing that cross-pillar correlation of attack paths is the platform's core value. Wiz is ranked best for graph-based correlation, Prisma Cloud for the broadest module set, and Microsoft Defender for Cloud for Azure economics. It also cites Google's approximately $32 billion agreement to acquire Wiz, announced in March 2025, as buyer leverage and a reason to seek roadmap and neutrality protections in multi-year contracts.

Cyber Security NewsSep 11, 07:21 UTC · 5d agoIndustry1

Linux Detection Engineering - Local Privilege Escalation

Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.

Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.

Elastic Security LabsSep 11, 00:00 UTC · 5d agoResearch

Top 10 Best Device Control & USB Security Tools in 2026

A 2026 scorecard ranks ten USB and device control tools, placing Safetica, Ivanti DeviceLock and DriveLock at the top.

The article compares ten device control and USB security tools using a weighted scorecard covering control granularity, platform coverage, DLP integration, operability and value. Safetica, Ivanti DeviceLock and DriveLock rank highest, with notes on cross-platform depth, Windows granularity and European compliance fit. The piece highlights ownership changes, including CoSoSys joining Netwrix and Digital Guardian joining Fortra, and stresses device control as part of Zero Trust and DLP strategies.

Cyber Security NewsSep 10, 08:05 UTC · 6d agoIndustry

Apple Doesn’t Want You to Worry About the New Apple Watch's Listening Features

Apple Watch Series 12 and Ultra 4 add opt-in audio intelligence features that process microphone audio on-device via a new Secure Exclave.

The Apple Watch Series 12 and Ultra 4 ship with four opt-in audio intelligence features: Sound Recognition, Shazam music identification, Siri Recap conversation summaries, and Live Rewind 15-second transcription. Audio is held and processed in an isolated Secure Exclave buffer on the new S11 chips, with on-device speech recognition on iPhone producing a distilled transcript that foundation models in Private Cloud Compute then summarize. Apple says no raw audio is stored or accessible to the operating system, apps, the user, or Apple, and untransferred audio is automatically deleted.

WIRED · Securityupdated Sep 10, 19:50 UTC · 5d agofirst Sep 9, 19:14 UTC · 6d agoAI industry 7 sources1

Introducing the CyberAgents Exchange AI Inspector: Rigorous review for community-built AI

Tenable and OpenAI launch the CyberAgents Exchange AI Inspector to security-review community-submitted AI agents, MCP servers, and skills using GPT Cyber models.

Tenable and OpenAI announced the CyberAgents Exchange AI Inspector, unveiled at OpenAI's "Intelligence at Work: Cyber Summit," to vet community-submitted AI agents, skills, MCP servers, and multi-agent playbooks in the CyberAgents Exchange registry. The process combines Tenable One AI Exposure scanning, OpenAI GPT Cyber model assessment, and human review, with reviews anchored to specific Git commits. The registry launched in August and hosts over 100 AI listings; the Inspector is expected to be available in September and has already detected prompt injection implemented via invisible Unicode tag characters in a SKILL.md file.

Tenable BlogSep 9, 13:00 UTC · 7d agoTools

Top 10 Best Application Control & Allowlisting Tools in 2026

Roundup ranks 2026 application allowlisting tools, scoring ThreatLocker 8.8/10, Airlock Digital 8.5/10 and free Microsoft WDAC among top default-deny options.

This is a scored buyer's guide to ten application control and allowlisting tools for 2026, with ThreatLocker ranked first (8.8/10), Airlock Digital second (8.5/10) and Microsoft's built-in WDAC/AppLocker third (7.2/10). It notes ransomware has renewed interest in default-deny execution controls and that CISA and other agencies list application control among the most effective yet under-deployed mitigations. The evaluation is research-based with no lab testing, weighting policy automation at 30% and operability at 25%.

Cyber Security NewsSep 9, 07:59 UTC · 7d agoIndustry1

Cross-modal learning for SAR target recognition using optical vision foundation models

Frozen DINOv3 optical prototypes supervise SAR target recognition without EO/SAR pairs, improving classification on the heavily imbalanced UNICORNv2 dataset.

The framework aligns SAR embeddings to class-level prototypes built from a frozen DINOv3 electro-optical encoder, requiring no strict EO/SAR image pairs. At inference the SAR model operates independently without access to optical imagery. On UNICORNv2, a civilian vehicle dataset with heavy speckle and severe class imbalance, EO prototype alignment improves accuracy over frozen DINOv3, SAR-only finetuning, and unpaired distribution alignment baselines.

arXiv cs.AI / cs.LG / cs.CLSep 7, 16:54 UTC · 9d agoAI research

LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.

LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.

arXiv cs.AI / cs.LG / cs.CLSep 4, 15:49 UTC · 12d agoAI research

Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal

A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.

The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.

Hugging Face daily papersSep 2, 20:00 UTC · 13d agoAI safety & security1

Utimaco HSM protects digital wallets and cryptocurrencies

Help Net SecurityJul 19, 00:00 UTC · Jul 19, 2018AI tools & infra

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:43 UTC · 1d agoAI research

Heart of the Matter: How a Major Children’s Hospital Uses Open Source NVIDIA AI for Cardiac Care

Children's Hospital of Philadelphia uses NVIDIA open-source MONAI, Warp and Newton to build pediatric heart models in seconds for surgical planning.

CHOP's cardiac modeling service uses MONAI, Auto3DSeg and SlicerHeart to turn CT, MRI and 3D ultrasound images into anatomically precise heart models in seconds instead of four hours of manual work. More than 20 US children's hospitals run similar programs, with Boston Children's supporting roughly 500 cardiac surgery cases a year. NVIDIA's Newton physics engine, built on the Warp Python framework, aims to reduce device simulations from hours to near real time in clinical workflows.

NVIDIA BlogSep 15, 09:00 UTC · 1d agoAI industry