Search: “ENEAS”

ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation

ENEAS adds text prompting and semantic verification to video segmentation to keep tracking targets through occlusion and reject lookalike distractors.

ENEAS is a unified text-promptable method for instance tracking and open-concept semantic discovery in video, designed to fix temporal hallucinations, spatial fragmentation, and semantic misclassification seen in SAM 3-class foundation models. It extends the geometrically robust SeC architecture with a text-prompting adapter and temporal memory, and uses a verification layer combining fast visual embedding matching with conditional VLM refinement for ambiguous candidates. It targets 3D reconstruction pipelines where a single misclassified distractor corrupts the asset. Code and models are open-sourced.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research

Deep Packet Inspection challenges for telecom and security vendors

Enea's DPI survey of telecom and security vendors finds traffic visibility is increasingly critical yet harder as 5G, cloud, IoT, and TLS 1.3 spread.

Enea's survey of high-tech product managers at telecom and security vendors found that real-time application-level traffic visibility is considered essential as cloud adoption, 5G, remote work, and IoT dissolve traditional network perimeters. Abnormal traffic detection was the top DPI use case, 70% of respondents require classification of connected devices in enterprise and IoT/industrial networks, and TLS 1.3 (mandatory in 5G) threatens payload-based visibility. Most vendors report having or developing cloud solutions, with half planning SASE offerings integrating security and networking.

Help Net Security·Aug 27, 06:51 UTC · 20d agoIndustry

CARDEA: Auditable Reasoning Grounded in Spatial Evidence for End-to-End Coronary Angiography Interpretation

CARDEA, a vision-language model trained only on public data, matches cardiologists on coronary angiography complexity assessment while exposing auditable bounding-box evidence.

CARDEA is a unified large vision-language model serving as the inference core of an end-to-end coronary angiography pipeline from multi-view videos to study-level diagnosis. It was trained on public datasets through visual alignment, self-distilled Chain-of-Box cold start, and reinforcement learning with verifiable rewards encouraging bounding-box reasoning. It reached 0.91 accuracy on dominance classification under domain shift and 0.90 on complexity assessment, comparable to two interventional cardiologists. RLVR raised zero-shot report generation vessel-severity macro-F1 from 0.513 to 0.686, while supervised imitation alone did not.

Hugging Face daily papers·Sep 6, 20:00 UTC · 10d agoAI research

Building a Production Greek-English Speech Recognizer

Engineering report details Sophea, a production Greek-English ASR reaching 4.26% WER on public English sets via ROVER ensemble and data-pipeline calibration.

Across 23 training iterations, two architectures, and nine production gates, no single data composition passed all gates; a three-model ROVER ensemble reached 9 of 9 gates and cut overlapping-speech WER from 53.35% to 37.87%. Calibrating an audio-quality filter against in-domain anchors reduced discarded scored Greek audio from 98.7% to 10.6%, and a pre-registered ablation traced a hallucination defect to one training-data package. The sophea/asr-k1 preview arbiter lists 4.26% average WER on eight public English test sets and 25.88% WER on live Greek noisy traffic; no weights or training data are released.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research

The EU CRA's Real Question: What Shipped, and When Did You Know?

ActiveState argues the EU CRA's 24-hour ENISA exploit-notification duty, effective September 11, 2026, makes current SBOMs and provenance visibility a legal necessity.

An ActiveState essay warns that the EU Cyber Resilience Act's reporting obligations take effect on September 11, 2026, requiring manufacturers of products with digital elements sold into the EU to notify ENISA within 24 hours of learning a vulnerability is actively exploited, with a fuller report within 72 hours. The law's engineering requirements only apply from December 11, 2027, leaving a visibility-first runway, and Article 13 requires the SBOM to stay current unlike one-time artifacts generated under US Executive Order 14028. The author contrasts the 24-hour notification clock with an industry-average 55 days to remediate high or critical vulnerabilities and recommends automated SBOM regeneration or consuming pre-vetted, attested open source components.

BleepingComputer·Sep 8, 20:24 UTC · 8d agoPolicy & legal

ETSI Proposes 17 Cybersecurity Standards to Support Cyber Resilience Act

ETSI has launched an approval process for 17 cybersecurity standards that vendors must meet under the EU Cyber Resilience Act.

The European Telecommunications Standards Institute (ETSI) initiated an approval process for 17 cybersecurity standards intended to support implementation of the EU Cyber Resilience Act. These standards will define requirements that vendors of products with digital elements must satisfy to comply with the regulation. The move advances the operational groundwork for CRA compliance in the European Union.

Infosecurity Magazine·Aug 17, 11:15 UTC · Aug 17, 2026Policy & legal

ENISA: Frontier AI Is Changing the Speed of Cyberattacks. Europe Needs to Catch Up

ENISA warns frontier AI compresses attack lifecycles to minutes, with exploits possible within 15 minutes of disclosure and median 72-minute breach-to-exfiltration times.

ENISA's July 2026 paper 'ENISA's view on Cybersecurity in the Frontier AI Era' argues AI-assisted attackers may weaponize vulnerabilities within 15 minutes of disclosure and achieve initial-access-to-data-exfiltration in a median 72 minutes, creating a 'negative time-to-exploit' problem. The report cites one organisation whose CVE volume rose from roughly 80 in Q1 2025 to almost 500 in Q1 2026, then about 500 reports per day when frontier-AI tools were used. ENISA recommends machine-speed defence under 'Cybersecurity as Code', EPSS and VEX-based prioritisation, AI-assisted incident response with human oversight, and an assume-breached architecture.

Security Affairs·Sep 14, 20:35 UTC · 1d agoAdvisory

NIS2 compliance: Fixing IAM and access control before the 2026 audit

EU NIS2 enforcement deadlines approach; organizations are urged to prioritize service account inventory, lifecycle offboarding, and phishing-resistant MFA before audits.

EU member states are moving from NIS2 transposition into enforcement, with fines up to 10 million euros or 2% of global turnover for essential entities and personal liability for management bodies. The article argues access management is the fastest high-ROI starting point, estimating 2-4 weeks to enforce fine-grained password policy, vault shared credentials, and deploy phishing-resistant MFA versus 6-12 months for supply chain risk management. It flags three common pre-audit failures: unmanaged service accounts and API keys, dormant accounts from broken offboarding, and SMS OTP instead of phishing-resistant MFA under NIST SP 800-63B. The piece promotes Passwork as a single control plane for credential storage, RBAC, and WebAuthn.

Help Net Security·Sep 1, 00:00 UTC · 15d agoIndustry

ENISA launched the CRA Single Reporting Platform for actively exploited vulnerabilities

ENISA launched the CRA Single Reporting Platform, making EU manufacturers report actively exploited vulnerabilities and severe incidents through one portal.

ENISA switched on the Cyber Resilience Act's Single Reporting Platform on 11 September 2026, the same day CRA reporting obligations became binding on manufacturers. Reports require an early warning within 24 hours, a fuller notification within 72 hours, and a final report within 14 days (one month after notification for severe incidents). Filings go through an EU Login account with MFA, are routed to a coordinating CSIRT chosen by the manufacturer, and no API is available in the first release. Open-source software stewards fall under the same obligations from 11 December 2027.

Help Net Security·Sep 14, 12:23 UTC · 2d agoPolicy & legal

Update modules/auxiliary/scanner/http/elasticsearch_tika_xfa_xxe.rb

Rapid7 updated a Metasploit auxiliary scanner module that detects XML external entity injection in Elasticsearch via Apache Tika.

A commit in the Metasploit Framework updated modules/auxiliary/scanner/http/elasticsearch_tika_xfa_xxe.rb, an auxiliary scanner module. The module targets XML external entity (XXE) injection in Elasticsearch through Apache Tika, and was co-authored by jheysel-r7. The terse commit message contains no additional details, CVE references, or exploitation notes.

Metasploit Framework commits·Sep 8, 19:22 UTC · 8d agoTools

18 ways to check whether data can be trusted for AI

ETSI published TR 104 180 defining 18 data quality metrics, including fairness and privacy, to assess whether datasets are fit for AI.

ETSI's technical report TR 104 180 defines 18 metrics across four groups - intrinsic quality, usability and lineage, fairness, and privacy - each with calculation formulas, plus an open-source tool that scores datasets. Testing on an aircraft engine sensor dataset and a US census dataset revealed a roughly threefold gender gap in high earners (about 31% of men versus 11% of women) and two privacy failures: re-identification via age, race, sex, and country, and sensitive fields stored in plaintext. The working group included Sejong University, EGM, TTA, Daejeon University, and CNIT.

Help Net Security·Sep 7, 00:00 UTC · 9d agoAI policy

Jev: New frontier model 40-400x cheaper and 20-200x faster

TypeSafe AI launches Jev, an early-access 'System One' model delivering calibrated structured outputs claimed 40-400x faster and cheaper than LLMs.

TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released its first 'System One Model' called Jev in early access. Jev forgoes string generation and is trained with Reinforcement Learning for Calibrated Decisions (RLCD) to produce type-safe structured values with calibrated probabilities. The company claims 70-500ms response times (40-200x faster), input pricing of $0.042 per million tokens, and free output tokens via a parallel sampling architecture. Target use cases include AI-powered workflows, real-time applications, and verification/guardrail tasks.

Hacker News · security·Sep 15, 19:25 UTC · 1d agoModel releaseHN 59↑ · 11 comments

ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:42 UTC · 1d agoAI research

EU's Cyber Resilience Act starts the 24-hour vulnerability clock

EU Cyber Resilience Act reporting rules take effect, requiring manufacturers to disclose actively exploited vulnerabilities to ENISA within 24 hours, with fines reaching €15 million.

The Cyber Resilience Act's Article 14 mandatory reporting duties became applicable, requiring makers of products with digital elements sold in the EU — regardless of where they are based — to file an early warning within 24 hours of becoming aware of an actively exploited vulnerability, a detailed notification within 72 hours, and a final report within 14 days of releasing a fix. Reports must be submitted through ENISA's Single Reporting Platform to the designated CSIRT, and non-compliance with these core duties can trigger fines up to €15 million or 2.5 percent of annual turnover. Manufacturers must also inform affected users of available fixes without undue delay, and most remaining CRA provisions, including mandatory SBOMs and security-by-design requirements, become applicable on December 11, 2027.

The Register · Security·Sep 11, 11:34 UTC · 5d agoPolicy & legal

Australia is replacing the Essential Eight with a new cyber framework. Here’s how exposure management can help you get ahead of it.

Australia's ASD is replacing the Essential Eight with an outcomes-based Essentials series covering IT, cloud, OT and likely agentic AI, with deprecation from mid-2027.

The Australian Signals Directorate announced in June 2026 that the Essential Eight will be replaced by an outcomes-focused Essentials series structured as chapters covering enterprise IT (including identity and SaaS), cloud, OT, and likely agentic AI. Deprecation begins around mid-2027 with full retirement around mid-2028, though timelines are targets; the Essential Eight is mandatory for roughly 98 non-corporate Commonwealth entities but voluntary for private firms. Tenable argues the shift demands continuous security posture evidence via exposure management rather than point-in-time checklist assessments.

Tenable Blog·Sep 15, 13:32 UTC · 1d agoPolicy & legal1

Risky Bulletin: The EU publishes its upcoming cybersecurity standards

ETSI releases 17 draft cybersecurity standards vendors must meet when the EU Cyber Resilience Act takes effect in December 2027.

The European Telecommunications Standards Institute published 17 interim draft standards covering operating systems, routers, firewalls, VPNs, SIEMs, browsers, password managers, smart home devices, toys and wearables. They mandate basic security features such as post-sale updates, shipped SBOMs, modern cryptography and secure-by-default settings; public comments run until November, with final versions expected in December, one year before CRA compliance begins in December 2027. The newsletter also reports Irregular taking responsibility for AI test-environment escapes involving Anthropic and Meta frontier models, a breach at France's tax agency exposing 678,000+ citizens' data claimed by hacker ZeroBytes, and Kazakhstan eGov data covering 15 million citizens listed for sale on an underground forum. Additional briefs cover a $3.2 million Harmony Protocol theft crashing the ONE token 40%, Columbus Police still restoring systems two years after ransomware, DDoS attacks on Threema's provider, and Ukraine's GUR claiming a cyberattack on Wildberries.

Risky Business News·Aug 17, 05:03 UTC · Aug 17, 2026Policy & legal2

SenseNova-U1.5: Towards Native Unified Visual Intelligence

SenseTime releases SenseNova-U1.5, an 8B-MoT encoder-free multimodal model unifying visual understanding, reasoning, and generation with native 4K resolution.

SenseNova-U1.5 is an 8B mixture-of-transformers multimodal model with an encoder-free, VAE-free architecture that understands, reasons about, and generates visual content at native resolutions up to 4K. Post-training optimizes specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing, consolidated through multi-expert on-policy distillation. Evaluations report gains in image fidelity, text rendering, multi-reference editing, and instruction following. The team plans to open-source training code including supervised fine-tuning, reinforcement learning, and on-policy distillation.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoModel release

Cyberattack on logistics giant Ceva hits retailers and Steam customers across Europe

Cyberattack on Ceva Logistics disrupted eight European warehouses, delaying shipments for Bol, De Bijenkorf, Ajax and exposing Steam hardware buyers' data.

A cyberattack on Ceva Logistics disrupted operations at eight European warehouses, delaying shipments for Bol, De Bijenkorf, Ace & Tate, Ajax and Steam hardware customers. Attackers accessed two Ceva systems processing Bol orders, potentially exposing names, addresses, phone numbers, email addresses and order details. Valve began notifying European Steam customers whose hardware shipping data may have been compromised and is contacting data protection authorities. Ceva, with about 110,000 employees and over 1,700 facilities, has not disclosed the attackers or whether ransomware was involved.

The Record·Aug 11, 15:49 UTC · Aug 11, 2026Data breach in the wild

Former OpenAI researcher builds an AI model that judges options instead of writing text

TypeSafe AI launches Jev, a judgment-only model built by ex-OpenAI staff that classifies inputs with 70-500 ms latency instead of generating text.

Startup TypeSafe AI, co-founded by former OpenAI researcher and InstructGPT co-author Diogo Almeida, introduced Jev, a model that scores developer-defined answer options with probabilities rather than generating free-form text. The company claims 70-500 ms responses, parallel multi-question evaluation, and $0.042 per million input tokens with free outputs, targeting request routing, sales intent scoring, and assistant guardrail checks. Benchmarks are self-built and not independently verified, the 'no hallucination' guarantee only covers output structure, and access is currently via waitlist.

The Decoder·15:19 UTC · 5h agoAI industry

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining

Climate-ModernBERT domain-adapted encoders reach 76.3 average F1 across nine climate benchmarks, 2.8 points above vanilla ModernBERT-Base.

The authors continue pretraining ModernBERT-Base on three climate corpora - academic text, climate-filtered web data, and synthetic documents - and compare joint mixtures against parameter-space merging of specialized checkpoints. The best model achieves 76.3 average F1 across nine climate NLP benchmarks, a 2.8-point improvement over the vanilla baseline. Academic climate corpora provide the strongest adaptation signal, and parameter-space merging outperforms joint multi-source training while preserving complementary corpus information; all variants are released.

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

Entrust turns cryptographic inventory data into security action

Entrust adds CBOM import/export, Ansible automation, and post-quantum readiness features to its Cryptographic Security Platform.

Entrust announced new capabilities for its Cryptographic Security Platform, including CBOM import/export to build cryptographic inventories, correlate assets and dependencies, and prioritize remediation of noncompliant or vulnerable crypto. New Ansible-based automation extends certificate lifecycle management across public and private PKI, while composite algorithm and SPIRE-based capabilities support phased post-quantum migration and trusted identities for AI agents. The platform is now deployable as-a-service or on-premises, aligning with DORA, NIS2, and recent U.S. post-quantum requirements.

Help Net Security·Sep 14, 13:54 UTC · 2d agoTools

Secure Chorus Standards Etsi

Infosecurity Magazine reports on ETSI's Secure Chorus standards effort for interoperable, encrypted enterprise voice and messaging communications.

The article covers developments around Secure Chorus, an ETSI standards framework intended to enable interoperable, encrypted voice and messaging between different providers and enterprise systems. Standards work of this kind affects how vendors implement secure communications and how enterprises procure interoperable secure telephony. Further details were not available in the provided text.

Infosecurity Magazine·Aug 17, 00:00 UTC · Aug 17, 2026Industry

Enoki: Efficient Multi-Level Hallucination Detection

Researchers introduce Enoki, an open information extraction framework unifying claim-level and span-level hallucination detection in LLMs at lower inference cost.

Enoki extracts text-anchored relational facts, verifies them against evidence, and projects unsupported facts back onto hallucinated spans, so claim-level verification and span-level localization share one representation without separate alignment. It supports LLM-based, encoder-based, and rule-based extraction regimes to balance accuracy against inference cost. Experiments show it stays competitive with strong claim-level systems while using fewer resources and outperforms them on fine-grained span- and entity-level localization. The authors also release EnokiQA, a dual-granularity dataset with aligned claim-level verification and span-level localization annotations.

Hugging Face daily papers·Aug 31, 22:21 UTC · 15d agoAI research

What the first year of EU AI Act transparency enforcement could look like

Help Net Security·Aug 7, 00:00 UTC · Aug 7, 2026AI policy

Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model

Ambient team wins EgoLongQA 2026 sub-2B division by distilling an agentic long-video perception pipeline into a 2B vision-language model.

Ambient's entry to the EgoLongQA track of the Wearable-AI Challenge at ECCV 2026 placed first in the <=2B parameter division with 0.8279 on the held-out test set. The system distills the junior perception module of a tool-using agentic pipeline into a 2B student, reaching 89% of the pipeline's accuracy with 1.1% of its parameters and lifting a 27.1% base model to 81.4%. To meet the division limit, the multilingual embedding table is pruned from 248,320 to 143,469 rows, reaching 1.9985B parameters with provably identical logits on retained rows.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research

The Pelican comparison grid for Astra is pretty interesting

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

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

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

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

Rosetta ranks 4th and 5th in AlexandriaX-2026 dialectal Arabic dialogue translation using a LoRA adapter on NileChat-3B, finding limited pretraining benefit.

The Rosetta system for the AlexandriaX-2026 shared task fine-tunes a LoRA adapter on NileChat-3B for context-aware English-to-dialectal Arabic dialogue translation. The adapter was additionally pretrained on MADAR and PADIC dialect corpora for the unconstrained track. It achieved spBLEU 26.10 (4th, constrained) and 25.09 (5th, unconstrained). External dialect pretraining improved only two of thirteen dialects while slightly degrading overall performance, indicating negative transfer.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:15 UTC · 7d agoAI research1

WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data

WearableQA benchmark introduces 4,084 questions over real longitudinal wearable data, showing 14 LLMs score 19.6-72.9% on health reasoning, far from solved.

WearableQA is a benchmark of 4,084 10-option multiple-choice questions built from wearable time series, blood biomarkers, and demographics of 200 real users with up to 500 days of daily measurements. It defines 16 question types along two axes: data versus health reasoning, and single- versus cross-signal reasoning, using a dual-grounding framework combining literature and population-validated patterns. Evaluations of 14 proprietary and open-source LLMs show accuracy ranging from 19.6% to 72.9% against a 10% chance baseline, with most models below 60%.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

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.CL·Sep 10, 17:39 UTC · 6d agoAI research1

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research