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CategoryAll
Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
Safety overview: GPT-6 Astra
OpenAI's GPT-6 Astra is its most capable broadly deployed model and first to reach Critical cybersecurity capability under the Preparedness Framework.
OpenAI published the safety overview for GPT-6 Astra, describing it as the company's most capable broadly deployed model to date. Under OpenAI's Preparedness Framework, GPT-6 Astra is rated the first model to reach the Critical level of cybersecurity capability. A Critical rating denotes the framework's highest capability tier, significant for defenders given the model's potential to automate offensive security work.
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Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization
ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.
Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).
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Introducing Unit 42’s Attribution Framework
Unit 42 releases its Attribution Framework, a systematic method using Diamond Model and Admiralty scores to attribute activity clusters to named threat actors.
Palo Alto Networks' Unit 42 introduced a structured framework for threat actor attribution built on the Diamond Model of Intrusion Analysis and Admiralty reliability/credibility scoring. The framework tracks activity at three levels: activity clusters (named CL-STA, CL-CRI, CL-UNK, or CL-MIX), temporary threat groups, and named threat actors using the constellation naming schema. Analysts score evidence across TTPs, tooling, malware code, OPSEC, infrastructure, timelines, and victimology to decide when to merge or elevate clusters, avoiding premature group naming.
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How to build an exposure management program the business trusts: Lessons from Tenable’s CSO
Tenable's CSO describes an AI-driven exposure management program that consolidates tool sprawl and translates cyber risk for boards.
A Tenable blog post shares lessons from CSO Robert Huber on moving to an AI-driven exposure management program. It argues tool sprawl and data silos hinder holistic risk assessment and that exposure management unifies attack-surface data into business-level metrics for the C-suite.
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Operational Resilience: IT Security Risks with Reduced Staffing | Huntress
Huntress blog advises security teams on managing change, risk, and incident response during reduced-staffing holiday periods.
The article discusses how holiday-period staffing reductions change organizational risk profiles around change management, monitoring, and incident response capability. It argues against blanket change freezes when critical vulnerabilities with high exploitation probability demand patching, and stresses retaining decision-making authority, escalation paths, and recovery knowledge. It concludes by promoting Huntress Managed Response, which lets the Huntress SOC take predefined containment actions on confirmed threats without customer intervention.
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Path to Astra: critical capabilities and frontier safeguards
OpenAI says Astra is its first model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, launching with stronger safeguards.
OpenAI published 'Path to Astra', announcing that Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under its Preparedness Framework. The company says the model will be released with strengthened frontier safeguards, marking a milestone in frontier capability and safety evaluation.
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Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.
The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.
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Guide to Cybersecurity Budget Planning: How Much + How To | Huntress
Huntress publishes guidance on planning cybersecurity budgets and getting more value from security spend.
Huntress released a guide aimed at helping businesses plan their cybersecurity budgets without overspending. The content is promotional guidance on prioritizing security spend rather than incident reporting. No vulnerabilities, incidents, or threat activity are described.
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AI's Vulnerability Surge May Be More Manageable Than First Feared
New research argues the coming surge of vulnerabilities will be manageable for enterprise security teams that adopt the right triage and prioritization strategies.
A Dark Reading write-up of new research suggests the anticipated explosion in vulnerability volume may be less overwhelming than feared. The analysis indicates enterprise security teams can cope if they apply appropriate strategies for handling vulnerability influx. The piece is framed as guidance rather than a disclosure of specific flaws or incidents.
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Causal Foundation Models
A paper introduces causal foundation models (CFMs): pretrained networks that estimate treatment effects on new datasets via in-context learning without fine-tuning.
Causal foundation models (CFMs) apply the foundation-model paradigm to causal inference, replacing bespoke per-problem estimator pipelines with networks pretrained once at scale. CFMs estimate causal quantities such as the average treatment effect on entirely new datasets through in-context learning, without model updates. The work serves as a practical introduction to the emerging area, covering background in causal inference and machine learning and including example code and Jupyter notebooks.
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Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness
Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.
A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.
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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.
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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.
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Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics
Researchers model curriculum learning as Wasserstein transport over difficulty distributions, finding curriculum benefits are strongly task- and budget-dependent with no dominant strategy.
The framework represents curricula as trajectories of training distributions over discrete difficulty levels, decoupling ordering, matched exposure, endpoint smoothness, and pacing. Across a calibrated suite of 12 tasks and 33 difficulty axes under fixed training budgets, no single strategy dominates, though easy-to-hard ordering improves hard-level performance relative to exposure-matched static sampling. Endpoint smoothness and pacing substantially affect where along the difficulty spectrum a curriculum is effective, and the transport view supports extensions to learned pacing and structured difficulty spaces.
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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.
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What is Proactive Threat Intelligence? | Recorded Future
Recorded Future publishes a vendor explainer on proactive threat intelligence, arguing external adversary context helps teams act before alerts fire.
Recorded Future published a conceptual blog on proactive threat intelligence, describing how external context on adversaries, infrastructure, stolen credentials, and vulnerability exploitation helps security teams act before intrusions surface internally. The piece outlines a four-step program: defining intelligence requirements, collecting external sources including OSINT and dark web, analyzing relevance to the organization, and driving security actions. Use cases include prioritizing CVEs by real-world exploitation activity and identifying external exposure before it becomes an internal incident.
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Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.
The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.
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Making Knowledge Distillation Cheap Enough to Run at Scale
Hugging Face blog by Multiverse Computing describes techniques making knowledge distillation cheap enough for large-scale training.
A Hugging Face blog post from Multiverse Computing (CAI) presents methods for reducing the cost of knowledge distillation so it can be run at scale. The post is aimed at practitioners compressing large models into smaller, cheaper ones for production use.
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Threat Matrix: Mapping threats across cloud web applications
Microsoft released a Cloud Web Applications Threat Matrix, a MITRE ATT&CK-aligned framework mapping threats to cloud-hosted web apps and serverless platforms.
Microsoft introduced the cloud web applications threat matrix, which organizes attack techniques for cloud-hosted web apps and serverless platforms using MITRE ATT&CK tactics, from resource development through impact. The framework covers attack paths spanning application code, managed runtimes, workload identities, and deployment pipelines, cataloging techniques such as subdomain takeover, code injection in connected repositories, compromised registry images, exposed admin interfaces, and serverless trigger injection. It builds on Microsoft's earlier Kubernetes and storage services matrices to help defenders identify visibility gaps, prioritize hardening, and plan investigations in cloud-native environments.
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Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence
New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.
An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.
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Pacing model development in an era of cyber-critical capabilities
OpenAI details strengthened monitoring, alignment, and security safeguards that will guide the pace of frontier model development as cyber capabilities grow.
OpenAI outlined new safeguards around monitoring, alignment, and security intended to guide the pacing of frontier model development. The post frames these measures in the context of models reaching cyber-critical capabilities. It describes safety process and deployment policy rather than a specific incident, model, or vulnerability.
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RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems
Researchers introduce RESCUE-Bench, a video benchmark of 191 couple and family conversations evaluating LLMs on relation-aware multi-party emotional support.
RESCUE-Bench is built from real couple and family interview conversations, containing 191 samples, 7,079 annotated turns, and 1,064.8 minutes of video. It defines six tasks measuring two capabilities: Relational Understanding and Relation-Sensitive Support. Experiments with ten LLMs show models handle local emotional cues but struggle with relation pattern prediction, viewpoint prediction, and support strategy prediction.
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Is Cyber Facing an Affordability Crisis?
Dark Reading analysis argues record breach costs and roughly $240 billion in cyber defense spending leave small businesses dangerously exposed, threatening supply chains.
The analysis examines an affordability crisis in cybersecurity, noting breach costs have reached record highs while defense spending approaches $240 billion. It argues small businesses are dangerously under-protected relative to rising attack costs. Weak small-business defenses are framed as a supply chain security risk for larger organizations.
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Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs
Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.
Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).
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An Evidence-First Multi-LLM Framework for Auditable Critical-Infrastructure Dependency Modeling
Evidence-first multi-LLM framework builds auditable critical-infrastructure dependency graphs while preserving provenance and unresolved cases.
The framework constructs Infrastructure Knowledge Bases and Infrastructure Dependency Graphs from heterogeneous infrastructure documentation using multiple open-weight LLMs that independently extract candidate entities and dependencies from normalized evidence. It separates evidence verification, ontology grounding, entity resolution, dependency alignment, validation, fusion, and human review, projecting the validated IKB deterministically into the IDG without new LLM-generated knowledge. Evaluation across nine infrastructure projects shows entity recovery achieves substantially higher recall than full dependency recovery, and cross-model overlap is much lower for dependencies than entities, indicating models often produce non-overlapping candidate assertions rather than stable consensus.
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From ‘High/Medium/Low’ to Dollars: Making Cyber Risk Legible to Your CFO
Cyble argues security teams should express cyber risk in financial terms for CFOs instead of high/medium/low ratings, citing its 2025 threat forecast results.
Cyble published guidance on cyber risk quantification, arguing qualitative high/medium/low ratings fail to convey financial exposure to executives. The piece notes that over 80% of its 2025 threat predictions, including AI-driven ransomware and supply-chain attacks, materialized as anticipated.
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K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.
K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.
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Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings
Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.
Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.
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One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation
A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.
The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.
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