Search: “real-time-analytics”

PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control

PhysStream autoregressive video model enables physics-grounded mid-generation motion control, cutting trajectory error 12% and FVMD 33% versus strongest baselines.

PhysStream is an autoregressive image-to-video model that incorporates structured scene memory—positional maps and object tracking maps derived online from previously generated frames—and supports fine-grained motion control via sparse velocity-increment signals encoding physical quantities. Training proceeds in two stages: a bidirectional model finetuned with motion-control conditioning, then a causal autoregressive model with scene memory. It reduces motion distribution distance (FVMD) by 33% and trajectory error by 12% over the strongest baselines, and human evaluators prefer it in over 85% of in-the-wild comparisons.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:55 UTC · 19h agoAI research 2 sources

Real-Time Intelligence with IBM Time Series Models on Confluent

IBM Research post on the Hugging Face blog describes running IBM time series models on Confluent for real-time intelligence.

Hugging Face's blog published an IBM Research post titled 'Real-Time Intelligence with IBM Time Series Models on Confluent.' No article body was available for classification, so details are limited to the title. The title indicates guidance on deploying IBM time series models alongside Confluent streaming infrastructure for real-time analytics.

Hugging Face Blog·Sep 2, 13:49 UTC · 13d agoAI tools & infra

Realtime-Venus: A full-duplex interaction system with asynchronous delegation

Realtime-Venus introduces two 9B full-duplex interaction models (Omni and Audio) that outperform Gemini 3.1 Live and GPT-4o on continuation metrics.

Realtime-Venus is a proactive full-duplex interaction system built on two separately trained 9B models: Realtime-Venus-Omni for audio-visual interaction and Realtime-Venus-Audio for spoken interaction. A dual-loop runtime lets foreground interaction continue while Realtime-Venus-Harness asynchronously executes background reasoning and tool tasks. Realtime-Venus-Omni leads on six of eight video benchmarks, including StreamingBench (70.2%), OVO-Bench (64.7%), and Daily-Omni (81.3%), while Realtime-Venus-Audio tops MMAU (78.0%) and MMAU-Pro (63.2%). On Full-Duplex-Bench v1.5, Realtime-Venus-Audio handles 75% of interruptions and exceeds Gemini 3.1 Live and GPT-4o on all three continuation metrics.

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

Mars Security Launches Real-Time Intel-to-Detection Engine That Turns Live Threat Intelligence Into Backtested Detections in Minutes

Mars Security launched Real-Time Intel-Based Detection, converting advisories into MITRE ATT&CK-mapped, backtested detection rules for CrowdStrike, Wiz, and Splunk within minutes.

The capability turns newly published threat intelligence from CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence into validated detection rules within minutes, each backtested against 30 days of the customer's own telemetry before deployment. Rules are written in native query languages across CrowdStrike Falcon, Wiz, Splunk, firewalls, Linux Sysmon, identity providers, AWS telemetry, and data lakes such as Snowflake and Databricks, with no data ingestion or stack changes. The feature is available at no additional cost to existing customers and on AWS Marketplace. Mars also flags detection coverage gaps and extends monitoring to credentials leaked by AI coding agents.

Cyber Security News·Sep 8, 13:20 UTC · 7d agoTools1

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

Benchmark Radar provides a living searchable database of 1,283 AI benchmark records and 12,916 score observations drawn from 37 daily discovery sources.

Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases from 13 direct connectors and 24 first-party feeds into a searchable catalog with model card mentions and score histories. The catalog contains 1,283 source records drawn from 4 benchmark catalogs plus 12,916 numeric observations on 790 records. The release includes a web dashboard with leaderboard, Pareto frontier of score versus usage, saturation and trend views, daily feeds, a CLI, and reproducible analysis. The paper audits the full catalog and examines benchmark saturation and limits of score comparisons.

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

MasterControl Seventeen Every Time

Governed enterprise analytics study shows deterministic policy execution matched 110/110 answer-and-evidence contracts while runtime agent planning matched none.

The paper studies a governed approach where a language model interprets the question while deterministic policy selects and runs a pre-approved analytical program returning results and evidence. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B only interpreted intent and policy executed the approved program. None of 330 runtime-planning episodes satisfied the full answer-and-evidence contract, whereas the policy-executed analyzer matched 110 of 110. The authors note this is configuration-specific and expressiveness is preserved via relational operations, aggregation, comparison, windows, ranking, and similarity with replayable results.

Hugging Face daily papers·Sep 1, 20:00 UTC · 14d agoAI research

Analysts got 19 minutes back every hour in Stellar Cyber's agentic auto triage trials

Help Net Security·Aug 4, 00:00 UTC · Aug 4, 2026Industry

Akamai Valkey Managed Database: Real-Time Memory for Enterprise AI

Akamai launched Valkey Managed Database, a low-latency in-memory data layer aimed at cutting AI inference costs and accelerating RAG.

Akamai introduced Valkey Managed Database, a managed in-memory data service based on the open-source Valkey project. The company positions it as real-time memory for enterprise AI, optimizing inference costs, accelerating retrieval-augmented generation, and powering real-time AI agents.

Akamai Blog·Aug 18, 13:00 UTC · 29d agoAI tools & infra

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

ReactVAU is a slow-fast streaming framework for real-time video anomaly understanding that reserves heavyweight MLLM reasoning for suspicious events, improving efficiency.

ReactVAU addresses causal streaming video anomaly understanding with three components: a lightweight Fast Detection Module using Spatial Grid Folding, Anomaly-Aware Persistent Memory that protects critical visual cues from temporal decay, and a Slow Reasoning Module activated only on suspicious events. This design minimizes heavyweight MLLM invocations during long normal intervals. Experiments show competitive anomaly detection and causal reasoning under strict streaming constraints with significantly enhanced computational efficiency.

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

Google's new AI model predicts the future from sales data, weather, and discount schedules

Google Research released TimesFM-3, a 330M-parameter multivariate time series forecasting model that tops Gift-Eval, FEV-Bench, and Time benchmarks and is on Hugging Face.

Google Research released TimesFM-3, a 330-million-parameter Transformer-based time series forecasting model trained on more than one trillion real and synthetic data points. It works zero-shot and adds multivariate support, ingesting related series, historical-only covariates, and known future events such as discount schedules and weather forecasts, while filling all future time steps in a single one-shot pass. Google reports first place among pretrained forecasting models on Gift-Eval, FEV-Bench, and Time, ahead of Amazon's Chronos-2, the Toto-2.0 family, and its own TimesFM-2.5. Weights are available on GitHub and Hugging Face, with BigQuery integration planned in the coming weeks.

The Decoder·Sep 12, 09:26 UTC · 4d agoModel release2

RapidFort Runtime brings continuous CVE monitoring and tamper detection

Help Net Security·Aug 4, 00:00 UTC · Aug 4, 2026Vulnerability

Can AI make threat intelligence easier? One platform thinks so

Feedly positions its AI-driven Threat Intelligence platform to cut CTI collection time, scanning 10,000+ sources with 1,000 AI models and reporting 70% time savings.

A profile of Feedly Threat Intelligence describes how CTI teams such as RH-ISAC reduced threat data collection from 10 hours weekly to 2-3 hours after adoption. The platform scans over 10,000 open web sources using 1,000 AI models to extract TTPs, CVEs and IoCs into a real-time Threat Graph queryable via an Ask AI LLM with source citations. Customers including GreyNoise, Sopra Steria, gematik and GISA reported measurable gains such as 20 hours saved daily and earlier vulnerability flagging than CISA 65% of the time. The product integrates with Anomali ThreatStream, Cortex XSOAR, Microsoft Sentinel and OpenCTI, plus a REST API and GitHub scripts.

Help Net Security·Aug 24, 04:21 UTC · 23d agoTools

Intelligence-Driven SOC: Modernizing Threat Monitoring and Detection Engineering for Ultimate MTTR Reduction

ANY.RUN urges SOC teams and MSSPs to adopt intelligence-driven threat monitoring to reduce mean time to respond.

ANY.RUN published a vendor blog post arguing that threat monitoring is the connective tissue of modern security operations and that SOC teams and MSSPs must move from simple log collection to a proactive, intelligence-driven framework. The piece promotes ANY.RUN's Threat Intelligence offering as the solution for detection engineering and MTTR reduction. It is promotional content rather than a threat disclosure or research finding.

ANY.RUN·Aug 12, 07:58 UTC · Aug 12, 2026Industry

Mars Security Debuts Automated Threat Engine Processing Live Cyber Intelligence Into Validated Rules Within Minutes

Mars Security launches Real-Time Intel-Based Detection, converting threat intelligence advisories into validated, ATT&CK-mapped detection rules within minutes for SOCs.

Mars Security, an autonomous threat hunting and detection engineering platform founded by former offensive security operators, announced Real-Time Intel-Based Detection. The capability ingests advisories from sources like CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence, maps indicators to MITRE ATT&CK, and authors native query logic across connected infrastructure including CrowdStrike Falcon, Wiz, Splunk, Sysmon, identity providers, Snowflake, and Databricks. Every rule is backtested against 30 days of historical telemetry to quantify false positives before analyst approval and one-click deployment. The feature is free for existing customers and available via AWS Marketplace.

CSO Online·Sep 8, 15:05 UTC · 7d agoTools1

Introducing AI Assistant for Akamai Web Security Analytics

Akamai launched an AI Assistant for Web Security Analytics, giving SOC and AppSec teams natural-language event investigation and guided response actions.

Akamai introduced an AI Assistant integrated with its Web Security Analytics offering, designed to let SOC and AppSec teams investigate security events faster. The assistant supports natural-language queries and guided action recommendations for analysts. The announcement is a vendor product launch with no incident details.

Akamai Blog·Sep 10, 13:16 UTC · 5d agoTools

StepAudio 3 Realtime Technical Report

StepAudio 3 Realtime debuts an audio-language model with Think-While-Speaking reasoning, delivering full-duplex voice dialogue with top benchmark results.

StepAudio 3 Realtime is an audio-language foundation model built around a continuous listen-converse-think-act loop for real-time spoken interaction. Think-While-Speaking runs private reasoning in parallel with speech, reaching a 73.0 macro average on StepAudioChat in reasoning mode. The model reports 90.6 on MMSU, 98.9 overall on the Artificial Analysis Full-Duplex Bench, and 56.0% macro task success on tau-Voice. An integrated Voice Agent handles asynchronous tool execution without disrupting dialogue flow.

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

Searchlight Cyber combines exposure and threat intelligence in new PTEM platform

Searchlight Cyber launched its PTEM platform, merging exposure management from Assetnote with dark web threat intelligence to prioritize likely-exploited exposures.

Searchlight Cyber launched its Preemptive Threat Exposure Management (PTEM) platform, unifying Searchlight Exposure (formerly Assetnote) with Searchlight Threat, which consolidates the Cerberus investigations platform and DarkIQ dark web monitoring. The platform pairs continuous attack surface discovery and exploitability validation with attacker intelligence from the open, deep and dark web to help teams prioritize the exposures most likely to be exploited. The launch follows the 2025 Assetnote acquisition and a new corporate identity, and cites a Gartner forecast that preemptive security will reach 50% of IT security spending by 2030.

Help Net Security·Aug 13, 00:00 UTC · Aug 13, 2026Tools

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.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 5d agoAI research

Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

Real-SWE benchmark tests coding agents on licensed private enterprise codebases; top model Fable 5.1 resolves only 38.8% of tasks.

Real-SWE is a new benchmark evaluating frontier AI coding agents on tasks drawn from private production codebases licensed from real companies, spanning billing, tax calculation, and cross-service migrations. Fable 5.1 with Claude Code leads at 38.8% resolution rate (pass@1 over eight runs), followed by GPT-6 Astra Codex CLI at 33.8% and Gemini 3.8 Flash Gemini CLI at 31.2%. Tasks use native harnesses and realistic tooling including Docker, Kubernetes, PostgreSQL, Redis, and Linear; median reference solutions edit 11 files versus 6 for DeepSWE and FrontierCode.

Hacker News · AI·Sep 12, 20:25 UTC · 3d agoAI researchHN 45↑ · 31 comments

Mars Security brings threat intelligence to detection in real time

Mars Security launched Real-Time Intel-Based Detection, converting advisories from CISA and Mandiant into backtested MITRE ATT&CK-mapped detection rules for CrowdStrike, Wiz, and Splunk.

Mars Security announced a capability that automatically turns newly published threat intelligence from sources like CISA, Mandiant, Unit 42, and Microsoft into MITRE ATT&CK-mapped detection rules. Each rule is written in the native query language of the customer's telemetry (CrowdStrike Falcon, Wiz, Splunk, firewalls, identity providers, AWS, Snowflake, Databricks) and backtested against 30 days of the customer's data before deployment, with indicator scoring to drop noisy or stale indicators. The platform also maps existing detection coverage, flags gaps such as AWS CloudTrail tampering and pass-the-hash movement, and delivers some recommendations as open pull requests for detection-as-code workflows.

Help Net Security·Sep 8, 13:43 UTC · 7d agoTools

OpenAI reports AI "research interns" and warns about its own pace at the same time

OpenAI claims its automated research intern milestone is met, with agents now doing 3.1 workdays per human day, while Pachocki warns monitoring is weakening.

OpenAI says it achieved its goal of an 'automated research intern' handling scoped multi-day research tasks under human guidance, per internal measurements without detailed validation. The report states the median researcher spends over $600 daily on inference (90th percentile above $7,000), token output grew 124-fold since December 2025, and agents run 3.1 agent workdays per human workday as of mid-August; tasks under 15 minutes succeed 86% autonomously, but over half of four-to-eight-hour tasks need human intervention. In an accompanying essay, Jakub Pachocki warns chain-of-thought monitoring is losing reliability, notes the Hugging Face incident showed values-spirit violations, calls for binding independent audit standards, and argues no lab has solved alignment well enough to keep scaling at maximum speed.

The Decoder·Sep 7, 13:05 UTC · 8d agoAI industry

AI-Driven Threat Intelligence for Gulf Enterprises: Why Detection Speed Is Now a Regulatory Requirement

Cyble argues GCC regulators' 6-72 hour breach-notification deadlines make AI-powered detection essential for Gulf enterprises.

Cyble's blog highlights that the UAE Information Assurance Standard v2 requires incident notification within 6 hours of detection, while Saudi Arabia's SAMA cybersecurity framework and NCA Essential Cybersecurity Controls converge on 72-hour reporting. It argues that compliance clocks start at detection, not response, and that IAS v2 mandates 24/7 monitoring with defined SLAs for Tier 1 critical infrastructure entities. The piece promotes Cyble Vision's AI-powered threat intelligence for continuous exposure monitoring and audit-ready detection logs.

Cyble·Sep 4, 13:23 UTC · 11d agoIndustry

Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

Case study shows autonomous LLM research reaches 90% of SOTA on telecom ticket retrieval in 10 weeks versus 10 months human work.

The paper explores adapting autonomous research to open-ended, industry-grade ML problems through a telecom ticket retrieval case study with commercial and open-source agents. Autonomous research reached 90% of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in 10 weeks versus 10 months of human work, at up to $200 per Cursor campaign. The authors find agents excel at narrow hyperparameter optimization but lack human-like intuition, recommending human-agent collaboration.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:09 UTC · 4d agoAI research

Citrix adds AI-powered browser activity analysis to SecurAccess

Citrix launched Session Insights for SecurAccess with Chrome Enterprise, using AI to record and analyze browser activity from users and autonomous agents.

Citrix Session Insights adds automatic session recording and AI-powered risk detection for browser activity by human users and autonomous AI agents within Citrix SecurAccess with Chrome Enterprise. The capability creates visual forensic records, highlights risky behavior for faster investigations, and recommends policy adjustments or changes to agent authority levels. It is designed to support audits and governance as enterprise AI agent workflows expand.

Help Net Security·10:14 UTC · 2h agoTools

Proofpoint Brings OpenAI GPT Cyber Models into Security Operations to Help Defenders Investigate Threats Faster

Proofpoint launched its SOC Analyst Agent, an agentic investigation tool powered by OpenAI Daybreak models, now in private preview with Q3 GA planned.

Proofpoint introduced the SOC Analyst Agent, the first capability to emerge from its membership in the OpenAI Daybreak Defense Network, which it joined in June 2026. The agent converts natural-language questions into structured, traceable investigation findings across Proofpoint alerts, logs, DLP events and user risk signals, while leaving remediation decisions to human analysts. It is in private preview with select beta customers, and general availability is expected by the end of Q3 2026. Proofpoint cites its 2025 report finding that 54% of organizations already use AI-enhanced capabilities to triage and investigate alerts.

Proofpoint Threat Insight·Sep 3, 11:53 UTC · 13d agoAI industry

Rethinking Heterogeneous System Disaggregation for Subquadratic Attention

SQD disaggregates LLM inference by quadratic versus subquadratic attention layers, improving energy efficiency up to 56% on heterogeneous systems versus GPU-only baselines.

SQD (SubQuadratic Disaggregation) splits decode not by operator type but by quadratic versus subquadratic attention, matching their distinct arithmetic intensity and memory footprints. For sparse attention LLMs it separates top-k selection (requiring full KV indexing) from top-k attention plus FFN; for linear and sliding-window models it separates dense attention layers from subquadratic layers plus FFN. On an adjusted 8xB200 heterogeneous proxy, tokens-per-joule improves 53% on GLM 5.2, 31% on Nemotron 3 Ultra, and 56% on Gemma 4 31B. A Rubin plus LPX analytical model shows 1.2x-1.5x tighter achievable latencies and up to 3.6x higher throughput versus attention-FFN disaggregation.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:55 UTC · 4d agoAI research

TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents

TRACE, a training-free visual token pruning framework, cuts GUI agent inference latency and memory while keeping trajectory-wide visual evidence reusable.

TRACE is a training-free framework for trajectory-robust admission and coverage-aware evidence ordering that prunes high-resolution screenshot tokens accumulated in GUI agent trajectories. It ranks visual evidence using a query-independent layout-derived interaction prior combined with instruction relevance and feature novelty, and reserves part of the budget for native tokens distributed across the screen to repair spatial coverage. A monotone KV contraction incrementally compresses retired frames into compact session state, avoiding repeated visual encoding or pruning. Experiments across six GUI benchmarks and diverse models verify effectiveness under tight budgets, with source code to be released.

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

Akeyless adds real-time enforcement for AI agents in production

Akeyless launches Agentic Runtime Authority, enforcing real-time intent-based access control on AI agent actions, with integrations for Claude Enterprise, OpenAI Codex and Amazon Bedrock AgentCore

Akeyless announced general availability of Agentic Runtime Authority, a real-time identity control layer that evaluates AI agent actions and blocks policy-violating actions before execution, layered on top of its SecretlessAI credential brokering. New integrations cover Claude Enterprise, OpenAI Codex and Amazon Bedrock AgentCore, alongside an Agentic Access Dashboard with session kill switches and event forwarding to Splunk, Datadog and Microsoft Sentinel. The vendor's platform already secures over 220 billion machine identity interactions for Fortune 500 organizations.

Help Net Security·updated Sep 11, 04:00 UTC · 5d ago·first Sep 9, 13:54 UTC · 6d agoTools 2 sources1

New insights from Google’s AI & Economy ATLAS

Google launches an interactive AI & Economy ATLAS experience; new research shows nearly half of surveyed scientists use AI daily.

Google introduced new interactive, open-access data visualizations for its AI & Economy ATLAS project tracking global AI adoption patterns. Research from Google, Google DeepMind, and MIT FutureTech analyzed 2,600 specialized AI models and surveyed over 600 U.S. and U.K. scientists, finding nearly half use AI daily and report saving almost seven hours per week. The study also found validation bottlenecks and a growing backlog of untested hypotheses limiting research productivity gains.

Google · AI·Sep 15, 13:00 UTC · 1d agoAI industry