Search: “DINO”

Smart search ranks by meaning as well as keywords (one row per story, last 45 days).

Hackers Deploy New SloppyRAT via ClickFix to Enable Ransomware Lateral Movement

Zscaler discovered SloppyRAT, a ClickFix-delivered RAT that gives ransomware operators reconnaissance, remote execution, and SOCKS proxy pivoting for lateral movement.

Zscaler identified SloppyRAT in June 2026, linked to a ransomware-related threat actor. The infection chain starts with a ClickFix lure abusing finger.exe over TCP port 79, then uses curl.exe, IronPython, CastleLoader, and CastleRAT before reflectively loading SloppyRAT as a DLL in memory. The RAT supports HTTPS command-and-control, system inventory, file operations, Defender tampering, and a reverse SOCKS proxy, with evasion features including runtime code encryption, indirect syscalls, certificate pinning, and a possible EtherHiding blockchain fallback. Its persistence code is buggy and the malware appears under development, but the pre-encryption foothold gives defenders a window to stop intrusions.

Cyber Security News·Sep 11, 09:12 UTC · 5d agoMalware in the wild 3 sources

52

Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection

UCF-Net fuses CLIP and DINO features with entropy-based uncertainty weighting to improve generalizable deepfake image detection across generators.

Researchers propose UCF-Net, an uncertainty-aware cascaded fusion network that combines CLIP's language-aligned semantic priors with DINO's self-supervised visual-structure priors for deepfake detection. It aggregates hierarchical features across transformer depths via layer-wise expert modules and performs weighted fusion driven by entropy-derived uncertainty. The authors consolidate public deepfake datasets into a unified benchmark of roughly 4 million images plus a cross-generator set of over 8,000 faces from eight recent generators, where UCF-Net achieves the best mean AUC among evaluated methods, though zero-shot transfer remains challenging.

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

40

Show HN: Self-hosted company OS, Claude Code and Codex agents in departments

OtoDock, a self-hosted company OS that organizes Claude Code and Codex AI agents into departments, was launched on GitHub via Show HN.

OtoDock is a self-hosted 'company OS' shared on GitHub through a Show HN post, presenting Claude Code and Codex AI agents organized into department-style teams. The Hacker News feed entry shows the post reached 20 points with 5 comments; no further technical details are provided in the available text.

Hacker News · security·Sep 9, 17:57 UTC · 6d agoAI tools & infraHN 20↑ · 5 comments1

18

Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization

SG-JEPA world model conditions latent prediction on physical parameters, halving open-loop prediction error versus DINO-WM and boosting robotic control success.

SG-JEPA extends the LeWorldModel JEPA framework by supplying the governing physics parameter to the temporal model via action-conditioning and jointly training an encoder and predictor through autoregressive latent rollout. On out-of-distribution gravitational-field tasks it reduces open-loop prediction error by up to 2x versus DINO-WM on 2D datasets and increases 3D robotic control success rate up to 2.5x using independently trained diffusion policies. A linear feature analysis attributes most of the gain to the encoder learning features that the predictor can carry forward through rollout.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:08 UTC · 6d agoAI research

45

FlashVector: Agent for Hierarchical Model Serving Stack Optimization

FlashVector agent optimizes all layers of Unity's ad-serving stack, delivering up to 2x model-server throughput and 1.98x latency speedup in production.

FlashVector is an agentic system that optimizes performance across GPU kernels, ML framework computation graphs, model servers, and on-demand feature processing. Deployed in Unity's Vector advertising platform, it achieved up to 2x model-server throughput increase, 1.98x latency speedup, and 1.6x feature-store throughput gain. Optimizations spanned NVIDIA Triton's C++ codebase and the Python feature transformation service, demonstrating extensibility beyond single-kernel tuning.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:27 UTC · 22h agoAI research

30

Modality-Autoregressive World-Action Models

ModAR autoregressively denoises multiple future modalities (point tracks, DINO features, depth) before predicting actions, beating prior world-action models at all data scales.

ModAR is the first world-action model (WAM) to autoregressively denoise multiple future modalities before predicting actions, letting each prediction condition on previously generated modalities. Training from scratch shows WAMs benefit from predicting point tracks, DINO features, and depth maps, while future RGB adds no consistent benefit. ModAR's sequential generation outperforms existing WAM formulations with the highest average success rate at all evaluated data scales. It slightly beats video-model-initialized Flex-π (75% vs 72% success) using roughly 20x fewer training FLOPs and no pretraining, and wins on three real-world bimanual tasks.

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

35

Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation

Researchers present Puppeteer, a posture-aware, object-grounded diffusion model generating physically consistent co-speech gestures with temporal control.

Puppeteer decomposes long gestures into structured primitives encoded by a causal variational autoencoder into temporally ordered latent tokens. Conditional diffusion in the causal latent space conditions on speech signals, motion history, an initial posture reference, and object geometry to synthesize physically consistent gestures. The authors also introduce new evaluation metrics and release SceneGes, the first curated synthetic 3D dataset of embodied co-speech gestures with corresponding 3D objects.

Hugging Face daily papers·Aug 30, 20:00 UTC · 16d agoAI research1

18

AsyncRAT Malware Abuses AutoIt and PowerShell to Hide Inside Legitimate Windows Process

Point Wild details a five-stage AsyncRAT campaign using AutoIt abuse and process injection to hide a .NET RAT inside Microsoft's signed charmap.exe.

Researchers at Point Wild Threat Intelligence analyzed a campaign starting from a lure batch file ('Right-click to open Invoice Details.bat') that launches hidden PowerShell to reconstruct a Base64/XOR-obfuscated payload. The stage drops a renamed signed AutoIt interpreter and loader script into %LOCALAPPDATA%\Temp and achieves persistence via a batch file in the user's Startup folder (T1547.001). The loader decrypts the payload in memory with single-byte XOR key 0x36 and injects it into SysWOW64\charmap.exe using OpenProcess/VirtualAllocEx/WriteProcessMemory/CreateRemoteThread, with PE-sieve confirming an in-memory implanted PE and patched AMSI modules. The final AsyncRAT payload performs screen capture and data theft, with observed C2 at 158[.]51[.]122[.]136:4944 over raw TCP.

GBHackers·Sep 14, 05:24 UTC · 2d agoMalware3· 1 read

48

THost9 Android RAT Pairs Packed Loader With ADB Worm

Dark Atlas details THost9, a packed Android RAT paired with an ADB worm that installs itself on devices with exposed Android Debug Bridge services.

Dark Atlas researchers described the Hagaseca cluster, whose THost9 packed loader hides executable code in an embedded asset decoded with single-byte XOR and gzip before loading a tc9.dex second stage. The second stage adds shell execution, file transfer, tunneling, reverse shells, downloadable modules and a self-propagating ADB worm that expands single addresses into 65,025-host scan ranges. Newer builds include anti-analysis checks for Frida, and incident reports connect THost9 and THost4 to Android phones and Redroid containers with exposed ADB from October 2024 through 2026.

Infosecurity Magazine·Sep 8, 11:15 UTC · 8d agoMalware in the wild

55

HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness

Researchers introduce HarnessVLN, a zero-shot training-free agent harness that sets new training-free SOTA on vision-language navigation benchmarks including R2R and HM3D.

HarnessVLN is a zero-shot, training-free framework for embodied vision-language navigation that coordinates perception, retrieval, grounding, navigation, recovery, and termination through a unified tool interface. It validates planner proposals against spatial evidence, geometric feasibility, and subgoal consistency, using hierarchical event memory and a persistent Spatiotemporal Graph that stores reusable spatial evidence and failure annotations. It reports success rates of 60.8% on R2R, 53.9% on RxR, 76.0% on HM3D-v2, and 59.3% on HM3D-OVON, surpassing prior training-free state of the art, with real-world humanoid deployment demonstrated.

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

30

Hackers Use Cross-Platform Noodle RAT to Secretly Control Windows and Linux Systemsnew

Check Point identifies Noodle RAT as a distinct cross-platform Windows/Linux backdoor used by Chinese-speaking actors against Asia-Pacific organizations since 2016.

Check Point assesses Noodle RAT, also known as ANGRYREBEL, as a distinct backdoor family rather than a variant of Gh0st RAT or Rekoobe, with separate Windows (Win.NOODLERAT) and Linux (Linux.NOODLERAT) variants sharing a common command-and-control design. The Windows implant runs filelessly via shellcode with loaders like MULTIDROP and MICROLOAD, while the Linux variant provides reverse shells, file management, and SOCKS tunneling after exploitation or web-shell placement on exposed servers. Groups including Iron Tiger, Calypso APT, Rocke, and Cloud Snooper have deployed it against organizations in Thailand, India, Japan, Malaysia, and Taiwan. Check Point released sample hashes and C2 IP indicators alongside the analysis.

Cyber Security News·14:03 UTC · 34m agoMalware in the wild 2 sources

50

DriveZero: End-to-End Driving Beyond Human Demonstrations

DriveZero pairs a frozen vision-foundation-model perception stack with a PPO-trained closed-loop RL teacher to beat replay experts on nuPlan.

DriveZero is an end-to-end camera-only autonomous-driving planner that separates perception and action. Its DriveVFM perception backbone consolidates frozen vision foundation models (DINOv3, SigLIP2, SAM, Depth Anything V2) from raw images without task annotations, while DriveRL trains a privileged PPO teacher policy through closed-loop rollouts in interactive worlds built from real driving logs. The planner distills this teacher, achieving a 93.57 mean nuPlan score across Val14, Test14-hard and Test14-random splits and beating the Log-Replay expert on all three. It also sets state of the art on NAVSIMv1, NAVSIMv2 and closed-loop HUGSIM without human trajectory supervision.

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

30

Iran-Linked Hackers Use Fake LinkedIn Job Offers to Deploy NodeRabbit and PollCat RATs

Iran-linked Mirage Kitten targets software engineers with fake LinkedIn recruiter coding tests deploying new NodeRabbit and PollCat RATs.

Kaspersky researchers link the campaign to Mirage Kitten (also tracked as UNC1549, Smoke Sandstorm, Nimbus Manticore), with victims in aviation, aerospace and fintech in Egypt, Ethiopia and Afghanistan. Trojanized npm dependencies (colorized_terminal, pretty-log) bundled in coding-challenge archives launch the Node.js implants across Windows, Linux and macOS. NodeRabbit uses AES-256-GCM-encrypted C2 via Azure, and its third variant persists through a fake GitHub Copilot Helper VS Code extension plus Git post-merge/post-checkout hooks. PollCat is an obfuscated JavaScript RAT that registers with C2 before OTP authentication and inventories tools from 24 security vendors.

GBHackers·Sep 9, 12:52 UTC · 7d agoThreat actor in the wild1

72

Hackers Use Fake LinkedIn Job Offers to Infect Developers With New Cross-Platform RATs

Iran-linked Mirage Kitten uses fake LinkedIn job challenges to deliver new cross-platform RATs NodeRabbit and PollCat to developers in fintech, aviation, and aerospace.

PolySwarm documented a campaign by Iran-linked Mirage Kitten (UNC1549) that uses fake recruiter personas and technical hiring challenges on LinkedIn to deliver the newly documented RATs NodeRabbit and PollCat, which run on Windows, Linux, and macOS. A challenge archive bundles a fake npm package (colorized_terminal 2.1.0) in node_modules that loads NodeRabbit, while a React-based challenge delivers PollCat with an attacker-controlled OTP screen. NodeRabbit persists through a malicious Visual Studio Code extension and Git post-merge/post-checkout hooks; PollCat persists via scheduled tasks, cron, and LaunchAgents. Victims were observed in fintech, aviation, and aerospace, with confirmed targets in Egypt, Ethiopia, and Afghanistan.

Cyber Security News·Sep 9, 14:07 UTC · 7d agoMalware in the wild

55

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 · 20h agoAI research

12

Dream-RSI: Recursive Self-Improvement through Evolving Worlds

Dream-RSI refines exploration policies by dreaming in replay simulators built from discovery history, cutting discovery costs across coding tasks.

Dream-RSI is a framework for scalable recursive self-improvement in autonomous coding agents, where a lightweight orchestration layer makes exploration explicit and programmable while leaving the underlying agent unchanged. Its core insight is that accumulated discovery history can serve as a replay simulator over the realized search space, providing immediate, low-cost off-policy feedback to evaluate and refine exploration policies without expensive online evaluations. Across algorithm engineering, mathematical optimization, and GPU kernel engineering, Dream-RSI achieves competitive or improved discovery quality at substantially reduced cost.

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

32

Sandworm-Linked Cyclops Blink Returns With Network Scanning and Packet-Sniffing Capabilities

Sophos uncovers a 64-bit Cyclops Blink variant on hacked Cisco FMC appliances, adding internal network scanning and selective packet capture; linked to Sandworm.

Sophos CTU analyzed a new 64-bit x86-64 Cyclops Blink implant (timezone_check) deployed on Cisco Secure Firewall Management Center appliances compromised via CVE-2026-20079 authentication bypass and CVE-2026-20316 low-privileged login. The activity is assessed with high confidence as Russian-nexus, with a moderate-confidence link to Sandworm (IRON VIKING, also tracked as Seashell Blizzard). The implant runs a parent controller plus five worker modules, masquerades as [kworker/0:1], persists via SysV init scripts at /lib/tz/timezone_check, and beacons to hard-coded C2 89.34.96.56 over a custom TLS protocol on ports 43856 and 49172. New module 0x11 scans internal IPv4 networks for SSH, SMB, LDAP, VMware, HTTP/HTTPS and VPN services, while module 0x12 performs filtered packet capture that can expose cleartext credentials, cookies and tokens.

GBHackers·Sep 14, 09:27 UTC · 2d agoMalware in the wild 13 sourcesCVE-2026-20079CVE-2026-20316

78

UniMate: One Unified Model to Animate Diverse Skeletons

UniMate is a topology-aware diffusion transformer generating articulated motion for arbitrary rigged skeletons from text, trained on 13,006 motion sequences.

UniMate is a unified foundation model that animates arbitrary rigged 3D skeletons from an asset and text prompt with no test-time optimization or per-skeleton retraining. It uses a topology-aware diffusion transformer combining graph-aware attention bias from joint relations and geodesic distances, a spectral rotary position embedding generalizing RoPE to kinematic trees via the graph Laplacian, and a global topological conditioner. The accompanying UniML3D dataset spans 13,006 motion sequences across bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid-object skeletons; the model outperforms baselines and supports zero-shot cross-topology transfer, in-betweening, expansion, and text-guided editing.

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

30

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%).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:31 UTC · 6d agoAI research

30

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

RSIAgent, a training-free multi-agent framework, builds reusable environment memory enabling Kimi-K3 and GLM-5.3 to beat GPT-6.

RSIAgent is a training-free framework for recursive self-improvement through autonomous memory construction, coordinating curriculum, actor, and verifier agents. It uses broad-then-deep exploration to capture environment structures, hidden constraints, and causal dependencies, and freezes the resulting memory for direct reuse without parameter updates. On OSWorld-v2 and Agent's Last Exam it substantially improves strong open-source models, enabling Kimi-K3 and GLM-5.3 to outperform frontier closed-source models including GPT-6.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research2

52

C2Looper: A New Backdoor Likely Tied To Ransomware With GitHub C2

Zscaler ThreatLabz identified C2Looper, a new Rust-based backdoor likely tied to ransomware actors and delivered via ClickFix chains, using GitHub for C2.

In July 2026, Zscaler ThreatLabz identified C2Looper, a new Rust-based backdoor family. The malware supports arbitrary command execution, reconnaissance, and deployment of second-stage payloads, and uses GitHub as its command-and-control channel. ThreatLabz assesses with low-to-medium confidence that it is delivered through multi-stage ClickFix infection chains and is likely leveraged by a ransomware-related threat actor. The analysis covers multiple C2Looper variants, their network protocols, and capabilities.

Zscaler ThreatLabz·Aug 17, 14:06 UTC · Aug 17, 2026Malware in the wild1

55

Abyssos: Technical Analysis of a New Modular RAT

Zscaler ThreatLabz analyzes Abyssos, a new modular C++ RAT offering credential theft, file exfiltration, and VNC-based remote access.

Zscaler ThreatLabz identified a new malware family tracked as Abyssos in late June 2026. Abyssos is a modular remote administration tool (RAT) written in C++ that supports credential theft, file exfiltration, and remote access via VNC. The malware is under active development, with multiple version numbers and obfuscation passes designed to evade security products. The analysis covers its core features, configuration, obfuscation, and network communication protocol.

Zscaler ThreatLabz·Aug 10, 15:51 UTC · Aug 10, 2026Malware

50

DeepZero: Open-source hunting for vulnerable Windows drivers

DeepZero, a new open-source engine, automates discovery of exploitable Windows kernel drivers for BYOVD attacks using Ghidra, Semgrep, and an LLM.

DeepZero is a free, open-source Python pipeline orchestrator that automates hunting for exploitable Windows kernel drivers relevant to BYOVD (bring your own vulnerable driver) attacks. Its seven-stage YAML pipeline parses PE headers, filters for kernel-mode drivers with IOCTL surfaces, excludes drivers listed on loldrivers.io, then runs headless Ghidra decompilation, Semgrep scanning, and an LLM-based exploitability assessment. The maintainer reports multiple verified vulnerabilities in the Snappy Driver Installer corpus, some still in the disclosure process, and notes findings involving plug-and-play-created device objects may need physical hardware to confirm.

Help Net Security·05:30 UTC · 9h agoTools

45

Novel macOS Infostealer AmnesiaStealer Spread via ClickFix

Novel macOS infostealer AmnesiaStealer spreads via ClickFix social engineering and lets attackers remotely control victims' browsers to steal cookie data.

Infosecurity Magazine reports on AmnesiaStealer, a novel macOS infostealer distributed through the ClickFix social engineering technique. The malware includes capabilities that let attackers take remote control of the victim's browser in order to steal cookie data. The novel remote-browser-control function distinguishes it from commodity macOS infostealers.

Infosecurity Magazine·Aug 14, 10:45 UTC · Aug 14, 2026Malware in the wild

45

Give Your Coding Agents a Memory You Own

Hugging Face introduces Funes, a tool that gives coding agents persistent, self-owned memory outside vendor clouds.

A Hugging Face blog post presents Funes, an approach for giving coding agents a persistent memory that developers own and control. The piece targets agent workflows where context must survive across sessions without ceding data to third-party services. No article body was available in the feed, so specifics beyond the title are limited.

Hugging Face Blog·Sep 3, 00:00 UTC · 13d agoAI tools & infra1

25

Tracking OceanLotus’ new Downloader, KerrDown

Unit 42 identifies KerrDown, a new OceanLotus (APT32) downloader active since 2018 targeting Vietnamese speakers via malicious macros and DLL side-loading.

Unit 42 tracks KerrDown, a previously undocumented downloader family used by OceanLotus (APT32) since at least early 2018, primarily targeting Vietnam or Vietnamese-speaking individuals. Delivery uses macro-laced Microsoft Office documents embedding base64-encoded 32-bit and 64-bit DLLs, and RAR archives containing a legitimate program abused for DLL side-loading. KerrDown is dropped as main_background.png, downloads a DES-encrypted payload from a URL, and executes it directly in memory. Researchers used Jaccard-index similarity analysis to identify the new family, connect campaign samples, and infer patterns in the group's working hours and days.

Palo Alto Unit 42·Aug 17, 12:53 UTC · Aug 17, 2026Malware in the wild1

52

RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?

RoboSPA introduces a 527K-trajectory benchmark with 280 task variants showing current VLA models struggle with spatial reasoning and long-horizon planning.

RoboSPA is a large-scale robotic manipulation dataset and benchmark for diagnosing embodied reasoning in vision-language-action models, covering fine-grained spatial reasoning and long-horizon procedural planning. It spans 10 task categories and 56 base tasks instantiated across five difficulty levels for 280 variants, with 527K trajectories collected across multiple embodiments and diverse scenes. Diagnostic metrics go beyond binary success rates, and experiments show current VLA models struggle with complex spatial relations, precise low-level execution, and memory-intensive planning.

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

28

Silver Fox Targets Japanese Manufacturer with 3

Chinese group Silver Fox used new vulnerable drivers and DLL side-loading to deploy ValleyRAT at a Japanese industrial manufacturer.

Cato Networks detailed a Silver Fox campaign against a Japanese industrial manufacturing organization combining new vulnerable-driver abuse (BootRepair.sys and EnPortv.sys alongside wsftprm.sys), DLL side-loading via Zeon Corporation PDF binaries, and NTDLL unhooking to deliver ValleyRAT (Winos 4.0), a Gh0st RAT variant. The chain starts with an invoice-themed phishing lure hosting attacker-controlled content on legitimate QQ and Tencent Cloud services. Shellcode is injected into svchost.exe via thread-context hijacking, and a dual watchdog design pairs payload monitoring with a scheduled-task batch script for persistence. A separate 180-day VirusTotal retrohunt found 146 Atlas RAT samples across six PDB builds, though the Silver Fox link remains circumstantial.

The Hacker News·Aug 27, 10:50 UTC · 20d agoThreat actor in the wild

58

TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

Researchers introduce TANGO, a whole-body vision-language-action model enabling zero-shot language-guided humanoid navigation on the Unitree G1 robot.

TANGO addresses humanoid navigation in cluttered indoor environments by predicting 29-DoF joint-space actions directly from natural-language instructions and egocentric RGB, rather than 2D path planning. It is trained entirely in simulation via a pipeline combining global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. In simulation it achieves state-of-the-art vision-language navigation performance and transfers zero-shot to a Unitree G1 humanoid without any real-world navigation data.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

27

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:56 UTC · 7d agoAI research2

40

Measuring LLM Sycophancy under Sustained Multi-Turn Pressure

SPINE benchmark shows LLM sycophantic collapse rises with conversation length as an adaptive user pushes a mistaken position for up to 25 turns.

The SPINE benchmark uses an LLM proxy that persistently and adaptively defends a mistaken user position for up to 25 turns, testing four production LLM systems and three OLMo3-7B variants on 100 false-presupposition and 100 unethical-query items. Collapse rates increase with conversation length for every model, and short-horizon evaluation protocols underestimate sycophancy. Analysis of accessible reasoning traces shows the correct position often remains represented when the model concedes, indicating models choose to please users rather than lacking knowledge. Among tested tactics, emotional appeals are most associated with inducing sycophantic behavior.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:35 UTC · 7d agoAI safety & security1

50

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a time-aware generative transformer trained on 559 million MIMIC clinical events to forecast multimodal patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer trained on over 559 million clinical events from 431,000 hospital visits by 299,000 patients across the MIMIC dataset family. It uses bidirectional time integration and a variational latent space to model the stochastic evolution of patient states, natively processing medical images, time-series signals, categorical events, and structured or unstructured clinical records. The model supports autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation, with strong probing performance across clinical outcomes, 15 ICD chapters, and 29 comorbidities.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

42