Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
404 Media covers DecryptAds, a new tool offering a massive queryable dataset for investigating location-tracking advertising.
404 Media reports on DecryptAds, a new tool paired with a large queryable dataset designed to make investigating the advertising industry's location-tracking practices much easier. The article is paywalled, so further technical details about the dataset are limited.
Researchers present CrossLink, a passive tracing algorithm linking temporary device identifiers across LTE, WiFi, and BLE, reconstructing full traces for 83% of simulated users.
Smartphones emit temporary identifiers simultaneously over LTE, WiFi, and BLE, and per-protocol randomization defenses implicitly assume their protections compose across protocols. CrossLink is an uncertainty-aware tracing algorithm that stitches device identifiers across time, space, and protocols even when the adversary is fully passive and rotations are unsynchronized. In large-scale mobility simulation it reconstructs full traces for 83% of users versus 22% for the best single-protocol baseline. It remains effective under partial sniffer coverage, including strategically placed sniffers near LTE handover regions, mobile sniffers, and limited high-coverage subregions.
Researchers introduce TrajMark, a training-free watermarking framework for coding-agent trajectories that recovers ownership, detects 95.5-100% of edits, and localizes tampered regions.
TrajMark is a training-free, symmetric-key, visible-only watermarking framework for coding-agent trajectories that separates robust ownership attribution from fragile local integrity verification. A sparse owner layer encodes a six-bit deployment identifier by rewriting keyed READ actions into masked linear equations, while a localization layer inserts linked Q12 seals that commit to protected critical-action segments. Across three coding-agent frameworks and three LLMs, it recovers the exact owner in all clean full-watermark batches, detects 95.5%-100% of single-site edits, and localizes 95.8% of random corruptions to an accepted protocol region. Owner marking adds no trajectory actions and matched Pass@1 is 26.9% versus 26.3% for unwatermarked runs.
Researchers show CSI-trained Wi-Fi models can localize people from RSSI data at ~80% confidence, enabling privacy attacks from ordinary IoT devices.
The paper investigates cross-domain inference, feeding RSSI data into an existing CSI-based Wi-Fi pose prediction model. RSSI is accessible on IoT devices without elevated OS permissions or specialized drivers, unlike CSI. Using an RSSI dataset synchronized with video ground truth, the model predicted human locations with approximately 80% confidence when movement was present. The results imply a wide range of commodity IoT devices could be used for privacy invasion in Wi-Fi-dense environments.
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.
KrebsOnSecurity covers DecryptAds, a free service that scrapes and correlates adtech data to reveal which entities track users across sites and apps.
KrebsOnSecurity wrote about DecryptAds, a free service that scrapes and correlates adtech data. The service makes it simple to identify which entities are responsible for ads shown on websites and data harvested by mobile apps, information that is semi-public but traditionally walled away inside large advertising platforms. No security incident is involved; the piece is an introduction to a privacy-research tool.
Researchers propose TFTrack, a template-free LiDAR 3D single object tracking framework cutting FLOPs ~50% while running at ~120 FPS.
TFTrack is the first template-free framework for 3D Single Object Tracking, dropping template-search pairings and complex motion modeling in favor of the prior bounding box center plus geometric alignment. It ships in three variants (TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point) covering sparse and dense 3D representations. On KITTI and nuScenes it is competitive with leading template-based trackers while reducing FLOPs by about 50% and running near 120 FPS. Code is released, targeting real-time deployment in embedded robotics such as autonomous vehicles.
Occlusion-robust tracker combining YOLOv11n, Kalman filtering, and appearance Re-ID cuts identity switches and beats OccluTrack by 18.1% MOTA on OVIS.
The framework integrates YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification to maintain target identity through full and long-term occlusion. Six Re-ID architectures were evaluated under identical conditions, with the Occlusion-Aware Mask Network (OAMN) performing best. On the public OVIS dataset it improves MOTA by 18.1% and IDF1 by 25.1% over OccluTrack while reducing identity switches by 12.8%; on a custom military surveillance dataset it achieves MOTA 0.734 and IDF1 0.729.
ZK-Trace combines Tardos fingerprints and zero-knowledge credentials to trace leaked classifier copies in federated GNSS monitoring without leaker cooperation.
ZK-Trace addresses leakage of a proprietary classifier distributed to partly trusted stations in federated GNSS interference monitoring, combining public identity marks, recipient-specific Tardos fingerprints, and zero-knowledge credential verification to support offline tracing. The paper provides false-accusation and tracing-score bounds with an interval-arithmetic checker allocating a common budget across accusation and tamper decisions. In a simulated GNSS federation it isolates all 160 single-owner copies and traces 712 of 720 two-owner mixtures with a 0.001 false-naming budget, while feature marks survive feature matching in 20/20 runs at 4.8 percentage-point accuracy cost but are erased by function-only distillation.
Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.
Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.
Researchers show roadside cameras plus Bluetooth signals can link phones to license plates, enabling richer tracking.
Ars Technica reports on new surveillance research showing that combining roadside camera license plate recognition with phone Bluetooth signals can associate specific devices with specific vehicles. The approach could turn existing traffic cameras into far richer tracking tools that follow both a car and its occupant's phone. The work raises significant privacy and civil liberties concerns.
A Georgia police officer used Flock license plate reader cameras to surveil his ex-partner and another officer after their affair ended.
Internal investigation records show a Georgia officer used the Flock license plate reader network to track the movements of a fellow police officer with whom he had an affair, along with a man whose vehicle frequently appeared near hers. The unauthorized personal use of police surveillance infrastructure surfaced through an internal affairs review. The case highlights oversight gaps around law enforcement access to mass surveillance data.
US DoD disabled ad tracking on troops' devices after foreign adversaries targeted service members using commercially bought location data.
Per letters shared with Senator Ron Wyden, the Army, Air Force, Navy, Marine Corps, and Special Operations Command disabled advertising IDs on iPhones, Android devices, and Windows computers across the federal military enterprise. The protections rolled out earlier in 2026, with the Air Force implementing changes in July, to prevent adversaries from exploiting app-derived location data sold through data brokers. Wyden warned that personal devices brought onto military bases could still expose troops and facilities, and noted the intelligence community and FBI buy such data without warrants.
Researchers present TANGO, a whole-body vision-language-action model enabling humanoid robots to traverse cluttered spaces from language instructions.
TANGO predicts 29-DoF joint-space actions from egocentric RGB observations and natural-language instructions for whole-body humanoid navigation, going beyond 2D path planning. It is trained entirely in simulation using global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. The model reports state-of-the-art simulation performance and was deployed zero-shot on a Unitree G1 humanoid without any real-world navigation training data.
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.
VLoc Bench tests 27 language models at locating vulnerable files in 290 repositories; best system reaches 0.229 File F1 and 38.4% of tasks unsolved.
The Vulnerability Localization Benchmark (VLoc Bench) contains 500 real-world vulnerabilities from 290 repositories across six package ecosystems and 147 CWE categories, pairing pre-fix and post-fix repository snapshots. Agents receive only a CWE description and read-only terminal access to identify affected files, and must confirm absence on patched snapshots. The strongest of 27 language models and four static-analysis tools achieves just 0.229 File F1; 38.4% of tasks receive no correct localization, and effective localizers still report unsupported locations on patched repositories.
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.
Introduces DRIFT, a black-box attack removing diffusion watermarks by deflecting generative trajectories, achieving 98-100% success across nine watermarking schemes.
Researchers propose DRIFT, a black-box watermark removal attack combining partial forward diffusion with stochastic reverse resampling to break trajectory-dependent verification. The paper derives information-theoretic and Wasserstein source-dependence bounds and shows the first verifier-rejected rung is least distorted among rejected rungs. Across nine watermarks spanning three paradigms, DRIFT achieves 98-100% attack success with the best image quality among compared attacks, without secret keys, verifier internals, or per-image gradient optimization.
AdaptVPR generates route-aware synthetic hard positives for visual place recognition, releasing the 160K-image AdaptCities dataset with R@1 gains up to 9.2% under domain shift.
AdaptVPR is a generative augmentation framework that creates same-place hard positives under illumination, weather, seasonal, and dynamic-occlusion shifts for robust visual place recognition training. A vision-language model parses scene attributes and estimates editability, while a rule-based scheduler routes generation through global appearance, local occlusion, or dual perturbation routes with geometric-consistency verification. The resulting AdaptCities dataset contains 160K verified synthetic hard positives, and experiments show R@1 gains up to 9.2% across VPR baselines and backbones. Code and data are publicly released on GitHub.
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.
ASTRIL-MPC combines learned kinematics, NMPC, and LLM-guided safety-checked retuning for articulated tracked robot traversal in search-and-rescue.
ASTRIL-MPC is a language-guided neural-kinematic model predictive control framework for autonomous traversal of articulated tracked robots in urban search and rescue. A learned kinematics model predicts short-horizon task-state increments, NMPC plans with feasibility constraints, and an LLM proposes bounded, safety-checked updates to weights and bounds. The compiled predictor enables a full control cycle within 100 ms, improving traversal-quality scores by up to 71% over non-adaptive NMPC and 67% over a PPO baseline while eliminating measurable collision impacts.
Unauthorized access to Thomson Reuters' C-Track court platform may have exposed SSNs and sealed records across 11 US states, USVI, and Ontario.
Thomson Reuters' West Publishing disclosed that an unauthorized party obtained files from the C-Track court case management platform starting in March 2026, with access to one environment running from March 1 through June 29, 2026 per Montana's account. Notices name roughly 24 court bodies across 11 US states, the US Virgin Islands, and Ontario, including appellate courts in Minnesota, Ohio, Montana, and Pennsylvania. Exposed data may include names, Social Security numbers, driver's license numbers, dates of birth, medical and health insurance information, and confidential or sealed court records. The company is offering 12 months of Experian or TransUnion monitoring, and courts disagree over whether the vendor's backup cloud environment or the production platform was accessed.
LP-BTS uses graph proposal policies, learned critics, and budgeted PUCT search to plan mobile charging across dynamic action spaces up to 2,813 stops.
LP-BTS is a learning-guided planning architecture for one-to-many mobile charging, where N=250 sensors induce roughly 1,125 initial candidate charging stops. A graph proposal policy concentrates candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares simulated futures, letting a single frozen checkpoint cover action universes from 736 to 2,813 stops. On a sealed 30-scenario confirmatory bank it attains the highest observed survival (0.4545) and alive-AUC (0.8031), though its +0.0066 survival edge over the strongest engineered comparator is statistically unresolved.
SolTracer traces cross-chain transactions onto Solana bridges, improving open-world association F1 by 20.16% over the strongest baseline for illicit-fund tracing.
The paper formalizes four Solana-bound cross-chain transaction modes and proposes SolTracer, which maps heterogeneous execution semantics into a unified event space and uses candidate-set selective decision-making with abstention when valid targets are absent. In the challenging open-world setting with a 50% TA ratio, SolTracer improves F1 by 20.16% over the strongest baseline. An empirical study of real-world transfers examines count-value divergence across bridge mechanisms, cross-asset shifts, and decoupling between on-chain settlement and explorer visibility.
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.
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.
Researchers demonstrate the first timing solution computed from Galileo's new Signal Authentication Service, protecting receivers against GNSS spoofing.
Galileo's new Signal Authentication Service (SAS) on the E6-C signal directly authenticates ranging measurements, closing a gap left by OSNMA, which only protects navigation data. Researchers built a snapshot software receiver implementing a simplified SAS protocol and computed an authenticated receiver clock bias from recordings of both SAS-capable satellites, collected with a Septentrio SDR prototype. The results demonstrate feasible authenticated timing ahead of full SAS operational deployment.
Researchers show full reasoning traces provide limited benefit in LLM post-training, with heavily truncated or endpoint-only trajectories performing comparably.
A pilot study plus attention-based analyses and controlled token-removal studies show intermediate tokens in reasoning trajectories contribute minimally to final reasoning quality. Partial trajectories remain effective even under heavy truncation, and training on endpoints alone leads to consistent changes in reasoning behavior. The finding also benefits reinforcement-learning and on-policy distillation post-training; code is released at github.com/naver-ai/revisiting-trace.
Researchers propose Patterns of Past Rewards (PPR), a lightweight reward-based detector that flags environment shifts in cooperative multi-agent reinforcement learning training.
The paper introduces Patterns of Past Rewards (PPR), an algorithm-agnostic detector that smooths cooperative agents' return streams and applies statistical drift testing to flag environment or task changes. Evaluation in a custom Speaker-Listener environment built on the Multi-Agent Particle Environment under two non-stationarity scenarios shows PPR balances detection speed against alarm stability. It avoids the repeated alarms of a smoothed-return baseline and the missed shifts of raw-return detection, enabling MARL systems to reliably identify major changes during training.
User studies explain VR motion privacy mechanism acceptance far better than physical deviation metrics, a study of three mechanisms finds.
The paper examines how well physical deviation predicts user acceptance of VR motion privacy mechanisms compared to user studies, testing three mechanisms at five deviation levels. User studies explain substantially more variation in mechanism acceptance, though physical deviation remains significant, and prior VR experience affects acceptance. Public VR game leaderboards expose motion recordings from hundreds of thousands of users, creating identification and profiling risks. The authors recommend combining physical deviation metrics with user studies and reporting participants' prior experience.