Detection
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ARC-SORT: Adaptive, Robust, Compensated Multi-Object Tracking with Interacting Multiple Models
2026-10-15 17:49:09Girish Kumar AdariAbstract
Multi-object tracking in sports broadcasts is hard for two reasons: athletes move fast and change direction abruptly, and the camera pans and zooms at the same time. We present ARC-SORT (Adaptive, Robust, Compensated SORT), a tracking-by-detection tracker that trains nothing and runs in real time. Its design rule: the tracker measures its own uncertainty, and that measurement decides how it acts. An Interacting Multiple Model (IMM) Kalman filter gives every track a predictive covariance, and we turn it into a per-track search region: the less reliable the motion prediction, the larger the region in which a detection may match (uncertainty-adaptive Expansion IoU, EIoU). On identical inputs, this continuous coupling is what matters: the same covariance used as a hard Mahalanobis gate hurts at every threshold we swept, and richer covariance summaries add nothing measurable. When two tracks cross, the assignment cost itself reports whether the decision was close. ARC-SORT keeps such pairs under review and applies at most one corrective identity swap once post-crossing appearance evidence shows the first decision was wrong; this adds +1.0 HOTA (higher order tracking accuracy) on both the SportsMOT and DanceTrack test sets at under a millisecond per frame. For players who leave the frame for seconds, motion extrapolation carries no information, so our offline re-identification drops the usual motion gate and links tracklets by appearance alone. ARC-SORT reaches 81.82 HOTA on the SportsMOT test set with the fewest identity switches of the 69 leaderboard entries as of August 2026 and tied-third multi-object tracking accuracy (MOTA, 97.09), and 62.1 HOTA on DanceTrack test with the same association settings. On Deep-EIoU's public detections and embeddings it reaches 80.31 HOTA, +3.1 over Deep-EIoU. It runs at about 50 frames per second on a laptop RTX 3060 GPU. Code will be released.
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URL
https://arxiv.org/abs/2610.22306
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Backward Compatibility in Zero-Bit Neural Audio Watermarking
2026-10-15 17:45:27Admir Krni\'c, Kosta Pavlovi\'c, Petar Nedi\'c, \v{Z}eljko Bolevi\'c, Slavko Kova\v{c}evi\'c, Igor Djurovi\'cAbstract
Neural watermarking systems are typically trained as tightly coupled embedder-detector pairs and implicitly assume static deployment. In real-world deployments, embedders and detectors are frequently updated and selectively decoupled for security and versioning purposes, which can break detection in previously embedded content and, more critically, increase false-positive rates on embedders that a given detector is intentionally not intended to recognize. In this paper the concept of backward compatibility in zero-bit audio watermarking is formalized. A training framework is proposed that pairs a new detector with a sequence of frozen legacy embedders (its legacy chain), using a performance-weighted sampling strategy so that the detector retains accuracy across all earlier versions. This coupling, while necessary for backward compatibility, is shown to induce an embedder-collapse effect that catastrophically degrades the true negative rate (TNR) against out-of-chain embedders. To counteract this, a Angular Diversity Loss (ADL) is introduced that explicitly forces each new embedder to occupy a distinct region of the output space. The combined approach restores near-perfect TNR with no measurable cost to detection accuracy or perceptual audio quality.
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https://arxiv.org/abs/2610.22296
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PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems
2026-10-15 16:23:17Shuo Lin, Jinzhe Li, Chenlu Guo, Yanhan Li, Yuan Wu, Yi ChangAbstract
Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that user queries are valid and answerable. As a result, models often generate executable SQL even for queries with flawed premises. We introduce premise critique ability in NL2SQL, namely the ability to recognize flawed queries and abstain from generating misleading SQL. To evaluate this capability, we propose PremiseSQL, a benchmark containing 1,800 flawed NL2SQL queries across four categories of premise failures, along with their 1,800 original queries. We further design an evaluation framework to measure intrinsic and prompt-induced premise-aware behavior across three prompting settings. Experiments on frontier LLMs show that current models are strongly premise-trusting by default and rarely critique flawed premises. Stronger critique-oriented prompting improves detection and abstention on flawed queries, but also increases false invalid judgments and suppresses SQL generation on originals. The code is available at this https URL.
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https://arxiv.org/abs/2610.22158
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Onboard Lightweight LLM Reasoning for Human-Aware Safe UAV Navigation
2026-10-15 15:43:49Siwei Cai, Yuwei Wu, Vijay Kumar, Lifeng ZhouAbstract
Aerial robots operating near people must treat a pedestrian, a parked car, and a wall differently, yet conventional perception--planning stacks reduce all of them to occupied geometry---a \emph{human-aware safety gap} in which category-specific clearance is unavailable to the planner and temporary occlusions remove pedestrian-specific constraints from its map. We present a modular, fully onboard pipeline that closes this gap by turning visual observations into context-dependent safety constraints for real-time UAV navigation. A shared BLIP encoder drives scene captioning and text-conditioned segmentation, which is fused with depth to localize obstacles in 3D. Obstacle-specific risk is inferred by a lightweight LLM (Qwen-2.5-0.5B) stabilized with retrieval-augmented generation and grammar-constrained decoding that restricts its output to a planner-compatible JSON schema. The LLM reasons once per object category rather than per frame: its verdicts are cached per class, so previously seen categories are resolved without inference and new ones trigger a fresh retrieval-grounded query; detections are additionally given persistent identities and held in the map across occlusions. The perception pipeline runs at 10\,Hz on a Jetson Orin NX, while fallback LLM queries take approximately 0.8\,s and remain off the control-critical path. In Gazebo, low-altitude urban navigation among pedestrians and vehicles shows a roughly $3\times$ reduction in collision events over a geometric planner, and semantics-aware landing among moving actors raises success from 34\% to 96\%. The 224-pixel encoder input limits reliable small-object perception at range; conservative pedestrian margins and short-term persistence mitigate this limitation during navigation.
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https://arxiv.org/abs/2610.22092
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From Signals to Evidence: Interpretable Underwater Acoustic Target Sensing with Large Language Models - Part I: Framework and Recognition
2026-10-15 15:27:49Yuge Zhang, Jie Chen, Zhengqiao Zhao, Xiling Yao, Lingji Xu, Jingdong Chen, Yixin YangAbstract
Underwater acoustic target detection and recognition are essential to marine surveillance and intelligent ocean sensing. Despite substantial advances in deep learning, existing systems typically produce binary detection decisions, categorical labels, or scalar confidence scores, offering limited insight into the physical evidence underlying their outputs. Large language models (LLMs) offer an opportunity to move underwater acoustic sensing toward interpretable, evidence-grounded understanding; however, generic audio encoders struggle to capture the distinctive physical characteristics of underwater acoustics, and physically annotated data are scarce. In this paper, the first of a two-part series, we propose a from-signals-to-evidence framework that integrates physics-aware acoustic encoding with a pretrained LLM, organized around a unified feature-evidence-decision chain with a shared reasoning core. An open input layer accommodates heterogeneous information-raw waveforms, time-frequency representations, environmental and geographic data, and textual physical priors-unifying detection and recognition within a common formulation. In Part I, we instantiate this framework for target recognition through an Underwater Acoustic Physics-aware Transformer (UPT), which tokenizes complementary acoustic representations and learns task-disentangled physical representations through four physics-oriented learning tasks. We develop a physics-parameterized data engine that provides traceable supervision at two levels of granularity and introduce an attribute-level evaluation protocol to assess the physical fidelity of generated descriptions. Experiments on simulated recordings of four vessels demonstrate accurate vessel recognition and reliable estimation of shaft frequency, blade-passing frequency, and machinery-related attributes.
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URL
https://arxiv.org/abs/2610.22064
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Time to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake Detection
2026-10-15 14:30:45Kajaree Das, Oliver B\"ose, Subarnaduti Paul, Gabriel Storch, Martin MundtAbstract
Deepfake detectors safeguard against misinformation, rendering their failures as much a societal as a technical concern. As generators constantly evolve, detectors trained on static datasets miss newly arising threats, while progressive training interferes catastrophically. Yet, mitigating forgetfulness in terms of accuracy, the prevalent focus of continual learning (CL), is insufficient to reflect real-world impact. We therefore scrutinize the effects of CL methods with respect to evolving fairness. To this end, we analyze four dimensions of fairness across diverse demographics for detectors trained progressively on a chronological sequence of 11 real-world deepfake generation methods. Tracking fairness metrics over time, we expose non-trivial interplays between performance and fairness that depend on both the choice of CL algorithm and demographic intersections. We find accuracy degradation to typically exacerbate unfairness and, conversely, fairness improvements to remain scarce over time. Dissecting the CL method families (rehearsal, prompts, regularization), we observe that inequitable forgetting is not explained by underrepresentation alone, but likely entangled with representation learning. We thus issue a call to action for CL methods to take social outcomes into account when balancing the stability-plasticity tradeoff.
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URL
https://arxiv.org/abs/2610.21974
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SurgStreamer: proactive, request-driven monitoring of surgical video streams with structured state and a reusable reasoning layer
2026-10-15 13:52:24Bowen Xiang, Jon S. Heiselman, Michael I. MigaAbstract
Surgical teams could benefit from assistants that watch the video for requested events, such as "tell me when the clip applier appears during Calot triangle dissection", and answer questions about what has already happened. We formalise this task as request-driven event detection with exact semantics and causal alert decisions, and present SurgStreamer. It separates procedure-specific perception, which maps the 1-FPS stream to a structured state over tools, actions and phases, from procedure-agnostic reasoning, which evaluates a formal monitoring condition over this state and an event memory, either with exact rules or with a small controller that addresses entities through pointers rather than names. We evaluate this monitoring core with gold conditions. On a pre-registered, sealed test set of 12 laparoscopic cholecystectomies, SurgStreamer reached a macro event-F1 of 0.598 over full procedures. On identical 75-s windows with only the window available to every method, it scored 0.677. The tested configurations of the streaming vision-language model OneStreamer-4B, including ones fine-tuned on SurgStreamer's state or on video with an adapted vision encoder, scored at most 0.540 (Holm-adjusted p <= 0.012). At three external centres, re-training only perception raised performance on held-out procedures from 0.239 to 0.470. On a new procedure, pituitary surgery, controller weights trained on cholecystectomy matched an in-domain controller on oracle state (0.947 vs 0.938). On predicted state they missed the pre-registered non-inferiority margin (-0.024 [-0.040, -0.008]) unless the alert thresholds were re-calibrated. The results support separating perception from reasoning and point to perception under distribution shift as the main limitation.
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URL
https://arxiv.org/abs/2610.21895
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Time-Optimal Ergodic Retrieval
2026-10-15 13:08:23Christian Sulana, Christian Hughes, Ian AbrahamAbstract
Retrieval tasks are a form of search task where a robot must explore, find an object, and return with it as quickly as possible. Recent work in ergodic control has demonstrated time-optimal path planning for search tasks where visitation is proportionally matched, within some tolerance, to a given target distribution. However, these planners minimize the duration of a full deterministic search, while retrieval missions end their search at an unknown point in the trajectory and must return to a given base. In this work, we propose a new formulation of the time-optimal ergodic search problem that models detection as a Poisson process and minimizes total mission time by including the return phase in the objective. Experimental results show that time-optimal ergodic retrieval (TOER) achieves up to $19\%$ shorter mission durations relative to ergodic baselines and that this advantage grows as the return phase becomes more costly. Simulation and hardware implementations validate TOER's compatibility with environments of non-convex geometry as well as both 2D and 3D search domains.
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URL
https://arxiv.org/abs/2610.21823
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NICE Toolbox: Nonverbal Interpersonal Communication Exploration Toolbox
2026-10-15 12:41:38Aleksandr Evgrashin, Carolin Schmitt, Timo L\"ubbing, Ashutosh Jha, Buket Naz Zeren, Sophie K. Bauer, Johannes Kopf-Beck, Anton K. G. Marx, Anamaria Semm, G\"okce Erg\"un, Senya PolikovskyAbstract
Understanding human communication is a long-standing goal in psychological research. People express themselves through many verbal and nonverbal channels: posture, gesture, gaze, facial expression, and speech. Measurement and annotation of these signals has traditionally required time-consuming manual work by trained human labellers. The Nonverbal Interpersonal Communication Exploration (NICE) Toolbox is a free, open-source Python framework that automatically measures these signals by combining existing state-of-the-art computer vision and speech-processing methods into a single software package. It includes support for multi-camera systems, interchangeable detection algorithms, 2D and 3D visualization of detector outputs, evaluation on the user's custom data, and export to third-party software. With the release of this tool, we aim to make data exploration of interpersonal communication signals scalable, reproducible, and accessible for psychological research.
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URL
https://arxiv.org/abs/2610.21786
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Beyond Generation: Using Multimodal LLMs as Detectors of LLM-Generated Deepfake Facial Images
2026-10-15 12:16:09Olivia Salamon, Virginia N. L. Franqueira, Shujun LiAbstract
As image generation models become increasingly capable of producing highly realistic content, traditional forensic techniques are struggling to keep up. This paper investigates whether MLLMs can instead be used as a form of forensic analysis by identifying synthetic media. Specifically, the study aims to determine whether these models detect fake images by recognising physical inconsistencies, or whether they rely on digital artefacts such as embedded this http URL MLLMs: GPT-4o, GPT-5, GPT-5.2, Gemini 2.5, Gemini 3, and Seed 1.6, are benchmarked using a dataset of 400 images. Fifty images are authentic photographs, while the remaining 350 are generated using seven leading image models: Gemini 3, Gemini 2.5, Flux.2 Pro, GPT-Image-1, GPT-Image-1.5, Seedream 4.0, and Seedream 4.5. The evaluation is structured in three stages: single-image classification, pairwise comparison, and a focused "self-detection" experiment on Gemini. Several MLLMs achieved a high detection accuracy, with GPT-5.2 reaching a peak of 97.9% in the pairwise experiment, against specific generators and an overall mean pairwise accuracy of 82.5% by identifying structural or physical inconsistencies within images. However, results from the self-detection experiment indicate that the presence of invisible SynthID watermarks leads to a 10% reduction in Gemini's detection accuracy compared to the clean control group. One possible explanation is that SynthID embedding reduces some of the visible artefacts the models rely on for this http URL results imply that MLLMs can detect structural artefacts without relying on watermarks, but performance varies substantially across model generations. The findings from comparison C5 also suggest that SynthID embedding may suppress some of the structural artefacts used during detection.
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URL
https://arxiv.org/abs/2610.21760
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Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection
2026-10-15 11:07:21Christos Panourgias, Roberto Stanzione, Adrien Petralia, Themis Palpanas, Paul BoniolAbstract
Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications. Despite the large number of anomaly detection methods proposed in the literature, recent benchmark studies have shown that no single detector performs best across highly heterogeneous time series. Therefore, a practical and scalable solution is to develop a model-selection method that, for a given time series, selects the anomaly detector most likely to perform well. Nevertheless, the model selection approaches proposed in the literature suffer from a significant drop in accuracy when applied in Out-of-Distribution (OOD) settings. In this paper, we tackle the aforementioned limitation and propose RAMSAD, a retrieval-based framework for model selection in time-series anomaly detection. Given a new time series, our method queries a knowledge base of previously observed instances, retrieves the top-k most similar series, and transfers detector recommendations from their performance profiles. The framework can operate with standard similarity measures as well as embedding-based representations. Our results show that RAMSAD outperforms existing baselines in In-Distribution (ID) settings and is the most robust selector in OOD, where, when selecting a small subset of detectors, it matches or exceeds ensembles that run the entire detector pool, at a fraction of their cost. Overall, we demonstrate that similarity-based retrieval constitutes a strong and efficient foundation for model selection in time-series anomaly detection.
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URL
https://arxiv.org/abs/2610.20243
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A Decision-Aware Evaluation Protocol for Uncertainty in Medical Image Segmentation
2026-10-15 09:59:55Saket Maganti, Shreyank N GowdaAbstract
Medical segmentation uncertainty is often evaluated with calibration error or pixel-level error-detection scores, but these metrics do not show whether uncertainty improves downstream review decisions. We propose a decision-aware evaluation protocol that separates uncertainty estimation from deferral policy and evaluates their combination under fixed review budgets: which pixels, regions, or images should be accepted, and which should be reviewed. The protocol reports fixed-budget error reduction, risk-coverage behaviour, region-level review proxies, structural failure modes, probability-derived baselines, and sensitivity to review assumptions. We instantiate it across retinal vessel, skin lesion, and polyp segmentation with multiple backbones and uncertainty estimators. Under matched inference budgets and canonical multi-layer MC Dropout, test-time augmentation (TTA) gives a stronger error-detection signal across matched MC--TTA comparisons, including architecture and dropout-placement checks. However, estimator rankings are not universal. For example, probability-derived scores remain competitive, deep ensembles can outperform TTA, learned deferral helps mainly when the base estimator is weak, and rankings can change with review unit, uncertainty target, correction assumptions, and cost model. Structural and second-observer analyses further show that boundary and thin-structure failures are partly tied to annotation ambiguity. These findings support evaluating uncertainty as an estimator-policy-review configuration, not as a standalone score map. Code: this https URL
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URL
https://arxiv.org/abs/2610.21616
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ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing
2026-10-15 08:38:23Hao Zhang (First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China), Hengrui Liu (First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China), Lina Sun (First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China), Junmin Meng (First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China)Abstract
Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stripe extraction, and propagation prediction. Here, we present ISW-Agent, which integrates large language model (LLM) interpretation with n8n workflows and independent scientific models through structured task constraints, geospatial transformations, and stage-specific output validation. In tests of 120 Chinese instructions per backend, GPT-5.6 Sol and DeepSeek both achieved a strict task-match rate of 94.2%, with all intents and execution decisions matching the reference. Sentinel-1 cases demonstrated continuous processing from dialogue requests to wavefront prediction for the specified scene. Of 50 timed scenes, 39 completed detection, extraction, and prediction with a median turnaround time of 48.04 s; 11 stopped after empty extraction. Across 3,408 controlled fault-injection runs, retries recovered 234 transient-failure conditions and output validation rejected 234 structural defects, while 61 provenance or completeness errors indicated the need for content-consistency checks. Overall, ISW-Agent thus connects natural-language requests to traceable spatial processing chains for multimodel ISW analysis.
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URL
https://arxiv.org/abs/2610.21502
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FlashAttention with Statistics: Low-Overhead Attention Features for Compact Hallucination Detectors
2026-10-15 08:23:42Andrei Volodichev, Maksim Savichev, Nikita Sviridov, Artem Yuliy, Dmitry Simakov, Alexey ZaytsevAbstract
Large language models can produce fluent answers that the supplied context does not support. A compact frozen proxy model can detect such answers by reading the context and another model's response, even from a proprietary generator. Attention-based detectors use summary statistics of the proxy's attention weights, but standard pipelines materialize the attention matrix, whose memory grows quadratically with sequence length. We compute these statistics without materializing attention, by accumulation in the FlashAttention pass, sparse lookups after it, or a second tiled pass over the scores, and classify them by this path into nearly free and costlier ones. On an H100, the statistics of five published detectors add 16% to the latency of a Qwen3-8B forward at 2K tokens, falling to 1.4% at 35K, and store 22.5 KiB per response token. On three benchmarks, probes trained on frozen Qwen3 proxies of 0.6B to 8B parameters prove strong detectors: a 4B proxy outperforms the strongest of nine zero-shot 4-9B LLM judges in F1 on two of the three, the proxies match or outperform specialized grounding checkers of up to 7B parameters, probing each proxy beats prompting it as a judge, and adding attention features significantly improves F1 in half of the 12 benchmark-proxy pairs, never significantly lowering it.
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URL
https://arxiv.org/abs/2610.21483
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RLDF: A Region-Level Deepfake Dataset and Benchmark for Partial-Face Manipulation Detection
2026-10-15 07:46:45Shukesh Reddy, Abhijit DasAbstract
Existing deepfake detection models are built upon manipu- lated images that are tampered with either exclusively fully swapped or fully synthesised faces, which leads us to an open question: Can off-the-shelf deepfake models detect manipulation effectively while tampering is confined to a local facial sub-region? To fill this gap, we introduce RLDF (Region-Level Deepfake), the first dataset for region-level deepfake evaluation. Employing the proposed region-aware partial swap synthesis model, we generate over 2M par- tial face-swap images by using eight face-swapping meth- ods. Altogether, we considered seven face regions, among which there are four discrete regions (eye, nose, mouth and eyebrow) and four composite facial regions (eye and nose; eye, nose and mouth; eye, nose, mouth and eye- brow; and full face). In addition, we define three evalu- ation protocols and benchmark 12 state-of-the-art detectors across the five popular standard cross-dataset evaluations, DF40, and the proposed RLDF, providing a comprehen- sive audit of where and why existing detectors underper- formed. Our evaluation reveals that existing detectors fail to generalize consistently across partial facial regions, re- sulting in an overall drop of 10.5% AUC and 10% accu- racy under region-level evaluation. Further, we propose a mask-guided cross-attention model as a baseline approach that can effectively detect region-level manipulation. The proposed dataset, code, and checkpoints are available at this https URL.
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URL
https://arxiv.org/abs/2610.21438
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Where, How, and When: Query-Guided Grounding for Video Understanding
2026-10-15 07:32:57Yogesh KumarAbstract
Understanding video content requires reasoning at multiple levels of granularity, from identifying objects to interpreting their interactions and localizing when specific actions occur. This thesis studies three query-guided grounding tasks that answer where key objects are (object-level grounding), how they interact (relationship-level grounding), and when specific moments occur (moment-level grounding). For object-level grounding, we present QDETRv, a query-guided extension of the Detection Transformer for one-shot localization of unseen objects in videos, which combines query-context fusion, a memory module for temporal consistency, and unsupervised pretraining on synthetic object trajectories. We further propose the Temporal Object-Aware Vision Transformer (TOViT) for few-shot video object detection, which builds on a language-aligned open-set vision encoder and propagates high-confidence object features across frames. For relationship-level grounding, we introduce few-shot referring relationships in videos: given a (subject, predicate, object) query and a few support videos sharing the predicate, the entities are localized by optimizing a T-partite random field using a meta-learned relation network with query-conditioned translational embeddings, Global Semantic Aggregation, Local Localization Aggregation, and belief-propagation-based message passing. For moment-level grounding, we propose the Moment Alignment Transformer (MATR) for video-to-video moment retrieval, which uses dual-stage Dynamic Time Warping for pre- and post-fusion alignment, learnable queries for boundary prediction, and self-supervised pretraining with temporal augmentations; MATR also extends to text-to-video moment retrieval with performance comparable to, or better than, the state of the art. These contributions advance query-guided video understanding across spatial, relational, and temporal grounding.
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URL
https://arxiv.org/abs/2610.21422
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Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction
2026-10-15 07:24:24SeungHyun Yi, Seungjun Yi, Sriram Sattiraju, Haeun Park, Joakim Nguyen, Terence Lim, Joseph Skrovan, Mehak Beri, Hitakshi Modi, Eloy Carrion, Andrew Well, Carlos M. Mery, Alexander Rasgon, Mia K Markey, Ying DingAbstract
Early stress detection and risk prediction are crucial for timely mental health assessment and support. While large language models have recently shown promise for social media stress prediction, relying on accuracy can obscure false positives. Also, prompt revisions yield inconsistent gains across models and tasks, with unclear links to strategy combinations and instruction changes. By comparing six methods on social media stress and emotion data across two model sizes, we found that GEPA and its extension combining instructions from two prompts (GEPA+merge) improved prediction over initial prompts in several settings. We also identified five prompting strategies associated with development gains. These strategies included directing attention to emotional evidence, distinguishing current from past distress, illustrating indirect cues, requiring emotional context, and guiding label selection. Moreover, we documented four observations across tasks and evaluation settings. In a separate set of searches, binary stress prediction improved early, whereas multilabel emotion prediction initially declined and partly recovered. The other three concerned incorrect or conflicting instructions, gains on small comparison samples that did not hold on full development data, and changes in selected prompts' development and reported test rankings. These findings identify effective prompt evolution methods, strategy combinations associated with gains, and task and evaluation limits, providing guidance for applying prompt evolution methods to social media stress detection.
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URL
https://arxiv.org/abs/2610.21407
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Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems
2026-10-15 07:08:51Ni Zhang, Jianan Zhou, Yue-Jiao Gong, Zhiguang CaoAbstract
Vehicle Routing Problems (VRPs) are fundamental to many real-world applications. Recent advances in large language models (LLMs) have spurred progress in solving these NP-hard problems. However, most existing approaches assume that input instances are error-free, neglecting the presence of faults commonly encountered in real-world data. In this work, we systematically investigate the capability of LLMs to detect (and correct) errors in VRP instances, and identify key limitations, particularly in handling complex instances or errors that substantially increase the LLM's reasoning burden. To overcome these challenges, we propose a self-guided reasoning framework in which LLMs autonomously generate code that provides structured insights to aid in error diagnosis. Experiments across diverse problem settings and LLMs demonstrate that our framework significantly improves error detection performance, achieving near-perfect accuracy.
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https://arxiv.org/abs/2610.21393
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A Black-Box Positioning Solution for Effortless Integration in Wearable Navigation
Abstract
Accurate localization is critical for a wide range of applications, including navigation, augmented reality, safety management, and healthcare. However, achieving accurate positioning remains challenging, especially in GPS-degraded environments. Pedestrian Dead Reckoning (PDR), which estimates position based on inertial sensor data, offers a promising alternative but typically demands substantial effort in algorithm design and system integration. This complexity often leads to prolonged development timelines and elevated costs. To address these challenges, we present the PDR Sensor (PDRS), a plug-and-play, black-box PDR module optimized for seamless integration into wearable and IoT platforms. PDRS integrates a microprocessor and an inertial measurement unit (IMU) within a compact, low-power module. It executes real-time sensor fusion onboard, providing position updates via standard interfaces (I2C, SPI, UART). Experimental evaluations demonstrate its high robustness, achieving a step detection error of just 0.16% over 1250 steps and a traveled distance error of 1.87% under diverse conditions. The system further exhibits low latency (2.5 ms) and modest power consumption (52.35 mA). Unlike conventional studies, the PDRS leverages a holistic co-design of hardware and software to enhance performance and integration aspects, simplifying adoption without requiring deep expertise. By lowering technical barriers, PDRS enables developers and researchers to prioritize innovation in IoT and wearable technologies, accelerating prototyping, reducing development time, and advancing industry progress. Future work aims to enhance accuracy, reduce power usage and module size, and integrate AI-driven techniques to better mitigate drift.
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https://arxiv.org/abs/2610.21353
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QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery
2026-10-15 06:20:09Yufan Zhu, Yangfan Jiang, Yang Fei, Shizheng Hou, Shubhankar Mohapatra, Xiaokui XiaoAbstract
Natural language messages often contain quasi-identifiers that are weak in isolation but can jointly reveal an individual's private attributes. We call such a jointly revealing set of cues a quasi-evidence group. Existing privacy benchmarks focus on explicit identifiers, attribute inference, or task-driven disclosures, leaving inference from quasi-evidence groups largely unmeasured. In practice, whether exposing such a quasi-evidence group violates privacy also depends on sender-specified disclosure policies. We introduce QuasiLeak-Bench, a benchmark for policy-conditioned quasi-evidence group recovery, which evaluates whether detectors recover complete quasi-evidence groups and flag the disclosures the given policy prohibits. To obtain reliable ground truth at scale, a reverse-construction pipeline first selects weak quasi-identifiers, composes them into quasi-evidence groups verified to jointly reveal a target attribute, and generates documents around this evidence, followed by automated quality control. Experiments show that existing privacy detectors transfer poorly to QuasiLeak-Bench, and locally deployable LLM-based detectors fall far behind proprietary models, mainly because they miss most quasi-evidence groups altogether. However, a 4B model fine-tuned on our training split surpasses the strongest prompted model on held-out documents, showing that the task is learnable.
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https://arxiv.org/abs/2610.21333
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