Networking
Centrality-Based Deployment of Queue Policies in Acyclic Multipath Routing Networks
Mahima Gupta, Acquin Biju, Rijul Jain et al. (+2 more)
Excessive queueing delays constitute a significant impediment to latency-sensitive network applications. Although effective deployment of Active Queue Management (AQM) strategies has been proposed as a necessary solution, deployment remains sparse. This paper studies AQM deployment in a specific class of networks where routers/switches have a topological hierarchy, form acyclic paths, and adopt multipath routing. Our approach rests on the well-established premise that AQM deployment impacts packet-forwarding dynamics in networks carrying TCP flows, thus establishing a direct link between stability and network performance. We use fluid models for TCP and queue dynamics in the network, along with a simple threshold-based queue policy to outline a closed-loop model for the network. Stability analyses reveal that while the network is vulnerable to instability as the average round-trip time (RTT) of the TCP flows increases, it tolerates a much larger RTT without losing stability when the threshold-based AQM is deployed in an appropriate router. We then define a Katz centrality-based metric to choose the most appropriate router for AQM deployment, and argue that doing so ensures the greatest stabilising effect. Finally, packet-level simulations corroborate that the proposed deployment strategy ensures low-latency operation of the network.
Read the full abstract ▾
All authors Mahima Gupta, Acquin Biju, Rijul Jain, Dipesh Sharma, Sreelakshmi Manjunath
Computer Vision
Syn2RealTrack: Bridging the Gap Between Synthetic and Real-World Datasets for Online Multi-View Multi-Target Tracking
Duong Nguyen-Ngoc Tran, Ngoc Doan-Minh Huynh, Cu Quoc Le et al. (+10 more)
Multi-camera 3D perception systems for warehouse scenes are trained largely on synthetic data and evaluated on physically captured environments. The resulting synthetic-to-real gap, which corrupts ground-plane localization and cross-camera identity association, is usually treated as one deficiency for a single domain-adaptation module to absorb; we argue instead that it enters the pipeline at three separable points: the camera calibration, the object shape prior, and the assumption that the object census is known, each admitting a different local remedy. Our online pipeline, Syn2RealTrack, follows this decomposition: lens distortion is recovered from images alone under a calibration that provides none, detections are fused across views by a visibility-weighted part-based descriptor that abstains on occluded parts rather than guessing, person height is measured in closed form from calibration instead of copied from a synthetic prior, and a closed-world cardinality prior is paired with a causal filter that removes the phantom boxes the prior manufactures. The system therefore adapts by reallocating trust between geometry and appearance without retraining a feature extractor. On the AI City Challenge 2026 Track~1 evaluation server it reaches a 3D Higher Order Tracking Accuracy (HOTA) of 52.0118%. The code will be released at https://github.com/SKKUAutoLab/aic26_mc3dp
Read the full abstract ▾
All authors Duong Nguyen-Ngoc Tran, Ngoc Doan-Minh Huynh, Cu Quoc Le, Hoang-Khang Nguyen, Long Hoang Pham, Huy-Hung Nguyen, Quoc Pham-Nam Ho, Trinh Le Ba Khanh, Chi Dai Tran, Duong Khac Vu, Son Hong Phan, Hyung-Min Jeon, Jae Wook Jeon
Cryptography and Security
Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate
Ethan Traister, Ankit Raj, Jiaqi Gan et al. (+7 more)
Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls – 913 hours of audio and 330,956 transcribed turns from 5,780 distinct numbers – collected over 54 days by an AI voice-
agent honeypot that answered callers and kept them talking, and introduced in a companion data descriptor. We separate outright scams, which solicit sensitive information, from the larger stream of predatory but legal lead generation ("spam") that feeds them. Scam operations keep office hours (6.6x more calls per weekday than weekend day); thousands of disposable numbers run a small catalog of recycled scripts (thirty opening clusters, half the traffic in the top five); and callers solicit identity anchors – a home address and a date of birth – far more often than payment credentials, pressing through persistence and manufactured authority rather than overt threats. Our central experiment asks: does it matter who picks up? Every seeded lead carried one of ten fictitious identities drawn uniformly at random, so the identity a fraud operation reaches is fixed before the caller exists. Across 1,823 randomized calls, scammers spent about 15% more conversational turns per decade of the target's apparent age (rate ratio 1.15, 95% CI 1.08-1.23; randomization p = 0.005) – yet what they asked for did not change (26.3% of calls reached a request for sensitive information; odds ratio 0.99 per decade, 95% CI 0.90-1.08). A second experiment casts early detection as a
benchmark: from a scammer's opening lines alone, on a caller-disjoint split, escalation is predictable at 0.72 ROC-AUC from the first line and 0.87 by the eighth, and a plain bag-of-words classifier matches a fine-tuned on-device
language model. Telephone fraud emerges as a templated industry that varies how hard it works a target, but not what it wants.
Read the full abstract ▾
All authors Ethan Traister, Ankit Raj, Jiaqi Gan, Xingyu Shen, Tyler Wu, Yuchen Zhou, Tommy Duong, Kidus Zewde, Siying Chen, Simiao Ren
Machine Learning
A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh et al. (+4 more)
Phase-field modeling of brittle fracture removes the need to track cracks explicitly by recasting their
evolution as the minimization of an energy functional. In return it requires a discretization dense enough to resolve a localization band whose width is set by a regularization length and whose path is not known in advance. We propose a mesh-free discretization in which a single neural network represents the displacement and phase fields and is trained by minimizing the incremental energy directly. The coordinates enter the network through a multiresolution feature encoding built from $C^1$ quadratic B-spline grids, so the finest scale the representation can express is set by choice rather than reached through slow training, and the energy is estimated by stratified Monte Carlo integration on points redrawn at every optimizer iteration. This pairing proves critical, since the crack fails to advance both when the integration points are held fixed and when the encoding is too coarse to represent the band, while each ingredient tolerates a wide range of settings once the other is in place. Because the representation is globally $C^1$, the second- and the fourth-order fracture energy densities run on the identical discretization. Across six problems, from single-edge-notched tension and shear to a thick-walled ring on a single spline patch, the computed load-displacement curves follow staggered finite element references at matched regularization length, with peak loads within about 1% on the single-edge-notched tests and within 8% where the crack pattern changes
topology. On a public
benchmark dataset of random multi-crack configurations the method classifies the active or dormant state of 90% of the seeded cracks in twenty zero-shot runs, where the deep Ritz baseline of the
dataset authors fails.
Read the full abstract ▾
All authors Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh, Mohammad Vahab, Cosmin Anitescu, Timon Rabczuk, Elena Atroshchenko
Computer Vision
Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models
Yingshu Li, Yunyi Liu, Zhanyu Wang et al. (+4 more)
Radiology report generation (RRG) has recently benefited from large
language models, which substantially improve report fluency. However, clinically faithful generation remains challenging because current supervision is still imposed mostly at the report level. This creates a granularity mismatch: radiology reports are composed of disease-grounded findings, while existing methods are trained mainly with whole-report objectives. To address this problem, we propose Graph-Supervised Hierarchical Clinical
Alignment, which reformulates image-report supervision as a hierarchical clinical
alignment problem. Our method structures this
alignment as a disease-conditioned process, where supervision is decomposed into two levels: Disease-Centric
Alignment for fine-grained disease-specific correspondence, and Global Clinical Semantic
Alignment for report-level semantic coherence. A clinical knowledge graph is used as a training-time-only structural prior that defines disease-specific supervision units and their clinical relationships, introducing no additional overhead at inference. Because standard contrastive
alignment could produce false negatives when studies share overlapping pathologies, we combine instance-conditioned discriminative matching with disease-conditioned soft regularization, enabling fine-grained yet clinically consistent cross-modal representations. Experiments on MIMIC-CXR, IU-Xray, and COV-CTR show that our method consistently improves performance on both conventional and clinical metrics. Notably, our 3B model surpasses several prior systems with larger 7B/13B backbones, suggesting that improving supervision structure, rather than increasing model size, can be more effective for RRG.
Read the full abstract ▾
All authors Yingshu Li, Yunyi Liu, Zhanyu Wang, Zailong Chen, Lingqiao Liu, Lei Wang, Luping Zhou
Numerical Analysis
Strang's Conjecture: Positive Result on Strong Collapsible Complexes and a Code to Check the Conjecture
Aranzazu Romero and Renaldi-Bradley Bodombo
Splines, which are piecewise
polynomial functions with given smoothness, are used in numerous applications such as computer aided
geometric design, algebraic geometry and for implementation of the finite element method. In many applications, it is important to know the dimension of the space of splines over a domain, but this continues to be an open problem. Strang's
conjecture predicts the dimension of the space of bivariate $C^1$ splines of degree at most $k$ over a polygonal domain with no holes. For the cases, $k=4$ and $k \geq 5$ the
conjecture has been proven true. For the case $k=2$, John Morgan and L. Ridgway Scott provided a counterexample. In this project, we begin by relating the space of bivariate $C^1$ splines of degree at most $k$ to various other function spaces. With the aid of some intrinsic operators on these spaces, we obtain a complex that yields an equivalence statement to Strang's
conjecture. The equivalent statement admits a computational approach to the
conjecture and thus we present an NGSolve code to check Strang's
conjecture for a given mesh. In addition, we prove that if the simplicial complex associated to a triangulation is strongly collapsible (with a non-collinear condition), then Strang's
conjecture holds for $k=2$.
Read the full abstract ▾
Computer Vision
TransPhy: Visual In-Context Learning for Physically Grounded Image Editing
Siyi Xie, Xuanke Shi, Jinsheng Quan et al. (+4 more)
Visual demonstrations provide a natural interface for specifying image transformations that are difficult to describe exhaustively with text. However, existing visual in-context learning (VICL) methods primarily focus on appearance-level relation transfer and provide limited support for physically grounded transformations, whose outcomes depend on material properties, geometry, object interactions, and environmental conditions. Given a source–target exemplar pair and a query image, physically grounded VICL requires a model to infer the demonstrated transformation, adapt its effects to the query-specific scene context, and preserve rule-irrelevant content. We introduce PhysVICL-74, comprising 74 physically grounded transformation rules and 5,240 source–target image pairs that form nearly 75K training and evaluation contexts. Its
benchmark split separately evaluates novel-instance transfer and unseen-rule generalization. We further propose TransPhy, a framework that decomposes physically grounded VICL into physical-rule induction and transition-aligned rendering. TransPhy first predicts the demonstrated rule and an explicit query-specific target-state description, and then synthesizes the target image through
token-wise mixture-of-experts adaptation, with expert routing guided by localized transition cues. Experiments show that TransPhy improves physical-rule adherence, query consistency, and unseen-rule generalization over existing visual in-context editing methods.
Read the full abstract ▾
All authors Siyi Xie, Xuanke Shi, Jinsheng Quan, Haoran Tang, Zukai Chen, Lei Yang, Quan Wang
Computation and Language
MC-CXR: A Multi-Context Chest X-ray Benchmark for Context-Induced Disruption in Vision-Language Models
Junhyeok Lee, Songsoo Kim and Kyu Sung Choi
Vision-
language models (VLMs) are increasingly used in clinical pipelines where a chest X-ray is interpreted alongside retrieved reports, preliminary notes, or prior imaging. Existing
benchmarks measure whether models answer correctly in isolation, but not whether they preserve a correct image-only decision when plausible context conflicts with the image. We introduce Multi-Context Chest X-ray (MC-CXR), a
benchmark of 240 cases expanded into 2,522 instances that isolates context-induced disruption through paired perturbation. Each case fixes the current image and target finding while presenting matched reliable and misleading context across text and prior CXR, with visual overlays where available. MC-CXR defines three task families and two paired metrics, the switch-to-wrong rate and the context-aligned error rate. We evaluate ten VLMs spanning open-source general, medical-domain, and closed-source systems. Image-only accuracy is necessary but insufficient. Mean switch rates range from 45.6-78.1% across misleading textual sources and 35.7-61.7% across misleading visual sources. Among switched predictions, 74.6% align with the misleading label for text versus 17.6% for visual context, a 57.0-point gap (95% CI 50.9-62.8). This text-visual asymmetry is observed under the standardized direct-answer protocol. The
dataset is available on PhysioNet.
Read the full abstract ▾
Robotics
PonderPounce: A Pretrained MLLM as an Episode Context Engine for Robot Control
Suhwan Choi, Jaeyoon Jung, Sungkyung Kim et al. (+2 more)
Multimodal large
language models (MLLMs) can integrate long visual histories and infer behavior from a few examples, yet
vision-
language-action models rarely use this capacity as episode
memory. Instead of a purpose-built
memory module, PONDERPOUNCE reuses an MLLM's native causal context. PONDER, a pretrained System 2 MLLM, integrates episode history and demonstrations to produce continuous cognition. POUNCE, a System 1 action model, asynchronously conditions control on the newest cognition and its age. Both are jointly trained end to end without separate bridge pretraining. Optimized per-call inference on an H100 achieves p50 latencies of 78 ms for cognition-only refresh and 25 ms for action-model invocation. On RoboMME, PONDERPOUNCE achieves 60.83% success at the base data scale and 75.54% with 9x data, compared with 44.51% and 57.88% for FrameSamp+Modul. At base scale, scaling PONDER from 0.8B to 9B adds 6.71 percentage points with the POUNCE architecture unchanged. A separately trained 9B PONDER without execution history achieves only 26.21% under matched supervision. PONDERPOUNCE also achieves 12.5% success on RoboCasa-DC and demonstrates real-world applicability on four tasks under asynchronous execution, with 60.98% mean success versus 40.67% for FrameSamp+Modul.
Read the full abstract ▾
All authors Suhwan Choi, Jaeyoon Jung, Sungkyung Kim, Yunsung Lee, Youngjae Yu
Artificial Intelligence
AHEAD: Adaptive Hindsight with Environment-Augmented Distillation for Agentic RL
Xiaolong Jin, Dingmin Wang, Vijay Lingam et al. (+1 more)
Training multi-turn LLM
agents with reinforcement learning typically relies on trajectory-level rewards, which assign a uniform advantage to every step and cannot identify which decisions led to success or failure. Self-
distillation methods can provide finer-grained supervision by augmenting RL with privileged information. However, existing approaches usually apply the same type of privileged information to every step in an indistinguishable manner, ignoring a key asymmetry: routine steps need little additional guidance, while critical error steps require corrective direction that environment feedback alone cannot provide. We propose AHEAD, a step-aware framework that matches different supervision sources to different step types. The teacher receives environment feedback on all steps as a grounded dense signal, and additionally receives LLM-generated corrective hints on error steps to supply the direction that environment feedback lacks. The method introduces minimal changes to the standard GRPO
algorithm. Across ALFWorld, WebShop, and Search-based QA, and across three model scales, AHEAD raises task success (+13.3 points on ALFWorld and +11.0 on WebShop at 7B over GRPO), reaches a given success rate in fewer training steps, and solves tasks within tighter interaction budgets than outcome-only RL and prior self-
distillation baselines.
Read the full abstract ▾
All authors Xiaolong Jin, Dingmin Wang, Vijay Lingam, Varun Kumar
Machine Learning
Structured Frequency-Domain Evidence for LLM-Based Time-Series Anomaly Detection
Jungwook Seo, Sangwon Son, Minjeong Kim et al. (+3 more)
Time-series anomalies can appear not only as pointwise deviations but also as changes in recurring temporal structure, such as shifted periodicity or localized oscillatory fluctuations. However, existing LLM-based time-series anomaly detection methods mainly expose time-domain evidence through indexed values, plots, or de-seasonalized representations, leaving spectral structure implicit. We propose an evidence-augmented zero-shot TSAD framework that preserves indexed de-seasonalized observations while adding compact frequency-domain evidence computed with the Fast
Fourier Transform (FFT). The evidence is constructed at two resolutions: global frequency-domain evidence summarizes sequence-level periodic context, while local frequency-domain evidence captures time-localized spectral departures. Experiments on AnomLLM with InternVL2-LLaMA3-76B, Qwen2.5-VL-72B-Instruct, Gemini-2.5-Flash, and GPT-4o, together with evaluation on the TSB-AD-U subset, show that explicit frequency-domain evidence improves LLM-based TSAD baselines. These results suggest that frequency-domain evidence can complement indexed and de-seasonalized time-domain inputs for zero-shot LLM-based TSAD.
Read the full abstract ▾
All authors Jungwook Seo, Sangwon Son, Minjeong Kim, Seungmin Han, Seojin Yoo, Sungyong Baik
Artificial Intelligence
Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking, Fine-Grained Diagnosis, and Cost-Aware Routing
Shaoan Zhao, Fang Zhao, Xueqiang Guo et al. (+9 more)
Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained
benchmarks decompose
prompts into questions, yet often return them to
prompt scores and fixed categories, weakening attribution and ignoring complexity. Related requirements are also scored separately or as one total, obscuring basic versus compositional failure. We present QC-T2I-Bench, a question-centric framework that converts open
prompts into attributed atomic questions and organizes their dependencies with Davidsonian Scene Graphs (DSGs). We use hierarchy-constrained question aggregation to exclude downstream questions after a prerequisite fails and to prevent simple and complex
prompts from receiving the same total weight. We then use the DSG structure to measure joint success within
prompts and compare repeated entities across
prompts, separating basic realization failures from failures under additional requirements. We evaluate multiple open-source T2I models on English and Chinese
prompts. The resulting question-level evidence supports reliable ranking and fine-grained diagnosis: joint completion falls from 80.7% for components with two capabilities to 37.2% for those with seven or more. Finally, we reuse the same records for training-free routing; our cost-aware router matches ERNIE's 89.51-point estimate with 21.3% less GPU-s/MP.
Read the full abstract ▾
All authors Shaoan Zhao, Fang Zhao, Xueqiang Guo, Xinpei Su, Huanlin Gao, Qiang Hui, Ting Lu, Fuyuan Shi, Chao Tan, Bikun Yang, Kai Wang, Shiguo Lian
Robotics
Trajectory-Level Continuous Action Representation for Robotic Manipulation
Tong Yang, Jingkai Jia, Yuecheng Xu et al. (+3 more)
We propose CAT, a trajectory-level continuous action representation framework for robotic manipulation. Existing visuomotor systems often entangle action representation with control frequency or rely on fixed temporal parameterizations. This leads to representational redundancy at high sampling rates and limits the modeling of critical motion. CAT instead encodes action trajectories within a fixed real-time interval into a set of continuous latent
tokens. To ensure temporal consistency across varying control frequencies, we further incorporate a frequency-aware positional encoding that establishs a shared temporal coordinate system. Trajectory-level regularization further stabilizes the latent representation. This approach prevents representation growth with timestep density and avoids reliance on predefined temporal parameterizations. Extensive system-level evaluations on LIBERO, MimicGen, and real-world long-horizon manipulation tasks demonstrate that CAT-based policies consistently outperform both competitive VQ-based and continuous visuomotor baselines under matched training settings. Across various model backbones and control frequencies, CAT consistently improves success rates. These results highlight the advantages of trajectory-level continuous action modeling for scalable robotic manipulation across varying control rates.
Read the full abstract ▾
All authors Tong Yang, Jingkai Jia, Yuecheng Xu, Xueyao Chen, Chi Zhang, Wenqiang Zhang
Graphics
ExMesh++: From Multi-View Images to Relightable UV-PBR Mesh Assets via Topology-Adaptive Reconstruction and Decomposition
Chuanjin Fan, Lifan Wu, Wenjie Chang et al. (+3 more)
Multi-view reconstruction extends beyond surface recovery to editable and relightable mesh assets. Such assets require well-formed
topology, valid UV parameterization, and explicit PBR material maps. Existing surface reconstruction approaches optimize implicit fields, Gaussian primitives, or other intermediate representations. Converting them into such assets often requires surface extraction and texture baking. Inverse-rendering methods estimate materials and illumination, yet these components often remain tied to neural fields or point-based primitives rather than the final mesh. Joint optimization of geometry, materials, and lighting may also allow these variables to compensate for one another, leading to ambiguous decomposition. To address these limitations, we present ExMesh++, a staged framework for reconstructing relightable UV-PBR mesh assets from multi-view images. The first stage refines explicit mesh geometry and
topology through adaptive vertex splitting and merging, while maintaining UV consistency as the
topology changes. The second stage fixes the resulting mesh-UV carrier and optimizes UV-space PBR maps together with environment lighting. Building on this stable carrier, ExMesh++ models one-bounce diffuse indirect illumination through secondary-ray tracing with shared UV-PBR materials. Experiments demonstrate competitive geometry accuracy, strong relighting performance, and direct usability of the exported assets in standard DCC workflows.
Read the full abstract ▾
All authors Chuanjin Fan, Lifan Wu, Wenjie Chang, Hanzhi Chang, Wenfei Yang, Tianzhu Zhang
Computer Vision
MatReplace: A Reference-Free, Conditioning-Aligned Benchmark for Material Replacement in Interior Scenes
Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy et al. (+2 more)
Material replacement is a common interior-design operation: changing the material of a selected surface while preserving its geometry, surroundings, and illumination. Despite its commercial relevance, no public
benchmark isolates this task, and evaluating it is challenging. Reference-based metrics penalize valid outputs in this inherently one-to-many setting, favor the style of the reference generator, and cannot fairly compare editors that receive different forms of guidance. We introduce MatReplace, a reference-free
benchmark that evaluates edits along four verifiable dimensions: local material correctness, global lighting harmony, outside preservation, and inside structure. It defines three tracks that vary one conditioning signal at a time: (A) instruction only, (B) instruction plus region mask, and (C) material reference image instead of instruction. Our results reveal a clear divide between naming and visually grounding materials. In Track A, leading closed-source editors achieve exemplar-level material rendering and surpass the exemplar anchor under our primary aggregate. In Track B, masks help only mask-compatible models with weak scene preservation, with task-paired, single-seed effects ranging from +0.137 to -0.090 across aligned model families. In Track C, reference-image conditioning degrades every family under both aggregates, by -0.031 to -0.508; in the worst cases, models repaint the reference image itself and perform worse than returning the input unchanged. Thus, named-material rendering is largely solved by the strongest closed editors on this distribution, but grounding materials from pixels remains an open challenge. Expert ratings validate our ranking (Kendall's tau = 0.68) and align with our aggregates more closely than GT-referenced or CLIP-based baselines.
Read the full abstract ▾
All authors Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy, See-Kiong Ng, Luu Anh Tuan
Computer Vision
DRRG: A Discrete Diffusion Framework for Radiology Report Generation
Shaoyang Zhoua, Yingshu Li, Yunyi Liu et al. (+4 more)
Purpose: Automatic radiology report generation (RRG) has been widely explored to improve reporting accuracy and reduce radiologists' workload. Most existing methods rely on autoregressive (AR) frameworks that generate reports
token by
token and cannot revise earlier content, making them prone to error propagation and inconsistent with the iterative refinement process of radiological reporting. In contrast, discrete
diffusion large
language models (DLLMs) generate text through iterative denoising, naturally enabling report refinement. However, DLLMs have not been extensively investigated for RRG. In this study, we developed and evaluated a discrete
diffusion framework for RRG that enables iterative refinement rather than conventional left-to-right autoregressive decoding. Materials and methods: We developed DRRG, a DLLM-based framework that formulates RRG as iterative masked-
token denoising. DRRG incorporates a clinical-entities-aware complementary mask to improve
token supervision coverage and emphasize clinically important entities, together with a concept-conditioning module that injects image-derived clinical concepts into visual representations. DRRG was trained and evaluated on MIMIC-CXR and CheXpert Plus. Results: On MIMIC-CXR, DRRG achieved BLEU-4 of 0.210, CheXpert-F1 of 0.549, RadGraph-F1 of 0.281, GREEN of 0.360, and RaTEScore of 0.604, outperforming the compared methods on most reported metrics, despite employing a substantially smaller LLM decoder. On CheXpert Plus, DRRG achieved the highest BLEU-4 (0.119) and CheXpert-F1 (0.347) among the compared methods. Conclusion: Discrete
diffusion provides an effective alternative to autoregressive radiology report generation by enabling iterative, bidirectional report refinement. Incorporating clinically focused masking and image-derived concept conditioning improves report quality and clinical consistency.
Read the full abstract ▾
All authors Shaoyang Zhoua, Yingshu Li, Yunyi Liu, Lijun Pu, Lingqiao Liu, Lei Wang, Luping Zhou
Machine Learning · Cross-listed from stat.ML
qshap: Fast Shapley Decomposition of $R^2$ for Gradient-Boosted Trees
Zhongli Jiang, Min Zhang and Dabao Zhang
Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications require global measures of feature contributions to overall model performance. Although local attribution scores can be aggregated to characterize feature importance, such summaries do not directly decompose measures of predictive performance, such as $R^2$. This article introduces qshap, available in both R and Python, which provides Shapley decomposition of $R^2$ values for gradient-boosted decision trees (GBDTs) to quantify feature-specific contributions to model performance. By decomposing the quadratic loss of individual observations, qshap provides flexible tools to explore the importance of individual features and observations. qshap currently supports widely used GBDT implementations, including xgboost, lightgbm, and catboost, through a unified tree representation and efficient C++ backends. Its modular design can accommodate other GBDT implementations built from binary decision trees. In addition, we introduce a specialized backend for oblivious trees that exploits their symmetric structure to substantially accelerate computation.
Read the full abstract ▾
Artificial Intelligence
ACE: A Self-Correcting Agentic Canvas Editor for Multi-Slide Presentation Automation
JooYoung Jang, Taegyeong Lee, Jihyeon Park et al. (+1 more)
Commercial design platforms increasingly edit documents through large
language model (LLM)
agents, but two practical problems block reliable deployment: legacy document formats expose only flat, absolutely positioned elements, so
agents must recompute coordinates and routinely break layouts; and design has no unique ground truth, so diff-against-reference metrics penalize valid-but-different outputs. We present ACE, an agentic canvas editor over a hierarchical scene-graph with a presentation-specialized action space (98 tools), paired with CARE, a content-aware router that feeds the
agent only the relevant slice of each deck (avg.\ $\sim$89% input-
token reduction), and a self-correction loop driven by a ground-truth-free instruction-following (IF) judge whose natural-
language critique is fed back as the next-turn instruction. With a fixed backbone, a scene-graph editor in a single turn already matches a same-backbone agentic HTML pipeline that iterates internally; adding self-correction lifts ACE significantly above it on instruction following (IF 4.23 vs.\ 3.81 on the full 94-task
benchmark, paired $p{=}.010$, replicated by an out-of-loop judge) at 1.75$\times$ the speed and $\sim$44% lower cost. VQ means are statistically indistinguishable, but 26 blind raters prefer ACE overall (58.7% decisive win-rate) and prefer the self-corrected output 81% of the time; the ranking is invariant across three judge families, and out-of-loop judges retain two-thirds of the self-correction gain, bounding circularity. 66% of cases halt after one pass, and a strict-peak rollback removes every observed regression.
Read the full abstract ▾
All authors JooYoung Jang, Taegyeong Lee, Jihyeon Park, Nojun Kwak
Distributed Computing
A Few Shared Random Bits Suffice for Constant-Round Almost Stable Matching
Yi-Jun Chang and Kushagra Chatterjee
We show that almost stable matching can be solved in constant distributed rounds on general bipartite graphs $G=(V,E)$ using only a few shared random bits. Specifically, in the $\congest$ model, we compute a matching whose expected number of blocking pairs is at most $\varepsilon |E|$ in $O\left(\frac{\log(1/\varepsilon)}{\varepsilon^4}\right)$ rounds using $O\left(\log(1/\varepsilon)\right)$ shared random bits. Thus, for every constant $\varepsilon>0$, the round complexity is $O(1)$, independent of the number of vertices and the maximum degree. Previous
algorithms achieve constant round complexity only for bounded-degree or almost-regular graphs; on general graphs, their round complexity depends polylogarithmically on $n$. Our main technical idea is a degree-guarded freezing rule that allows widely varying degrees to be handled by a single global charging argument, avoiding the $\Theta(\log n)$ successive degree thresholds used in previous work. The shared random bits are used only to select a common random output iteration. As consequences, we obtain an $O\left( \frac{\log(1/\varepsilon)}{\varepsilon^4} + \frac{\log n}{\varepsilon} \right)$-round $\congest$
algorithm without pre-shared randomness, via a low-diameter decomposition, and an $O\left(\frac{\log(1/\varepsilon)}{\varepsilon^4}\right)$-round
algorithm in the fully-scalable Massively Parallel Computation ($\mpc$) model with linear total
memory.
Read the full abstract ▾
Robotics
TrAct: Bridging Robot Control and Visual Prediction with Visual Tracks
Zhi Cao, Howard Ji, Kevin Zhang et al. (+4 more)
Robot actions are inherently embodiment-specific and only weakly aligned with image-space visual changes, limiting their effectiveness as conditioning signals for
robot world models. In contrast, visual tracks provide an embodiment-agnostic representation of how task-relevant points move through a scene, offering dense image-space guidance for accurate and spatially precise future
video prediction. Building on this observation, we propose TrAct, a world-model-based
robot decision-making framework that uses visual tracks as an intermediate interface between control and prediction. TrAct consists of three components: a
Vision-
Language-Action-and-Track model (VLAT) that jointly predicts candidate actions and corresponding visual tracks from the current observation and
language instruction; a track-conditioned world model (TWM) that predicts future visual outcomes conditioned on the proposed tracks; and a
vision-
language reward model (VLAC) that scores the predicted outcomes. At inference time, VLAT generates candidate action-track pairs, TWM rolls out their visual consequences, and VLAC selects the track whose predicted outcome best satisfies the instruction; the action paired with the selected track is then executed by the
robot. Experiments on the proposed LIBERO-INTEGRAL
benchmark and real-world Franka manipulation show that TrAct improves success rates from 27% to 55% in simulation and from 49% to 76% on real-world tasks compared with the strong VLA baseline $\pi_{0.5}$. Furthermore, TWM consistently improves
video prediction quality over the action-conditioned world model (AWM). These results demonstrate that visual tracks provide an effective shared interface between
robot control and visual prediction, enabling more accurate world modeling and stronger
robot generalization.
Read the full abstract ▾
All authors Zhi Cao, Howard Ji, Kevin Zhang, Kuangzhi Ge, Li Fei-Fei, Jiajun Wu, Huang Huang
Artificial Intelligence
Are Android GUI Agents Robust Against Runtime Anomalies? AnTrap: Evaluating Agents in Dynamic Adversarial Environments
Guo Gan, Yilun Zhao, Cong Chen et al. (+5 more)
GUI
agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing
benchmarks lack systematic evaluation of
agent robustness against runtime anomalies. We introduce AnTrap, a comprehensive
benchmark that injects dynamic perturbations into
agent execution trajectories. We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round) with ten fine-grained subcategories, and develop a construction pipeline that preserves task solvability while introducing realistic
adversarial conditions. Evaluating 16 leading GUI models, we reveal universal vulnerability to dynamic anomalies, with even the strongest models suffering significant performance degradation. Furthermore, we conduct GRPO training in both original and
adversarial environments to validate our
benchmark, separating environment-learnable anomalies from
reasoning-bottlenecked ones. Our findings show that while single-step traps at state and action layers are largely addressable through
adversarial reinforcement learning, deep contextual traps, like state deadlock, expose intrinsic limitations that cannot be resolved by training in environments with traps alone.
Read the full abstract ▾
All authors Guo Gan, Yilun Zhao, Cong Chen, Jinbiao Wei, Tingyu Song, Zheyuan Yang, Lin Fu, Hong Zhou
Machine Learning
The Sharp Tail of Uniform Stability
Pahan Dewasurendra
Uniform stability controls how much one training example can change the loss at any test point. A new logarithmic-free upper bound shows that a $\gamma$-uniformly stable
algorithm with loss in $[0,L]$ has generalization gap at most $O \left(\gamma\log(1/\delta) +L\sqrt{\frac{\log(1/\delta)}{n}}\right)$ with probability $1-\delta$. Whether an actual bounded-loss learning
algorithm can realize the linear dependence on $\log(1/\delta)$ has remained open. The known construction realizes it only for auxiliary weakly dependent random variables whose pointwise range grows with $n$. The known learning lower bound holds only at constant probability. We close this gap. For every $n$, stability level $\gamma$, and loss bound $L$, we construct one deterministic $\gamma$-uniformly stable learning problem whose tail satisfies, simultaneously for $1\le p\le c n$, $\mathbb P \left( R(A_S)-R_S(A_S) \ge c'\min \left\{L,\gamma p+L\sqrt{p/n}\right\} \right)\ge e^{-p}.$ The construction is ordinary bounded absolute-loss regression with constant labels. Its key is a multiscale collection of rare Rademacher features. A coordinatewise ramp is stable in sup norm, while an odd symmetrized maximum converts a unique extreme feature into a gap of order $\gamma p$ without violating the loss bound. Geometrically spaced ramps put all confidence levels into the same problem. Together with the logarithmic-free upper bound, this determines the optimal high-probability and moment dependence of uniform stability up to universal constants.
Read the full abstract ▾
Robotics
SIREN-Bench: Behavior-Driven Generation and Evaluation of Emergency-Vehicle Interactions
Yicheng Zhu, Tianmu Zhao, Haoxin Leng et al. (+3 more)
Emergency vehicles (EMVs) can reorganize surrounding traffic as civilian vehicles brake, change lanes, or form rescue corridors in response to their passage. Evaluating these safety-critical interactions requires behavior-level control over both EMV privileges and civilian responses, together with consistent sensing and ground truth. Existing
datasets and simulation
benchmarks do not directly provide this combination. We present SIREN, a behavior-driven SUMO–CARLA co-simulation platform for generating EMV–civilian interactions. SIREN couples SUMO's network-level traffic
evolution and behavior logic with CARLA's continuous vehicle control and synchronized onboard sensing; depending on the active behavior, the interaction is controlled by SUMO, CARLA, or jointly. We instantiate the platform as SIREN-Bench-v1, comprising seven parameterized interaction templates across emergency levels L1–L3 and three behavior families, with synchronized sensor observations and simulator-native annotations. We demonstrate the
benchmark through three representative tasks: 3D object detection, trajectory prediction, and
vision-
language risk understanding. Evaluations of nine trajectory predictors, four
LiDAR-based detectors, and five
vision-
language models reveal behavior-dependent failure modes. Traffic-clearance interactions are hardest for detection, privileged intersection traversal is hardest for prediction, and no learned predictor outperforms the constant-velocity reference on average.
Vision-
language models perform substantially better on normal traffic than on near-miss and collision events. These results demonstrate the value of behavior-centered benchmarking and establish SIREN as an extensible data-generation and evaluation platform for autonomous-driving and transportation safety research.
Read the full abstract ▾
All authors Yicheng Zhu, Tianmu Zhao, Haoxin Leng, Fan Zuo, Tao Li, Zilin Bian
Computer Vision
Joint-Embedding Prediction of Masked Point Tubes for Self-Supervised Learning on 4D Point Cloud Videos
Jheng-Ling Lee and Shang-Tse Chen
Self-supervised representation learning for 4D point cloud
videos is challenging because annotations are costly and reconstruction-based pretraining can overemphasize low-level
geometric details. We propose a JEPA-style framework that learns from unlabeled spatiotemporal point clouds through latent point-tube prediction. Instead of reconstructing raw coordinates, the model masks spatiotemporal regions and predicts their target representations from visible context representations in feature space. To stabilize latent prediction, we incorporate Sketched Isotropic Gaussian Regularization, which encourages non-collapsed
embeddings without relying on explicit reconstruction targets. This formulation aims to capture both spatial structure and temporal dynamics while keeping the pretraining objective aligned with downstream semantic recognition. Experiments on action and gesture recognition
benchmarks show that the learned representations improve downstream fine-tuning, limited-label learning, and cross-
dataset transfer. These results suggest that JEPA-style latent prediction is a promising alternative to reconstruction-centered pretraining for 4D point cloud
videos.
Read the full abstract ▾
Information Retrieval
Native Multimodal Representation Learning for Click-Through Rate Prediction in E-Commerce Scenarios
Chao Yi, Feifan Yang, Jiawei Feng et al. (+4 more)
Multimodal representations have been widely adopted in industrial e-commerce recommendation systems. Due to their strong semantic understanding and generalization capabilities, they enhance the performance of traditional sparse ID-based Click-Through Rate (CTR) prediction models. Current
multimodal application frameworks in the CTR prediction task typically follow a two-stage paradigm: first, pre-training a
multimodal encoder on data from specific recommendation scenarios; second, extracting items'
multimodal representations using this pre-trained
multimodal encoder and integrating them into the CTR prediction model. However, the training objectives and data distribution of
multimodal pre-training tasks often differ from those of the CTR prediction task, which limits the effectiveness of
multimodal representation on downstream tasks. In this paper, we focus on how to learn Native
Multimodal Representation for the CTR prediction task. One intuitive solution is to jointly train the
multimodal encoder and CTR model end-to-end on the CTR task, with the expectation that the encoder can automatically learn downstream-relevant knowledge. However, we find that the end-to-end training does not bring performance improvements to existing
multimodal application paradigms. Our analysis reveals that user behaviors in raw CTR data are driven by both
multimodal semantics and non-
multimodal factors, leading to ambiguous supervision and inconsistent encoder updates. To address this, we propose a Mine-Then-Train method that mines high-quality, multimodally interpretable training samples from CTR data and uses them to fine-tune the
multimodal encoder for better
alignment with user click preferences. Offline and online experiments demonstrate the effectiveness of our approach.
Read the full abstract ▾
All authors Chao Yi, Feifan Yang, Jiawei Feng, Sishuo Chen, Zhangming Chan, Xiang-Rong Sheng, Han Zhu
Numerical Analysis
Derivative-Free Recovery of a Nonlinearity in a Free-Boundary DCIS Model
De-Han Chen, Hongyu Liu and Keji Liu
We investigate an inverse coefficient problem for a multidimensional free-boundary model of ductal carcinoma in situ (DCIS), in which the tumor interface is governed by the nonlinear coupling of nutrient concentration, tissue pressure and
curvature, and the unknown nutrient consumption function is recovered from a temporal trace of the nutrient concentration obtained by needle aspiration biopsy. For the forward problem, we establish uniform local well-posedness over an admissible class of consumption functions. The inverse problem is recast as a fixed-point problem: approximating the admissible set by finite-dimensional spaces yields discrete iteration operators, for which we prove the existence of fixed points, and the strong convergence of a subsequence of discrete fixed points to a fixed point of the continuous operator, which solves the inverse problem under a consistency condition. To approximate these fixed points, we develop a homotopy-continuation method combining a linearly convergent Picard iteration with a cubical Sperner search, without differentiating an objective functional or computing an adjoint state. Several numerical experiments on radially symmetric and non-symmetric DCIS models corroborate the theoretical findings.
Read the full abstract ▾
Information Retrieval
CodeHID: Learning an Addressable Hierarchical Code Index for Generative Code Retrieval
Zhen Li, Yuhong Chen, Wenhao Xu et al. (+2 more)
Code
retrieval models have predominantly relied on a flat matching paradigm that treats code snippets as independent candidates, making them less capable of distinguishing similar code candidates. Generative
retrieval offers a solution by constructing a learnable index over the code corpus, guiding the retriever to better understand how code candidates are semantically organized and addressed. However, naively applying generative
retrieval in the code
retrieval task may result in operating over an identifier space whose prefixes do not correspond to meaningful code-semantic regions. In this paper, we propose CodeHID, a generative code
retrieval framework that reformulates the code
retrieval task from flat candidate matching to coarse-to-fine semantic address generation. CodeHID relies on two core components. First, Pseudo-Neighbor Guided DocID Learning constructs a globally static hierarchical index by applying multi-level residual quantization and $k$-nearest-neighbor pseudo-supervision, ensuring that semantically related code snippets share prefixes while preserving target-level separability. Second, Dual-Phase DocID Generation Guidance reliably navigates this fixed index by combining training-side ranking enhancements, using hard negatives and rank
distillation, with inference-side candidate constraints and prefix-aware decoding. Extensive experiments on CoSQA and ProCQA
benchmarks demonstrate that CodeHID outperforms existing sparse
retrieval, pre-trained code models, dense code
retrieval, and generative
retrieval baselines by a large margin in most cases, achieving particularly strong improvements in rank-one
retrieval metrics.
Read the full abstract ▾
All authors Zhen Li, Yuhong Chen, Wenhao Xu, Xiaodong Li, Hui Li
Machine Learning
Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents
Nadeem Shaikh
Current LLM
agent systems decide delegation before
reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and may retry). We study a third regime: an
agent that recognises, during its own
reasoning, that it is unlikely to succeed and transfers control to a stronger model. We formulate intra-generation delegation as a
Bayesian optimal-stopping problem over a learned competence posterior – an online estimate of the
agent's eventual task success whose sufficient statistics are learned from labelled trajectories, not read off raw
entropy. We derive the myopic escalation threshold in closed form, characterise the optimal policy via dynamic programming, and prove that the optimal policy is a time-varying threshold with no shape assumption on the raw signal. We further prove exponential separation of the oracle belief at the Chernoff-information rate of the signal, a regret bound governed by the calibration of the posterior, and a finite-sample guarantee: with n labelled calibration trajectories the deployed plug-in policy's regret decays as 1/sqrt(n). A controlled simulation study confirms each prediction of the theory, including the predicted 1/sqrt(n) rate. We additionally report a real-model validation on a Qwen2.5-Coder 1.5B->7B code cascade (MBPP, 257 tasks), confirming two of three pre-registered predictions: the escalation frontier dominates post-hoc routing at equal cost, and the cumulative competence belief's discrimination rises over generation.
Read the full abstract ▾
Artificial Intelligence
EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals
Mingxu Zhang, Ying Sun, Yuhan Li et al. (+4 more)
Large
language models (LLMs) are increasingly used as code
agents for scientific and engineering analysis, but their ability to analyze raw physical-layer measurements remains untested. We introduce EMRB (Electromagnetic
Reasoning Benchmark), which evaluates whether LLMs can analyze raw I/Q data by writing and running code. EMRB contains 200 problems across five difficulty levels and 27 question types, from signal detection to OFDM design, generated from 11 signal types with verified ground truth. Unlike
benchmarks built on preprocessed features or structured tables, EMRB provides only the raw capture; the quantities each question refers to must first be discovered through code. We evaluate 14 LLMs spanning proprietary, open-weight, and
reasoning-oriented families. Scores range from 24.1% to 78.9%, with the mean dropping from 84.9% on basic measurement to 21.2% on system design. We also propose ReconPilot, a structured method that separates signal reconnaissance, targeted analysis, and self-verification. Across three backbones, ReconPilot raises the overall score by 3.8 to 17.6 points and improves 13 of 15 backbone-level combinations tested. All data and code are publicly released in \href{https://github.com/mingxuZhang2/EMRB}{\textcolor{blue}{our GitHub repository}}.
Read the full abstract ▾
All authors Mingxu Zhang, Ying Sun, Yuhan Li, Yang Ji, Dazhong Shen, Ke Zhang, Shan Huang
Computation and Language
PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding
Md Mahadi Hasan Nahid and Davood Rafiei
Large
Language Models (LLMs) have shown strong capabilities in table
reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and difficulty localizing the evidence required for
reasoning. Existing approaches typically reason over either the full table or a single reduced view, which can still obscure important row-column relationships. We introducePARTAB (Partition-Aware
Reasoning overTables), a framework that constructs a structured evidence interface between the LLM and the table. PARTAB represents query-relevant evidence as semantically coherent, row-linked table regions and performs hierarchical selection over column groups and row-level partitions before composing the selected evidence for answer generation. We evaluate PARTAB on multiple table
reasoning benchmarks, covering question answering, fact verification, and numerical
reasoning. PARTAB consistently improves over full-table prompting and several recent table
reasoning methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical
reasoning. Additional analyses show that semantic partitioning and targeted evidence selection improve evidence localization, substantially reduce the
reasoning context, and provide larger benefits on complex tables. These results demonstrate the value of structured, partition aware evidence construction for scalable table
reasoning.
Read the full abstract ▾