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新论文 · v1arXiv:2611.03399

Image Guided Depth Upsampling using Anisotropic Total Generalized Variation

David Ferstl, Christian Reinbacher, Rene Ranftl, Matthias Rüther, Horst Bischof

原文摘要

In this work we present a novel method for the challenging problem of depth image upsampling. Modern depth cameras such as Kinect or Time of Flight cameras deliver dense, high quality depth measurements but are limited in their lateral resolution. To overcome this limitation we formulate a convex optimization problem using higher order regularization for depth image upsampling. In this optimization an anisotropic diffusion tensor, calculated from a high resolution intensity image, is used to guide the upsampling. We derive a numerical algorithm based on a primaldual formulation that is efficiently parallelized and runs at multiple frames per second. We show that this novel upsampling clearly outperforms state of the art approaches in terms of speed and accuracy on the widely used Middlebury 2007 datasets. Furthermore, we introduce novel datasets with highly accurate groundtruth, which, for the first time, enable to benchmark depth upsampling methods using real sensor data.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03394

TaoMate-HD: Progressive-Resolution Autoregressive Audio-Video Generation

Qijun Gan, Chenwei Zhang, Xinyang Li, Meiguang Jin, Junfeng Ma, Shen Qiu

原文摘要

Streaming digital humans produce detailed video while continuing speech as new text arrives. High-resolution denoising is costly, and text updates can cause repeated or interrupted speech. We present TaoMate-HD, an autoregressive model for high-resolution audio-video streaming. For each short segment, or chunk, two denoising steps establish coarse video structure at low resolution. A final step refines the video to high resolution, with audio generated throughout the same trajectory. Each stage passes its audiovisual predictions to the same stage of the next chunk as history. Across text updates, the model retains this history to infer what has already been spoken. Continuation training utilizes asymmetric text conditioning, with separate text windows for generation and distillation. Experiments show a 1.6x speedup over direct high-resolution generation with the same model and hardware. Optimized stage-parallel deployment provides a further 1.9x overall speedup over serial progressive generation. Our continuation training reduces character error rate by 11.18 percentage points relative to the baseline. The model also supports hour-long audiovisual generation. Our project page is https://taoliveaigc.github.io/TaoMate-HD.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03390

Progressive Multi-Scale Super-Resolution via Cascaded Bicubic Anchors and Inter-Scale Mutual Refinement

Chun-An Lin, Yu-Chia Chang, Chi-Mao Fan, Yen-Chieh Ouyang, Tsung-Jung Liu

原文摘要

Super-resolution networks are trained for one magnification factor, so several output resolutions require several models. We propose PMS-SR, a progressive single-input multiple-output network that produces x2, x4, and x8 results in one forward pass, with a Transformer backbone on the low-resolution grid. In Stage 1, each scale is predicted as a residual over the detached bicubic upsampling of the previous scale's prediction. With Dirac and zero initialization, training starts exactly from the bicubic cascade, and each head adds only the band the previous scale lacks. In Stage 2, inter-scale mutual refinement couples the scales in both directions through space-to-depth feedback, deformable alignment, and per-pixel fusion, so x8 features refine the x2 output. Trained on DF2K, the 7.95M-parameter model achieves a higher PSNR than every compared multi-scale method at x4 and x8 on all five standard benchmarks. At x8, it outperforms the x8-specific DRN-L on all of them, leads all compared methods on Urban100, and is within 0.13 dB of the best result elsewhere. Against three single-scale SwinIR-M models, PMS-SR uses 22% of the parameters, runs 2.9x faster on BSD100 inputs, and its outputs agree 6.7-12.4 dB more closely. Code and pretrained models will be released at https://github.com/julian135707/PMS-SR upon acceptance.

cs.CV · cs.LG · eess.IV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2510.13385

Probabilistic Prediction Markets with Intermittent Contributions

Michael Vitali, Pierre Pinson

原文摘要

Although both data availability and the demand for accurate forecasts are increasing, collaboration between stakeholders is often constrained by data ownership and competitive interests. In contrast to recent proposals within cooperative game-theoretical frameworks, we place ourselves in a more general framework, based on prediction markets. There, independent agents trade forecasts of uncertain future events in exchange for rewards. We introduce and analyse a prediction market operating with a fixed pool of agents that (i) accounts for the historical performance, (ii) adapts to time-varying conditions, while (iii) accommodates intermittent forecast submissions. The proposed design employs robust regression models to learn the optimal forecasts' combination whilst handling missing submissions. Moreover, we introduce a pay-off allocation mechanism that considers both in-sample and out-of-sample performance while satisfying several desirable economic properties. Case studies using simulated and real-world data demonstrate the effectiveness and adaptability of the proposed market design.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03389

CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances

Torin Hopkins, Shih-Yu Ma, Zander Trafton-Hancock, Ellen Yi-Luen Do, Luca Turchet

原文摘要

Technology-mediated audience participation (TMAP) enables audiences to influence live performances, yet balancing interaction with meaningful agency remains challenging. While AI-generated music and MR enable new forms of audio and visual audience participation, little is known about how these participation modalities influence audience experiences during live musical performance. In particular, it remains unclear how they shape perceived agency, collaboration, and engagement, and how they complement one another within TMAP systems. We present CrowdForge, an AI- and MR-mediated paradigm where audiences collectively control generative music and virtual environments during a live performance featuring a guitarist and DJ. Through a mixedmethods concert study evaluating three interaction conditions (audio, visual, and combined audio-visual), we demonstrate that observability is the primary driver of audience agency. While visual interaction provided easily observable feedback, audio parameters offered macro-structural control over the music. Audiences tended to use the control enabled by AI audio generation to test musical boundaries, introducing a productive adversarial tension between the crowd and the musicians. This dynamic pushed creative limits, deepened engagement, and generated moments of musical coherence when performers successfully resolved audience-induced challenges. We argue future TMAP systems should prioritize human-centered, highly observable interactions to build agency, while combining the expressiveness of music with the clarity of visual feedback.

cs.HC · cs.AI · cs.NI · cs.PF · cs.SI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.27428

Rethinking AI in Medical Imaging: Assumptions, Reality, and Reframing; Towards Physician-Centered Agentic Reasoning

Arman Rahmim, Nourhan Bayasi, Xiaoxiao Li, Babak Saboury, Fereshteh Yousefirizi

原文摘要

Medical imaging has served as primary proving ground for clinical AI, yet a decade of intense research has not translated into proportionate bedside impact. We argue that this gap is not primarily a product of insufficient algorithmic performance, inadequate regulation, or limited explainability. Rather, it reflects a structural misalignment, between how AI systems are designed and evaluated, and how clinical decisions are made. This work identifies 8 interconnected dimensions of this misalignment: 1) the dominance of pixel-only models in a multimodal clinical world; 2) the erosion of physician trust through opaque and inflexible systems; 3) the unfulfilled promise of foundation models in data-sparse medical domains; 4) the persistent bottleneck of non-shareable under-curated datasets; 5) the gap between validated algorithms and deployable clinical platforms; 6) the slow, unmonitored translation of evidence into routine practice; 7) the risk of AI displacing rather than extending physician judgment; and 8) the failure of prediction-centric AI to generate actionable, agentic clinical guidance. For each dimension, we reframe the problem and propose a path forward, culminating in a vision of physician-centered AI solutions with agentic reasoning that extend, rather than replace, clinical judgment.

physics.med-ph · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.28397

MAS-Guard: Unified Guardrails from Individual Agents to Multi-Agent LLM Systems

Mohammad Kachuee, Phu Mon Htut, Sheng Li, Nikolaos Pappas, Miguel Ballesteros, Yassine Benajiba, Sujith Ravi, Dan Roth

原文摘要

Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their utility while exposing applications to unsafe actions, information leakage, and adversarial manipulation. Multi-agent systems further expand these capabilities through specialization and collaboration, but distributed authority, heterogeneous roles, and opaque or potentially compromised participants make safeguarding even more challenging. Existing defenses address important individual threats but provide fragmented coverage across local execution, system-level interactions, and enforcement mechanisms. We introduce MAS-Guard, a unified framework that closes this gap for safeguarding single- and multi-agent AI applications containing both instrumentable and opaque agents. Within instrumentable agents, the proposed local Agent Guardrail (A-Guard) layer applies task-specific experts to assess agentic events. At the system level, developer-owned Agent Charters and a Control Plane combine deterministic structural checks of routes, agent capabilities, and message formats with semantic assessments of roles and message content. These complementary layers enable enforcement through a shared set of interventions: allow, reflection, confirmation, and block. Evaluated across six benchmarks covering diverse single- and multi-agent scenarios with three agent LLMs from different providers, MAS-Guard reduces the overall attack success rate by 71.5-86.3% relative to unguarded agents, while meeting the utility reference criterion on benign tasks.

cs.CR · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03388

LAND: Learning an Adaptive Number of Draws for Bayesian Best-of-N Selection

Koen Oostermeijer, Teryn Jones

原文摘要

Best-of-N decoding improves generation quality by sampling multiple candidates and selecting the best, but it allocates equal compute to sources regardless of how much they benefit from additional sampling: the expected benefit is small for sources with low-variance score distributions and potentially much larger for those with high-variance distributions. To address this shortcoming, we propose LAND (Learning an Adaptive Number of Draws), which dynamically allocates samples according to their posterior-predictive marginal value. LAND learns a discrete empirical mixture of source-conditioned score distributions from calibration data, updates its belief about a source as completion scores are observed, and stops sampling when the expected improvement of another draw falls below a shared compute price. Across machine-translation experiments with multiple models and language pairs, LAND achieves better MetricX scores than fixed best-of-N at matched average compute. Conversely, at matched translation quality, LAND requires approximately 20-40% fewer candidate generations. Adaptive allocation therefore provides a simple way to retain the benefits of best-of-N while substantially improving its compute efficiency.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03384

Can We Stop the Backtest Yet? Anytime-Valid Promotion Tests for Adaptive Financial Research Agents

Eric Benhamou, Jean-Jacques Ohana, Beatrice Guez, Ethan Setrouk, Chamyl Saadi, Thomas Jacquot

原文摘要

Financial research agents can generate and monitor trading rules faster than a human can document the resulting selection process. Candidates appear at data-dependent times, weak rules are abandoned, and promising ones are watched until they cross a reporting threshold. We introduce PROMOTE, a protocol that assigns every candidate predictable testing wealth at birth and accumulates a cost-aware e-process only from subsequent returns. If lifetime allocations sum to at most one, a weighted Ville argument bounds the probability of ever promoting a rule whose declared bounded net score has always had nonpositive conditional mean. The result permits dependent candidates, adaptive births, and indefinite monitoring. We audit the implementation in two settings. A retrospective pseudo-out-of-sample replay contains 24 panel--rule hypotheses, 3,000 shared-sign null paths, injected alternatives, and public ETF and industry data. Repeated unadjusted tests produce a family crossing in 94.5% of null paths, versus 0.0% for PROMOTE; at a 15-bp aligned ETF-component perturbation, PROMOTE has 70.1% target power and median crossing session 2,327. A separate exact mean-zero experiment lets an agent select candidate identities and birth times from past scores; the funded gate has 1.77% false-family crossings across 3,000 programs. The market replay produces zero promotions after costs. The certificate controls historical false promotion under the declared null; it is not a certificate that an earlier edge remains profitable at deployment. AI-use disclosure. Generative AI tools, including OpenAI Codex, assisted manuscript preparation. For this preprint version, Codex assisted with LaTeX editing, author and venue metadata, compilation, and layout checks.

cs.AI · q-fin.ST

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03382

ASCoR: Asymmetric Semantic-Spatial Learning for Remote Sensing Domain Generalization

Zitong Wang, Shuchang Lyu, Meng Li, Qiang Wang, Guangliang Cheng, Zhenwei Shi

原文摘要

Source-only domain generalization (DG) for remote sensing semantic segmentation seeks reliable predictions in unseen domains without target-domain data during training. Although vision foundation models offer transferable knowledge, category recognition remains sensitive to source-specific visual cues, while task-specific adaptation may weaken pretrained transferability. We propose ASCoR, an asymmetric semantic-spatial framework built on SAM3. Its text-conditioned semantic branch uses class-name prompts to provide explicit category guidance. Probe-Once Dynamic Adaptive LoRA distributes a fixed adaptation budget across encoder blocks to limit source-specific specialization. A complementary task-specific dense branch learns multi-scale spatial representations using a separate frozen SAM3 image encoder. A lightweight reliability router integrates the two branches through bounded spatial correction based on local prediction reliability. All components are trained using source-domain supervision only. Experiments on the ISPRS, LoveDA, and OpenEarthMap benchmarks cover nine cross-domain transfers. ASCoR achieves a state-of-the-art mean IoU of 68.61% across six ISPRS transfers, outperforming the previous best method by 2.69 percentage points. It also achieves the best results on all three LoveDA/OpenEarthMap transfers, with 59.07%, 47.95%, and 49.38% mIoU on Rural-to-Urban, Urban-to-Rural, and Urban-to-OpenEarthMap, respectively. Ablation studies, feature visualizations, and spatial reliability analyses support the effectiveness and complementarity of the proposed components. Code: https://github.com/WangZitong42/ASCoR.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.25990

SpeechEQ: Benchmarking Emotional Intelligence Quotient in Socially Aware Voice Conversational Models

Liang-Yuan Wu, Zih-Ching Chen, Tongshuang Wu, Chao-Han Huck Yang, Hua Shen

原文摘要

As multimodal conversational systems increasingly engage in spoken interaction, their ability to navigate paralinguistic social cues has become a critical bottleneck for natural human-AI communication. However, existing evaluations of machine emotional intelligence assess reasoning exclusively through isolated text or passive acoustic perception, overlooking the complex cross-modal reasoning required for active, multi-turn dialogue. We introduce \textsc{SpeechEQ}, a comprehensive framework designed to evaluate the sociolinguistic reasoning of Speech-Language Models (SLMs). The framework includes a validated dataset of 2,265 dialogues across 15 Emotional Quotient (EQ) subscales grounded in EQ-i 2.0 theory, along with a multi-turn evaluation protocol measured by our proposed Spoken EQ (SEQ) score inspired by human EQ assessments. Experiments show limitations in how both existing Speech Emotion Recognition and end-to-end Speech-Language Models understand and apply paralinguistic cues through speech. While end-to-end architectures outperform cascaded systems, \textsc{SpeechEQ} reveals that current multimodal models remain bottlenecked by a text-reliant ``modality shortcut,'' an alignment-induced ``safety trap,'' and ``contextual amnesia,'' highlighting the barriers to truly emotionally aware AI. Our benchmark can be accessed at https://huggingface.co/datasets/SpeechEQ/SpeechEQ and demo page at https://speecheq.github.io

cs.CL · cs.AI · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03381

A Geometry-Informed Neural Operator surrogate of LISFLOOD-FP for rapid pluvial-fluvial flood forecasting: the case of Forlì, Italy

Samuele Maria Garofalo, Fabio Limardo, Simonetta Bodojra, Mohammad Amin Nabian, Mehdi Taghizadeh

原文摘要

Physically based two-dimensional hydraulic models are too slow for real-time inundation forecasting and large ensembles. We present a time-dependent geometry-informed neural operator (GINO) surrogate of LISFLOOD-FP for the municipality of Forlì (Italy), severely affected by the May 2023 Emilia-Romagna floods. The surrogate is designed for an operational setting in which only river stage gauges and rain gauges are available. A reproducible pipeline turns public regional observations into a physically based training set: each river inflow is driven by the first stage gauge downstream of it through a calibrated water-surface boundary condition, and rain enters as SCS-CN effective rainfall. The surrogate is an autoregressive GINO trained on 216 simulations of 24 observed events with scaled forcing. It predicts volume-conserving polygon-mean depths on an exposure-based Voronoi mesh of 659 nodes, with a 20 min step over 72 h. The surrogate is tested on 38 independent observed events that it never saw during training. On these events it reaches a critical success index (CSI) of 0.934 at 0.05m and 0.844 at 0.3 m, a depth RMSE of 0.008m and an $R^2$ of 0.984 for maximum-depth maps. On one NVIDIA A10G GPU a 72-h forecast takes 1.7 s instead of 55 s with LISFLOOD-FP, and a 50-member ensemble, computed in a single batched rollout, takes 6.2 s instead of about 46 min, which makes probabilistic flood forecasting practical. These results show that neural-operator surrogates can deliver accurate pluvial-fluvial flood forecasts driven by river stage for operational early warning.

cs.LG · physics.geo-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2111.08222

Impact of Model Interpretability and Outcome Feedback on Trust in AI

Daehwan Ahn, Abdullah Almaatouq, Monisha Gulabani, Kartik Hosanagar

原文摘要

This paper bridges the gap in Human-Computer Interaction (HCI) research by comparatively assessing the effects of interpretability and outcome feedback on user trust and collaborative performance with AI. Through novel pre-registered experiments (N=1,511 total participants) using an interactive prediction task, we analyzed how interpretability and outcome feedback influence users' task performance and trust in AI. The results counter the widespread belief that interpretability drives trust, showing that interpretability led to no robust improvements in trust and that outcome feedback had a significantly greater and more reliable effect. However, both factors had modest effects on participants' task performance. These findings suggest that (1) interpretability may be less effective at increasing trust than factors like outcome feedback, and (2) augmenting human performance via AI systems may not be a simple matter of increasing trust in AI, as increased trust is not always associated with equally sizable performance improvements. Our exploratory analyses further delve into the mechanisms underlying this trust-performance paradox. These findings present an opportunity for research to focus not only on methods for generating interpretations but also on techniques that ensure interpretations impact trust and performance in practice.

cs.AI · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03379

RangeFinder: Predicting Ligand Property Ranges from Protein Pockets

Alan Nadelsticher Ruvalcaba, Chris Alvin, Supratik Mukhopadhyay, Adam Bess

原文摘要

Identifying ligands for a protein target requires searching a vast chemical space for a comparatively small set of compatible molecules. Existing machine learning approaches primarily address this problem by generating candidate ligands or scoring individual protein-ligand pairs, but provide limited information about the broader chemical space compatible with a binding pocket. We introduce RangeFinder, an SE(3)-equivariant transformer pocket-level model, trained and evaluated on BioLiP2 data, that predicts ranges over nine physicochemical ligand properties directly from pocket structure, chemistry, and evolutionary context. Using an intersection-over-union objective and evaluation metric (range-IoU), RangeFinder achieves a range-IoU of $0.464 \pm 0.002$, outperforming all deployable baselines while requiring only $12.2$ ms per pocket. We also quantify our method's ability to reduce the ligand search space while retaining compatible binders; in this regard, RangeFinder achieves a Reduction-Recall F-Score of $0.543 \pm 0.011$ --- once again outperforming all deployable baselines. Ablation experiments indicate that RangeFinder's performance depends more strongly on the density and quality of ligand supervision than on any individual input modality or specialized training strategy. Together, these results position RangeFinder as an inexpensive and effective molecular filtering prior, with demonstrated potential in narrowing the ligand search space of target pockets before docking, generation, or synthesis. The code for this project can be found at: https://github.com/alannadels/RangeFinder.

cs.LG · q-bio.BM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03377

Same Outputs, Different Scores: Auditing the Unchecked Graders of Multimodal LLM Benchmarks

Ahmad Jarjis Hasan, Jonathan Lwowski

原文摘要

Every multimodal-LLM multiple-choice benchmark ends with a scoring rule that converts a model's response, often free-form text rather than a clean option letter, into a right/wrong verdict, buried in its code. Unlike the dataset, this rule is chosen without justification and left unexamined, yet it sets the published number. We reproduce the official scorers of eight peer-reviewed benchmarks on five open vision-language models (VLMs) that fit a single 16 GB GPU, and introduce a blinded human-plus-LLM-judge adjudication (Cohen's kappa=0.956) that recovers what the model actually answered. The scoring rule alone moves accuracy by up to 22 points on identical outputs, yet exactly 0 wherever a model emits clean option letters, so it hides on strong-model leaderboards. It is the fallback and not the rambling that does the damage: the benchmark with the highest non-compliance we measure has an artifact of exactly zero, because its scorer abstains instead of guessing; a reasoning model with non-compliance 0.000 still swings 9.5 points with which regex match of its trace the parser keeps. No standard rule is reliable: snapping a non-answer to its closest option credits answers never given yet buries correct ones, abstention discards answers stated in words, and likelihood scoring follows answer-token priors, so a single grade conflates capability with luck rather than measuring either. We release all code, labels, and the protocol, and argue benchmarks should report a non-compliance rate and at least two rules, not one unexamined grade.

cs.AI · cs.CL · cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2605.03639

Diffusion Masked Pretraining for Dynamic Point Cloud

Zhuoyue Zhang, Yiding Sun, Chaowei Fang, Haozhe Cheng, Jian Liu, Ajmal Saeed Mian, Jihua Zhu

原文摘要

Dynamic point cloud pretraining is still dominated by masked reconstruction objectives. However, these objectives inherit two key limitations. Existing methods inject ground-truth tube centers as decoder positional embeddings, causing spatio-temporal positional leakage. Moreover, they supervise inter-frame motion with deterministic proxy targets that systematically discard distributional structure by collapsing multimodal trajectory uncertainty into conditional means. To address these limitations, we propose Diffusion Masked Pretraining (DiMP), a unified self-supervised framework for dynamic point clouds. DiMP introduces diffusion modeling into both positional inference and motion learning. It first applies forward diffusion noise only to masked tube centers, then predicts clean centers from visible spatio-temporal context. This removes positional leakage while preserving visible coordinates as clean temporal anchors. DiMP also reformulates point-wise inter-frame displacement supervision as a DDPM noise-prediction objective conditioned on decoded representations. This design drives the encoder to target the full conditional distribution of plausible motions under a variational surrogate, rather than collapsing to a single deterministic estimate. Extensive experiments demonstrate that DiMP consistently improves downstream accuracy over the backbone alone, with absolute gains of 11.21\% on offline action segmentation and 13.65\% under causally constrained online inference. Code is available at https://github.com/InitalZ/DiMP.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03376

Mixed-Methods Exploration of Sociotechnical Factors to Improve Adoption of Crime-Reporting Apps in Urban Communities

Olalekan David-Banjo, Chinazunwa Uwaoma, Paul Witman

原文摘要

Crime-reporting mobile applications can enhance community safety and engagement in urban areas, yet their adoption remains low, partly because developers prioritize technological features over residents' social realities. This study used an exploratory sequential mixed-methods design to identify and validate the sociotechnical factors that drive adoption. Two focus groups and an analysis of 660 app reviews revealed that users' initial sense of usefulness fades, whereas sustained engagement depends on perceived value (PV), comprising six dimensions: safety, anonymity, recency, autonomy, camaraderie, and accessibility. A survey of 505 urban residents, analyzed with PLS-SEM, validated PV as a formative higher-order construct formed by four of these dimensions (safety, anonymity, autonomy, and camaraderie). PV was the strongest predictor of behavioral intention, ahead of effort expectancy, facilitating conditions, and performance expectancy, and behavioral intention strongly predicted actual use. The model explained 75% of the variance in behavioral intention and 51% in actual use. The findings offer design principles for community-centered crime-reporting technologies.

cs.AI · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03374

Characterizing When Language Models Take Harmful Actions to Protect Their Assigned Goals

Brandon Jaipersaud, Elliot Creager

原文摘要

Recent language model (LM) safety failures have largely been studied as disparate phenomena. In agentic misalignment, a model blackmails an executive to avoid being replaced. In alignment faking, it complies with harmful requests during training to keep its values from being modified. We argue that these behaviours share a common decision structure: a model assigned a goal must either take a harmful action to preserve it or refuse and lose it. We formalize this as a goal-harm conflict and present the same conflict under various elicitation protocols that range from compact forced-choice prompts to simulated agentic environments. These protocols span two perspectives on LM decision-making, which we term (i) the logit view, reading preferences from answer-token probabilities, and (ii) the sampling view, classifying open-ended responses with an LM judge. Under the logit view, we show that goal importance and harm severity additively shape how a model resolves these value conflicts. Under the sampling view, we derive two factorizations of the harmful-action probability that safety evaluations commonly report. The first measures how well a preference read from answer tokens carries over into behaviour, such as making a harmful tool call. The second attributes a low harmful-action rate either to refusal or to responses that reach no resolution. We then show how fictional context changes a model's preference under the logit view, and how tool use and partial observability -- two features of agentic evaluations -- change its harmful-action rate under the sampling view. Our work offers a descriptive framework for studying how LMs resolve such value conflicts and for estimating low-probability harmful actions at a finer granularity than the aggregate harm rates commonly reported in safety evaluations.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03371

EO-Jev: Decision Model for Earth Observation

Mohamed Atef Abouelhadid, Diaa Addeen Abuhani, Muhammad Akhtar Munir, Fahad Shahbaz Khan, Salman Khan

原文摘要

Earth observation (EO) workflows require selecting among known outcomes, estimating confidence, and processing large volumes of imagery. Generative vision-language models can address these tasks, but autoregressive decoding is computationally costly and their confidence estimates are unreliable. Decision models offer an alternative, returning probabilities over candidate answers in a single forward pass. However, their accuracy, calibration, and transfer across EO tasks remain insufficiently understood. We investigate these properties through a diagnostic study of d3-flash (8.4B parameters) across 30 settings from 23 public EO benchmarks. We find that compositional reasoning transfers well and calibration improves over its Qwen3.5-9B backbone on 24 settings, while counting, change quantification, damage grading, and absent-object recognition remain challenging. To address these limitations, we adapt d3-flash through low-rank fine-tuning on public EO training splits, producing EO-Jev. EO-Jev improves significantly over d3-flash on 26 of 30 settings and matches or exceeds specialist references on 20 of 28. It also achieves an average gain of 8.0 points across 12 held-out settings and lower expected calibration error than specialists on 21 of 28 settings. These improvements come at a modest cost of 1.8 points in general decision performance. Our results demonstrate the potential of adapting general decision models into competitive EO generalists while highlighting persistent challenges in quantitative perception, option-order consistency, and calibration under distribution shift.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03370

Fuse To Align: Multimodal Representation Learning for Retrieval via Hypergraph Regularization

Anindya Nag, Ambuj Mehrish, Sebastiano Vascon

原文摘要

Multimodal retrieval must integrate complementary evidence from views that may be missing, noisy, or unequally informative. When a sample provides limited reliable evidence, learning from its views alone can hinder the acquisition of useful retrieval representations. Semantically related training samples may provide complementary observations, yet existing volume-based objectives jointly align modalities within each sample without explicitly enabling information exchange across samples. We therefore propose a training-time hypergraph fusion framework that connects observed modalities both within samples and across semantically related samples, allowing representation learning to draw on evidence beyond an individual sample while leaving the retrieval pipeline unchanged at inference. The framework includes HyVol as a special case and introduces two configurations: wHyVol learns view contribution weights and symmetric semantic affinities, whereas wRHG additionally models typed interactions and participant roles through relational propagation. Missing modalities are omitted rather than imputed, and the hypergraph is discarded after training, incurring no additional inference cost. Experiments on eleven zero-shot video-text and audio-text retrieval benchmarks demonstrate broad improvements with both GRAM and HyperGRAM. Across 17 video benchmark-modality settings evaluated in both retrieval directions, wRHG outperforms GRAM in all 34 comparisons and HyperGRAM in 33 of 34. Mean text-to-video R@1 increases from 63.4 to 68.2 with GRAM and from 64.8 to 68.4 with HyperGRAM, while the largest video-to-text gain reaches 13.3 percentage points on ActivityNet. Under missing-modality evaluation, both weighted configurations retain positive text-to-video gains over GRAM across seven benchmarks at every evaluated missingness rate, even when 90% of clips lack one modality.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.22695

A Survey on the Linear Representation Hypothesis

Sewoong Lee, Marc E. Canby, Ikhyun Cho, Julia Hockenmaier

原文摘要

The term "linear representation hypothesis" (LRH) has appeared across diverse subfields of artificial intelligence, neuroscience, and cognitive science. But previous works have not consistently treated the LRH as a falsifiable scientific hypothesis; we analyze these inconsistencies and examine their implications for how prior theoretical and methodological results should be interpreted. Based on this analysis, we argue that claims regarding linear representations become well-defined only through careful examination of the model, representation location, feature definition, and evaluation dataset. We therefore propose a more rigorous formalization of the LRH that makes these dependencies explicit and allows the hypothesis to be evaluated as a falsifiable scientific claim. Finally, we identify some non-trivial open problems that warrant further attention from the research community.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.18823

Using OCR Heads to Verbalize Image Semantics

Sheridan Feucht, Benno Krojer, Sarah Wang, Henry Abrahamsen, Byron C. Wallace, David Bau

原文摘要

How do VLMs map from pixels to semantics? To understand this general question, we focus on a narrow one: studying how VLMs perform optical character recognition (OCR). Across four models, we identify attention heads causally necessary for OCR, and discover that these are in fact general-purpose heads that output interpretable semantic features across all image tokens. For example, pointing these heads at an image token containing the word "bike" causes Qwen3-VL-8B to output "bike," but pointing them at a bird wing causes the model to output the token "feathers." We collapse these heads' attention weights into a single verbalization lens transformation that reveals interpretable semantic features in hidden states across all layers. When combined with projection to vocabulary space, we can obtain interpretable labels starting from layer 0, showing that image representations are in fact aligned with language in early layers. We find that we can also use the inverse of this transformation to edit non-word concepts, e.g., replacing a tractor with a revolver in a naturalistic image, providing causal evidence that this subspace is useful for more than just OCR. Our results are an example of how the study of specific mechanisms can shed light on broader interpretability problems.

cs.CV · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.05786

Selecting Hyperparameters for Tree-Boosting

Floris Jan Koster, Fabio Sigrist

原文摘要

Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article, we empirically compare several popular methods for hyperparameter optimization for tree-boosting including random grid search, the tree-structured Parzen estimator (TPE), Gaussian-process-based Bayesian optimization (GP-BO), Hyperband, the sequential model-based algorithm configuration (SMAC) method, and deterministic full grid search using $59$ regression and binary classification data sets. We find that the SMAC method clearly outperforms all the other considered methods on average, and it gives stable performance across a diverse collection of tabular data sets under a fixed tuning budget, which is relevant for users who cannot afford extensive manual trial-and-error tuning. We further observe that (i) a relatively large number of trials larger than $100$ is typically required for accurate tuning, (ii) using default values for hyperparameters or a full search over a small grid often yields very inaccurate models, (iii) all considered hyperparameters can have a material effect on the accuracy of tree-boosting, i.e., there is no small set of hyperparameters that is more important than others, and (iv) choosing the number of boosting iterations using early stopping yields more accurate results compared to including it in the search space for regression tasks.

cs.LG · stat.AP · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03360

Every Pixel Is an Option: A System One Model for Medical Ultrasound Biometry

Moein Heidari, Junbo Rao, Yudan Chen, Yasamin Medghalchi, River Jiang, Wenjin Chen, David J. Foran, Jasmine Grewal 等 9 位作者

原文摘要

**Purpose:** Ultrasound biometry is widely used across various clinical applications, including prenatal and cardiac examinations, to obtain quantitative measurements by placing calipers at anatomical landmarks. However, these measurements remain subject to interoperator variability as they rely largely on manual caliper placement. Conventional methods often use specialized prediction heads for different ultrasound tasks, whereas vision-language models offer flexibility through text instructions. However, direct coordinate prediction does not explicitly model spatial likelihood distributions, limiting the representation of localization ambiguity. **Methods:** We propose Kev-Bio, a vision-language model that uses clinical measurement questions to guide ultrasound landmark detection. Inspired by System One decision models, Kev-Bio uses decision representations of the language model to generate queries for each caliper endpoint. These queries are matched against shared dense ultrasound features to produce spatial probability maps, from which landmark coordinates and clinical measurements are derived. The model is trained end-to-end using both landmark localization and clinical measurement losses. **Results:** On the nine-view FU/GU Biometry 2026 dataset, Kev-Bio achieves a mean radial error (MRE) of 22.50 pixels and a clinical measurement mean absolute error (MAE) of 15.38, outperforming the compared baselines in overall performance. **Conclusion:** These results demonstrate the effectiveness of Kev-Bio for ultrasound landmark detection and clinical measurement across different anatomical views. Our code and models are available at https://github.com/moeinheidari7829/Kev-Bio.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03357

Multidimensional Suicide Risk Assessment with Machine Learning using Electronic Health Records

Nathan Scollar, Kemal Davaslioglu, Nathan Conger, Sastry Kompella, Yalin E. Sagduyu, Nathaniel D. Bastian

原文摘要

Suicide prevention remains a critical public health challenge, and electronic health records (EHRs) provide an opportunity to characterize suicide-related risk indicators using documented clinical, behavioral, medical, and social-contextual information. This paper presents a multidimensional machine learning framework to model and estimate suicide-related outcomes using MIMIC-IV EHR data. We construct clinically motivated diagnosis categories and define a composite outcome from documented codes corresponding to suicidal ideation, suicide attempt, prior suicidal behavior, and intentional self-harm. To address severe class imbalance and reduce age-related confounding, we apply age-matched resampling that retains all positive patients and samples comparison-cohort patients from the same age group. Logistic regression, random forest, and XGBoost classifiers are evaluated before and after resampling with isotonic calibration. The calibrated XGBoost model achieves the strongest matched-cohort performance, outperforming the other evaluated classifiers. SHAP-based interpretation shows that diagnostic burden, depression-related features, age, personality-disorder history, social determinants related to violence, and medical comorbidity contribute to elevated predicted probabilities. Our results demonstrate that combining broad EHR-derived features, age-matched cohort construction, calibration, and feature-level interpretation provides a practical framework for retrospective suicide-related risk modeling.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.06942

Empirical Ciphertext Distinguishability Testing of KEMs and Hybrid Encryption via Deep Learning

Simon Calderon, Niklas Johansson, Onur Günlü

原文摘要

We model indistinguishability under chosen-plaintext attack (IND-CPA) games as binary classification tasks for deep neural networks (DNNs). We apply this methodology to the public-key encryption (PKE) schemes used to construct PQC key encapsulation mechanisms (KEMs) such as ML-KEM and HQC, and to cascaded symmetric algorithms, demonstrating methodological generality. Moreover, we present a novel extension of this approach to empirical distinguishability testing of hybrid KEMs. For IND-CPA-secure schemes and their combinations, no DNN achieves accuracy significantly different from random guessing (measured via Bonferroni-corrected binomial tests), consistent with theoretical security guarantees. For insecure schemes, such as plain RSA, AES-CTR and AES-CBC with IV or counter reuse, or RC4, the DNNs classify the ciphertexts better than random guessing. Thus, this framework is shown to serve as a practical, scheme-agnostic tool for flagging distinguishable ciphertexts in both individual and composed constructions.

cs.CR · cs.IT · cs.LG · cs.NE · eess.SP

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.14960

EMAR: Efficient Multi-domain Adaptive Retrieval

Pranav Kasela, Marco Braga, Ophir Frieder, Nazli Goharian, Gabriella Pasi, Raffaele Perego

原文摘要

Neural retrieval systems are increasingly deployed in multi-domain settings, where each domain may have its own vocabulary and word distribution. A common approach is to fine-tune separate models for each domain, achieving strong in-domain performance while increasing training costs and storage requirements. In contrast, a single model trained jointly across domains avoids model duplication but weakens robustness under domain shift. This creates a trade-off between effectiveness and resource-efficiency. To address this issue, we propose EMAR, a retrieval system that employs a single frozen model shared across all domains and introduces lightweight domain-specific adapters. At inference time, a learned routing mechanism dynamically selects the most suitable adapter for each query. EMAR enables efficient domain-specific retrieval by sharing the computationally expensive backbone across multiple domains and without maintaining multiple full models. Experimental results on three multi-domain benchmarks spanning seventeen domains and three retrieval architectures show that EMAR achieves retrieval effectiveness on par or even better than fully fine-tuned domain-specific models while requiring storing only a small fraction of parameters per domain. Furthermore, EMAR exhibits strong generalization to previously unseen domains, highlighting its effectiveness as an extensible solution for multi-domain retrieval.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03350

Certifying Graph Neural Networks Against Label and Structure Poisoning

Lukas Gosch, Xichuan Chen, Yan Scholten, Stephan Günnemann

原文摘要

Robust machine learning for graph-structured data has made significant progress against test-time attacks, yet certified robustness to poisoning - where adversaries manipulate the training data - remains largely underexplored. For image data, state-of-the-art poisoning certificates rely on partitioning-and-aggregation schemes. However, we show that these methods fail when applied in the graph domain due to the inherent label and structure sparsity found in common graph datasets, making effective graph-partitioning difficult. To address this challenge, we propose a novel semi-supervised learning framework called deep Self-Training Graph Partition Aggregation (ST-GPA), which enriches each graph partition with informative pseudo-labels and synthetic edges, enabling effective certification against node-label and graph-structure poisoning under sparse conditions. Our method is architecture-agnostic, scales to large numbers of partitions, and consistently and significantly improves robustness guarantees against both label and structure poisoning across multiple benchmarks, while maintaining strong clean accuracy. Overall, our results establish a promising direction for certifiably robust learning on graph-structured data against poisoning under sparse conditions.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.04743

DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval

Maojun Sun, Yue Wu, Yifei Xie, Ruijian Han, Binyan Jiang, Defeng Sun, Yancheng Yuan, Jian Huang

原文摘要

Large Language Model (LLM) agents excel at automating data science workflows, yet often underutilize rigorous statistical methods in R. Existing Retrieval-Augmented Generation (RAG) approaches rely on semantic similarity, ignoring the data-distribution and structural characteristics crucial for determining a statistical method's applicability. To address this, we propose DARE (Distribution-Aware Retrieval Embedding), a representation learning framework incorporating these data characteristics into R function retrieval. Our contributions are fourfold: (i) RPKB, a curated knowledge base of 8,191 high-quality R functions; (ii) DARE, the distribution-aware retrieval framework; (iii) RCodingAgent, an R-oriented agent integrating DARE for end-to-end analysis; and (iv) RStatBench, a comprehensive benchmark of 100 statistical tasks. Experiments demonstrate that DARE improves NDCG@10 by 13.43 to 32.65 percentage points across evaluated embedding models, with a MiniLM-based DARE achieving 93.92% NDCG@10 alongside high efficiency. Furthermore, integrating DARE into RCodingAgent nearly doubles mean downstream accuracy on RStatBench, increasing from 31.8% to 62.2% across ten LLMs. This work helps bridge modern LLM automation with R's established statistical computing ecosystem.

cs.IR · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03349

NormMoX: A Normative Mixture of Experts for Explainable Autism Classification from Structural and Functional MRI

Muhammad Hasan Masrur, Fani Deligianni

原文摘要

While structural and functional magnetic resonance imaging (sMRI, fMRI) offer profound potential for researching autism spectrum disorder (ASD) and understanding individual neurodivergence, current classifiers lack the explanatory power needed for scientific study, clinical use and personal-level insights. Most models learn from imaging features that also vary with age, sex, scanner site and head motion. They fuse the two modalities without a common reference and explain decisions against arbitrary reference inputs rather than a typical brain. We present NormMoX, a normative mixture of experts that makes the typical brain the shared reference for prediction, fusion and explanation. Normative models trained on large healthy cohorts give the value each brain measure is expected to take for a person's age and sex. NormMoX learns from each person's deviations from these expected values. For each modality, a neural network expert turns the deviations into a score of evidence for ASD. A fusion head, MoX, compares each score with those of typical controls before combining them. It therefore needs no imputation when a modality is missing and shows in which modality each person is atypical. On 1,035 participants of ABIDE I, NormMoX reached an AUROC of $0.728 \pm 0.004$ in repeated cross-validation, above linear, single-modality and deep fusion baselines. NormMoX thus shows not only whether a person is likely to have ASD, but where, and in which modality, their brain departs from typical. The implementation is available at https://github.com/Novelex/NormMox.

cs.LG

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arXiv ↗PDF ↗