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版本更新 · v5arXiv:2607.04171

XS-VLA: Teaching Tiny Vision-Language-Action Models with Spatial Supervision and Demonstration Conditioning

Iok Tong Lei, Ying Jie Yap, Wei Huang, Qingchen Xie, Qianzhi Li, Yujie Zhang, Xiaolong Liu, Zhidong Deng

原文摘要

How can richer training supervision improve robot control while keeping the deployed policy compact? We present XS-VLA, a staged training framework using teacher-derived spatial labels and demonstration-conditioned action learning. Coarse-Grained Spatial Distillation (CSD) initializes the backbone through an auxiliary region-label task. Latent Flow Matching (LFM) then conditions an action-space velocity field on a demonstration latent, using KL regularization while jointly optimizing the backbone and action modules. The deployed policy contains 243.99M parameters and operates without the teacher or posterior encoder. XS-VLA achieves 90.25% average LIBERO success in each of two training seeds, compared with 86.00% for a SmolVLA-0.25B base trained under our settings. Ablations examine both training stages through matched image pretraining and Huber/MSE controls. On three Mobile ALOHA tasks, average strict success increases from 21.7% to 65.0%. These results demonstrate the control utility of auxiliary representation initialization and regularized demonstration-conditioned flow learning for compact VLA policies

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.04072

The Exact Complexity of Majority in Depth-Three Circuits with Bottom Fan-in Three

Mohit Gurumukhani, Daniel Kleber, Ramamohan Paturi, Christopher Rosin, Michael Saks, Navid Talebanfard

原文摘要

Lecomte and Ramakrishnan (arXiv'26) recently presented a depth-three circuit of size $2^{O(\sqrt{n})}$ that computes the Majority function, refuting a long held conjecture. Nevertheless, as a fundamental computational primitive which ubiquitously appears across computer science, determining the exact complexity of Majority down to the constant in the exponent, remains important. Gurumukhani et al. (CCC'24) initiated the study of optimal $Σ^k_3$ circuits computing the Majority function. In this work, we determine the exact $Σ^3_3$ complexity of Majority up to polynomial factors. We show that for any $t \le n/2$, given an $n$-variate $t$-threshold 3-CNF, i.e., with no satisfying assignment with Hamming weight less than $t$, the number of weight-$t$ satisfying assignments is at most $2^t(3/2)^{(n-t)/2}$. Along with a matching construction for $t = n/2$, the result implies that the size of the smallest $Σ^3_3$ circuit computing Majority is exactly $poly(n)(2/6^{1/4})^n$. The proof involves two main ingredients: - Reduction to monotone: We formulate a linear program (LP) which is defined only with respect to the monotone part of the formula. We show that bounding the cost function of this LP implies our desired upper bound on the number of minimum weight solutions of the general formula. - Structure of extremal formulas: To bound the cost function of the LP, we develop an extensive inductive argument. The inductive steps are determined by a close inspection of the local structure of extremal formulas with respect to minimum weight. Our result has a natural hypergraph interpretation: it bounds the number of independent sets of size $n-t$ under the condition that the independence number is $n-t$. This is a generalized hypergraph Turán problem and coincides with the classical hypergraph Turán problem at threshold $t=n-k$ for $k$-uniform hypergraphs.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13927

Safety and Connectivity Certificates for Finite-Time Formation Transitions

Yash Purswani, Shivansh Agarwal, Anuj Tiwari

原文摘要

Finite-time formation transitions are useful for coordinating multi-agent systems in time-critical missions, but convergence to a prescribed formation in finite time does not guarantee collision avoidance or communication connectivity during the transition. Existing approaches address finite-time coordination and safety-constrained formation control, yet explicit connections between disagreement during transitions, formation geometry, and coupling-gain selection need to be developed for certifying safe maneuvers. This paper develops a disagreement-based framework for certifying transient safety and connectivity during finite-time tracking of arbitrary prescribed formation trajectories. Local feedback guarantees exact finite-time tracking with a graph-independent settling-time bound, while a graph-dependent disagreement bound is used to derive sufficient geometric conditions for collision avoidance and connectivity preservation. These conditions further yield an offline coupling-gain selection rule based on the available geometric clearance. Numerical simulations illustrate the trade-off between convergence rate and control effort as the coupling gain increases. They also demonstrate the effectiveness and conservatism of the proposed certificates.

eess.SY · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13926

Beyond Proxy Metrics: MLLMs as Human Surrogates for Explainable AI Evaluation

Eoin M. Kenny, Salim I. Amoukou, Tom Bewley

原文摘要

Explainable AI (XAI) has expanded into a broad and impactful research area, but the fundamental question of how best to evaluate explanation techniques has proven frustratingly elusive. Methodologies typically revolve around either proxy metrics which tell us little about the utility of an explanation, or human evaluation which---while generally considered the gold standard---is expensive, difficult to scale, and challenging to reproduce. In this paper, we investigate if Multimodal Large Language Models (MLLMs) can act as surrogate human participants to automate the evaluation of XAI. To do this, we first propose a ``purpose-grounded'' evaluation framework with metrics applicable to both MLLMs and human users alike. Then, we offer some conceptual insights as to why and when we can trust these models to automate this process. Lastly, we conduct extensive empirical testing across five purpose-grounded areas such as model improvement and fourteen popular MLLMs. Results indicate that all models trend positively, but OpenAI and Qwen models generally show higher alignment compared to other variants, which would be the recommended choices in practice. We expect our insights not to replace user testing, but rather to help XAI researchers complement their current evaluation methodologies and move the field away from heavy reliance on proxy metrics for measuring explanation utility.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13925

OpenRE-Bench: A Benchmark Harness for Multi-Agent Requirements Engineering with Mined GitHub Issues

Haowei Cheng, Milhan Kim, Bowen Jiang, Weixing Zhang, Yuhong Fu, Zhixu Liu, Naoyasu Ubayashi, Hironori Washizaki

原文摘要

OpenRE-Bench is an open-source benchmarking infrastructure for large language model (LLM) based multi-agent requirements-engineering (RE) frameworks. Such frameworks are increasingly common, but they use different agent roles, negotiation policies, and output shapes, and are typically evaluated on their own runtimes, metrics, and small sets of curated system descriptions, which makes fair, reproducible comparison difficult. OpenRE-Bench addresses this with four reusable pieces: a shared run harness with one input, phase-artifact, and metric contract; an adapter mechanism through which a framework joins by implementing a single adapter, leaving cases, metrics, and other adapters untouched; a reproducible GitHub issue-mining pipeline that grows the corpus from real feature requests; and a versioned case suite. As a demonstration, we mine 3,000 fetched issues across five projects (three languages, five domains) into five cases, package them with five hand-built cases as the compact v1.0 suite, and run three heterogeneous frameworks (MARE and iReDev as paper-faithful reimplementations, plus QUARE) under one protocol. OpenRE-Bench originated as the evaluation back-end of our QUARE study and has since been reused by two further studies of our group. It is released with a pinned environment, a Docker image, frozen API snapshots, a 651-test suite, and the full pool of 1,396 filtered issues behind the mined cases.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.19181

Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning

Aixiu An, Michael Jungo, Eloi Eynard, Mark Drenhaus, Andreas Fischer, Jean Hennebert, Sébastien Rumley

原文摘要

Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity of legal language and the high level of precision it requires. The recent emergence of reasoning-capable language models opens new possibilities for tackling such challenges. They add to a set of other previously proposed techniques to enhance the translation quality, which includes supervised fine-tuning and reinforcement learning. In this work, we perform a comparison between these various approaches. More particularly, we evaluate small language models such as Qwen3.5 4B, Qwen3.5 9B, and Gemma 3 12B enhanced with various re-training paradigms and compare their performances against frontier reasoning models. We focus on the Swiss legal system, which -- with its unique multilingual statutes -- offers a particularly challenging testbed for reasoning-augmented models. Our results show that the quality of small ``base'' models can be greatly enhanced, and that reinforcement learning with verifiable rewards can be applied to NMT in the legal domain and surpasses the translation quality of supervised fine-tuning. The performance of enhanced small models is close to the one of state-of-the-art reasoning models yet remains inferior. We also note that re-training paradigms yield diminishing returns as model size increase. The code and models are publicly available at https://github.com/aixiuxiuxiu/Legal-MT-SFT-RL.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24490

Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems

Yunhua Pei, Zerui Ge, Jin Zheng, John Cartlidge

原文摘要

Multi-agent LLM decision systems for portfolio management still lack a principled way to assign credit across specialist agents, remain vulnerable to cold-start dominance under regime shifts, and offer limited transparency into how final allocations are formed. We propose Market Regime Council (MRC), an online credit-assignment mechanism over coalitions of specialist agents, with portfolio management as the downstream task. MRC computes exact Shapley credits across all single, pairwise, and Grand-coalition outputs for online agent weighting. Instantiated with N=3 specialist agents, at each trading period, MRC recomputes coalition-based Shapley weights from exponentially weighted performance histories, uses a Bayesian adaptive mixture to stabilize early periods, applies regime-dependent multipliers to adjust agent authority, and records each rebalance through a five-layer causal trace. Over 1,037 trading days across 13 crypto assets and five seeds, MRC achieves a Sharpe ratio of 1.51 and a cumulative return of 440.1%, ranking first on CR, SR, and IR among active baselines and attaining the lowest MDD among active methods. Ablation results show that the gains come from Shapley-weighted integration across coalition outputs rather than from any single stage in isolation. Code and demo data are available open source.

cs.AI · cs.LG · q-fin.PM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13922

Domain-Invariant and Language-Model-Assisted Acoustic Side-Channel Attacks on Keyboards

Bikrant Bikram Pratap Maurya

原文摘要

Acoustic side-channel attacks (ASCA) recover typed text from the sound of keystrokes, using only a commodity microphone and no access to the victim's device. Prior work establishes that such attacks succeed under idealized laboratory conditions - a single keyboard, a fixed microphone, a quiet room, and training and testing on the same device but leaves open the question that matters for security: whether keystroke inference survives the open world of unseen keyboards, diverse users, noisy environments, and compressed transmission channels. The answer developed here is that it does, provided the acoustic model is made explicitly domain-invariant and its noisy predictions are corrected by a language model.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13921

N -phase flow simulations using X-Mesh and an explicit marker interface representation

Antoine Quiriny, Jonathan Lambrechts, Michel Henry, Nicolas Moës, Jean-François Remacle

原文摘要

We present a marker-based X-Mesh framework for incompressible N-phase flow simulations with sharp, explicitly tracked interfaces. The interface is represented by Lagrangian marker chains, while the computational mesh is deformed so that selected mesh edges conform to the tracked geometry without changing mesh connectivity. This combination preserves the advantages of body-fitted finite element discretizations while allowing highly distorted and nearly degenerated elements to capture fine interfacial features on relatively coarse meshes without requiring mesh refinement. The arbitrary Lagrangian-Eulerian (ALE) finite element solver uses a pressure field that is continuous within each phase and discontinuous across the fitted interface. Surface tension is introduced through a variational membrane, so the pressure jump is resolved without prescribing a curvature-dependent correction; a Young wall energy provides the corresponding treatment of fluid-fluid-solid triple points. Topological changes are handled geometrically from the marker representation. Numerical experiments illustrate the ability of the approach to transport complex interfaces and to control topology changes through marker-based feature-size criteria.

cs.CE · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13920

CAppleNet: A Coordinate Attention-Based YOLO11 for Real-Time Apple Stem Cavity and Calyx Detection

Alican Güneş, Robert Selim Hill, Gökhan Hasanoğlu, Murat Kurt

原文摘要

Automated fruit grading, sorting, and orientation systems require real-time, accurate detection of fruit-level morphological landmarks rather than the fruit boundary alone. This paper presents a custom, fully self-collected and self-annotated dataset of 1506 apple images (1054 training, 301 validation, 151 test) captured under controlled, fixed-camera conditions and annotated for three target classes: fruit body, stem cavity, and calyx. A YOLO11s detector trained with default hyperparameters is adopted as the baseline, and a systematic, one-factor-at-a-time ablation study is conducted across input resolution, data augmentation policy, optimizer configuration, and lightweight architectural enhancement to identify the factors that most improve detection of these fine-grained landmarks under high inter-cultivar color and texture variability. Neither increased input resolution, nor any of six tested augmentation variants, nor manual optimizer tuning improved on the untuned baseline (F1 = 0.9047); only a same-slot architectural substitution of the backbone's C2PSA block with Coordinate Attention (CA) matched the baseline's overall F1 (0.9038) while reducing parameter count by 10.2% and achieving the study's best mAP@0.5 (0.935) and best stem-cavity F1 (0.8217). A confidence-threshold sweep identified 0.15 as the deployment operating point for this model, and a confirmatory experiment combining the CA-swap architecture with the best-performing augmentation variant showed the two effects do not combine additively, reversing the architectural gain rather than compounding it. The resulting design recommendations --- the CA-swap architecture alone, at a 0.15 confidence threshold --- target real-time apple landmark detection for orientation-aware industrial sorting and alignment systems.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13919

EAGLE: Early Identification of Information Propagation Sources through Generalized Bayesian Inference in Large-scale Social Networks

Tao Wen, Yu-wang Chen, Jie Zhao, Changchun Pan, Tahir Abbas Syed, Yong-Yeol Ahn, Kang Hao Cheong

原文摘要

In the era of infodemics, rapidly pinpointing information propagation sources is essential for mitigating negative impacts and enhancing societal resilience. This study presents EAGLE (Early identificAtion of propagation sources through GeneraLized Bayesian infErence), a novel and effective framework for early source identification in large-scale social networks, which integrates evolutionary computation to strategically deploy sparse observers with generalized Bayesian inference to aggregate observational data of varying credibility. Unlike existing approaches, our method relies on limited input from human observers and explicitly incorporates the uncertainty and noise of the data. Comprehensive evaluations across diverse networks and propagation scenarios demonstrate that our approach consistently surpasses state-of-the-art models, without relying on assumptions about propagation characteristics or user attributes. Utilizing real-world datasets with empirical propagation paths from Twitter, our method is among the first capable of identifying potential sources in large-scale networks using limited observers, demonstrating its broad applicability. These findings highlight the effectiveness of generalized Bayesian inference in uncovering propagation dynamics and equip organizations and policymakers with critical tools to proactively address threats to digital trust and security.

cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13917

Feedback Vertex Set, Max Cut, and Edge Coloring on the Strong Product of a Graph and a Path

Jan Diester, Kolja Kühn, Manuel Rapp, Samuel Schneider, Torsten Ueckerdt

原文摘要

We examine the computational complexity of well-known problems on subgraphs of the strong product of two paths. Namely, for Max Cut we show NP-hardness on induced subgraphs of $P \boxtimes P$ answering a question by Dvořák, Huynh, Joret, Liu, and Wood (2021). For 3-Edge Coloring we show NP-hardness on subgraphs of $P \boxtimes P$ and that it is equivalent to 3-Edge Coloring on planar graphs for induced subgraphs of $P \boxtimes P$. It is a long standing open question whether 3-Edge Coloring on planar graphs is NP-hard. As a second contribution, we consider graphs of the form $G \boxtimes P$ and show that Feedback Vertex Set on these graphs is FPT in the clique-width of $G$. Furthermore, we show that it can be solved in polynomial time if $G$ is chordal or co-comparable and that the computational complexity depends on the parity of $P$ if $G$ is bipartite. Namely, it is solvable in polynomial time if $P$ has an even number of vertices but is NP-complete otherwise. For Max Cut, we show that it can be solved in polynomial time if $G$ is bipartite.

cs.CC · cs.DM · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13916

Dual-Space Preconditioning for Variational Inequalities and Root-Finding Problems

Jan Quan, Konstantinos Oikonomidis, Alexander Bodard, Panagiotis Patrinos

原文摘要

This paper develops a dual-space preconditioning framework for variational inequalities and root-finding problems beyond classical cocoercivity and Lipschitz continuity assumptions. To this end, we introduce both a novel cocoercivity-type condition and a relaxed Lipschitz continuity condition, and derive convergence of deterministic and stochastic methods under these generalized conditions. For isotropic preconditioners, the resulting algorithms can be interpreted as variable-stepsize variants of classical methods, extending recent analyses based on $(L_0, L_1)$-type growth conditions while allowing simpler proofs and larger stepsize ranges. We also show that the framework naturally handles variational inequalities through weak Minty-type arguments. Finally, we study the associated continuous-time dynamics and derive a variational and optimal control interpretation of the preconditioned flow. Overall, the results show that dual-space preconditioning provides a flexible mechanism for designing and analyzing operator methods beyond the standard Euclidean Lipschitz setting.

math.OC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.29630

BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation

Johann-Ludwig Herzog, Mathis Jürgen Adler, Leonard Hackel, Yan Shu, Angelos Zavras, Ioannis Papoutsis, Paolo Rota, Begüm Demir

原文摘要

Vision-langugage models (VLMs) have shown strong performance in computer vision (CV), yet their performance on remote sensing (RS) data remains limited due to the lack of large-scale, multi-sensor RS image-text datasets with diverse textual annotations. Existing datasets predominantly include aerial Red-Green-Blue imagery, with short or weakly grounded captions, and provide limited diversity in annotation types. To address this limitation, we introduce BigEarthNet$.$txt, a large-scale, multi-sensor image-text dataset designed to advance instruction-driven image-text learning in Earth observation across multiple tasks. It contains 464044 co-registered Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral images with 9.6M text annotations, including: i) geographically anchored captions describing land-use/land-cover (LULC) classes, their spatial relations, and environmental context; ii) visual question answering pairs relevant for different tasks; and iii) referring expression detection instructions for bounding box prediction. A comparative statistical analysis shows that BigEarthNet$.$txt surpasses existing RS image-text datasets in textual richness and annotation type variety. We further establish a manually-verified benchmark split and extensively evaluate existing VLMs in RS and CV. The results show the limitations of these models on tasks that involve complex LULC classes, whereas fine-tuning using BigEarthNet$.$txt results in consistent performance gains across all considered tasks. Our code and data are publicly available at https://txt.bigearth.net.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13914

From Counterfactual Edits to Human Narratives: LLM-Driven Interpretations for Recommender System Explanations

Vojtech Cahlik, Amir Reza Mohammadi, Rodrigo Alves, Pavel Kordik, Eva Zangerle

原文摘要

Recommender systems increasingly influence what users discover and consume online, yet the decision logic behind their outputs often remains opaque. Counterfactual explanation (CFX) methods provide an effective way to show which changes to a user's interaction history would alter a recommendation. In recommendation settings, however, CFXs often require many edits, producing long, low-level lists that are hard for users to interpret. Moreover, most evaluations emphasize edit counts while largely overlooking whether the resulting CFXs are coherent and actionable for users. We argue that this gap between generating valid CFXs and communicating model behavior stems from the lack of user-centered abstraction and limited qualitative evaluation. To bridge this gap, we investigate large language models as mediators that summarize raw CFX interaction edit lists into concise natural language explanations describing higher-level preference patterns. We optimize these mediators for correctness, informativeness, and linguistic quality through preference optimization, and evaluate the resulting explanations with human-calibrated metrics derived from separate human-labeled datasets. On the MovieLens-1M dataset, across multiple counterfactual generators and language models, the resulting summaries achieve high overall linguistic quality, while the strongest semantic results are obtained with high-success CFX methods such as ACCENT and SPINRec. Direct Preference Optimization improves the semantic metrics in almost all evaluated settings.

cs.AI · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13913

Learning from First Discrepancies with Joint Reset and Pilot Guarantees

Michal Čertík

原文摘要

A run that stops at its first wrong decision shows the queries and their correct labels up to and including that decision, and nothing after it. We study how many such runs are needed for learning. Each run tests a hypothesis, the pilot, chosen beforehand from a fixed binary class of VC dimension at most $d$, on a fresh record of at most $D$ queries drawn from one unknown distribution that does not depend on the pilot. The terminal information of a record is shown only if the pilot makes no discrepancy, and a bounded loss, fixed in advance, is attached to the position where a hypothesis stops. If the discrepancy probabilities of members of the class have infimum at most $f$ and their risks have infimum at most $r$, one learner uses $\widetilde O((d+1)[D(f+\varepsilon)+r]/\varepsilon^2)$ runs and $\widetilde O(D(1+f/\varepsilon))$ distinct pilots on every history. It returns a member of the class and an interval of width at most $\varepsilon$ containing, with probability at least $1-δ$, both the output's risk and the optimal risk. We also treat unknown ceilings and expected costs. Lower bounds for finite classes show that each order is necessary in the worst case, possibly on different instances, at positive dimension, small accuracy and fixed confidence. The learner enumerates label patterns of the class on displayed queries, possibly in exponential time; the results, under standard measurability conditions, concern statistical resources only. The proof combines a gain computable from stopped displays, exponential weights, a sparse depth-robust graph and a harmonic-mean bound.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13912

MicroBT: Centroid-Guided Synergistic Learning for Multi-Modal Micro Brain Tumor Segmentation and Counting

Zhenye Lou, Qing Xu, Stefanie Thust, Dorothee P. Auer, Zhen Chen, Wenting Duan

原文摘要

Multi-modal brain tumor segmentation is essential for clinical diagnosis, radiotherapy planning, and longitudinal monitoring. While recent studies have achieved remarkable overall segmentation performance, they rely on overlap-based objectives that inherently favor large regions, causing sub-3 mm micro-lesions to be systematically suppressed during training. Moreover, existing pixel-level segmentation frameworks lack explicit instance-level modeling, leading to unreliable lesion counting that directly impacts clinical staging and therapeutic decisions. To address these challenges, we propose MicroBT, a unified multi-modal framework for micro brain tumor segmentation and counting. Our core insight is that a fundamental geometric property is inherently scale-invariant: regardless of size, each lesion possesses a well-defined centroid. Leveraging this, we first devise a centroid-anchored synergistic paradigm that utilizes predicted centroids as shared anchors to simultaneously condition dense segmentation and instance-level localization. To counteract the tumor size imbalance, we further devise a Volume-Calibrated Loss (VC-Loss) that adaptively amplifies the gradient contribution of micro-lesions, alongside a Size-Aware Curriculum Optimization (SACO) that stabilizes multi-task training by establishing global representations before progressively unlocking fine-grained localization. Extensive experiments demonstrate that MicroBT outperforms state-of-the-art methods, particularly on metastases, where micro-lesion ($\leq 3$ mm) localization precision improves by approximately 7% and counting estimation errors decrease by approximately 26%. The code is available at https://github.com/leonlou921/MicroBT.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13911

Adapting a Vision Foundation Model for Fracture Characterization in Acoustic Image Logs

Guangyu Wang, Xiaodong Ma, Xinming Wu

原文摘要

Natural fractures (NFs) and drilling-induced tensile fractures (DITFs) in acoustic image logs provide insights into rock mass structure and in situ stress, but manual characterization is time-consuming. Deep learning can automate this task, although expert-labeled data are scarce. We investigate whether adapting a pretrained vision foundation model (VFM) outperforms task-specific models trained from scratch with limited annotations. Using 80 annotated acoustic image samples, we fine-tuned DINOv3, pretrained on satellite imagery, and evaluated it on four boreholes excluded from fine-tuning. The adapted model estimated NF and DITF parameters comparably to human interpretation. Relative to U-Net, YOLOv8, and Swin Transformer models trained from scratch, it yielded fewer false positives and negatives and more accurate fracture counts and orientations. However, its performance advantage generally narrowed as training sample size decreased. We also assessed decoder architecture, fine-tuning strategy, and image enhancement. A progressive upsampling decoder with full fine-tuning performed best among the DINOv3 configurations tested, while fracture-focused image enhancement did not consistently improve accuracy. These results demonstrate the potential of VFM adaptation for fracture characterization in acoustic image logs with limited labeled data and suggest a transferable workflow for other annotation-scarce geoscientific imaging tasks.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.11841

Minimax Rates and Spectral Distillation for Tree Ensembles

Binh Duc Vu, David S. Watson

原文摘要

Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theoretical properties remain incompletely understood. We adopt a spectral perspective on these algorithms, with two main contributions. First, we derive minimax-optimal convergence for RF regression, showing that, under mild regularity conditions on tree growth, the eigenvalue decay of the induced kernel operator governs the statistical rate. Second, we exploit this spectral viewpoint to develop compression schemes for tree ensembles. For RFs, leading eigenfunctions of the kernel operator capture the dominant predictive directions; for GBMs, leading singular vectors of the smoother matrix play an analogous role. Learning nonlinear maps for these spectral representations yields distilled models that are orders of magnitude smaller than the originals while maintaining competitive predictive performance. Our methods compare favorably to state of the art algorithms for forest pruning and rule extraction, with applications to resource constrained computing.

stat.ML · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13907

EVAThink: Evidence Sufficiency-Guided Training for Adaptive Thinking in Video Understanding

Changti Wu, Ruizhe Fan, Bin Wang, Lei Zhang, Yuxiang Zhang

原文摘要

Under a fixed frame budget, video understanding with vision-language models (VLMs) depends on whether the sampled frames provide sufficient evidence for the question. Sufficient frame evidence supports direct answering, whereas less informative observations may benefit from reasoning that connects the available cues. We introduce EVAThink, which enhances video understanding through evidence sufficiency-guided training for adaptive thinking. First, Recoverable Evidence Gap Mining constructs evidence-dense and evidence-sparse video views that share the same question and answer under an identical frame budget. It retains pairs where sparse views yield lower direct-answer accuracy than dense views but benefit from explicit reasoning, providing supervision for response-mode selection. Second, Evidence-Guided Two-Stage Reinforcement Learning uses stage-specific rewards to guide training with evidence-based mode preferences and then refine response efficiency, while first-token advantage regularization mitigates mode collapse. Trained on only 6.7K curated samples, EVAThink consistently improves upon the base model across video understanding benchmarks under both 32- and 64-frame budgets, while outperforming strong baselines on average. The code, model, and datasets are available at \href{https://github.com/ChangtiWu/EVAThink}{EVAThink}.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13906

Target Selection with a Disappearing-Stimulus Mixed-Reality P300 Interface: Feasibility and Object-Distinguishable Versus Uniform Cues

Runqi Pan, Takuto Niinuma, Yunshan Huang, Masato Sugino, Kenta Shimba, Kiyoshi Kotani

原文摘要

Mixed-reality (MR) P300 interfaces can register selectable destinations, such as wheelchair goals, directly in the user's surroundings. When the cues disappear between flashes, however, the user must retain an intended target that is no longer visible. We tested whether such an interface supports target selection and whether giving each cue a stable colour-shape identity improves it compared with visually uniform spheres. Six healthy adults completed four trials in each cue condition of a 5x5 row/column task on a head-mounted MR display, with EEG from Fz, Cz, and Pz. Shrinkage linear discriminant analysis was evaluated with nested validation in which complete trials were held out from class balancing, hyperparameter selection, and fitting. Both conditions evoked a parietal target-related positivity. The decoded cell was correct in 18 of 48 held-out trials, against 1.9 expected by chance (binomial p < 1e-12), and in three participants individually. Cue format made no detectable difference: complete-target accuracy was 9/24 in both conditions (paired difference 0.0 percentage points, 95% CI [-37, 37]), and held-out AUC was 0.609 for spheres and 0.585 for object cues. Disappearing-stimulus MR P300 selection is therefore feasible under strict trial-level evaluation, but the benefit of object identity, if any, is too small to resolve in a pilot of this size.

cs.LG · cs.HC · eess.SP

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新论文 · v1arXiv:2611.13904

Thermodynamic Formalism for Non-Invariant Probabilities: Entropy, Divergence, Channels

Artur O. Lopes, Elismar R. Oliveira

原文摘要

Let $\mathcal{M}$ and $\mathcal{M}_σ$ denote, respectively, the sets of Borel and $σ$-invariant probabilities on the uncountable set $Ω=\{1,2,...,d\}^\mathbb{N}$. Given a Hölder Jacobian $J$, the dual Ruelle operator $μ\in \mathcal{M} \mapsto \mathcal{L}_{J}^* (μ)$ will play the role of a dynamical channel; generalizes the action of a stochastic matrix on probability vectors. Since $\mathcal{L}_{J}^*$ generally breaks $σ$-invariance, we introduce the \emph{ni-entropy} $h_{ni}(μ)$ for non-invariant measures, employing the concept of the expected value function on cylinders of size one. $h_{ni}(μ)$ differs from Shannon entropy but extends the Kolmogorov-Sinai entropy from $\mathcal{M}_σ$ to $\mathcal{M}$; $h_{ni}$ is concave and u.s.c. This leads us to the concept of ni-pressure $\mathcal{P}(A)$ of a potential $A:Ω\to \mathbb{R}$. $μ\in \mathcal{M}$ is not a density with respect to a dominating measure. We define an {\it ni-Kullback-Leibler divergence} $D_{ni}(ρ|| ν)$ not assuming absolute continuity. We investigate concepts of Information Theory such as Minimal Output Entropy, Donsker-Varadhan variational principle, a variational formula for $h_{ni}$, bounds for divergence, information projections, and their connections to the ni-pressure $\mathcal{P}$.

math.DS · cond-mat.stat-mech · cs.IT · math.PR

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版本更新 · v2arXiv:2605.26074

StakeBench: Evaluating Language Understanding Grounded in Market Commitment

Yunhua Pei, Jingyu Hu, Yiwei Shi, Hongnan Ma, Weiru Liu, John Cartlidge

原文摘要

Existing financial NLP benchmarks often rely on labels supplied by outside observers, measuring how language is perceived rather than what speakers have committed to in the market. We introduce StakeBench, an evaluation framework for language understanding grounded in market commitment. StakeBench links 560,876 comments from 2,261 resolved markets to verified position, action, and market-odds records across Polymarket and Manifold. Supervision is derived from observable market behavior. Position sides, post-comment trading actions, and market-odds trajectories replace human annotation. Four diagnostic tasks test whether models detect market commitment, identify the revealed side, anticipate future action, and perform collective odds projection. Three commitment-aware metrics measure alignment with revealed preferences rather than perceived sentiment. Validity audits and explicit interpretation boundaries help distinguish observable commitment signals from latent belief and causal market-odds impact. Across 15 LLMs and 18 topics and platform settings, models partially recover position-side signals, with Directed Accuracy from 0.506 to 0.599, but show structural failures on later tasks. Ten of the fifteen models collapse to one or two action labels in future action anticipation, and no model consistently improves on the naive odds-direction baseline in collective odds projection. Model scale is not correlated with performance, finance-domain tuning does not improve revealed-side identification, and platform incentives strongly shape higher-order results. StakeBench is packaged with evaluation code and dataset under CC-BY 4.0.

cs.CL · cs.AI · q-fin.GN

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新论文 · v1arXiv:2611.13903

Persistent Metric-Semantic Maritime Scene Memory from Open-Vocabulary 3D Scene Graphs

Kim Alexander Christensen, Dina Zimmermann, Martin Steinert, Eleni Kelasidi

原文摘要

Three-dimensional scene graphs (3DSGs) can provide robots with persistent metric-semantic representations for spatial reasoning, but the existing methods have primarily been developed for indoor and terrestrial environments. In this paper we investigate how a representative outdoor open-vocabulary method transfers to maritime environments and which adaptations are needed. Our experiments show that zero-shot representative outdoor 3DSG descriptions agree with our labeled ground truth for a majority of geometry-supported objects on our data, but the resulting map exhibits considerable physical-entity fragmentation, and its terrestrial higher-level assumptions don't capture maritime spatial organization needs. We therefore keep the underlying mapping method and post-process its accumulated evidence. We show a metric-semantic consolidation method followed by geometry-grounded multi-view vision-language model (VLM) reasoning that reconciles remaining fragments into physical entities and groups entities through observations into local places. In parallel, we construct maritime macro regions using map priors and onboard water and structure observations, and associate them with the local places. The resulting representation reduces physical-entity fragmentation beyond both the baseline and our geometry-semantic consolidation component alone, supports correspondence of fixed local places across repeated maritime traversals, and improves contextual retrieval on our reviewed queries over tested baselines. These results show how an outdoor open-vocabulary map can be adapted into persistent maritime scene memory with our methods for object/place identity and spatial abstractions.

cs.CV · cs.RO

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版本更新 · v2arXiv:2605.23291

Maximum Probability of Independence in Transitive Matroids

Mladen Kovačević

原文摘要

Let $M$ be a matroid on a finite ground set $E$, and suppose that the automorphism group of $M$ acts transitively on $E$. We show the following: if $X_1,\ldots,X_K$ are sampled independently from a distribution $p$ on $E$, then the probability that the samples are distinct and that $\{X_1,\ldots,X_K\}$ is an independent set in $M$ is quasi-concave in $p$ and maximized when $p$ is uniform. For $2\le K\le\rank(M)$, we characterize all maximizers by uniform total mass on the parallel classes and, in particular, show that the maximizer is unique and has global quadratic stability when $M$ is simple. As a corollary, for a random $K\times N$ matrix over a finite field with i.i.d.\ rows, uniform sampling from the nonzero vectors maximizes the probability of full row rank. Finally, we show that uniform sampling maximizes $\Prob\big(\rank_M(X_1,\ldots,X_L)\ge r\big)$, and hence also $\E\rank_M(X_1,\ldots,X_L)$, for every $L, r$.

math.CO · cs.DM · math.PR

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版本更新 · v2arXiv:2602.04851

Pose Distance Fields for Humanoid Robots

Yi Gu, Yukang Gao, Yangchen Zhou, Xingyu Chen, Yixiao Feng, Mingle Zhao, Yunyang Mo, Zhaorui Wang 等 10 位作者

原文摘要

Pose and motion priors play a crucial role in humanoid robotics. Although such priors have been widely studied in human motion recovery (HMR) domain with a range of models, their adoption for humanoid robots remains limited, largely due to the scarcity of high-quality humanoid motion data. In this work, we introduce Pose Distance Fields for Humanoid Robots (PDF-HR), a lightweight prior that represents the robot pose distribution as a continuous and differentiable manifold. Given an arbitrary pose, PDF-HR predicts its distance to a large corpus of retargeted robot poses, yielding a smooth measure of pose plausibility that is well suited for optimization and control. PDF-HR can be integrated as a reward shaping term, a regularizer, or a standalone plausibility scorer across diverse pipelines. We evaluate PDF-HR on various humanoid tasks, including single-trajectory motion tracking, general motion tracking, style-based motion mimicry, and general motion retargeting. Experiments show that this plug-and-play prior consistently and substantially strengthens strong baselines. Code and models are publicly available at https://gaoyukang33.github.io/PDF-HR.

cs.RO · cs.CV

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新论文 · v1arXiv:2611.13894

Beyond Fluent Responses: Finding User Dissatisfaction in Human-AI Interactions

Yuka Saito, Ikumi Numaya, Jun Suzuki, Reina Akama

原文摘要

As large language models generate increasingly high-quality responses, recent work on response evaluation has moved beyond response-centered criteria, such as correctness and coherence, toward user-centered criteria, including user preferences and perceived usefulness. This shift is fundamentally important for developing technologies that are truly useful to real-world users. While conventional response evaluation quantifies the quality of the response itself, users' satisfaction in real conversations also depends on their subjective preferences and perceptions of usefulness, which do not necessarily align with response quality. This study investigates whether such user-centered signals can be detected from conversational text. Specifically, we analyze user utterances with self-reported dissatisfaction labels to examine whether user dissatisfaction in text can be detected. In English interaction, models using user utterances exceeded the 17.8% dissatisfaction label prevalence in AUPRC by up to 15 percentage points. These results suggest that user utterances reveal cues to implicit dissatisfaction. Furthermore, changes from the immediately preceding user utterance may improve dissatisfaction detection, with anomaly detection methods appearing particularly effective.

cs.CL

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新论文 · v1arXiv:2611.13893

Conformal Risk Control for QoT-Aware Lightpath Feasibility Under Technology-Driven Domain Shift

Kiarash Rezaei, Omran Ayoub, Paolo Monti, Carlos Natalino

原文摘要

Artificial intelligence and machine learning (AI/ML)-based regression models support operational decision-making in optical networks but typically expose only point predictions, which carry no statistically controlled information about their reliability. This paper proposes a post-hoc conformal risk control (CRC) framework that keeps a pretrained AI/ML regressor fixed, provides a finite-sample guarantee on uncertainty band miscoverage, and maps the band to operational decisions through policy rules. The guarantee applies to interval miscoverage, while decision-error rates are evaluated empirically. We instantiate the framework for quality of transmission (QoT)-aware lightpath (LP) feasibility assessment, in which a regressor predicts optical signal-to-noise ratio (OSNR) under domain shifts caused by fiber, amplifier, and switching upgrades. Feature shift contribution scores identify the LP features whose relation to QoT changes the most across a technology transition, and two policies translate the bands into feasibility decisions, allowing operators to tune the trade-off between conservatively rejecting or aggressively accepting LP realizations. Experiments on open space-division multiplexing (SDM) QoT datasets over the CONUS topology show that, for a target coverage of 90%, the uncertainty bands meet the target in all 170 trials spanning 17 domain shifts and ten calibration-test partitions, whereas a source-only quantile regression (QR) baseline meets it in none. Under severe shifts, the efficiency-oriented (EO) policy empirically improves zero-shot (ZS) feasibility-decision accuracy from about 85% to above 99%, approaching a transfer learning (TL) policy while avoiding retraining. The framework provides finite-sample control of uncertainty band miscoverage for regression-based optical network decision support, with potential applications beyond QoT-aware LP feasibility assessment.

cs.LG

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新论文 · v1arXiv:2611.13892

A 10K drone image dataset with component-level annotations for rooftop photovoltaic inspection

Penglei Zhang, Wan Zhang, Ruizhe Deng, Bo Chen, Wenhao Duan, Jinhe Zhang, Yanzhen Zhang, Qi Chen 等 11 位作者

原文摘要

Component-level information supports rooftop photovoltaic (PV) inspection, but recognizing individual components across complex scenes remains challenging. We present PVIS-10K, a dataset of 10,065 drone images with component-level annotations covering 63,632 physical PV modules across 22 rooftop installations. The high-resolution red-green-blue (RGB) images were acquired by unmanned aerial vehicles (UAVs, also known as drones) in Ningbo, Shaoxing and Shenzhen, China. Multi-angle oblique observations capture installation-height and scale variation, dense arrangements, occlusion, shadows and reflections. Three-dimensional delineation, reprojection and image-by-image review produced 3,516,675 image-level polygon annotations, stored in Common Objects in Context (COCO) format. Station-disjoint partitions support evaluation on unseen stations within and across cities. Four supervised models and text-prompted zero-shot Segment Anything Model 3 (SAM 3) provide full-resolution benchmarks of instance matching and foreground coverage. PVIS-10K supports component localization, delineation and image-based counting for drone-based rooftop PV inspection research.

cs.CV

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新论文 · v1arXiv:2611.13890

DroneSegEdit: Adapting Pretrained Generative Editors for UAV Segmentation with Language-Grounded Verification

Dehua Chen, Long Xin, Jiangtao Wei

原文摘要

Recent work has shown that pretrained image generators can support diverse visual tasks. Yet directly applying them to language-guided unmanned aerial vehicle (UAV) segmentation exposes a key limitation: a plausible mask may correspond to the wrong instance among small and repeated objects. We call this mismatch between mask rendering and expression-level target selection the Grounding-Rendering Gap. We present DroneSegEdit, which combines RGB proposal rendering with language-grounded, proposal-constrained verification to select supported foreground while retaining rendered geometry. DroneSegEdit adapts the open-weight LongCat-Image-Edit model using task-specific LoRA and fixed color-to-label decoding. For referring segmentation, a Language-Grounded Spatial Verifier (LGSV) reads hierarchical states from the frozen multimodal large language model backbone to derive expression-conditioned spatial evidence, which is mapped back to the rendered proposal without introducing foreground outside it. On RIS-LAD, DroneSegEdit reaches a new state of the art at 52.78 mIoU, 7.16 points above the previous best. It remains competitive overall on UAVid-RIS and VDD-RIS, with its clearest class-wise gains on compact, application-relevant categories, notably vehicles and humans. A separate verifier-free experiment extends the RGB interface to full-scene parsing: a task-specific adapter jointly renders all classes from one task-level instruction without enumerating class names or palette colors.

cs.CV

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