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版本更新 · v4arXiv:2603.25251

Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding

Gregor Baer, Chao Zhang, Isel Grau, Pieter Van Gorp

原文摘要

Explainable AI (XAI) methods are commonly evaluated using functional correctness metrics, also called faithfulness or fidelity, which estimate how closely an explanation reflects the model's reasoning. Higher correctness is assumed to produce better human understanding, but this link has not been tested with controlled levels. We conducted a user study (N=200) that manipulated explanation correctness at four levels (100%, 85%, 70%, 55%) in a synthetic time series classification task where participants could not rely on domain knowledge or visual intuition. Explanations were constructed to resemble feature attributions, with correctness defined against a known ground truth rather than estimated from a trained model. Participants predicted a simulated AI's decisions from these explanations (forward simulation), and we used their forward simulation accuracy as a proxy for understanding. Correctness affected forward simulation accuracy, but not at every level: accuracy dropped at 70% and 55% relative to 100%, with no further reduction below 70% and no conclusive difference between 85% and 100%. Lower correctness reduced how many participants predicted accurately rather than lowering accuracy uniformly, and even with fully correct explanations, only a subset of participants predicted well above chance. In exploratory analyses, some participants predicted decisions accurately but described the wrong pattern when asked what the AI used, and self-reported ratings correlated with forward simulation accuracy only when explanations were fully correct. These findings suggest that, for feature-attribution-style explanations, not all differences in functional correctness translate to differences in human understanding, highlighting the need to validate functional metrics against human outcomes.

cs.HC · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.11007

TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings

Jingxiang Zhang, Lujia Zhong, Zijie Zhu, Shuo Huang, Yuang Xu

原文摘要

Few-shot multimodal classification commonly attaches a lightweight head, such as $k$-nearest neighbors, logistic regression, or a linear SVM, to a frozen pretrained encoder. Although computationally efficient, these heads can produce poorly calibrated confidence scores. We ask whether TabPFN can provide reliable confidence estimates on multimodal embeddings without sacrificing predictive accuracy, and under what conditions. We systematically evaluate TabPFN as a zero-gradient head for frozen image, text, and audio encoders. Across 22{,}820 evaluation episodes spanning 14 datasets, 11 encoders, and three modalities, TabPFN achieves the best mean rank among nine classification heads on both negative log-likelihood (NLL) and expected calibration error (ECE). At a representative setting, it reduces NLL by 48--62\% and ECE by 2.1--5.3$\times$ relative to the average of eight baselines while matching or exceeding their average accuracy. This calibration benefit transfers broadly, whereas the accuracy advantage is conditional: it concentrates at moderate-to-high shot counts and low-to-moderate feature dimensions ($k \ge 50$, $d \le 32$), and diminishes when labeled data are scarce, feature dimensions are high, or competing methods approach ceiling accuracy. After backbone adaptation, replacing the trained linear head with TabPFN improves calibration while preserving competitive accuracy, showing that representation adaptation and reliable head choice are complementary. Together, these results identify when TabPFN can serve as a training-free head for calibration-sensitive multimodal classification. To support transparency and reproducibility, we publicly release the source code, experiment configurations, and evaluation scripts in our GitHub repository: https://github.com/Jingxiang-Zhang/tabpfn-multimodal-embeddings.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00951

Why Koopman Structure Does Not Help Differentiable World Models for Humanoid Whole-Body Tracking

Haizhou Ge, Mengshuo Wang, Yufei Jia, Lei Han, Lu Shi, Guyue Zhou, Ruqi Huang

原文摘要

We test whether Koopman structure helps a differentiable world model used to train a simulated Unitree G1 whole-body tracking policy by backpropagation through imagined rollouts, replacing the MLP world model of an otherwise unchanged pipeline. It does not: return is at best equal (0.823 versus 0.830), one-step error on identical data is 1.3 to 1.6 times the MLP's, and training is about 15% slower. Lifted rollouts diverge or fail to train policies; of the schemes tested, re-lifting at every imagined step trains best. Re-lifting never uses the lifted linear dynamics and leaves a model affine in the action, with state-dependent offset and slope; an action-affine MLP also predicts worse, so the accuracy cost belongs to this class. At a fixed state, the dynamics' curvature in the action explains only part of it. Across states, the policy's actions span a curved region, so an affine model fits policy data only as the tangent at the policy's action, the operating point: on G1, the MLP's tangent there is as accurate as the MLP; at the mean action, 31 times worse. The offset must encode, from the state alone, where the policy acts, whereas an MLP reads the action. Supplying the policy's action removes most of the remaining gap on G1, Ant and Humanoid. The only tested online repair approaching the MLP feeds the action into the nonlinear lift, abandoning the structure. For this use, the MLP world model remains the better choice.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07599

Binding Paraphrased Instructions to Encountered Objects in sLLM Agents under Partial Observability

Chanyoung Kim, Harim Song, Byoungchan Ko, Keonyoung Lee, Myungwon Lee, Dahuin Jung

原文摘要

Reliable deployment of LLM agents requires grounding diverse user expressions in their environments. Although RL-finetuned smaller large language model (sLLM) agents rival larger prompted LLMs on embodied tasks, their robustness to instruction paraphrases remains unclear. We introduce ALFWorld-Para to evaluate this robustness under controlled paraphrases, revealing 21-52% declines in success rates (SRs), predominantly driven by object synonyms. Despite retaining knowledge of synonym referents, agents often fail to take the corresponding objects even after observing them, exposing a gap between referential knowledge and object binding. Partial observability further complicates binding, as referents may remain unseen or have already been encountered under different labels. To address this challenge, we propose BINDIntent, a training-free framework that probabilistically binds referring expressions to observed labels using semantic proximity and estimated persistence against unseen candidates. Without additional training or LLM calls, BINDIntent improves synonym-paraphrase success of RL-finetuned sLLMs by 17.5-49.1 percentage points (pp) with only 0.4 ms of CPU overhead per step. It also raises GiGPO-7B's overall SR from 61.8% to 77.4%, surpassing a prompted 24B LLM. Code and benchmark are available at https://github.com/cau-hai-lab/ALFWorld-Para.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07598

Align Before Aggregation: Basis-Consistent Federated LoRA under Heterogeneous Ranks

Pengpeng Qiao, Yang Cao, Lingling Zhang, Guo Cheng, Junwei Chen, Manjiang Yu, Wei Yang Bryan Lim, Masatoshi Yoshikawa

原文摘要

Federated fine-tuning of large language models (LLMs) under heterogeneous client capacities is increasingly important, and low-rank adaptation (LoRA) with heterogeneous ranks makes it practical. Existing server-side aggregators compose local matrix updates $ΔW_i$ from client-specific LoRA factors $(A_i,B_i)$ before aggregating them. Although these matrix updates lie in the same weight space $\mathbb{R}^{m\times n}$ and can be averaged directly, the LoRA factors that generate them may expressed client-specific local basis. In the typical compose--aggregate--factorize--dispatch loop, this basis inconsistency can make the rank-$r_i$ reference prefix dispatched to lower-rank clients unstable after server-side refactorization. In this work, we propose \textbf{FedAbA}, a basis-consistent federated LoRA aggregation framework for heterogeneous ranks. FedAbA first extracts each client's local basis, aligns it to the corresponding prefix of the global reference basis, and constructs a basis-consistent reconstruction of each local matrix update before aggregation. It then aggregates these reconstructions with weights that combine client data size and consistency score, followed by exact coefficient-space SVD refactorization and gauge fixing before rank-prefix dispatch. Our theoretical analysis clarifies the role of basis consistency and supports the proposed consistency-aware aggregation mechanism. Extensive experiments show that FedAbA outperforms representative federated LoRA baselines, with the largest gains under the strongest rank heterogeneity.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07595

Evolver: Turning Agent Exploration into Evolving Capabilities

Haoyang Zhang, Qumeng Sun, Xiao Zhang, Xiang Liu, Zhidong Fan, Junjie Wang

原文摘要

Agent exploration produces valuable procedural knowledge, yet much of this experience remains confined to transient execution traces and is not systematically reused. We introduce Evolver, an experience-driven framework that transforms agent exploration into persistent, reusable capabilities without updating foundation model parameters. Built around structured Gene assets, Evolver integrates experience acquisition, retrieval, and feedback-guided evolution to support experience-conditioned execution across existing agent harnesses. We evaluate Evolver using Claude Code and Codex, two foundation model configurations, and four benchmarks: Terminal-Bench 2.1, SWE-bench Multilingual, SWE-rebench, and GAIA. On a selected diagnostic set of 320 paired executions, Evolver improves task success from 83.13% to 87.50% (+4.38 percentage points) while reducing recorded task-execution token consumption by 24.24%. Furthermore, among 265 pairs successful under both conditions, token consumption decreases by 17.57%, demonstrating efficiency benefits even when task success is preserved. These findings suggest that persistent procedural experience can improve both task effectiveness and execution efficiency, providing a practical foundation for transforming agent exploration into evolving capabilities.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07593

TACT-KV: Tri-Axis Cosine Transform for Compressing Volumetric KV Caches in Medical VLMs

Chongyu Qu, Ritchie Zhao, Yufan He, Dong Yang, Zhengyi Lu, Junchao Zhu, Tianyuan Yao, Juming Xiong 等 15 位作者

原文摘要

Vision-language models (VLMs) applied to 3D medical scans face a structural memory wall. A single CT or MRI volume produces tens of thousands of vision tokens, and the resulting key-value (KV) cache scales linearly with both batch and context. Existing KV compression methods operate on the cache's feature or token statistics, or apply 1D and 2D transforms, leaving the three-axis spatial structure in the cache unexploited. We propose TACT-KV (Tri-Axis Cosine Transform), a calibration-free KV compression method that exploits strong spatial correlations in visual KV states along all three anatomical axes of 3D scans. This correlation concentrates the KV cache energy under a frequency-domain transform. TACT-KV applies a separable 3D discrete cosine transform to the KV cache, allocates a per-layer bit budget across frequency bins via reverse water-filling (more bits to high-energy components), and quantizes each bin with a fixed scalar codebook. We derive a close-form relation: the distortion reduction from adaptive bit allocation is dominated by the spectral concentration of the transformed cache. Across six medical VLMs and two 3D CT benchmarks, TACT-KV achieves the lowest 1-bit prediction-flip rate on eleven of twelve model and dataset pairs in our main comparison. The same advantage reproduces on four general-purpose VLMs evaluated on video tasks, indicating that the benefit of exploiting three-axis structure generalizes beyond medical imaging. At equal high-bandwidth memory (HBM), TACT-KV scales the decoding batch size on a single GPU by up to $8\times$.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07591

Beyond Independent Predictions: Learning the Browsing Journey for Sequence-Level Re-ranking

Jiayi Zhang, Jingxin Hai, Zhenjia Ma, Hongbin Pei, Dagui Chen, Han Zhu, Jian Xu

原文摘要

Re-ranking aims to present users with an ordered list that maximizes sequence-level utility. Industrial deployments widely adopt the Generator--Evaluator framework, where the generator proposes candidate sequences, and the evaluator scores them to select the best one for display. The fidelity of the evaluator's utility estimates thus directly governs recommendation quality. However, existing evaluators predominantly follow the \textbf{Independent Behavior Modeling (IBM)} paradigm, which simplifies the continuous user \emph{browsing journey} into independent per-item predictions (e.g., CTR). This oversimplification ignores two physical realities: (i) a click can occur only after the item has been reached and impressed, and (ii) items compete for finite user attention. As a result, IBM produces systematically biased evaluations and suboptimal sequence selection. To address this, we propose a \textbf{Joint Behavior Modeling (JBM)} perspective that explicitly models the \emph{display} $\rightarrow$ \emph{impression} $\rightarrow$ \emph{click} process to capture causal and competitive dependencies within a sequence. Building on JBM, we develop the \textbf{Impression-Click Evaluator (ICE)}, a plug-and-play framework that (i) predicts a browsing-depth distribution to estimate per-position impression probabilities, (ii) leverages predicted impressions to suppress spurious context from unexposed items during click modeling, and (iii) introduces a click distribution matching objective to capture intra-sequence dependencies. Extensive offline experiments across diverse backbones, together with a 14-day online A/B test in the Taobao App (+0.70\% clicks, +3.67\% revenue) and full-traffic deployment, validate the effectiveness and robustness of our approach.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.14494

Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty

Tianjue Lin, Jianan Zhou, Jieyi Bi, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

原文摘要

Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates this issue by selecting a small, representative subset of scenarios to enable tractable computation. However, existing methods are largely problem-agnostic, operating solely on the uncertainty set without consulting the feasible region or recourse structure. In this paper, we introduce PRISE, a problem-driven sequential lookahead heuristic that constructs reduced scenario sets by evaluating the marginal impact of each scenario. While PRISE yields high-quality scenario subsets, each selection step requires solving multiple subproblems, making it computationally expensive at scale. To address this, we propose NeurPRISE, a neural surrogate model built on a GNN-Transformer backbone that encodes the per-scenario structure via graph convolution and captures cross-scenario interactions through attention. NeurPRISE is trained via imitation learning with a gain-aware ranking objective, which distills marginal gain information from PRISE into a learned scoring function for scenario ranking and selection. Extensive results on three 2RO problems show that NeurPRISE consistently achieves competitive regret relative to comprehensive methods, maintains strong calability with varying numbers of scenarios, and delivers 17-200x speedup over PRISE. NeurPRISE also exhibits strong zero-shot generalization, effectively handling instances with larger problem scales (up to 5x), more scenarios (up to 4x), and distribution shifts.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07589

Learning and Steering in Incomplete-Information Games

Asrın Efe Yorulmaz, Tamer Başar

原文摘要

In many modern multi-agent systems, a mediator seeks to influence strategic decision dynamics using only outcome-level feedback and without knowing agents' utilities. In this work, we study how a mediator can learn an unknown game and steer adaptive agents toward outcomes that maximize her utility using only trajectory feedback. The mediator knows the game form and her own objective, but not the agents' utilities. Moreover, she commits to nonnegative payments and provides recommendations and contexts, but receives only a single realized terminal outcome rather than full-information feedback. We develop halving-type algorithms that learn utility differences in normal-form games and conditional continuation-value differences in extensive-form games, achieving a sample complexity of $\widetilde O(\varepsilon^{-2})$ that matches worst-case lower bounds up to logarithmic factors. We then combine these learning procedures with a Lagrangian formulation in an online Learn-Compute-Steer algorithm that interleaves learning, equilibrium computation, and steering toward mediator-optimal extensive-form correlated equilibria (EFCE). With high probability, the Learn-Compute-Steer algorithm achieves a directness gap and an optimality gap of $\widetilde O(T^{-1/4})$, while time-average payments are of $\widetilde O(T^{-1/8})$.

cs.GT · cs.AI · cs.LG · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07588

What Gets Hacked in a Collaborative Reward for LLM Recommenders: The Part That Does Not Depend on the User

Zhuangsheng Li, Liang Dong, Yu Wu, Dailin Li, Yu Xia, Yunhan C. Zhang, Rui Cai, Yi Wu 等 9 位作者

原文摘要

Collaborative-filtering (CF) scores provide graded, personalized rewards for training large language model (LLM) recommenders through reinforcement learning. However, these rewards can cause exposure collapse: even though the CF model itself produces diverse top recommendations across users, an LLM optimized with its rewards concentrates on fewer items. We decompose each score into an item-mean component, the item's average CF score across user histories, and a residual component, the current user's score deviation from the average. Through controlled interventions, we identify the item-mean component as a contributor to exposure collapse. Accordingly, we propose two reward corrections: per-item centering removes each item's estimated mean score, while per-item standardization further rescales the residual using its estimated standard deviation. Experiments on two datasets with two identifier schemes show that both corrections substantially improve catalog coverage and exposure evenness. Compared with training on the uncorrected CF reward, our corrections increase the number of distinct items ranked first across users by up to $9.8\times$, while maintaining comparable ranking accuracy. Code is available at https://anonymous.4open.science/r/LLM4Rec-CF-Bias-27EF/.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07587

Fine-Tuning a Transducer for Impaired Speech: Where the Gains Appear and What They Mean for Evaluation

Kien Nguyen, Tuan Dinh, Bao Phan

原文摘要

Speech recognition still works poorly for many people whose speech is affected by conditions such as ALS, Parkinson's disease, cerebral palsy, Down syndrome or stroke. We took part in the Speech Accessibility Project Challenge 2 (SAPC2) with a public speech recognizer that we fine-tuned on the challenge data. Our best entry scored 7.81% character error rate on Test1, the first of two hidden test sets (official baseline 15.26%). Our live entries ran on CPUs with a combined median latency of about 0.9 seconds at 7.96-8.07% error. On new speakers, fine-tuning reduced errors most for the speakers that an off-the-shelf recognizer finds hardest to understand. Sentences already seen in training had far lower error rates than new ones (2.58% vs 6.42%), a larger difference than being a familiar speaker made. We close with lessons for evaluating such systems.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07585

CasHyper: Modeling Multi-Factor Interactions and Asymmetric Influence in Cascade Prediction via Hypercomplex Algebra

Yansong Wang, Qisen Chai, Junjie Huang, Tao Jia

原文摘要

Cascade prediction aims to forecast the future evolution of information diffusion and is an important task in social network analysis. Most existing methods represent users and cascades in real-valued Euclidean spaces, which may offer limited capacity to model multi-factor interactions and asymmetric influence in complex diffusion processes. To address these challenges, we propose CasHyper, a hypercomplex-based cascade prediction framework that integrates hypercomplex algebra with graph neural networks and Transformers. CasHyper organizes user and cascade embeddings into multiple hypercomplex components, with each component encoding distinct latent factors, and uses structured algebraic operations to model cross-component interactions. The non-commutative hypercomplex product further yields order-dependent interaction representations, enabling the model to learn asymmetric influence among users. In the network encoding stage, a static heterogeneous graph channel captures user-user and user-cascade relations, while a Neural ODE-based dynamic channel models the continuous evolution of node states and edge weights. A hypercomplex Transformer with norm-preserving rotary transformations is then introduced to incorporate relative positional information into cascade sequence modeling. Finally, an interaction-aware module and a linear projection module are combined to estimate the probability of each candidate user participating next. Experiments on four real-world datasets show that CasHyper achieves competitive or better performance than the compared methods under the evaluated settings, including relative improvements of up to $43.24\%$ on Android and $18.43\%$ on Christianity in Hits@50.

cs.SI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.29475

Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks

Dmitrii Seletkov, Paul Hager, Georgios Kaissis, Rickmer Braren, Daniel Rueckert, Raphael Rehms

原文摘要

Survival analysis is crucial for many medical applications, but remains challenging for modern machine learning due to limited data, censoring, and the heterogeneity of tabular covariates. While the prior-fitted paradigm, which relies on pretraining models on large collections of synthetic datasets, has recently facilitated tabular foundation models for classification and regression, its suitability for time-to-event modeling remains unclear. We propose a flexible survival data generation framework that defines a rich survival prior with explicit control over covariates and time-event distributions. Building on this prior, we introduce Survival In-Context (SIC), a prior-fitted in-context learning model for survival analysis that is pretrained exclusively on synthetic data. SIC is trained to approximate Bayesian posterior predictive inference under the synthetic survival prior, enabling individualized survival prediction in a single forward pass, requiring no task-specific training or hyperparameter tuning. Across a broad evaluation on real-world survival datasets, SIC achieves competitive or superior performance compared to classical and deep survival models, particularly in small and medium-sized data regimes, highlighting the promise of a prior-fitted paradigm for survival analysis. The code and pretrained models are available at https://github.com/yayapa/sic

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05637

Contextual Pictograms for Repairing Multilingual ASR Errors

Mariana Hernandez, Hector Luna, Carlos Bruno, Luis Ricardo Santos, Elena Simoes

原文摘要

Speech transcription errors can be difficult to recognize when competing word hypotheses are unfamiliar or visually similar. We describe an interface that combines alternative transcripts, replay and contextually checked pictograms for selected uncertain concepts. The system will initially support French and Spanish functional utterances. A counterbalanced study will assess whether pictorial feedback helps listeners recover the speaker's intended action beyond text alternatives alone. We will also quantify semantic mismatch and additional interaction time, since an inappropriate image can reinforce the wrong interpretation rather than repair it.

cs.CL · cs.HC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.11063

EmboTeam: Asymmetric State Grounding for Heterogeneous Multi-Robot Collaboration

Haishan Zeng, Mengna Wang, Peng Li

原文摘要

Heterogeneous multi-robot collaboration over long horizons requires both global task planning and robust coordination during execution. Existing plan- ners using large language models (LLMs) can generate task decompositions or symbolic plans, but often execute them as rigid action sequences, making them fragile to local state mismatches and synchronization failures. We propose Em- boTeam, a neuro-symbolic framework for asymmetric state grounding. An LLM generates structured state expectations, which are verified and coordinated through symbolic search using Planning Domain Definition Language (PDDL) and then grounded into a team-level parallel behavior tree. Robot-specific subtrees per- form precondition checking, skill execution, effect validation, local replanning, and blackboard-based synchronization. We further introduce MACE-THOR, con- taining 42 heterogeneous multi-robot tasks across 8 indoor layouts. EmboTeam improves task success rate from 12% to 55% and goal condition recall from 32% to 75% over LaMMA-P, demonstrating the effectiveness of synchronized reactive grounding for collaboration over long horizons.

cs.RO · cs.AI · cs.CV · cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.23481

VTAM: Efficient Visuo-Tactile World-Action Models for Contact-Rich Manipulation without Tactile Pretraining

Haoran Yuan, Weigang Yi, Zhenyu Zhang, Wendi Chen, Yuchen Mo, Jiashi Yin, Xinzhuo Li, Yue Luo 等 14 位作者

原文摘要

Tactile vision-language-action models (VLAs) and tactile world-action models (WAMs) are a promising new paradigm for contact-rich manipulation, but existing models either underutilize touch, rely on large-scale tactile pretraining, or incur high inference latency due to iterative video generation. We present VTAM, an efficient visuo-tactile world-action model for contact-rich manipulation that requires no tactile or action pretraining. RGB and tactile observations share a frozen video VAE and the pretrained video transformer weights. VTAM first adapts the video backbone to jointly predict future visual and tactile latents, then fine-tunes it end-to-end with a randomly initialized action expert that predicts actions, proprioceptive states, and a light flow-force proxy computed from tactile optical flow. Trained only on task demonstrations, VTAM outperforms all baselines on four real-robot tasks across two platforms with different tactile sensors, outperforms all baselines without tactile pretraining in average success on eight UniVTAC simulation tasks, and is comparable to tactile VLAs and WAMs pretrained on over 30K hours of largely visuo-tactile robot data (83.4% vs. 84.5% for N0-TWAM). Without full video denoising at inference, VTAM is approximately 6x and 16x faster than N0-TWAM and LingBot-VA without compilation, with compiled latency comparable to Fast-WAM. Ablations show that feeding frozen-VAE tactile features directly to the action expert, bypassing tactile world modeling, fails on every tested task, that Stage I visuo-tactile world-model fine-tuning improves UniVTAC success on most tasks, and that flow-force proxy keeps the policy from fully ignoring touch. The tactile encoder transfers zero-shot across GelSight Mini and Xense Photon sensors.

cs.RO · cs.AI · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07580

Subspace Learning with Outlier Exposure for Out-of-Distribution Detection

Hongxu Shen, Kun Fang, Jiahui Zheng, Jian Sun, Qinghua Tao

原文摘要

Out-of-Distribution (OoD) detection is pivotal for reliable deployments of Deep Neural Networks (DNNs), aiming to distinguish anomalous samples from the In-Distribution (InD) data in training. By introducing outliers as surrogate OoD data to finetune DNNs, Outlier Exposure (OE) has greatly advanced OoD detection in recent years. Existing OE-based research remains predominantly logit-centric, either by generating diverse outliers or by regularizing them with objectives operating at the prediction-output level. Nonetheless, these approaches leave much of the rich information in feature spaces untapped. In this work, we propose an OE-based detection framework, named RecOE, that learns appropriate feature subspaces from InD data and auxiliary outliers, such that the InD-OoD discrepancy can be effectively characterized through the learned feature subspaces. Specifically, the feature subspaces are optimized to capture the dominant variations of InD and outliers by minimizing reconstruction errors therein. Besides, a sample-level objective discourages reconstruction in the opposing subspace, while an orthogonality regularizer reduces subspace overlap, further promoting discriminative subspaces. At inference, the difference in reconstruction errors between the learned subspaces serves as the OoD detection score. Extensive experiments show the superiority of RecOE on multiple benchmarks, in which significant performance gains are achieved over recent strong baselines with only slight extra overhead from two small-size matrices of subspace bases. The source code is available at https://github.com/shenhongxu123/RecOE.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07576

Digital Security Under Compound Threats: Surveillance, Threat Reasoning, and Habituation Among North Korean Defectors in China

Yibo Meng, Bingyi Liu, Lionel Z. Wang, Hongkun Yang

原文摘要

North Korean defectors residing in China face persistent uncertainty regarding digital security and privacy. Through semi-structured interviews with 19 defectors, we examine how individuals understand digital threats and adapt their security behaviors. We identify three interrelated conditions: transnational surveillance pressure from North Korea, identity-linked digital infrastructure and intermediary dependency in China, and threat reasoning shaped by prior surveillance schemas. Participants developed uneven protective practices and followed two trajectories: adaptive habituation driven by knowledge acquisition and erosive habituation driven by the prolonged absence of adverse events. Our findings show how compound threats, attribution uncertainty, and constrained agency shape security reasoning among undocumented users.

cs.CY · cs.CR · cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02545

FindLink: Learning to Assess Scientific Connections from Source-Grounded Premises

Jingxuan Wei, Song Tan, Xiaohan Liu, Chao Sang, Changwei Wang, Linzhuang Sun, Bihui Yu, Fuqiang Wang 等 9 位作者

原文摘要

Scientific discovery from the literature requires assessing connections among findings obtained under different study conditions. Source attribution makes individual findings traceable, but does not determine what they jointly support. We introduce GI-TraceBench, a benchmark of 10,282 questions in gastrointestinal oncology that unifies provenance question answering, knowledge discovery, and hypothesis evaluation through shared Paper, Claim, and Evidence Span records. We further propose FindLink, a two-stage framework for provenance-grounded scientific reasoning. Premise Grounding constructs source-bound premises with explicit study context and support status. Structured Reasoning organizes these premises into support plans that represent both supported relations and unresolved conditions, then generates evidence-conditioned answers using deterministic compiler feedback. The compiler verifies structural and source-binding consistency, but does not assess scientific entailment. On the 1,545-question fixed-evidence test set, FindLink improves the balanced family macro score from 67.95 to 87.25 over direct supervised fine-tuning of the same backbone. A retrained variant without Structured Reasoning achieves slightly higher premise-identifier F1, but substantially lower relation and evidence-sufficiency F1. These results indicate that scientific reasoning requires not only identifying grounded premises, but also assessing what connections those premises support.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.15992

Player Disjoint Multi-Task Tennis Stroke Analysis from Monocular Pose Sequences

Jigyashman Hazarika, Prince Kumar, Karthik Vasu

原文摘要

Analyzing tennis technique from monocular RGB video could make coaching more accessible, but prior methods usually rely on hand-trimmed strokes, test on random splits that leak player identity, or fail on players with different body proportions. We present an end-to-end, player-disjoint framework with an autonomous kinetic stroke event detector, a 78-dimensional causal velocity representation ([X, V]), a 569,095-parameter multi-task Transformer Encoder (E7), exploratory 9-dimensional kinematic deviation profiling, and a heuristic feedback prototype. All models are evaluated with Clean LOPO v2: Leave-One-Player-Out cross-validation over 7 players and 3 seeds, giving 21 outer evaluations and 147 training runs per configuration, with nested inner cross-validation. Against 206 frozen consensus impact events, the detector reaches an F1-score of 85.65% (+3.24% over baseline thresholding) with a mean absolute timing error of 80.6 ms. Under heavy class imbalance, Stroke Type reaches 74.27% accuracy, 65.37% balanced accuracy, and Macro-F1 = 0.6453. Direction reaches 43.90% accuracy, 37.75% balanced accuracy, and Macro-F1 = 0.3141, a 17.9% relative Macro-F1 gain over the static baseline E0, though balanced accuracy is only a little above the 33.3% chance level. Posture Quality reaches 52.13% accuracy, 50.56% balanced accuracy (essentially chance), and Macro-F1 = 0.4232. Single-task specialists did not differ significantly from the joint model on Stroke Type (p = 0.4062) or Posture Quality (p = 0.1406). On Direction the joint model did somewhat better (exact permutation p = 0.0469), but this is suggestive only and does not pass a Bonferroni threshold. Deviation profiling shows a nominal, unadjusted separation between good and bad posture (|d| = 0.341, p = 4.07 x 10^-8), which drives the feedback prototype. We report negative ablations, seeds, and limits as a baseline for pose-based coaching.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.25820

Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models

Yulin Yuan, Hongshuo Zhao, Xiangming Meng

原文摘要

Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel. This turns each decoding step into a position-selection problem: the model must choose not only which predictions are reliable in isolation, but also which positions should be committed together as context for later decoding steps. Existing confidence-based decoding ranks masked positions independently and commits the top-K positions, largely ignoring whether the committed tokens provide complementary visual grounding. We identify a step-level limitation of this strategy in multimodal settings: high-confidence tokens selected in the same step can rely on overlapping visual grounding, introducing visual redundancy among the committed tokens and leaving less complementary visual grounding available for later decoding. To quantify this effect, we introduce the Visual Redundancy Index (VRI), which measures visual grounding overlap among tokens committed in parallel. To control this redundancy during decoding, we propose Visual-Redundancy-Controlled Decoding (VRCD), a training-free inference-time decoding method that uses token-to-image attention to prioritize visually complementary positions. Across diverse multimodal benchmarks, VRCD reduces visual redundancy and remaining-position entropy with modest runtime overhead. In longer decoding experiments, it also achieves relative accuracy gains of up to 18.8% on M^3CoT and 6.9% on MMBench over confidence-based decoding. Code is available at https://github.com/infiniteYuanyl/VRCD.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.07729

Foveated Compression: Selective High-Resolution Preservation for Token-Efficient VLMs

Donghyun Han, Jangho Park, Yuseok Bae

原文摘要

Visual tokens are a major source of inference cost in vision-language models, yet simple image downsampling remains a surprisingly strong compression baseline. This raises a complementary question: under a fixed token budget, where should visual fidelity be preserved? We introduce Foveated Compression, which encodes a full-resolution image once and represents it with a mixture of native- and compressed-resolution visual tokens. A behaviorally self-distilled Foveated Merger compresses local visual tokens while preserving compatibility with their native counterparts, and a lightweight Foveated Selector chooses one of nine spatial cells to retain at native resolution using exhaustive budget-matched intervention supervision. At 11.11% visual tokens, uniform Foveated Compression shows no significant paired difference from iso-token downsampling. At 20.99%, the learned selector significantly outperforms random and fixed allocation, but remains below strong whole-image resizing, showing that localized fidelity is not universally preferable. A budget-matched region-choice oracle reaches 82.73 macro accuracy versus 69.61 for the learned selector, revealing substantial headroom within the same spatial action space. Matched probing further shows that signals predicting when compression breaks the answer are substantially more accessible after language-model computation than to the lightweight prefill-free selector. These results expose complementary bottlenecks in region selection and compressed-region fidelity.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07573

Integrating Learned Priors through Prior-Space Reparameterization for Physically Based Inverse Rendering

Hun Hwangbo, Minjun Park, Yongsung Kim, Wooseok Song, Jungmin Ko, Junghyun Hwang, Sungroh Yoon

原文摘要

Physically based inverse rendering decomposes appearance into materials and illumination, an ambiguous problem for which learned material predictions provide useful prior information. Most existing methods incorporate these cues through auxiliary regularization losses, which alter the optimization objective and require balancing their influence against photometric fitting. We propose a prior-guided material reparameterization that uses the predicted feature relationships to restrict the optimization space while leaving the objective unchanged. We fuse multi-view material predictions onto a mesh and construct a permutohedral lattice in the resulting prior feature space. Surface points with similar predictions share material variables, which are optimized jointly with geometry and illumination using a purely photometric objective. Experiments with two learned predictors on three synthetic benchmarks and Stanford-ORB demonstrate improved material decomposition and relighting. Our method improves relighting PSNR by 1.5-2.5 dB over the strongest evaluated mesh-based baseline on Synthetic4Relight and TensoIR-Synthetic, and outperforms a regularization loss using the same prior in controlled comparisons. We further show that our reparameterization pools noisy photometric gradients, producing updates that are more consistent across sampled views. This consistency enables a shortened joint optimization schedule that takes less than a minute while retaining relighting quality above all evaluated baselines on TensoIR-Synthetic.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.06113

Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback

Huaisong Zhang, Hao Yu, Yuxuan Zhang, Jiahe Wang, Xinrui Chen, Haoxiang Cao, Feng Lu, Wendong Zhang 等 10 位作者

原文摘要

Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still formulate diagnosis as pixel-field regression, making it difficult to localize variable-cardinality defects and bind semantic reasons to individual failures. To address this representation bottleneck, we propose Structured Defect Grounding (SDG), which casts T2I diagnosis as structured set prediction by modeling each defect as a (location, type, reason, importance) tuple. To make this formulation trainable and measurable, we introduce SDG-30K, a 30K-image dataset with box-grounded annotations across four modern T2I generators, together with a dedicated evaluation protocol, SDG-Eval. Building on this structured representation, we further present a diagnosis-to-alignment framework in which a Vision-Language Model (VLM) serves as the SDG detector, and BoxFlow-GRPO converts predicted defect sets into box-derived, importance-weighted spatial rewards for diffusion model alignment. Extensive experiments show that our SDG detector outperforms leading proprietary VLMs on structured defect grounding, while SDG-guided rewards consistently improve T2I alignment and support localized image refinement. These results establish SDG as a unified, instance-level interface for diagnosing, evaluating, and enhancing modern generative models.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07569

MambaTG: Candidate-Aware State Space Modeling for Future Link Prediction on Temporal Graphs

Nguyen Hong Quan, Sitao Luan, Yilun Zheng, Lihui Chen

原文摘要

Future link prediction on temporal graphs involves both repeated interactions between previously connected node pairs and first-time interactions between pairs with no prior interaction. While repeated-edge prediction can exploit direct pairwise evidence, unseen-edge prediction must generalize beyond historical recurrence. Existing temporal graph models often exploit signals tied to recurring interactions or historical overlap, which are highly informative for repeated edges but become limited or unavailable for unseen edges, contributing to substantial performance degradation on unseen-edge prediction. Based on this observation, we identify two complementary signals for modeling sequential dynamics beyond historical recurrence: (1) historical-sequence evolution, which captures dependencies among the source node's chronologically ordered interactions, and (2) candidate--history compatibility, which captures how well a candidate fits the source node's evolving history. Based on the decomposition, we propose MambaTG, a candidate-aware Mamba-based framework that jointly models these two signals. Mamba provides a natural mechanism for this design: its selective recurrence captures the evolution of ordered interaction histories, while its input-dependent state transitions allow the history state to adapt to different candidate inputs. Extensive experiments on 13 temporal graph datasets against 13 representative baselines show that MambaTG achieves strong and consistent performance across both repeated and unseen edges while remaining computationally efficient.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.19633

Keeping Jerk in Check: Jointly Trained Perception-Gated Experts for Smooth Terrain Transitions

Francisco Affonso, Matheus P. Angarola, Ana Luiza Mineiro, Aditya Potnis, Marcelo Becker, Girish Chowdhary

原文摘要

Perceptive legged locomotion across discontinuous terrain requires shared locomotion knowledge, task-specific maneuvers, and smooth transitions between them. Existing approaches train specialized policies independently and either switch among them with a hierarchical selector or distill them into a monolithic policy. Both fix the constituent behaviors before they are composed, and both are assessed through aggregate course outcomes that leave behavior at terrain boundaries uncharacterized. We instead propose CTS-MoE, which formulates multi-terrain locomotion as multi-task reinforcement learning and jointly trains a perception-gated dense mixture-of-experts actor with task-specific critics: soft routing composes expert knowledge into continuously varying behavior, while separated critics isolate heterogeneous objectives so that specialization survives joint optimization. A concurrent teacher-student formulation transfers this composition from privileged terrain information to onboard depth, so that at deployment routing is inferred from perception alone, with no terrain label, selector, or distillation stage. Against hierarchical and DAgger-distilled baselines, the policy raises connected-course success to 93.3% from 73.3% and 53.3%, lowers mean action jerk by about a third across the course, and avoids the switching transient that hard selection introduces at boundaries. On a Unitree Go1, it completes an extended outdoor course in 10/10 trials and transfers to four outdoor terrain types. Project Website: https://cts-moe.github.io/ .

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07566

Guidance Is a Field, Not a Scalar: Interval-Integrated Classifier-Free Guidance for One-Step Video Generation

Zijie Meng, Xixin Cao

原文摘要

One-step generators built on MeanFlow synthesize a video in a single function evaluation, yet inherit a rigid treatment of classifier-free guidance (CFG): the guidance scale is frozen during training or, at best, exposed as a single scalar. This conflicts with mounting evidence that the optimal guidance is not a constant but a field that varies with the noise level, with spatial content, and, for video, with frame position. Every existing remedy presupposes a multi-step sampler along which guidance can be re-scheduled--an assumption one-step models violate by construction. We resolve this tension through a single observation: because the MeanFlow target is an integral over a noise interval, an arbitrary guidance field can be absorbed into the target itself and compiled into one evaluation. We formalize this as the interval-integrated CFG identity, prove that the compiled guidance carries no schedule-discretization error and can thus match or exceed any finite multi-step sampler, and instantiate it as AGAVE, which equips the average-velocity field with a learnable temporal schedule, a free motion-saliency spatial modulation, and a frame-position asymmetry. Adding fewer than ten scalars at zero inference cost, AGAVE surpasses all one-step baselines on VBench, UCF-101 and MSR-VTT while matching multi-step guided sampling. Project repository: https://github.com/YMlinfeng/AGAVE.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15843

WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes

Jichen Hu, Jiawei Guo, Jiazhong Cen, Chen Yang, Sikuang Li, Wei Shen

原文摘要

Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability and physical interaction. This restricts their use in immersive content creation and embodied simulation, where generated worlds must be actively modified and manipulated. To tackle this challenge, we present WorldAct, a framework that converts static generated 3D worlds into editable and interaction-ready scenes. WorldAct uses a multimodal agent to guide scene decomposition, identify actionable objects, reconstruct geometrically aligned object-level meshes for interaction, and restore the residual background via 3D inpainting. The resulting scenes support object-level editing, collision-aware manipulation, and embodied task execution while preserving global scene coherence. Experiments show that WorldAct enables richer interaction scenarios than the original generated scenes, suggesting a practical path toward editable and interactive 3D world models. Project page: https://sjtu-deepvisionlab.github.io/WorldAct

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03953

P$^2$-MOPD: Accelerating Multi-Teacher On-Policy Distillation via Parameter Priors

Sinuo Wang, Jiacheng Lu, Zeyu Zhang, Maojia Song, Rui Sun, Beidi Luan, Yutong Xie, Jing Li 等 11 位作者

原文摘要

Multi-teacher on-policy distillation consolidates expert capabilities by supervising student-generated trajectories, whereas task arithmetic offers a training-free alternative through linear parameter combinations. We view the linear combination of student and expert parameters as a single differentiable model and propose P$^2$-MOPD, which jointly optimizes the mixing coefficients and the student weights under the same on-policy distillation objective. Unlike task arithmetic, which stays within the span of the frozen student and expert parameters for any choice of merging coefficients, P$^2$-MOPD keeps the student trainable, so the experts act as parameter priors without confining it to this span. Across four domains at 1.7B and 4B scales, P$^2$-MOPD achieves faster and more balanced recovery than MOPD from the same initialization, surpassing its final average recovery in half the training steps. At the end of training, our 1.7B model reaches 94.7% average recovery, versus 80.9% for MOPD.

cs.LG · cs.AI

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