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版本更新 · v3arXiv:2412.17254

ViDE: Tuning-Free Video Coherence via Temporal Attention Reweighting and Prompt Blending

Xingyao Li, Hao Zhang, Fengzhuo Zhang, Jiachun Pan, Yunlong Hou, Vincent Y. F. Tan, Zhuoran Yang

原文摘要

Despite the substantial progress in long video generation, multi-prompt synthesis often encounters inconsistencies resulting from the training-inference gap caused by length extension techniques and coarse prompt interpolation. To overcome these issues, we propose the Video Diffusion with hidden states Editing (ViDE) framework, which consists of two key components. The first is the Time-frequency based Temporal Attention Reweighting (TiTAR) algorithm, which leverages the relationship between inconsistencies and diagonal elements of temporal attention. By reweighting the attention scores via the Discrete Short-Time Fourier Transform (DSTFT), TiTAR effectively reduces frame inconsistencies, a capability further corroborated by a Fourier-based analysis. The second component, PromptBlend, reduces inconsistencies in multi-prompt settings through fine-grained prompt alignment and adaptive interpolation, enabling smooth semantic transitions. Extensive experiments demonstrate the effectiveness of ViDE, demonstrating consistent and significant improvements over multiple baselines.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2407.16151

FUSE-PnPL: Flexible, Unified, Statistically Consistent, and Asymptotically Efficient Camera Pose Estimation from Points and Lines

Guangyang Zeng, Yuan Shen, Yiguo Peng, Guodong Shi, Biqiang Mu, Junfeng Wu

原文摘要

Camera pose estimation from 3D-2D correspondences is fundamental to robotics. Three practical considerations motivate this work. First, point and line features provide complementary geometric information, motivating a unified estimator. Second, the number and composition of available features vary across scenarios, requiring support for minimal and arbitrary non-minimal configurations. Third, modern visual front ends extract abundant features from individual images, enabling highly accurate pose estimation. We propose FUSE-PnPL, a flexible and unified camera pose estimator that is statistically consistent and asymptotically efficient. Specifically, we represent each line using two distinct points and adopt a unified image-space noise model for point and line measurements. By employing Plücker coordinates for line parameterization, we explicitly characterize the propagation of image endpoint noise into line residuals and formulate a joint maximum likelihood (ML) estimation problem for combined point and line measurements. To efficiently solve this problem, FUSE-PnPL employs a two-stage estimation scheme. First, noise variance estimation and bias elimination yield a consistent initial pose estimate. A single Gauss-Newton iteration then refines this estimate, achieving asymptotic efficiency as its mean squared error asymptotically attains the Cramér-Rao lower bound. Moreover, the proposed estimator has linear computational complexity with respect to the number of correspondences, making it suitable for high-precision and real-time robot pose estimation. Extensive experiments on synthetic data and real-world benchmarks validate the theoretical properties of FUSE-PnPL and demonstrate its advantages in both static global localization and dynamic SLAM systems.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.13194

HARENA: A Living Benchmark for Sensor-Based Human Activity Recognition on Resource-Constrained Edge Devices

Maximilian Burzer, Tobias King, Till Riedel, Michael Beigl, Tobias Röddiger

原文摘要

Sensor-based human activity recognition runs on edge devices, trading performance against latency, memory, and energy. Yet new architectures are usually compared with few, often outdated baselines on few datasets with study-specific data preparation and hardware, leaving open which perform best at what cost. We present HARENA, a living benchmark with a public leaderboard, to which new architectures and datasets can be added under one protocol. Its initial suite evaluates 16 neural architectures and three classical baselines on 29 datasets, measuring performance on unseen subjects and deployment cost on a smartphone, smartwatch, and microcontroller. CNN-HAR, the suite's oldest deep architecture, leads overall, but 8 architectures lie within 2.5 points of its mean test macro-F1, while their cost differs by orders of magnitude. On the microcontroller, Random Forest leads on latency and peak RAM, LITEWAY-Light on flash, and TinyHAR is most accurate within the real-time budget. Rankings shift with dataset and channel count, so general claims require many datasets, which a growing benchmark can provide. We find no sign of benchmark overfitting, but performance drops by a median of 15.0 points on unseen subjects, about three times the typical spread between architectures, making generalization and adaptation the most promising future directions.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.06281

INEUS: Iterative Neural Solver for High-Dimensional PIDEs

Jean-Loup Dupret, Davide Gallon, Patrick Cheridito

原文摘要

In this paper, we introduce INEUS, a meshfree iterative neural solver for partial integro-differential equations (PIDEs). The method replaces the explicit evaluation of nonlocal jump integrals with single-jump sampling and reformulates PIDE solving as a sequence of recursive regression problems. Like Physics-Informed Neural Networks (PINNs), INEUS learns global solutions over the entire space-time domain, yet it offers a more efficient treatment of nonlocal terms and avoids the computationally expensive differentiation of full PIDE residuals. These features make INEUS particularly well suited for high-dimensional PDEs and PIDEs. Supported by a contraction-based convergence proof for linear PIDEs, our numerical experiments show that INEUS delivers accurate and scalable solutions for various high-dimensional linear and nonlinear examples.

cs.LG · math.NA · q-fin.CP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.16789

Conservative Continuous-Time Treatment Optimization

Nora Schneider, Georg Manten, Niki Kilbertus

原文摘要

Finding optimal treatment plans in continuous time is crucial for personalized medicine. Existing methods, however, do not explicitly optimize treatment plans and instead focus solely on predicting treatment effects. Naive optimization over these treatment-response models can exploit model errors, resulting in plans that appear optimal under the learned model but perform poorly in practice. To mitigate model exploitation, we propose SigCTO (Signature-based Conservative Treatment Optimization), a conservative framework for continuous-time treatment optimization. We derive a path-space regularizer based on signature-kernel maximum mean discrepancy that penalizes treatment plans for which predicted and true responses differ. Under suitable assumptions, the regularizer allows for consistent estimation, and the regularized objective upper bounds the true treatment cost. Experiments on pharmacometric simulators show that SigCTO finds treatment plans with lower ground-truth costs than state-of-the-art baselines. On real-world ICU data, SigCTO predicts treatment cost more accurately than baselines, and the regularizer detects prediction errors.

cs.LG · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.09843

Kelix Technical Report

Boyang Ding, Chenglong Chu, Dunju Zang, Han Li, Jiangxia Cao, Kun Gai, Muhao Wei, Ruiming Tang 等 31 位作者

原文摘要

Autoregressive large language models (LLMs) scale well by expressing diverse tasks as sequences of discrete natural-language tokens and training with next-token prediction, which unifies comprehension and generation under self-supervision. Extending this paradigm to multimodal data requires a shared, discrete representation across modalities. However, most vision-language models (VLMs) still rely on a hybrid interface: discrete text tokens paired with continuous Vision Transformer (ViT) features. Because supervision is largely text-driven, these models are often biased toward understanding and cannot fully leverage large-scale self-supervised learning on non-text data. Recent work has explored discrete visual tokenization to enable fully autoregressive multimodal modeling, showing promising progress toward unified understanding and generation. Yet existing discrete vision tokens frequently lose information due to limited code capacity, resulting in noticeably weaker understanding than continuous-feature VLMs. We present Kelix, a fully discrete autoregressive unified model that closes the understanding gap between discrete and continuous visual representations.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2512.13197

Parameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator

Ayrat Abdullin, Umair Bin Waheed, Leo Eisner, Naveed Iqbal

原文摘要

Seismic phase picking is fundamental for microseismic monitoring and subsurface imaging. Manual processing is impractical for real-time applications and large sensor arrays, motivating the use of deep learning-based pickers trained on extensive earthquake catalogs. On a broader scale, these models are generally tuned to perform optimally in high signal-to-noise and long-duration networks and often fail to perform satisfactorily when applied to campaign-based microseismic datasets, which are characterized by low signal-to-noise ratios, sparse geometries, and limited labeled data. In this study, we present a microseismic adaptation of a network-wide earthquake phase picker, Phase Neural Operator (PhaseNO), using transfer learning and parameter-efficient fine-tuning. Starting from a model pre-trained on more than 57,000 three-component earthquake and noise records, we fine-tune it using only 200 labeled and noisy microseismic recordings from hydraulic fracturing settings. We present a parameter-efficient adaptation of PhaseNO that fine-tunes a small fraction of its parameters (only 3.6%) while retaining its global spatiotemporal representations learned from a large dataset of earthquake recordings. We then evaluate our adapted model on three independent microseismic datasets and compare its performance against the original pre-trained PhaseNO, a STA/LTA-based workflow, and two state-of-the-art deep learning models, PhaseNet and EQTransformer. We demonstrate that our adapted model significantly outperforms the original PhaseNO in F1 and accuracy metrics, achieving up to 30% absolute improvements in all test sets and consistently performing better than STA/LTA and state-of-the-art models. With our adaptation being based on a small calibration set, our proposed workflow is a practical and efficient tool to deploy network-wide models in data-limited microseismic applications.

physics.geo-ph · cs.LG

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2410.01308

How Hard Is It for Message-Passing GNNs to Simulate One Step of the Weisfeiler-Lehman Test?

Guanyu Cui, Yuhe Guo, Zhewei Wei, Hsin-Hao Su

原文摘要

Message-passing graph neural networks (MPGNNs) are commonly compared with the Weisfeiler-Lehman (WL) test, but this comparison does not quantify the resource parameters a network needs to implement a step of the WL test with bounded-size messages and finite numerical precision. We study the cost of simulating a single step of the WL test on unattributed graphs. We distinguish input-independent, or oblivious, simulation from instance-dependent simulation. In the former, the parameters, or their distributions in randomized models, are fixed before the input instance is known. Our results show that the local update rule of the WL test hides a global relabeling problem. In the oblivious setting, deterministic and zero-error randomized MPGNNs cannot solve this problem in the worst case using only shallow networks with small messages. We complement this lower bound with a nearly matching construction in a stronger rooted, port-aware model. By contrast, when the color set is large, bounded-error randomness can greatly reduce the cost, and a one-layer MPGNN with messages of logarithmic size and a logarithmic number of random bits suffices. We show that this logarithmic number of random bits is essentially necessary for shallow, small-message simulations. When the color set is small, we still obtain a rooted, port-aware simulation, but this construction requires more layers or larger messages. We also prove that this extra cost is partly unavoidable, as small color sets force a nontrivial trade-off between the number of layers and the message size. Finally, instance-dependent simulation can be much shallower, but the required instance-specific parameters are not necessarily easy to find. Together, these results reveal quantitative structure hidden behind the statement that MPGNNs match the WL test.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09218

BOXBOX: Recursive Harness Self-Improvement for Long-Running Performance Optimization

Yuhao Tan, Hanyun Jiang, Fangkai Yang, Yuan Yao, Taolue Chen, Bo Qiao, Yu Kang, Lu Wang 等 11 位作者

原文摘要

Automated harness optimization improves agents by learning from trajectories and task feedback. Existing offline methods use reference answers to optimize harnesses for reuse on held-out tasks. However, many real-world tasks are open-ended, while long runs expose new difficulties that offline-optimized harnesses may not address. To study harness optimization in these settings, we introduce PerfRace, a benchmark for long-running performance optimization of software projects, and investigate whether agents can use task-specific experience to recursively improve their harnesses during an ongoing task. To this end, we introduce BOXBOX, which enables agent-triggered harness revision and kernel-managed hot reloading while preserving execution context. We evaluate BOXBOX on PerfRace under matched test-time budgets, comparing it with Frozen Harness, offline Harness Evolution, and Harness Scaling. BOXBOX achieves higher speedup with more efficient use of task time and model cost, reaching a Speedup@4h of 1.70x, compared with 1.54x for the strongest baseline.

cs.SE · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.06874

Induced Cycles of Many Lengths

Maria Chudnovsky, Ilya Maier

原文摘要

Let $G$ be a graph and let $\mathrm{cl}(G)$ be the number of distinct induced cycle lengths in $G$. We show that for $c,t\in \mathbb N$, every graph $G$ that does not contain an induced subgraph isomorphic to $K_{t+1}$ or $K_{t,t}$ and satisfies $\mathrm{cl}(G) \le c$ has bounded treewidth. As a consequence, we obtain a polynomial-time algorithm for deciding whether a graph $G$ contains induced cycles of at least three distinct lengths.

math.CO · cs.DM · cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41336

HandMimicX: Learning Human-Like Dexterous Manipulation via an Object-Centric Policy from Egocentric Videos

Zihan Gao, Yufei Liu, Yuhong Zhang, Zhen Yu, Xinhao Huang, Zhinan You, Kai W. Ho, Zipeng Ye 等 10 位作者

原文摘要

Learning dexterous manipulation from human videos remains an open challenge, particularly in the context of transferring a single demonstration into a spatially generalizable robotic skill and maintaining physically plausible human-like interactions. Unlike prior works that emphasize hand-motion imitation, we introduce an object-centric framework for acquiring dexterous skills from egocentric human RGB-D videos. Within our framework, we formulate the reinforcement learning (RL) objective in terms of object motion to decouple the underlying skill from the specific hand trajectory and impose constraints on hand-object interaction states to promote more anatomically natural hand configurations. Extensive experiments in simulation and on real robots demonstrate that our method enables efficient learning from human videos and generalizable skill transfer across novel spatial configurations.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09497

Hardness of Unique Shortest Vectors and Bounded Distance Decoding

Sergei Kudria, Jason Luo

原文摘要

We prove randomized NP-hardness of the unique shortest vector problem (uSVP) at every constant uniqueness factor greater than one, and of bounded distance decoding (BDD) at every fixed positive fraction less than one half of the minimum distance, in both their threshold decision and search versions. Both results hold in every fixed $\ell_p$ norm with $1\le p<\infty$. Previous Euclidean NP-hardness results for the search versions reached uniqueness factors tending to one and decoding radii greater than the minimum distance divided by $\sqrt2$. For linear codes over every fixed finite field and each fixed ${0<ρ\le1/2}$, the decision version of BDD with radius strictly less than $ρ$ times the minimum distance is equivalent under deterministic polynomial-time reductions to satisfiability for circuits promised to have at most one satisfying input. It is therefore NP-hard under randomized polynomial-time reductions. Earlier hardness for decoding general codes applied beyond half the minimum distance. For every fixed $p>2$ and for $p=\infty$, there is a constant $ε>0$ such that, under randomized polynomial-time reductions, uSVP in the $\ell_p$ norm is NP-hard at uniqueness factor $n^ε$, and BDD in the $\ell_p$ norm is NP-hard at radius $λ_1/n^ε$, where $n$ is the lattice rank and $λ_1$ is its minimum distance.

cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08904

MotionCore: A Latent Neural Simulator for Video Generation with Rigid-Body Dynamics

Zheng Wang, Zhengwei Gong, Yuanzhi Liang, Haibin Huang, Chi Zhang

原文摘要

Video generation models can produce highly realistic videos while generating physically inconsistent object motion. This limitation suggests that visual modeling alone may not provide a transferable representation of physical dynamics. We introduce MotionCore, a framework for learning physical dynamics via a learnable partial differential equation solver and focuses on rigid-body motion. We introduce a latent neural simulator learning to predict the evolution of object-level physical states from observed motion. Its predictions are then converted into spatially aligned latent controls for a controllable video diffusion model. The generator serves as a visual interface: it provides appearance and scene detail, while the learned dynamics model specifies the physical trajectory. To preserve this trajectory through the interface, MotionCore combines a diffusion-matching objective with a low-capacity decodability constraint on object identity and position. We evaluate MotionCore on public physical-consistency benchmarks and on paired data with known trajectories. Results indicate that MotionCore improves physical consistency and trajectory obedience in the evaluated rigid-body settings, while maintaining comparable visual quality. A controlled ablation protocol compares learned dynamics with an analytic prior and studies the choice of latent evolution processor.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07594

Scaling Inherently Interpretable Language Models

GuideLabs, Andreas Madsen, Aya Abdelsalam Ismail, Giang Nguyen, Isaac Plant, Muawiz Chaudhary, Nathaniel Monson, Saqib Azim 等 10 位作者

原文摘要

Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods whose reliability is difficult to establish. In this work, we challenge this premise. Rather than reverse-engineering a model, we make interpretability a constraint of the training pipeline, optimized alongside the language modeling objective. Across three orders of magnitude of compute, on both autoregressive and diffusion language models, interpretability scales with capability rather than against it. Surprisingly, model representations become more disentangled and aligned with human-understandable concepts with scale. We instantiate the training-time recipe with Steerling-8B, a diffusion language model with a causal attention mask. For any group of generated tokens, Steerling-8B attributes the output to relevant input tokens, human-understandable concepts, and training data. This enables closed-loop intervention: diagnose an output through its concept or feature attribution, retrieve similar training data, and correct the behavior through concept steering without retraining. Steerling-8B remains competitive with open peer models trained on substantially 2-16x more compute, suggesting a different scaling paradigm: interpretability can be designed into training, and it improves with scale.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.28444

EvaOmni: An Evidence-Grounded Benchmark for Audio-Visual Reasoning

Xiaoxing Hu, Wenxi Yao, Chaoyuan Shi, Ziqi Ye, Xiaorong Guo, Peixuan Li, Zhuoran Sun, Ziyang Gong 等 20 位作者

原文摘要

Omni LLMs can naturally locate key audio and visual evidence and integrate information across modalities for audiovisual reasoning. Existing audio-visual reasoning benchmarks that mainly rely on multiple-choice questions usually score models solely based on answer correctness, making it difficult to assess whether a model truly forms a reasoning chain from evidence to the correct answer. In addition, textual or unimodal shortcuts may allow models to answer correctly without analyzing the necessary audio-visual evidence, further weakening the reliability of evaluation benchmarks. To address these issues, we introduce EvaOmni, an evidence-grounded benchmark containing 392 videos and 1,027 purely human-annotated question-answer pairs, covering 15 capabilities and three levels: retrieval and localization, basic understanding, and complex reasoning. We design a series of rigorous annotation procedures in which experts annotate audio and visual evidence and construct video-based distractors; humans review whether questions meet strict inspection criteria, and finally models filter textual and unimodal shortcuts. Beyond high-quality question-answer pairs, we require models to simultaneously return the answer and supporting evidence intervals in order to evaluate the complete reasoning chain. We propose the Coverage Grounded Score (C.G.) to measure how much annotated evidence a model identifies, and the Bottleneck Grounded Score (B.G.) to penalize poor temporal localization in either modality; both are incorporated into answer correctness. Across 7 open-weight and 17 proprietary Omni LLMs, Gemini3.7-Flash achieved the highest overall accuracy (79.4%), C.G. (70.8%), and B.G. (35.1%). At Level 3, it reached 79.1% accuracy, but its B.G. was only 12.3%. These results show that even the most advanced models still have room for development in grounded audio-visual reasoning.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.18654

Kwai Summary Attention V2 Technical Report

OneRec Team, Chenglong Chu, Guohong Mu, Guowang Zhang, Hao Peng, Hui Wang, Jian Liang, Jiangxia Cao 等 33 位作者

原文摘要

Summary-token architectures such as Kwai Summary Attention (KSA) compress distant context into learnable Summary token per chunk, and hybrid variants interleave these layers with full attention to retain exact access to the original text. In such hybrid KSA-Full architecture, the interleaving inserted summary tokens are served as a streaming flow role across different layers. Although effective, however, it also introduce some bottlenecks: (1) the different layers summary compression chunk must be same, therefore hard to reach a flexible KV Cache compression, e.g., different layer set different summary compression chunk window. (2) in the full-attention layers, the summary token introduced additional computation cost, but such layer ignore them and still pay quadratic prefilling, therefore unfriendly to the computation side. Motivated by them, we developed the KSA V2, which not only achieves a more flexible KV cache memory compression but also reduces the computation cost in Full attention layer. Specifically, we found that the summary tokens can serve two roles: the KV memory and sparse indexer.

cs.LG · cs.AI · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.19911

CitySTAR: Structured and Topology-Aware Reasoning for Open-Vocabulary Urban 3D Grounding

Shuai Zhang, Hongye Hou, Qinghe Liu, Zhuoxiao Li, Dongli Wu, Jing Ou, Yuan Liu, Wufan Zhao

原文摘要

3D grounding aims to localize target entities in complex scenes from natural language and plays a fundamental role in embodied perception and spatial reasoning. However, existing approaches mostly rely on feature similarity or direct matching, making it difficult to connect natural-language intent with the implicit semantic and geometric structures hidden in billion-scale urban point clouds. We reformulate city-scale 3D grounding as structured constraint reasoning, where description semantics are organized into computable cross-modal constraints over open-vocabulary 3D entities, attributes, and spatial relations. We present CitySTAR, a training-free framework for reasoning-driven urban 3D grounding. CitySTAR lifts raw billion-scale urban point clouds into a query-ready scene graph of open-vocabulary 3D instances, with CodeLLM-driven tools supplying multimodal evidence for node attributes and 3D spatial relations. It then models target-context topology with paired hypergraphs and performs bidirectional topology verification for structural disambiguation. Finally, a Reflective Cross-modal Grounding module integrates topology consistency and candidate-centered 2D visual evidence to make decisions over a metric-aware 3D context graph. To further support this setting, we introduce CitySTAR-3D, an enhanced benchmark that improves semantic coverage, instance completeness, bounding-box fidelity, and spatial-relation complexity in city-scale 3D grounding. Extensive experiments show that CitySTAR consistently improves open-world urban 3D grounding while maintaining strong interpretability and generalization.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.28093

Meteosat Third Generation imagery improves CNN-based SSI retrieval

Gordei Pribõtkin, Piia Post, Velle Toll

原文摘要

Accurate Surface Solar Irradiance (SSI) estimation is increasingly important for photovoltaic energy monitoring and forecasting. The recently introduced Meteosat Third Generation (MTG) satellite constellation provides imaging data with higher spatial resolution compared to the Meteosat Second Generation (MSG) satellite constellation, but its benefits for machine-learning-based SSI retrieval are not well established. We compare multi-resolution convolutional neural network models for 10-minute SSI retrieval over Northern Europe (Estonia) using either MSG/SEVIRI or MTG/FCI satellite imagery. We evaluate model performance against ground-based pyranometer measurements from eight Estonian meteorological stations using site-based cross-validation with a temporally independent test year and multiple training seeds. Model performance is also compared with the SARAH-3 physics-based satellite SSI product. The FCI-only model significantly outperformed the SEVIRI-only model under overcast and cloudy conditions, reducing RMSE by 5.1 W/m^2 and 6.5 W/m^2, respectively. However, under partly cloudy or clear skies, there was no statistically significant difference in RMSE between the FCI-only and the SEVIRI-only models. In all-sky conditions, the RMSE of the FCI-only model was 5.1 W/m^2 lower on average, but this difference was not statistically significant after Bonferroni correction. Compared with physics-based SARAH-3, the FCI-only model yielded skill scores of 28% under overcast conditions, 19% under cloudy conditions, and 17% overall. Furthermore, both models underperformed SARAH-3 in clear-sky conditions. These results show that higher-resolution MTG/FCI imagery improves CNN-based SSI retrieval when clouds dominate irradiance variability, but also indicate that higher resolution alone is insufficient to address clear-sky limitations in machine-learning-based SSI retrieval.

physics.ao-ph · cs.LG · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.24005

Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing

Haotian Zhang, Shucun Wang, Jinze Wu, Liang Ding, Shuochen Liu, Zhenya Huang, Jing Sha, Shijin Wang 等 9 位作者

原文摘要

Knowledge Tracing (KT) aims to assess students' dynamic knowledge states from their learning histories. While most existing KT methods focus on single-domain learning with notable success, real-world learning scenarios often involve multiple domains simultaneously, introducing two critical factors: 1) Cognitive load, arising from managing learning across domains in both temporal and knowledge dimensions. 2) Knowledge transfer, where knowledge states in one domain influence related states both within and across domains. In this paper, we focus on exploring these factors to improve students' knowledge state assessment in multi-domain learning scenarios and propose a novel method incorporating cognitive Load and knowledge Transfer for Multi-domain Knowledge Tracing (LT-MKT). Specifically, to bridge isolated domains, LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph, leveraging the advanced representational capabilities of large language models (LLMs). Then, cross-domain features in both the temporal and knowledge dimensions are explicitly modeled to capture the effects of cognitive load. Additionally, a knowledge transfer module is designed to model the propagation of knowledge states within and across domains. By jointly modeling these factors, LT-MKT enables more accurate prediction of students' future performance. Finally, extensive experiments on real-world datasets demonstrate that our method achieves state-of-the-art performance.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.03894

Vision Transformers for Zero-Shot Clustering of Animal Images: A Comparative Benchmarking Study

Hugo Markoff, Stefan Hein Bengtson, Michael Ørsted

原文摘要

Manual labelling of animal images remains a significant bottleneck in ecological research, limiting the scale and efficiency of biodiversity monitoring efforts. This study investigates whether state-of-the-art Vision Transformer (ViT) foundation models can reduce thousands of unlabelled animal images directly to species-level clusters. We present a comprehensive benchmarking framework evaluating five ViT models combined with five dimensionality reduction techniques and four clustering algorithms, two supervised and two unsupervised, across 60 species (30 mammals and 30 birds), with each test using a random subset of 200 validated images per species. We investigate when clustering succeeds at species-level, where it fails, and whether clustering within the species-level reveals ecologically meaningful patterns such as sex, age, or phenotypic variation. Our primary, fully unsupervised finding is that DINOv3 embeddings with t-SNE and HDBSCAN reach a species-level V-measure of 0.943 with no prior knowledge of the number of species, rejecting only 1.14% of images as outliers requiring expert review. Assigning one label per cluster reduces manual labelling actions by 98.3%, with 94.6% of clustered images labelled correctly, close to the accuracy of manual annotation. When the true number of species is supplied, supervised hierarchical clustering raises this to 0.958, which we report as an upper bound as the species count is typically unknown in real deployments. We further demonstrate robustness to realistic long-tailed distributions of species and show that intentional over-clustering can reveal intra-specific variation including age classes, sexual dimorphism, and pelage differences. We introduce an open-source benchmarking toolkit and provide recommendations for ecologists to select appropriate methods for sorting their specific taxonomic groups and data.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.08810

What Simple Readouts Miss in Continuous Language Diffusion

Zhicheng Du, Lan Ma

原文摘要

How much of a learned decoder can a simple readout preserve? On generated ELF and AURORA states, token-wise affine maps recover $97.17\%$ and $95.74\%$ of native lexical decisions. Contextual computation has already produced these states; only the terminal readout is simplified. The remaining disagreements matter beyond their count: equal-count rare-first and low-native-margin-first repairs have opposing pooled evaluator effects in both interfaces. On conditional summarization, rare-first improves reference similarity, while source consistency remains unresolved. Score accounting locates part of the benefit on unchanged text despite increased unigram cost, without identifying rarity as a cause. Repeatable repair effects do not ensure prediction transfer: a frozen predictor improves on two ELF checkpoints but fails on a distilled student. A separate task-fitted selector improves BERTScore over random selection by $0.261$ points on 256 fresh articles at a $50\%$ native-call budget; all-native decoding remains higher-scoring and acceleration is unestablished. Compressed and directional interface tests further distinguish fitting-state coverage, native decisions, and soft inputs. Together, the results motivate testing a simplification at three levels: preserved decisions, output consequences, and computational savings. Native decisions are fidelity targets, not semantic truth.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.27287

Alternating Abduction and Planning over Temporal Dynamic Knowledge Graphs

Amir Hossein Sharafi, Alireza Shahbazi

原文摘要

Knowledge graphs change over time, but the effects of actions on them are only partly stated: the ontology entails further consequences, facts that an action needs are often unknown, and an action may make the graph inconsistent. We study these problems for Temporal Dynamic Knowledge Graphs (TDKGs), that is, SROIQ knowledge bases whose actions are rules with delete/insert templates and whose events lie on an intrinsic timeline. We present three tools that use a SROIQ reasoner only through a few operations: temporal projection, which plays the events along the timeline and is the exact test of plans; DDA (Direct-Derivation Abduction), which explains an observation by assuming its negation and negating what the reasoner newly derives; and APT (Abduction and Planning over TDKGs), which plans backwards from a goal, assumes the facts that the plan needs but nobody stated, and lets DDA explain the assumptions, each explanation becoming a new goal for a sub-plan, up to a depth bound. The same machinery synthesises action sequences that end in a chosen contradiction. We prove that projection is decidable and that the indirect effects of an action are exactly the entailments of the updated state; that DDA is sound, terminates and finds every single-assertion explanation; and that APT is sound and terminates, while bounded planning is decidable but 2NExpTime-hard. We compare the tools analytically with hitting-set abduction and classical planning, compare DDA with the abduction solver CATS on shared problems, and run them on six benchmarks. Five reasoners (HermiT, Konclude, Pellet, Openllet, JFact) return identical results. Their incremental modes answered some checks wrongly after removals and gave no speed-up; parallel calls help only when a single call is expensive.

cs.AI · cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.01835

ReFrame: Silence-Aware Compute Reclamation for Full-Duplex Speech Models

Zheng Zhou, Guanyi Chen, Jing Wei, Haoyuan Liang, Jian Zhang, Haohuan Fu

原文摘要

Full-duplex speech models execute dense model inference for every audio frame throughout a live interaction. This uniform framewise schedule creates sustained GPU load across dialogue states whose computation has strong, reusable temporal structure. ReFrame formulates full-duplex inference as a silence-aware computation problem. Mutual Silence Speculation (MSS) operates when both participants are silent: it evaluates multi-frame Temporal continuations, verifies text and hidden-state stability, and applies batched Mimi audio-token correction. Listening Frame Folding (LFF) operates while the user speaks and the agent listens: it processes already-arrived user frames through parallel Temporal execution and frame-batched audio-token generation while accepting only the verified prefix. Frame-Budget Reinvestment (FBR) assigns the released frame intervals to resumable external retrieval and language-model workers, then injects timely references through Moshi's conditioning interface. We instantiate ReFrame on five full-duplex models spanning Moshi-family, phase-structured, and flattened autoregressive generation. Across the complete 1,225-conversation FDBench, ReFrame reduces measured GPU execution time by 18.80-31.01% and retains 96.92-100% non-PAD text agreement on five different models. On HaluEval tests, FBR increases the number of correct generated spoken answers from 86 to 205 under the same auxiliary-service budget. ReFrame turns recurring dialogue structure into lower inference cost and higher end-to-end service capability.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.01044

CROWD: A global dataset of continuous urban dashcam driving

Md Shadab Alam, Olena Bazilinska, Pavlo Bazilinskyy

原文摘要

We introduce CROWD (City Road Observations With Dashcams), a manually curated dataset of ordinary, variable duration, temporally contiguous, unedited, front facing urban dashcam segments screened and segmented from publicly available YouTube videos. CROWD is designed to support cross-domain robustness and interaction analysis by prioritising routine driving and explicitly excluding crashes, crash aftermath, and other edited or incident-focused content. The release contains 107,407 segment records spanning 36,204.26 hours (82,484 unique uploads), covering 10,538 named inhabited places in 238 countries and territories across all six inhabited continents (Africa, Asia, Europe, North America, South America and Oceania), with segment level manual labels for time of day (day or night) and vehicle type. To lower the barrier for reproducible baseline analysis, we provide per-segment CSV files of automatic detections for all 80 MS-COCO classes produced with YOLOv11x, together with segment-local multi-object tracks generated with BoT-SORT; e.g. person, bicycle, motorcycle, car, bus, truck, traffic light, stop sign, etc. CROWD is distributed as video identifiers with segment boundaries and derived detection and tracking pseudo-labels, enabling reproducible research without redistributing the underlying videos. To support leakage-free evaluation, we also provide canonical splits grouped by source channel or by locality, and weights that balance the contribution of localities and countries.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.29322

VACP: Visual Analytics Context Protocol

Tobias Stähle, Péter Ferenc Gyarmati, Thilo Spinner, Rita Sevastjanova, Dominik Moritz, Mennatallah El-Assady

原文摘要

The rise of AI agents introduces a fundamental shift in Visual Analytics (VA), in which agents act as a new user group. Current agentic approaches - based on computer vision and raw DOM access - fail to perform VA tasks accurately and efficiently. This paper introduces the Visual Analytics Context Protocol (VACP), a framework designed to make VA applications "agent-ready" that extends generic protocols by explicitly exposing application state, available interactions, and mechanisms for direct execution. To support our context protocol, we contribute a formal specification of AI agent requirements and knowledge representations in VA interfaces. We instantiate VACP as a library compatible with major visualization grammars and web frameworks, enabling augmentation of existing systems and the development of new ones. Our evaluation across representative VA tasks demonstrates that VACP-enabled agents achieve higher success rates in interface interpretation and execution compared to current agentic approaches, while reducing token consumption and latency. VACP closes the gap between human-centric VA interfaces and machine perceivability, ensuring agents can reliably act as collaborative users in VA systems.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02076

Scene-Adaptive Readout and Decoding in Diffusion Driving VLAs

Seungjun Yu, Jiho Choi, Inha Kang, Hojun Choi, Seungwook Kim, Hyunjung Shim

原文摘要

Diffusion driving vision-language-action models repeatedly execute the backbone at every denoising stage, incurring substantial inference cost, while it remains unclear whether all scenes require the same decoding computation. To distinguish when a trajectory becomes readable from how much further refinement it requires, we analyze trajectory readout across network depth and decoding progress. We identify a depth-decoding dissociation in which late-layer representations already support useful trajectory readout after the first forward sweep, whereas early-layer representations benefit more from continued decoding. We further find that both the preferred readout layer and decoding depth vary across scenes. Based on these observations, we propose a scene-adaptive readout and decoding framework. A scene-conditioned readout MoE adaptively weights and combines trajectory predictions from layer-specific late-layer representations after a single backbone sweep, while an adaptive decoding policy applies additional native refinement only when required by the scene. We evaluate our framework on two diffusion driving VLAs across the Waymo E2E and nuScenes benchmarks. Our method improves validation RFS by up to 0.56 while also improving test performance, and achieves an end-to-end speedup of up to $6.0\times$ on Waymo E2E, while consistently reducing trajectory error on nuScenes. These results demonstrate that scene-level readout and decoding allocation improves planning quality while avoiding unnecessary decoding. Our code is available at https://github.com/sjyu001/SARD

cs.CV · cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.06467

No Triangulation Without Representation: Generalization in Topological Deep Learning

Johannes S. Schmidt, Martin Carrasco, Ernst Röell, Guy Wolf, Nello Blaser, Bastian Rieck

原文摘要

Despite an ever-increasing interest in topological deep learning models that target higher-order datasets, there is no consensus on how to evaluate such models. This is exacerbated by the fact that topological objects permit operations, such as structural refinements, that are not appropriate for graph data. In this work, we extend MANTRA, a benchmark dataset containing manifold triangulations, to a larger class of manifolds with more diverse homeomorphism types. We show that, contrary to prior claims, graph neural networks can saturate the benchmark, while higher-order message passing methods lag behind. However, we find that this is contingent on the right representation and feature assignment, emphasizing their importance in baseline models. Hence, we present a novel evaluation protocol based on representational diversity and triangulation refinement. Even when including refinement-like changes in the training data, existing models fail to generalize beyond the combinatorial structure of the data. This points towards a research gap in developing models that understand topological structure independent of scale. Our work thus provides the necessary scaffolding to evaluate future models and enable the development of topology-aware inductive biases.

cs.LG · math.AT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.16897

Contrastive-Difference CKA Reveals Concept-Specific Structural Alignment Across Language Model Architectures

Xueping Gao

原文摘要

Do language models encode the same prompted concepts in structurally compatible ways, beyond generic similarity induced by shared questions? We study contrastive-difference centered kernel alignment ($\mathrm{CKA}_Δ$), which compares per-question activation differences between opposing concept instructions. A matched same-concept versus cross-concept comparison separates the question of concept-conditioned structure from the magnitude of representational similarity. Across four checkpoints and eight personality-related traits, a fixed-final-layer analysis gives mean same- and cross-trait $\mathrm{CKA}_Δ$ of 0.7259 and 0.6828. Trait-label randomization detects correspondence conditional on this model and prompt set ($p=1/10001$, one-sided), but a model-and-trait-weighted 95% interval for the difference is $[-0.0330,0.1017]$, limiting broader generalization. Strict question-group holdouts retain high same-trait linear recoverability. More stringently, maps and preprocessing fitted on seven traits transfer a source polarity classifier for the eighth trait to handwritten test questions: mean affine and orthogonal accuracies are 88.10% and 88.57%, versus 47.70% for shuffled pairing. Performance varies substantially across traits and model directions. Synthetic controls show why moderate CKA and high affine accuracy alone do not establish functional equivalence. The results support a scoped diagnostic of concept-specific structure and held-out linear recoverability; they do not establish universal invariance or a robust advantage over raw CKA across architectures.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27726

Anytime-Valid Runtime Monitoring of Learned Barrier Certificates

Zhipeng Cao, Taoran Wu, Weiru Cui, Luke Ong, Bai Xue

原文摘要

Learned barrier certificates provide a principled mechanism for establishing safety of stochastic systems, but their validity may deteriorate during deployment when the underlying dynamics differ from those used for training. We propose an anytime-valid runtime monitor that audits a learned barrier certificate using observed closed-loop trajectories. The monitor is intended to detect loss of statistical support for the deployed barrier condition; it does not by itself guarantee detection before unsafe entry or safety of a subsequent intervention. Specifically, the conditional barrier-drift condition is formulated as an episode-wise sequential hypothesis test, and observed barrier increments are transformed into a nonnegative test supermartingale, yielding finite-sample false-alarm control under arbitrary stopping times. Inspired by finite-horizon safety verification, we further introduce a deterministic temporal shaping of the monitoring boundary, making the monitor more conservative early in a prescribed deployment horizon while preserving anytime-validity. The monitor is designed to detect statistical evidence that the safety certificate is no longer consistent with the observed dynamics, rather than to predict unsafe entry. To assess the monitor empirically, we evaluate empirical alarm behavior under valid and invalid certificates. Code and data are available at https://github.com/FormalCert-AI/runtime-barrier-monitor.

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

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.11370

Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers

Wenhan Ma, Hailin Zhang, Liang Zhao, Yifan Song, Yudong Wang, Zhifang Sui, Fuli Luo

原文摘要

Reinforcement learning (RL) has emerged as a crucial approach for enhancing the capabilities of large language models. However, in Mixture-of-Experts (MoE) models, the routing mechanism often introduces instability, even leading to catastrophic RL training collapse. We analyze the training-inference consistency of MoE models and identify a notable discrepancy in routing behaviors between the two phases. Moreover, even under identical conditions, the routing framework can yield divergent expert selections across repeated forward passes. To address this foundational inconsistency, we propose Rollout Routing Replay (R3), a method that records routing distributions from the inference engine and replays them during training. R3 significantly reduces training-inference policy KL divergence and mitigates extreme discrepancies without compromising training speed. Extensive experiments on various settings confirm that R3 succeeds in stabilizing RL training, preventing collapse and outperforming methods such as GSPO and TIS. We believe this work can offer a new solution for stabilizing RL in MoE models.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗