OpenRaiser/Intern-Atlas huggingface dataset:
TrackNetV5: Residual-Driven Spatio-Temporal Refinement and Motion Direction Decoupling for Fast Object Tracking
Venue: arXiv cs.CV 2026
Year: 2026

The TrackNet series has established a strong baseline for fast-moving small object tracking in sports. However, existing iterations face significant limitations: V1-V3 struggle with occlusions due to a reliance on purely visual cues, while TrackNetV4, despite introducing motion inputs, suffers from directional ambiguity as its absolute difference method discards motion polarity. To overcome these bottlenecks, we propose TrackNetV5, a robust architecture integrating two novel mechanisms. First, to recover lost directional priors, we introduce the Motion Direction Decoupling (MDD) module. Unlike V4, MDD decomposes temporal dynamics into signed polarity fields, explicitly encoding both movement occurrence and trajectory direction. Second, we propose the Residual-Driven Spatio-Temporal Refinement (R-STR) head. Operating on a coarse-to-fine paradigm, this Transformer-based module leverages factorized spatio-temporal contexts to estimate a corrective residual, effectively recovering occluded targets. Extensive experiments on the TrackNetV2 dataset demonstrate that TrackNetV5 achieves a new state-of-the-art F1-score of 0.9859 and an accuracy of 0.9733, significantly outperforming previous versions. Notably, this performance leap is achieved with a marginal 3.7% increase in FLOPs compared to V4, maintaining real-time inference capabilities while delivering superior tracking precision.
A Comparative Study on How Data Normalization Affects Zero-Shot Generalization in Time Series Foundation Models
Venue: arXiv cs.LG 2026
Year: 2026

We investigate input normalization methods for Time-Series Foundation Models (TSFMs). While normalization is well-studied in dataset-specific time-series models, it remains overlooked in TSFMs where generalization is critical. Time-series data, unlike text or images, exhibits significant scale variation across domains and channels, coupled with non-stationarity, can undermine TSFM performance regardless of architectural complexity. Through systematic evaluation across four architecturally diverse TSFMs, we empirically establish REVIN as the most efficient approach, reducing zero-shot MASE by 89\% relative to an un-normalized baseline and by 44\% versus other normalization methods, while matching the best in-domain accuracy (0.84 MASE) without any dataset-level preprocessing -- yielding the highest accuracy-efficiency trade-off. Yet its effect utilization depends on architectural design choices and optimization objective, particularly with respect to training loss scale sensitivity and model type (probabilistic, point-forecast, or LLM-based models).
Unsupervised Multimodal Graph-based Model for Geo-social Analysis
Venue: arXiv cs.SI 2026
Year: 2026

The systematic analysis of user-generated social media content, especially when enriched with geospatial context, plays a vital role in domains such as disaster management and public opinion monitoring. Although multimodal approaches have made significant progress, most existing models remain fragmented, processing each modality separately rather than integrating them into a unified end-to-end model. To address this, we propose an unsupervised, multimodal graph-based methodology that jointly embeds semantic and geographic information into a shared representation space. The proposed methodology comprises two architectural paradigms: a mono graph (MonoGrah) model that jointly encodes both modalities, and a multi graph (MultiGraph) model that separately models semantic and geographic relationships and subsequently integrates them through multi-head attention mechanisms. A composite loss, combining contrastive, coherence, and alignment objectives, guides the learning process to produce semantically coherent and spatially compact clusters. Experiments on four real-world disaster datasets demonstrate that our models consistently outperform existing baselines in topic quality, spatial coherence, and interpretability. Inherently domain-independent, the framework can be readily extended to diverse forms of multimodal data and a wide range of downstream analysis tasks.
Beyond Additivity: Sparse Isotonic Shapley Regression toward Nonlinear Explainability
Venue: arXiv cs.LG 2026
Year: 2026

Shapley values, a gold standard for feature attribution in Explainable AI, face two key challenges. First, the canonical Shapley framework assumes that the worth function is additive, yet real-world payoff constructions--driven by non-Gaussian distributions, heavy tails, feature dependence, or domain-specific loss scales--often violate this assumption, leading to distorted attributions. Second, achieving sparse explanations in high-dimensional settings by computing dense Shapley values and then applying ad hoc thresholding is costly and risks inconsistency. We introduce Sparse Isotonic Shapley Regression (SISR), a unified nonlinear explanation framework. SISR simultaneously learns a monotonic transformation to restore additivity--obviating the need for a closed-form specification--and enforces an L0 sparsity constraint on the Shapley vector, enhancing computational efficiency in large feature spaces. Its optimization algorithm leverages Pool-Adjacent-Violators for efficient isotonic regression and normalized hard-thresholding for support selection, ensuring ease in implementation and global convergence guarantees. Analysis shows that SISR recovers the true transformation in a wide range of scenarios and achieves strong support recovery even in high noise. Moreover, we are the first to demonstrate that irrelevant features and inter-feature dependencies can induce a true payoff transformation that deviates substantially from linearity. Extensive experiments demonstrate that SISR stabilizes attributions across payoff schemes and correctly filters irrelevant features; in contrast, standard Shapley values suffer severe rank and sign distortions. By unifying nonlinear transformation estimation with sparsity pursuit, SISR advances the frontier of nonlinear explainability, providing a theoretically grounded and practical attribution framework.
Adaptive Aggregation with Two Gains in QFL
Venue: arXiv cs.LG 2026
Year: 2026

Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic teleportation fidelity, device instability, and geometric mismatch between local and global models. Classical aggregation rules assume euclidean topology and uniform communication reliability, limiting their suitability for emerging quantum federated systems. This paper introduces A2G (Adaptive Aggregation with Two Gains), a dual gain framework that jointly regulates geometric blending through a geometry gain and modulates client importance using a QoS gain derived from teleportation fidelity, latency, and instability.
Movable Signals with Dual-Polarized Fixed Intelligent Surfaces: Beyond Diagonal Reflection Matrices
Venue: arXiv cs.IT 2026
Year: 2026

This paper investigates wireless systems aided by dual-polarized intelligent surfaces. We compare reconfigurable intelligent surface (RIS), which adjust their reflection matrices, with movable signals operating with fixed intelligent surface (FIS), which adjust the signal frequency while the surface properties remain fixed. For both RIS and FIS, we consider surfaces with a diagonal reflection matrix, named diagonal RIS/FIS, and surfaces with a reflection matrix not limited to being diagonal, named beyond-diagonal RIS/FIS. Movable signals with FIS always outperform RIS, achieving at least a fourfold gain. When transmitter and receiver polarizations differ, beyond-diagonal FIS further enhances performance.
MDE-AgriVLN: Agricultural Vision-and-Language Navigation with Monocular Depth Estimation
Venue: arXiv cs.RO 2026
Year: 2026

Agricultural robots are serving as powerful assistants across a wide range of agricultural tasks, nevertheless, still heavily relying on manual operations or railway systems for movement. The AgriVLN method and the A2A benchmark pioneeringly extended Vision-and-Language Navigation (VLN) to the agricultural domain, enabling a robot to navigate to a target position following a natural language instruction. Unlike human binocular vision, most agricultural robots are only given a single camera for monocular vision, which results in limited spatial perception. To bridge this gap, we present the method of Agricultural Vision-and-Language Navigation with Monocular Depth Estimation (MDE-AgriVLN), in which we propose the MDE module generating depth features from RGB images, to assist the decision-maker on multimodal reasoning. When evaluated on the A2A benchmark, our MDE-AgriVLN method successfully increases Success Rate from 0.23 to 0.32 and decreases Navigation Error from 4.43m to 4.08m, demonstrating the state-of-the-art performance in the agricultural VLN domain. Code: https://github.com/AlexTraveling/MDE-AgriVLN.
Learning Steerable Clarification Policies with Collaborative Self-play
Venue: arXiv cs.LG 2026
Year: 2026

To handle underspecified or ambiguous queries, AI assistants need a policy for managing their uncertainty to determine (a) when to guess the user intent and answer directly, (b) when to enumerate and answer multiple possible intents, and (c) when to ask a clarifying question. However, such policies are contextually dependent on factors such as user preferences or modality. For example, enumerating multiple possible user intentions is cumbersome on small screens or in a voice setting. In this work, we propose to train steerable policies for managing this uncertainty using self-play. Given two agents, one simulating a user and the other an AI assistant, we generate conversations where the user issues a potentially ambiguous query, and the assistant needs to determine how to respond. Importantly, the model takes as input the numerical cost of each clarification question, and each generated word, and is asked to take the action that will maximize its final reward, which is the cost-penalized accuracy. We use Reinforced Self-Training (ReST) to train our model to achieve high reward and show this leads to a steerable policy that changes its behavior predictably conditioned on the provided costs, leading to higher reward and accuracy. Moreover, our procedure also generalizes to numerical cost values that were unobserved at training time.
BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training
Venue: arXiv cs.LG 2026
Year: 2026

Binary Neural Networks (BNNs), which constrain both weights and activations to binary values, offer substantial reductions in computational complexity, memory footprint, and energy consumption. These advantages make them particularly well suited for deployment on resource-constrained devices. However, training BNNs via gradient-based optimization remains challenging due to the discrete nature of their variables. The dominant approach, quantization-aware training, circumvents this issue by employing surrogate gradients. Yet, this method requires maintaining latent full-precision parameters and performing the backward pass with floating-point arithmetic, thereby forfeiting the efficiency of binary operations during training. While alternative approaches based on local learning rules exist, they are unsuitable for global credit assignment and for back-propagating errors in multi-layer architectures. This paper introduces Binary Error Propagation (BEP), the first learning algorithm to establish a principled, discrete analog of the backpropagation chain rule. This mechanism enables error signals, represented as binary vectors, to be propagated backward through multiple layers of a neural network. BEP operates entirely on binary variables, with all forward and backward computations performed using only bitwise operations. Crucially, this makes BEP the first solution to enable end-to-end binary training for recurrent neural network architectures. We validate the effectiveness of BEP on both multi-layer perceptrons and recurrent neural networks, demonstrating gains of up to +6.89% and +10.57% in test accuracy, respectively. The proposed algorithm is released as an open-source repository.
Small Models Achieve Large Language Model Performance: Evaluating Reasoning-Enabled AI for Secure Child Welfare Research
Venue: arXiv cs.CY 2026
Year: 2026

Objective: This study develops a systematic benchmarking framework for testing whether language models can accurately identify constructs of interest in child welfare records. The objective is to assess how different model sizes and architectures perform on four validated benchmarks for classifying critical risk factors among child welfare-involved families: domestic violence, firearms, substance-related problems generally, and opioids specifically. Method: We constructed four benchmarks for identifying risk factors in child welfare investigation summaries: domestic violence, substance-related problems, firearms, and opioids (n=500 each). We evaluated seven model sizes (0.6B-32B parameters) in standard and extended reasoning modes, plus a mixture-of-experts variant. Cohen's kappa measured agreement with gold standard classifications established by human experts. Results: The benchmarking revealed a critical finding: bigger models are not better. A small 4B parameter model with extended reasoning proved most effective, outperforming models up to eight times larger. It consistently achieved "substantial" to "almost perfect" agreement across all four benchmark categories. This model achieved "almost perfect" agreement (\k{appa} = 0.93-0.96) on three benchmarks (substance-related problems, firearms, and opioids) and "substantial" agreement (\k{appa} = 0.74) on the most complex task (domestic violence). Small models with extended reasoning rivaled the largest models while being more resource-efficient. Conclusions: Small reasoning-enabled models achieve accuracy levels historically requiring larger architectures, enabling significant time and computational efficiencies. The benchmarking framework provides a method for evidence-based model selection to balance accuracy with practical resource constraints before operational deployment in social work research.
RNNs perform task computations by dynamically warping neural representations
Venue: arXiv cs.LG 2026
Year: 2026

Analysing how neural networks represent data features in their activations can help interpret how they perform tasks. Hence, a long line of work has focused on mathematically characterising the geometry of such "neural representations." In parallel, machine learning has seen a surge of interest in understanding how dynamical systems perform computations on time-varying input data. Yet, the link between computation-through-dynamics and representational geometry remains poorly understood. Here, we hypothesise that recurrent neural networks (RNNs) perform computations by dynamically warping their representations of task variables. To test this hypothesis, we develop a Riemannian geometric framework that enables the derivation of the manifold topology and geometry of a dynamical system from the manifold of its inputs. By characterising the time-varying geometry of RNNs, we show that dynamic warping is a fundamental feature of their computations.
ConsentDiff at Scale: Longitudinal Audits of Web Privacy Policy Changes and UI Frictions
Venue: arXiv cs.HC 2026
Year: 2026

Web privacy is experienced via two public artifacts: site utterances in policy texts, and the actions users are required to take during consent interfaces. In the extensive cross-section audits we've studied, there is a lack of longitudinal data detailing how these artifacts are changing together, and if interfaces are actually doing what they promise in policy. ConsentDiff provides that longitudinal view. We build a reproducible pipeline that snapshots sites every month, semantically aligns policy clauses to track clause-level churn, and classifies consent-UI patterns by pulling together DOM signals with cues provided by screenshots. We introduce a novel weighted claim-UI alignment score, connecting common policy claims to observable predicates, and enabling comparisons over time, regions, and verticals. Our measurements suggest continued policy churn, systematic changes to eliminate a higher-friction banner design, and significantly higher alignment where rejecting is visible and lower friction.
Efficient Reinforcement Learning with Semantic and Token Entropy for LLM Reasoning
Venue: arXiv cs.AI 2026
Year: 2026

Reinforcement learning with verifiable rewards (RLVR) has demonstrated superior performance in enhancing the reasoning capability of large language models (LLMs). However, this accuracy-oriented learning paradigm often suffers from entropy collapse, which reduces policy exploration and limits reasoning capabilities. To address this challenge, we propose an efficient reinforcement learning framework that leverages entropy signals at both the semantic and token levels to improve reasoning. From the data perspective, we introduce semantic entropy-guided curriculum learning, organizing training data from low to high semantic entropy to guide progressive optimization from easier to more challenging tasks. For the algorithmic design, we adopt non-uniform token treatment by imposing KL regularization on low-entropy tokens that critically impact policy exploration and applying stronger constraints on high-covariance portions within these tokens. By jointly optimizing data organization and algorithmic design, our method effectively mitigates entropy collapse and enhances LLM reasoning. Experimental results across 6 benchmarks with 3 different parameter-scale base models demonstrate that our method outperforms other entropy-based approaches in improving reasoning.
GraphBench: Next-generation graph learning benchmarking
Venue: arXiv cs.LG 2026
Year: 2026

Machine learning on graphs has recently achieved impressive progress in various domains, including molecular property prediction and chip design. However, benchmarking practices remain fragmented, often relying on narrow, task-specific datasets and inconsistent evaluation protocols, which hampers reproducibility and broader progress. To address this, we introduce GraphBench, a comprehensive benchmarking suite that spans diverse domains and prediction tasks, including node-level, edge-level, graph-level, and generative settings. GraphBench provides standardized evaluation protocols -- with consistent dataset splits and performance metrics that account for out-of-distribution generalization -- as well as a unified hyperparameter tuning framework. Additionally, we benchmark GraphBench using message-passing neural networks and graph transformer models, providing principled baselines and establishing a reference performance. See www.graphbench.io for further details.
Prescriptive Artificial Intelligence: A Formal Paradigm for Auditing Human Decisions Under Uncertainty
Venue: arXiv cs.AI 2026
Year: 2026

We formalize Prescriptive Artificial Intelligence as a distinct paradigm for human-AI decision collaboration in high-stakes environments. Unlike predictive systems optimized for outcome accuracy, prescriptive systems are designed to recommend and audit human decisions under uncertainty, providing normative guidance while preserving human agency and accountability. We introduce four domain-independent axioms characterizing prescriptive systems and prove fundamental separation results. Central among these is the Imitation Incompleteness theorem, which establishes that supervised learning from historical decisions cannot correct systematic decision biases in the absence of external normative signals. Consequently, performance in decision imitation is bounded by a structural bias term epsilon_bias rather than the statistical learning rate O(1/sqrt(n)). This result formalizes the empirically observed accuracy ceiling in human decision imitation tasks and provides a principled criterion for when automation should be replaced by epistemic auditing. We demonstrate the computational realizability of the framework through an interpretable fuzzy inference system, applied as a stress test in elite soccer decision-making, where it reveals systematic decision latency and risk states obscured by outcome and status quo biases. The proposed framework establishes Prescriptive AI as a general, realizable class of decision-support systems applicable across safety-critical domains in which interpretability, contestability, and normative alignment are essential.
Hoi! -- A Multimodal Dataset for Force-Grounded, Cross-View Articulated Manipulation
Venue: arXiv cs.RO 2026
Year: 2026

We present a dataset for force-grounded, cross-view articulated manipulation that couples what is seen with what is done and what is felt during real human interaction. The dataset contains 3048 sequences across 381 articulated objects in 38 environments. Each object is operated under four embodiments - (i) human hand, (ii) human hand with a wrist-mounted camera, (iii) handheld UMI gripper, and (iv) a custom Hoi! gripper - where the tool embodiment provides synchronized end-effector forces and tactile sensing. Our dataset offers a holistic view of interaction understanding from video, enabling researchers to evaluate how well methods transfer between human and robotic viewpoints, but also investigate underexplored modalities such as force sensing and prediction. Further information can be found on the Website.
Capturing Classic Authorial Style in Long-Form Story Generation with GRPO Fine-Tuning
Venue: arXiv cs.CL 2026
Year: 2026

Evaluating and optimising authorial style in long-form story generation remains challenging because style is often assessed with ad hoc prompting and is frequently conflated with overall writing quality. We propose a two-stage pipeline. First, we train a dedicated style-similarity judge by fine-tuning a sentence-transformer with authorship-verification supervision, and calibrate its similarity outputs into a bounded $[0,1]$ reward. Second, we use this judge as the primary reward in Group Relative Policy Optimization (GRPO) to fine-tune an 8B story generator for style-conditioned writing, avoiding the accept/reject supervision required by Direct Preference Optimization (DPO). Across four target authors (Mark Twain, Jane Austen, Charles Dickens, Thomas Hardy), the GRPO-trained 8B model achieves higher style scores than open-weight baselines, with an average style score of 0.893 across authors. These results suggest that AV-calibrated reward modelling provides a practical mechanism for controllable style transfer in long-form generation under a moderate model size and training budget.
SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations
Venue: arXiv cs.CV 2026
Year: 2026

Achieving character animation that meets studio-grade production standards remains challenging despite recent progress. Existing approaches can transfer motion from a driving video to a reference image, but often fail to preserve structural fidelity and temporal consistency in wild scenarios involving complex motion and cross-identity animations. In this work, we present \textbf{SCAIL} (a framework toward \textbf{S}tudio-grade \textbf{C}haracter \textbf{A}nimation via \textbf{I}n-context \textbf{L}earning), a framework designed to address these challenges from two key innovations. First, we propose a novel 3D pose representation, providing a more robust and flexible motion signal. Second, we introduce a full-context pose injection mechanism within a diffusion-transformer architecture, enabling effective spatio-temporal reasoning over full motion sequences. To align with studio-level requirements, we develop a curated data pipeline ensuring both diversity and quality, and establish a comprehensive benchmark for systematic evaluation. Experiments show that \textbf{SCAIL} achieves state-of-the-art performance and advances character animation toward studio-grade reliability and realism.