OpenRaiser/Intern-Atlas huggingface dataset:
Explaining Time Series Classifiers with PHAR: Rule Extraction and Fusion from Post-hoc Attributions
Venue: arXiv cs.LG 2026
Year: 2026

Explaining machine learning (ML) models for time series (TS) classification remains challenging due to the difficulty of interpreting raw time series and the high dimensionality of the input space. We introduce PHAR--Post-hoc Attribution Rules--a unified framework that transforms numeric feature attributions from post-hoc, instance-wise explainers (e.g. LIME, SHAP) into structured, human-readable rules. These rules define human-readable intervals that indicate where and when decision-relevant segments occur and can enhance model transparency by localizing threshold-based conditions on the raw series. PHAR performs comparably to native rule-based methods, such as Anchor, while scaling more efficiently to long TS sequences and achieving broader instance coverage. A dedicated rule fusion step consolidates rule sets using strategies like weighted selection and lasso-based refinement, balancing key quality metrics: coverage, confidence, and simplicity. This fusion ensures each instance receives a concise and unambiguous rule, improving both explanation fidelity and consistency. We further introduce visualization techniques to illustrate specificity-generalization trade-offs in the derived rules. PHAR resolves conflicting and overlapping explanations--a common effect of the Rashomon phenomenon--into coherent, domain-adaptable insights. Comprehensive experiments on UCR/UEA Time Series Classification Archive demonstrate that PHAR may improve interpretability, decision transparency, and practical applicability for TS classification tasks by providing concise, human-readable rules aligned with model predictions.
ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflection
Venue: arXiv cs.AI 2026
Year: 2026

The NP-hard Dynamic Flexible Job-Shop Scheduling (DFJSP) problem involves real-time events and complex routing. While traditional rules are efficient but rigid, deep learning is opaque and requires feature engineering. Large Language Models (LLMs) promise adaptive reasoning without this engineering overhead, yet we find their direct application is suboptimal. Baseline LLMs suffer from three key pitfalls: the long-context paradox, where crucial data is underutilized; an underutilization of expert heuristics; and myopic decision-making. To address this, we propose ReflecSched, a framework that empowers the LLM beyond a direct scheduler by equipping it with a strategic analysis capability. ReflecSched tasks the LLM to analyze heuristic-driven simulations across multiple planning horizons and distill them into a concise, natural-language summary termed Strategic Experience. This summary is then integrated into the prompt of a final decision-making module, guiding it to produce non-myopic actions. Experiments demonstrate ReflecSched achieves superior performance, with its best variants attaining an average RPD of 6.09% and rank of 4.39 on GEN-Bench, significantly outperforming strong traditional and learning-based methods including HMPSAC and IDDQN. It also statistically and decisively surpasses direct LLM baselines, securing a 71.35% Win Rate while being, on average, 15.1% more token-efficient on Normal-scale problems. Furthermore, cumulative runtime analysis reveals that ReflecSched's zero-shot nature eliminates the training bottleneck, providing a decisive efficiency advantage in high-variability manufacturing environments. Ablation studies attribute this performance to a robust reflection mechanism that leverages high-quality, contrastive experience. Ultimately, the framework's performance is statistically on par with an oracle-like strategy, showcasing its effectiveness and robustness.
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
Venue: arXiv cs.AI 2026
Year: 2026

Model Context Protocol (MCP) has become a key infrastructure for connecting LLMs with external tools, scaling to 10,000+ MCP servers with diverse tools. Unfortunately, there is still a large gap between real-world MCP usage and current evaluation: they typically assume single-server settings and directly inject tools into the model's context, bypassing the challenges of large-scale retrieval and multi-tool composition. To bridge this gap, we propose LiveMCPBench, which evaluates 95 real-world daily tasks explicitly constructed to stress diverse tools and scaled multi-server routing. The benchmark includes a ready-to-deploy tool suite of 70 servers with 527 tools, ensuring reproducibility without scattered API configuration. We further introduce an LLM-as-a-Judge evaluation framework that directly verifies task outcomes, handling dynamic data sources and multiple valid solution paths. We benchmark 12 state-of-the-art LLMs and observe a substantial performance gap: while Claude-Sonnet-4 reaches 78.95% task success, most models achieve only 30-50%. Our analysis reveals that the active tool composition strongly correlates with task success, whereas retrieval errors account for nearly half of all failures, highlighting retrieval as the dominant bottleneck. Together, these results provide the first large-scale, reproducible diagnosis of MCP agent capabilities and point towards future research on improving retrieval robustness and encouraging effective tool composition. Our code and data are publicly available at https://icip-cas.github.io/LiveMCPBench.
SpeechRole: A Large-Scale Dataset and Benchmark for Evaluating Speech Role-Playing Agents
Venue: arXiv cs.CL 2026
Year: 2026

Speech is essential for realistic role-playing, yet existing work on role-playing agents largely centers on text, leaving Speech Role-Playing Agents (SRPAs) underexplored and lacking systematic evaluation. We introduce SpeechRole, a unified framework for developing and assessing SRPAs. SpeechRole-Data contains 98 roles and 111k speech-to-speech conversations with rich timbre and prosodic variation, providing large-scale resources for training SRPAs. SpeechRole-Eval offers a multidimensional benchmark that directly evaluates generated speech, preserving paralinguistic cues and measuring interaction ability, speech expressiveness, and role-playing fidelity. Experiments show that end-to-end SRPAs such as GPT-4o Audio achieve strong fluency and naturalness, but remain limited in prosody consistency and emotion appropriateness. In contrast, current open-source end-to-end models exhibit substantial performance gaps across multiple evaluation dimensions. Cascaded and end-to-end systems achieve comparable results in interaction ability and role-playing fidelity, suggesting that these aspects are still largely influenced by the underlying text-based language models. We release all data, code, and evaluation tools at https://github.com/yuhui1038/SpeechRole.
AttriCtrl: Fine-Grained Control of Aesthetic Attribute Intensity in Diffusion Models
Venue: arXiv cs.CV 2026
Year: 2026

Diffusion models have recently become the dominant paradigm for image generation, yet existing systems struggle to interpret and follow numeric instructions for adjusting semantic attributes. In real-world creative scenarios, especially when precise control over aesthetic attributes is required, current methods fail to provide such controllability. This limitation partly arises from the subjective and context-dependent nature of aesthetic judgments, but more fundamentally stems from the fact that current text encoders are designed for discrete tokens rather than continuous values. Meanwhile, efforts on aesthetic alignment, often leveraging reinforcement learning, direct preference optimization, or architectural modifications, primarily align models with a global notion of human preference. While these approaches improve user experience, they overlook the multifaceted and compositional nature of aesthetics, underscoring the need for explicit disentanglement and independent control of aesthetic attributes. To address this gap, we introduce AttriCtrl, a lightweight framework for continuous aesthetic intensity control in diffusion models. It first defines relevant aesthetic attributes, then quantifies them through a hybrid strategy that maps both concrete and abstract dimensions onto a unified $[0,1]$ scale. A plug-and-play value encoder is then used to transform user-specified values into model-interpretable embeddings for controllable generation. Experiments show that AttriCtrl achieves accurate and continuous control over both single and multiple aesthetic attributes, significantly enhancing personalization and diversity. Crucially, it is implemented as a lightweight adapter while keeping the diffusion model frozen, ensuring seamless integration with existing frameworks such as ControlNet at negligible computational cost.
Eye2Recall: Exploring the Design of Enhancing Reminiscence Activities via Eye Tracking-Based LLM-Powered Interaction Experience for Older Adults
Venue: arXiv cs.HC 2026
Year: 2026

Photo-based reminiscence has the potential to have a positive impact on older adults' reconnection with their personal history and improve their well-being. Supporting reminiscence in older adults through technological implementations is becoming an increasingly important area of research in the fields of HCI and CSCW. However, the impact of integrating gaze and speech as mixed-initiative interactions in LLM-powered reminiscence conversations remains under-explored. To address this, we conducted expert interviews to understand the challenges that older adults face with LLM-powered, photo-based reminiscence experiences. Based on these design considerations, we developed Eye2Recall, a system that integrates eye tracking for detecting visual interest with natural language interaction to create a mixed-initiative reminiscence experience. We evaluated its effectiveness through a user study involving ten older adults. The results have important implications for the future design of more accessible and empowering reminiscence technologies that better align with older adults' natural interaction patterns and enhance their positive aging.
Large AI Models for Wireless Physical Layer
Venue: arXiv cs.IT 2026
Year: 2026

Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in applying LAMs to physical layer communications, addressing obstacles of conventional AI-based approaches. LAM-based solutions are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. The motivations and key frameworks of these approaches are comprehensively examined through multiple use cases. Both strategies significantly improve performance and adaptability across diverse wireless scenarios. Future research directions, including efficient architectures, interpretability, standardized datasets, and collaboration between large and small models, are proposed to advance LAM-based physical layer solutions for next-generation communication systems.
Alleviating Attention Hacking in Discriminative Reward Modeling through Interaction Distillation
Venue: arXiv cs.CL 2026
Year: 2026

The reward model (RM), as the core component of reinforcement learning from human feedback (RLHF) for large language models (LLMs), responsible for providing reward signals to generated responses. However, the mainstream discriminative reward modeling is inadequate in terms of token-level interaction, making its judgment signals vulnerable to being hacked by misallocated attention to context. This stems from two fundamental limitations: (1) Current preference modeling employs decoder-only architectures, where the unidirectional causal attention mechanism leads to forward-decaying intra-sequence attention within the prompt-response sequence. (2) The independent Siamese-encoding paradigm induces the absence of token-level inter-sequence attention between chosen and rejected sequences. To address this "attention hacking", we propose "Interaction Distillation", a novel training framework for more adequate discriminative reward modeling via attention-level optimization. The method introduces an interaction-based natural language understanding model as the teacher to provide sophisticated token interaction patterns via comprehensive attention, and guides the reward modeling to simulate teacher model's interaction pattern through an attentional alignment objective. Through extensive experiments, interaction distillation has demonstrated its ability to provide more stable and generalizable reward signals compared to state-of-the-art RM optimization methods that target data noise, highlighting the attention hacking constitute a more fundamental limitation in discriminative RM.
ECGTwin: Personalized ECG Generation Using Controllable Diffusion Model
Venue: arXiv cs.LG 2026
Year: 2026

Personalized electrocardiogram (ECG) generation is to simulate a patient's ECG digital twins tailored to specific conditions. It has the potential to transform traditional healthcare into a more accurate individualized paradigm, while preserving the key benefits of conventional population-level ECG synthesis. However, this promising task presents two fundamental challenges: extracting individual features without ground truth and injecting various types of conditions without confusing generative model. In this paper, we present ECGTwin, a two-stage framework designed to address these challenges. In the first stage, an Individual Base Extractor trained via contrastive learning robustly captures personal features from a reference ECG. In the second stage, the extracted individual features, along with a target cardiac condition, are integrated into the diffusion-based generation process through our novel AdaX Condition Injector, which injects these signals via two dedicated and specialized pathways. Both qualitative and quantitative experiments have demonstrated that our model can not only generate ECG signals of high fidelity and diversity by offering a fine-grained generation controllability, but also preserving individual-specific features. Furthermore, ECGTwin shows the potential to enhance ECG auto-diagnosis in downstream application, confirming the possibility of precise personalized healthcare solutions.
DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening
Venue: arXiv cs.LG 2026
Year: 2026

Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We propose DeepGB-TB, a non-invasive system that instantly assigns TB risk scores using only cough audio and basic demographic data. The model couples a lightweight one-dimensional convolutional neural network for audio processing with a gradient-boosted decision tree for tabular features. Its principal innovation is a Cross-Modal Bidirectional Cross-Attention module (CM-BCA) that iteratively exchanges salient cues between modalities, emulating the way clinicians integrate symptoms and risk factors. To meet the clinical priority of minimizing missed cases, we design a Tuberculosis Risk-Balanced Loss (TRBL) that places stronger penalties on false-negative predictions, thereby reducing high-risk misclassifications. DeepGB-TB is evaluated on a diverse dataset of 1,105 patients collected across seven countries, achieving an AUROC of 0.903 and an F1-score of 0.851, representing a new state of the art. Its computational efficiency enables real-time, offline inference directly on common mobile devices, making it ideal for low-resource settings. Importantly, the system produces clinically validated explanations that promote trust and adoption by frontline health workers. By coupling AI innovation with public-health requirements for speed, affordability, and reliability, DeepGB-TB offers a tool for advancing global TB control.
Real-World Evaluation of Protocol-Compliant Denial-of-Service Attacks on C-V2X-based Forward Collision Warning Systems
Venue: arXiv cs.CR 2026
Year: 2026

Cellular Vehicle-to-Everything (C-V2X) technology enables low-latency, reliable communications essential for safety applications such as a Forward Collision Warning (FCW) system. C-V2X deployments operate under strict protocol compliance with the 3rd Generation Partnership Project (3GPP) and the Society of Automotive Engineers Standard (SAE) J2735 specifications to ensure interoperability. This paper presents a real-world testbed evaluation of protocol-compliant Denial-of-Service (DoS) attacks using User Datagram Protocol (UDP) flooding and oversized Basic Safety Message (BSM) attacks that 7 exploit transport- and application-layer vulnerabilities in C-V2X. The attacks presented in this study transmit valid messages over standard PC5 sidelinks, fully adhering to 3GPP and SAE J2735 specifications, but at abnormally high rates and with oversized payloads that overload the receiver resources without breaching any protocol rules such as IEEE 1609. Using a real-world connected vehicle 11 testbed with commercially available On-Board Units (OBUs), we demonstrate that high-rate UDP flooding and oversized payload of BSM flooding can severely degrade FCW performance. Results show that UDP flooding alone reduces packet delivery ratio by up to 87% and increases latency to over 400ms, while oversized BSM floods overload receiver processing resources, delaying or completely suppressing FCW alerts. When UDP and BSM attacks are executed simultaneously, they cause near-total communication failure, preventing FCW warnings entirely. These findings reveal that protocol-compliant communications do not necessarily guarantee safe or reliable operation of C-V2X-based safety applications.
Affine-Equivariant Kernel Space Encoding for NeRF Editing
Venue: arXiv cs.CV 2026
Year: 2026

Neural scene representations achieve high-fidelity rendering by encoding 3D scenes as continuous functions, but their latent spaces are typically implicit and globally entangled, making localized editing and physically grounded manipulation difficult. While several works introduce explicit control structures or point-based latent representations to improve editability, these approaches often suffer from limited locality, sensitivity to deformations, or visual artifacts. In this paper, we introduce Affine-Equivariant Kernel Space Encoding (EKS), a spatial encoding for neural radiance fields that provides localized, deformation-aware feature representations. Instead of querying latent features directly at discrete points or grid vertices, our encoding aggregates features through a field of anisotropic Gaussian kernels, each defining a localized region of influence. This kernel-based formulation enables stable feature interpolation under spatial transformations while preserving continuity and high reconstruction quality. To preserve detail without sacrificing editability, we further propose a training-time feature distillation mechanism that transfers information from multi-resolution hash grid encodings into the kernel field, yielding a compact and fully grid-free representation at inference. This enables intuitive, localized scene editing directly via Gaussian kernels without retraining, while maintaining high-quality rendering. The code can be found under (https://github.com/MikolajZielinski/eks)
TIC-GRPO: Provable and Efficient Optimization for Reinforcement Learning from Human Feedback
Venue: arXiv cs.LG 2026
Year: 2026

Group Relative Policy Optimization (GRPO), recently introduced by DeepSeek, is a critic-free reinforcement learning algorithm for fine-tuning large language models. GRPO replaces the value function in Proximal Policy Optimization (PPO) with group-normalized rewards while retaining PPO-style token-level importance sampling based on an old policy. Our theoretical analysis reveals that the GRPO update rule estimates the policy gradient at the old policy rather than the current one; however, since the old policy is refreshed every few steps, the resulting discrepancy remains small and the induced bias is negligible in practice. To empirically validate this insight, we conduct an ablation study that entirely removes importance sampling and performs multiple optimization steps using gradients estimated at a fixed old policy. Remarkably, this simplified variant attains performance comparable to standard GRPO. Motivated by this finding, we propose Trajectory-level Importance-Corrected GRPO (TIC-GRPO), a new algorithm that replaces token-level importance ratios with a single trajectory-level probability ratio, thereby yielding an estimate of the current policy gradient while preserving the critic-free structure. Furthermore, we present the first convergence analysis for GRPO-style methods and show that TIC-GRPO converges faster than GRPO. Finally, empirical results across math reasoning and coding tasks demonstrate the superiority of TIC-GRPO.
Critical Challenges in Content Moderation for People Who Use Drugs (PWUD): Insights into Online Harm Reduction Practices from Moderators
Venue: arXiv cs.HC 2026
Year: 2026

Online communities serve as essential support channels for People Who Use Drugs (PWUD), providing access to peer support and harm reduction information. The moderation of these communities involves consequential decisions affecting member safety, yet existing sociotechnical systems provide insufficient support for moderators. Through interviews with experienced moderators from PWUD forums on Reddit, we examine the unique nature of this work and its implications for HCI and content moderation research. We demonstrate that this work constitutes a distinct form of public health intervention characterised by three challenges: (1) high-stakes risk evaluation requiring pharmacological expertise, (2) time-critical crisis intervention spanning platform content and external drug market surveillance, and (3) navigation of structural conflicts where platform policies designed to minimise legal liability directly oppose community harm reduction goals. Our findings extend existing HCI moderation frameworks by revealing how legal liability structures can systematically undermine expert moderators' protective work, with implications for other marginalised communities facing similar regulatory tensions, including abortion care and sex work contexts. We identify two necessary shifts in sociotechnical design: moving from binary classification to multi-dimensional approaches that externalise competing factors moderators must balance, and shifting from low-level rule programming to high-level example-based instruction. However, we surface unresolved tensions around volunteer labour sustainability and risks of incorporating automated systems in high-stakes health contexts, identifying open questions requiring HCI research attention. These findings inform the design of platforms that better accommodate vulnerable populations whose health needs conflict with regulatory frameworks.