OpenRaiser/Intern-Atlas huggingface dataset:
BNMusic: Blending Environmental Noises into Personalized Music
Venue: arXiv cs.SD 2026
Year: 2026

While being disturbed by environmental noises, the acoustic masking technique is a conventional way to reduce the annoyance in audio engineering that seeks to cover up the noises with other dominant yet less intrusive sounds. However, misalignment between the dominant sound and the noise-such as mismatched downbeats-often requires an excessive volume increase to achieve effective masking. Motivated by recent advances in cross-modal generation, in this work, we introduce an alternative method to acoustic masking, aiming to reduce the noticeability of environmental noises by blending them into personalized music generated based on user-provided text prompts. Following the paradigm of music generation using mel-spectrogram representations, we propose a Blending Noises into Personalized Music (BNMusic) framework with two key stages. The first stage synthesizes a complete piece of music in a mel-spectrogram representation that encapsulates the musical essence of the noise. In the second stage, we adaptively amplify the generated music segment to further reduce noise perception and enhance the blending effectiveness, while preserving auditory quality. Our experiments with comprehensive evaluations on MusicBench, EPIC-SOUNDS, and ESC-50 demonstrate the effectiveness of our framework, highlighting the ability to blend environmental noise with rhythmically aligned, adaptively amplified, and enjoyable music segments, minimizing the noticeability of the noise, thereby improving overall acoustic experiences. Project page: https://d-fas.github.io/BNMusic_page/.
Monotone Classification with Relative Approximations
Venue: arXiv cs.LG 2026
Year: 2026

In monotone classification, the input is a multi-set $P$ of points in $\mathbb{R}^d$, each associated with a hidden label from $\{-1, 1\}$. The goal is to identify a monotone function $h$, which acts as a classifier, mapping from $\mathbb{R}^d$ to $\{-1, 1\}$ with a small {\em error}, measured as the number of points $p \in P$ whose labels differ from the function values $h(p)$. The cost of an algorithm is defined as the number of points having their labels revealed. This article presents the first study on the lowest cost required to find a monotone classifier whose error is at most $(1 + ε) \cdot k^*$ where $ε\ge 0$ and $k^*$ is the minimum error achieved by an optimal monotone classifier -- in other words, the error is allowed to exceed the optimal by at most a relative factor. Nearly matching upper and lower bounds are presented for the full range of $ε$. All previous work on the problem can only achieve an error higher than the optimal by an absolute factor.
(De)composing Craft: An Elementary Grammar for Sharing Expertise in Craft Workflows
Venue: arXiv cs.HC 2026
Year: 2026

Craft practices rely on evolving archives of skill and knowledge developed through generations of craftspeople experimenting with designs, materials, and techniques. Better documentation of these practices enables the sharing of knowledge and expertise between sites and generations. However, most documentation focuses on the linear steps leading to final artifacts, neglecting the distinct tacit knowledge, improvisational actions, and situated adaptations needed to meet the unique demands of each craft project. This omission limits knowledge sharing and reduces craft to a mechanical endeavor, rather than a sophisticated and contextual way of seeing, thinking, and doing. Drawing on expert interviews and literature from HCI, CSCW and the social sciences, we develop an elementary grammar to document improvisational actions of real-world craft practices. We demonstrate the utility of this grammar with a MLLM-powered interface called CraftLink that can be used to analyze expert videos and generate documentation to share material and contextual variations of practices with other knowledgeable but non-master craftspeople. Our user study with expert crocheters (N=7) evaluates our grammar's effectiveness in capturing and sharing expert knowledge with other craftspeople, offering new pathways for computational systems to support collaborative archives of knowledge and practice across time, space, and skill levels. We conclude by showing how our grammar address four key tensions of the craft learning environment: personal and shareable documentation, fragmented and discoverable expertise, linear and iterative practices, and data privacy and ownership.
Spurious Rewards: Rethinking Training Signals in RLVR
Venue: arXiv cs.AI 2026
Year: 2026

We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have little, no, or even negative correlation with the correct answer. For example, RLVR training with GRPO improves MATH-500 performance for Qwen2.5-Math-7B by 21.4 percentage points using randomly assigned rewards, nearly matching the 29.1-point gain from ground-truth rewards. To explain this counterintuitive observation, we show that GRPO exhibits a clipping bias from the clip term, which can amplify high-prior behaviors learned during pretraining even without informative rewards. As a case study, we identify one such behavior in Qwen2.5-Math models, which we call code reasoning -- reasoning in code without actual code execution; code-reasoning frequency increases from 65 percent to over 90 percent with spurious rewards. However, the presence of such amplifiable behaviors is highly model-dependent. In practice, spurious rewards that are effective for Qwen models often fail to produce gains for other model families, such as Llama3 or OLMo2. Our results highlight the importance of validating RL methods across diverse models rather than relying on a single de facto choice: large gains can arise on Qwen models even from random rewards that do not reflect genuine capability improvements.
CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling
Venue: arXiv cs.CL 2026
Year: 2026

Large reasoning models (LRMs), such as OpenAI's o1 and DeepSeek-R1, harness test-time scaling to perform multi-step reasoning for complex problem-solving. This reasoning process, executed before producing final answers, is often guided by special juncture tokens that prompt self-evaluative reflection. These transition markers and reflective cues are referred to as "reflection tokens" (e.g., "wait", "but", "alternatively"). In this work, we treat reflection tokens as a "resource" and introduce the problem of resource allocation, aimed at improving the test-time compute performance of LRMs by adaptively regulating the frequency and placement of reflection tokens. Through empirical analysis, we show that both excessive and insufficient use of reflection tokens, referred to as over-reflection and under-reflection, can degrade model performance. To better understand this trade-off, we draw an analogy between reflection token usage and learning rate scheduling in optimization. Building on this insight, We propose cyclical reflection token scheduling (termed CyclicReflex), a training-free decoding strategy that dynamically modulates reflection token logits with a bidirectional, position-dependent triangular waveform, incurring no additional computation cost. Experiments on MATH500, AIME2024/2025, AMC2023, GPQA Diamond and LiveCodeBench demonstrate that CyclicReflex consistently improves performance across model sizes (1.5B-14B), outperforming standard decoding and recent approaches such as TIP (thought switching penalty) and S1. Codes are available at https://github.com/OPTML-Group/CyclicReflex.
JAFAR: Jack up Any Feature at Any Resolution
Venue: arXiv cs.CV 2026
Year: 2026

Foundation Vision Encoders have become essential for a wide range of dense vision tasks. However, their low-resolution spatial feature outputs necessitate feature upsampling to produce the high-resolution modalities required for downstream tasks. In this work, we introduce JAFAR, a lightweight and flexible feature upsampler that enhances the spatial resolution of visual features from any Foundation Vision Encoder to an arbitrary target resolution. JAFAR employs an attention-based module designed to promote semantic alignment between high-resolution queries, derived from low-level image features, and semantically enriched low-resolution keys, using Spatial Feature Transform (SFT) modulation. Notably, despite the absence of high-resolution supervision, we demonstrate that learning at low upsampling ratios and resolutions generalizes remarkably well to significantly higher output scales. Extensive experiments show that JAFAR effectively recovers fine-grained spatial details and consistently outperforms existing feature upsampling methods across a diverse set of downstream tasks. Project page at https://jafar-upsampler.github.io
Needling Through the Threads: A Visualization Tool for Navigating Threaded Online Discussions
Venue: arXiv cs.HC 2026
Year: 2026

Navigating large-scale online discussions is difficult due to the rapid pace and large volume of user-generated content. Prior work in CSCW has shown that moderators often struggle to follow multiple simultaneous discussions, track evolving conversations, and maintain contextual understanding--all of which hinder timely and effective moderation. While platforms like Reddit use threaded structures to organize discourse, deeply nested threads can still obscure discussions and make it difficult to grasp the overall trajectory of conversations. In this paper, we present an interactive system called Needle to support better navigation and comprehension of complex discourse within threaded discussions. Needle uses visual analytics to summarize key conversational metrics--such as activity, toxicity levels, and voting trends--over time, offering both high-level insights and detailed breakdowns of discussion threads. Through a user study with ten Reddit moderators, we find that Needle supports moderation by reducing cognitive load in making sense of large discussion, helping prioritize areas that need attention, and providing decision-making supports. Based on our findings, we provide a set of design guidelines to inform future visualization-driven moderation tools and sociotechnical systems. To the best of our knowledge, Needle is one of the first systems to combine interactive visual analytics with human-in-the-loop moderation for threaded online discussions.
Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly
Venue: arXiv cs.CL 2026
Year: 2026

There has been considerable interest in using surprisal from Transformer-based language models (LMs) as predictors of human sentence processing difficulty. Recent work has observed an inverse scaling relationship between Transformers' per-word estimated probability and the predictive power of their surprisal estimates on reading times, showing that LMs with more parameters and trained on more data are less predictive of human reading times. However, these studies focused on predicting latency-based measures. Tests on brain imaging data have not shown a trend in any direction when using a relatively small set of LMs, leaving open the possibility that the inverse scaling phenomenon is constrained to latency data. This study therefore conducted a more comprehensive evaluation using surprisal estimates from 17 pre-trained LMs across three different LM families on two functional magnetic resonance imaging (fMRI) datasets. Results show that the inverse scaling relationship between models' per-word estimated probability and model fit on both datasets still obtains, resolving the inconclusive results of previous work and indicating that this trend is not specific to latency-based measures.
Curriculum-Guided Layer Scaling for Language Model Pretraining
Venue: arXiv cs.CL 2026
Year: 2026

As the cost of pretraining large language models grows, there is continued interest in strategies to improve learning efficiency during this core training stage. Motivated by cognitive development, where humans gradually build knowledge as their brains mature, we propose Curriculum-Guided Layer Scaling (CGLS), a framework for compute-efficient pretraining that synchronizes increasing data difficulty with model growth through progressive layer stacking (i.e. gradually adding layers during training). At the 100M parameter scale, using a curriculum transitioning from synthetic short stories to general web data, CGLS outperforms baseline methods on the question-answering benchmarks PIQA and ARC. Pretraining at the 1.2B scale, we stratify the DataComp-LM corpus with a DistilBERT-based classifier and progress from general text to highly technical or specialized content. Our results show that progressively increasing model depth alongside sample difficulty leads to better generalization and zero-shot performance on various downstream benchmarks. Altogether, our findings demonstrate that CGLS unlocks the potential of progressive stacking, offering a simple yet effective strategy for improving generalization on knowledge-intensive and reasoning tasks.
Do Not Immerse and Drive? Prolonged Effects of Cybersickness on Physiological Stress Markers And Cognitive Performance
Venue: arXiv cs.HC 2026
Year: 2026

Extended exposure to virtual reality environments can induce motion sickness, often referred to as cybersickness, which may lead to physiological stress responses and impaired cognitive performance. This study investigates the aftereffects of VR-induced motion sickness with a focus on physiological stress markers and working memory performance. Using a carousel simulation to elicit cybersickness, we assessed subjective discomfort (SSQ, FMS), physiological stress (salivary cortisol, alpha-amylase, electrodermal activity, heart rate), and cognitive performance (n-Back task) over a 90-minute post-exposure period. Our findings demonstrate a significant increase in both subjective and physiological stress indicators following VR exposure, accompanied by a decline in working memory performance. Notably, delayed symptom progression was observed in a substantial proportion of participants, with some reporting peak symptoms up to 90 minutes post-stimulation. Salivary cortisol levels remained elevated throughout the observation period, indicating prolonged stress recovery. These results highlight the need for longer washout phases in XR research and raise safety concerns for professional applications involving post-exposure task performance.
Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse but have been found to consistently exhibit a progressive left-leaning bias. At the same time, so-called persona or identity prompts have been shown to produce LLM behavior that aligns with socioeconomic groups with which the base model is not aligned. In this work, we analyze whether zero-shot persona prompting with limited information can accurately predict individual voting decisions and, by aggregation, accurately predict the positions of European groups on a diverse set of policies. We evaluate whether predictions are stable in response to counterfactual arguments, different persona prompts, and generation methods. Finally, we find that we can simulate the voting behavior of Members of the European Parliament reasonably well, achieving a weighted F1 score of approximately 0.793. Our persona dataset of politicians in the 2024 European Parliament and our code are available at the following url: https://github.com/dess-mannheim/european_parliament_simulation.
Auditory-Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients
Venue: arXiv cs.RO 2026
Year: 2026

Misdiagnosis can result in delayed treatment and patient harm. Robotic patient simulators (robopatients) provide a controlled framework for training and evaluating clinicians in rare and complex cases. We investigate auditory tactile congruence in the synthesis of adaptive vocal pain expressions for robopatients. The system generates pain vocalizations in response to tactile stimuli applied during abdominal palpation. Haptic input is captured through an abdominal phantom and processed using an internal palpation-to-pain mapping model that drives acoustic output. To evaluate perceptual congruence between palpation force and synthesized pain expressions, we conducted a study comprising 7,680 trials with 20 participants. Participants rated perceived pain intensity based solely on auditory feedback. We analyzed the influence of acoustic parameters on agreement between applied force and perceived pain. Results indicate that amplitude and pitch significantly affect perceptual agreement, independent of pain sound category. Increased palpation force was associated with higher agreement ratings, consistent with psychophysical scaling effects. Among the tested acoustic features, pitch exerted a stronger influence than amplitude on perceived congruence. These findings demonstrate that modulation of pitch and amplitude is critical for achieving perceptually coherent auditory tactile mappings in robotic pain simulation. The proposed framework supports the development of high fidelity robotic patient simulators and provides a platform for studying multidimensional representations of pain in embodied robotic systems.
Confidence-Based Self-Training for EMG-to-Speech: Leveraging Synthetic EMG for Robust Modeling
Venue: arXiv cs.SD 2026
Year: 2026

Voiced Electromyography (EMG)-to-Speech (V-ETS) models reconstruct speech from muscle activity signals, facilitating applications such as neurolaryngologic diagnostics. Despite its potential, the advancement of V-ETS is hindered by a scarcity of paired EMG-speech data. To address this, we propose a novel Confidence-based Multi-Speaker Self-training (CoM2S) approach, along with a newly curated Libri-EMG dataset. This approach leverages synthetic EMG data generated by a pre-trained model, followed by a proposed filtering mechanism based on phoneme-level confidence to enhance the ETS model through the proposed self-training techniques. Experiments demonstrate our method improves phoneme accuracy, reduces phonological confusion, and lowers word error rate, confirming the effectiveness of our CoM2S approach for V-ETS. In support of future research, we will release the codes and the proposed Libri-EMG dataset-an open-access, time-aligned, multi-speaker voiced EMG and speech recordings.
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers
Venue: arXiv cs.LG 2026
Year: 2026

The comparison between discriminative and generative classifiers has intrigued researchers since Efron's seminal analysis of logistic regression versus discriminant analysis. While early theoretical work established that generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, these trade-offs remain unexplored in the transformer era. We present the first comprehensive evaluation of modern generative and discriminative architectures - Auto-regressive modeling, Masked Language Modeling, Discrete Diffusion, and Encoders for text classification. Our study reveals that the classical 'two regimes' phenomenon manifests distinctly across different architectures and training paradigms. Beyond accuracy, we analyze sample efficiency, calibration, noise robustness, and ordinality across diverse scenarios. Our findings offer practical guidance for selecting the most suitable modeling approach based on real-world constraints such as latency and data limitations.
ViSTA: Visual Storytelling using Multi-modal Adapters for Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2026
Year: 2026

Text-to-image diffusion models have achieved remarkable success, yet generating coherent image sequences for visual storytelling remains challenging. A key challenge is effectively leveraging all previous text-image pairs, referred to as history text-image pairs, which provide contextual information for maintaining consistency across frames. Existing auto-regressive methods condition on all past image-text pairs but require extensive training, while training-free subject-specific approaches ensure consistency but lack adaptability to narrative prompts. To address these limitations, we propose a multi-modal history adapter for text-to-image diffusion models, \textbf{ViSTA}. It consists of (1) a multi-modal history fusion module to extract relevant history features and (2) a history adapter to condition the generation on the extracted relevant features. We also introduce a salient history selection strategy during inference, where the most salient history text-image pair is selected, improving the quality of the conditioning. Furthermore, we propose to employ a Visual Question Answering-based metric TIFA to assess text-image alignment in visual storytelling, providing a more targeted and interpretable assessment of generated images. Evaluated on the StorySalon and FlintStonesSV dataset, our proposed ViSTA model is not only consistent across different frames, but also well-aligned with the narrative text descriptions.
GroupNL: Low-Resource and Robust CNN Design over Cloud and Device
Venue: arXiv cs.CV 2026
Year: 2026

Deploying Convolutional Neural Network (CNN) models on ubiquitous Internet of Things (IoT) devices in a cloud-assisted manner to provide users with a variety of high-quality services has become mainstream. Most existing studies speed up model cloud training/on-device inference by reducing the number of convolution (Conv) parameters and floating-point operations (FLOPs). However, they usually employ two or more lightweight operations (e.g., depthwise Conv, $1\times1$ cheap Conv) to replace a Conv, which can still affect the model's speedup even with fewer parameters and FLOPs. To this end, we propose the Grouped NonLinear transformation generation method (GroupNL), leveraging data-agnostic, hyperparameters-fixed, and lightweight Nonlinear Transformation Functions (NLFs) to generate diversified feature maps on demand via grouping, thereby reducing resource consumption while improving the robustness of CNNs. First, in a GroupNL Conv layer, a small set of feature maps, i.e., seed feature maps, are generated based on the seed Conv operation. Then, we split seed feature maps into several groups, each with a set of different NLFs, to generate the required number of diversified feature maps with tensor manipulation operators and nonlinear processing in a lightweight manner without additional Conv operations. We further introduce a sparse GroupNL Conv to speed up by reasonably designing the seed Conv groups between the number of input channels and seed feature maps. Experiments conducted on benchmarks and on-device resource measurements demonstrate that the GroupNL Conv is an impressive alternative to Conv layers in baseline models. Specifically, on Icons-50 dataset, the accuracy of GroupNL-ResNet-18 is 2.86% higher than ResNet-18; on ImageNet-C dataset, the accuracy of GroupNL-EfficientNet-ES achieves about 1.1% higher than EfficientNet-ES.