OpenRaiser/Intern-Atlas huggingface dataset:
The Homogeneity Trap: Spectral Collapse in Doubly-Stochastic Deep Networks
Venue: arXiv cs.LG 2026
Year: 2026

Doubly-stochastic matrices (DSM) are increasingly utilized in structure-preserving deep architectures -- such as Optimal Transport layers and Sinkhorn-based attention -- to enforce numerical stability and probabilistic interpretability. In this work, we identify a critical spectral degradation phenomenon inherent to these constraints, termed the Homogeneity Trap. We demonstrate that the maximum-entropy bias, typical of Sinkhorn-based projections, drives the mixing operator towards the uniform barycenter, thereby suppressing the subdominant singular value σ_2 and filtering out high-frequency feature components. We derive a spectral bound linking σ_2 to the network's effective depth, showing that high-entropy constraints restrict feature transformation to a shallow effective receptive field. Furthermore, we formally demonstrate that Layer Normalization fails to mitigate this collapse in noise-dominated regimes; specifically, when spectral filtering degrades the Signal-to-Noise Ratio (SNR) below a critical threshold, geometric structure is irreversibly lost to noise-induced orthogonal collapse. Our findings highlight a fundamental trade-off between entropic stability and spectral expressivity in DSM-constrained networks.
A Differentiable Adversarial Framework for Task-Aware Data Subsampling
Venue: arXiv cs.LG 2026
Year: 2026

The proliferation of large-scale datasets poses a major computational challenge to model training. The traditional data subsampling method works as a static, task independent preprocessing step which usually discards information that is critical to downstream prediction. In this paper, we introduce the antagonistic soft selection subsampling (ASSS) framework as a novel paradigm that reconstructs data reduction into a differentiable end-to-end learning problem. ASSS uses the adversarial game between selector network and task network, and selector network learning assigns continuous importance weights to samples. This direct optimization implemented by Gumbel-Softmax relaxation allows the selector to identify and retain samples with the maximum amount of information for a specific task target under the guidance of the loss function that balances the fidelity and sparsity of the prediction. Theoretical analysis links this framework with the information bottleneck principle. Comprehensive experiments on four large-scale real world datasets show that ASSS has always been better than heuristic subsampling baselines such as clustering and nearest neighbor thinning in maintaining model performance. It is worth noting that ASSS can not only match, but also sometimes exceed the training performance of the entire dataset, showcasing the effect of intelligent denoising. This work establishes task aware data subsampling as a learnable component, providing a principled solution for effective large-scale data learning.
HFRWKV: A High-Performance Fully On-Chip Hardware Accelerator for RWKV
Venue: arXiv cs.AR 2026
Year: 2026

RWKV is a modern RNN architecture that approaches the performance of Transformers, with the advantage of processing long contexts at a linear memory cost. However, its sequential computation pattern struggles to efficiently leverage GPU parallelism, which leads to low compute resource utilization. Furthermore, frequent off-chip weight accesses create a memory bottleneck. To address these challenges, we propose HFRWKV, an FPGA-based hardware accelerator specifically designed for RWKV. Within the matrix operation module, we propose a novel hardware-friendly hybrid-precision quantization strategy, which enhances performance while maintaining acceptable accuracy. For the complex operations including exponentiation and division, we introduce a method featuring reusable architectures combined with lookup tables or piecewise linear approximation, which is algorithmically refined to effectively balance precision and hardware resource consumption. Based on this foundation, we adopt a fully on-chip computing system integrating parallel matrix-vector processing array and an efficient pipeline architecture. Through computation reordering and chunked double buffering, it effectively eliminates data transfer bottlenecks and improves overall throughput. We implement HFRWKV on the Alveo U50 and U280 platform. Experimental results show that compared to a CPU, a throughput improvement of 63.48$\times$ and an energy efficiency improvement of 139.17$\times$. Compared to GPUs, achieves a throughput improvement of 32.33$\times$ and an energy efficiency improvement of 171.36$\times$.
BiPrompt: Bilateral Prompt Optimization for Visual and Textual Debiasing in Vision-Language Models
Venue: arXiv cs.CV 2026
Year: 2026

Vision language foundation models such as CLIP exhibit impressive zero-shot generalization yet remain vulnerable to spurious correlations across visual and textual modalities. Existing debiasing approaches often address a single modality either visual or textual leading to partial robustness and unstable adaptation under distribution shifts. We propose a bilateral prompt optimization framework (BiPrompt) that simultaneously mitigates non-causal feature reliance in both modalities during test-time adaptation. On the visual side, it employs structured attention-guided erasure to suppress background activations and enforce orthogonal prediction consistency between causal and spurious regions. On the textual side, it introduces balanced prompt normalization, a learnable re-centering mechanism that aligns class embeddings toward an isotropic semantic space. Together, these modules jointly minimize conditional mutual information between spurious cues and predictions, steering the model toward causal, domain invariant reasoning without retraining or domain supervision. Extensive evaluations on real-world and synthetic bias benchmarks demonstrate consistent improvements in both average and worst-group accuracies over prior test-time debiasing methods, establishing a lightweight yet effective path toward trustworthy and causally grounded vision-language adaptation.
LocoScooter: Designing a Stationary Scooter-Based Locomotion System for Navigation in Virtual Reality
Venue: arXiv cs.HC 2026
Year: 2026

Virtual locomotion remains a challenge in VR, especially in space-limited environments where room-scale walking is impractical. We present LocoScooter, a low-cost, deployable locomotion interface combining foot-sliding on a compact treadmill with handlebar steering inspired by scooter riding. Built from commodity hardware, it supports embodied navigation through familiar, physically engaging movement. In a within-subject study (N = 14), LocoScooter significantly improved immersion, enjoyment, and bodily involvement over joystick navigation, while maintaining comparable efficiency and usability. Despite higher physical demand, users did not report increased fatigue, suggesting familiar movements can enrich VR navigation.
Streaming Hallucination Detection in Long Chain-of-Thought Reasoning
Venue: arXiv cs.AI 2026
Year: 2026

Long chain-of-thought (CoT) reasoning improves the performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. We suggest that hallucination in long CoT reasoning is better understood as an evolving latent state rather than a one-off erroneous event. Accordingly, we treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level hallucination signal that tracks the global evolution of the reasoning state over the entire trajectory. Overall, our approach enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence.
Toward Global Large Language Models in Medicine
Venue: arXiv cs.CL 2026
Year: 2026

Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed the landscape of medicine and holds promise for improving health care quality and expanding access to medical information globally. However, existing LLMs are primarily trained on high-resource languages, limiting their applicability in global medical scenarios. To address this gap, we constructed GlobMed, a large multilingual medical dataset, containing over 500,000 entries spanning 12 languages, including four low-resource languages. Building on this, we established GlobMed-Bench, which systematically assesses 56 state-of-the-art proprietary and open-weight LLMs across multiple multilingual medical tasks, revealing significant performance disparities across languages, particularly for low-resource languages. Additionally, we introduced GlobMed-LLMs, a suite of multilingual medical LLMs trained on GlobMed, with parameters ranging from 1.7B to 8B. GlobMed-LLMs achieved an average performance improvement of over 40% relative to baseline models, with a more than threefold increase in performance on low-resource languages. Together, these resources provide an important foundation for advancing the equitable development and application of LLMs globally, enabling broader language communities to benefit from technological advances.
NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation
Venue: arXiv cs.CV 2026
Year: 2026

We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation within a unified autoregressive architecture, NextFlow natively activates multimodal understanding and generation capabilities, unlocking abilities of image editing, interleaved content and video generation. Motivated by the distinct nature of modalities - where text is strictly sequential and images are inherently hierarchical - we retain next-token prediction for text but adopt next-scale prediction for visual generation. This departs from traditional raster-scan methods, enabling the generation of 1024x1024 images in just 5 seconds - orders of magnitude faster than comparable AR models. We address the instabilities of multi-scale generation through a robust training recipe. Furthermore, we introduce a prefix-tuning strategy for reinforcement learning. Experiments demonstrate that NextFlow achieves state-of-the-art performance among unified models and rivals specialized diffusion baselines in visual quality.
Unraveling MMDiT Blocks: Training-free Analysis and Enhancement of Text-conditioned Diffusion
Venue: arXiv cs.CV 2026
Year: 2026

Recent breakthroughs of transformer-based diffusion models, particularly with Multimodal Diffusion Transformers (MMDiT) driven models like FLUX and Qwen Image, have facilitated thrilling experiences in text-to-image generation and editing. To understand the internal mechanism of MMDiT-based models, existing methods tried to analyze the effect of specific components like positional encoding and attention layers. Yet, a comprehensive understanding of how different blocks and their interactions with textual conditions contribute to the synthesis process remains elusive. In this paper, we first develop a systematic pipeline to comprehensively investigate each block's functionality by removing, disabling and enhancing textual hidden-states at corresponding blocks. Our analysis reveals that 1) semantic information appears in earlier blocks and finer details are rendered in later blocks, 2) removing specific blocks is usually less disruptive than disabling text conditions, and 3) enhancing textual conditions in selective blocks improves semantic attributes. Building on these observations, we further propose novel training-free strategies for improved text alignment, precise editing, and acceleration. Extensive experiments demonstrated that our method outperforms various baselines and remains flexible across text-to-image generation, image editing, and inference acceleration. Our method improves T2I-Combench++ from 56.92% to 63.00% and GenEval from 66.42% to 71.63% on SD3.5, without sacrificing synthesis quality. These results advance understanding of MMDiT models and provide valuable insights to unlock new possibilities for further improvements.
Avatar Exposure and Strategic Coordination in Virtual Reality: Evidence from a Threshold Public Goods Experiment
Venue: arXiv cs.HC 2026
Year: 2026

Digital platforms increasingly support collective action initiatives, yet coordinating geographically dispersed users through digital interfaces remains challenging, particularly in threshold settings where success requires critical mass participation. This study investigates how avatar-based social representation in Virtual Reality (VR) influences coordination in threshold collective action problems. Through a randomized controlled experiment with 188 participants organized in 94 pairs, we examine whether brief avatar exposure affects perceived co-presence and coordination outcomes in a two-player threshold public goods game implemented as a real-effort recycling task. We manipulate a single design feature: participants either briefly interact through avatars before the main task (Pre-Task Avatar treatment) or complete an equivalent activity individually without peer visibility (No Pre-Task Avatar treatment). Our findings reveal that minimal avatar exposure significantly increases perceived co-presence and improves strategic coordination, though not through increased contribution quantity. Participants exposed to peer avatars achieve higher social welfare by coordinating to avoid wasteful over-contribution beyond the threshold. Additionally, we identify VR presence-the sense of 'being there' in the virtual environment-as a stronger predictor of task performance than co-presence itself. This research contributes to Information Systems theory by establishing causal pathways from specific design features to presence to coordination outcomes, demonstrates VR as a rigorous experimental methodology for IS research, and provides actionable insights for designing collaborative platforms supporting sustainability initiatives and threshold collective action problems.
LLM-Empowered Functional Safety and Security by Design in Automotive Systems
Venue: arXiv cs.SE 2026
Year: 2026

This paper presents LLM-empowered workflow to support Software Defined Vehicle (SDV) software development, covering the aspects of security-aware system topology design, as well as event-driven decision-making code analysis. For code analysis we adopt event chains model which provides formal foundations to systematic validation of functional safety, taking into account the semantic validity of messages exchanged between key components, including both CAN and Vehicle Signal Specification (VSS). Analysis of security aspects for topology relies on synergy with Model-Driven Engineering (MDE) approach and Object Constraint Language (OCL) rules. Both locally deployable and proprietary solution are taken into account for evaluation within Advanced Driver-Assistance Systems (ADAS)-related scenarios.
Deciding Serializability in Network Systems
Venue: arXiv cs.FL 2026
Year: 2026

We present the SER modeling language for automatically verifying serializability of concurrent programs, i.e., whether every concurrent execution of the program is equivalent to some serial execution. SER programs are suitably restricted to make this problem decidable, while still allowing for an unbounded number of concurrent threads of execution, each potentially running for an unbounded number of steps. Building on prior theoretical results, we give the first automated end-to-end decision procedure that either proves serializability by producing a checkable certificate, or refutes it by producing a counterexample trace. We also present a network-system abstraction to which SER programs compile. Our decision procedure then reduces serializability in this setting to a Petri net reachability query. Furthermore, in order to scale, we curtail the search space via multiple optimizations, including Petri net slicing, semilinear-set compression, and Presburger-formula manipulation. We extensively evaluate our framework and show that, despite the theoretical hardness of the problem, it can successfully handle various models of real-world programs, including stateful firewalls, BGP routers, and more.
Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization
Venue: arXiv cs.CR 2026
Year: 2026

We study differentially-private statistics in the fully dynamic continual observation model, where many updates can arrive at each time step and updates to a stream can involve both insertions and deletions of an item. Earlier work (e.g., Jain et al., NeurIPS 2023 for counting distinct elements; Raskhodnikova & Steiner, PODS 2025 for triangle counting with edge updates) reduced the respective cardinality estimation problem to continual counting on the difference stream associated with the true function values on the input stream. In such reductions, a change in the original stream can cause many changes in the difference stream, this poses a challenge for applying private continual counting algorithms to obtain optimal error bounds. We improve the accuracy of several such reductions by studying the associated $\ell_p$-sensitivity vectors of the resulting difference streams and isolating their properties. We demonstrate that our framework gives improved bounds for counting distinct elements, estimating degree histograms, and estimating triangle counts (under a slightly relaxed privacy model), thus offering a general approach to private continual cardinality estimation in streaming settings. Our improved accuracy stems from tight analysis of known factorization mechanisms for the counting matrix in this setting; the key technical challenge is arguing that one can use state-of-the-art factorizations for sensitivity vector sets with the properties we isolate. Empirically and analytically, we demonstrate that our improved error bounds offer a substantial improvement in accuracy for cardinality estimation problems over a large range of parameters.
POSEIDON: Physics-Optimized Seismic Energy Inference and Detection Operating Network
Venue: arXiv cs.LG 2026
Year: 2026

Earthquake prediction and seismic hazard assessment remain fundamental challenges in geophysics, with existing machine learning approaches often operating as black boxes that ignore established physical laws. We introduce POSEIDON (Physics-Optimized Seismic Energy Inference and Detection Operating Network), a physics-informed energy-based model for unified multi-task seismic event prediction, alongside the Poseidon dataset -- the largest open-source global earthquake catalog comprising 2.8 million events spanning 30 years. POSEIDON embeds fundamental seismological principles, including the Gutenberg-Richter magnitude-frequency relationship and Omori-Utsu aftershock decay law, as learnable constraints within an energy-based modeling framework. The architecture simultaneously addresses three interconnected prediction tasks: aftershock sequence identification, tsunami generation potential, and foreshock detection. Extensive experiments demonstrate that POSEIDON achieves state-of-the-art performance across all tasks, outperforming gradient boosting, random forest, and CNN baselines with the highest average F1 score among all compared methods. Crucially, the learned physics parameters converge to scientifically interpretable values -- Gutenberg-Richter b-value of 0.752 and Omori-Utsu parameters p=0.835, c=0.1948 days -- falling within established seismological ranges while enhancing rather than compromising predictive accuracy. The Poseidon dataset is publicly available at https://huggingface.co/datasets/BorisKriuk/Poseidon, providing pre-computed energy features, spatial grid indices, and standardized quality metrics to advance physics-informed seismic research.
An Automatic Pipeline for the Integration of Python-Based Tools into the Galaxy Platform: Application to the anvi'o Framework
Venue: arXiv cs.SC 2026
Year: 2026

The integration of command-line tools into the Galaxy platform is crucial for making complex computational methods accessible to a broader audience and ensuring reproducible research. However, the manual development of tool wrappers is a time-consuming, error-prone, and knowledge-intensive process. This bottleneck significantly affects the rapid deployment of new and updated tools, creating a gap between tool development and its availability to the scientific community. We have developed a novel, automated approach that directly translates Python tool interfaces into Galaxy-compliant tool wrappers. Our method leverages the argparse library, a standard for command-line argument parsing in Python. By embedding structured metadata within the metavar attribute of input and output arguments, our system programmatically parses the tool's interface to extract all necessary information. This includes parameter types, data formats, help text, and input/output definitions. The system then uses this information to automatically generate a complete and valid Galaxy tool XML wrapper, requiring no manual intervention. To validate the scalability and effectiveness of our approach, we applied it to the anvi'o framework, a comprehensive and complex bioinformatics platform comprising hundreds of individual programs. Our method successfully parsed the argparse definitions for the entire anvi'o suite and generated functional Galaxy tool wrappers. The resulting integration allows for the seamless execution of anvi'o workflows within the Galaxy environment. This work presents a significant advancement in the automation of tool integration for scientific workflow systems. By establishing a convention-based approach using Python's argparse library, we have created a scalable and generalizable solution that dramatically reduces the effort required to make command-line tools available in Galaxy.
Power-of-Two Quantization-Aware-Training (PoT-QAT) in Large Language Models (LLMs)
Venue: arXiv cs.CL 2026
Year: 2026

In Large Language Models (LLMs), the number of parameters has grown exponentially in the past few years, e.g., from 1.5 billion parameters in GPT-2 to 175 billion in GPT-3 to possibly more than trillion in higher versions. This raises a significant challenge for implementation, especially for Edge devices. Unlike cloud computing, memory and processing power for Edge devices are very limited, which necessitates developing novel ideas to make such applications feasible. In this work, we investigate compressing weights with a special quantization that limits numbers to only power-of-two (PoT). This helps save a huge amount of memory as only exponents need to be stored, more importantly, it significantly reduces processing power by replacing costly multiplication with low cost bit shifting. To overcome performance loss due to this strict quantization, we investigate Quantization Aware Training (QAT) to enhance performance through additional training. Results on GPT-2 124M show a major enhancement for quantized PoT model after additional training, with a perplexity enhancement of 66% and BERT-Score loss to baseline GPT-2 of 1%. The memory saving is estimated to be 87.5% while the inference speed is expected to be 3-10x faster with PoT quantization versus full-precision.
Classifying several dialectal Nawatl varieties
Venue: arXiv cs.CL 2026
Year: 2026

Mexico is a country with a large number of indigenous languages, among which the most widely spoken is Nawatl, with more than two million people currently speaking it (mainly in North and Central America). Despite its rich cultural heritage, which dates back to the 15th century, Nawatl is a language with few computer resources. The problem is compounded when it comes to its dialectal varieties, with approximately 30 varieties recognised, not counting the different spellings in the written forms of the language. In this research work, we addressed the problem of classifying Nawatl varieties using Machine Learning and Neural Networks.