OpenRaiser/Intern-Atlas huggingface dataset:
Exploring Spatiotemporal Feature Propagation for Video-Level Compressive Spectral Reconstruction: Dataset, Model and Benchmark
Venue: arXiv cs.CV 2026
Year: 2026

Recently, Spectral Compressive Imaging (SCI) has achieved remarkable success, unlocking significant potential for dynamic spectral vision. However, existing reconstruction methods, primarily image-based, suffer from two limitations: (i) Encoding process masks spatial-spectral features, leading to uncertainty in reconstructing missing information from single compressed measurements, and (ii) The frame-by-frame reconstruction paradigm fails to ensure temporal consistency, which is crucial in the video perception. To address these challenges, this paper seeks to advance spectral reconstruction from the image level to the video level, leveraging the complementary features and temporal continuity across adjacent frames in dynamic scenes. Initially, we construct the first high-quality dynamic hyperspectral image dataset (DynaSpec), comprising 30 sequences obtained through frame-scanning acquisition. Subsequently, we propose the Propagation-Guided Spectral Video Reconstruction Transformer (PG-SVRT), which employs a spatial-then-temporal attention to effectively reconstruct spectral features from abundant video information, while using a bridged token to reduce computational complexity. Finally, we conduct simulation experiments to assess the performance of four SCI systems, and construct a DD-CASSI prototype for real-world data collection and benchmarking. Extensive experiments demonstrate that PG-SVRT achieves superior performance in reconstruction quality, spectral fidelity, and temporal consistency, while maintaining minimal FLOPs. Project page: https://github.com/nju-cite/DynaSpec
Dial E for Ethical Enforcement: institutional VETO power as a governance primitive
Venue: arXiv cs.CY 2026
Year: 2026

The persistent militarization of large reasoning models stems not from technical necessity but from governance arrangements that strip researchers of meaningful authority to refuse harmful transfers and deployments. Existing accountability mechanisms such as model cards and responsible AI statements operate as reputational signals detached from decision making architecture. We identify institutional veto power as a missing governance primitive: a formal authority to halt subsequent use or distribution of research when credible risks of weaponization emerge. Drawing on precedents in nuclear nonproliferation and biomedical ethics, the paper maps unprotected veto points across the research lifecycle, diagnose why compliance without enforceable constraints fails, and offer concrete institutional designs that embed veto authority while reducing the risk of political capture. The paper argues that communities most vulnerable to military uses must lead governance design, and that institutional veto power is a prerequisite for converting symbolic safeguards into enforceable responsibility and for achieving meaningful model disarmament.
Retrodictive Forecasting: A Proof-of-Concept for Exploiting Temporal Asymmetry in Time Series Prediction
Venue: arXiv cs.LG 2026
Year: 2026

We propose a retrodictive forecasting paradigm for time series: instead of predicting the future from the past, we identify the future that best explains the observed present via inverse MAP optimization over a Conditional Variational Autoencoder (CVAE). This conditioning is a statistical modeling choice for Bayesian inversion; it does not assert that future events cause past observations. The approach is theoretically grounded in an information-theoretic arrow-of-time measure: the symmetrized Kullback-Leibler divergence between forward and time-reversed trajectory ensembles provides both the conceptual rationale and an operational GO/NO-GO diagnostic for applicability. We implement the paradigm as MAP inference over an inverse CVAE with a learned RealNVP normalizing-flow prior and evaluate it on six time series cases: four synthetic processes with controlled temporal asymmetry and two ERA5 reanalysis datasets (wind speed and solar irradiance). The work makes four contributions: (i) a formal retrodictive inference formulation; (ii) an inverse CVAE architecture; (iii) a model-free irreversibility diagnostic; and (iv) a falsifiable validation protocol with four pre-specified predictions. All pre-specified predictions are empirically supported: the diagnostic correctly classifies all six cases; the learned flow prior improves over an isotropic Gaussian baseline on GO cases; the inverse MAP yields no spurious advantage on time-reversible dynamics; and on irreversible GO cases, it achieves competitive or superior RMSE relative to forward baselines, with a statistically significant 17.7% reduction over a forward MLP on ERA5 solar irradiance. These results provide a structured proof-of-concept that retrodictive forecasting can constitute a viable alternative to conventional forward prediction when statistical time-irreversibility is present and exploitable.
Position: Evaluation of Visual Processing Should Be Human-Centered, Not Metric-Centered
Venue: arXiv cs.CV 2026
Year: 2026

This position paper argues that the evaluation of modern visual processing systems should no longer be driven primarily by single-metric image quality assessment benchmarks, particularly in the era of generative and perception-oriented methods. Image restoration exemplifies this divergence: while objective IQA metrics enable reproducible, scalable evaluation, they have increasingly drifted apart from human perception and user preferences. We contend that this mismatch risks constraining innovation and misguiding research progress across visual processing tasks. Rather than rejecting metrics altogether, this paper calls for a rebalancing of evaluation paradigms, advocating a more human-centered, context-aware, and fine-grained approach to assessing the visual models' outcomes.
Stateful Cross-layer Vision Modulation
Venue: arXiv cs.CV 2026
Year: 2026

Recent multimodal large language models (MLLMs) widely adopt multi-layer visual feature fusion to enhance visual representation. However, existing approaches typically perform static concatenation or weighted aggregation after visual encoding, without intervening in the representation formation process itself. As a result, fine-grained details from early layers may be progressively suppressed during hierarchical abstraction. Moreover, directly introducing shallow-layer features into the language model often leads to semantic distribution mismatch with the visual feature space that the LLM's cross-attention layers were pretrained on, which typically requires additional adaptation or fine-tuning of the LLM. To address these limitations, we revisit visual representation learning from the perspective of representation evolution control and propose a cross-layer memory-modulated vision framework(SCVM). Specifically, we introduce a recursively updated cross-layer memory state inside the vision encoder to model long-range inter-layer dependencies. We further design a layer-wise feedback modulation mechanism that refreshes token representations at each layer based on the accumulated memory, thereby structurally regulating the representation evolution trajectory. In addition, we incorporate an auxiliary semantic alignment objective that explicitly supervises the final memory state, encouraging progressive compression and reinforcement of task-relevant information. Experimental results on multiple visual question answering and hallucination evaluation benchmarks demonstrate that SCVM achieves consistent performance improvements without expanding visual tokens, introducing additional vision encoders, or modifying or fine-tuning the language model.
Polynomial Mixing for Efficient Self-supervised Speech Encoders
Venue: arXiv cs.CL 2026
Year: 2026

State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms. However, the quadratic complexity of self-attention in both memory and computation imposes significant constraints on scalability. In this work, we propose a novel token-mixing mechanism, the Polynomial Mixer (PoM), as a drop-in replacement for multi-head self-attention. PoM computes a polynomial representation of the input with linear complexity with respect to the input sequence length. We integrate PoM into a self-supervised speech representation learning framework based on BEST-RQ and evaluate its performance on downstream speech recognition tasks. Experimental results demonstrate that PoM achieves a competitive word error rate compared to full self-attention and other linear-complexity alternatives, offering an improved trade-off between performance and efficiency in time and memory.
SCOUT: Fast Spectral CT Imaging in Ultra LOw-data Regimes via PseUdo-label GeneraTion
Venue: arXiv cs.CV 2026
Year: 2026

Noise and artifacts during computed tomography (CT) scans are a fundamental challenge affecting disease diagnosis. However, current methods either involve excessively long reconstruction times or rely on data-driven models for optimization, failing to adequately consider the valuable information inherent in the data itself, especially medical 3D data. This work proposes a reconstruction method under ultra-low raw data conditions, requiring no external data and avoiding lengthy pre-training processes. By leveraging spatial nonlocal similarity and the conjugate properties of the projection domain to generate pseudo-3D data for self-supervised training, high-fidelity results can be achieved in a very short time. Extensive experiments demonstrate that this method not only mitigates detector-induced ring artifacts but also exhibits unprecedented capabilities in detail recovery. This method provides a new paradigm for research using unlabeled raw projection data. Code is available at https://github.com/yqx7150/SCOUT.
Towards Non-Latin Text and Layout Personalization for Enhanced Readability
Venue: arXiv cs.HC 2026
Year: 2026

Reading has always been an integral part of both professional and personal life. Character and layout recognition and understanding by computers are well-explored areas. Nevertheless, how characters and layout are read and perceived by humans remains relatively underexplored. This work contributes to the field of human-document interaction (HDI) by investigating the effects of character and layout personalization on readability. The paper presents an empirical study on how parts-of-speech (POS)-based character and layout modifications can lead to overall improvements in both reading comprehension and memorization for two non-segmented, non-Latin writing systems: Khmer and Japanese. The experimental results from 43 participants suggest that, by bolding POS-derived content words, Khmer readers perform better on both reading comprehension and memorisation tasks, with a significant effect (p-values of 0.03 and 0.04, respectively). A similar overall tendency is also observed in a pilot study among Japanese readers (10 participants) using syntactic color-coding. In addition, the analyses of reading time, answering time, and perceived difficulty reveal that the proposed text styling technique does not increase any perceived difficulty, cognitive load, or reading effort for the Khmer readers. However, the Japanese readers experienced a decrease in reading speed. This study and its findings represent a significant step towards enabling dynamic, script-dependent personalization of character and layout to optimize human readability.
Local Differential Privacy for Molecular Communication Networks
Venue: arXiv cs.IT 2026
Year: 2026

Molecular communication (MC) enables information exchange in nanoscale sensor networks operating in biological environments, yet privacy remains largely unaddressed. We integrate local differential privacy (LDP) into diffusion-based MC by privatizing each user's measurement at the transmitter and conveying the resulting randomized report over the MC channel. To our knowledge, this is the first systematic LDP implementation for diffusion-based MC, enabling privacy-preserving aggregate data analysis for in-body health monitoring and other population-scale sensing applications. We benchmark major LDP mechanisms under a realistic channel model. Simulation results show that k-ary Randomized Response (KRR) and Optimized Local Hashing (OLH) achieve the lowest average $\ell_1$ distribution-estimation error under the MC channel: OLH is preferable when channel resources are sufficient and the number of possible user values (alphabet size) $k$ is moderate to large, whereas the KRR is more robust as the MC transmission quality deteriorates. We further propose RLIM-LDP, which combines run-length-limited ISI-mitigation (RLIM) coding with LDP coding. Extensive simulation results demonstrate that RLIM-LDP improves end-to-end reliability and reduces the final distribution-estimation error when time and molecule resources are limited.
STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification
Venue: arXiv cs.CV 2026
Year: 2026

Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose STMI, a novel multi-modal learning framework consisting of three key components: (1) Segmentation-Guided Feature Modulation (SFM) module leverages SAM-generated masks to enhance foreground representations and suppress background noise through learnable attention modulation; (2) Semantic Token Reallocation (STR) module employs learnable query tokens and an adaptive reallocation mechanism to extract compact and informative representations without discarding any tokens; (3) Cross-Modal Hypergraph Interaction (CHI) module constructs a unified hypergraph across modalities to capture high-order semantic relationships. Extensive experiments on public benchmarks (i.e., RGBNT201, RGBNT100, and MSVR310) demonstrate the effectiveness and robustness of our proposed STMI framework in multi-modal ReID scenarios.
Towards Universal Khmer Text Recognition
Venue: arXiv cs.CV 2026
Year: 2026

Khmer is a low-resource language characterized by a complex script, presenting significant challenges for optical character recognition (OCR). While document printed text recognition has advanced because of available datasets, performance on other modalities, such as handwritten and scene text, remains limited by data scarcity. Training modality-specific models for each modality does not allow cross-modality transfer learning, from which modalities with limited data could otherwise benefit. Moreover, deploying many modality-specific models results in significant memory overhead and requires error-prone routing each input image to the appropriate model. On the other hand, simply training on a combined dataset with a non-uniform data distribution across different modalities often leads to degraded performance on underrepresented modalities. To address these, we propose a universal Khmer text recognition (UKTR) framework capable of handling diverse text modalities. Central to our method is a novel modality-aware adaptive feature selection (MAFS) technique designed to adapt visual features according to a particular input image modality and enhance recognition robustness across modalities. Extensive experiments demonstrate that our model achieves state-of-the-art (SoTA) performance. Furthermore, we introduce the first comprehensive benchmark for universal Khmer text recognition, which we release to the community to facilitate future research. Our datasets and models can be accessible via this gated repository\footnote{in review}.
The On-Chain and Off-Chain Mechanisms of DAO-to-DAO Voting
Venue: arXiv cs.CR 2026
Year: 2026

Voting is the primary mechanism through which Decentralised Autonomous Organisations (DAO) reach decisions. Although transparent, the voting process can be complex: it can involve many interacting smart contracts. The nexus of the decision-making process can be relocated and the true voter demographic obfuscated. Furthermore, DAOs can govern other DAOs -- metagovernance. We present a method for identifying DAO-to-DAO metagovernance on the Ethereum blockchain. We focus on the links between DAOs and token contracts. We employ a signature-matching algorithm to flexibly handle a variety of DAO frameworks and voting schemes. Once we establish token-to-DAO relationships, we gather and process voting data to produce a list of metagovernance relationships. We apply this algorithm to an initial set of sixteen DAOs and we extend the dataset as more DAOs are identified. We produce a metagovernance network with 61 DAOs and 72 metagovernance relationships. We examine three case studies that show metagovernance of various forms: strategic, decisive, and centralised where a DAO becomes a nexus for metagovernance. We demonstrate that metagovernance obscures voting context and introduces entities driven by self-interest that can significantly influence governance. We highlight instances of metagovernance between DAOs operating on the Ethereum blockchain where current governance tools inadequately reveal such dynamics. To preserve the transparency-centric ethos of DAOs and mitigate risks associated with metagovernance, there is a pressing need for enhanced tools to address such issues.
LLM-Powered Automatic Theorem Proving and Synthesis for Hybrid Systems and Game
Venue: arXiv cs.LO 2026
Year: 2026

Hybrid games model cyber-physical systems (CPS), like cars, trains, and airplanes, where discrete control decisions interact with continuous physical dynamics. We use Large Language Models (LLMs) to scale formal verification and synthesis for hybrid systems and games for a high-level hybrid games symbolic logic, differential game logic (dGL). This combination of a logic with the right expressivity and automation of the interactive theorem proving process using LLMs brings within reach a challenging class of CPS verification/synthesis problems, that were previously well out of range of automatic theorem proving. We demonstrate it on five challenging case studies, all beyond the reach of existing automatic techniques. Verification succeeds for all five, and the synthesis of control solutions succeeds for four of the five.
ResGene-T: A Tensor-Based Residual Network Approach for Genomic Prediction
Venue: arXiv cs.LG 2026
Year: 2026

In this work, we propose a new deep learning model for Genomic Prediction (GP), which involves correlating genotypic data with phenotypic. The genotypes are typically fed as a sequence of characters to the 1D-Convolution Neural Network layer of the underlying deep learning model. Inspired by earlier work that represented genotype as a 2D-image for genotype-phenotype classification, we extend this idea to GP, which is a regression task. We use a ResNet-18 as the underlying architecture, and term this model as ResGene-2D. Although the 2D-image representation captures biological interactions well, it requires all the layers of the model to do so. This limits training efficiency. Thus, as seen in the earlier work that proposed a 2D-image representation, our ResGene-2D performs almost the same as other models (3% improvement). To overcome this, we propose a novel idea of converting the 2D-image into a 3D/ tensor and feed this to the ResNet-18 architecture, and term this model as ResGene-T. We evaluate our proposed models on three crop species having ten phenotypic traits and compare it with seven most popular models (two statistical, two machine learning, and three deep learning). ResGene-T performs the best among all these seven methods (gains from 14.51% to 41.51%).
The Disintegration of Free Speech
Venue: arXiv cs.CY 2026
Year: 2026

This Article examines the constitutional status of AI-mediated communication under the First Amendment. Social media platforms, increasingly integrated with generative AI systems, now function as core public communication infrastructures. Within this environment, AI-generated pornography and large-scale political misinformation have produced significant dignitary and democratic harms. In response, states have enacted regulations requiring platforms to remove certain content, disclose recommendation practices, or redesign moderation systems. These measures, however, collide with prevailing First Amendment doctrine. The Article argues that under existing jurisprudence, AI-generated content is protected speech, and regulations targeting platform moderation practices are likely unconstitutional. Since the 1970s, the Supreme Court has shifted from a structural concern with the free circulation of information toward a strong protection of editorial autonomy, understood as control over authorship, expressive identity, and freedom from compelled attribution. Once content moderation is characterized as editorial judgment, regulatory mandates that compel or restrict such practices presumptively violate the Free Speech Clause. The Article concludes that this doctrinal trajectory risks severing the First Amendment from its democratic foundations and calls for a reconstruction attentive to automated content production, platform infrastructure, and concentrated communicative power.
Scalable overset computation between a forest-of-octrees- and an arbitrary distributed parallel mesh
Venue: arXiv cs.DC 2026
Year: 2026

We introduce an algorithm that performs a one-directional mesh overset of a parallel forest of octrees with another distributed mesh of unrelated partition. The forest mesh consists of several adaptively refined octrees. Individual smooth mappings for every tree allow to represent a broad range of geometric domains. The other mesh is generic and defines a distributed set of query points, e.g. stemming from a quadrature rule applied to each cell. We face the problem of finding data for all queries in the remote forest. The forest is partitioned according to its natural Morton ordering. Thus, the partition boundaries can be encoded globally with one Morton index per process, which allows for precise, communication-free searching of the queries in the partition geometry. This is necessary to organize non-blocking communication of the queries to the relevant processes only. In a subsequent local search of the forest, we process the incoming queries and return the data of interest to each query's origin. The algorithm can be generalized, for example to load balancing of the overset, or adaptive refinement of the meshes around their intersection area. In 2D and 3D example scenarios we demonstrate the algorithm's performance and scalability to 12,288 processes.
Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2026
Year: 2026

Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free sampling acceleration methods are typically developed independently, leaving the overall performance and compatibility among these methods unexplored. In this paper, we bridge this gap by systematically elucidating the design space, and our comprehensive experiments identify the sampling time schedule as the most pivotal factor. Inspired by the geometric properties of diffusion models revealed through the Frenet-Serret formulas, we propose constant total rotation schedule (TORS), a scheduling strategy that ensures uniform geometric variation along the sampling trajectory. TORS outperforms previous training-free acceleration methods and produces high-quality images with 10 sampling steps on Flux.1-Dev and Stable Diffusion 3.5. Extensive experiments underscore the adaptability of our method to unseen models, hyperparameters, and downstream applications.