Artificial Intelligence
EviAnchor: Mitigating Hallucinations in Large Vision-Language Models via Regional Visual Evidence Compensation
Sihang Jia, Shuliang Liu, Songbo Yang et al. (+1 more)
Large
vision-
language models (LVLMs) frequently generate content unsupported by visual inputs. Preliminary experiments show that visual evidence is primarily incorporated into answer-side representations in early-to-middle decoder layers, while its direct influence progressively weakens in later layers. This attenuation suggests that visual evidence acquired earlier may be insufficiently utilized during subsequent generation. Based on this observation, we propose EviAnchor, a training-free and single-branch inference framework that preserves and reactivates visual evidence throughout generation. EviAnchor introduces Regional Evidence Anchor (REA) slots to progressively aggregate dense visual
tokens into spatially structured representations. It then strengthens the current decision state's access to these visual anchors through decision-conditioned evidence routing, mitigating excessive dependence on textual context. Finally, the model resumes its native
Transformer computation to integrate the retrieved visual evidence with question semantics and generation history. Experiments across POPE, CHAIR, and MMHal-Bench demonstrate consistent improvements in visual grounding.
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All authors Sihang Jia, Shuliang Liu, Songbo Yang, Xuming Hu
Artificial Intelligence
HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering
Zafar Ali, Asad Khan, Nimbeshaho Thierry et al. (+3 more)
Multimodal question answering remains sensitive to noisy, incomplete, and weakly grounded evidence. Long unstructured contexts can introduce redundancy and encourage unsupported generation, while flat
retrieval may overlook relations needed for multi-step
reasoning. We present HANIA, a planner-guided
multimodal graph framework for evidence-grounded question answering. HANIA processes the supplied image and text using a frozen
vision-
language model to extract concise question-relevant visual evidence with explicit abstention. It then constructs an input-grounded
multimodal graph and applies a two-group finite-state planner to coordinate descriptive and relational evidence. Coverage-aware
pruning retains a compact evidence set based on relevance, graph confidence, concept coverage, and modality diversity. The selected passages, visual statements, and graph triples are provided to a frozen instruction-tuned decoder. We evaluate HANIA on ScienceQA using answer accuracy, evidence-filtering quality, evidence-budget sensitivity, and efficiency. The results show that structured evidence planning and compact graph-guided
retrieval can support competitive
multimodal question answering without target-
dataset fine-tuning or iterative
retrieval. The code is available at https://github.com/Zafar-southeast/HANIA.
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All authors Zafar Ali, Asad Khan, Nimbeshaho Thierry, Nabila Amir, Adam A. Q. Mohammed, Pavlos Kefalas
Game Theory
The Art of Calling the Winner by Asking Just Enough Questions: Competitive Preference Elicitation with Next-Best Queries
Nisarg Shah and Ziqi Yu
We study active elicitation of
agent preferences for collectively choosing among $m$ alternatives using prominent voting rules. We focus on the next-best query model, in which an
agent responds to a query by revealing their next favorite alternative, and measure the competitive ratio, which is the worst-case ratio between the number of queries made by the active elicitation
algorithm and the minimum number of queries needed to reveal the winning alternative(s) in hindsight. We show that sublinear competitive ratios are achievable for many positional scoring rules, whereas every Condorcet-consistent rule has competitive ratio linear in $m$. For Borda count, we develop two complementary techniques: level-wise
pruning, whose analysis extends to general concave scoring rules, and multi-scale score thresholding, which gives an $O(\sqrt m)$ worst-case guarantee for Borda. We also demonstrate strong empirical performance of level-wise
pruning on real data.
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Cryptography and Security
UiAs: User-Independent 3D Facial Anti-Spoofing via Multi-modal Wireless Signals
Zhiwei chen, Lebin Lyu, Yimo Zhang et al. (+7 more)
Face authentication is widely deployed in security-sensitive applications, while increasingly realistic 3D spoofing attacks pose growing threats. High-fidelity 3D masks can reproduce facial appearance and geometry but cannot replicate the intrinsic physical responses of living tissue, which can be actively probed by wireless signals. However, the resulting liveness cues captured by wireless signals are entangled with user-dependent facial geometry, limiting cross-user generalization. We present UiAs, a
multimodal user-independent 3D facial anti-spoofing system using electromagnetic (mmWave) and mechanical (acoustic) waves. The two modalities share similar user-dependent
geometric variations, allowing UiAs to suppress them through cross-modal subtraction while preserving modality-specific liveness cues. Their complementary physical responses further improve live/spoof discrimination. In practical deployments, multiple materials (e.g., skin, hair, eyeglasses, or face coverings) may also bias liveness representations, while spoofing materials are diverse and open-ended. UiAs addresses both through skin-anchored contrastive learning. We evaluate UiAs with real 3D spoofing attacks, which achieves 93.25% accuracy for unseen users without user-specific physical-signal enrollment.
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All authors Zhiwei chen, Lebin Lyu, Yimo Zhang, Dingyu Zhong, Yijie Li, Yichao Chen, Dian Ding, Jiguo Yu, Xiaosong Zhang, Yongzhao Zhang
Numerical Analysis
A Learnable Multigrid Framework via Graph Convolutions
Xiuping Wang and Li Luo
This paper presents a novel framework that integrates learnable graph convolutions with the
geometric multigrid method for solving partial differential equations (PDEs). The discretization of PDEs is first represented as a graph structure, enabling the application of graph convolutions to enhance the multigrid performance. By incorporating graph convolutions into the multigrid components such as smoothing and inter-grid transfer operators, we develop a learnable multigrid that can adaptively optimize its performance based on the underlying problem characteristics. In this framework, the graph convolutions are embedded directly within the multigrid cycle, effectively transforming the entire multigrid solver into a specialized neural network architecture, rather than combining a classical solver with surrogate models. The learnable multigrid framework is lightweight in terms of parameter count and requires minimal training effort to achieve good performance. Numerical experiments demonstrate the effectiveness of the proposed approach in solving some challenging problems, showing improved convergence rates compared to traditional multigrid methods. The generalizability of the learned parameters across different problem settings, including varying source terms, coefficients, geometries, and mesh sizes, is also investigated with proper weight-sharing and transfer-learning strategies.
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Computer Vision
AdapToPASS: Ambiguity-aware Adaptive Spherical Transformer for Panoramic Semantic Segmentation
Soumyaratna Debnath, Weiming Zhang, Shriram Damodaran et al. (+2 more)
Spherical
Transformers have emerged as a promising framework for panoramic semantic
segmentation (PASS) by operating directly on spherical geometry and alleviating projection-induced distortions. However, existing architectures often assume canonical spherical structure and stable viewpoints, which are frequently violated in real-world imagery due to unconstrained camera motion, introducing contextual and
geometric ambiguity. Consequently, they lack adaptive mechanisms to handle such ambiguity, limiting robustness to unseen spherical transformations. In contrast, biological perception is inherently ambiguity-aware, adapting to fluctuations in cue reliability caused by
geometric and contextual variations to maintain stable interpretation under complex transformations. Motivated by this, we first systematically analyze existing PASS architectures under various unseen spherical transformations. We then introduce AdapToPASS, a novel bio-inspired Spherical
Transformer that adaptively models contextual and
geometric ambiguities for robust PASS. At its core, Adaptive Spherical
Attention (AdaSpA) blocks dynamically modulate
attention according to local contextual ambiguity, mimicking adaptive, context-driven biological perception. To address
geometric ambiguity, AdapToPASS employs Bifocal Spherical Representation to balance field of view and spatial resolution, together with boundary supervision inspired by the boundary-sensitive nature of biological
vision. Across indoor and outdoor semantic
segmentation, AdapToPASS consistently outperforms prior state-of-the-art methods. Under unseen spherical transformations, it surpasses the next-best method by +13.38% relative mIoU on Stanford2D3D and +18.77% on WildPASS. We further introduce AdapToPASS-Swift, a lightweight variant with fewer than 2M parameters, which surpasses compact baselines while retaining robustness to spherical transformations.
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All authors Soumyaratna Debnath, Weiming Zhang, Shriram Damodaran, Dingwen Xiao, Addison Lin Wang
Robotics
GHOST in the Robots: Real-Time Exocentric Dual-Robot VR Teleoperation from Onboard Cameras
Yichen Wei, Faisal Zaghloul, Soujanya C Aryal et al. (+5 more)
Teleoperating multiple
robots simultaneously enables additional views and coordinated control. Yet, it poses fundamental challenges: the system must present sensor data cohesively and allow operators to manage multiple
robot bases, arms, and cameras while maintaining low latency. Current multi-
robot teleoperation systems require multiple operators, rely on autonomy, or restrict operators to high-level commands. We present GHOST: an open-source VR teleoperation system that enables single operator control of two mobile manipulators via direct lowlevel commands using only onboard sensing. GHOST creates an exocentric 3D workspace by aligning real-time point clouds from the
robots' RGB-D cameras, where scene coverage is improved through learning-based completion to aid operator spatial awareness. For control, the operator uses a mode-switching architecture to command either
robot individually or both
robots simultaneously. Experiments with 15 novice participants demonstrate 1.6-4x the success rate of an off-the-shelf tablet interface. For experts across nine challenging dual-
robot tasks, our system enabled completion of two tasks that were infeasible with the tablet, and was 1.47x faster on average than the tablet. Website and code: https://h2r.github.io/GHOST/.
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All authors Yichen Wei, Faisal Zaghloul, Soujanya C Aryal, Aanya K. Agrawal, Chengfan Li, Jason Xinyu Liu, James Tompkin, Stefanie Tellex
Robotics
DREAM: Deployment-Time Demonstration Generation via Real-to-Sim for Scalable Policy Adaptation
Makoto Sato, Tatsuya Matsushima, Yutaka Matsuo et al. (+1 more)
Vision-
language-action (VLA) models have made strong progress in
language-conditioned
robot manipulation, but improving their performance in a new workspace still often requires action-labeled data from that environment. Collecting such data by human teleoperation is costly, especially when each workspace, object arrangement, or task may require new demonstrations. We present DREAM, a framework that generates fine-tuning data for a pretrained VLA from a captured workspace and a
language instruction, without requiring a task-specific human demonstration. DREAM reconstructs the workspace, automatically translates the instruction into symbolic task goals and success criteria using a large
language model, and uses task-and-motion planning to generate feasible
robot trajectories. The planned trajectories are augmented across randomized object configurations, verified by the generated success criteria, and rendered into image-action examples for VLA fine-tuning. Through real-
robot experiments on
language-conditioned manipulation tasks, we study whether DREAM can serve as a scalable data-collection system for the deployment workspace by examining whether fine-tuning on its automatically generated data improves success over direct deployment and how its data-collection cost compares with human teleoperation when adapting a VLA to a new workspace.
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All authors Makoto Sato, Tatsuya Matsushima, Yutaka Matsuo, Yusuke Iwasawa
Cryptography and Security
A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges
Ruiyi Yan, Chenhui Chu, Zhongliang Yang et al. (+1 more)
Linguistic steganography hides secret messages in natural
language text. Large
language models (LLMs) have reshaped the field, but a systematic account of how these scattered advances collectively reshape the field in this new era is still missing. We provide one along four axes: 148 steganographic methods, 60 linguistic steganalysis countermeasures, 23 evaluation metrics, and 9 open challenges, each with taxonomies, reviews, and adoption analyses. Cutting across these axes, we identify five specific paradigm shifts in the LLM era: (1) from covertext modification to
prompt-only generation, (2) from heuristic to provable security, (3) from white-box symmetric LMs to black-box or asymmetric access, (4) from security-centric designs to joint optimization, and (5) from text-quality concerns to engineering issues. The survey aims to serve as both a reference and a roadmap for practical and responsible linguistic steganography in the LLM era.
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All authors Ruiyi Yan, Chenhui Chu, Zhongliang Yang, Yugo Murawaki
Artificial Intelligence
Nested Convex-Body Chasing for Online Optimization with Evolving Feasible Sets
Dhruv Sarkar and Aprameyo Chakrabartty
We study online optimization with nested shrinking feasible regions in two settings: convex optimization with nested evolving feasible sets (CONES) and
adversarial constrained online convex optimization (COCO). Our
algorithms separate loss control from
geometric movement: constrained minimizers and cumulative-loss tests preserve regret guarantees, while a deterministic resettable nested convex-body chaser limits movement. For CONES with a $G$-Lipschitz, $\mu$-strongly convex objective on a diameter-$D$ domain, we chase intersections of the current feasible set with adaptive objective sublevel sets. Using the Euclidean chasing ratio $O(\sqrt{d\log(1+d)})$, we obtain nonpositive regret at every prefix and movement $O(\sqrt{d\log(1+d)\,GD\log(eT)/\mu})$. The bound adapts to the increase in the constrained optimum value. In dimension two, with all other parameters fixed, every randomized
algorithm with terminal expected regret $O(T^\beta)$, $\beta<1$, suffers $\Omega(\sqrt{\log T})$ expected movement on some deterministic nested sequence, proving optimal horizon dependence. Under linear growth away from the constrained minimizer set, Steiner-point tracking yields movement independent of $T$. For general convex COCO, one-step-delayed chasing with regularized-leader resets gives regret $O(G_fD\sqrt{d\log(1+d)T})$ and cumulative constraint violation $O(G_gD\sqrt{d\log(1+d)T})$. For strongly convex losses, both are $O(d\log(1+d)\log(eT))$ when other parameters are fixed. These reductions replace the $O(d^{d/2})$ projection-path factor in prior analyses by the
polynomial dimension dependence of Euclidean nested convex-body chasing.
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Artificial Intelligence
Revolutionizing Turn-by-Turn Navigation with Cloud-Edge Deep Learning
Yiming Yang, Hao Fu, Fanxiang Zeng et al. (+3 more)
Turn-by-turn (TBT)
navigation systems are integral to modern driving experiences, providing real-time audio instructions to guide drivers safely to destinations. However, existing audio instruction policy often relies on rule-based approaches that struggle to balance informational content with cognitive load, potentially leading to driver confusion or missed turns in complex environments. To overcome these difficulties, we first model the generation of
navigation instructions as a multi-task learning problem by decomposing the audio content into combinations of modular elements. Then, we propose a novel deep learning framework that leverages the powerful spatiotemporal information processing capabilities of
Transformers and the strong multi-task learning abilities of Mixture of Experts (MoE) to generate real-time, context-aware audio instructions for TBT driving
navigation. A cloud-edge collaborative architecture is implemented to handle the computational demands of the model, ensuring scalability and real-time performance for practical applications. Experimental results in the real world demonstrate that the proposed method significantly reduces the yaw rate (the proportion of vehicles deviating from
navigation routes) compared to traditional methods, delivering clearer and more effective audio instructions. This is the first large-scale application of deep learning in driving audio
navigation, marking a substantial advancement in intelligent transportation and driving assistance technologies.
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All authors Yiming Yang, Hao Fu, Fanxiang Zeng, Xikai Yang, Yue Liu, Ning Guo
Machine Learning
Selective Disclosure of Hidden Directives in Reasoning Models: Behavioral Asymmetry and Steering
Zimo Shi, Xander Tifft and Wen Xing
Chain-of-thought (CoT)
reasoning traces are increasingly proposed as a mechanism for AI oversight: a monitor inspecting a model's
reasoning can, in principle, detect misbehavior invisible from outputs alone. This assumes CoT surfaces what a model is instructed to do regardless of the instructions given. We test this assumption along two axes. First, we introduce the Instruction-Compliance Gap (ICG): the difference in probability that a model's CoT explicitly references a hidden system
prompt directive when that directive is malign versus benign. Across 100 task pairs and 8 frontier
reasoning models from 5 families, we find consistent asymmetric disclosure, a higher probability of leaking malign hidden instructions than benign ones, in Qwen3-14B (Wilcoxon $p=0.0001$, $+13.9$pp), Qwen3-32B ($p=0.0011$, $+13.0$pp), Qwen3-235B ($p=0.035$, $+5.8$pp), and similar results with MiniMax-M2.5 and DeepSeek-R1. The detector has 100% precision against two independent blinded labelling passes, and an LLM monitor reading only the
reasoning trace reproduces the asymmetry in all 8 models against directive-free controls, identifying the specific directive in 82% of malign traces which the detector classifies as clean. Second, steering vectors extracted in MiniMax-M2.5 via Contrastive Activation Addition causally induce hiding from bare
prompts and suppress it from
prompts that would otherwise produce it, replicating in Qwen3-14B under a pre-registered design. Benign and malign-derived hiding vectors are highly similar (cosine $0.804$ in MiniMax-M2.5; $0.970$ in Qwen3-14B), implying that in these models the disclosure asymmetry arises from differential activation of a shared hiding direction rather than separate mechanisms.
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Machine Learning · Cross-listed from physics.flu-dyn
Spectral-Embedded Operator Learning for Three-Phase Interfacial Flow: A Ternary Cahn-Hilliard-Navier-Stokes Benchmark
Muhammad Abid, Arth Sojitra and Omer San
Operator-learning surrogates have been benchmarked largely on single-field, single-interface problems, leaving unclear whether architectural choices validated in those settings transfer to constrained, multiphase flows. We introduce a three-phase interfacial-flow
benchmark to examine whether the trunk coordinate representation matters for a multi-channel, interface-dominated target. The configuration consists of an air bubble rising through water, piercing a water-oil interface, and entraining a water plume into the oil within a bounded, wall-confined domain. Reference data are generated using a structure-preserving ternary Cahn-Hilliard-Navier-Stokes solver that algebraically preserves the simplex constraint. From 1,024 Sobol-sampled simulations spanning a nine-dimensional parameter space, we learn the mapping from physical parameters to five-channel space-time fields. We compare three parameter-matched DeepONet variants differing only in trunk representation: raw coordinates (DeepONet), random
Fourier features (FEDONet), and a fixed tensor-product Chebyshev dictionary (SEDONet). SEDONet reduces the test relative L2 error by 16.8% compared with FEDONet and by 24.0% compared with DeepONet, while improving all five output channels. Spatial and temporal error analyses localize the principal gains near the diffuse interfaces and after bubble breakthrough. The results indicate that the Chebyshev representation is particularly effective for the strongly non-periodic wall-normal and temporal structure of this three-phase flow.
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Computation and Language
Not All or None: Dynamic Construction of Target-aware Memory Graph for Conversational Stance Detection
Yifan Xiang, Bin Liang, Yuqi Huang et al. (+2 more)
Stance detection is crucial for understanding the underlying attitude of an expression towards a target. Conversational stance detection is a more challenging stance detection task in real-world social media scenarios, as it involves detecting the user's stance by leveraging the target-related historical statements across conversational sessions. In this paper, we propose target-aware
Memory Graph TamGraph, a novel method that dynamically leverages target-related statements for conversational stance detection. Instead of considering all preceding historical conversations or using no prior conversation information for stance detection, our TamGraph employs a stepwise,
entropy-guided backtracking mechanism to selectively activate
memory from historical conversations and dynamically constructs a target-aware graph to model the stance relations among utterances. This allows the exploitation of target-related information from the conversation history for stance detection while preventing the introduction of noise. Experimental results on both English and Chinese
benchmarks demonstrate that our TamGraph substantially improves LLM performance on conversational stance detection.
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All authors Yifan Xiang, Bin Liang, Yuqi Huang, Ruifeng Xu, Kam-Fai Wong
Artificial Intelligence
Agent2UCB: Agentic System for Generative Engine Optimization
Sheldon Yu, Rui Wang, Tong Yu et al. (+4 more)
Large
language model driven search engines such as Google AI Overviews and Perplexity have created new opportunities for Generative Engine Optimization (GEO) the practice of refining content to increase its likelihood of being cited or summarized by generative systems. We demonstrate
Agent2UCB, an agentic GEO system that autonomously improves content visibility through customized, feedback-driven optimization. For each content item, the system evaluates nine GEO strategies, identifies the most effective method, and accelerates selection using a bandit-based
Agent2UCB policy that integrates LLM priors with online reward signals. To monitor side effects, the system also provides a lightweight, text-only SEO readiness evaluation covering readability, topical coverage, and EEAT-style credibility. Experiments on GEO-Bench show consistent visibility gains while preserving SEO quality. The demo allows users to choose the websites of interest, observe the optimization workflow, and compare GEO/SEO outcomes across methods.
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All authors Sheldon Yu, Rui Wang, Tong Yu, Sungchul Kim, Doga Dogan, Junda Wu, Julian McAuley
Machine Learning
PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning
Soohyun Choi, Seonvin Cho and Songnam Hong
Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-horizon offline GCRL remains challenging because sparse goal-reaching signals must be propagated over many steps, while execution errors cannot be corrected through additional environment interaction. Existing methods address these challenges by improving long-range value estimation or reducing the effective decision horizon through subgoals, options, and action chunks. In several hierarchical methods, however, a selected subgoal specifies where to go, while the intervening state-space path remains implicit in an endpoint-conditioned low-level policy. To address this interface, we propose PathBridger, a hierarchical offline GCRL method that explicitly connects subgoal selection to short-horizon execution. PathBridger constructs a state-space bridge toward the selected intermediate endpoint and decodes it into a short executable action chunk using an inverse dynamics model. Experiments across the evaluated OGBench tasks demonstrate strong aggregate performance, with particularly large gains on the multi-object Cube manipulation tasks. Code: https://github.com/SChoish/PathBridger
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Computational Geometry
Bellman–Shoreline Search in Arbitrary Dimension: Exponential Vector Oscillators, Active Memory, Precession, and Effective Computability
Florentin Koch
We study online search for an unknown affine hyperplane in $\mathbb{R}^D$, for arbitrary fixed finite dimension. Building on a companion self-similar
cell reduction and support-function formulation, we ask how the mechanism changes as the normal space grows from $\mathbb{S}^0$ to $\mathbb{S}^{D-1}$. In $D=1$, alternation and productivity yield an equal-ripple principle and the exact stationary constant $9$. In $D=2$, the analogous relative
equilibrium is a logarithmic spiral whose bottleneck chord imposes tangency and selects the pitch. For exponential orbits $\Gamma(\sigma)=e^{\kappa\sigma}\omega(\sigma)$, we develop log-directional geometry, exponentially discounted
memory, gauges, and recursive hyperspherical parametrizations. Without a shape ansatz, the bottleneck admits a certificate supported by at most $D$ historical suppliers, and at globally worst phases the current point lies on the active face. Within regular chambers we derive exact variation, tangency, pitch, age, and, in $D=3$, delay-system identities. Odd-dimensional obstructions, antipodal subclasses, and harmonic towers provide constraints and explicit candidate families but are not claimed globally optimal. Finally, the N-COMP
theorem shows that $C_D^*$ is a computable real for every fixed finite $D$ and that algebraic polygonal $\varepsilon$-optimal
cells can in principle be synthesized. Numerical screening through $D=10$ is kept separate from the proved results.
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Computation and Language
RouteSparse: Input-Conditional Pattern Routing for Budgeted Long-Context Prefilling
Chao Zhang, Yifan Ji, Ziyan Zhang et al. (+2 more)
Dynamic sparse
attention can reduce the quadratic cost of long-context prefilling without changing model weights. MInference assigns each
attention head one pattern offline and estimates that pattern's sparse indices for every
prompt. This design is efficient, but it assumes that a head's preferred pattern and sparsity budget remain suitable across inputs. We introduce RouteSparse, which routes each head and
prompt segment among a small library of GPU-efficient sparse patterns. A low-cost probe estimates pattern utility and uncertainty; a latency-aware router then selects a pattern and budget, while uncertain cases fall back to a denser mask. We formulate routing as constrained risk minimization, derive an
attention-output error certificate from omitted probability mass, and evaluate the method on long-context
retrieval, question answering, summarization, and
language modeling. On Llama 3.1-8B-Instruct with 128K-
token prompts, RouteSparse achieves $6.5\times$ dense prefill speed with a 0.2-point RULER drop relative to dense
attention, compared with $7.3\times$ speed and a 1.6-point drop for fixed per-head routing. Ablations confirm that input-conditional routing, hardware profiling, and selective dense fallback each contribute to the quality–latency tradeoff.
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All authors Chao Zhang, Yifan Ji, Ziyan Zhang, Kai Song, Fei Lin
Machine Learning
Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems
Aidan Li, Uday Kiran Reddy Tadipatri, Mahan Fathi et al. (+2 more)
Koopman autoencoders (KAEs) seek a higher-dimensional latent representation in which nonlinear dynamics evolve linearly. However, many interesting systems have multiple basins of attraction, and both theoretical and empirical work has shown these multibasin systems cannot generally admit a single finite-dimensional global Koopman
embedding under standard assumptions. We posit that encoders with a sparsity-inducing objective encouraging few active latent coefficients will provide latent supports as an inspectable basin-modeling principle for Koopman autoencoders. We use these encoders producing sparse latents in training Sparse Koopman Autoencoders (SKAEs) without basin labels or other regime annotations, and treat the learned latent supports as model-produced regime variables after training. Across a range of procedurally generated multibasin systems and chaotic flows, we show that SKAEs have superior
forecasting performance compared to dense-latent KAEs. We also perform a mechanistic study that shows latent supports produced by SKAEs are both essential for the quality of the representation and useful for identifying basins on basin-interior states, whereas dense-latent KAEs collapse to an uninformative single support family. These results identify sparse latents and their corresponding supports as label-free, interpretable regime variables for Koopman learning in nonlinear systems with multiple local dynamical laws.
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All authors Aidan Li, Uday Kiran Reddy Tadipatri, Mahan Fathi, Sarath Chandar, Ross Goroshin
Information Theory
Neural Network-Based Delay-Doppler-Assisted Channel Estimation for OFDM
Mingcheng Nie, Hao Chang, Shuangyang Li et al. (+3 more)
Conventional orthogonal frequency division multiplexing (OFDM) channel estimation relies on single-tap estimation and time-frequency (TF) interpolation, which becomes unreliable in high-mobility channels because Doppler-induced inter-carrier interference (ICI) invalidates the underlying element-wise TF model. This paper proposes a neural-network-based delay-Doppler (DD)-assisted channel estimation framework for OFDM over doubly selective channels. We first derive an ICI-aware TF domain input-output relation and formulate channel estimation as a DD recovery problem. Unlike conventional sparse recovery approaches, the proposed framework does not require the equivalent DD domain channel vector to be strictly sparse, thereby accommodating the leakage induced by fractional delay and Doppler shifts. Since the data symbols are unknown during channel estimation, the sensing
matrix is constructed using only the known pilot symbols. As a result, data-induced interference is not explicitly modeled, leading to a structured mismatch in the pilot observations. To tackle this challenge, the adopted network iteratively exchanges observation- and channel-domain features through the sensing
matrix to learn the mapping from these contaminated observations to the equivalent DD domain channel, which is subsequently used to reconstruct the TF-domain channel. Simulation results show that the proposed method achieves lower normalized mean-square error and bit-error rate than conventional OFDM
estimators.
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All authors Mingcheng Nie, Hao Chang, Shuangyang Li, Haiyao Yu, Jiafu Hao, Yonghui Li
Computers and Society
Why Organizational Rules Fail AI: O-I-B-A-R and the Externalization of Decision Boundaries
Chao Li and Chunyi Zhao
AI systems increasingly enter organizations through policies, procedures, playbooks,
prompts, and other explicit representations of work. Yet formal descriptions often differ from situated practice, and captured know-what can omit the contextual know-how experts use when judgments are uncertain. We argue that a recurring class of organizational AI failures arises partly from a knowledge representation problem at the sociotechnical interface: the AI receives the procedure, while the organization operates on the procedure plus negative boundaries, runtime judgments, responsibility assignments, and learning history. We introduce O-I-B-A-R (OPEN, IS, BUT, ACTION, RESULT), a scaffold for externalizing these missing decision boundaries. IS records when a judgment holds. BUT records a concrete failure containing information beyond the logical negation of IS. Comparable success and failure cases are decomposed toward a minimally sufficient changing variable, which becomes a value-bearing decision dimension. A suspension represents the state in which the dimension is known but its current value is unresolved, specifying what must be measured, asked, retrieved, or escalated to a human. RESULT confirms a boundary, shifts a threshold, or exposes a new dimension. Incidents can generate new dimensions, unresolved values can define human-AI handoffs, and feedback can expand the decision space. We also identify a sociotechnical tension: durable and attributable failure histories can suppress the candor on which useful boundary knowledge depends. Externalization must therefore be designed as an organizational intervention with real costs and incentives.
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Artificial Intelligence
Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL
Shuomin Xue, Jingyuan Li, Ju Jia et al. (+2 more)
Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly in cross-platform user interest modeling and cross-modal semantic
alignment. In the real world, a practical defense against graph
adversarial perturbations is needed. However, we observe that the prevailing
adversarial purification methods are essentially domain-restricted defenses, which leads to the following shortcomings: (1) single-domain data provides insufficient structural and semantic diversity for learning robust purification criteria; (2) training of domain-specific defense strategies from scratch consumes substantial computational cost. To address the above limitations, we propose a transferable graph purification scheme, named ProGAP, to bridge
adversarial defense knowledge via vulnerability-aware graph
prompt learning. Firstly, to capture universal
adversarial patterns, a perturbation-capture edge detector is pretrained on data-rich graphs by jointly modeling topological and semantic information. Subsequently, to achieve more knowledge transfer w.r.t. robustness, vulnerability-aware
prompts are designed that inject targeted purification guidance into biased nodes, during which the pretrained detector adapts to distribution shifts in downstream graphs without parameter-laborious updates. Experimental results demonstrate that compared with state-of-the-art baselines, our ProGAP achieves 1%-9% improvement, and reduces the time consumption by up to 2.2x. The code for ProGAP is available at https://github.com/Lieyoufffff/ProGAP.
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All authors Shuomin Xue, Jingyuan Li, Ju Jia, Jingxuan Yu, Xiaojun Jia
Distributed Computing
Performance Evaluation of RED-ONION: A High-Speed Disk-to-Disk Transfer System
Keichi Takahashi, Hiroaki Kataoka, Takeo Hosomi et al. (+5 more)
Modern experimental instruments produce data faster than general-purpose file transfer interfaces can move it, so delivery to the computing infrastructure has become a bottleneck in the research process. At many universities and research institutes, moreover, the instruments that generate research data and the high-performance computing systems that analyze it are separated both geographically and organizationally, because each demands its own expertise and installation environment. Connecting the two seamlessly is a pressing challenge for data-driven science. This article presents RED-ONION, a high-speed disk-to-disk transfer system that connects research facilities, on campus and beyond, to a computing center. The system combines data transfer nodes, a dedicated high-bandwidth network, an all-flash parallel file system, and multi-threaded transfer software that parallelizes network transmission and storage access. The design targets the wire rate both along the entire path, from the read on the sender storage to the write on the receiver storage, and for a single file between one pair of nodes rather than only in aggregate over many files or nodes. We describe the end-to-end optimizations across the transfer software, the operating system, and the storage that this requires. We evaluate a prototype deployed over a 100 Gbps transpacific path between Atlanta and Tokyo with a 150 ms round-trip time, on which a single 1 TB file transfer reached 90 Gbps, delivering a terabyte in approximately 95 s. Moving a
dataset of this size therefore becomes a routine step, and the computing center serves an instrument as if the two were co-located.
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All authors Keichi Takahashi, Hiroaki Kataoka, Takeo Hosomi, Ayahiro Takaki, Yasunori Kakizawa, Shuichi Ihara, Nobuaki Hashizume, Susumu Date
Information Theory
DeepHSIC: Deep Learning-based Signal Detector for Hybrid Downlink IM-NOMA
Dung Nguyen Tran, Toan D. Gian, Tien-Hoa Nguyen et al. (+3 more)
DeepHSIC is introduced as a neural receiver for hybrid downlink IM-NOMA transmission. The considered scheme combines power-domain NOMA with a composite OFDM/OFDM-IM waveform, so that user information is mapped jointly onto constellation symbols, subcarrier-index patterns, and different power levels. Although maximum-likelihood detection can achieve strong reliability for this model, its search space grows rapidly with the number of users and subcarriers. Conventional SIC reduces part of this burden, but its sequential cancellation may still accumulate errors and does not fully exploit the structure of IM-NOMA signals. To address this limitation, the proposed detector embeds dedicated deep neural network modules into the receiver and replaces the most computationally demanding SIC operations with learned inference blocks. The receiver is trained for Rayleigh fading channels and uses preprocessed channel-output features to recover user symbols. Simulation results show that DeepHSIC reaches BER performance close to model-based detectors under both perfect and imperfect CSI while requiring substantially lower detection time. These results indicate that learned SIC-style detection is a practical candidate for scalable hybrid downlink IM-NOMA receivers.
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All authors Dung Nguyen Tran, Toan D. Gian, Tien-Hoa Nguyen, Mai Xuan Trang, Tien-Cuong Nguyen, Thien Van Luong
Computer Vision
DReSG: Diffusion Residuals for Stylized Gaussian Splatting
Zhongliang Liu, Wenjie Liu and Yang Li
Reference-guided stylization of scenes represented by 3D Gaussian Splatting (3DGS) is important for efficient and controllable 3D content creation. Existing VGG-feature-based 3D stylization methods provide stable rendered-view optimization, but often under-represent expressive reference style cues;
diffusion models offer stronger image priors, yet direct per-view or score-based
diffusion guidance can lead to view drift, local artifacts, and hard-to-control appearance updates. We present DReSG, a 3D-grounded residual-feedback framework for stylized Gaussian splatting. DReSG represents
attention-guided
diffusion proposals as residual targets relative to the current render, and progressively absorbs these residuals into a shared Gaussian scene through multi-view Gaussian feedback. To make this feedback stable and controllable, DReSG modulates residual strength during target construction and combines coverage-aware view selection with conflict-filtered color updates during multi-view fitting. Extensive experiments demonstrate that DReSG achieves competitive reference-guided stylization while better preserving scene structure and cross-view stability. Our project page is available at https://vpx-ecnu.github.io/DReSG-website/.
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Machine Learning
NVE: A Separability and Coverage-Aware Internal Validation Metric for Biclustering
Paritosh Tiwari, I Navin Kumar, James C. Bezdek et al. (+1 more)
Biclustering, or co-clustering, aims to discover coherent submatrices by grouping rows and columns of a data
matrix simultaneously. This local two-dimensional structure makes validation more difficult than in ordinary clustering, where internal indices usually rely on compactness and separation in a single shared feature space. Existing popular internal biclustering measures such as Mean Squared Residue (MSR), and Virtual Error (VE) mainly evaluate within-bicluster coherence. Although useful, these measures do not directly assess whether the extracted biclusters are mutually distinct or whether they explain a meaningful portion of the data
matrix. This paper investigates Normalised Virtual Error (NVE), an internal validation metric that extends VE using a super-bicluster normalization strategy. By comparing the VE of each bicluster with the VE obtained after merging it with other biclusters, NVE introduces a relative notion of separability and redundancy. We also study a coverage-adjusted variant, NVE\textsubscript{cov}, which penalizes solutions that obtain low error by selecting only very small submatrices. Through controlled synthetic
benchmarks and yeast gene-expression
datasets, we examine whether NVE and NVE\textsubscript{cov} provide information beyond standard coherence-based metrics. The results show that NVE is sensitive to redundant and poorly separated biclusters, while NVE\textsubscript{cov} changes solution rankings when low-error biclusters cover only a negligible part of the
matrix. These findings suggest that NVE-based measures are useful complementary criteria for internal co-clustering validation, especially when coherence, separability, and coverage must be considered jointly.
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All authors Paritosh Tiwari, I Navin Kumar, James C. Bezdek, Punit Rathore
Computer Vision
Di$^2$CycleSB: Towards High-Quality Unsupervised Nighttime Visibility Enhancement via Schrödinger Bridge Transformer
Hanting Li, Xin Sun, Wei Ye et al. (+2 more)
Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and decomposing them through prior-driven regularization, yet they are often limited by hand-crafted priors and ill-posed nature of decomposition. This work proposes Di$^2$CycleSB, a unsupervised Cycle Schrödinger Bridge
Transformer framework guided by dynamic integral image priors, for high-quality unsupervised nighttime visibility enhancement. Specifically, a novel light-effect
estimator is introduced to parameterize Gaussian-like adaptive priors by aggregating dynamic integral image representations for non-uniform glow estimation. Then, we propose a prior-informed Generator that exploits light-effect representations to guide long-range dependency modeling within our specific
Transformer blocks. We formulate light-effect suppression as a Schrödinger bridge problem and construct forward and backward bridges with cycle consistency constraints to achieve visually pleasing enhancement. Extensive experiments on real-world
datasets demonstrate the remarkable effectiveness of our Di$^2$CycleSB in enhancing nighttime visibility. In particular, it achieves effective end-to-end light-effect suppression without any regularization constraints and image decomposition. The code and models are available at https://github.com/LHTcode/Di2CycleSB.
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All authors Hanting Li, Xin Sun, Wei Ye, Jungong Han, Liang-jie Zhang
Human-Computer Interaction
Sharing Roughness with Hand-Outline Visualization to Reduce Sensory Asymmetry in VR Collaboration
Minju Baeck, Yoonseok Shin, Hyunjin Lee et al. (+3 more)
In collaborative VR, asymmetric access to haptic hardware creates a critical information gap: tactile evidence remains private to the haptic user, hindering the shared understanding needed for joint decision-making. While prior work has explored crossmodal sensory cues in virtual environments, it remains unclear how such cues should be designed for asymmetric collaboration, where collaborators receive information through different modalities. In our setting, the haptic user feels roughness through fingertip vibration, whereas the non-haptic user relies on
vision alone. To reduce this asymmetry, we propose externalizing an object's tactile state through a glanceable hand-outline visual proxy. Specifically, we examine whether abstract visual roughness cues based on line shape and motion can encode three discrete roughness levels for both haptic and non-haptic users. Two preliminary studies establish a shared visual semantics by identifying visually distinguishable cues for non-haptic users and validating their visuo-haptic correspondence for haptic users. In a main study of a collaborative sorting task, showing this visualization on both users' hands significantly reduced completion time relative to a no-visualization baseline. Moreover, NU-side cue visibility was associated with higher confidence and perceived contribution for the non-haptic user. These findings show that hand-anchored abstract visual cues provide a lightweight means of externalizing object tactile state, reducing information asymmetry without compromising social presence.
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All authors Minju Baeck, Yoonseok Shin, Hyunjin Lee, Boram Yoon, Sang Ho Yoon, Woontack Woo
Computer Vision
sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows
Dongjin Kim, Donggoo Jung, Sungyong Baik et al. (+1 more)
Noise poses a widespread challenge in signal processing, particularly when it comes to denoising images. Although convolutional neural networks (CNNs) have exhibited remarkable success in this field, they are predicated upon the belief that noise follows established distributions, which restricts their practicality when dealing with real-world noise. To overcome this limitation, several efforts have been taken to collect noisy image
datasets from the real world. Generative methods, employing techniques such as generative
adversarial networks (GANs) and normalizing flows (NFs), have emerged as a solution for generating realistic noisy images. Recent works model noise using camera metadata, however requiring metadata even for sampling phase. In contrast, in this work, we aim to estimate the underlying camera settings, enabling us to improve noise modeling and generate diverse noise distributions. To this end, we introduce a new NF framework that allows us to both classify noise based on camera settings and generate various noisy images. Through experimental results, our model demonstrates exceptional noise quality and leads in denoising performance on
benchmark datasets.
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All authors Dongjin Kim, Donggoo Jung, Sungyong Baik, Tae Hyun Kim
Computer Vision
DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering
Zihan Huang, Shihang Wu, Junle Liu et al. (+4 more)
Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of
Multimodal Large
Language Models (MLLMs). Applying restoration tools before Visual Question Answering (VQA) is an intuitive solution. However, existing restoration approaches remain limited, as manually designing and executing restoration strategies is labor-intensive and requires domain expertise. Agentic restoration offers new possibilities for automation, yet existing frameworks primarily target natural images and pursue perceptual quality, overlooking that restoration should serve downstream tasks rather than optimize generic image quality metrics. To this end, we explore the value of agentic restoration for real-world degraded document VQA and propose DocIntent, a training-free Answerability-Guided Agentic Restoration framework. DocIntent first assesses question answerability, then identifies task-relevant degradations and selectively invokes restoration tools. A Comparison-Based Rollback mechanism validates each restoration step and reverts it when question-relevant evidence becomes less decipherable. The entire process requires no additional pretrained degradation classifier or image quality assessment model. Extensive experiments on the WildDoc
benchmark show that DocIntent consistently improves the average score and consistency of different open- and closed-source MLLMs. The code and experimental data will be publicly available.
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All authors Zihan Huang, Shihang Wu, Junle Liu, Peirong Zhang, Yongxin Shi, Xuhan Zheng, Lianwen Jin