Hardware Architecture
Zephyr: An Efficient Audio Denoising System Using Spiking Neural Networks Enabled With A Sparsity-Aware Flexible FPGA PE Array
Cheng-En Chang, Chi-Wei Kao, Chung-Lun Yang et al. (+4 more)
In this work we look to neuromorphic computing to solve the power consumption problem that audio denoising neural networks face on edge devices like smartphones, wireless headphones and hearing aids. Spiking neural networks (SNNs) have the potential to solve this problem due to their high activation sparsity and low complexity, however many SOTA SNNs require hardware that supports a mixture of operations to be able to fully perform inference. To solve this problem, we convert SOTA audio denoising neural network Spiking-FullSubNet to a hardware friendly version showing that via QAT and activation function simplification we can achieve $\approx28\times$ improvement in power consumption to 52.9nJ per 32ms audio frame when calculated for custom digital hardware in a 45nm process node. We then propose a digital
circuit which by means of a sparsity-aware flexible PE array can perform inference of the heterogeneous compute load of Spiking-FullSubNet, and validate this
circuit on a PYNQ-Z1 FPGA achieving a real-time factor of 0.727 at 100MHz.
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All authors Cheng-En Chang, Chi-Wei Kao, Chung-Lun Yang, Yan-Lin Jiang, Yi-Chen Huang, Sebastian Fieldhouse, Kea-Tiong Tang
Sound
Do Music Generative Models Understand Musical Qualities? Automatic Music Evaluation with Model-Intrinsic Signals
Xiaosha Li, Chun Liu and Ziyu Wang
Current music generative models can produce high-quality music, but does this ability imply that they “understand” the musical qualities of their outputs, and is that understanding aligned with human evaluation? Previous attempts to use the likelihood of a generative model to evaluate music, an approach commonly used in text, have proven unsuccessful, leading researchers to rely on standalone supervised music evaluation models. In this paper, we answer this question affirmatively: we show that a model's intrinsic signals—derived from its hidden representations and predictions—are strongly correlated with human ratings. In particular, we study MusicGen and consider three types of features: (1) prediction loss, (2) prediction
entropy, and (3) concepts extracted from the model using a sparse autoencoder (SAE). Using these features, we train a lightweight prediction model to estimate subjective ratings. We evaluate these features both individually and in combination. We hypothesize that these signals parallel the listening process: the temporal and frequency-domain structure of loss and
entropy reflects listeners' expectation and surprise, while gradient directions in SAE latent space predict perceived quality. Experiments on five human-evaluation
benchmarks spanning continuous ratings and pairwise preferences confirm this hypothesis, with SAE latents carrying most of the predictive signal.
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Computer Vision
VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation
Yuta Oshima, Masakazu Yoshimura, Masahiro Suzuki et al. (+2 more)
Recent
multimodal image generation models can take multiple images and textual instructions as input, enabling reference-based generation guided not only by text but also by visual instructions such as layouts, arrows, and pose cues. However, existing
benchmarks do not evaluate the joint setting in which multiple references must be composed under multiple and heterogeneous visual-instruction images. To address this gap, we introduce VIF-Bench, a
benchmark of 1,241 tasks designed to assess the edge of model capabilities in this joint setting by covering: (i) multi-reference generation (up to 7) under multiple heterogeneous visual instructions (up to 6), (ii) cases where reference images can potentially compete with visual instructions (e.g., a strongly posed subject vs. a target pose), and (iii) controlled comparison of visual instructions with text descriptions at different levels of specificity. Using these capabilities, we uncover three findings: (1) models face an adherence-artifact trade-off: once models reach stronger visual instruction adherence, stronger adherence tends to coincide with more instruction artifacts in generated images, (2) visual instruction adherence tends to be lower on tasks whose reference images carry a salient state of the controlled attribute (e.g., a neon-lit subject under a light-direction instruction), most consistently for light and wind, and (3) for models that can understand visual instructions, it is often better to provide visual constraints directly rather than describe them in text; when using text, a moderate level of detail works better than an exhaustive description. VIF-Bench is released as an open
benchmark to establish a basis for fair comparison in controllable multi-reference image generation.
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All authors Yuta Oshima, Masakazu Yoshimura, Masahiro Suzuki, Yutaka Matsuo, Hiroki Furuta
Artificial Intelligence
Generative Interactions: Weaving Multiparty Human Motion with Bilevel Latent Dynamics
Ojas Shirekar, Yash Surange, Augustinas Jučas et al. (+1 more)
Human social behaviour is not a collection of independent motions, but a jointly organised process in which group dynamics and individual variation continuously shape one another. Yet existing social motion models often prioritise plausible trajectories while leaving interaction state implicit, limiting their ability to transfer across groups, tasks, and partial-observation regimes. To address this gap, we introduce Bilevel Representations for
Agent Interaction Dynamics (BRAID), a hierarchical sequential latent-variable model for generative multi-person interaction. BRAID explicitly formulates social motion generation as a meta-transfer learning problem: shared interaction priors are learned across
datasets and adapted through arbitrary context sets of observed people and joints. The model represents each scene through a group-level latent state that captures shared interaction dynamics and person-level latent states that capture individual behaviour conditioned on the evolving group context. This modelling choice enables coherent generation under full, sparse, or partial observations while exposing compact social-state vectors that can serve as an interface for downstream
embodied-
agent systems. We evaluate BRAID under a unified SMPL-based representation on social
forecasting, tracking and in-filling, and response generation, using metrics that assess not only reconstruction accuracy but also realism, diversity, temporal
alignment, and interpersonal coordination. We further analyse the hierarchical latent space, showing that it captures separable group- and individual-level structure.
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All authors Ojas Shirekar, Yash Surange, Augustinas Jučas, Chirag Raman
Machine Learning
Width Expansion as a Method for Class Incremental Learning
A. L. S. Conde, Y. Elkhatib and C. M. Ranieri
Class Incremental Learning (Class-IL) requires models to learn new classes over time while preserving previously acquired knowledge without access to past data or task identity. This setting intensifies the stability-plasticity dilemma and makes catastrophic forgetting a central challenge. Existing approaches include regularization, knowledge
distillation, replay, and architectural expansion. However, many expansion methods rely on explicit task identifiers or predefined growth strategies, limiting their applicability when task boundaries are unavailable at inference time. This work proposes a dynamic width expansion method that increases the number of
neurons within existing layers according to a normalized loss criterion, without requiring task-specific information. An
attention mechanism with persistent key-value
memory is also incorporated to stabilize feature representations and reduce interference between previously learned and newly introduced classes. The approach is evaluated on Split MNIST and Split CIFAR-100 under the standard Class-IL protocol. Experiments compare fixed-capacity and dynamically expanding architectures, both with and without
attention, combined with established continual learning methods including EWC, LwF, and A-GEM. Results show that progressive width expansion consistently improves performance over fixed architectures, particularly when combined with functional methods and A-GEM. The combination of width expansion and
attention provides the most consistent gains. Overall, dynamic width expansion based on representational demand provides an effective and flexible strategy for Class-IL, although uncontrolled growth may increase overfitting and computational cost.
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Artificial Intelligence
Locating Answer-Correctness Signals in Frozen Large Language Models
Yuansen Liu, Yixuan Tang and Anthony Kum Hoe Tung
Language models expose internal signals that predict whether an answer is correct, readable from a single forward pass of a frozen model without additional generations. Yet existing probes often commit to one signal family or layer and can be brittle under distribution shift; in
retrieval-augmented settings, many specialized detectors instead target passage faithfulness, which can diverge from correctness when retrieved evidence is unhelpful or conflicting. We therefore ask where answer correctness is readable, which internal signal families carry it, and how they should be combined. We search over hidden states,
token probabilities, residual-stream features,
attention, and their fusion, treating the selected readouts as a predictive measurement rather than a mechanistic localization. We run this analysis separately in closed-book and with-context settings, since context can change which readouts are informative. A consistent anatomy emerges: correctness concentrates in the answer span, recovered from the answer
tokens even under
retrieval, and the families carry it complementarily, so fusing them helps most out of distribution, where a single signal is weakest. The protocol is effective across two backbones and gates a
retrieval controller as one downstream use.
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Networking
Sequence Models for Layer-3 Protocol Emulation
Alix Jeannerot, Petko Petkov and Alvaro Valcarce Rial
This article investigates whether Layer-3
radio-protocol behavior can be represented by compact sequence models suitable for deployment inside the RAN. We introduce the RRC sequence engine (RSE), a hybrid architecture in which a sequence model predicts protocol-dependent message structure while deterministic components retain control over security-sensitive or configured fields and over transport containers. Using NR protocol traces, we show that protocol-aware tokenization, cross-stack context, and explicit placeholders matter more than general-sequence model scale. A purpose-built 11M-parameter Mamba engine achieves 0.90 exact match on the evaluated next-generation Node B (gNB)-side test set with a median generation latency of 115 ms, sufficient for some timers of Layer-3, outperforming a fine-tuned 0.6B-parameter model. We discuss applications in testing, simulation, deployment specialization, and future trainable 6G control planes, as well as the validation, latency, robustness, and security challenges that remain before operational use.
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Machine Learning
GARDiff: Graph-Aligned Residual Diffusion for Probabilistic Multivariate Time-Series Forecasting
Rui Han, Min Yang, Xu Zhang et al. (+3 more)
Diffusion models have recently shown strong potential for probabilistic multivariate time-series
forecasting by modeling complex conditional distributions. Recent decoupled
diffusion frameworks further separate
forecasting into deterministic prediction and
stochastic residual generation, making it natural to derive dependency graphs from deterministic representations and use them to guide residual
diffusion. However, we show that this direct structural transfer is unreliable. Although deterministic-derived graphs encode useful global dependency priors, they exhibit substantial edge-level misalignment with residual dependency structures, introducing inaccurate or redundant conditions during residual generation. This reveals a previously overlooked deterministic-to-residual structural
alignment problem in decoupled
diffusion forecasting. To address this problem, we propose GARDiff, a Graph-Aligned Residual
Diffusion framework for probabilistic multivariate time-series
forecasting. Instead of treating deterministic-derived graphs as fixed
diffusion conditions, GARDiff progressively adapts them to residual generation. Specifically, GARDiff estimates residual uncertainty to distinguish high- and low-uncertainty regions, enabling uncertainty-aware structural refinement, and further performs timestep-aware edge sparsification during reverse
diffusion to evolve graph conditions from broad dependency aggregation to localized residual refinement. Extensive experiments on six real-world
benchmarks demonstrate that GARDiff consistently improves probabilistic
forecasting performance and uncertainty calibration over strong baselines.
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All authors Rui Han, Min Yang, Xu Zhang, Xinghao Yang, Wei Liu, Yongshun Gong
Distributed Computing
FP64 Is All You Want, INT8 Is All You Need, FP4/6/8 Is All You Have
Pratyai Mazumder, Alexandru Calotoiu and Torsten Hoefler
Ozaki scheme II emulates FP64
matrix products with INT8 ones through residues modulo pairwise coprime
moduli, and variants for FP8 and FP4 have followed. We treat these schemes as one family and pose the choice of a scheme as a combinatorial program that minimizes the number of low-precision GEMMs. Given, for each modulus, a finite set of ways to compute products modulo it from low-precision GEMMs, we find the choice of
moduli and ways with the fewest GEMMs, for any format, accumulator and inner dimension, and derive lower bounds on the GEMM count over the whole family. Applied to the formats of current GPUs, the method gives the first FP6 schemes, an FP8 scheme with fewer GEMMs than any previous one, and an FP4 scheme that the bounds show needs the fewest GEMMs of any scheme in the family whose
moduli lie in a stated range. Implemented on three Blackwell GPUs, the INT8, FP8 and FP4 schemes run faster than native FP64, up to 83x on B300.
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Computer Vision
Honeycomb: Constant-Size Scene Memory Representation for Video World Models
Jack Wei Lun Shi, Kaichen Zhou, Haoyu Chen et al. (+6 more)
Video world models require persistent scene
memory to maintain consistency during long-horizon
video generation. Existing spatial
memories accumulate RGB observations or latent features, increasing storage requirements as generation proceeds. We introduce Honeycomb, a
video world model built on HexMemory, our proposed low-rank representation for storing scene features in a fixed-size
memory with a total of six spatial and spatiotemporal planes. A feed-forward writer maps each generated chunk into new plane features. As the spatial coverage or temporal range expands, we warp the previous planes while preserving their dimensions, then fuse them with the new features through confidence-weighted pooling and a learned residual correction. A reader retrieves latents from HexMemory to condition subsequent
video generation. The writer processes only observations from the new chunk, avoiding per-scene optimization and repeated processing of the full history. Experiments on WorldScore and RealEstate10K demonstrate strong
video generation quality and robust revisit consistency while keeping HexMemory feature storage constant throughout generation. Code and additional visualizations are available on our project page at https://jackswl.github.io/honeycomb/.
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All authors Jack Wei Lun Shi, Kaichen Zhou, Haoyu Chen, Yufeng Weng, Keane Ong, Ruojin Cai, Hang Hua, Justin K. W. Yeoh, Mengyu Wang
Computation and Language
Reader Proficiency Shapes Layer-wise Surprisal Profiles
Akio Hayakawa and Horacio Saggion
Reading behaviour varies not only with linguistic input, but also with reader proficiency. In this study, we investigate whether the layer-wise relationship between surprisal from large
language models (LLMs) and human gaze behaviour differs across readers with different levels of proficiency and across gaze measures. Using eye-tracking data from the MECO L2 corpus, we compare readers with high and low vocabulary proficiency on first-pass gaze duration (FPGD) and total gaze duration (TGD). We quantify the distribution of the predictive power of surprisal across model layers using Predictive Depth. Across 12 tested LLMs, we find that readers with lower vocabulary proficiency tend to show deeper Predictive Depth for FPGD, while this difference is smaller for TGD. Also, TGD itself shows deeper Predictive Depth than FPGD in both proficiency groups. These patterns suggest that where predictive power is concentrated across LLM layers may be related to the timing and breadth of the reading processes captured by different gaze measures, and that this relationship can vary with reader proficiency. Our leave-one-out analysis further shows that the advantage of informative internal layers extends to unseen texts, although the practical improvements in prediction are limited. Overall, our results show that layer-wise LLM surprisal provides a useful perspective on variation in reading behaviour across both reader groups and gaze measures.
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Artificial Intelligence
WISE-ATTA: When to Ask for Labels in Budgeted Active Test-Time Adaptation
Muhammad Huzaifa, Lea Schönherr and Thorsten Eisenhofer
Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively querying supervision. However, most existing ATTA methods implicitly assume that supervision can be requested for every incoming test batch, which can incur substantial annotation cost over long test streams. In this work, we introduce budgeted ATTA in which labels are available for only a fraction of test batches. This formulation shifts the central challenge from deciding what to label within a batch to deciding when supervision should be applied over time. To address this challenge, we propose a budget-aware approach WISE-ATTA that allocates supervision over the test stream based on lightweight signals computed online, prioritizing periods where supervision is likely to be most useful. When a batch is selected for supervision, we further employ a drift-based sample selection criterion that targets samples exhibiting ongoing, unconverged adaptation dynamics, enabling effective updates from a single labeled example. We evaluate this approach on synthetic corruptions (ImageNet-C) and natural distribution shifts (ImageNet-R/K/A). Across settings, WISE-ATTA achieves competitive or improved performance compared to recent ATTA methods while requiring substantially fewer labels. Overall, we find that the timing of supervision is a key, yet underexplored, aspect of active test-time adaptation. Code: https://github.com/Muhammad-Huzaifaa/WISE-ATTA
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Artificial Intelligence
EngiWorld: What Can Frontier Agents Deliver in Professional Engineering Environments?
Hongcheng Gao, Hailong Qu, Yu Lei et al. (+19 more)
Autonomous
agents have made rapid progress in general-purpose computer use, yet reliable automation of professional industrial engineering remains out of reach. Engineering workflows require
reasoning over
geometric and physical constraints and keeping dependencies consistent across software and design stages. We present EngiWorld, a
benchmark designed around the capabilities that real engineering projects require, from operating a single application to solving engineering problems end to end. It contains 1,301 expert-curated tasks spanning six engineering domains (CAD, CAE, CAM, BIM, EDA, and DCC) and 26 professional software applications, with both GUI and CLI interfaces. The tasks are organized into six types, corresponding to capabilities that real engineering projects require. We further introduce an artifact-centric evaluation methodology built on a unified suite of domain verifiers. The verifiers programmatically check whether final and intermediate artifacts are geometrically valid, physically feasible, and compliant with design rules. Beyond binary success, they also score how well optimized designs meet their specifications. Evaluation of seven frontier models reveals a substantial capability gap: the strongest model scores only 44.7/100, and just 3.6% of runs on tasks that require cross-software coordination succeed. EngiWorld provides a rigorous foundation for measuring progress toward
agents that operate professional engineering software end to end.
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All authors Hongcheng Gao, Hailong Qu, Yu Lei, Henghui Sun, Haoyang Li, Yipeng Wei, Naihao Xue, Xiaohan Yu, Zhuo Tao, Yihe Zang, Yajiao Wang, Jingyi Tang, Yi Li, Jingjing Zhou, Jie Luo, Bohan Zeng, Chengyu Shen, Hao Jiang, Chong Chen, Bowen Qu, Olive Huang, Zeqiang Wang
Computer Vision
PAIQ: Patch-Aligned Semantic Injection via Residual Rotation
Pinze Ren, Yuwei Zhang, Hao Chen et al. (+3 more)
Language-aligned and self-supervised visual encoders offer complementary strengths in semantic abstraction and spatial detail. Harnessing this complementarity requires enriching local features while retaining distinctions between semantically related patches. We introduce PAIQ, a patch-aligned semantic injection framework that combines content-based cross-encoder matching with orthogonally constrained residual updates. Using DINOv3 patch features as the spatial base, PAIQ aggregates complementary SigLIP features through joint source allocation and injects the aggregate–base differences through a shared orthogonal transformation Q. This rotation adapts update directions while preserving residual norms and pairwise angles. For fixed projected features, we derive conditions for patch separability under similar semantic aggregates and show that rotation adds a nonnegative separation term over direct interpolation when the aggregate is shared. Only the projection and fusion parameters are trained; both visual encoders and the
language model remain frozen, and fusion retains 196 visual
tokens. Across diverse
language backbones, PAIQ yields broad gains in judge-assessed correctness and reductions in
hallucination severity over single-encoder interfaces on image description and visual question answering. On the 2B and 9B Qwen backbones, this compact interface outperforms the strongest evaluated fusion or
token-compression baselines by about 2.9 correctness points on average.
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All authors Pinze Ren, Yuwei Zhang, Hao Chen, Linghao Meng, Chang Li, Qiankun Li
Artificial Intelligence
Learning from Shared-Control Overrides: Context-Driven Acceleration Profile Prediction for Personalized Overtaking
Ruizheng Xu, Lounis Adouane, Javier Ibañez-Guzmán et al. (+1 more)
Adaptive Cruise Control (ACC) systems are typically calibrated for an average driver, often resulting in a mismatch between vehicle behavior and individual expectations during time-critical maneuvers such as highway overtaking. When the ACC is perceived as too conservative and inconsistent, drivers intervene through throttle overrides, providing implicit feedback on the system's behavior. This paper reframes these override actions as human-in-theloop supervisory signals and proposes a data-driven framework for personalized vehicle adaptation, termed Context-driven Personalized ACC (CoP-ACC). Rather than relying solely on end-to-end regression, which tends to over-smooth dynamic responses, we introduce a hybrid pipeline combining: (i) unsupervised hierarchical clustering to extract representative acceleration profiles from override events; (ii) a context classifier that maps pre-maneuver driving conditions to the appropriate profile; and (iii) a residual regressor that refines the selected profile into a smooth, personalized acceleration profile tailored to the immediate context. Evaluated on real-world public-road data against a withheld forced-ACC baseline, the approach demonstrates high reconstruction fidelity and generates acceleration profiles that tend toward the driver's expected behavior in potential override contexts. The results highlight the potential of learning from shared-control overrides to enable anticipatory, personalized ACC behavior, reducing manual interventions and improving ride comfort.
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All authors Ruizheng Xu, Lounis Adouane, Javier Ibañez-Guzmán, Clément Zinoune
Systems and Control
Active Informativity: Online Input Design for Data-Driven Control
Yishu Wang, Paolo Rapisarda, Jianquan Lu et al. (+1 more)
We develop a unified framework for online input design in data-driven control from noisy data. We introduce a performance measure and analyze how such quality measure changes as new data are added. Based on such analysis we propose an online method for selecting inputs that ensures that data quality does not decrease over time. We demonstrate the validity of our approach by formulating six common noisy data-driven control problems in our framework, and by solving numerical examples for three of them.
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All authors Yishu Wang, Paolo Rapisarda, Jianquan Lu, Yang Liu
Computer Vision
Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation
Chu Zhang, Haoyu Jiang, Hongyuan Zhang et al. (+2 more)
The rapid expansion of large-scale medical
datasets and computational resources has driven significant progress in medical foundation models. Given the inherent heterogeneity of medical imaging modalities, current research mainly follows two paths: specialized models optimized for specific modalities, and generalist models designed to handle multiple modalities. However, medical generalist models suffer from both insufficient training data scale relative to natural image generalists and inadequate domain-specific depth relative to medical specialists. Empirically, generalist models establish a cross-modality performance baseline, while specialists define the performance ceiling within their respective domains. To elevate this baseline toward these ceilings, we propose Med-
RADIO, a medical multi-teacher
distillation framework that Reduces All Domains Into One by compressing complementary expertise from multiple domain-specific teachers into a unified medical
vision foundation model. Our method curates both generalist and specialist teachers, allocates modality-aligned
distillation streams to reorganize generalist pretraining data so it matches specialist domains, and uses a balanced loss to prevent any single teacher from dominating the
distillation process. On internal and external classification
benchmarks spanning five modalities, Med-
RADIO improves over strong medical generalists under linear probing and remains competitive with representative specialists on most evaluated modalities. Code is available at https://github.com/CAIR-HKISI/Med-
RADIO.
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All authors Chu Zhang, Haoyu Jiang, Hongyuan Zhang, Hongbin Liu, Dong Yi
Robotics
ProAct-VLM: Pre-Failure Vision-Language Task Replanning with Continuous Perception Feedback
Ahmed Nader Ahmed, Omar Moured, Mughni Irfan Mohammed Abdul et al. (+2 more)
Long-horizon robotic tasks are vulnerable to unexpected environmental changes that can render planned actions ineffective or unsafe. To address this,
robots must detect such changes as they occur, interpret their impact, and adjust their actions accordingly. Traditional rule-based decision-making pipelines are brittle in open-world conditions, as they are hand-tuned for specific scenarios and lack generalization.
Vision-
Language Models (VLMs) offer a promising alternative as they combine broad world knowledge with unified visual–text
reasoning, enabling them to generalize across diverse scenarios and generate accurate, grounded task plans. However, for effective deployment in dynamic real-world settings, VLMs must be embedded into frameworks capable of handling uncertainty and environmental changes. Existing frameworks broadly address this reactively, triggering replanning only after execution failures or post-task checks, risking failed actions. Some methods verify conditions before actions, but these discrete checks miss changes occurring during execution. To address this, we present ProAct-VLM, an adaptive, physically grounded task planning framework that integrates VLMs within a real-time perception–feedback loop. ProAct-VLM continuously monitors the environment and re-plans as soon as relevant changes are detected, enabling adaptation before failure occurs. Evaluations against multiple baselines and across different VLM backbones show that our framework improves both success rates and efficiency in dynamic, long-horizon manipulation tasks. Project page: https://github.com/moured/ProAct-VLM
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All authors Ahmed Nader Ahmed, Omar Moured, Mughni Irfan Mohammed Abdul, Muhayy Ud Din, Irfan Hussain
Machine Learning
When Models Don't Manipulate Manifolds: The Geometry of a Comparison Task
Sai Sumedh R. Hindupur, Hadas Orgad, Thomas Fel et al. (+1 more)
One of the current premises of mechanistic interpretability research is that detailed accounts of the geometry of neural network representations can tell us how models perform computations, and how to effectively intervene on them. While low dimensional
manifolds have been observed for multiple concepts in the literature (e.g. numbers encoded on helices, days of the week on a circle, ...), with structure believed to reflect properties of data and tasks, the extent to which models rely on them for computation, and how they manipulate them, remains unclear. We characterize precisely the geometry of computation in a number-comparison task, as an abstraction of comparison for decision making, and how models utilize geometry in an elegant fashion to implement it. Specifically, we study the causal geometry of number comparison in Qwen2.5-7B-Instruct, a capable and widely studied open-weight model, and find Qwen largely uses linear representations of numbers despite the presence of curved geometry. To compare two numbers, the model first encodes each number along a vector and adds the two representations using
attention and the residual connection, bringing them into a shared space in the residual stream. Then, the model uses MLP
neurons to compare the pair of numbers on local regions in this shared space, which correspond to smaller intervals of input numbers, and combines these to obtain the position of the maximum. In fact, this reliance on linear representations for comparison also persists when the model compares three numbers. Our findings demonstrate that the
manifold hypothesis can co-exist with linear representations: while concepts that are ordered may have
manifold structure in representations, the model may use an underlying linear structure of the concept in certain computations.
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All authors Sai Sumedh R. Hindupur, Hadas Orgad, Thomas Fel, Demba Ba
Robotics
Learning Expressive and Compositional Motion Representation via Spectral Skills
Feiyang Wu, Chenxiao Gao, Chen Yang et al. (+3 more)
Robotic foundation models offer a promising path toward general-purpose
humanoid robot control, often through hierarchical architectures. However, their effectiveness depends on the command interface between the planner and the controller, which must support accurate execution while remaining easy to predict, and ideally allow new behaviors to be composed from prior ones. In this work, we introduce spectral skills, a latent representation of this interface that meets these requirements through predictive representation learning. By design, spectral skills compactly encode short motion segments and are learned by predicting subsequent motion rather than reconstructing the encoder input. On a 29-DoF
humanoid, a controller conditioned on spectral skills reduces global tracking error by 62% relative to the state of the art. The same frozen controller chains independently encoded skills without a separate transition policy. It also composes new behaviors by adding orthogonal directions to any compatible base skill, producing combinations unseen in the training data. We demonstrate tracking, chaining, and composition, as well as control through a
language-conditioned planner, on Unitree G1 hardware. Project page: https://spectral-skill.github.io
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All authors Feiyang Wu, Chenxiao Gao, Chen Yang, Ye Zhao, Bo Dai, Anqi Wu
Cryptography and Security
She Spoofed Sea Ships by the Sea Shore: Measuring Large-Scale GPS Spoofing in Global Maritime Traffic
Anna Raymaker, Ryan Von Brock, Ryan Pickren et al. (+4 more)
GPS spoofing has emerged as a serious threat to maritime security, yet its global prevalence, persistence, and structure remain largely unmeasured. In this paper, we present the first large-scale measurement study of maritime GPS spoofing, using global Automatic Identification System (AIS) data, which contain the GPS coordinates broadcasted over time by ships across the world. We focus on large-scale regional spoofing, where external interference displaces many vessels across an area at once, leaving a recognizable signature of physically implausible motion correlated across ships; our motion-aware, marine-specific framework identifies this signature and grades the evidence for GPS spoofing in each region it finds. Applying our approach to AIS data from over 367,000 vessels collected between late November 2024 and early February 2025, we identify 31 persistent anomalous hotspots across high-traffic maritime regions, at least 22 of which show strong evidence of GPS spoofing, with spatial and temporal structure aligning with regional conflict and economic sanctions. Notably, our method found that the spoofing activity in the Red Sea responsible for the highly-publicized grounding of the 75,000-ton container ship, MSC Antonia, was ongoing months before the incident, which has not been previously documented. Similarly, we detected persistent spoofing in the Strait of Hormuz over a year before the 2026 Iran war brought commercial shipping through the Strait to near-standstill. Together, this work establishes GPS spoofing as a widespread, recurring, and measurable threat to global maritime
navigation.
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All authors Anna Raymaker, Ryan Von Brock, Ryan Pickren, Animesh Chhotaray, Frank Li, Saman Zonouz, Raheem Beyah
Machine Learning
LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling
Biswajit Banerjee, Claudia Alvarez Carreno and Anton S. Petrov
Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural
language processing across sequence- and structure-based tasks, yet the potential of tokenization remains underexploited. Unlike human
language,
proteins preserve structure despite extensive sequence variation a property standard tokenization strategies fundamentally fail to capture. We introduce ZEST (Zoned Encoding of Sequence Traits), an
evolution-informed vocabulary derived from conserved regions of multiple sequence
alignments. ZEST allows
embedding domain-level biological priors directly at the tokenization stage rather than learning them implicitly through scale. ZEST natively compresses sequences to an average
token length of 4 residues, enabling our model to process 4,000 residues within a standard 1024-
token context window. Building on this, we present LEMON (Layered Extraction of Molecular Ordering from Nature), a compact 200M-parameter sequence-based model for detection of remote homology between
protein sequences trained on a single H100 GPU for one week. Despite its modest size, LEMON outperforms state-of-the-art models ranging from 600M to 3B parameters. Our results demonstrate that
evolution-informed tokenization can substitute for massive parameter scaling, opening a new direction for efficient, biologically-grounded
protein representation learning. All code, model weights, and results are publicly available under the MIT license.
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Artificial Intelligence
KUPAS MASTER: Distilling the Tacit Expertise of Master Practitioners into Agent-Ready Experience Corpora
Changmian Wang, Yuchao Ma, Xuchao Lu et al. (+13 more)
Experienced professionals know more than just facts and conclusions. They know which cues matter, why a judgment is reasonable, and which action to take. Routine work records often leave out this tacit knowledge, making it difficult for Large
Language Model (LLM)
agents to use professional experience effectively. We introduce KUPAS MASTER, an experience engineering platform built around nine-layer cognitive corpus construction. It turns heterogeneous work records and practitioner interviews into traceable, reusable experience corpora for
agents. Six case elements preserve the task process: context, cues, judgment, action, boundaries, and outcomes. Nine-layer cognitive corpus construction organizes tacit experience along nine extraction dimensions and stores the resulting assets in six libraries: rules, constraints, best practices, negative examples, corner cases, and skills. Semantic
alignment, individual experience
distillation, organizational consolidation, and cross-review preserve source evidence, conditions of use, and unresolved disagreements. The platform packages these assets into callable skills with explicit inputs, steps, dependencies, and stopping conditions, connecting experience collection to task execution and evaluation feedback. Using authorized samples from 20 randomly selected practitioners, the platform processed 1,576 source files into 23,024 individual experience records and 13,113 organizational assets. The evaluation spans multiple professional domains. Under common task inputs and scoring criteria, the base model, raw corpus
retrieval-augmented generation (RAG), and KUPAS MASTER
agent scored 70.63, 79.75, and 89.58, respectively. The KUPAS MASTER
agent improved on raw-corpus RAG in all seven scoring dimensions. The platform provides a practical path from individual tacit experience to organizational knowledge and
agent capabilities.
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All authors Changmian Wang, Yuchao Ma, Xuchao Lu, Chen Zhang, Ping Sun, Jiazheng Wang, Shan Wang, Xuanwen Chen, Yihe Sun, Ziyu Lu, Jianqiang Huang, Hongzhi Li, Ziqing Xia, Kaihua Tang, Xian-Sheng Hua, Qinghua Zheng
Networking
NetLexicon: Learning Discrete Behavioral Representations for Encrypted Web Traffic Analysis
Xiangyu Gao, Tong Li, Ziqiang Wang et al. (+9 more)
Encrypted Web traffic analysis requires effective representations of observable communication behavior. Existing pretraining methods often adapt NLP/CV objectives and sequence architectures, motivating learning objectives that capture traffic-specific interaction patterns. We present NetLexicon, a discrete pretraining framework that learns reusable behavioral states from unlabeled traffic. It converts contextual traffic windows into discrete states through vector quantization, constructing a compact traffic lexicon. We design two complementary pretraining objectives. State Transition Prediction (STP) forecasts subsequent sequence structure and packet features from observed history, while Statistical Feature
Alignment (SFA) grounds learned states in window-level traffic statistics. Together, they guide the lexicon to capture recurring communication behaviors and their
evolution. We evaluate NetLexicon on four
benchmarks covering Web application identification, service type identification, and malware detection. NetLexicon improves Macro-F1 by up to 25.5 percentage points over the strongest baseline on each
benchmark and reduces fine-tuning time per epoch by up to 23.6 times relative to the evaluated baselines. Further analysis shows that the learned discrete states capture recognizable patterns in packet size, timing, and data transfer. These results demonstrate that incorporating observable behavioral structure into pretraining supports effective, efficient, and interpretable representations for encrypted traffic analysis.
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All authors Xiangyu Gao, Tong Li, Ziqiang Wang, Yinchao Zhang, Rongbang Wu, Zhenxing Zhang, Jing Hu, Hanlin Huang, Xinle Du, Su Yao, Qi Li, Ke Xu
Artificial Intelligence
MeanFlowAdvantage: Stable Reward Fine-Tuning for Few-Step Average-Velocity Generators
Haocheng Tang, Tianchi Xie and Xingqiao Lin
MeanFlow enables efficient few-step generation by predicting interval-average velocities, but this representation creates a mismatch for reward fine-tuning: existing advantage-based objectives are typically defined on instantaneous velocities or equivalent $x_0$-space predictions, whereas inference directly uses the learned average-velocity map. We introduce MeanFlowAdvantage, a signed advantage-weighted least-squares objective for average-velocity generators. Our key construction uses a shared, detached MeanFlow derivative correction to express the reward objective in prediction space while making rollout and reference regularization exact penalties on the average-velocity network deployed at inference. The resulting formulation preserves MeanFlow's native few-step sampler and provides a direct mechanism for transferring reward improvements to the deployed flow map. On SD3.5-Medium, MeanFlowAdvantage improves all eight reported metrics over the matched four-step MeanFlowNFT baseline and, with only four NFEs, matches or exceeds the 40-step DiffusionNFT baseline on six of eight metrics. The same objective also transfers to DNA promoter design, where it supports both teacher-free on-policy RL for a generator defined on a
manifold and teacher-guided reward-graded
distillation, with the latter yielding the lowest one-step Sei profile MSE among the compared configurations.
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Software Engineering
Retrieve, Reproduce, Reveal: Dissecting Retrieval-Augmented Software Vulnerability Detection
Sabrina Kaniewski, Tim Krämer, Julius Bächle et al. (+3 more)
Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large
Language Model (LLM)-based software vulnerability detection by grounding predictions in retrieved vulnerability knowledge, such as vulnerability reports. However, existing RAG-based software vulnerability detection (RAG4SVD) systems are often evaluated using proprietary models, which challenges open science and reproducibility. Further, studies use different
datasets, custom knowledge bases, different backbone models, and diverse metrics, which hinders meaningful cross-system comparison. In this work, we study six open-source RAG4SVD systems and address these reproducibility and comparability challenges through (i) reproduction of their experimental settings under an open-weight setting, and (ii) a unified
benchmark using a common
dataset, metric suite, and pool of open-weight models. Further, RAG4SVD systems typically consist of multiple components, yet are often evaluated only as a whole system, i.e., end-to-end. Therefore, we perform (iii) a component-level analysis that decomposes representative RAG4SVD pipelines into input abstraction, knowledge
retrieval, and detection. Our results demonstrate that reproducibility varies substantially across systems. Under the presented unified
benchmark, published RAG4SVD performance does not transfer under a controlled open-weight evaluation and depends strongly on the used model. The component analysis shows that effective RAG4SVD depends on the
alignment between pipeline stages. For example, oracle knowledge raises
retrieval to near-optimal, yet performance remains low (0.51 pairwise accuracy), demonstrating that
retrieval effectiveness alone is insufficient for reliable detection. These findings motivate evaluating RAG4SVD not only end-to-end, but at the level of pipeline components, and provide a basis for more standardized, RAG-aware evaluation practices.
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All authors Sabrina Kaniewski, Tim Krämer, Julius Bächle, Markus Enzweiler, Michael Menth, Tobias Heer
Machine Learning
Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs
Yuxiang Yao and Zijun Zhao
Counterfactual explanations for graph neural networks (GNNs) find the minimal intervention that flips a node's prediction–but computing one requires reading sensitive graph structure, and releasing it discloses that structure. Both existing placements fail. Privatizing the graph before explaining corrupts the target on exactly the borderline nodes needing recourse, manufacturing spurious flips that flip the privatized graph but not the true one. Explaining on the clean graph and perturbing the released explanation resists certification: re-
auditing the standard heuristic shows an implied full-release budget of 573–753 on Cora and 256 on CiteSeer–orders of magnitude beyond its advertised budget–with worst-case single-entry leakage at AUC 1.0. We propose PrivCFS, which replaces certification-by-optimization with certification-by-construction: counterfactual selection over a fixed, data-independent candidate universe–edge interventions from a public prior graph, feature interventions from a public schema–whose no-op semantics give neighboring graphs the same output support. A validity-gated, clipped utility of global sensitivity $\Delta u \le 1$ released through the exponential mechanism gives pure $\varepsilon$-DP for the complete released object, composable over queries–to our knowledge the first such guarantee on graphs.
Privacy noise is the cheapest stage: at $\varepsilon$=8 the release retains 94–97% of its support-restricted non-private optimum on the recourse population and 83–95% on the general one; the optimal edge-inference audit attains AUC 0.50 on average and 0.59 worst-pair, versus the heuristic's worst entry 1.0; and transfers to a 15K-node graph at 0.96 valid rate. The dominant cost is a measurable, monotone price in public disclosure, readable off one table before any budget is spent–turning explanation
privacy from an accounting risk into a purchasable decision.
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Robotics
Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordination
Abdulqader Dhafer, Qi Wang and Zhou Daniel Hao
Search and Rescue (SAR) operations increasingly deploy heterogeneous teams of aerial and ground
robots. However, conventional coverage methods typically do not translate perceived terrain into platform-specific reachability, while continuous image exchange imposes a high communication cost. We propose an edge-centric, semantic-aware coverage planning framework that integrates aerial terrain perception,
robot-specific traversability
reasoning, and payload-efficient semantic state sharing. Aerial observations are converted into compact semantic grid maps, enabling reachability-constrained area decomposition and capability-aware coverage paths that assign only regions admitted by each
robot's capability profile. The resulting perception-sharing-planning loop feeds semantic corrections into traversability
reasoning and replanning, forming an application-level mechanism motivated by AI-enabled goal-oriented communication envisioned for AI-native 6G networks. For the high-update case, transmitting semantic corrections reduces the application payload by a factor of approximately $82$ relative to periodic full-map sharing. Across matched
benchmark scenarios, the proposed method achieved $91.5\%$ coverage with no capability-infeasible allocations, compared with $78.8\%$ coverage and a $21.5\%$ capability-infeasible allocation rate for LS-MCPP. Semantic corrections update the shared planning state without requiring repeated transmission of the complete map.
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Machine Learning
Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood
Trung-Dung Hoang, Alceu Bissoto, Tim Flühmann et al. (+4 more)
Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training data. We introduce MissCNF, which trains CNFs directly on incomplete data by maximizing the marginal likelihood of each partially observed sample, without discarding rows or constructing a completed
dataset. Thanks to the causal structure encoded in the autoregressive factorization of CNFs, only missing variables in the ancestral closure of the observed set are integrated out, while the others are dropped without computation. We further establish the conditions under which MissCNF recovers the true joint distribution, and introduce causal-family positivity, where identification is possible even when no record in the
dataset is ever complete. We compare MissCNF with two common strategies for handling missing data: listwise deletion and impute-then-fit pipelines. Across eight synthetic causal
benchmarks, three missingness mechanisms, and missing rates up to $90\%$, MissCNF achieves the lowest KL divergence in 23 of 24 nonlinear MCAR and MAR settings and in all nonlinear MNAR settings, as well as the lowest counterfactual error in 20 of 24 settings. On linear SCMs, where linear imputation performs best, MissCNF ranks in the top two in 22 of 24 settings.
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All authors Trung-Dung Hoang, Alceu Bissoto, Tim Flühmann, David Herzig, Christos Nakas, Lia Bally, Lisa M. Koch
Computation and Language
Corpus-Guided Dual-Path Propagation for Graph Retrieval-Augmented Generation
Baoxian Liu and Tong Wei
Graph-based
retrieval-augmented generation supports multi-hop
retrieval by organizing corpus information into graphs. However, existing relation-free graph
retrieval methods rely primarily on query-sentence similarity to search for evidence. This can exclude useful bridging evidence with low query similarity and activate incidental entities unrelated to the
reasoning chain. In this paper, we propose a simple and effective approach called NexusRAG, which augments the relation-free Tri-Graph with a corpus-level entity neighborhood structure derived from joint entity co-occurrence and semantic similarity. NexusRAG employs this structure to guide two complementary propagation paths: neighborhood-constrained semantic propagation through sentences identifies the query-relevant entity frontier, while direct structural propagation between neighboring entities expands that frontier to structurally related entities. The propagated entity weights also inform neighborhood-aware passage initialization for Personalized PageRank. Experiments on three multi-hop QA
benchmarks and a domain-specific subset of GraphRAG-Bench show that NexusRAG consistently outperforms existing approaches. On the GraphRAG-Bench subset, NexusRAG achieves the highest evidence recall in all question categories, exceeding baselines by 4.2-8.1 points. The implementation code is available at https://github.com/Jacob-biu/NexusRAG.
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