Machine Learning
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Alex Kipnis, Marcel Binz and Eric Schulz
Hierarchical data is ubiquitous in the empirical sciences and most commonly analyzed with generalized linear mixed-effects models (GLMMs), which have interpretable parameters.
Bayesian inference for GLMMs yields calibrated uncertainty estimates but requires Markov Chain Monte Carlo (MCMC); the gold-standard No-U-Turn Sampler (NUTS) is slow and must restart for every new
dataset, model and prior specification. We introduce MetaBeta, a pretrained amortized model for in-context
Bayesian inference over GLMMs. The full prior and model specification are supplied as context at test time, and the model returns an asymptotically exact posterior at the speed of a forward pass. Two set
transformers and two conditional neural spline flows mirror the GLMM's hierarchical structure and are trained jointly on millions of realistic simulated
datasets with continuous, binary and count outcomes. The flow posterior is refined by Independence Metropolis-Hastings against the unnormalized posterior, yielding tuning-free asymptotically correct inference. Alternatively, MetaBeta warm-starts NUTS, yielding nearly identical inference 3.5-13x faster than its cold-started counterpart, while removing over 98% of its divergent transitions. On controlled
benchmarks with ground-truth parameters, MetaBeta matches NUTS in parameter recovery, posterior calibration and out-of-sample prediction; on real
datasets with unknown parameters, priors and model structure, its posteriors closely agree with those of NUTS; and it remains faithful where amortized models typically struggle: under misspecified likelihoods, misspecified priors, out-of-distribution predictors, collinear designs and data-poor regimes. On real data, a full prior sensitivity analysis including model comparison takes a few seconds with MetaBeta against 14-18 core-hours for NUTS, tracing its posteriors throughout. Our model is open-source and open-weights.
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Systems and Control · Cross-listed from math.OC
Benign Geometry and Distributional Robustness of $\mathcal{H}_2$ Synthesis
Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamás Keviczky et al. (+1 more)
In this paper, we study standard and distributionally robust $\mathcal{H}_2$ synthesis problem of a stabilizing state-feedback controller for discrete-time linear time-invariant systems. Without requiring a nonsingular disturbance controllability Gramian or a positive-definite control penalty, we establish that stationarity of a stabilizing gain in standard $\mathcal{H}_2$ synthesis is equivalent to global optimality and derive an exact Pythagorean identity for the performance difference. We then generalize the $\mathcal{H}_2$ synthesis problem by considering i.i.d. disturbances with zero mean and uniformly bounded second moments, and extend the stationarity-optimality equivalence to the corresponding distributionally robust $\mathcal{H}_2$ synthesis. Moreover, we show that every standard $\mathcal{H}_2$-optimal gain is simultaneously optimal for every such distributionally robust problem. Finally, we specialize the framework to Frobenius, Kullback–Leibler, and Wasserstein-2 ambiguity sets and obtain explicit characterizations of their worst-case covariances, which, in turn, provide performance certificates for a common optimal controller.
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All authors Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamás Keviczky, Mohamad Amin Sharifi Kolarijani
Machine Learning
ARM: Attention with Routed-Memory for Learnable Sparse Control
Qiuhao Zeng, Jerry Huang, Peng Lu et al. (+7 more)
Despite advances in long-context inference, large
language models (LLMs) remain fundamentally limited by the key-value (KV) caching mechanisms that are necessary for stable computation. Techniques such as selective
token eviction and
pruning have vastly mitigated these issues, but often discard core information to manage the growing cache. In this paper, we propose
Attention with Routed
Memory (ARM) a novel KV caching structure that introduces a fully differentiable, fixed-size
memory system organized as a hierarchical router. Via a Gumbel-Softmax, ARM learns to select
memory slots and perform sigmoid-gated updates that softly combine new and stored information, avoiding hard eviction and reducing information loss. By further training a policy to dynamically select varying amounts of
memory at inference, ARM adapts its accesses for both simple contexts and inputs that require deeper
reasoning, enabling more scalable and effective
retrieval on both short- and long-contexts. Experimental results on standard commonsense and long-context
reasoning benchmarks demonstrate that ARM achieves superior performance and efficiency compared to fixed KV-caching approaches, while remaining efficient and scalable in terms of both
memory and generation latency.
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All authors Qiuhao Zeng, Jerry Huang, Peng Lu, Ruiyi Fang, Gezheng Xu, Zihao Jing, Yufei Cui, Charles Ling, Gang Niu, Boyu Wang
Robotics
Robotic Valve Turning: Axial Misalignment Correction Using Reaction Torque Feedback
Amit Kumar, Sri Harsha Turlapati, Gautami Golani et al. (+3 more)
In this work, we propose a haptic update control law that uses reaction torques to correct axial misalignment during robotic valve manipulation. Unlike
vision-based estimates, which can be affected by calibration errors, occlusion, and uncertainty in the contact geometry, reaction torques arise directly from the physical interaction between the gripper and valve. A
geometric relationship exists between the error (misalignment) vector and these torques. The primary aim of this work is to propose a stable controller exploiting this
geometric property. Our control law is proven to be uniformly asymptotically stable. Simulations are performed for verification. Furthermore, we experimentally test the robustness of our method using a Kinova Gen3 robotic arm for initial misalignments ranging from $-15^\circ$ to $15^\circ$ at 3 different valve positions and report the resulting data distribution. The absolute value of the median misalignment across all 18 test cases is found to be within $2.46^\circ$ and that of reaction torques within $0.23\mathrm{Nm}$.
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All authors Amit Kumar, Sri Harsha Turlapati, Gautami Golani, Yang Lin, Ravi N. Banavar, Domenico Campolo
Robotics
Zeva-Ego: Egocentric Mid-Training with In-Context Causal Learning for Robot Manipulation
Bingjia Huang, Xin Ding, Fu Chen et al. (+5 more)
Egocentric video offers a scalable source of physical interaction experience, yet translating it into
robot-executable knowledge and enabling continual adaptation remain challenging. We introduce Zeva-Ego, a unified framework that learns physical priors from human experience and evolves through
robot interaction. An Action-Centric Encoder (ACE) converts
egocentric visual transitions into action-centered supervision for VLA mid-training, while In-Context Causal Learning (ICCL) enables parameter-free adaptation from action-effect feedback at deployment. Scaling Ego data to 10K hours improves RoboTwin success from 63.8% to 75.3%, matching 2K hours of
robot demonstrations (74.7%), corresponding to an empirical data ratio of roughly 4-5:1. With accumulated interaction experience, ICCL further improves success from 58% to 89% within four attempts without parameter updates. These results demonstrate a scalable path toward
embodied intelligence that learns from human experience and continuously improves through its own interaction.
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All authors Bingjia Huang, Xin Ding, Fu Chen, Kun Li, Wei Sun, Hao Wu, Yunxin Liu, Ting Cao
Computation and Language
End-to-end Jordanian dialect speech-to-text self-supervised learning framework
Ali A. Safieh, Ibrahim Abu Alhaol and Rawan Ghnemat
Speech-to-text engines are extremely needed nowadays for different applications, representing an essential enabler in human-
robot interaction. Still, some
languages suffer from the lack of labeled
speech data, especially in the Arabic dialects or any low-resource
languages. The need for a self-supervised training process and self-training using noisy training is proven to be one of the up-and-coming feasible solutions. This article proposes an end-to-end,
transformers-based model with a framework for low-resource
languages. In addition, the framework incorporates customized audio-to-text processing
algorithms to achieve a highly efficient Jordanian Arabic dialect
speech-to-text system. The proposed framework enables ingesting data from many sources, making the ground truth from external sources possible by speeding up the manual annotation process. The framework allows the training process using noisy student training and self-supervised learning to utilize the unlabeled data in both pre- and post-training stages and incorporate multiple types of data augmentation. The proposed self-training approach outperforms the fine-tuned Wav2Vec model by 5% in terms of word error rate reduction. The outcome of this work provides the research community with a Jordanian-spoken data set along with an end-to-end approach to deal with low-resource
languages. This is done by utilizing the power of the pretraining, post-training, and injecting noisy labeled and augmented data with minimal human intervention. It enables the development of new applications in the field of Arabic
language speech-to-text area like the question-answering systems and intelligent control systems, and it will add human-like perception and hearing sensors to intelligent
robots.
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Computer Vision
DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding
Xiaoqiang Lu, Licheng Jiao, Long Sun et al. (+5 more)
Multi-task visual grounding requires models to jointly understand linguistic semantics and perform accurate visual localization and
segmentation. Despite the success of
multimodal large
language models, effectively adapting them to multiple grounding objectives remains challenging. Existing methods commonly enforce task cooperation through shared representations, while overlooking the intrinsic conflict between task-oriented feature interests. In this paper, we introduce $\textbf{DeCo}$, an efficient $\textbf{De}$couple-to-$\textbf{Co}$uple learning framework that resolves this dilemma through a two-stage paradigm: task-specific representation decoupling followed by complementary prior coupling. Specifically, we first propose Task-aware Semantic Decoupling (TSD) to route shared visual cues into individual features under salient word-level guidance, alleviating representation interference between localization and
segmentation. Furthermore, we observe that
segmentation naturally provides informative localization priors due to dense supervision. Based on this insight, we introduce Hybrid Prior Coupling (HPC), which integrates sentence-level semantic prior with mask-derived spatial prior for enhanced grounding. Built upon a frozen
multimodal encoder, DeCo requires lightweight trainable parameters while achieving strong generalization across multiple grounding objectives. Extensive experiments on RefCOCO/+, G-Ref, ReferIt, Flickr, DIOR-RSVG, SARVG1.0, RRSIS-D, RIS-LAD, and RefDIOR demonstrate that DeCo achieves state-of-the-art performance on both natural and remote sensing
benchmarks. The code and models are available at https://github.com/xiaoqiang-lu/DeCo.
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All authors Xiaoqiang Lu, Licheng Jiao, Long Sun, Yuting Yang, Xu Liu, Lingling Li, Wenping Ma, Fang Liu
Information Retrieval
Auditing Source Exposure in Baidu and Google AI Search
Yibo Li, Enci Guan, Yuedan Cai et al. (+2 more)
AI-generated overviews are becoming an increasingly prominent layer of search interfaces, yet their behavior in Chinese-
language search remains underexplored. We conduct a cross-lingual audit of AI overview behavior on Baidu and Google using English queries sampled from MS MARCO and their translated Chinese counterparts. Our analysis examines when overviews are triggered across platform-
language settings, which host domains receive visible exposure in Chinese-
language overviews, how concentrated that exposure is, and how source overlap varies across settings. We also compare the
embedding-based semantic similarity of generated answers for matched query intents. The results reveal substantial differences across platform-
language settings in overview availability and visible source exposure. At the aggregate level, the settings exhibit low overlap in visible host-domain inventories, while matched-query answers yield median cosine similarities ranging from 0.701 to 0.813. These findings indicate that answer-level semantic similarity and aggregate source exposure capture distinct dimensions of AI-mediated search. Evaluations of AI search should therefore consider not only the content of generated answers but also how source visibility is distributed across platforms,
languages, and information environments.
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All authors Yibo Li, Enci Guan, Yuedan Cai, Geng Liu, Francesco Pierri
Cryptography and Security
SkelOT: Reusing AOT Compilation Across EVM Contract Families
Sipeng Xie, Qianhong Wu, Minghang Li et al. (+3 more)
Ahead-of-time (AOT) compilers (e.g., revmc, evmone, and DTVM) for the Ethereum Virtual Machine (EVM) reuse compilation artifacts at contract-code-hash granularity. This granularity is poorly matched to real EVM workloads dominated by contract families: factory-, proxy-, and template-driven deployments that share instruction structure but differ in a small set of embedded constants. Across four EVM chains (Base, Ethereum, BSC, and Arbitrum), we find that 23.1–47.6% of unique compilable bytecodes map to shared family skeletons within 10K-block windows. Per-hash AOT therefore redundantly recompiles structurally equivalent code, inflating compile time and artifact footprint while reducing workload coverage under finite compile budgets. We present SkelOT, an AOT framework that lifts the unit of compilation reuse from code hash to family skeleton. SkelOT compiles one native artifact per family, bakes invariant constants into the artifact, and reads variant constants from a per-contract runtime table. Built on revmc/LLVM and evaluated on a 10K-block Base mainnet corpus (3.52M transactions), SkelOT reduces compilation units by 47.5%, artifact footprint by 57.4%, and compile time by $2.19\times$, while preserving byte-identical execution outcomes versus per-hash AOT. At runtime, SkelOT delivers a $1.31\times$ median per-contract speedup across family members. Under a compile budget targeting 75% execution-time coverage, SkelOT needs far fewer artifacts than per-hash AOT, and the advantage holds at every coverage target.
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All authors Sipeng Xie, Qianhong Wu, Minghang Li, Qin Wang, Zhipeng Wang, Bo Qin
Computer Vision
Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target tracking
Mazdak Fatahi, Šárka Pryjmaková, Pierre Boulet et al. (+1 more)
Biological visual systems achieve continuous, low-latency motion perception by processing sparse, asynchronous spiking signals, enabling real-time tracking under strict energy constraints. Event-based cameras, inspired by the mammalian retina, replicate this efficiency by capturing only local brightness changes as asynchronous events, offering a natural substrate for spiking neural networks (SNNs) to parallelise computation and adapt to fast-changing scenes. Pinball provides a controlled yet dynamic testbed, requiring precise motion estimation and fast reaction to a small, rapidly moving target. This work presents a fully spiking, real-time perception-to-action pipeline for closed-loop pinball gameplay. A dynamic
vision sensor observes a small, fast-moving ball, and a network of spiking Time-Difference Encoders on the SpiNNaker neuromorphic platform jointly estimates its position, speed, and direction. The system is characterised across receptive field size, accumulation window, and angular tuning width for real-time operation, and benchmarked in closed loop against human players across two flipper regimes of increasing physical realism. It achieves a hit rate of 56.1%, nearly double the human average, reacting within 21.7 ms (5 ms network latency) and consuming an estimated 148 {\mu}W using fewer than 25k
neurons, among the fastest and most energy-efficient event-based closed-loop demonstrators benchmarked. Under more realistic flipper dynamics, tuning a single interpretable policy parameter reproduces the full spectrum of human play styles, from cautious to aggressive, with no change to the perception pipeline. A physical demonstrator, tracking a real ball and actuating real flippers in closed loop, confirms the principle operates beyond simulation. Its fully spiking, learning-free design offers a compact, energy-efficient example of real-time neuromorphic perception-to-action.
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All authors Mazdak Fatahi, Šárka Pryjmaková, Pierre Boulet, Giulia D'Angelo
Machine Learning
Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning
Mindula Illeperuma, Rafael Pina, Charuka Herath et al. (+2 more)
Structured
pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constrained devices. Popular methods of
pruning rely on opaque heuristics or weight-based criteria that give no indication as to the structural dependencies in the network. To address these limitations we present Artificial Structure Function Search (ASF-S): a novel structured
pruning framework. ASF-S utilizes Principle Gradient Importance (PGI): a novel prune-candidate selection criteria that is inspired by structure-function relationships in the
brain. By ensuring the pruned structure of the model respects topographical organization of the output layer, we define Artificial Functional Connectivity (AFC) for artificial neural networks. AFC provides evidence to demonstrate that accurate smaller networks can be found using careful prune candidate selection criteria. We present results for PGI as a selection criterion and for ASF-S as a
pruning framework against recent
benchmarks, demonstrating that our method yields model variants with 70% parameter reduction, that can recover baseline accuracy without re-training the pruned layers.
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All authors Mindula Illeperuma, Rafael Pina, Charuka Herath, Sharmarke A. Gabayre, Varuna De Silva
Social and Information Networks
Optimal and heuristic strategies for evaluating the influence of coordinated behavior in information cascades and retweet networks
Niccolò Di Marco, Matteo Cinelli, Shinichi Nakano et al. (+1 more)
Coordinated Inauthentic Behavior (CIB) has become a major concern in online social platforms, yet its actual impact on information
diffusion remains poorly understood. Existing research has primarily focused on detecting coordinated activity, while comparatively little
attention has been devoted to quantifying its influence once detected. In this work, we introduce two complementary frameworks for the post-hoc evaluation of coordinated accounts. First, we formulate the problem on information cascades as a constrained influence maximization problem over directed trees and develop a
polynomial-time dynamic programming
algorithm that computes the optimal placement of coordinated nodes, providing an upper bound on their achievable influence. Second, motivated by the limited availability of
diffusion cascades in real-world platforms, we propose a network-based framework that estimates influence directly from retweet networks using the independent cascade model and compares the observed placement of coordinated accounts against established heuristic baselines. We evaluate both approaches on Twitter/X data from the 2019 UK General Election and on a collection of verified state-backed information operation campaigns spanning multiple countries. While coordinated accounts exhibit limited influence in the UK cascades, the network-based analysis reveals substantial differences across campaigns, with several operations achieving influence comparable to or exceeding that of structurally central seed sets. Finally, by reconstructing cascades from the retweet networks, we show that the two frameworks produce consistent results, suggesting that the observed effects reflect intrinsic structural properties of coordinated activity rather than artifacts of the underlying methodology.
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All authors Niccolò Di Marco, Matteo Cinelli, Shinichi Nakano, Andrea Frosini
Machine Learning
Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systems
Hans-Werner Wiesbrock
The conventional testing process quickly fails when applied to ML-based systems such as obstacle detection in vehicles: if an obstacle is not detected in a test, classical bug fixing is impossible and an AI system will always retain shortcomings. Test results can therefore only be interpreted statistically, which in turn requires test sets that are not only complete with respect to the operational design domain (ODD) of the system, but also representative of it. To this end, we introduce probabilistically extended ontologies (PEONs): ontologies describing the ODD, augmented with a probability distribution over the partitioning they induce. Instead of unmaintainable conditional probability tables, only marginal distributions and functionally described dependencies need to be specified;
algorithms based on couplings and optimal transport complete this specification to a
Bayesian network. From a PEON we derive the sampling of representative test cases, rigorous end-of-test criteria for given quality targets and significance levels, and methods for re-evaluating existing test results and for assessing the balance of training data. We demonstrate the practical modelling of a complex ODD using the example of automatic train operation.
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Machine Learning
Climate Variability Modulates the Impact of Price Spikes on Food Insecurity
Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, Homer Durand et al. (+3 more)
Climate variability influences whether a
market disruption escalates into a food crisis, yet broad
climate patterns like El Niño, tracked months before they alter hydro-climatic conditions, are still not incorporated as an early-warning component in food-security responses. We address this gap by introducing sensitivity regimes, a stratification of regions by the direction and strength of their vegetation response to the El Niño Southern Oscillation, and using them to estimate how food price spikes affect acute food insecurity across sub-Saharan Africa. Integrating remote sensing, socioeconomic data, and causal machine learning, we find that in regions where ENSO systematically suppresses vegetation, a price spike raises the share of the population at acute risk by 5.4 percentage points in the following month. In regions where vegetation is unaffected by or positively linked to ENSO, the estimated effect is smaller (around 2 percentage points) and statistically insignificant. These results demonstrate that
climate context is critical for understanding food security vulnerabilities. Sensitivity regimes can be combined with operational price-spike triggers to stage anticipatory action: the ENSO state flags vulnerable regions months ahead, and a pre-positioned response in those regions to a price spike would avert the largest jump in acute food insecurity.
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All authors Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, Homer Durand, Gherardo Varando, Michele Ronco, Gustau Camps-Valls
Cryptography and Security
Passive Hybrid Network-Based Intrusion Detection System (Hybrid-NIDS) Combining Suricata and Random Forest
Quoc-Cuong Tang, Hoang-Lam Huynh, Van-Tri Phan et al. (+1 more)
This paper evaluates a passive Hybrid Network-based Intrusion Detection System (Hybrid-NIDS) prototype that combines Suricata with Random Forest flow classification and centralized ELK-based alert handling. The study explicitly separates
benchmark evaluation from PCAP/live operational validation and controls exact feature-duplicate leakage using feature hashing and group-aware splitting. From 2,540,047 UNSW-NB15 records, 453 conflicting-label hash groups containing 1,879 rows were removed; the resulting Development and Hold-out sets have zero exact feature-hash overlap. RF-41 achieved F1 = 0.971360 and ROC-AUC = 0.999671, while the NFStream-compatible RF-21 achieved F1 = 0.970148 on the same prepared hold-out boundary. However, operational validation revealed substantial
benchmark-to-deployment domain shift: on a labeled laboratory PCAP, RF-21 and the strictly correlated branch achieved recall of only 0.0095, and RF-21 produced no alerts in five additional 60-second attack sessions. An unlabeled normal-traffic test produced 439 alerts from 2,375 flows; this value is reported only as an alert ratio and is not interpreted as a false-positive rate. These results show that strong performance on a public
benchmark does not directly translate into operational effectiveness. Accordingly, the current Hybrid-NIDS should be interpreted as a passive prototype and evaluation framework, and the reported experiments do not demonstrate that Suricata-Random Forest correlation provides better operational detection than Suricata alone.
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All authors Quoc-Cuong Tang, Hoang-Lam Huynh, Van-Tri Phan, Khuong Nguyen-An
Machine Learning
NAVIR: Neuromorphic Audio-Visual Speech Recognition for Robust Human-Robot Interaction on Edge Hardware
Leonidas Delimpasis, Panagiota Moraiti, Antonis Porichis et al. (+2 more)
Voice-controlled interaction in industrial settings is hampered by acoustic noise, which severely degrades audio-only
speech recognition. Audio-visual
speech recognition (AVSR) addresses this by fusing lip-motion cues with the audio stream, but state-of-the-art pipelines rely on three-dimensional convolutions, recurrent units, and
attention modules that exceed the budget of typical edge devices. We present NAVIR, an end-to-end AVSR system targeting the BrainChip Akida neuromorphic processor, which natively supports only sequential two-dimensional convolutional inference. The pipeline factorises spatial and temporal encoding into separate AkidaNet-based modules: a per-frame visual encoder, a temporal
video encoder, and a spectrogram audio encoder, fused by a lightweight predictor head and decoded by constrained beam search. Models are trained with connectionist temporal classification on noise-augmented audio and then fine-tuned with quantization-aware training. On the GRID
benchmark, the quantized audio-visual model reaches 14.0% word error rate (WER) under noise on the unseen-speaker split and 3.3% WER on the overlapped-speaker split, against 22.5% and 11.8% for audio-only baselines, and it attains 98.6% command accuracy at 1.5% WER on a task-specific industrial-command corpus. Operation-count analysis indicates a 13-fold energy advantage of the spiking formulation over its artificial neural network counterpart at 27.6% mean firing rate. On-board measurements show roughly 5-fold lower energy per inference than a Raspberry Pi central processing unit on the lip-reading model, and over 100-fold lower than a laptop graphics processing unit, while sustaining 14.5 inferences per second. To the best of our knowledge, this is the first complete
multimodal AVSR pipeline running on neuromorphic hardware of this class.
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All authors Leonidas Delimpasis, Panagiota Moraiti, Antonis Porichis, Panos Chatzakos, Michail Karamousadakis
Cryptography and Security
Name2Pkg: Lightweight One-Class Android Malware Screening via Name-Package Correspondence Modeling
Changyeop Sung, Yeonjae Kang, Jaeho Shin et al. (+1 more)
Deep learning-based malware detection has been widely adopted in security-critical services. Most detection methods rely on internal features extracted from APK files or runtime behavior. However, extracting these features is computationally expensive. This limits their use in large-scale, early-stage screening. Malicious apps may exhibit weak correspondence between their user-facing app names and package names, providing a low-cost screening signal. We present Name2Pkg, a lightweight one-class classification method. It leverages only the app name and the package name. We formulate malware screening as a sequence anomaly detection problem. A character-level sequence-to-sequence model estimates the conditional likelihood of a package name given the app name. The length-normalized negative log-likelihood serves as the anomaly score. We train the model and calibrate the threshold using only benign data. Using a
dataset of 67,129 real-world applications, Name2Pkg achieves an area under the receiver operating characteristic curve (ROC-AUC) of 0.982 and malware recall of 0.885 at an achieved false-positive rate of 0.044 on held-out test data. It has a 3.57 MiB checkpoint and a CPU inference latency of 28.20 ms per sample. Name2Pkg provides an efficient and effective pre-filtering signal for large-scale security systems.
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All authors Changyeop Sung, Yeonjae Kang, Jaeho Shin, Huy Kang Kim
Computers and Society
Critical Data Studies in the Anthropocene
Ana Valdivia
This chapter introduces the concept of the Anthropocene into critical data studies, a field that has, for the past decade, explored the entanglements between datafication and politics. With the scaling up of contemporary datafication alongside generative computing, it is essential to expand these debates, both theoretically and methodologically, to consider the logics of extraction and exploitation inherent in the politics of artificial intelligence. Critical data scholars are called to interrogate how dominant narratives around the materiality of contemporary datafication either obscure or reveal its environmental impacts, along other key questions such as who benefits from these logics. To illustrate this, this chapter brings two case studies from Spain and Chile and explores how the infrastructure of contemporary datafication intersects with existing power structures, influencing which social and environmental consequences are recognized, addressed, and neglected. Finally, it invites critical data scholars to keep exploring the politics of data, infrastructure, and the Anthropocene by blending discipline boundaries between science and technology studies, media, geography, and political ecology.
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Machine Learning
Machine Learning-Based Prediction of Childhood Stunting in Bangladesh: Fairness and Temporal Robustness Assessment
Md Ahshanul Haque and Muhammad Ashad Kabir
Childhood stunting remains a major public health concern in Bangladesh and reflects long-term growth failure influenced by child, maternal, household, socioeconomic, and health-service factors. This study used nationally representative Bangladesh Demographic and Health Survey data from 2007 to 2022 to develop machine learning models for population-level prediction of childhood stunting and to assess temporal robustness and subgroup
fairness. Children aged 0-59 months with complete anthropometric and predictor data were included. Data from the 2007, 2011, and 2014 survey rounds were used for model development, while the 2018 and 2022 rounds were retained as temporal test
datasets. Twelve feature-selection approaches were assessed, and the KNN permutation importance-selected predictor set was used for final model evaluation. Eleven machine learning models were evaluated: ten conventional
algorithms and one pretrained tabular foundation model, TabPFN. Performance was assessed using balanced accuracy, AUROC, F1-score, Brier score, and expected calibration error. Subgroup
fairness was examined by child sex, place of residence, and socioeconomic status. The final analytic sample included 18,844 children, of whom 35.05% were stunted. In the development hold-out test
dataset, TabPFN showed the highest observed balanced accuracy overall at 67.58%, while AdaBoost showed the highest observed balanced accuracy among conventional models at 67.51%. In temporal testing, the highest observed balanced accuracy was found for Gradient Boosting in BDHS 2018 and XGBoost in BDHS 2022. Model performance varied across survey rounds and subgroups, highlighting the importance of temporal validation, subgroup
fairness assessment, and transparent interpretation in public health prediction modeling.
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Robotics
Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors
Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy et al. (+1 more)
Tactile sensing is an essential modality for
robots performing contact-rich, dexterous manipulation, particularly under visual occlusion. While pre-trained image encoders are standard in
robot learning pipelines, tactile encoders are still commonly trained from scratch from raw, noisy signals, which might limit their expressivity. Existing self-supervised learning (SSL) approaches focus predominantly on
vision-based tactile sensors, leaving distributed electronic skins largely unaddressed. These sensors, however, have a distinctive property: their sensing elements are sparse and irregularly arranged over the surface they cover, which makes direct reuse of visual SSL methods suboptimal. We present Tactile-JEPA, an efficient self-supervised pre-training method that uses the spatial arrangement of tactile sensors to learn
topology-aware representations. Specifically, it is trained to predict the
embeddings of masked sensing elements from the unmasked remainder, using the sensor connectivity graph to guide spatial masking. Our analysis shows that effective tactile representations require capturing both local contact details and the global state of the tactile surface, which we achieve through dual-scale masking. Across three diverse
datasets spanning
magnetic and piezoresistive sensors, different
robot embodiments, and single- and paired-sensor configurations, Tactile-JEPA reduces force estimation error by 6.3% and in-hand orientation error by 20.8% over the prior state-of-the-art, with consistent gains in other downstream applications, including policy learning. Overall, our results demonstrate that the benefit of tactile sensing depends critically on the quality of encoder pre-training, a problem which Tactile-JEPA addresses directly. Code is available at https://github.com/E-Kovtun/tactile.
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All authors Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy, Semen Budennyy
Computer Vision
A Lightweight Convolutional Neural Network for Real-Time Recognition of Hand-Drawn Geometric Shapes
Shahir Abdullah
Recognizing hand-drawn
geometric shapes is a foundational sub-problem of sketch recognition, with applications in education, human-computer interaction, and diagram digitization. This paper presents the design, implementation, and evaluation of a desktop application that recognizes four basic hand-drawn
geometric shapes, circle, square, rectangle, and triangle using a compact Convolutional Neural Network (CNN). A
dataset of 2,000 labeled 28x28-pixel shape images was collected independently and released publicly. The classifier consists of three convolutional blocks (16, 32, and 64 filters) with max-pooling, an in-model data-augmentation stage (random horizontal flip, rotation, and zoom), a dropout-regularized dense layer of 128 units, and a 4-way linear output layer, totaling 97{,}956 trainable parameters. The network is trained with the Adam optimizer on a sparse categorical cross-
entropy objective computed directly on logits. On an 80/20 train-validation split, the model achieves 94.80% training accuracy and 96.01% validation accuracy with a validation loss of 0.1437. A Tkinter-based graphical interface allows a user to draw a shape with the mouse and receive an immediate class prediction with a confidence score. We situate this system within the broader sketch and shape-recognition literature, compare its accuracy against related hand-drawn shape classification studies, and discuss the limitations inherent to a small, single-contributor
dataset. The complete source code, trained model, and per-class
datasets are released publicly to support reproducibility.
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Machine Learning
Credit Access is Associated with Improved Food Security in the Horn of Africa
Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, José Manuel Veiga López-Peña et al. (+3 more)
The intensification of
climate change poses a growing threat to food security, especially in vulnerable communities. This study employs an observational machine-learning framework to estimate the causal association between access to credit and acute food insecurity in Somalia and across the Horn of Africa, drawing on a harmonized
dataset spanning key environmental, socioeconomic, and conflict-related factors from 2015 to 2022. Results indicate that greater credit access is associated with a 2% reduction in acute food insecurity at the population level over the study period. Given that, on average, 16% of the population is in crisis, this effect represents a meaningful shift within the at-risk group. We interpret these estimates under explicit identification assumptions and complement them with robustness and refutation tests. The results provide context-specific evidence on how financial access correlates with food security outcomes in data-scarce, crisis-affected settings, and offer a transparent framework for integrating heterogeneous data sources when randomized evaluations are infeasible.
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All authors Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, José Manuel Veiga López-Peña, Duccio Piovani, José María Tárraga, Gustau Camps-Valls
Machine Learning
Information-Time Proximal Policy Optimization
Yongcheng Zeng, Xinyu Cui, Yan Song et al. (+9 more)
RLVR has substantially improved the
reasoning capabilities of LLMs. However, existing methods typically parameterize temporal progression in the Markov Decision Process by
token-by-
token generation, despite the highly non-uniform information flow along autoregressive trajectories. In this paper, we propose InfoPPO, which reparameterizes temporal progression using information density rather than raw
token count. This reparameterization induces a common state-dependent structure for both temporal credit propagation and policy updates. InfoPPO restores the effectiveness of non-trivial discounting in long-horizon
reasoning, retaining effective-horizon contraction while avoiding excessive attenuation of terminal supervision over long
token sequences. Moreover, the information-time policy-improvement analysis naturally leads to a state-dependent update constraint, which we implement through adaptive clipping. By adapting the clipping threshold at each
token position to the information density of its corresponding state, this mechanism enables more targeted policy updates while preserving proximal control. Theoretically, we extend performance-difference and policy-improvement analyses to the information-time MDP, deriving a policy-improvement lower bound when policy changes are regulated by information density. We further connect the general information-time analysis to practical LLM policy optimization by relating state-wise information density to local policy movement, while also providing theoretical grounding for the adaptive update mechanism. Experiments on Qwen3 models demonstrate consistent gains over competitive baselines across five challenging competition-style mathematical
reasoning benchmarks. InfoPPO also maintains stable accuracy and response length across non-trivial discount settings under which
token-time PPO deteriorates.
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All authors Yongcheng Zeng, Xinyu Cui, Yan Song, Guoqing Liu, Hongsheng Xin, Kaike Zhang, Cheng Deng, Kun Zhan, Jian Ying, Jian Zhao, Haifeng Zhang, Jun Wang
Computer Vision
Topographic Training Concentrates Causal Circuits Without Improving Neuron Monosemanticity
Gautam Ranka, Shubham Santosh Pandere and Aiden Dsouza
Mechanistic interpretability of
vision transformers seeks to decompose model computation into human-readable units, but learned representations entangle many concepts in each
neuron. Feature superposition is widely treated as the central obstacle to this decomposition, yet most mitigations (sparse autoencoders, dictionary learning) are post-hoc and leave the underlying network unchanged. We ask whether a spatial-locality training loss (TopoLoss) can act as a lightweight, training-time prior that improves interpretability of standard mech-interp tools. Training ViT on ImageNet-100 across multiple TopoLoss weights $\alpha$, we measure causal sufficiency of topographic clusters via activation patching and feature geometry via sparse autoencoders fit to the same residual stream. At $\alpha=1.0$, topographic clusters are 2.79$\times$ more causally sufficient than random unit sets of the same size, with the effect increasing monotonically in $\alpha$. SAE L0 sparsity decreases by 11% and dead-feature fraction rises 19-fold, yet standard
neuron-level monosemanticity scores are unchanged, indicating that topographic pressure acts at
circuit level, concentrating causal mass into spatially local structures without disentangling individual
neurons. This dissociation suggests current
neuron-level monosemanticity metrics are insensitive to a class of real interpretability gains, and positions cheap architectural priors as a viable training-time complement to post-hoc tooling.
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Information Theory
On the Information-Theoretic Limits of Latent-Space Watermarking Through Pretrained Generators
Jinwan Jeon, Minju Lee and Sung Hoon Lim
We study latent-space watermarking through a pretrained generator using a prescribed latent-to-output
stochastic mapping, called the renderer. A watermark encoder selects the latent input using a message and secret key. For every message and semantic context, the released output must have exactly the desired conditional output distribution. For finite alphabets, we derive rate–key inner and outer bounds and characterize the coding and coordination requirements for realizing watermark communication through the prescribed latent interface. When the target output distribution of the generator uniquely determines the corresponding latent input distribution through the renderer, a strengthened converse yields the capacity region; the same region governs explicit preservation of the pretrained latent distribution. We extend the analysis to general jointly Gaussian models and identify a sufficient statistic of the latent that captures both the watermark-bearing information available at the generated output and the latent coordination required to preserve its target distribution. For the vector Gaussian model, we further characterize the optimal allocation of the secret-key resource across the resulting modes. Finally, we turn to an emerging robustness threat that is particularly natural in generative watermarking: an adversary can regenerate the released sample to obtain a fresh realization of the same underlying content while attenuating or destroying the embedded watermark. We incorporate this robustness axis into our framework and characterize the one-pass compound capacity of the scalar Gaussian model when the semantic context is known to the encoder but hidden from the detector, while the regeneration attack may depend on that context. Extending the analysis to multiple rounds of repeated canonical regeneration, we characterize the resulting watermark-capacity decay.
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Robotics
Multi-Agent Transportation of Free-Flyers in Microgravity Via Pushing Interaction Under Human-in-the-Loop Control
Gregorio Marchesini, Nicola De Carli, Sihyun Cho et al. (+5 more)
We propose a safety-critical framework for the cooperative transportation of passive targets in microgravity, where a team of chaser
robots acts through unilateral pushing contacts to track a human-provided desired twist while ensuring safe target motion. The pushing-only nature of the interaction introduces sparse, configuration-dependent actuation constraints requiring chasers to physically relocate on the target body when the desired pushing allocation changes. To address these challenges, we formulate a delay-aware feedback control architecture leveraging Control Lyapunov Function (CLF) and Control Barrier Function (CBF) constraints within a mixed-integer thrust allocation program to enforce stability and safety of the target, respectively. The proposed framework enables reference tracking while guaranteeing obstacle avoidance with a circular obstacle despite intermittent control authority, providing a foundation for human-supervised cooperative transportation of free-flyers in space environments. The proposed framework is validated through Gazebo simulations.
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All authors Gregorio Marchesini, Nicola De Carli, Sihyun Cho, Youngkyoung Kong, Elias Krantz, Mani Hemanth Dhullipalla, Dimos V. Dimarogonas, H. Jin Kim
Computation and Language
URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER
Jingyu Wang, Shijie Wu and Fusheng Jin
In-context learning (ICL) based on large
language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limited availability of annotated data in Named Entity Recognition (NER). However, existing methods still face issues of
retrieval misalignment and generation uncertainty, making their performance heavily dependent on the LLM's capabilities. As the parameter scale of LLMs decreases, their performance in few-shot settings deteriorates significantly. In this paper, we propose a novel unified
retrieval-augmented framework, URA-NER, including three key components: Progressive Granularity
Retrieval (PGR), Model-aware Representation Enhancement (MaRE), and Reason-aware Knowledge Verification. PGR is a two-stage
retrieval mechanism that achieves stage
alignment. It first retrieves demonstrations for span detection based on the query's global semantics, and then for type classification based on the specific entity context, providing fine-grained local information. Moreover, MaRE employs entity pre-recognition to guide the construction of representations, ensuring the query and demonstrations are aligned within the LLM's semantic space and
attention pattern. In addition, to mitigate generation uncertainty, we propose RaKV, a closed-loop "generation-
retrieval-verification" process. It explicates the LLM's
reasoning paths, leverages them for the
retrieval of external knowledge, and reorganizes the knowledge into verification evidence aligned with the original
reasoning paths. We conduct extensive experiments on multiple low-resource NER
datasets. Results demonstrate that URA-NER significantly enhances the performance of LLMs under low-resource settings, with particularly pronounced gains for smaller LLMs, achieving new state-of-the-art results on several
benchmarks.
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Machine Learning
Prescriptive SVD-Inspired Attention via Spectral Energy Retention
Vasileios Arampatzakis, Vasileios Sevetlidis and George Pavlidis
Self-
attention is central to modern
Transformer architectures, but its dense dot-product formulation makes it difficult to identify which internal directions are structurally important and which can be modified without disrupting the model. SVD-Inspired
Attention (SVDA) addresses part of this problem by introducing a learned diagonal spectrum into the query-key score interaction, making latent
attention directions explicitly inspectable through indicators such as spectral
entropy, effective rank, sparsity,
alignment, selectivity, and perturbation response. This paper examines the transition from diagnostic interpretation to operational intervention. A diagnosis–intervention–verification framework is proposed, and one intervention is evaluated: spectral energy retention in the
attention-score pathway. Across FashionMNIST, CIFAR-10, CIFAR-100, and Food-101, the $\rho=0.90$ prescription removes 24.5–53.7% of score directions, reduces parameters by 2.6–4.3%, and reduces estimated MACs by 2.8–5.4%. The paired mean accuracy change of the dimension-reduced model ranges from $-0.03$ to $+0.05$ percentage points over three seeds. These results support SVDA as an intrinsically interpretable
attention mechanism whose learned spectrum exposes an operational coordinate system for deterministic and verifiable modification of
attention-score formation.
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Human-Computer Interaction
DeceptionAnalyser: A Web-Based AI Tool for Performing Structured Deception Analysis with Argumentation Schemes and LLMs
Stefan Sarkadi, Xabier Garmendia, Jack Mumford et al. (+1 more)
Deception plays a central role in Intelligence operations, yet it remains difficult to analyse systematically without expert knowledge of
reasoning patterns and cognitive manipulation. In computational argumentation, for instance, no scheme-level ground-truth corpora currently exist to support statistical validation. In this paper, we address this by introducing a set of ten argument schemes designed to model distinct forms of deception, each accompanied by structured premises and critical questions. In doing so, we introduce the first dedicated library of argumentation schemes specifically designed for deception analysis, providing a structured foundation for systematically modelling and analysing deception in narrative text. We then present DeceptionAnalyser, a browser-based tool that implements these schemes through a two-stage methodology combining LLM-based premise extraction with critical-question-driven evaluation. Our aim is to provide a conceptual and methodological foundation for analysing deceptive
reasoning in narrative text. This is precisely what we address in this paper by demonstrating how structured argumentation theory and AI-assisted analysis can support transparent, explainable assessments of potential deception. Because the schemes are designed to flag claims for scrutiny rather than to output a deception verdict, we do not
benchmark classification accuracy; instead, we assess the reliability of the methodology by measuring the consistency of the tool's premise and conclusion assessments across ten contemporary large
language models and repeated runs. We find that scheme detection is highly stable for clear-cut deception and degrades gracefully, in interpretable ways, on more ambiguous intelligence-style narratives.
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All authors Stefan Sarkadi, Xabier Garmendia, Jack Mumford, Trevor Bench-Capon
Computer Vision
TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting
Fanqi Yu, Shengming Ma, Stefano Fiorini et al. (+4 more)
Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale. We propose TReViS, a self-supervised
video synthesis framework that enables training RAC models without any repetition labels. TReViS estimates the underlying temporal repetition structure of an unlabeled
video via a Temporal Self-Similarity
Matrix, infers its cycle statistics, and synthesizes new training sequences that preserve realistic repetition patterns while introducing controlled temporal variability. These synthesized
videos are paired with pseudo-labels and used to train existing RAC architectures from scratch. Across multiple
datasets and backbones, TReViS consistently outperforms prior self-supervised methods and achieves performance competitive with several supervised baselines, while remaining fully label-free, demonstrating the effectiveness of structure-aware
video synthesis for label-free RAC. The source code is available at https://github.com/yfqi/TReViS.
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All authors Fanqi Yu, Shengming Ma, Stefano Fiorini, Vito Paolo Pastore, Xuan Qi, Vittorio Murino, Cigdem Beyan