Robotics
InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies
Sai Puneeth Reddy Gottam, Elmar Rueckert and Vedant Dave
Vision-
Language-Action (VLA) policies often rely strongly on the camera viewpoints seen during training, causing substantial performance degradation when deployed from unseen perspectives. Collecting demonstrations from sufficiently diverse physical viewpoints is expensive and still provides only sparse coverage of the viewpoint space. We introduce InfiNoVA, a data-augmentation framework that converts synchronized multi-camera demonstrations into a dense distribution of geometrically consistent training views. InfiNoVA reconstructs each manipulation trajectory as a time-varying 3D Gaussian representation and renders novel observations from sampled camera poses while preserving the original state-action correspondence. This explicit scene representation improves frame-level fidelity and temporal consistency while reducing task-critical
hallucinations observed in generative novel-view synthesis. Across four real-world manipulation tasks, policies trained with InfiNoVA achieve 5.4x higher average success under unseen randomized viewpoints than both VISTA-based augmentation and the unaugmented policy. InfiNoVA further achieves 1.7x higher success than training directly on all five physical camera views. These results show that dense, geometrically grounded viewpoint augmentation provides a practical route toward camera-robust
robot policies without modifying the underlying policy architecture.
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Systems and Control · Cross-listed from eess.SP
Analytical Framework of Radial Resolution for Near-Field Communications
Viviana Centritto, Sotiris Droulias and Angeliki Alexiou
As extremely large
antenna arrays (ELAAs) become central to next-generation wireless systems, the transition into the near-field propagation regime enables the exploitation of spherical wavefronts for radial-domain beamfocusing. This capability is pivotal for emerging applications requiring precise spatial isolation, such as Space Division Multiple Access (SDMA), hierarchical localization and advanced sensing. However, fully realizing these technologies requires specific design rules to dimension multi-user systems without relying on computational expensive full-wave simulations. To address the gap in modeling contiguous focal regions with controllable radial resolution, this paper expands the Angular Spectrum Representation (ASR) approach to propose a comprehensive analytical framework. Through the introduction of a tunable inter-beam overlap parameter $\rho$, we derive closed-form expressions to synthesize multiple focal regions, providing the flexibility to tailor their radial resolution. Furthermore, the resolution capabilities are analyzed to characterize the interplay between key operational variables, such as the transmitter size, beam radius and operation frequency. System-level assessment of per-user and sum-rate spectral efficiencies across varying signal-to-noise (SNR) regimes reveals how the inter-beam overlap dictates a fundamental trade-off between user capacity and inter-user interference, delivering design guidelines for future near-field communications.
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Information Theory · Cross-listed from math.CO
A new upper bound for Sidon sets in $\mathbb{F}_2^{4k+3}$
Darrion Thornburgh
A subset $S \subseteq \mathbb{F}_2^n$ is called a Sidon set if no four distinct points of $S$ have zero sum. It is shown that if $n \geq 7$ and $n \equiv 3 \mod 4$, then $|S|\leq 2^{\frac{n+1}{2}}-3$. As a consequence, for any even $t \geq 4$, there does not exist a binary linear $[2^t-3,2^t-2t-2,5]$-code, strengthening a nonexistence result of Brouwer and Tolhuizen from 1993. As a second consequence, we also show that for an even integer $n \geq 4$, every almost perfect nonlinear (APN) function $F \colon \mathbb{F}_2^n \to \mathbb{F}_2^n$ has nonlinearity at least $3$.
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Artificial Intelligence · Cross-listed from q-bio.NC
AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets
Marco Rothermel, Madleen Stenger, Soroush Daftarian et al. (+7 more)
In neuropsychiatry, the primary goal is often not only to decode
brain activity but to change it, for example to lessen a negative affective bias or an overly salient
memory. Motivated by control theory, we develop an AI-driven neural-surrogate framework that proposes candidate representational changes and tests their predicted perceptual effects from snapshots of stimulus-evoked fMRI activity, without physical stimulation. The framework combines fMRI decoding, deep generative modeling, and constrained latent-space steering. Valence and memorability are used only as worked examples. Using more than 36,000 image-fMRI observations from four deeply sampled Natural Scenes
Dataset participants, subject-specific models recovered coarse generative structure from visually responsive cortex (two-way identification, 0.79-0.88; chance, 0.5). Graded perturbations were reconstructed as images and evaluated with automated scorers and human ratings from 7,200 trials by 18 participants. In the primary VDVAE model, valence shifted from -0.61 to +1.03 SD and memorability from -1.34 to +1.45 SD; a later Versatile
Diffusion refinement reduced or altered these effects. Across five perturbation levels, human valence ratings moved in the predicted direction under the linear time-correction model (mean slope, 0.038 SD per unit of alpha; 95 percent CI, 0.003-0.074; positive in 16 of 18 participants). Perceived memorability did not change reliably. Baseline agreement with the automated assessor was suggestive for valence (r = 0.30) and weak for memorability (r = 0.10). Extreme perturbations drifted from the original stimulus, so intended change must be weighed against loss of fidelity. These findings provide a falsifiable upstream method for designing and behaviorally testing candidate representational targets for future neuromodulation in psychiatry, while marking the limits of the present static approximation.
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All authors Marco Rothermel, Madleen Stenger, Soroush Daftarian, Svenja Jule Francke, Bita Shariatpanahi, José C. García Alanis, Mohammad-Ali Nikouei Mahani, Stefan G. Hofmann, Tim Hahn, Hamidreza Jamalabadi
Computer Vision
NeuralSRNF: Neural Square Root Normal Fields for the Statistical Shape Analysis and Generation of Nonrigid 3D and 4D Objects
Awais Nizamani, Hamid Laga, Guanjin Wang et al. (+3 more)
We introduce NeuralSRNF, a novel framework for the statistical shape analysis and generation of genus-zero 3D and 4D objects that undergo nonrigid deformations. Traditional methods rely on complex and computationally expensive nonlinear elastic metrics that measure bending and stretching. Recent advances in elastic shape analysis achieve computational efficiency by mapping input 3D shapes to the space of Square Root Normal Fields (SRNFs) where the L2 metric approximates the partial elastic metric, significantly facilitating the process of computing geodesics and summary statistics. SRNFs, however, are not invertible, and the numerical
algorithms used to map SRNFs back to the original space of surfaces remain computationally very expensive and often lead to approximate results. This paper addresses this fundamental SRNF inversion problem using a novel neural representation, termed NeuralSRNF. Unlike the commonly used numerical SRNF, NeuralSRNF is (1) continuous, and thus resolution-agnostic, enabling full functional shape analysis, (2) more accurate, and (3) computationally more efficient as it can compute inverse SRNF maps along a geodesic path in less than 3 s compared to over 10 min for the numerical SRNF. We demonstrate, using various
datasets, the utility and efficiency of the proposed NeuralSRNF in multiple elastic 3D and 4D shape analysis tasks such as geodesic computation, deformation transfer, statistical summaries computation, and 3D shape generation. We show that it outperforms competing methods on most evaluated
datasets and metrics by a wide margin in both accuracy and computational efficiency. The source code and additional results are available at https://awaisnizamani16.github.io/awais/NeuralSRNF/.
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All authors Awais Nizamani, Hamid Laga, Guanjin Wang, Farid Boussaid, Mohammed Bennamoun, Anuj Srivastava
Distributed Computing · Cross-listed from econ.GN
Sovereign Grassroots Currencies: A CBDC Architecture for Credit and Monetary Policy (Full Version)
Ehud Shapiro
A Central Bank Digital Currency (CBDC) is central-bank money in digital form, held by the public. Leading designs have two limitations: conversion from bank deposits into CBDC can accelerate deposit flight, requiring safeguards, and the CBDC stays outside credit creation and monetary-policy operations. Here we present a CBDC architecture based on grassroots currencies that overcomes these limitations. The architecture has three components: (1) Money: sovereign grassroots coins, which are digital debts of one unit of fiat currency issued by the central bank, constituting a direct CBDC; (2) Credit and Liquidity: non-sovereign grassroots coins, which are digital debts of one unit of the same fiat currency, redeemable at par, that can be issued by any person, natural or legal, thus adding credit; and (3) Interest: grassroots bonds, sovereign and non-sovereign, adding maturity and thus interest, standard banking instruments, and the central bank's instruments of monetary policy. The central bank can therefore lend, absorb liquidity, set its rates and buy and sell securities in the coins and bonds the public holds, choosing the counterparties and terms of its credit operations, and without converting bank deposits into newly issued central bank money on demand. We prove that the arbitrage-free price of any non-sovereign grassroots coin whose issuer redeems it on demand is one unit of the fiat currency. The central bank can choose to deal with any counterparty, not just banks, and we argue that the central bank's interest rates on lending and bonds bound from above and below the corresponding interest rates of its counterparties. Sovereign and non-sovereign grassroots coins and bonds have been implemented and tested on a small scale.
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Human-Computer Interaction
Open Questions Towards Skill-Sustaining Reliance in Reflective AI Engagement
Sander de Jong
As AI systems are increasingly integrated into professional work, reflection strategies such as cognitive forcing and
prompts that foster critical engagement have shown promise in reducing overreliance and improving decision quality. However, these strategies have primarily been evaluated as short-term interventions within single sessions. The next challenge is to assess whether such mechanisms sustain human agency and expertise over time. Drawing on prior work in AI-assisted decision-making, metacognition, and reflective AI engagement, we examine the challenges of designing and evaluating reflective mechanisms for long-term skill sustainability, considering individual differences in how users engage with such support, the organisational conditions under which it is implemented, and the gap between short-term evidence and long-term claims. We introduce open questions for the research community about the conditions under which reflective AI engagement can be sustained in practice.
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Systems and Control
GA-Agent: Large Language Models as Hyperparameter Optimizers for Evolutionary Controller Synthesis
Mohammad Narimani and Seyyed Ali Emami
Tuning PID controllers to satisfy competing objectives - low tracking error, fast settling, limited overshoot, and moderate control effort - is labor-intensive and requires expertise. Genetic
algorithms (GAs) offer gradient-free optimization of controller gains against a weighted fitness function, but success depends on meta-level choices: population size, generation budget, gain bounds, and fitness weights. These are usually set by manual trial-and-error or costly bilevel optimization, exposing a tension: GAs excel at dense numerical search, but configuring them needs high-level, context-dependent semantic
reasoning. We propose GA-
Agent, which decouples these modes. A standard GA handles low-level PID gain optimization. A large
language model (LLM)
agent operates at the meta-level: it observes completed GA runs, diagnoses gaps versus user control objectives, and proposes updated GA configurations. The architecture uses structured
memory, quantitative goal translation, resource-aware termination, and outcome-driven routing. We evaluate GA-
Agent on eight control case studies with diverse dynamics (DC motor, inverted pendulum, aircraft pitch, autonomous
underwater vehicle, and others). GA-
Agent achieves 100% success on all
benchmarks, outperforming a Regular GA with fixed hyperparameters in solution quality and sample efficiency. It matches or surpasses a Cascade-GA baseline while reducing function evaluations by one to two orders of magnitude, typically converging in one to three optimization attempts. Sensitivity analysis shows robustness across LLM backbones and
memory configurations. A compact
memory buffer (size 2-3) and cost-effective models (DeepSeek-V4-Flash at about $0.002 per run) achieve superior performance.
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Robotics
CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation
Jinyu He, Zihao Mao, Haonan Jin et al. (+2 more)
Spatially constrained semantic
navigation requires
robots to identify targets specified not only by semantic categories but also by relations to surrounding objects. In unknown environments, resolving such goals requires efficient exploration together with sufficient target and contextual evidence for reliable relation verification. Existing methods leave relation-aware verification and multi-
robot collaboration largely disconnected: relational
navigation is predominantly single-
agent, while multi-
robot systems seldom coordinate distributed observations for instance-specific relation verification. We propose CoRelNav, whose core is coupling task-conditioned multi-
robot exploration with candidate-driven collaborative verification. A spatial-semantic field converts task constraints, scene nodes, and object features into exploration utility; as candidate information accumulates,
robots are reallocated toward complementary evidence under team
navigation costs, while instance-consistent observations are aggregated across
topology nodes. This coupling reduces redundant search and enables relation hypotheses to be resolved from distributed partial evidence that independent exploration or isolated-view verification can leave ambiguous. Experiments in photorealistic simulation demonstrate consistent improvements over representative baselines, with ablations validating the proposed exploration and verification mechanisms. We further deploy the complete system on two physical mobile
robots, demonstrating its applicability to real-world collaborative
navigation.
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All authors Jinyu He, Zihao Mao, Haonan Jin, Mengyin Fu, Wenjie Song
Data Structures and Algorithms
Vertex-Coloring Edge-Weighting: Kernelization and Generalization
Shubhada Aute, Fahad Panolan and Geevarghese Philip
An edge weighting of a graph induces a coloring of its vertices in which the color of a vertex is the total weight of the edges incident with it. Such an edge weighting is proper if adjacent vertices always receive distinct colors. Deciding whether a graph admits a proper weighting is known to be NP-complete for the weight set $\{0,1\}$, and also for $\{1,2\}$. In recent work (arXiv:2604.12363) we showed that both problems are FPT parameterized by the vertex cover number $k$, but it was open – to the best of our knowledge – whether either parameterized problem had a
polynomial kernel. In this work, we show that both problems have
polynomial kernels when parameterized by $k$. We also show that both problems are W[1]-hard parameterized by treedepth, answering another question from our earlier work. We then study the pre-weighted versions of the two problems, in which the weights of some edges are fixed in advance, and the task is to extend the assignment to a proper weighting of the whole graph. We show that both pre-weighted problems are FPT parameterized by the vertex cover number $k$. For the $\{1,2\}$ version the running time is $2^{O(k \log k)} \cdot n$; for the $\{0,1\}$ version we obtain the same running time when every pre-weight is $1$, and a slower FPT
algorithm in the general case. We also show that both pre-weighted problems are W[1]-hard parameterized by either of (i) the feedback vertex set number or (ii) the treedepth of the input graph. Since a graph with no pre-assigned weights is a special case, our
algorithms for the pre-weighted versions solve the two original problems as well, in time $2^{O(k \log k)} \cdot n$, significantly improving on the bound of $2^{O(k^4)} \cdot n^{O(1)}$ from our earlier work.
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Computation and Language
SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving
Zhilong Ge, Yuting Shao, Yutao Yang et al. (+6 more)
Human-written
agent skills encode rich workflows for real-world problem solving, but are typically used as external inference-time instructions rather than internalized as reusable model capabilities. We introduce SkillGym, a framework that transforms these skills into executable, verifiable training environments for large
language model
agents. Its skill-to-task pipeline instantiates concrete tasks, verifies outcomes with code-based checkers, and assesses empirical skill dependence through contrastive executions. We construct and release 2,756 environments across 12 categories and collect 8,364 successful trajectories from multiple models and harnesses, averaging 49 tool calls and over 60k logged text
tokens. These resources support supervised fine-tuning on verified workflows and reinforcement learning with outcome-based rewards. Under Claude Code, supervised fine-tuning improves Qwen3.5-35B-A3B by 199 Elo on GDPval-AA v2, 19.10 percentage points on Terminal-Bench 2.1, and 28.13 and 12.38 points on SkillsBench v1.1 with and without skills, respectively. Our 35B SkillGym-
Agent reaches 51.47% on skill-assisted SkillsBench, exceeding reported scores for Claude Sonnet 4.6, GPT-5.4 Mini, and DeepSeek V4 Pro. Without skills, it also surpasses skill-assisted bases under Codex and Claude Code, suggesting reusable procedural competence.
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All authors Zhilong Ge, Yuting Shao, Yutao Yang, Yuxuan Cai, Jie Zhou, Kai Chen, Bo Zhang, Qin Chen, Liang He
Robotics
Wave-Robust Passive AUV Localization Using FP-MUSIC
Usama Saqib, Ola Rønning and Andrzej Wąsowski
Localizing an autonomous
underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challenge. We present a receiver-passive 3-D localization and spatial mapping system utilizing a single floating surface buoy equipped with a hydrophone array and an inertial measurement unit (IMU). The central difficulty is that surface wave motion induces six-degree-of-freedom (6-DOF) perturbations that rotate the array between snapshots, degrading conventional subspace processing. We resolve this by introducing a fixed-point iterative MUltiple SIgnal Classification
algorithm (FP-MUSIC) that uses IMU measurements to de-warp snapshot covariances prior to direction-of-arrival estimation. Furthermore, we employ a subspace-projected wideband matched filter to resolve beacon ranges and use power asymmetry for independent front-back identification. Evaluations across simulated sea states demonstrate that FP-MUSIC substantially reduces localization error relative to uncompensated methods and sustains robust 3-D tracking and vehicle orientation estimation under wave-induced motion. At moderate sea state, FP-MUSIC increases the 2-m beacon-separation accuracy from approximately 45% to 75%.
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Computer Vision
FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology
Anh-Tien Nguyen, Trung DQ. Dang, Nghiem Tuong Diep et al. (+9 more)
Pathology
vision-
language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high cost of expert pathology annotation can also limit the labeled data available for task-specific adaptation. Combining complementary pretrained representations is a potential approach to these limitations, yet learning an effective fusion from few labeled examples remains challenging. We introduce Few-shot Fusion Foundation Models of Computational Pathology (FFM-CP), which is a framework that combines multiple pathology
vision-
language models in the few-shot learning setting. The framework first aligns heterogeneous representations using a closed-form Orthogonal Procrustes transformation estimated from corresponding support images. This
alignment preserves within-model feature geometry without training an additional
alignment network. Within the aligned space, a unified graph enables information exchange across backbones by jointly refining support-image features and visual and textual class prototypes. These refined representations support complementary text-prototype and case-
retrieval branches that capture semantic class knowledge and within-class visual variation, respectively. Each branch learns to combine predictions from all ordered backbone pairs, allowing queries encoded by one model to draw on evidence represented by another. We evaluate three backbone combinations on six histopathology
datasets at 4, 8, and 16 shots per class. FFM-CP achieves higher mean macro-F1 than the strongest individually adapted member of each fused set in 50 of 54 comparisons. These findings suggest that combining complementary pretrained representations can improve histopathological classification when annotations are limited.
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All authors Anh-Tien Nguyen, Trung DQ. Dang, Nghiem Tuong Diep, Bui Ngoc Han Nguyen, Tan-Ha Mai, Miriam Cindy Maurer, Phuong Hoa Nguyen, Thi Thuy Uyen Nguyen, Youngjun Park, Daniel Sonntag, Duy Minh Ho Nguyen, Anne-Christin Hauschild
Numerical Analysis
Conservative Three-Layer Schemes for Kirchhoff-Type Equations
Felix Krumbiegel, Jemal Rogava, Andreas Rupp et al. (+1 more)
We study a Kirchhoff-type nonlinear integro-differential equation in two spatial dimensions whose coefficients are allowed to depend on time, and we construct conservative discretizations in time for the associated initial–boundary value problem. We consider two symmetric three-layer schemes of Crank–Nicolson type, a locally linear one and a genuinely nonlinear one, and we show that each of them preserves a discrete analogue of the total mechanical energy of the homogeneous problem with constant coefficients. For the nonlinear scheme we establish uniform apriori bounds on the discrete solution and on its discrete velocity by working directly with the discrete energies, without invoking a nonlinear discrete Grönwall inequality, the constants still grow exponentially in the final time, and we prove local second-order convergence in time, both for the solution and for the central-difference approximation of its first time derivative. The nonlinear system arising at each time level is solved by a fixed-point iteration: given iterates at the two preceding levels that satisfy the apriori bounds, that equation has exactly one solution and the iteration converges to it at a
geometric rate once the time step is small enough. Since the apriori bounds are index-local, alternating them with that one-step solver constructs the trajectory stepwise, for time-dependent coefficients as well, on the local interval on which those bounds hold. Numerical experiments, carried out in a setting in which the spatial discretization contributes no error, exhibit the conservation of the discrete invariants, confirm the second order in time and verify the
geometric convergence of the fixed-point iteration.
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All authors Felix Krumbiegel, Jemal Rogava, Andreas Rupp, Zurab Vashakidze
Hardware Architecture
MVP: A Motion-Predictive Speculative Vision Pipeline with Non-Blocking Drift Correction
Raul Taranco and Antonio González
Continuous
Vision (CV) systems underpin real-time applications such as autonomous driving and augmented reality, where latency, throughput, and energy are tightly constrained on mobile platforms. Modern CV SoC pipelines, however, still serialize image capture and processing, leading to high end-to-end latency. Prior work reduces this latency by predicting future frames and running pixel-domain backend inference speculatively, but incorrect predictions force re-execution on real frames, increasing energy and complexity. We present MVP, a motion-predictive speculative
vision pipeline that operates entirely in the motion domain. Instead of
forecasting full images, MVP predicts future motion vectors and uses them to extrapolate perception results from previously processed frames before the next frame arrives. A lightweight hardware extension in the Image Signal Processor (ISP) reuses existing motion-estimation logic to predict motion with minimal area and energy cost. MVP introduces a scheduling model that treats motion extrapolation as the default path, while full backend inference runs periodically in the background for drift correction off the critical path. It also supports optional frontend scaling, allowing the system to reduce sensor sampling under low or predictable motion to save energy. We evaluate MVP on object detection, demonstrating up to 66.8% reduction in tail latency and 46% energy savings, at a small accuracy cost.
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Robotics
DAVIO: Dense Monocular-Inertial SLAM with Feed-Forward Initialization and Pose-Conditioned Mapping
Jaafar Mahmoud, Arthur Movsesyan, Mikhail Iumanov et al. (+1 more)
A camera and an IMU are the minimal sensor setup for metric localization and dense mapping, yet classical visual–inertial filters must wait for parallax before they start and then retain only sparse landmarks. Feed-forward geometry models, in contrast, predict dense structure from a few images but provide neither metric scale nor
gravity. We present DAVIO, which uses a single multi-view depth model, Depth Anything~3, for both start-up and mapping. At start-up, a five-image window and preintegrated IMU measurements form a feature-free linear system. Its robust, conditioning-checked solution bootstraps a VIO filter through buffered replay. During tracking, the filter's metric poses condition the depth model. Residual scale is corrected only along viewing rays, which preserves the metric camera baselines, and a
gravity-preserving submap graph with drift-gated revisits refines the map. On EuRoC, DAVIO starts markedly earlier, reduces the localization error, and maps more accurately than SOTA feed-forward mappers given identical poses. On building-scale ORI sequences, DAVIO is on bar or better than SOTA mappers on the same odometry, and degrades far less when GT poses are replaced by real odometry. We release the code of DAVIO, a real-time dense metric SLAM system, to the community.
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All authors Jaafar Mahmoud, Arthur Movsesyan, Mikhail Iumanov, Sergey Kolyubin
Systems and Control
Forced Oscillations in Power Systems Induced by Data Centers Hosting AI Workloads
Jovan Krajacic, Georgia Pierrou, Maitraya Desai et al. (+2 more)
Power swings in large Data Centers (DTCs) running Artificial Intelligence (AI) workloads can excite poorly damped modes in power systems. The resulting forced oscillations can lead to flicker, equipment disconnection, or blackouts. This paper investigates the risks of such load fluctuations for different system strengths and damping conditions. Using an analytical approach based on transfer functions, we identify critical DTC locations in the power grid at which load fluctuations could induce the largest forced oscillations and further characterize the harmonic spectrum of the resulting system response. Depending on the frequency and magnitude of the DTC load fluctuations, forced oscillations can become unbounded. The underlying instabilities are classified into saddle-node bifurcations of the forced periodic response and impasse-surface encounters, using Floquet multipliers and the minimum singular value of the algebraic Jacobian. Furthermore, the impact of different duty cycles and harmonic components beyond the fundamental oscillation frequency in square-wave load profiles is analyzed. Finally, the interaction of two oscillating DTCs is investigated for different locations and forcing frequencies, considering both synchronized and unsynchronized operation. The findings can help system operators to define new regulations on the maximum load fluctuations permitted for DTC facilities at specific grid locations, without negatively affecting the stability and operation of the system.
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All authors Jovan Krajacic, Georgia Pierrou, Maitraya Desai, Gabriela Hug, Gustavo Valverde
Computer Vision
SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos
Christoph Baumann, Ronny Schweitzer, Noemi Pavo et al. (+3 more)
The SYNTAX score is an established tool for assessing coronary artery disease and guiding revascularization treatment decisions. However, its manual estimation from coronary angiography
videos by clinical experts is time-consuming and subject to inter-reader variability. While machine learning has shown promise in automating this process, prior work has primarily focused on lesion detection, characterization, or binary disease classification, leaving direct SYNTAX score prediction relatively unexplored. We propose SynSeq, a
video-based method for direct SYNTAX score prediction. It combines targeted preprocessing with a tailored training strategy using a zero-inflation-aware loss and linear target scaling. Evaluated on the public CardioSyntax
dataset, SynSeq significantly outperforms previous state-of-the-art methods, improving $R^2$ by 0.55, reducing prediction bias by 93.1% and achieving more consistent performance across annotations from three independent expert graders. In addition, SynSeq achieves a weighted $F_1$-score of 0.80 for revascularization treatment recommendations, slightly below inter-expert agreement. These results demonstrate the potential of SynSeq to provide consistent, automated SYNTAX score assessment and reliable decision support for coronary revascularization planning.
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All authors Christoph Baumann, Ronny Schweitzer, Noemi Pavo, Ulrike Attenberger, Christian Loewe, Philipp Seeböck
Robotics
GLoTouch: Global-to-Local Haptic Perception Using a Parallel Gripper for Object Search, Recognition, and Grasping Without External Vision
Zonglin Li, Wanruo Zhang, Yiming Wang et al. (+3 more)
Perceiving objects in the environment is a fundamental capability of autonomous
robots. In dark or low-light environments, external cameras often fail to reliably perceive object positions and geometry; when visual sensing is unavailable, completing target search, recognition, and grasping through touch alone becomes a key
robot manipulation capability. This task must simultaneously address container-scale spatial exploration and object-scale fine-grained
geometric perception, which is particularly challenging for low-degree-of-freedom parallel grippers. However, a unified framework remains lacking for connecting container-scale spatial exploration with object-scale fine-grained
geometric perception and grasping. To address this challenge, we present GLoTouch, a global-to-local haptic perception and manipulation framework built on a parallel gripper. In the global stage, the gripper holds a passive long-reach probe, combining force measurements with known tool geometry to localize contacts and actively estimate candidate-object positions, coarse contours, and heights. In the local stage, the
robot sets down the probe and uses the bilateral visuotactile sensors on the same gripper to directly acquire local haptic observations, which are matched against a given target 3-D model without object-specific training. We evaluate the framework in both simulation and real-
robot experiments. Source code will be open-sourced.
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All authors Zonglin Li, Wanruo Zhang, Yiming Wang, Kun Song, Xinyi Zhou, Daolin Ma
Computation and Language
Consequential Behaviour and Representational Fairness in the Validation of Synthetic Research
Florian Kutzner, Celina Kacperski, Laura de Molière et al. (+4 more)
Researchers in industry and academia use synthetic survey respondents powered by large
language models as substitutes for human samples. These synthetic populations require validation against real-world data, so researchers often address them using ad hoc comparisons with human surveys. Inspired by the intention-behaviour gap in behavioural science, we argue that these validations test the wrong thing for most applied cases where decision makers commission synthetic research to anticipate consequential behaviour. To address this problem, we propose a validation framework with two requirements. First, every validity claim must state its level of correspondence with human data: does the sample predict what the represented people do, which of four diagnostics (location, dispersion, response process and structure) does the validation address, and does the validation compare against experimental effects? Second, researchers must report validity claims for subgroups, since these groups are often the most affected by consequential decisions and aggregate accuracy hides their misrepresentation. Our validation framework operationalises three justice dimensions (distributional, procedural, and recognition) as measurable quantities and treats within-persona counterfactual experiments as a design that itself requires validation. We then apply the framework to electric vehicle charging tariffs, before closing with a reporting checklist that researchers can use to make convincing validity claims.
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All authors Florian Kutzner, Celina Kacperski, Laura de Molière, Edoardo Chidichimo, Min Jun Jung, Felix P. S. Wallis, James K. He
Information Retrieval
Test-Time Adaptation with Query-Dependent Residuals for Visual Document Retrieval
Zeliang Li, Xiaofen Xing, Kailing Guo et al. (+1 more)
Visual document
retrieval (VDR) systems depend on page
embeddings computed before deployment, which makes adaptation difficult when encoder parameters or corpus re-encoding are unavailable. Rerankers provide useful relevance signals, but conventional reranking applies them only to selected queries and candidate pages. We introduce Q-REACT, a query-side test-time adaptation method that converts limited reranker feedback into reusable
retrieval improvements. Q-REACT learns a shared low-rank transformation that produces query-dependent residuals, combines adapted query scores with document-level context, and distills reranker preferences with a student distribution normalized over the complete task-specific page index. This design lets unscored pages compete through cached
embeddings while keeping the encoders and page index fixed. Across eight ViDoRe V3 tasks and five open-weight and proprietary backbones, Q-REACT improves average
retrieval over evaluated baselines at sparse and full-coverage budgets, transfers to held-out queries and tasks, and adds little inference overhead. The results show that finite reranker feedback can be amortized across a query collection without retraining or rebuilding the retriever.
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All authors Zeliang Li, Xiaofen Xing, Kailing Guo, Xiangmin Xu
Computational Engineering
Topology optimization of multimaterial aircraft pylons using generalized shape function approach
Swagatam Islam Sarkar and Prabhat Kumar
As the primary structural component connecting the engine to the wing or fuselage, an aircraft pylon requires optimized structural efficiency; this paper provides
topology optimization of multimaterial pylons using the generalized shape function (gSF) approach. The gSF method uses $n$ natural-coordinate design variables per element to provide optimized designs up to $2^n$ distinct material phases while promoting close to discrete material layouts in conjunction with the density and formulated Heaviside projection filters. Pylon structural compliance is minimized subject to volume constraints. Exploiting the
geometric features of a typical pylon structure, multimaterial
evolution is performed on a corresponding 2D design domain representing the midplane, with up to 14 candidate materials. The optimized two-dimensional layout is then extruded to achieve the corresponding three-dimensional optimized pylon structure. The Method of Moving Asymptotes is employed to achieve the final design variables. The resulting convergence histories exhibit smooth and stable objective minimization. The results highlight the capability of the multimaterial
topology optimization framework to effectively optimized aircraft pylons with multiple candidate materials, without requiring a considerable expansion of the design variable set.
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Computer Vision
Gender Bias in Vision-Language In-Context Learning
Tong Xiang, Yuta Nakashima and Noa Garcia
In-context learning (ICL) enables large
vision-
language models (LVLMs) to perform tasks by following patterns from in-context examples, yet its potential to amplify societal biases remains underexplored. We systematically investigate how ICL influences gender bias in LVLMs through VL-BICLE, an evaluation framework comprising six ICL settings, three tasks, and four
datasets. Our experiments on six LVLMs reveal that gendered ICL demonstrations act as a directional force, shifting model bias toward the demonstrated gender through a cross-gender mechanism that disproportionately degrades performance on the opposite gender. This effect appears in image captioning and pronoun prediction but not in visual question answering, indicating that gendered ICL influences bias only when the task output involves gendered
language. Similarity-based
retrieval methods inherit the training pool's gender imbalance and offer no debiasing advantage, while standard quality metrics remain blind to these bias shifts. To mitigate this bias, we replace real in-context images with synthetic ones from stable
diffusion models while keeping captions unchanged. This simple intervention reduces gender bias without degrading caption quality.
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Computer Vision
CasCVS-Net: A Staged Multi-Task Cascade for Critical View of Safety Assessment
Bock-Zien Toh, Yuanchuan Ren, Tay Aw Yu et al. (+3 more)
Automated assessment of the Critical View of Safety (CVS) in laparoscopic cholecystectomy requires both recognition of the three CVS criteria and anatomical grounding in small, rare, and often occluded hepatocystic structures. Learning-based methods differ in the anatomical information they use, from image-level classification to detection,
segmentation, or graph-based
reasoning, yet grounding the safety-critical anatomy remains the main bottleneck. We propose CasCVS-Net, a staged multi-task cascade that jointly performs object detection, semantic
segmentation, and CVS assessment, trained on the Endoscapes
dataset. The model couples the tasks through predicted anatomy: predicted boxes guide
segmentation, and predicted masks provide region-level features for CVS classification, so CVS assessment at inference uses only model predictions rather than ground-truth annotations. To reduce optimisation instability in this coupled setting, training progresses from detection to detection-
segmentation and then to the full three-task cascade, followed by task-wise fine-tuning. Evaluation on the public unseen test set shows that CasCVS-Net improves over matched single-task baselines on all three tasks, achieving 32.0 detection mAP, 46.8 semantic mIoU, 15.3 rare-anatomy mIoU, and 67.2 CVS mAP. It outperforms the state-of-the-art LG-CVS and SV2LSTG by 6.3% and 4.5% relative CVS mAP, respectively, corresponding to 4.0 and 2.9 mAP points. These results show that staged task coupling through predicted boxes and masks improves anatomical grounding for CVS assessment, particularly for rare hepatocystic structures.
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All authors Bock-Zien Toh, Yuanchuan Ren, Tay Aw Yu, Ng Khee Ong, Zhehua Mao, Sophia Bano
Machine Learning
What Do Tabular Foundation Models Compute In Context? In-Situ Representation Refinement through Attention-Gated Updates
Tian Zhou, Beverly Jin, Linxiao Yang et al. (+6 more)
A tabular foundation model must discover which distinctions matter for each new table without updating its parameters. We develop in-situ representation refinement: support labels guide changes to the episode's representations, improving the information available to later queries. A regularized leave-one-out objective yields a support correction and its query extension. The leading term separates
attention-based reading from state-dependent scaling, motivating RefineICL: an
attention-gated, FFN-free contextual stack with selected low-rank feature interaction and typed
memory. A direct intervention tests the role of evolving support states: removing one intermediate support update while preserving the block's query output increases final query cross-
entropy in all 72 tested episodes. RefineICL-L24 reaches 0.93836 OVR-AUC and 0.87173 accuracy on AMLB29. A
benchmark-informed continuation reaches 1644.8 Elo on the 38-
dataset TabArena snapshot, 31.4 Elo above TabPFN-3 under the same evaluation. It also improves all four reported metrics over TabPFN-v3 on both TabZilla views. In a matched 100K-update depth grid, an expanded FFN gives no consistent validation benefit and uses 60.2% more peak inference
memory at L8. These results connect learning within a forward pass to representation refinement and show how this view guides a competitive,
memory-efficient model.
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All authors Tian Zhou, Beverly Jin, Linxiao Yang, Xue Wang, Wenwei Wang, Bingqing Peng, Mengni Ye, Jinjie Gu, Liang Sun
Computation and Language
Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding
Fan Zhang, Yankai Chen, Zhuohan Xie et al. (+11 more)
Aggregate accuracy summarizes how often a model is correct, but not which judgments remain correct when requests change. We evaluate Jev and nine
language models on ContractNLI, with repeated requests that preserve the contract and target while varying visible hypotheses, requested answers, and output order. A four-model follow-up adds an equal-call repetition control and coverage of the remaining test contracts. Corrections and regressions can cancel, leaving unchanged accuracy despite changed decisions; consistent answers can also preserve errors. Majority voting over recorded responses yields small, uncertain gains, with repeated wrong answers accounting for much of the observed error. Gold-class diagnostics further reveal different error profiles behind aggregate model rankings. Resource comparisons add distinct trade-offs: Jev is cheapest and fastest for the joint baseline, while hosted
language models achieve higher accuracy. Together, these findings connect decision-level evaluation to whether additional calls improve correctness and what the resulting decisions cost.
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All authors Fan Zhang, Yankai Chen, Zhuohan Xie, Yixi Zhou, Sijia Peng, Lei Fan, Xinhua Ji, Cunyuan Zheng, Huangyong Shan, Philip S. Yu, Xue Liu, Yu Chen, Preslav Nakov, Songwei He
Computer Vision
RoadOcc Learns When to Persist, Transport, or Refresh Memory for Roadside Occupancy Prediction
Xiaokai Bai, Lei Yang, Songkai Wang et al. (+3 more)
Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal
memory can recover weak observations, but reusing moving evidence at stale locations can corrupt occupancy predictions. Motion compensation addresses displacement, while reliance on the resulting history remains a separate learning problem. We introduce RoadOcc, which learns soft routing among fixed-coordinate history (Persist), velocity-addressed history (Transport), and current evidence (Refresh). Motion state and class-consistent historical support supervise these source choices. Dynamic-aware cross-
attention (DCA) updates candidate locations, multi-scale voxel velocity estimation (VVE) constructs transport addresses from multi-scale current–history correspondence, and velocity-guided dynamic sparse fusion (VDSF) combines routed evidence under fixed sparse-
token budgets. On InfraOcc, RoadOcc reaches 65.29 mIoU and 32.37 dynamic mIoU, gains of 4.44 and 4.71 over STCOcc. Controlled address experiments show that VVE raises dynamic mIoU by 0.87 over fixed-coordinate reading. Across three seeds, supervised P/T/R adds 1.40 dynamic points over motion-corrected
retrieval, while removing Refresh costs 0.32 points. Results from two transfer models, Occ3D-nuScenes, and longer intervals provide additional support. Code will be released.
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All authors Xiaokai Bai, Lei Yang, Songkai Wang, Lianqing Zheng, Si-Yuan Cao, Hui-liang Shen
Software Engineering · Cross-listed from astro-ph.IM
A ready-to-deploy MLOps software platform for satellite and NEO detection at meter-class ground-based observatories
Rafael Carrillo Navarro, Pablo García-Martín, René Duffard et al. (+2 more)
Ground-based astronomical observations frequently contain streaks produced by artificial satellites, space debris, and potentially Near-Earth Objects (NEOs). While machine-learning models can reliably detect these features, their practical adoption in observatory operations is often limited by the lack of integrated tools for visual inspection, validation, workflow management, and structured data storage. This paper presents the StreakMind Workbench, a framework that applies MLOps practices to bridge research-oriented machine-learning pipelines with routine observatory operations. Rather than introducing new detection
algorithms, the Workbench addresses a software-engineering challenge in astronomical computing: maintaining a single authoritative source of scientific processing code while providing astronomers with an operational environment for workflow execution and result inspection. Integrated with the reference StreakMind AI model of Carrillo et al. (2026) and implemented in Python using PyQt5, the Workbench supports the complete workflow from FITS ingestion to database storage, including inference, result inspection, database exploration, training management, and Minor
Planet Center formatted observations. Validation on 273 images from La Sagra Observatory demonstrates successful end-to-end workflows while maintaining consistency with the underlying StreakMind scientific code. The platform facilitates operational use of a research ML pipeline in meter-class observatories and moderate-scale campaigns, supporting Space Situational Awareness and planetary defence.
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All authors Rafael Carrillo Navarro, Pablo García-Martín, René Duffard, Juan Luis Orellana Montes de Oca, Oscar Ortega Ríos
Computer Vision
Track2Art: Articulated Object Model Recovery with Visual-Geometric Track Representations
Xiaotong Li, Yixiong Jing, Junsheng Ding et al. (+4 more)
Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinematic relations. Existing approaches often treat articulation as a by-product of reconstructed geometry or recover it through per-instance optimization. We instead build on the hypothesis that articulation is directly observable from persistent motion: points on the same rigid part move coherently, while relative motion between parts reveals their kinematic constraints. We present Track2Art, a motion-centric framework for recovering structured articulated objects from RGB-D interaction
videos. Track2Art lifts tracked image points into persistent 3D trajectories and combines pretrained tracking features, visual descriptors, and explicit trajectory geometry. These representations are grouped into a variable number of rigid-part hypotheses and subsequently used to recover directed kinematic relations, joint types, and joint geometry through rotation-equivariant learned–analytic
reasoning. On the aligned 20-object PartNet-Mobility suite, Track2Art achieves 0.695 Point IoU and 0.410 end-to-end J@20, while requiring neither ground-truth part counts nor test-time optimization.
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All authors Xiaotong Li, Yixiong Jing, Junsheng Ding, Weihang Li, Benjamin Busam, Guangming Wang, Brian Sheil
Logic in Computer Science
Coloring Queens with Thousands of Encodings
Bernardo Subercaseaux, Benjamin Przybocki and Marijn J. H. Heule
In The Art of Computer Programming, Knuth benchmarked 10 encoding techniques for computing the chromatic number of the queen's graph: the minimum number of colors needed to color the squares of an $n \times n$ chessboard so that no two squares sharing a row, column, or diagonal receive the same color. In this paper, we extend his analysis much further by comparing thousands of encodings for the same problem, which allows us to identify additional factors that are important for solver performance. We obtain 1584 encodings for this problem by varying (a) the constraints that encode which color is assigned to each
cell, (b) the constraints that forbid the same color appearing in a row, column, or diagonal line, and (c) the
symmetry-breaking constraints. We find that the three most impactful encoding factors are (i) the choice of
symmetry-breaking constraints, (ii) enabling so-called clique hints, and (iii) enforcing that each
cell is assigned exactly one color through blocked clauses. Furthermore, while Knuth proposed clique hints as an advantage of the order encoding, we show in fact that they can be effectively employed for the one-hot encoding as well.
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