Machine Learning
Robust Non-Clairvoyant Scheduling with Classification Models
Anthony Dugois, Vincent Fagnon and Giorgio Lucarelli
We study the classical single-machine scheduling problem of minimizing the sum of completion times of jobs in a non-clairvoyant setting, where the processing time of each job remains unknown until its completion. This is a hard problem for which no constant competitive
algorithm is possible. Inspired by robust optimization and learning-augmented
algorithms, we introduce a novel robustness framework that leverages structural information provided by a classification model to overcome this limitation. Specifically, we assume that jobs are partitioned into classes and we have access to the confusion
matrix of the classifier, whose entry $(k,\ell)$ indicates the number of jobs predicted to belong to class~$k$ but that actually belong to class~$\ell$. In this manner, we are able to characterize uncertainty as a set of permutations within each predicted class, rather than as a collection of discrete numerical scenarios, avoiding the computational difficulty of classical robust metrics, such as Min-Max and Min-Max Regret. In addition to these worst-case metrics, we also consider the expected objective over all scenarios. We first propose an optimal non-adaptive strategy that is oblivious with respect to all three robust criteria. We then investigate adaptive and randomized
algorithms, showing that they can outperform the optimal non-adaptive strategy when the
matrix exhibits particular structural properties.
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Symbolic Computation · Cross-listed from math.RA
Rational linear forms of linearizable ordinary differential equations
Dmitry Lyakhov
Every scalar ordinary differential equation of order at least three with rational right-hand side that is locally linearizable by a point transformation admits a linear form with rational coefficients over the same coefficient field. We prove this by restricting the derived
symmetry algebra to a coordinate line and recovering a scalar differential operator from rational
symmetry-jet data. The construction uses differential elimination and linear
algebra; it does not require the
symmetry generators or a linearizing transformation to be solved for. A single integer parameter suffices to choose the line.
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Computational Geometry
Thomas Bläsius, Lennart Großkreutz and Jean-Pierre von der Heydt
The sphericity of a graph is the minimum dimension d such that the graph has an intersection representation of d-dimensional balls of equal radius. While sphericity has been studied in Euclidean space, we initiate the study of
hyperbolic sphericity. The
hyperbolic sphericity of a graph can be significantly smaller than its Euclidean counterpart, but, contrary to the Euclidean setting, depends strongly on the radius of the balls. We show that, if the radius of the balls can be chosen depending on the graph, the
hyperbolic sphericity is upper bounded by the Euclidean sphericity. This extends a previous result for 2-dimensional
hyperbolic space, i.e., uniform disk graphs, to arbitrary dimensions. Moreover, our proof is significantly simpler. If we fix the radius, i.e., do not make it dependent on the graph, we show that
hyperbolic sphericity can be larger than Euclidean sphericity, but by at most 1. Additionally, we study how
hyperbolic sphericity changes with the ball radius. We show that choosing a larger radius can substantially decrease the sphericity while increasing it by at most 1. We also provide a construction of a graph where the sphericity oscillates between different values as the radius increases. Besides being theoretically interesting, we note that these results are relevant for graph
embeddings in machine learning, where one is interested in low-dimensional numeric representations of symbolic data like graphs.
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Computer Vision
CLASP: Continual Low-rank Adapters for Spatially Placed Concepts from One Hypernetwork
Wojciech Gromski, Patryk Krukowski, Jan Miksa et al. (+2 more)
Continual personalization of text-to-image
diffusion models requires sequentially acquiring new concepts while retaining previously learned ones. However, existing methods either suffer from catastrophic forgetting or rely on storing additional concept-specific parameters and spatial components, causing their parameter footprint to grow with the concept stream. This limits their ability to scale to long sequences of personalization tasks. We propose a rehearsal-free approach that uses a single fixed-size hypernetwork to continually personalize a frozen
diffusion model. Instead of expanding the model as new concepts are acquired, the hypernetwork dynamically produces the concept-specific adaptations required for personalization while preserving previously learned concepts. Our framework further integrates spatial control into the personalization process, allowing users to specify where a personalized concept should appear without introducing additional per-concept components. This formulation enables continual personalization with a parameter footprint that remains independent of the number of learned concepts, aside from compact concept representations. Experiments demonstrate strong retention of previously learned concepts and reliable spatial grounding, matching or improving upon existing methods while scaling effectively to long streams of personalization tasks.
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All authors Wojciech Gromski, Patryk Krukowski, Jan Miksa, Maciej Zieba, Przemysław Spurek
Artificial Intelligence
An ontology for cross-sectoral crisis management: core and public health modules
Aldo Gangemi, Rita T. Sousa, Luigi Asprino et al. (+6 more)
This paper presents the European Crisis Management Ontology (ECMO), a modular OWL-based ontology intended as a cross-sectoral reference for disaster risk reduction and response. ECMO is designed to be organised as a network of ontological modules. Among the modules, ECMO-CORE captures fundamental crisis management concepts such as hazard, event, exposure, impact, and response measure and uses ontology design patterns and the OWL2 punning technique to resolve ambiguities between hazard types and event manifestations. In addition, domain-specific modules are defined as in the case of the public health module aligned with SNOMED CT and ICD-11. To demonstrate the resource's utility, we used ECMO to represent the data of the Epidemic Intelligence from Open Sources system of the Joint Research Centre to generate an end-to-end pipeline that populates an ECMO-compliant knowledge graph from unstructured epidemiological news. Initial results demonstrate that ECMO provides the formal guardrails necessary for consistent and unified knowledge representation and integration. The ontology is publicly available at https://doi.org/10.5281/zenodo.20070268 and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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All authors Aldo Gangemi, Rita T. Sousa, Luigi Asprino, Giorgia Lodi, Andrea G. Nuzzolese, Valentina Presutti, Johannes Gysen, Diana F. Sousa, Luigi Spagnolo
Artificial Intelligence
PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading
Duong Hien Chi Kien and Thanh Trung Huynh
Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passive long exposure on upward-drifting assets, while aggressively risk-penalized rewards can become too defensive during volatile periods. This paper proposes PPO-HRAP, a hybrid regime-aware policy that combines Proximal Policy Optimization with an interpretable regime prior. The
agent observes both
market features and portfolio-state variables, receives a reward combining portfolio log return, VIX-conditioned drawdown-increase penalty, target-exposure deviation, and turnover cost, and executes a blended action between the PPO actor output and a regime-derived target exposure. On the held-out 2020-2022 SPY test window, PPO-HRAP achieves 27.62% total return, 8.48% annualized return, 0.6447 Sharpe ratio, 0.8588 Sortino ratio, and 0.4592 Calmar ratio, while reducing maximum drawdown from 34.10% for Buy and Hold to 18.47%. Across five SPY seeds, PPO-HRAP remains stable with mean total return $0.2725 \pm 0.0109$ and mean Sharpe ratio $0.6219 \pm 0.0565$. Single-run cross-asset tests on QQQ and DIA further show that the proposed method ranks first on total return and Sharpe ratio for all three reported assets. These results suggest that blending learned actions with a
volatility-aware regime prior is a practical way to improve risk-adjusted trading behavior, although the current policy still incurs high turnover and cross-asset robustness beyond SPY remains limited to single-run evidence.
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Computation and Language
Evaluating Biomedical Reranking for LLM-Based Question Answering over Longitudinal Clinical Notes
Maryam Shahbaz Ali, Laura B. Strachan, Caitlin Sherman et al. (+3 more)
Patient-specific clinical question answering requires locating the right evidence within long, heterogeneous longitudinal clinical records in which relevant facts may be scattered across encounters, repeated in copied-forward notes, or expressed using different clinical terminology. We evaluated whether biomedical reranking can improve evidence selection and downstream answer quality in a locally deployed
retrieval-augmented generation pipeline for longitudinal clinical notes. The pipeline combines PubMedBERT dense
retrieval, BM25 lexical
retrieval, weighted reciprocal-rank fusion, and MedCPT cross-encoder reranking. Across 1,000 open- and closed-ended question-answer pairs from a cohort of 200 bariatric surgery patients, reranking increased exact source-chunk
retrieval within the top 10 items, Hit@10 from 46.6% to 60.6% and mean reciprocal rank from 0.2371 to 0.3252. With Qwen3-8B generation, local judge-assessed answer correctness increased from 44.8% to 48.6%. These results show that biomedical reranking can improve the placement of relevant clinical evidence within a limited context window, although gains in
retrieval do not translate proportionally into gains in answer correctness.
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All authors Maryam Shahbaz Ali, Laura B. Strachan, Caitlin Sherman, Mark Kovler, Eleanor Mackey, Syed Muhammad Anwar
Artificial Intelligence
TRACE: Trajectory Return Attribution and Contrastive Erasure for Multi-Turn Safety
Fengpeng Li, Kemou Li, Qizhou Wang et al. (+3 more)
Safety-aligned large
language models (LLMs) often refuse a harmful request but comply once the same goal is spread over several turns. Preference objectives score whole responses to single
prompts, so their training loss alone cannot control risk on unseen histories. Our analysis gives sufficient conditions under which suppression at supervised single-turn contexts yields a bound on multi-turn trajectory risk. The bound accounts for coverage, transfer slack, and leakage, and characterizes contraction relative to a base-policy risk budget evaluated on the trained policy's contexts. TRACE (Trajectory Return Attribution and Contrastive Erasure) turns this principle into a
token-level objective. On the safe response, each
token is weighted by the discounted return of a refusal-attributable advantage. The advantage compares a frozen reference model with its refusal-ablated copy, allowing earlier response
tokens to receive credit from later refusal-related evidence. At high-gap positions on rejected responses, TRACE combines the observed
token with policy-selected alternatives in the erasure target. A gradient-norm penalty replaces the retain set. Across five open-weight models and seven multi-turn attacks, TRACE gives the lowest attack success rate (ASR) in all 35 model and attack pairs, while the model utility evaluated on MMLU and HellaSwag drop by at most 1\.23 points. Source code can be found in the supplemental material.
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All authors Fengpeng Li, Kemou Li, Qizhou Wang, Haiwei Wu, Jiantao Zhou, Di Wang
Machine Learning
Clifford Sheaf Neural Networks
Kotaro Kamiya and Joel Nicholls
We introduce the Clifford Sheaf Neural Network (CSNN), an equivariant sheaf neural network for
geometric graphs that places a Clifford
algebra on each stalk of a cellular sheaf and transports multivector features along edges. The canonical choice of restriction map for sheaves with
algebra-valued stalks is
algebra homomorphism. Adding the constraint of equivariance, the naive choice becomes versor conjugation. However, versor conjugation is expressively weak, so we drop
algebra homomorphism and arrive at the K-term sandwich. The resulting sheaf
Laplacian is positive semidefinite by construction, needs no versor constraint, and still mixes grades. Our main contribution characterizes the resulting family of restriction maps along three axes: which grades a map couples, how much of the endomorphism space it reaches, and how well it is conditioned. The K-term sandwich spans half of the endomorphism space, and in Cl(3, 0, 0) it corresponds to the maps that commute with the central pseudoscalar. The number of terms controls expressivity. CSNN is the reversion member, a first-order model by construction and the grade-mixing corner of this family, developed as a sheaf construction for graph-level equivariant regression.
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Systems and Control · Cross-listed from math.OC
Control Allocation with Adaptive Augmentation for Aerodynamic Optimization of Trailing Edge Morphing Aircraft
Mark Spiller, Lennart Kracke and Johannes Autenrieb
This paper presents a control allocation framework with adaptive augmentation for a trailing edge morphing aircraft, providing stability guarantees under uncertainty while exploiting the available morphing degrees of freedom to optimize aerodynamic efficiency. A baseline controller is designed using the nominal aircraft model to establish the desired closed-loop reference dynamics. For the aerodynamics, the wing shape is optimized dependent on the flight state to achieve a target elliptical lift distribution corresponding to minimum induced drag. The adaptive augmentation is tailored to the control allocation problem to account for the uncertain system dynamics and stabilize the aircraft around the reference dynamics. The resulting stabilization condition is formulated as hard constraint in the allocation problem while minimizing deviations from the corresponding state-dependent aerodynamically optimal wing shape. Numerical results demonstrate that the adaptive augmentation compensates for matched uncertainties while the control allocation optimizes the wing shape with respect to the reference elliptical lift distribution.
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Artificial Intelligence
ProtoFlow: Prototype-Guided Flow Matching for Multivariate Time Series Forecasting
Shibo Feng, Wanjin Feng, Yang Qiu et al. (+3 more)
Generative modeling has shown strong promise for multivariate time mseries (MTS)
forecasting, especially scale to high-dimensional settings.
Diffusion-based methods achieve competitive performance but typically require many sampling steps at inference. VAE-based non-iterative
forecasting frameworks have therefore emerged as an efficient alternative. Within this line of work, vector quantization (VQ) enables controllable latent space modeling by mapping multivariate series into compact discrete representations. Existing VQ-based
forecasting methods, however, typically rely on autoregressive (AR)
token generation, which suffers from exposure bias and training-inference mismatch. Flow matching provides an efficient non-autoregressive alternative for latent
forecasting, but existing formulations usually initialize transport from a generic Gaussian prior. We instead observe that the trained VQ codebook already captures representative latent prototypes and can thus serve as a more informative prior for flow matching. Based on this insight, we propose ProtoFlow, a
forecasting framework that combines vector-quantized autoencoding with Prototype-prior Flow matching. Our method first maps multivariate sequences into a discrete latent space, then constructs a structured prior from the learned codebook, and finally learns a DiT-based rectified flow to transport samples from this prior to future latent representations conditioned on historical observations. By replacing generic noise initialization with a learned prototype prior, ProtoFlow avoids the rollout mismatch of AR
token prediction and promotes faster training convergence. Extensive experiments on
benchmark datasets show that it consistently achieves superior
forecasting performance with efficient inference.
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All authors Shibo Feng, Wanjin Feng, Yang Qiu, Deheng Ye, Peilin Zhao, Chunyan Miao
Robotics
Cross-entropy optimization with prioritized constraints
Francisco Roldan Sanchez, Pau de las Heras Molins, David Fridovich-Keil et al. (+1 more)
When constraints conflict, an optimizer must determine which requirements to preserve and which to relax. On the one hand, a priority ordering specifies which requirements take precedence. On the other hand, penalty-based formulations encode their relative importance through numerical weights. Depending on these weights, a solution can improve its weighted score while violating intended priorities. We introduce TierCEM, a variant of the cross-
entropy method that incorporates strict constraint priorities directly into elite selection without requiring per-constraint importance weights. TierCEM works by sequentially filtering sampled candidates, from highest- to lowest-priority constraint. If and when a constraint eliminates all remaining candidates, TierCEM returns to the last nonempty set and selects elites with the smallest violations of that blocking constraint, recursively preserving satisfaction of all higher-priority constraints. We evaluate TierCEM on 2D
navigation and contact-rich pushing tasks in proprioceptive and learned world-model settings. Experiments show that reversing the constraint ordering changes which constraints are violated under conflict. Prioritizing progress toward the task objective also enables TierCEM to relax lower-priority constraints when they would otherwise prevent further progress.
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All authors Francisco Roldan Sanchez, Pau de las Heras Molins, David Fridovich-Keil, Georgios Bakirtzis
Machine Learning
Feature Selective Model Collapse in Diffusion Models: Total Replacement versus Fixed-Budget Training
Hanna Malet and Gabriel Turinici
Model collapse arises when generative models are trained on synthetic data produced by earlier models. The phenomenon has attracted considerable
attention because of its societal and technical implications. However, previous studies have reached seemingly contradictory conclusions: replacing real data with synthetic data causes collapse (Shumailov et al.), yet accumulating real data alongside synthetic data can prevent it. For
diffusion models, we study an intermediate regime typical of finite-budget pipelines: all past
datasets and the real data are kept, but each new model is trained on a fixed-size sample from this growing pool, so the real fraction vanishes without any data being removed. Experiments on a 2D spiral
dataset as well as the image
benchmarks (MNIST, Fashion-MNIST, and CIFAR-10) show that replacement protocol degrades
dataset rapidly as in the literature, whereas the fixed budget degrades only partially, sparing some features. A linear-response model of the multi-generational parameter dynamics, analyzed by
stochastic recursion, confirms that the two protocols differ: some features will be fragile and lost within a few generations for both protocols, while some will be robust and preserved over practically unbounded horizons under the fixed budget protocol.
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Machine Learning
Prediction-powered Neural Architecture Search
Pascal Janetzky, Yuxin Wang, Michael Klar et al. (+1 more)
Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are limited because training and evaluating architectures is costly; on the other hand, zero-cost proxies (ZCPs) are cheap to compute at large scale but can be noisy. Yet, how to effectively combine these two sources of supervision remains unclear. In this paper, we propose PPNAS, a novel prediction-powered inference (PPI) approach for NAS. PPNAS fuses (1) a small set of architectures with observed performance labels and (2) a large set of architectures with ZCP information. To combine these two sources of supervision, PPNAS exploits the ordinal information provided by ZCPs to construct additional pairwise ranking supervision, while PPI debiases systematic discrepancies between ZCP-based and true performance rankings. We evaluate PPNAS in end-to-end predictor-based NAS, where it achieves state-of-the-art under limited evaluation budgets. To the best of our knowledge, PPNAS is the first prediction-powered approach for label-efficient NAS.
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All authors Pascal Janetzky, Yuxin Wang, Michael Klar, Stefan Feuerriegel
Computation and Language
What Wins a Vote? Formatting, Length, and Lexical Diversity in the French Compar:IA LLM Arena
Simonas Zilinskas, Maayeesha Farzana and Christophe Benavent
LLM arenas turn pairwise human preferences into model rankings. Those preferences may reflect how an answer is presented as well as what it says. We take a stylometric approach to 137,293 decisive French-
language votes from the July 2026 Compar:IA release; the primary formatting analysis includes 137,113 battles across 116 models, and the joint estimates use the 127,092 battles with all required measurements. For each battle, we reconstruct the response visible when the user voted. We then compare the raw ranking with rankings adjusted for formatting, length, readability, vocabulary variety, and sentence structure. Presentation is associated with winning, but length, bold text, and lists tend to occur together, making their individual contributions hard to separate. Across the measured features, two associations change least across specifications: bold usage (+11.0% win odds per standard deviation in the joint model) and moving-average type-
token ratio (MATTR), a measure of vocabulary variety that is less sensitive to answer length (+16.8%). The bold association is substantially smaller in observed multi-turn conversations, whereas the MATTR association changes little; because users choose whether to continue, this difference is descriptive rather than causal. The full adjustment moves 36 of 116 models by at least ten ranks. Yet comparisons with external
benchmarks do not show that adjusted rankings better measure capability. We therefore recommend publishing raw and adjusted rankings side by side as a transparent sensitivity analysis.
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Machine Learning
EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations
Qiuliang Liu, Liming Wu, Qi Li et al. (+5 more)
Generative models have made rapid progress in ordered
crystal structure prediction, yet many functional materials are intrinsically disordered, with substitutional mixing, vacancies, or interstitial species controlling their properties. Existing
crystal generators either assume deterministic site occupations or require site-level disorder annotations, which are often unavailable when the chemical formula is the primary input. We formulate disordered
crystal structure prediction through an Occupancy Distribution
Matrix (ODM), a continuous site-by-species representation that unifies ordered
crystals, solid solutions, vacancy disorder, and interstitial occupancy. A valid ODM must satisfy coupled site-wise occupancy, mass-conservation, and non-negativity constraints, placing each sample on a formula-dependent transportation polytope. We propose Entropic Polytope Flow (EP-Flow), a marginal-constrained flow matching framework that canonicalizes heterogeneous polytopes into a shared double-centered space, learns a marginal-preserving flow, and recovers feasible occupancies through a Sinkhorn inverse map. By jointly generating occupancies, fractional coordinates, and
lattice parameters, EP-Flow achieves state-of-the-art performance on formula-conditioned disordered CSP
benchmarks derived from COD and MPDS, substantially outperforming adapted ordered-
crystal generators. Analyses further show that EP-Flow recovers sparse and chemically meaningful local disorder patterns rather than merely matching global composition statistics.
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All authors Qiuliang Liu, Liming Wu, Qi Li, Zhonglong Peng, Chang Chen, Xiaolong Chen, Wenbing Huang, Shifeng Jin
Computer Vision
ARROW: Arbitrary Reconstruction and Tracking of 4D Observations in the Wild
Ilya Fradlin, Christian Schmidt, Jens Piekenbrinck et al. (+3 more)
Dynamic scenes may be captured by a moving camera, multiple
video streams, or images taken at different times. These observations reveal complementary aspects of scene geometry and motion, yet bringing them together requires establishing correspondence across viewpoints, capture times, and visibility changes. We introduce ARROW, a feed-forward model that unifies 3D reconstruction and 3D point tracking from arbitrary image sets. At its core is a novel order-invariant querying approach, which allows the association of queries with observations across arbitrary inputs. We show that exposing the model to more diverse sets of inputs during training results in improved task performance. Moreover, the resulting model is capable of generalization to a wider range of tasks including multi-view tracking. Trained with this strategy, ARROW establishes a new state of the art in 3D tracking on WorldTrack and TAPVid-3D and outperforms dedicated multi-view trackers on an adapted RGB-only MVTracker
benchmark, while remaining competitive across 3D reconstruction tasks. Code and weights are publicly available.
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All authors Ilya Fradlin, Christian Schmidt, Jens Piekenbrinck, Karim Knaebel, Gonzalo Martin Garcia, Bastian Leibe
Data Structures and Algorithms
Factor Three Approximation for Edit Distance
Egor Gorbachev
We give randomized
algorithms for $3$-approximate edit distance in $\widetilde{\mathcal{O}}(N^{11/6})$ time for unweighted edit distance and in $\widetilde{\mathcal{O}}(N^{40/21})$ time for arbitrary metric edit weights, where $N$ is the total input length. For non-metric costs, we prove an unconditional $\Omega(N^2)$ oracle-query lower bound for every approximation factor depending only on $N$, even for symmetric weights or weights satisfying the triangle inequality (but not both). Under the Orthogonal Vectors Hypothesis, we show a similar result for constant-size alphabets. This holds even for symmetric weights over a size-$3$ alphabet or triangle-inequality weights over a size-$2$ alphabet. In contrast, for symmetric weights over a binary alphabet we show an $\widetilde{\mathcal{O}}(N^{40/21})$-time $3$-approximation
algorithm.
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Robotics
Ex vivo breach detection using electrical conductivity during robotic pedicle drilling in the spine
Jorge Andrés Pérez Velásquez, François Teyssere, Thibault Chandanson et al. (+2 more)
Purpose: Pedicle screw placement is technically demanding in scoliosis treatment. High precision is required due to limited visibility, anatomical variability, and the risk of complications. Although robotic systems assist CT-based planning and execution, they still rely on ionizing intraoperative imaging and complex registration. This study proposes robotic pedicle drilling with real-time preventive breach detection using electrical bioimpedance sensing. Methods: We developed a robotic approach combined with a pedicle-drilling tool equipped with a proprioceptive electrical bioimpedance sensor developed by SpineGuard. A real-time detection
algorithm was designed to analyze the electrical bioimpedance signal during drilling and identify abrupt changes in conductivity associated with potential breaches towards the spinal canal. The method operates without external devices or sensors. Results: The ex vivo experiments showed that the proposed method prevented breaches in 100 of the 51 drilling cases. These findings demonstrate the system's ability to detect potentially hazardous events during drilling and to stop the procedure before. The ex vivo experiments demonstrated that the proposed method prevented breaches in all 51 drilling cases. Conclusions: This work demonstrates the feasibility of robotic pedicle drilling with electrical bioimpedance sensing for real-time breach prevention. Using only the tool signal, the method eliminates the need for external sensing systems and supports safer pedicle screw placement.
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All authors Jorge Andrés Pérez Velásquez, François Teyssere, Thibault Chandanson, Quentin Grimal, Brahim Tamadazte
Information Theory
Exploiting UAV Attitude for Covert Communications: Dynamics-Consistent Trajectory-Attitude Co-Design
Jinpeng Xu, Lin Zhou, Xin Xie et al. (+2 more)
Existing trajectory designs for covert communications with uncrewed aerial vehicles (
UAVs) typically rely on point-mass kinematics, which overlook the inherent coupling between flight maneuvers and
antenna orientation. Motivated by recent studies linking
UAV acceleration to attitude, we investigate dynamics-consistent trajectory and attitude co-design for rotary-wing
UAV covert communications. We consider a
UAV equipped with a directional
antenna transmitting confidential information to a legitimate ground receiver in the presence of a warden. The acceleration-induced reduced attitude of the
UAV is explicitly modeled together with its impact on the attitude-dependent directional channel gain. To ensure covertness, we derive a per-slot constraint based on the Kullback–Leibler (KL) divergence at the warden. We then formulate a joint trajectory and attitude optimization problem to maximize the average achievable covert rate subject to covertness, motion, thrust-magnitude, attitude-smoothness, and acceleration-domain safety constraints induced by roll and pitch limits. The resulting problem is non-convex because the channel gains depend jointly on position, acceleration, and attitude. To solve it, we develop a minorization–maximization (MM)-based
algorithm that constructs a concave lower bound on the transmission rate and a convex upper bound on the covert constraint at each iteration. We further extend the framework to imperfect attitude control and develop a robust design against attitude tracking errors caused by actuator limitations, sensor noise, and control delays. Numerical results demonstrate the effectiveness of the proposed dynamics-consistent design and highlight the benefit of jointly optimizing
UAV trajectory and attitude for covert communications.
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All authors Jinpeng Xu, Lin Zhou, Xin Xie, Mengyuan Zhang, Jingjing Wang
Artificial Intelligence
DAYJOB: A Benchmark for Long-Horizon Professional Work
Stephanie Finley, Liudas Panavas, Thomas Mikkelson et al. (+12 more)
Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the request's premise holds. We introduce DAYJOB, a
benchmark of 130 tasks built by professionals in healthcare (50) and finance (80). The tasks are estimated to take a professional 13.6 hours on average in healthcare and 16.6 in finance. Each task is a containerized Harbor environment with an expert rubric of binary criteria (median 47.5 and 57.5 per task) that an agentic judge applies to the delivered files, and an attempt passes only if it meets every criterion. Across 30 model configurations from 13 developers, the strongest, Claude Opus 5.5, passes 24.7% of healthcare and 23.9% of finance attempts, and the median configuration passes 0.6% and 2.5%. In case studies,
agents accept premises that the record contradicts and carry wrong inputs through otherwise consistent analyses. We release all healthcare tasks, 50 of the 80 finance tasks, the evaluation harness, and the leaderboard.
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All authors Stephanie Finley, Liudas Panavas, Thomas Mikkelson, Cam Hinton, Stacey Ganss, Bradley Monton, Emily Kendall, Michelle Spradlin, Lydia Bye, Michael O'Brien, Lauren Ylvisaker, Derek Ray, Suhaas Garre, Sushant Mehta, Edwin Chen
Social and Information Networks
Beyond the Clique: Comparing Clique and Dowker Complexes for Co-occurrence Data in Learning Analytics
Koichi Yasutake, Wakana Tsuji and Hitoshi Inoue
Learning analytics increasingly represents the relations in co-occurrence data (codes in a window of discourse, participants in a thread, tags on a post) as simplicial complexes and analyses them with persistent homology. The standard approach uses the clique complex. Because its simplices are determined by the pairwise network alone, it cannot distinguish "three elements co-occurred together" from "each of the three pairs co-occurred separately". The Dowker complex, in contrast, takes as simplices the sets of entities actually observed to co-occur. We compare the two. First, we show that, on the same 1-skeleton, the Dowker complex is a subcomplex of the clique complex, the map on first homology induced by the inclusion is surjective, and its kernel is generated by phantom triangles that never co-occurred; that is, the clique construction can only erase holes. We also give a criterion for agreement that can be checked on triples of groups. We then test this on real data. On four Stack Exchange data sets, 46-91% of clique triangles are phantom and 522-1,970 holes are erased. On the example data of the learning-analytics R packages tna/Nestimate, the first Betti number $\beta_1$ of the clique complex is 0 at every threshold, whereas the Dowker complex detects holes. Against a degree-preserving null model, the Dowker complex departs strongly in five of the six data sets. This difference is invisible at the fixed thresholds used in practice. We conclude that the construction should follow the data type and that, for observed groups, the Dowker complex is the appropriate choice. Code is available at https://github.com/igu-lab/beyond-the-clique.
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Networking
Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport
Emilio Paolini, Andrea Pinto, Flavio Esposito et al. (+1 more)
Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring and deadline-sensitive communication workloads across access and transport networks. This challenge is particularly relevant in mobile RANs, where wireless variability, mobility, and device heterogeneity cause model updates to arrive asynchronously. Although these updates belong to the same learning round and share a common destination and deadline, conventional transport networks treat them as independent device-originated flows, hiding their underlying structure and limiting the ability to efficiently provision transport resources. This mismatch is particularly problematic for optical
circuit switching and all-photonics transport, which benefit from predictable and schedulable traffic demands. We argue that future RANs should act as learning-aware traffic shapers by exposing the communication structure of distributed model adaptation to the transport layer. Through in-network aggregation at the gNB, asynchronous UE updates can be transformed into fewer aggregate transfers with bounded size and delivery requirements. Once shaped in this way,
federated LLM traffic becomes a suitable candidate for selectively provisioned optical connectivity, where high-capacity paths can be established during aggregate-transfer windows and released between learning rounds. The resulting architecture combines the flexibility of packet-based mobile access with dynamically provisioned optical capacity, illustrating a broader approach for coordinating distributed AI workloads across programmable access and transport networks.
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All authors Emilio Paolini, Andrea Pinto, Flavio Esposito, Luca Valcarenghi
Databases · Cross-listed from q-bio.NC
A High-Density EEG Dataset for Stimulus-Driven Auditory Attention
Ruofan Yan, Na Lu, Shu Peng et al. (+6 more)
Stimulus-driven auditory
attention determines which sound gains priority when multiple sources compete without an explicit listening goal, yet most computational studies focus either on acoustic salience or on decoding predefined attended targets. This study investigates instruction-free auditory competition using the Stimulus-driven Auditory
Attention (SAAD) paradigm and develops a neurophysiologically informed framework that integrates stimulus-derived sound priority with trial-specific
EEG evidence. Behavioral analysis using a Bradley–Terry model showed that sound priority estimated from previous competitions generalized to unseen sound pairings, improving held-out prediction from an AUC of 0.577 to 0.718.
EEG analysis further revealed mid-to-late centro-temporal lateralization associated with the reported selection side, with neural information remaining predictive beyond acoustic asymmetry. Guided by these findings, the proposed model first estimates a latent priority for each competing sound and forms relative stimulus evidence from their difference. A multi-scale
EEG pathway with complementary signed and power-based readouts then extracts trial-specific neural evidence, which is incorporated through gated decision-level integration. The framework is evaluated using mirror-constrained and pairing-held-out protocols, together with representative acoustic,
EEG,
multimodal baselines, and systematic ablations. The results support a computational account in which spontaneous auditory selection reflects the interaction between generalizable stimulus priority and trial-specific neural variability.
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All authors Ruofan Yan, Na Lu, Shu Peng, Wenlong You, Zhige Chen, Yuxuan Yan, Yan Liu, Kay Chen Tan, Jibin Wu
Computer Vision
STAGE: Subspace-Targeted Affine Generative Erasure for Text-to-3D Models
Karol Dziekan, Przemysław Spurek and Dawid Malarz
Concept erasure suppresses a target concept while preserving behavior on unrelated inputs. Existing closed-form methods were designed for 2D image
diffusion and assume a single generative pathway, so one edit must cover geometry and texture at once. Native 3D generators, which synthesize structured 3D representations directly rather than by lifting 2D samples, violate this assumption. We show that shape and object concepts must be erased in the structural stage of the pipeline and material concepts in the appearance stage. We therefore formulate erasure in native text-to-3D as a stage-aware editing problem and introduce STAGE, a training-free, closed-form framework. STAGE confines each edit to the low-dimensional subspace spanned by the differences between erase and anchor
embeddings, and relaxes the norm-preserving (orthogonal) constraint of prior editors into a least-squares affine correction that maps target activations onto safe anchors subject to a penalty on the displacement of retained
prompts. The correction applies to the structural stage, the appearance stage, or both. We find that the stage an edit must reach is determined by concept type. On TRELLIS, the standard open native 3D generator, across 15 shape, material, and object concepts, STAGE reaches 66.7 on a composite score that balances forgetting the target concept against preserving everything else, aggregating CLIP-based semantic and physical metrics, versus 53.2 for the strongest adapted baseline. Code: https://github.com/gmum/STAGE/ Project Page https://gmum.github.io/STAGE/
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Robotics
Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations
Giulio Schiavi, Andrei Cramariuc, Michael Pantic et al. (+1 more)
Most current grasp synthesis systems are trained offline and remain fixed during deployment. While this works well when deployment conditions resemble the training data, performance can degrade when
robots encounter conditions they have not seen before, such as unfamiliar objects. In this work, we present a continual-learning framework for single-view 6-DoF grasp synthesis for a parallel-jaw gripper in cluttered scenes. Rather than finetuning a large parametric model, our method adapts through
memory in a learned
embedding space: grasp outcomes update future grasp scores, while optional user demonstrations are recalled and transferred to new scenes as additional candidate grasps. We evaluate our method in simulation and in extensive real-world experiments comprising over 1500 grasp trials. We show that our method matches the performance of existing 6-DoF grasping baselines even before adaptation, improves online on unseen objects from categories absent or underrepresented during training, and supports long-horizon continual learning with limited forgetting. In real-world experiments, our method reaches over 90% success rates on several challenging object categories after only 50 online grasp attempts.
Videos and code at https://giuschio.github.io/cl_grasping/.
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All authors Giulio Schiavi, Andrei Cramariuc, Michael Pantic, Roland Siegwart
Cryptography and Security
A Systematization of Knowledge on DeFi Vaults: Architectures, Curation Mechanisms, and Strategy Design
Davide Mancino and Luca Pennella
Decentralized finance (DeFi) vaults are smart-contract-based asset management systems that pool deposits, execute programmable strategies, and mint tokenized shares representing claims on underlying assets and strategy performance. As vault designs have evolved from early yield aggregators to modular, actively managed systems, a new control layer, curation, has emerged to select strategies, configure risk parameters, and coordinate operational execution, introducing principal-
agent dynamics and new failure modes. This paper systematizes DeFi vault architectures and curator-mediated control planes through (i) a unified system model and formal definitions for share accounting, roles, and operational dependencies, and (ii) three complementary taxonomies covering vault exposures and objectives, curator governance and accountability mechanisms, and strategy execution patterns together with their failure modes. We further map a representative set of production protocols to the proposed dimensions. The frameworks in this work aim to support rigorous analysis and safer design of blockchain-based financial applications.
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Artificial Intelligence
Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data
Achal Nanjundamurthy, Rupam Misra, Suzanne Little et al. (+1 more)
Ireland's smart metering programme records electricity use at 30-minute resolution, with smart meters installed in over 80% of households as of late 2025. While this is useful for billing of smart, time-of-use tariffs, it is too coarse to capture use of domestic appliances. We present a label-free disaggregation system that breaks usage data into 9 appliance categories by combining event detection for high-power loads with questionnaire-guided estimation. Our evaluation draws on four
datasets: a calibration household with a commercial comparator, two public
benchmarks (UK-DALE and REFIT) with per-appliance sub-metering, and a smart meter
dataset of more than 4,800 years of use from 2,968 Irish consumers. Compared against two independently developed disaggregation systems our hybrid method combining analysis of usage data with questionnaire results, achieves the lowest whole-decomposition error on all buildings across the
datasets, with better month-level performance over 54 paired months ($p<0.001$, Holm-corrected). Our method provides useful advice on a household's energy consumption patterns and advice on how to reduce or shift usage on some appliances in order to reduce costs.
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All authors Achal Nanjundamurthy, Rupam Misra, Suzanne Little, Alan F. Smeaton
Artificial Intelligence
ITC-MoE: Importance-guided Token-aware Compression for MoE Diffusion Language Models
Lianjun Liu, Shipeng Li, You Huang et al. (+5 more)
Mixture-of-Experts (MoE)
Diffusion Language Models (DLMs) offer flexible parallel decoding and increased model capacity, but their large number of expert parameters incurs substantial computation and storage costs. Existing low-rank MoE compression methods largely rely on static factorization and fixed rank allocation, which overlook the distinctive properties of MoE DLMs. Specifically, we identify two properties: cross-mode non-uniform redundancy, where parameter redundancy and sensitivity to rank truncation vary across the input, output, and expert modes, and
token-wise utilization variation, where hot and cold
tokens exhibit distinct spectral characteristics and expert activation patterns. To address these challenges, we propose ITC-MoE, an Importance-guided
Token-aware Compression framework for MoE DLMs. ITC-MoE consists of two complementary components. First, Importance-guided Adaptive Tucker Compression (IATC) incorporates activation and gradient importance into expert weight transformation, jointly factorizes expert weights across multiple modes, and adaptively allocates ranks under a fixed parameter budget. Second,
Token-aware Compensation and Routing (TCR) applies lightweight low-rank compensation to compression-sensitive hot
tokens and restricts the candidate expert set for cold
tokens with concentrated routing patterns. By jointly adapting compression capacity and inference execution to both parameter redundancy and
token-wise variation, ITC-MoE substantially reduces the computation and storage costs of MoE DLMs while preserving their generation quality. For example, on SDAR-30B-A3B-Chat-b32, ITC-MoE maintains an accuracy of 96.33% on MultiArith under a 30% compression budget, while achieving up to a 7.22x end-to-end speedup. The code is publicly available at https://github.com/lianjunl13-sudo/ITC-MoE.
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All authors Lianjun Liu, Shipeng Li, You Huang, Weiqi Yan, Mingte Qiu, Huazhong Liu, Xiaofeng Zhu, Yunshan Zhong
Systems and Control · Cross-listed from math.OC
Petrov-Galerkin operator inference with application to stability-encouraging identification
Johannes Rettberg, Jonas Nicodemus, Harsh Sharma et al. (+3 more)
Data-driven model order reduction methods such as operator inference enable the efficient construction of reduced-order models directly from high-dimensional time-domain data. Standard operator inference typically seeks a Galerkin-type reduced model in a prescribed low-dimensional subspace by identifying its reduced operators from projected snapshot data. The resulting inference problem is formulated as a least-squares problem admitting an efficient closed-form solution. However, it is well known from intrusive model order reduction for linear time-invariant systems that Petrov-Galerkin projections can additionally preserve important system properties such as stability and passivity. To overcome the limitations of standard operator inference, we extend the framework for linear time-invariant systems to incorporate Petrov-Galerkin projections and provide explicit error expressions and bounds between the intrusive and nonintrusive reduced operators, thus generalizing results from the literature. We demonstrate the proposed approach in the context of dissipative and port-
Hamiltonian systems. Furthermore, we introduce a novel convex optimization formulation that explicitly enforces the port-
Hamiltonian structure on the inferred operators. The effectiveness of the proposed methods is demonstrated on several well-established
benchmark problems, including the CD player, an atmospheric model, a mass-spring-damper system, and a poroelasticity system.
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All authors Johannes Rettberg, Jonas Nicodemus, Harsh Sharma, Boris Kramer, Jörg Fehr, Benjamin Unger