Emerging Technologies · Cross-listed from quant-ph
Benchmarking the computational power of quantum computers
Timothy Proctor, Oliver Hart, Oliver Widzowski Maupin et al. (+27 more)
Quantum computing hardware is advancing rapidly toward utility-scale machines that will enable scientific breakthroughs. Many teams are pursuing distinct and difficult-to-compare routes to this goal, using different
qubit technologies and logical architectures. Tracking progress toward
quantum utility therefore requires rigorous
benchmarks that measure computational capability relative to utility-scale challenge problems and enable fair comparison across disparate platforms. Here we demonstrate direct, cross-platform measurement of
quantum computational capability using a new
benchmark that quantifies the size of the largest computationally relevant
quantum circuits that a machine can execute successfully and the speed at which it can execute them. We apply this
quantum universal operation performance system (QUOPS) experimentally to leading processors from Quantinuum, Google, and IBM, computing directly on physical
qubits. Translating state-of-the-art resource requirements for recognized challenge problems that represent useful
quantum computation into effective QUOPS
circuit sizes shows that computational capability must grow by 5 orders of magnitude, motivating fault-tolerant approaches. We use the same
benchmark to assess the performance of a simple fault-tolerant logical-
qubit processor implemented on up to eight [[7,1,3]]-encoded logical
qubits using Quantinuum Helios-1, and project the growth of capability across successive generations of fault-tolerant
quantum computers to show how QUOPS can track progress toward
quantum scientific utility.
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All authors Timothy Proctor, Oliver Hart, Oliver Widzowski Maupin, Matthew Girling, Daniel Hothem, Daniel Mills, Jordan Hines, Karl Mayer, Jacob S. Nelson, Tyler LeBlond, Zohim Chandani, Diego Forlivesi, Piper C. Wysocki, Boldizsár Poór, Joan M. Dreiling, Annie Park, Adam P. Reed, Brian Estey, Cameron Foltz, Akhil Isanaka, M. S. Allman, Michael Mills, Maxwell D. Urmey, Peter E. Siegfried, Audrey Faricy, Jin-Sung Kim, Cristina Cîrstoiu, Andrew D. Baczewski, Charles H. Baldwin, Robin Blume-Kohout
Cryptography and Security
Hardware Fingerprinting FTQC via Quantum Decoder Timing
Friedrich Doku, Jakub Szefer and Kaitlin N. Smith
As the
quantum computing field transitions toward Fault-Tolerant
Quantum Computing (FTQC), intensive efforts are focused on scaling architectures and realizing active error correction. However, this shift introduces security surfaces that remain largely unexplored. Fault-tolerant
quantum computers pair a
quantum processor with a classical decoder that sits on the critical path of every syndrome-extraction round. For the first time, this work demonstrates that the wall-clock time each decoder takes to process a syndrome measurement and decoding round constitutes a novel, exploitable hardware side channel on physical
quantum hardware. Using per-shot decoder timings from three IBM Heron processors collected over a 68-day window, the decode-time distribution alone allows a passive observer to (i) reconstruct the shot-by-shot detector-firing distribution and estimate the workload's logical error rate $p_L$, (ii) infer the code distance in use, and (iii) fingerprint the specific physical device with up to 89% accuracy (a random guess is 33%), with a pooled two-sample Kolmogorov-Smirnov test confirming the decode-time distributions are statistically distinct. In noisy simulation inspired by public data from Google's 105-
qubit Willow processor, decoder timing further distinguishes 9 surface-code patches at different locations on the chip with 81% accuracy, showing the side channel persists on below-threshold fault-tolerant hardware from a different vendor and code family.
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Networking
On Identifying Adversarial Intent Injection in AI-Native 6G Networks
Nilesh Chakraborty, Petar Djukic and Burak Kantarci
AI-native 6G networks have brought Intent-Based Networking (IBN) to the forefront, enabling high-level goals to be translated into network configurations. However, this abstraction opens new attack surfaces, primarily
adversarial intent injection, where malicious policies are disguised within benign intent flows. The detection of attack instances might become significantly more difficult if the adversaries adopt a stealthy mode of malicious intent injection. With all these in mind, we first define a fine-grained threat model that facilitates the threat of malicious intent injection in an AI-native network. Alongside, we investigate four malicious intent injection strategies$-$ stealth-mode, random distribution, increasing frequency, and decreasing frequency- and propose a dual-path detection framework: (i) a CNN using TF-IDF features for supervised malicious intent detection, and (ii) an AutoEncoder trained exclusively on benign data for one-class malicious intent detection. Our evaluation demonstrates strong detection performance, with accuracy improving to 0.97 (~9% gain) and F1-score to 0.98 (~36% gain) over the state-of-the-art baseline.
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Distributed Computing
Consensus-based Decentralized Distributed Swarm Learning with Heterogeneous Big Data
Zhuoyu Yao, Dong Yang, Yue Wang et al. (+4 more)
Artificial intelligence increasingly relies on large-scale, distributed, and heterogeneous data collected by edge devices. However, the practice of edge intelligence remains challenging due to non-convex objectives, data heterogeneity, and complex wireless network
topology. To address these issues, this paper proposes a consensus-based decentralized distributed swarm learning (CD-DSL) framework for wireless edge networks. Our CD-DSL integrates consensus optimization with particle swarm optimization (PSO), by reaching the model consensus among neighboring devices while leveraging the PSO exploration and exploitation. The consensus mechanism supports decentralized coordination without raw-data exchange, while PSO-inspired updates utilize historical and neighbor-shared experience to enhance exploration for non-convex optimization, improve robustness to data heterogeneity, and accelerate convergence. We further develop an adaptive neighbor-mixing strategy that learns performance-aware consensus weights, improving decentralized collaboration among heterogeneous edge devices. Theoretical analysis establishes that CD-DSL maintains participant consistency and achieves non-ergodic convergence to a neighborhood of a stationary point under non-convex objectives. Experimental results show that CD-DSL can mitigate the performance degeneration of existing decentralized baselines caused by heterogeneous data.
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All authors Zhuoyu Yao, Dong Yang, Yue Wang, Songyang Zhang, Yingshu Li, Zhi Tian, Zhipeng Cai
Robotics
A Data-Driven Distributed Control Scheme: Learning Multi-Objective Agent-Based MPC for Path-Tracking
Jiaming Zhong, Reza Valiollahi Mehrizi, Yash Vardhan Pant et al. (+1 more)
Agent-based model predictive control (AMPC) has recently been proposed for vehicle systems with various controllers, such as differential braking and torque vectoring, where controllers are regarded as distributed
agents contributing to the same objective. However, this scheme is challenging in handling multiple conflicting objectives with coupled
agents. A common approach for such tasks is the integrated MPC, where all objectives and
agents are stacked together in one optimization. Nevertheless, as more
agents and objectives are involved, the integrated MPC will face challenges like computational burdens and maintenance difficulties in practice. To this end, this paper proposes a learning multi-objective AMPC that can improve design flexibility and computing efficiency. First, under the assumption of information exchange, a multi-objective AMPC tailored from the alternating direction method of multipliers (ADMM) is proposed to decouple the system and achieve the same performance as the integrated scheme iteratively. Second, a learning-based method for initializing iterations is proposed to accelerate convergence. In addition, a data management method is proposed for real-time efficiency, and an authentication module is designed for learning reliability. We compare the proposed scheme against the integrated scheme via a combined path-tracking simulation for autonomous vehicles with various controllers. The proposed scheme achieves the same control performance as the integrated one while reducing the computational time by 43.5%. Furthermore, the learning-based method saves 88.6% more computational time than without learning, making it suitable for real-time implementation.
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All authors Jiaming Zhong, Reza Valiollahi Mehrizi, Yash Vardhan Pant, Amir Khajepour
Information Theory
Random Access and Localization in Cell-Free User-Centric Networks with Multipath Channels
Simon Tarboush, Eleni Gkiouzepi and Giuseppe Caire
In a wireless network, the initial/random access mechanism (RACH) allows idle/new users to join the network and (possibly) request allocated transmission resources for subsequent traffic. Building on our own previous work, for
cell-free user-centric networks, we consider location-dependent random access codebooks such that users in a certain geographic area (location) make use of the corresponding set of random access preambles (codewords). We expand our previous work in two ways: (1) we consider multipath channels with line-of-sight (LoS) propagation within a given radius; (2) we consider two different approaches. The first makes use of Zadoff-Chu (ZC) sequences and GLRT detection to cope with the unknown delay, and it is conceptually similar to the 3GPP 2-step RACH specification (here extended to the
cell-free case). The second builds on our previous work on multisource approximate message passing (AMP). For both schemes, we also consider a novel near Maximum-Likelihood approach for localization of the random access users directly from the detected RACH preambles, implicitly using angle of arrival and time difference of arrival information embedded into the LoS components. Simulation results show that the AMP approach achieves generally better performance for random access user detection, while both approaches have similar localization capability with a slight superiority for the frequency-domain scheme.
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Information Theory
Joint Random Access and Localization in Cell-Free User-Centric Networks with Frequency-Selective Fading Channels
Simon Tarboush, Eleni Gkiouzepi and Giuseppe Caire
We study random access (RACH) schemes for
cell-free (CF) user-centric networks to handle many geographically distributed users with sporadic traffic and intermittent activity. The RACH must allow the system to: 1) detect preambles sent by the (yet unknown) random access users in the RACH slot; 2) localize them for fast allocation of user-centric
radio-unit (RU) clusters. Most prior work uses simplified models, neglecting frame-synchronous but chip-asynchronous transmission, possible line-of-sight (LoS) propagation for certain user-RU pairs, and multipath non-line-of-sight (NLoS) propagation yielding frequency-selective channels. Building on our previous work, we consider location-dependent partitioned random access codebooks where users in a geographic area (location) use the corresponding subset of random access preambles. We present a unified framework for joint detection and localization over a spatially consistent network-wide channel model, incorporating these neglected aspects. We evaluate two schemes: 1) a “legacy” scheme using Zadoff-Chu (ZC) sequences, extending the 3GPP 2-step RACH to the CF case; 2) our multisource approximate message passing (AMP) approach extended to multipath frequency-selective fading. For both schemes, we develop novel approximated GLRT preamble detection and Maximum-Likelihood position
estimators with super-resolution refinement, implicitly exploiting received signal strength, angle of arrival, and time-difference of arrival information embedded into LoS components. Numerical results show that the AMP-based scheme achieves superior preamble detection, while both schemes have similar and excellent localization capability.
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Artificial Intelligence
Mined from Scientific Literature: Process Schemas for Atomic Layer Deposition and Etching in Materials Science
Sameer Sadruddin, Eleni Poupaki, Alex Watkins et al. (+5 more)
Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials science, hindering comparison and machine-actionable reuse. We present four domain-expert-reviewed JSON Schemas for ALD and ALE experimental and simulation processes. Curated with schema-miner and grounded in QUDT using schema-miner pro, the schemas structure materials, process conditions, configurations , and measured or predicted results. We compare their scope, structure, and semantic grounding, and demonstrate their use for schema-guided literature extraction and publication of structured records through ORKG templates.
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All authors Sameer Sadruddin, Eleni Poupaki, Alex Watkins, Bora Karasulu, Adriaan J. M. Mackus, Erwin Kessels, Sören Auer, Jennifer D'Souza
Machine Learning
GUIDE: Generative Utility Inference and Decision Engine
Anagha Tiwari, Alexander G. Gray, Nick Feamster et al. (+3 more)
Measuring the preferences of human users remains a fundamental challenge of AI
alignment. Existing elicitation approaches struggle to efficiently discover multidimensional preferences or accurately ground these inferences in domain knowledge. To address this, we introduce GUIDE, an LLM-driven elicitation architecture that infers user preferences through conversations by combining
Bayesian adaptive sampling for question selection and symbolic representation learning to initialize domain-specific preference models. GUIDE generalizes adaptive sampling to diverse elicitation questions through an extensible type system of transforms on a parameterized preference state. GUIDE produces domain-specific preference representations through an initialization process using symbolic rule-based learning to capture world knowledge and set priors over preference dimensions grounded in data about decision alternatives. The architecture provides observability and steerability to facilitate deployment and analyze elicitation processes. In silico experiments on investment portfolio optimization demonstrate that GUIDE improves cold-start and minimizes recommendation regret consistently within early elicitation interactions across user personas compared to prior work, LLM-only baselines, and ablated GUIDE versions.
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All authors Anagha Tiwari, Alexander G. Gray, Nick Feamster, Brian Jabarian, Alex Imas, Alex Kale
Artificial Intelligence · Cross-listed from stat.ME
The Anatomy and Boundary of Adaptation under Temporal Tabular Shift
Tianyu Wang, Xi Vincent Wang, Lihui Wang et al. (+2 more)
Prequential adaptation of frozen tabular foundation models under temporal drift, with each label revealed only after prediction, helps some deployments and harms others, yet current practice does not predict which. We study the sources and limits of these gains. A diagnostic anatomy attributes gains to four recurring mechanisms under a streaming protocol that removes three optimistic biases and quantifies a fourth. Within an agnostic total-variation drift class, the target conditional is only partially identified: its identified-set diameter, the wall, is irreducible from unlabeled data uniformly in sample size. A second, orthogonal $L^2$ projection wall quantifies what the frozen representation cannot express. Two canonical mechanism priors collapse the first wall. Under stated nuisance-rate conditions, the wall can be estimated from labeled historical windows at a $\sqrt N$ rate above the margin threshold $\gamma^\star=d_0/(2\alpha_s)$. At $\gamma=0$, the conditional lower-bound program depends on an open affinity estimate; the positive-margin lower branch also remains open. Semi-synthetic data illustrate the finite-sample mechanism with calibrated exponents. Stream-level proxies on eight industrial streams fall on the difficult side under a stated roughness bound, while the equality case $\gamma=\gamma^\star$ remains unresolved.
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All authors Tianyu Wang, Xi Vincent Wang, Lihui Wang, Mian Li, Zhihao Liu
Computation and Language
Population-level measures of perceived food access reveal barriers beyond geographic proximity
Teresa Groton and Benjamin rachunok
Food access is multidimensional, but population-level measurement still relies heavily on geography because perceived dimensions of access are difficult to measure at scale. Here, we use 25,125 Google Maps reviews from 49 grocery stores in Raleigh, North Carolina, to measure five dimensions of food access: availability, accessibility, affordability, accommodation, and acceptability. We identify review topics with unsupervised topic modeling and assign them to access dimensions using zero-shot classification, with 85.4% agreement against manual coding. The resulting store-level measures capture distinct aspects of food access and reveal barriers that geographic proximity alone does not capture. Comparisons between nearby stores in the same chain further show that identical store policies can be perceived very differently across locations, consistent with food access reflecting the fit between residents and their food environment. Perceived food access also follows systematic socioeconomic and demographic patterns that broadly parallel, but do not replicate, those observed for geographic access. These results show that online grocery reviews can provide a scalable complement to geographic measures of food access.
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Software Engineering
Missing Dimensions: Integrating Human and Social Systems into Digital Twin Engineering
Francis Bordeleau and Mark van den Brand
Digital twins (DTs) have emerged as a key technology at the core of digital transformation, yet their engineering practice remains too narrowly focused on engineered and natural systems. This paper argues that four system dimensions must be explicitly recognized in DT engineering: Engineered, Natural/Biological, Human, and Social. Each dimension brings distinct properties, modeling requirements, and ethical obligations that fundamentally shape what a DT must represent and how it must be built. We further argue that as DTs extend into the Human and Social dimensions, the emphasis should shift from automated control toward decision support and occupant empowerment. We illustrate our arguments using a smart building as a running example and identify four open research challenges for multi-dimensional DT engineering.
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Social and Information Networks
Exploring Climate-Related Anxiety Through Social Media Content
Karim El-Sharkawy, Zainab Rehman, Vikrant Kumar et al. (+2 more)
This study explores
climate-related anxiety as expressed through social media discussions on Reddit. Using natural
language processing techniques, we analyse large-scale textual data to identify recurring themes, emotional patterns, and how these evolve over time. Text data was preprocessed and analysed using BERTopic for topic modelling and a
transformer-based model for
emotion classification across 28 categories using the RoBERTa-base GoEmotions model. Results show that
climate-related discourse is structured around a small number of core themes, primarily separating action-oriented discussions like advocacy and policy from informational and reflective content. Emotional analysis reveals that negative
emotions such as fear and sadness are more prominent in posts, while comments often introduce a wider range of responses, including care, encouragement, and neutral reactions. These findings suggest that online
climate discourse is shaped not only by the topics being discussed, but also by how users engage with and respond to one another. This work provides an initial framework for understanding
climate-related anxiety through large-scale social media analysis and highlights opportunities for improving future models, expanding platform coverage, and incorporating youth-centred perspectives.
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All authors Karim El-Sharkawy, Zainab Rehman, Vikrant Kumar, Ardavan Shahrabi, Bobbie Williams
Computation and Language
Local Edits, Global Ripples: Replay-Informed Policy Adaptation for Workflow Synthesis
Manqing Mao, Hong Wang, Samson Koelle et al. (+8 more)
Prompt-policy editing offers a practical way to improve
agents that synthesize executable workflows without updating the underlying model. However, persistent
prompt editing has two coupled properties. First, edit locality does not imply effect locality: an edit confined to one policy segment can ripple through downstream execution, altering behavior beyond the edited segment. Second, edit effects are composition-sensitive: edits that work in isolation can interfere after composition, causing one or both to lose their benefit or become harmful. Persistent adaptation must therefore support two distinct decisions: identifying where the policy should change from execution feedback, and determining whether the resulting edit remains safe to persist after composition. To address these challenges, we introduce RIPPLE (Replay-Informed Persistent Policy Localization and Editing), which separates where an edit is made from whether it remains safe after composition. It diagnoses failed trajectories, maps each actionable failure to a predefined policy segment, and restricts the correction to that part of the policy. RIPPLE then evaluates candidates against the same iteration-start policy to compare their isolated gains, before replaying promising edits after previously accepted updates to expose downstream effects and interactions. Only edits that remain safe under composition are retained. We evaluate RIPPLE on Flow-HO, a synthetic held-out
benchmark for executable workflow synthesis. RIPPLE improves validation success by up to 23.1% and yields positive gains on two additional frozen
language-model backbones, while maintaining edit efficiency and low execution cost. Targeted interaction analysis further demonstrates both properties: a segment-local tool-use edit changes downstream resource resolution and validation, while an edit beneficial in isolation becomes harmful after composition.
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All authors Manqing Mao, Hong Wang, Samson Koelle, Jie Yuan, Zhuoer Wang, James Feng, Yanjun Lin, Daniel Edmiston, Nikki Lijing Kuang, Zhecheng Sheng, Wei Niu
Machine Learning
Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
Zihan Zhu, Zhehang Du, Xuyang Chen et al. (+5 more)
Low-Rank Adaptation (LoRA) is an effective approach for adapting large pretrained models by learning low-rank weight updates. In practice, the LoRA rank is used to control an adapter's parameter budget and representational capacity. We show that this view is incomplete: while the nominal rank determines the representational capacity, the optimizer shapes how much of that capacity is used in the induced weight-space updates. In a case study of GPT-2 adaptation with LoRA, we observe a strong rank-dependent optimizer effect. Despite using the same nominal rank, AdamW often produces per-step updates with concentrated singular spectra and low effective rank, whereas
Muon uses a richer set of directions and benefits more consistently from increasing LoRA rank. These observations motivate ISO-LoRA, an optimizer that couples the LoRA factor updates through spectral descent on the induced tangent perturbation in weight space. ISO-LoRA promotes updates that distribute energy more evenly across singular directions, improving rank utilization while preserving compatibility with the LoRA parameterization. We complement this design with theoretical guarantees showing that ISO-LoRA can achieve higher effective rank than standard factor-wise optimizers through a one-step analysis under a stylized spiked-gradient model. We validate this design on
language-model adaptation across 0.1B-7B-parameter models, where ISO-LoRA improves effective rank and downstream performance, with the strongest gains at moderate-to-large LoRA ranks. Our results highlight rank utilization as a key factor in LoRA optimization and suggest that optimizer design offers an important path toward stronger parameter-efficient adaptation.
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All authors Zihan Zhu, Zhehang Du, Xuyang Chen, Tim Tsz-Kit Lau, Jiayuan Wu, X. Y. Han, Qi Long, Weijie Su
Computation and Language
Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking
Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia et al. (+4 more)
Consonants contribute unequally to whether a word is understood. Given the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritize intervention targets in motor
speech disorders. However, measuring this contribution relies on perceptual studies that are difficult to scale. This paper presents a scalable method that measures consonant contribution using acoustic masking. We silence one consonant at a time in an isolated word and test whether an automatic
speech recognition (ASR) model still recognizes the word. We define a consonant's contribution score as the proportion of its masked instances for which the word becomes misrecognized, which we refer to as the mask-induced misrecognition rate (MMR). We relate MMR to two linguistic factors previously reported to correlate with consonant contribution, namely phoneme frequency and functional load. We apply this analysis across four
languages, English, Spanish, German, and Czech, using three ASR architectures, MMS (encoder-only), Whisper (encoder-decoder), and Qwen3-ASR (LLM-based). Using partial Spearman correlations, we find that phoneme frequency correlates negatively with MMR while functional load correlates positively. In other words, more frequent consonants are less disruptive when masked, whereas consonants carrying more lexical contrast are more disruptive. Further cross-
language analysis shows that consonant rankings agree only partially across
languages, indicating that consonant contribution is
language-dependent.
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All authors Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia, Visar Berisha, Julie M. Liss, David R. Mortensen, David Harwath
Machine Learning
Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise
Amartya Mukherjee and Jun Liu
Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, particularly in applications requiring robustness or
privacy guarantees. However, clipping introduces a bias in
stochastic gradients, while additive noise introduces additional variance, making the long-run behaviour of individual optimization trajectories difficult to characterize. In this work, we prove that SGD with clipping and additive Gaussian noise (SGD-CN) converges almost surely (a.s.) under smoothness and uniformly bounded
stochastic-gradient noise assumptions, provided the step sizes satisfy some standard decaying conditions. Our analysis extends to momentum variants such as the
stochastic heavy ball and Nesterov's accelerated gradient, where we show that careful energy constructions yield similar guarantees. These results provide stronger theoretical foundations for understanding the pathwise behaviour of clipped
stochastic gradient methods and suggest that, despite the bias and noise introduced by clipping and perturbation, the
algorithm remains stable in both convex and nonconvex regimes.
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Artificial Intelligence
When Successful Knowledge Graph Edits Displace Correct Answers: Rank-Level Locality beyond Parameter Support
Yi-Cheng Lai, Jerry Wang, Hsin-Ling Hsu et al. (+3 more)
Editing a knowledge graph
embedding (KGE) model to promote a desired answer can displace correct answers from the returned list. Locality tests based only on facts that reuse the edited parameter can miss this ranking effect. We introduce a common rank-displacement audit at three scopes: facts supported by the edited parameter, other correct answers to the target query, and correct answers across queries with the same relation. We also derive dimensional and
geometric conditions for an update to improve the target while exactly preserving selected scores. On FB15k-237 with DistMult and ComplEx, direct promotion always moves the target into the top ten, but does so without damage in only 23.0–23.2% of edits. Strict preservation causes no measured damage, yet succeeds in only 1.3–1.4%. Support-regularized entity editing gives the highest joint success, 36.3–37.7%, while rank-truncated preservation reaches 32.8–34.7% and reduces the mean number of displaced answers from about 14 to 1.2. Experiments across dimensions, scorers, ranking conventions, and a learned editor show that locality depends on both the protected scope and the editing mechanism. KGE editing should therefore report correction success together with the incidence and severity of rank displacement.
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All authors Yi-Cheng Lai, Jerry Wang, Hsin-Ling Hsu, Li-Chu Chi, Ya-Wen Teng, Hen-Hsen Huang
Artificial Intelligence
DU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling
Enrique Hernandez Noguera, Md Meftahul Ferdaus, Nathan Cooper et al. (+2 more)
Phase-resolving wave models such as FUNWAVE-TVD are the accuracy standard for nearshore dynamics, resolving the shoaling, refraction, and breaking of individual waves, but their cost rules them out for the ensembles, uncertainty quantification, and real-time warning that operational
forecasting demands. Neural operators promise solver-level accuracy at a fraction of that cost, yet on wave-dominated fields the accurate ones are large: hybrid spectral-convolutional operators such as U-FNO (the strongest baseline in our study after DU-NO) buy their fidelity with tens of millions of parameters. We introduce DU-NO (Double U-shaped Neural Operator), a multiscale U-shaped spectral operator that attaches lightweight convolutional U-Net branches only at its two shallowest encoder and decoder levels. The placement follows a sampling argument: high-wavenumber content exists only on fine grids, so the local, full-band pathways go where that content lives, while the coarse, band-limited levels stay purely spectral. A depth-decaying mode schedule holds the model to 3.64M parameters, an order of magnitude below U-FNO. On our publicly released FUNWAVE-TVD
benchmark, DU-NO attains the best autoregressive rollout error of six identically trained architectures, improving on U-FNO by 14.9% with 10.8x fewer parameters, and a frequency-band analysis shows the gain holds across all bands, including the high-wavenumber band where truncated-spectral operators collapse. Parameter-matched controls confirm the gain is architectural: rescaled to the same 3.6M budget, the best baseline still trails DU-NO by 28.6%. The advantage carries beyond nearshore waves: DU-NO matches the strongest baselines on 2D Navier-Stokes and wins clearly on PDEBench shallow-water rollouts. Code, trained models, and evaluation artifacts are available at https://anonymous.4open.science/r/duno-code-5A7B/.
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All authors Enrique Hernandez Noguera, Md Meftahul Ferdaus, Nathan Cooper, Elias Ioup, Mahdi Abdelguerfi
Numerical Analysis · Cross-listed from math.OC
Global and Unconditional $R$-Linear Convergence of Rank-Compressed Weighted LMSD Sweeps for Strictly Convex Quadratics
Shutai Yang and Ya-xiang Yuan
We study limited
memory steepest descent (LMSD) with exact algebraic rank compression for strictly convex quadratic optimization. At each cycle, the method restricts the gradient history to its column space and applies the reciprocals of all Ritz values of the compressed projection in the next sweep. If the block-start gradient has at most $p$ active distinct
eigenvalues, the delayed sweep terminates finitely. For nonterminating trajectories, a determinant and Cauchy–Binet formula for the complete-sweep
polynomial yields global convergence without a full-rank history or a run-wise normalized-conditioning bound. Consecutive sweep endpoints define a continuous positively homogeneous map on a closed cone of compatible states. Compactness then gives an $R$-linear endpoint estimate, uniform over compatible initial states, with constants depending only on $H$ and $p$; the estimate extends to inner gradients, iterate errors, and objective gaps. Every fixed positive spectral weight $W=\omega(H)$ is linearly conjugate to the standard method. The weighted methods therefore share its decay factor, while the Euclidean-norm prefactor is increased by at most $\sqrt{\kappa_2(W)}$. This includes harmonic-Ritz LMSD, fixed power weights, and the delayed BB1 and BB2 recurrences.
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Machine Learning
Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative
Amartya Mukherjee, Tristan Milne, Kry Yik-Chau Lui et al. (+2 more)
Outliers are important for stress-testing
algorithms and understanding system behaviour under rare conditions. Despite being commonly described as low-likelihood events, existing generative approaches rarely control likelihood explicitly. In this work, we introduce a measure-theoretic notion of outliers based on the distribution of log-likelihood values, which is guaranteed to assign higher probability mass to low-likelihood events with a specifiable magnitude. Building on this formulation, we derive how likelihood reweighting modifies the
diffusion score and use this relation to motivate a controlled modification of the reverse-time dynamics. In particular, likelihood reweighting implies a scaling of the score function with a control term derived from the Radon-Nikodym derivative of the likelihood distributions. Correspondingly, the updated score function can be obtained with no retraining of the
diffusion model. We exploit the Ornstein-Uhlenbeck
semigroup underlying
diffusion models to motivate an exponentially interpolated controller which approximates the true control. Experiments demonstrate controlled generation of low-likelihood samples while remaining consistent with the data geometry.
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All authors Amartya Mukherjee, Tristan Milne, Kry Yik-Chau Lui, Stephanie Hazlewood, Jun Liu
Computation and Language
The Cost of Compression: A Rate-Distortion Limit on Factual Hallucination
Xi Wang, Shijia Xu and Rongfeng Guo
Factual
hallucination in closed-book question answering is often treated as a coverage problem: a model fails because the relevant fact is absent from its internal
memory. This view misses a second source of error. Even when a fact has been observed, finite
memory may force it to be stored only approximately. We study this effect through a simple coverage–compression model of factual recall. We consider an unstructured question-answering task with $N$ possible queries and $K$ possible answers. A learner observes $M$ training facts, compresses them into at most $B$ bits, and answers uniformly drawn test queries without
retrieval. For a uniformly random ground-truth mapping, we prove $\mathcal{E} \geq \frac{M}{N}\delta^\star\!\left(\frac{B}{M}\right) + \left(1-\frac{M}{N}\right)\left(1-\frac{1}{K}\right)$, where $\delta^\star(r)$ is the inverse rate-distortion function of a uniform $K$-ary source under zero-one loss. The two terms separate compression distortion on observed facts from missing coverage on unobserved facts. The bound gives a compact way to reason about selective
memory, forced compression, structure,
retrieval, abstention, and long-context organization. We study the predicted signatures with theory-implied simulations and controlled fact-injection probes in modern
language models that vary fact load and effective trainable
memory. The result is not a complete theory of
hallucination, but an information-theoretic account of a separable failure mode: lossy recall of observed facts under finite
memory.
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Robotics
Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control
Jiaming Zhong, Ladan Khoshnevisan, Shucheng Huang et al. (+3 more)
Functional integration is a growing trend in vehicle control, often involving the coordination of multiple controllers to achieve various objectives simultaneously. The need for flexibility and reliability has led to a "plug-and-play" approach in control system design, which presents challenges for traditional integrated model predictive control (MPC).
Agent-based model predictive control (AMPC) has recently emerged as a distributed solution that treats controllers as
agents, creating a collaborative framework among them to reach a common goal. However, this approach struggles to manage distributed conflicting objectives when
agents are coupled or interdependent. To address this, we propose a novel, practical distributed control scheme called multi-objective AMPC, which adapts the alternating direction method of multipliers (ADMM) into a general control strategy that approximates global optimization while decoupling objectives. We systematically develop three formulations that maintain convergence while addressing control regularization and inequality constraints, applying them to complex vehicle control systems for the first time. The proposed method has been tested on two vehicle control scenarios with a multi-objective
topology. Different formulations are compared through simulations, and the most computationally efficient one was implemented on an electric vehicle for real-world evaluations. The results demonstrate that the proposed multi-objective AMPC can converge approximately to the same global optimum as integrated MPC with greater flexibility and the potential to reduce computational costs.
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All authors Jiaming Zhong, Ladan Khoshnevisan, Shucheng Huang, Mohammad Pirani, Yash Vardhan Pant, Amir Khajepour
Computation and Language
Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement
Rafael Macalaba, Aivin V. Solatorio, Patrick Michael Brock et al. (+1 more)
Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy, and operations, yet systematically identifying where these
datasets are referenced remains difficult. Such references are dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace data use and to identify potential gaps in data availability or dissemination. We present a weakly supervised framework for adapting
dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without first constructing a large manually labeled training corpus. A lightweight model trained on general research literature generates candidate
dataset mentions from unlabeled domain documents, which a frontier large
language model (LLM) reviews in context, validating or rejecting candidates and correcting their extraction boundaries. The resulting annotations are supplemented with targeted synthetic and contrastive examples and used to fine-tune the lightweight model for large-scale extraction. We evaluate the resulting model on an independent gold-standard
benchmark of 1,706 text passages spanning research, humanitarian, and operational documents. Across the full
benchmark, the model achieves 74.1% precision and 70.5% recall at the mention level; among passages containing
dataset references, precision reaches 89.5%. At the passage level, the model achieves 88.2% accuracy and 88.6% specificity in distinguishing passages with
dataset references from those without them. These results demonstrate a practical approach for constructing domain-specific supervision when labeled data are limited, and provide a technical foundation for larger-scale analysis of data use and potential gaps in the displacement data landscape.
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All authors Rafael Macalaba, Aivin V. Solatorio, Patrick Michael Brock, Olivier Dupriez
Data Structures and Algorithms
A Non-constant Lower Bound for Grammar-Based Compression with Greedy
Danny Hucke
We prove a lower bound of {\Omega}(log n/ log log n) on the approximation ratio of the global grammar-based compression
algorithm Greedy. To our knowledge, the previously best lower bound was a constant, and the existence of a nonconstant lower bound had remained open for more than twenty years. Our bound holds on an infinite family of words of length n, over alphabets of growing size, for every execution using left-to-right occurrence replacement and arbitrary tie-breaking. The lower bound is also formally verified in Lean 4.
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Artificial Intelligence
Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models
Reuben Vandeventer, David Imrem and David J. Wild
The prevailing assumption in applied machine learning is that progress on consequential quantitative decisions such as pricing risk, allocating capital, triaging patients, or containing a network intrusion will follow from progress in large
language models (LLMs). A
language model is trained on a representation of the world that was produced by human description; description is a lossy encoding of the quantitative record, and the loss is irreversible: no downstream model, at any scale, can recover from a description what the description did not encode. We formalize this as a property of the representation on which a model is trained rather than of the model capacity, and we identify three further properties that consequential settings demand of a model and that a
language substrate cannot supply by construction: reproducibility, lineage from every output back to the source records that produced. it, and calibrated uncertainty. We argue that these properties define a distinct model class, which we call the Large Quantitative Model (LQM).
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Robotics
RodForesight: A World Model Enhanced Diffusion Policy for Slender Rod Insertion
Chuanbo Yu, Mingyu Yue, Yan Lyu et al. (+2 more)
Slender rod insertion arises in precision manufacturing, where millimetre scale diameter and tight clearances demand accurate perception and control. Conventional peg-in-hole methods assume a rigid object whose tip pose is fixed relative to the gripper. This assumption breaks down for a high aspect ratio rod, which can bend during manipulation, making its tip motion dependent on the rod configuration, grasp, material properties, and contact. We present RodForesight, a learning framework that factorises the task into two stages: 1) coarse approaching, which uses visual servoing to map diverse initial configurations into a compact near hole hand-off region; and 2) predictive insertion, which performs fine
alignment and completes the insertion. It is worth noting that the two stages can be wrapped into an end-to-end design. During insertion, a
diffusion policy generates candidate action chunks, while an action conditioned world model predicts their effects on rod-hole
alignment. This pre-execution evaluation enables RodForesight to select the best action chunk based on predicted tilt and radial errors before execution. Experiments investigate the performance of different stages and the end-to-end setting, where RodForesight improves the success rate from 88.9% to 96.7%, compared to baseline methods such as
diffusion policy.
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All authors Chuanbo Yu, Mingyu Yue, Yan Lyu, Chuhan Song, Peng Wang
Artificial Intelligence
When Do Language Models Add Value? Relative Competence in Hybrid Event Forecasting
Aditi Tiwari, Aashrith Bandaru and Heng Ji
When a
forecasting system already has a prediction from a
market, crowd, or statistical model, when should it use a
language model? Standalone accuracy cannot answer this question because a less accurate model may still improve the existing forecast. We study this distinction through relative competence, separating a model's replacement advantage from its complementary value. Under Brier loss (squared error between predicted probabilities and binary outcomes), we characterize when model disagreement can correct external forecast errors and derive the population gain from regime-specific affine pooling. We introduce a competence gate that estimates source weights from resolved outcomes, shrinks regime-specific estimates toward a global solution, and chronologically recalibrates the pooled forecast. Across 4,974 resolved binary questions and four
language models, the best standalone model has 24.94% higher Brier loss than the calibrated external forecast, yet the gate improves that reference by 3.35% (95% CI, 1.80 to 4.90%). Model contributions vary across
forecasting regimes, with larger corrections in thinner
markets and
forecasting crowds and smaller corrections in higher-participation settings. The gate also shows no statistically detectable performance difference from more flexible contextual aggregators. These results demonstrate that standalone superiority is not necessary for complementary predictive value and that
language models should be evaluated relative to the forecasts already available.
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Numerical Analysis · Cross-listed from math.OC
$R$-Linear Convergence of Barzilai-Borwein Methods on Strictly Convex Quadratics via Closed-Cone Homogeneous Dynamics
Shutai Yang and Ya-xiang Yuan
We give an $R$-linear convergence proof for BB1, BB2, and fixed positive spectral-weight variants on finite-dimensional strictly convex quadratics. The delayed recurrence is written as a first-order system on the closed cone of compatible consecutive-gradient pairs. Its transition is continuous and positively homogeneous of degree one, including at finite termination. A compactness
theorem shows that pointwise convergence of every cone orbit implies a uniform finite-step contraction and hence a global $R$-linear estimate. By a realization lemma, every compatible state initiates a BB1 trajectory, so Raydan's
theorem yields pointwise stability on the entire cone. Finally, the transformation $g\mapsto W^{1/2}g$ conjugates every fixed positive spectral-weight rule to BB1. Consequently, BB1, BB2, and all such weighted rules have the same homogeneous growth radius.
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Computer Vision
Beyond Argmax: A Mechanistic Study of Semantic Retention in Frozen Foundation-Model Composition for Generalized Few-Shot 3D Segmentation
Silas Kwabla Gah and Ebenezer Owusu
Classical classifier-combination work distinguishes score-level fusion from hard decision-level voting. We revisit this distinction where independently pretrained, frozen foundation models are composed at inference time for generalized few-shot 3D
segmentation. We ask: how much useful semantic information is lost when heterogeneous sources are collapsed to a single class before they can interact? We answer with a same-input semantic-retention intervention. Dense RegionPLC and sparse cross-view SAM3 evidence, model weights, masks, geometry, vocabularies, and fusion rules are frozen; only the number of semantic alternatives retained before interaction is varied via a matched top-k ladder. On 156 held-out ScanNet200 scenes, top-1 reaches 28.47 harmonic-mean (HM) IoU while full distribution fusion reaches 34.87 HM (+6.40, 95% CI [+5.24,+7.64]). The pattern replicates on 50 ScanNet++ scenes: 23.02 vs. 26.50 HM (+3.48, 95% CI [+1.64,+5.93]). The conclusion is robust: full-distribution HM is stable across sparse-source weights 0.3–0.7; alternative operators (max,
geometric pooling) also outperform top-1; and a GroundingDINO–SAM2.1 source-replacement diagnostic shows monotonic HM increase from 14.77 to 18.75 with full retention. Calibration diagnostics reveal opposite miscalibration of the two sources, yet correcting calibration does not eliminate the retention advantage. Across
datasets and source stacks, most information is recovered by retaining a compact set of plausible alternatives. The contribution is a controlled diagnosis of premature semantic collapse as a repeatable information bottleneck in heterogeneous frozen-model composition.
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