Cryptography and Security
Static Detection of Post-Quantum Cryptographic Algorithms in Stripped Binaries for Digital Forensic Examination and Migration Assurance
Muhammad Shaheer Bin Junaid
Identifying which post-
quantum algorithm a compiled binary implements is a core problem in binary analysis, and it becomes acute once the binary has been stripped, statically linked, and optimised, since the symbols, library dependencies, and runtime behaviour that conventional discovery tools rely on are then gone. This paper presents Kestrel, a static analysis method that identifies an
algorithm from the number-theoretic transform constant tables its arithmetic depends on, and applies it to the standardised
lattice schemes ML-KEM and ML-DSA, for which no prior method could confirm, from a shipped binary alone, that a
quantum-vulnerable
algorithm had been replaced by its approved successor. The fingerprints Kestrel derives from public scheme parameters are localised by a normalisation-and-multiset-matching procedure; the false-positive probability is established analytically. Across four independent implementation lineages and all build transformations, including compiler-level obfuscation, Kestrel achieved recall of 128 of 128 with zero false positives. Applied to 6,224 binaries on a production Linux system, it disclosed twelve uncatalogued programs containing ML-KEM, among them the OpenSSH key-exchange program and the container-management stack, where post-
quantum code had entered production through the
language runtime without the awareness of the projects distributing them. Kestrel distinguishes genuine implementations from advertised claims not backed by the underlying code, attributes each detection to its originating codebase, and, in a forensic disk-image trial, recovered a detection from unallocated space after the deleted binary could no longer be reconstructed. It thus provides a practical basis for cryptographic migration assurance, compliance verification, software supply-chain inspection, and post-
quantum forensic examination.
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Machine Learning
Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events
Heyang Gong
Machine learning is usually formalized through samples, while the persistent individual to which multiple observed or possible events refer often remains implicit. We propose the unit as an explicit primitive at the level of task semantics. A learning task first declares a population of persistent referents and a sameness criterion; the realized value $u$ denotes the selected referent. Supervised learning is the main formal specialization. Its semantic object is a family of unit-conditioned response laws. Homogeneity is the special case in which those laws coincide; a sample-only conditional is silent as to whether the world is homogeneous or the observed law is only the marginal of a heterogeneous family. What is learned from data is a pair $(T_\phi,R_\theta)$: a tokenizer that produces a contextual unit
token and one shared response-law form that reads it. The structured class takes that form to be a simple relation in the
token; a linear predictor is the running instance. The
token is the learner-side representation through which the task-side unit affects prediction, while a learner specification that omits unit information is unit-insensitive; homogeneity remains a property of the world-side response family. When identity is unresolved, the world-side law mixes unit-conditioned targets, while the learner composes its shared form with a
token. A trusted resolver may fix the unit and supply a lookup
token; otherwise unit abduction forms a
token of the same type from factual evidence. Unlinked single-row observations can fail to distinguish a heterogeneous unit world from a homogeneous pooled world; trusted same-unit pairs separate a restricted witness. The formal results concern this supervised specialization.
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Human-Computer Interaction
Teaching Geometric Proof with Tech: Pitfalls and Possibilities
Hwei-Shin Harriman, Wode Ni, Yuchen Jin et al. (+2 more)
Geometric proof is a foundational yet challenging topic in mathematics, requiring students to integrate visual, logical, and notational skills. While technology has enhanced learning in other mathematical domains, its impact on
geometric proof remains limited. To investigate this gap, we interviewed 18 geometry teachers to establish the technical requirements of educational proof tools. These requirements inform our review of 33 commercial and research tools. Our findings reveal a critical mismatch: while teachers value certain digital tools for initial planning and exploration activities, they revert to pen-and-paper for formal proof because it supports diagram annotation and provides space for multiple approaches to proof-solving. Annotating the diagram is a key component of the proof-solving workflow that existing tools do not support. We propose four technical and human-centered design guidelines for educational proof tools to meet teacher needs at scale: integrating diagram and proof, generating problems and feedback automatically, supporting multiple proof formats, and reducing accidental complexity in the user experience.
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All authors Hwei-Shin Harriman, Wode Ni, Yuchen Jin, Dominik Moritz, Joshua Sunshine
Machine Learning
GRAPE: Gradient Refinement and Progress-Aware Exploitation for Query-Efficient High-Dimensional Bayesian Optimization
Richard Cornelius Suwandi and Feng Yin
Optimizing expensive, high-dimensional black-box functions remains a central challenge in modern machine learning and scientific discovery. While local
Bayesian optimization mitigates the curse of dimensionality, existing techniques often prioritize the probability of descent over the magnitude of progress. This leads to overly conservative steps that yield negligible improvement, wasting queries on directions that are nearly certain to descend but offer little decrease. We introduce Gradient Refinement and Progress-Aware Exploitation (GRAPE), a two-stage framework that first sharpens the local gradient posterior via a closed-form acquisition function, then selects update directions by maximizing the expected decrease conditional on descent. Theoretical analysis proves that this gradient refinement stage monotonically minimizes local uncertainty and that the progress-aware direction converges to true steepest descent as the posterior sharpens. Empirically, GRAPE demonstrates superior query efficiency across high-dimensional tasks: in black-box
adversarial attacks, it achieves an average 5.4$\times$ speedup over baselines, and on large
language model
prompt optimization tasks, it outperforms the second best method by a reduction of 3.8 log-units in the final average regret.
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Computation and Language
Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting
Weibin Cai and Reza Zafarani
Existing methods for improving
Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or serving optimization. However, RAG is an end-to-end system, and its bottleneck can shift between upstream reranking and downstream generation under different serving loads and reranking budgets.In this paper, we first empirically characterize this shifting-bottleneck behavior and show that upstream reranking can become the dominant bottleneck under high query rates or large reranking budgets. Reducing the reranking budget can relieve this bottleneck, but it may also drop supporting evidence and degrade recall. To address this problem, we propose \textbf{PACE} (Prioritized Adaptive Coverage of Evidence), a training-free framework that combines evidence frontloading with pressure-adaptive budgeting. PACE first reorders candidates by marginal evidence coverage, prioritizing documents that are query-relevant, complementary, and useful for forming multi-hop evidence chains. We show that this objective is monotone submodular, giving greedy selection a $(1-1/e)$ approximation guarantee. PACE then dynamically adjusts the reranking budget according to the relative pressure of the reranker and the LLM. Experiments on three multi-hop QA
datasets and online serving simulations show that PACE improves evidence recall, reduces p95 latency under ranking-heavy workloads. More importantly, the two components together reveal that less can be more: an evidence-dense top-ranked candidates enable higher final recall with fewer reranked documents.
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Machine Learning
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Yan Gao, Mohammad Naseri, Javier Fernandez-Marques et al. (+19 more)
Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Existing evaluations are often tied to custom infrastructure, released as incomplete research code, and conducted primarily in simulation, which limits portability and practical relevance. We present Flower Hub, a platform for publishing, discovering, and executing decentralized and
federated applications. We show how it enables reproducible benchmarking by packaging
benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows. We instantiate this approach with a multi-domain
benchmark suite spanning cross-silo and cross-device settings, and including tasks in medical imaging, financial tabular learning, legal instruction tuning, phishing URL detection, and audio tagging. We further demonstrate that the same benchmarking application can run across both simulation and deployment runtimes without changing the application code, enabling unified evaluation across varying learning environments. Beyond model quality, our
benchmark design supports system-aware reporting, including runtime and communication metrics. This work advances benchmarking in FL settings from ad hoc code artifacts towards portable, executable, and reusable
benchmark applications.
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All authors Yan Gao, Mohammad Naseri, Javier Fernandez-Marques, Dimitris Stripelis, Lorenzo Sani, Davide Eynard, Fan Zhang, Hong Jia, Ting Dang, D. B. Emerson, Fatemeh Tavakoli, Ole Werger, Lars Wulfert, Petros Demetrakopoulos, Sofia Tsekeridou, InSeo Song, KangYoon Lee, Honghao Li, Lingjuan Lyu, John P Dickerson, Daniel Janes Beutel, Nicholas D. Lane
Computer Vision · Cross-listed from eess.IV
Improving Cross-Site Whole-Heart Segmentation
Tanish Mudaliar, Justin Li, Daniel Lin et al. (+4 more)
Whole-heart
segmentation from CT and MRI is essential for quantitative cardiac image analysis, but remains challenging under multi-center and multi-modality distribution shift. In the CARE whole-heart
segmentation task, models must generalize from limited labeled sites to unseen acquisition distributions, where variation in spacing, intensity, reconstruction texture, and anatomy can degrade out-of-distribution performance. We propose a modality-routed 3D cardiac
segmentation pipeline that combines TotalSegmentator-initialized nnU-Netv2 models with site-characterized, label-preserving appearance augmentation. We first characterize the available sites using measurable image properties and use this analysis to motivate candidate data-space generalization routes. The final retained recipe applies Bias Field + Bezier appearance augmentation, combining smooth spatial intensity perturbation with nonlinear intensity remapping, followed by lightweight class-wise largest-connected-component cleanup. On the primary held-out-site validation splits, the final configuration improves CT mean Dice from 0.8350 to 0.9135 and MRI mean Dice from 0.7695 to 0.7830, while also reducing HD95. These results suggest that site-motivated appearance augmentation is a practical strategy for improving cross-site robustness in limited-data whole-heart
segmentation. Our code can be found in https://github.com/Purdue-M2/Improving-Cross-Site-Whole-Heart-
Segmentation
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All authors Tanish Mudaliar, Justin Li, Daniel Lin, Julianna Vo, Kaitao Liao, Xin Wang, Shu Hu
Information Retrieval
SWIM: Step-Wise Integrated Measure for Session-supervised List Evaluation in Generative Re-ranking
Yuanhao Pu, Chenghao Zhang, Chao Feng et al. (+6 more)
Modern industrial recommender systems have increasingly adopted the Generator-Evaluator (G-E) framework for the re-ranking stage. Within this paradigm, the generator produces candidate item lists from a pool filtered by upstream
retrieval and ranking modules, while the evaluator scores these lists and selects the highest-scoring one for final exposure per request. However, on sequential platforms (e.g., short-
video apps), users consume items continuously, ignoring artificial list boundaries. Conventional evaluators score lists by aggregating point-wise values, implicitly assuming exposure independence. This fails to capture critical session-level dynamics, such as contextual dependencies, user continuation, and diminishing marginal utility from repetitive content. To bridge this gap, we propose SWIM (Step-Wise Integrated Measure), a list-level evaluator that models user behaviors as a finite-horizon prefix session-level survival process. SWIM estimates the prefix-conditioned contribution of the current list to the session-level objective by factorizing it into a recursive survival distribution and reached-position conditional rewards. Leveraging a causally-masked
Transformer, SWIM efficiently estimates continuation probabilities and utilities in parallel, satisfying strict industrial latency constraints. Extensive experiments demonstrate that SWIM significantly outperforms baselines in listwise reranking tasks, yielding substantial improvements in overall recommendation engagement.
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All authors Yuanhao Pu, Chenghao Zhang, Chao Feng, Xunyong Yang, Xiang Li, Yongqi Liu, Defu Lian, Kaiqiao Zhan, Kun Gai
Robotics
ROS2 Connect: A new ROS2 over WAN Solution
Daniel Schott, Lakshminarasimhan Srinivasan, Christian Herrmann et al. (+1 more)
The
Robot Operating System 2 (ROS2) has become a widely adopted framework for the development of distributed robotic systems. However, its communication architecture, based on DDS and RTPS, relies on multicast discovery mechanisms that are typically unavailable in wide-area network (WAN) environments, making remote operation challenging. This work presents ROS2 Connect, a WebSocket-based communication framework that enables transparent and secure ROS2 interaction across routed networks without requiring modifications to network infrastructure or DDS configurations. The proposed client-server architecture supports bidirectional exchange of topics, services, actions, and system data while integrating authentication and access control mechanisms. Experimental evaluation over a real WAN connection demonstrates significantly lower latency, higher stability, and improved scalability compared to existing solutions, including DDS Router, rosbridge and Zenoh. Initial results show that ROS2 Connect provides a reliable foundation for teleoperation and distributed robotics applications over wide-area networks.
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All authors Daniel Schott, Lakshminarasimhan Srinivasan, Christian Herrmann, Andreas Nüchter
Artificial Intelligence
Towards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning
Xuzhong Wang, Maiqi Jiang, Tejal Nair et al. (+3 more)
Large
Language Models (LLMs) are powerful but limited by static parametric knowledge that becomes outdated once pretraining ends. Knowledge editing addresses this problem by updating model behavior on target facts without full retraining. In particular, in-context knowledge editing has gained
attention because it is training-free and readily applicable to black-box LLMs. Recent reinforcement learning (RL)-based approaches improve over fixed
retrieval strategies by adapting
prompt construction to the quantity-quality trade-off. Despite initial success, they fail to model the
prompt as a structured entity under the distinct and often competing objectives of reliability, generality, and specificity. Previous methods largely optimize a single objective and make decisions over only part of the
prompt construction process, thereby overlooking both the balance of different objectives and the global organization of demonstrations. We propose Multi-Objective In-context Knowledge Editing (MO-IKE), a multi-objective RL
algorithm that formulates
prompt construction for in-context knowledge editing as a Constrained Markov Decision Process. MO-IKE trains a dynamic retriever to optimize competing objectives in knowledge editing, enabling more balanced and globally coherent
prompt construction. On Llama-3.2, MO-IKE improves edit success (reliability) from 85.0% to 92.0%, paraphrase consistency (generality) from 77% to 79%, while increasing retention rate (specificity) by 23.0% compared to prior RL-based methods.
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All authors Xuzhong Wang, Maiqi Jiang, Tejal Nair, Girija Bhusal, Yanfu Zhang, Haipeng Chen
Discrete Mathematics
Parallelizable Gradient-Based Optimization For Multi-Objective MaxCut
Jingjuan Huang, Cheng-Han Huang, Alvaro Velasquez et al. (+3 more)
Multi-objective combinatorial optimization arises in a wide range of problems and applications, including the canonical multi-objective MaxCut problem. Differentiable single-instance quadratic methods have recently achieved remarkable performance in single-objective combinatorial optimization. In this paper, we develop a differentiable framework for multi-objective MaxCut by combining an adjacency-based quadratic formulation with linear scalarization, thereby reducing the problem to a preference-conditioned single-objective signed-weight MaxCut problem. Theoretically, we characterize projected gradient ascent (PGA) fixed points and their local dynamics under the signed-weight adjacency formulation. We further characterize how these fixed points depend on preferences and establish their connection to Pareto optimality. Computationally, unlike conventional heuristics and branch-and-bound methods, our approach is GPU-parallelizable and can therefore benefit from substantial performance speedups. We term our
algorithm Multi-objective QUadratic Combinatorial Optimization (MO-QUCO) and its parallelized variant pMO-QUCO. Empirically, we evaluate our methods on multi-layered graphs with different sizes and edge-weight distributions. Both our CPU-only and GPU-based
algorithms outperform state-of-the-art exact and heuristic methods in terms of wall-clock runtime and objective quality. Despite operating under different computational settings, MO-QUCO also outperforms the SOTA
quantum method.
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All authors Jingjuan Huang, Cheng-Han Huang, Alvaro Velasquez, Rongrong Wang, Jia Liu, Ismail Alkhouri
Artificial Intelligence
PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?
Ruoran Xu, Wending Gao, Liyunfeng Chen et al. (+5 more)
Understanding how (
multimodal) large
language models perform on physics problems requires
benchmarks that reflect the difficulty and breadth of expert-level physical
reasoning. Existing physics
benchmarks remain limited in the following two important ways: (1) short of high-difficulty
datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current
datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present PhysElite, a large-scale bilingual
multimodal benchmark for Olympiad-level physics
reasoning. PhysElite contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese-English solution derivations, and the final answer. We
benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the
reasoning chain. Our
datasets are released at https://github.com/PremiLab-Math/PhysElite.
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All authors Ruoran Xu, Wending Gao, Liyunfeng Chen, Aixin Shi, Haoyu Cheng, Zixiang Fang, Yiqiang Zou, Qiufeng Wang
Machine Learning
Understanding the Energy Scaling of Large Language Model Inference Across Context Lengths and Attention Architectures
Molka Chkir, Syed Muhammad Danish, Jos Höll et al. (+1 more)
The growing adoption of large
language models (LLMs) has raised increasing concerns about the energy consumption and environmental impact of inference. This paper presents a systematic empirical study of decode-phase energy consumption across representative open-source LLMs employing Multi-Head
Attention (MHA), Grouped Query
Attention (GQA), and Grouped Query
Attention with Sliding Window
Attention (SWA) to characterize how
attention architecture influences decode-phase energy consumption under varying inference workloads. We evaluate four models across different context lengths, batch sizes, and generation workloads while measuring GPU energy using NVIDIA hardware counters. We examine the effects of context length,
attention mechanism, Key-Value (KV) cache growth, and batching on decode-phase energy consumption. Results show that
attention mechanism is the primary factor governing how decode energy scales with context length. MHA models exhibit substantially steeper energy growth than GQA models, whereas GQA with SWA maintains nearly constant energy consumption. We further show that model size primarily determines absolute energy consumption, while batching reduces both energy per generated
token and request latency by up to 87%. These findings provide practical guidance for selecting energy-efficient LLM architectures and inference configurations.
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All authors Molka Chkir, Syed Muhammad Danish, Jos Höll, Arghavan Asad
Systems and Control
Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration
Hoang T. Nguyen, Shaohui Liu, Reetam Sen Biswas et al. (+4 more)
The proliferation of distributed energy resources (DERs) in distribution grids enables the active coordination of these assets to reduce costs and enable cleaner operations. Realizing this potential requires solving multiphase AC optimal power flow (AC-OPF) quickly across varying loads, DER availabilities, and
topology reconfigurations, at much greater speed and scale than conventional nonlinear solvers. Learning-based surrogates can offer millisecond inference, yet existing methods target largely balanced transmission systems and do not scale to the multiphase, unbalanced, and reconfigurable nature of distribution feeders at utility scale. We present the Penalty + Sequential Linearized Feasibility Seeking (SLFS)
algorithm, a self-supervised learning framework for multiphase distribution AC-OPF under switch-induced
topology changes. Penalty+SLFS requires no labeled optimal solutions and trains directly from the AC-OPF objective and constraints through a differentiable fixed-point power flow solver, avoiding expensive label generation and admitting robust training procedures.
Topology changes are handled efficiently using Sherman-Morrison-Woodbury updates of the admittance-
matrix inverse, while an M-step Jacobian approximation accelerates differentiation through the power flow solver. At inference, SLFS repairs any infeasible predictions, providing feasibility guarantees with low computational overhead. On IEEE feeders ranging from 13 to 8,500 nodes, Penalty+SLFS achieves negligible optimality gaps and near-zero constraint violations, delivers up to three orders of magnitude speedups over IPOPT, and remains robust under large distributional shifts, demonstrating a viable path toward real-time,
topology-aware AC-OPF for large-scale distribution grids.
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All authors Hoang T. Nguyen, Shaohui Liu, Reetam Sen Biswas, Varsha Pendyala, Nurali Virani, Deepjyoti Deka, Priya L. Donti
Artificial Intelligence
Auto-Policy, not Auto-Skill: Compiled Agent Skills for the Physical World
Zhonghao Zhan and Hamed Haddadi
Self-evolving Skill harnesses (AutoSkills, Hermes
Agent) generate more advisory orchestration automatically; their reported gains are efficiency, not safety. This misses the actual gap: a Skill describes how an
agent should behave; a Policy decides which behavior is allowed to become an action. Today's format covers the first with markdown and scripts; the second is left to the model. Generating more Skills scales the gap, not the safety, especially when a wrong invocation can unlock a door or move money. Two adjacent attacks are documented: malicious skills compromising cloud software, and jailbroken LLM-controlled
robots causing physical harm. Their intersection, malicious
agent skills causing physical harm, follows directly but has not been reported. We name this class Borrowed Authority: Skills format gives the receiving
agent no typed way to reject an inter-
agent permission claim, so a malicious or misused Skill can drive actuation by attaching one. We propose Edge Skillguard, a typed authority layer that lives inside the Skill artifact rather than between tools as workflow engines do, with guards over world state and sensor evidence. On a live edge control-plane testbed, the guards reject 60/60 borrowed-authority requests across five attack variants without blocking benign requests, and the result holds at 5x scale and across hosts over a Tailscale mesh. These results suggest that high-risk Skills should co-package typed invocation policy with procedural knowledge, so that physical actions depend on machine-checkable evidence rather than peer-
agent claims.
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Computation and Language
Apples to Apples? Towards Comparable Crosslingual Language Model Evaluation
Xiulin Yang, Ethan Gotlieb Wilcox and Catherine Arnett
Crosslingual evaluation of
language models that enables fair comparisons remains a fundamental challenge in
multilingual NLP. Existing studies adopt a variety of downstream tasks and intrinsic metrics with different theoretical justifications, yet there has been little empirical investigation into whether these approaches yield meaningful crosslingual conclusions. We systematically examine crosslingual evaluation approaches using controlled monolingual
language models trained on parallel data with varying tokenizer vocabulary sizes and model sizes, and further validate our findings on
multilingual LLMs. We further discuss challenges in achieving comparable downstream evaluation across
languages. Our results show that several widely used normalized metrics introduce crosslinguistic biases rooted in tokenization, encoding, and orthographic differences. In contrast, sentence-level negative log-likelihood computed over semantically equivalent sequences provides more meaningful and consistent crosslingual comparisons.
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Machine Learning
The Von-Neumann State-Space Transformer for neural decoding
Morteza Sarafyazd
Cortical computation is strikingly low-dimensional: a handful of latent variables, carried in a neural population's activity, steer the higher-dimensional responses of individual
neurons. Our aim is sample efficiency-models that decode well from limited data and at small parameter budgets. In a standard
Transformer layer, the feed-forward block applies the same operator to every
token. We suggest a von-Neumann inspired hypothesis of efficient computation as an alternative for neural decoding: a controller decodes an instruction and then executes a
token-specific operator; the usual realization-a soft mixture of experts-only blends their outputs, not operators. We introduce a von-Neumann State-Space
Transformer (VN-SST), a
memory-augmented
Transformer whose feed-forward block is a low-rank instruction bank: a shared base operator plus a small set of learned low-rank instructions, from which a per-
token code synthesizes the weight
matrix actually used at that
token. The code is read from a low- dimensional projection of a carried state-space
memory, so a slow latent trajectory acts as an instruction pointer-mirroring how low-dimensional dynamics may route cortical computation. On three motor-cortex neural-decoding
benchmarks, VN-SST is far more data-efficient than a modern
Transformer, each jointly predicting spikes and decoding behavior. This model wins by a wide margin on the scarcest
benchmark, leads on the other two, and turns longer context into rising rather than falling accuracy. We evaluated that the network compresses a large instruction bank to a few bits per
token, so program capacity acts as a control channel, not an accuracy lever. The same model is also more parameter-efficient on two small text
benchmarks used for
language modeling (LLMs), suggesting a generic mechanism.
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Distributed Computing
Probabilistic Performance Analysis of Parallel Signature Search Strategies in Multi-Level Tree Networks
Jingwei Li and Thomas G. Robertazzi
Hierarchical distributed search, locating a data pattern, or signature, across a tree-structured collection of files, underlies distributed index traversal, deep packet inspection and sequence
alignment. A practitioner must decide how much parallelism to employ: scan each layer sequentially, fan out within subtrees, or launch the whole tree at once. Existing analyses answer this only partially: they characterize every node by the statistics of a signature-holding file and, for multi-signature files, need quantities revealed only at run time. We develop a probabilistic framework predicting the completion time of five search strategies, spanning sequential to full-tree parallelism, before any file is read. Node scan times are modeled as a mixture over signature presence, layer times as order statistics, and parallel subtree scans by extreme-value arguments; when signature counts are known, occupancy under capacity constraints is treated by generating functions. Each performance formula carries an exactness label: exact (or exact-in-regime), plug-in, asymptotic or bound, with each approximation quantified against Monte Carlo simulation and its regime identified. A multicore prototype reproduces the coarse separation between full-tree, layer- and subtree-level parallelism, but shows that synchronization overhead can erase the predicted separation between close strategies. The framework delivers a priori completion-time predictions with explicit accuracy regimes and negligible computational cost, the design example evaluated in under a millisecond; these timing models can support subsequent resource-cost optimization.
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Computation and Language
MTDiag: A Multi-Turn Diagnostic Dataset Towards Clinically Meaningful LLM Evaluation
Pia Chouayfati, Alexander M. Fichtl, Miriam Anschütz et al. (+2 more)
Clinical diagnosis is fundamentally interactive and incremental, yet the dominant paradigm for evaluating Large
Language Models (LLMs) in medicine remains static QA
benchmarks or template-based dialogues. These
benchmarks say little about whether a model can serve as a diagnostic
agent in a dynamic clinical encounter, with LLMs showing significant accuracy and reliability degradation in multi-turn settings. To address this issue, we present MTDiag, a large multi-turn diagnostic dialogue
dataset constructed from three heterogeneous sources: DDXPlus, MIMIC-IV, and published case reports (AJCR), covering common ED presentations as well as long-tail rare and atypical conditions. All cases are normalized into a canonical schema anchored in the most comprehensive and widely-adopted medical knowledge bases (UMLS concept identifiers, with ICD-10 diagnosis codes). We release the schema, a UserLM-8B-based utterance-generation pipeline, and the physician-validated
dataset that converts structured clinical evidence into natural-
language utterances. Importantly, we introduce and motivate clinical knowledge-grounded metrics for evaluating LLMs as diagnostic
agents, beyond diagnostic accuracy, for the task of multi-turn differential diagnosis.
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All authors Pia Chouayfati, Alexander M. Fichtl, Miriam Anschütz, George Doumat, Georg Groh
Machine Learning
The Frame Kernel Method for Multiscale Operator Learning
Branden Frieden, Ryan Whitehead, M. Keith Ballard et al. (+2 more)
We present a natively multiscale operator learning method for the surrogate modeling of (numerical solvers for) multiscale partial differential equations (PDEs). The primary novelty of our method lies in a novel multiscale kernel frame function approximation technique. Leveraging this new kernel frame technique, we cast the operator learning problem as one of learning frame coefficients of output functions as a function of frame coefficients of input functions. The generalization step then automatically allows for a multiscale decomposition of the output functions. Our method is applicable to both tensor-product grids and point clouds. We present interpolation proofs, error estimates, and numerical convergence rates for our frame approximation. We the demonstrate the applicability of our method for the surrogate modeling of inherently multiscale PDEs. The new multiscale frame kernel method is significantly more accurate than popular neural operators on challenging problems from the literature, while simultaneously admitting an a posteriori multiscale decomposition upon generalization.
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All authors Branden Frieden, Ryan Whitehead, M. Keith Ballard, Robert M. Kirby, Varun Shankar
Machine Learning
NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates
Muyan Anna Li, Manikandan Ravikiran and Aditi Gautam
Time series
forecasting models are widely used in high-stakes settings, yet their predictions remain difficult to interpret because existing post-hoc methods often ignore temporal dependence and fail to provide horizon-specific explanations. We propose a model-agnostic explainability framework that explains
forecasting predictions by attributing each forecast horizon to temporally relevant historical lags. The framework models
forecasting as a latent trajectory and introduces semantic flow to quantify how information evolves across time in the model's internal representations. By aggregating semantic flow, it constructs a lag-horizon attribution
matrix that captures horizon-resolved temporal influence. To improve explainability, we further generate structure-preserving perturbations and fit sparse local surrogate models, producing human-readable and temporally coherent explanations. We evaluate the method using faithfulness and stability diagnostics across multiple
benchmark datasets. Results show that the semantic-flow variant achieves competitive or superior faithfulness compared to standard post-hoc baselines, while being substantially more computationally efficient. Stability analysis further demonstrates that the explanations are robust and identifies regimes where interpretation should be applied with caution.
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Machine Learning
DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task Learning
Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi et al. (+1 more)
Digital mobility outcomes (DMOs) derived from wearable sensors characterise mobility in daily life and offer a promising means of monitoring disease progression. However, existing DMO studies have typically focused on either a single disease or a single visit. To the best of our knowledge, we are the first to define and study the practical problem of cross-disease longitudinal DMO modelling. We argue that this problem should satisfy at least two requirements. First, the temporal progression of DMOs should be modelled within each disease, as mobility-limiting diseases evolve over time. Second, multiple mobility-limiting diseases should be modelled jointly, as different diseases affect different aspects of human mobility. To address this problem, we propose DeMMO, an interpretable framework for longitudinal, multi-disease, and multi-outcome learning. Its central technical contribution is an interpretable cross-disease and cross-outcome relation-learning mechanism that infers signed relations directly from learned longitudinal DMO-outcome mappings, thereby enabling selective information sharing across cohorts without requiring paired participants. We evaluate DeMMO on the recently released, large-scale, multicentre Mobilise-D
dataset, which presents a challenging modelling setting involving longitudinal observations, multiple clinical outcomes, and four participant-disjoint cohorts representing distinct mobility-limiting diseases. Compared with eight strong structural longitudinal and deep-regression baselines, DeMMO achieves the best overall predictive performance and outperforms the baselines for most individual outcomes. Stability selection further identifies reliable longitudinal DMO patterns that can inform subsequent clinical validation and disease monitoring. The implementation code is available at https://github.com/menghui-zhou/DeMMO.
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All authors Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi, Po Yang
Computational Engineering · Cross-listed from physics.soc-ph
The complex relationship between anti-immigrant sentiment and exposure in the Netherlands
Benedikt Meylahn, Tommaso Giommoni, Mike Lees et al. (+1 more)
We study the relationship between exposure to immigrants and right-wing votes used as a proxy for anti-immigrant sentiment in the Netherlands. Exposure to immigrants is measured using a novel indicator, adapted from the residential segregation literature, applied to grid data available for the entire country at the aggregate level. Using the proxy of election results, we are able to study the entire voting population instead of a limited subset of panel respondents. In our cross-sectional study of the 2023 national election, we find that municipalities and neighbourhoods with greater exposure have less anti-immigrant sentiment. However, looking at the increase in right-wing vote share from the 2021 to the 2023 national elections, we find evidence of an “overexposure effect”, as this relationship reverses, and the areas that saw significant increases in migrants subsequently experienced an increase in right-wing votes. Furthermore, we see that at high levels of exposure, wealth (and inequality) plays an important role in the voting dynamics, indicating that relative deprivation theory may play a role.
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All authors Benedikt Meylahn, Tommaso Giommoni, Mike Lees, Alessandro Nai
Computation and Language
HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench
Matthew Flathers, Phuong Anh Nguyen, Jill Noorily et al. (+7 more)
General-purpose health
benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, making domain-specific performance hard to isolate. Mental health is of acute public-health concern as millions of people turn to LLMs for psychological support, and most existing evaluations are bespoke academic
benchmarks that are difficult to integrate into developer workflows. We introduce HealthBench-Psych and HealthBench-Psych-Hard. We screened HealthBench's 5,000 physician-rubric conversations for mental-health relevance with a transparent LLM-applied rubric, then validated the subset through two rounds of blinded clinician review with concealed known-exclude controls, yielding 610 conversations (12.2% of the corpus). Evaluating 20 frontier and open models under a cross-vendor panel of three LLM judges, we find a statistically tied frontier cluster, measurable refusal behavior in two models, and near-identical rankings across judges ($\tau \ge 0.92$). We release the subset, pipeline, model responses, grades, and analysis code as a reusable resource.
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All authors Matthew Flathers, Phuong Anh Nguyen, Jill Noorily, Julian Herpertz, Meiting Chen, Jasreen Multani, Samuel Powell, Mason Granof, Mark Kalinch, John Torous
Computer Vision
SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs
Ali Bahri, Hang Li, Hongliang Li et al. (+1 more)
Depth
pruning removes entire
Transformer blocks to reduce the inference cost of large
language models, but disrupts the hidden-state distributions expected by downstream layers, leading to significant accuracy loss. We introduce SHIFT-LLM, a training-free post-
pruning correction framework that inserts a Linear Residual Adapter (LRA) at each
pruning site. Each LRA preserves the identity pathway of the original residual block and adds a lightweight affine residual correction. This correction is calibrated via closed-form least-squares regression on a small held-out set, without gradient computation, to approximate the missing residual update produced by the pruned block. Together with the preserved identity pathway, the resulting LRA output approximates the hidden state produced by the original block, thereby mitigating the distributional mismatch introduced by layer removal while avoiding the expensive
attention and feed-forward computations of the removed blocks. The resulting LRAs support low-rank factorization and exact merging across consecutive pruned layers for additional compression, and combine naturally with parameter-efficient fine-tuning for further recovery beyond fine-tuning the pruned model alone. Experiments on five model families, six layer-selection criteria, and seven zero-shot
benchmarks show that SHIFT-LLM consistently recovers accuracy lost to depth
pruning across most configurations, achieving gains up to +15.7 points on Llama-3.1-8B-Instruct while requiring only a few hundred calibration samples and no gradient computation.
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All authors Ali Bahri, Hang Li, Hongliang Li, Zhitang Chen
Artificial Intelligence
SimVerity: When Does Simulated Agent Success Survive Physical Deployment?
Zhonghao Zhan, Yefan Zhang, Krinos Li et al. (+1 more)
Simulated evaluation is widely used to
benchmark AI
agents, yet how much evidence a simulated pass provides about physical deployment has not been systematically quantified. We present SimVerity, a verdict-transfer assurance framework: it replays matched scenarios on target smart home deployments and cross-validates
agent execution against independently qualified physical witnesses. Our evaluation highlights that deployment success is a real-world process, not a static property in simulation: completion, reported state, observable effect, and settled outcome diverged within the same execution. Although an advanced simulator cleared all 240 light trials, a camera caught 42 sub-second failures invisible to settled-state checks. False clearance was predictable: a risk profile learned from measured trials and locked before evaluation predicted failures on a path it never physically measured, beating a property-blind baseline in all eleven held-out sessions across two cohorts.
Agent auditability was also measurable: switching one
agent loop's model-client/serving configuration raised its scenario-matching share from 52-88% to 100%. Finally, a second qualified simulator added no independent cross-check: it never disagreed on any overlapping case, and only physical measurement exposed their shared blind spots. SimVerity turns verdict transfer into an explicit decision: clear, abstain, or escalate before deployment.
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All authors Zhonghao Zhan, Yefan Zhang, Krinos Li, Hamed Haddadi
Computers and Society
The AI Adaptation Gap in Higher Education: Students, Faculty, and Administrative Staff
Yuriy S. Braun and Salavat M. Khafizov
The purpose of this study was to analyze patterns of artificial intelligence (AI) use and attitudes toward AI among students, faculty, and administrative staff at a large university specializing in teacher education. The analytical sample comprised 1809 students, 250 faculty members, and 62 administrative staff members (N = 2121). Three role-adapted 75-item questionnaires covered the frequency and contexts of AI use, perceived usefulness, trust and control, academic integrity concerns, responsible-use norms, institutional policy clarity, and perceived improvement in output quality. Data analysis included descriptive statistics, Welch group comparisons, pooled ordinary least squares (OLS) models, reliability and dimensionality checks for observed indices, and exploratory student-only K-means clustering. The results revealed a pronounced AI adaptation gap across university groups. Students reported higher current AI-use intensity and perceived usefulness than faculty and administrative staff, whereas faculty and administrative staff reported stronger academic integrity concerns and greater endorsement of responsible-use norms. In the pooled OLS trust model, perceived usefulness had the strongest standardized positive association with trust in AI (beta = 0.402); institutional policy clarity also had a positive but weaker association (beta = 0.223). Students reported higher perceived policy clarity than faculty, while neither group differed significantly from administrative staff. Exploratory clustering indicated heterogeneity among students in experience, competence, usefulness, trust, and control, but did not establish a latent typology across university groups. The cross-sectional, self-reported data show associations and group differences rather than causal effects on learning or objective outcomes.
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Hardware Architecture
FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration
Geraldo F. Oliveira, Arash Tavakkol, Xiangyu Zhu et al. (+10 more)
LLM inference is increasingly constrained by accelerator
memory capacity rather than compute throughput. This constraint is especially acute in single-accelerator and small-node inference systems, where limited on-package
memory capacity restricts the size of deployable models. HBF is an emerging 3D-stacked NAND flash technology that provides multi-terabyte near-accelerator capacity, making it a promising capacity tier for storing LLM weights. However, existing HBF-based proposals face three adoption challenges: they (1) rely on coarse-grained static prefetching for LLM weights aiming to hide the microsecond-level read latency of the NAND flash device while maximizing HBF's read throughput, (2) expose NAND flash management tasks (e.g., refresh operations) to the accelerator-visible critical inference path, and (3) miss optimization opportunities to specialize and optimize the flash-management mechanisms to the workload behavior. Our goal is to design an efficient HBF substrate that integrates HBF as a
memory-capacity tier alongside HBM while addressing these three challenges. To this end, we propose FLINT, a workload-driven HBF substrate for capacity-scalable LLM inference. FLINT introduces three mechanisms: (1) a hardware burst-buffer controller that dynamically coalesces and pipelines HBF reads aiming to utilize existing NAND flash buffers while sustaining high HBF bandwidth, (2) a phantom-plane refresh mechanism, which removes refresh from the critical inference path by moving refresh-related NAND flash operations outside the read foreground back via low-cost resource duplication, and (3) a read-only FTL, which replaces SSD-class support for arbitrary writes with a compact table that translates logical weight bursts to physical HBF locations.
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All authors Geraldo F. Oliveira, Arash Tavakkol, Xiangyu Zhu, Ahmet Caner Yüzügüler, Vamanan Arulchelvan, Lukas Cavigelli, Renzo Andri, Mohammad Sadrosadati, Jia Xinglei, Onur Mutlu, Zhou Ke, Shai Bergman, Ji Zhang
Computation and Language
DataKernelBench: Can LLMs Optimize Database Queries on GPUs?
Gokul Karthik Kumar, Yotam Perlitz, Corey Lammie et al. (+2 more)
GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel
benchmarks focus on machine learning operators, leaving irregular, heterogeneous, data-movement-heavy database-style operators untested. We introduce DataKernelBench, which translates SQL into validated PyTorch TorchPlan programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair. Across ten proprietary and open-weight models on TPC-H SF10 with an H100 GPU, the strongest full-query CUDA configuration achieves $2.11\times$ speedup over the TorchPlan baseline at full pass rate. We find that higher-performing implementations commonly use kernel fusion and execution-strategy changes, stronger models benefit most from full-query specialization, and workload context matters more than hardware context. To handle data larger than GPU
memory, we extend TorchPlan with Dask-cuDF for on-demand partition loading on TPC-H SF100 with four H100 GPUs, achieving $2.54\times$ speedup. Project page: https://kerneldf.github.io/datakernelbench
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All authors Gokul Karthik Kumar, Yotam Perlitz, Corey Lammie, Andrea Giovannini, Katja Hose
Numerical Analysis · Cross-listed from math.FA
On exact discretization of the $L_2$-norm in the space spanned by the first $N$ Rademacher functions
Anna Kazakova
We study the exact discretization of the $L_2$-norm in the space spanned by the first $N$ Rademacher functions. It is shown that the sufficient number of nodes for discretization with the minimal number of nodes is equal to $N$ or $N+1$ and depends on the dimension $N$. The connection with Hadamard
matrices and the Hadamard
conjecture is demonstrated.
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