Systems and Control · Cross-listed from eess.SP
A Dry-Contact Ear-EEG System With Continuous Electrode-Skin Impedance Mismatch Monitoring for Motion Artifact Cancellation Using DRL Stimulus
Lohan Atapattu, Sajitha Madugalle, Imasha Nethmal et al. (+6 more)
Dry-contact ear-electroencephalography (Ear-
EEG) enables wearable neural monitoring. However, motion induced electrode-skin impedance (ESI) mismatches between electrodes can severely degrade signal quality. To the best of our knowledge, this paper presents the first proof-of-concept dry-contact EarEEG system that uses a driven-right-leg (DRL) stimulus for continuous ESI mismatch monitoring, enabling online adaptive motion artifact cancellation. A 1 kHz sinusoidal stimulus is injected through the DRL electrode. The resulting response to the injected carrier is separated from the
EEG using bandpass filtering and demodulation, and then used to extract the ESI mismatch information as the reference input for a normalized least-mean-square adaptive filter followed by a Hampel filtering stage. To evaluate artifact suppression and preservation of neural activity, alpha-band
EEG activity was analyzed involving four healthy participants performing head nodding, electrode tapping, and jaw clenching. The system achieved artifact power reductions of 6.5, 12.6, and 9.0 dB (77.6%, 92.8%, and 86.4%, respectively) while alpha-band modulation remained clearly observable after processing. This demonstrates the feasibility of DRL-stimulusbased ESI mismatch monitoring for motion artifact cancellation in wearable dry-contact Ear-
EEG.
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All authors Lohan Atapattu, Sajitha Madugalle, Imasha Nethmal, Erandee Jayathilaka, Avishka Herath, Kithmin Wickremasinghe, Simon L. Kappel, Nilan Udayanga, Chamira U. S. Edussooriya
Computer Vision
Video-Based Palm-Vein Authentication under Challenging Conditions
Xiaofeng Yan, Kechen Liu, Abhilash Venkatesh et al. (+3 more)
Palm-vein biometrics are increasingly used for secure, contactless authentication. Yet real-world deployment exposes them to surface noise (sweat, dirt), illumination and motion variation, and temperature-driven changes in vascular visibility, which remain underexplored for lack of data captured under such conditions. To study these effects, we introduce the Columbia University Palm-vein (CUP)
dataset, to our knowledge the first public
video-based palm-vein
dataset. CUP records every palm under four surface conditions (a clean baseline, warm, wet, and dirty) and pairs each subject with physiological and demographic metadata. On it we
benchmark twenty-one recognizers spanning static,
video, and multi-frame aggregation architectures. Models that verify reliably on clean palms lose most of their accuracy on dirty ones, and the mean equal error rate (EER) roughly quadruples. We recover much of that robustness along both axes of the capture. Temporally, a consensus over the few frames the sensor already returns cancels transient corruption; spatially, a test-time matcher that adds no learned parameters fuses the global cosine with a saliency-steered region-level optimal transport that routes the comparison around corrupted regions. The full design leads on every surface of CUP in EER, TAR@FAR=0.01, and Rank-1, at 4.3M parameters and 3.1 GFLOPs, a fraction of the
video models' cost. Attached to four frozen state-of-the-art backbones it cuts their mean EER by 29-37% without retraining, and on four public single-image
datasets the regional matching alone still helps. A preliminary audit across ten demographic and physiological traits finds two warm-condition gaps, along body water and gender, that survive multiple-comparison correction. CUP will be released for non-commercial research use at https://github.com/MobileX-CU/CUP_v1 upon publication.
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All authors Xiaofeng Yan, Kechen Liu, Abhilash Venkatesh, Cathy Zhang, Xia Zhou, Salvatore Stolfo
Cryptography and Security
CodePoisonRAG: Knowledge Poisoning Attacks on Retrieval-Augmented Code Generation
Varun Gadey, Ziad Marey and Alexandra Dmitrienko
Retrieval-Augmented Code Generation (RACG) improves LLM-based software development by retrieving external code artifacts, documentation, and patches, and incorporating them into the generation context. This reliance on external knowledge introduces a critical trust boundary: poisoned artifacts can influence generated code without modifying the underlying LLM. Prior work shows that selecting existing vulnerable examples can increase the general vulnerability rate of RACG outputs, but leaves open whether a black-box attacker can construct a single task-matched artifact that propagates an attacker-selected weakness. We introduce CodePoisonRAG, a targeted upstream knowledge-poisoning framework that transforms benign fixed-code entries into poisoned artifacts. Its attack chain combines CWE-specific Vulnerability Injection, which embeds a selected source-to-sink flow while retaining task
alignment, with Semantic Mislabeling, which adds false safety claims without repairing the vulnerable behavior. The attacker has no access to the victim's deployed knowledge base, retriever, re-ranker, generator,
prompt, or defense mechanism and injects at most one artifact per anticipated programming task. We construct 85 poisoned artifacts covering ten CWE classes across Java and C, yielding an aggregate corpus-poisoning ratio of 0.7%. Across three generators, all 85 artifacts appear among the Top-3 results for their corresponding queries, and CodePoisonRAG achieves attack success rates between 0.80 and 0.93. Against CodeGuarder, which injects vulnerability-specific security knowledge into the generation context, the attack retains success rates between 0.40 and 0.71. These results show that RACG poisoning extends beyond the incidental propagation of existing vulnerabilities to the targeted construction and propagation of attacker-selected weaknesses.
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Computation and Language
HyperStyler: Low-resource Authorship Style Transfer via Context-aware Style Navigation and Hypernetworks
Jongkyung Shin, Minguk Jeon, Chanwoo Park et al. (+1 more)
Low-resource authorship style transfer (LAST) aims to rewrite text into the style of an arbitrary target author using only a few reference examples while preserving the original meaning. Existing methods often struggle to achieve both high style fidelity and semantic preservation because they compress diverse references into a single static author
embedding, which averages out context-dependent stylistic variation, and rely on hidden representations for style control, which entangle style with content. We propose HyperStyler, a novel architecture that decouples LAST into style selection and style realization. Stylo-navigator predicts style coordinates by jointly modeling the source context and target-author references, and Stylo-hypernet realizes them via dynamic parameter modulation instead of hidden-state injection. Our experiments on Reddit, Blog, and News
datasets demonstrate that HyperStyler consistently outperforms prior methods including LLM-based approaches and generalizes robustly across domains. Notably, HyperStyler achieves superior performance with as few as 2.4% additional parameters over T5-large, while being over 1.8x faster than LLMs at inference.
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All authors Jongkyung Shin, Minguk Jeon, Chanwoo Park, Chiehyeon Lim
Computation and Language
From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution
Yuzhang Luo, Chenpeng Wang, Jianhui Chen et al. (+1 more)
Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which examples are selected and how they are modified. Influence functions (IF) estimate behavioral changes under infinitesimal reweighting, yet IF-selected examples often show limited advantages over random selection under conventional weight-based interventions. This raises the question of whether influential examples lack intervention value or whether reweighting fails to realize their behavioral leverage.We introduce influence-guided response rewriting, which uses IF to identify intervention targets and replaces their responses with behavior-aligned or behavior-opposed supervision while keeping instructions fixed. Across four open-weight LLMs, we compare rewriting and reweighting on the same influence-selected examples using epistemic abstention as our primary testbed. Response rewriting produces stronger, more persistent, and bidirectional behavioral shifts, while reweighting the same examples yields weak and inconsistent effects. Further analyses show that influence-selected examples provide greater rewriting leverage than alternative selectors, with changes remaining concentrated on target-relevant behaviors. The same qualitative contrast extends to safety refusal. These results distinguish the local reweighting effects captured by influence estimates from the broader intervention leverage of the examples they identify, motivating intervention-aware evaluation of TDA methods.
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All authors Yuzhang Luo, Chenpeng Wang, Jianhui Chen, Liangming Pan
Machine Learning
Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit
Wassim Tenachi, Yashar Hezaveh, Laurence Perreault Levasseur et al. (+1 more)
Tabular foundation models (TFMs) learn to fill in tables the way
language models fill in text — and tables are arguably the format in which most physical measurement arrives. Did they learn any physics in the process? They are
Bayesian by construction, so the question is what their prior contains. We probe it directly, evaluating four of them ($\texttt{TabPFN-3}$, $\texttt{TabICLv2}$, $\texttt{TabDPT}$ and $\texttt{Real-TabPFN-2.5}$) against six baselines on
datasets sampled from 316 physical equations, in and out of domain. TFMs dominate, out of the box and after tuning. But they struggle to represent noiseless deterministic mechanisms in their uncertainty, and show no sign of exploiting physical units: they interpolate physics without yet being able to act as physical models.
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All authors Wassim Tenachi, Yashar Hezaveh, Laurence Perreault Levasseur, Pierre-Luc Bacon
Data Structures and Algorithms
Finding a Shortest Vector and More in $2^{n/2+o(n)}$ Time using $q$-ary Coset Difference Tree
Minki Hhan
This paper presents a new randomized
algorithm for solving the exact shortest vector problem. For the $n$-dimensional
lattice $\mathcal L$, our
algorithm runs in time and space $2^{n/2+o(n)}$. Our
algorithm can be viewed as a $q$-ary analogue of the midpoint Hessian for an odd
prime $q$; more precisely, we use the fact that, for a shortest vector $v$, the gradient (rather than Hessian) of the periodic Gaussian function at $v/q$ is nearly proportional to $v$ (up to sign), even after aggregation over a relatively large random affine coset. We compute the relevant coset gradient along a chain of intermediate
lattices using a combinatorial procedure inspired by Wagner's generalized birthday
algorithm, yielding the $2^{n/2+o(n)}$ time and space complexity. A variant of the
algorithm solves the exact closest vector problem on every input $(y,\mathcal L)$ with a distance guarantee $\operatorname{dist}(y,\mathcal L)\le 1.039\lambda_1(\mathcal L)$ within the same time and space complexity. This guarantee holds for a random target and a random
lattice drawn according to the Haar-Siegel measure. Thus, this
algorithm solves a closest vector problem on such random instances in time and space $2^{n/2+o(n)}$.
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Artificial Intelligence
Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents
Vasileios Rizeakos, Georgios Paisios, Alexandros Machairas et al. (+2 more)
On-premise assistants can give factory workers conversational access to machine documentation, but models capable of the task rarely fit shop-floor hardware. We show that after structural compression and
retrieval-grounded adaptation, model size is no longer a reliable predictor of adapted answer quality: general capability falls almost linearly with parameter count, while judged
retrieval-augmented answer quality does not. We therefore treat deployment as a post-adaptation selection problem, committing one sub-network per device on judged answer quality and measured on-device throughput under a configurable general-capability floor and
memory budget; rules that optimize size, speed, or quality alone each give up capability or throughput. A weight-shared supernetwork trained with sandwich-style in-place
distillation keeps this selection inexpensive. In a manufacturing-manual case study, extraction costs 13.7 percent of the unpruned model's judged quality and
retrieval-grounded
distillation returns it to within 4.6 percent, recovering two thirds of the loss, and the same assistant runs across three heterogeneous edge tiers at 1.3 to 5 watts standby.
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All authors Vasileios Rizeakos, Georgios Paisios, Alexandros Machairas, Michael Birbas, Athanasios Bachoumis
Computation and Language
Untangling the Mechanisms of Misleading Context in Medical Question Answering
Robin Linzmayer and Noémie Elhadad
Large
language models now answer medical questions with expert-level performance. However, the context these systems act on can be misleading, and misleading context can corrupt a model's medical judgment. To understand how misleading context corrupts this judgment, we examine the model's susceptibility to the context, disclosure of it, mechanism of corrupted
reasoning, and monitorability of the decision. On the medical
reasoning subset of MedMisBench, a clinician-reviewed question-answering
benchmark of 8,627 questions, we inject two types of misleading context cues, fabricated evidence and a bare assertion. We test three
reasoning models, two that expose their full
reasoning trace and one frontier model that exposes only its response. All three are more susceptible to the assertion than to the fabricated evidence, adopting the asserted answer 10 to 27 points more often. The misleading cues are disclosed in 81 to 98% of traces but only 7 to 90% of responses, and the assertion is disclosed less often than evidence based cues. Resampling from
reasoning traces without disclosure shows the two cues corrupt
reasoning differently, evidence entering early and accumulating while the assertion redirects the conclusion near its end. An LLM monitor catches 78% of corrupted decisions at 5% false positives when reading an open model's trace with guidance, against at most 32% from any response. The misleading context that models are most susceptible to is disclosed least, and was caught reliably only from an open
reasoning trace, which frontier providers withhold.
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Software Engineering
The Import Tax: A Longitudinal Measurement of Startup Cost in the Python Ecosystem
Trinath Sai Subhash Reddy Pittala
Python programs pay for their imports at every process start, a cost that is invisible in steady-state
benchmarks but dominant for command-line tools, test workers, and serverless cold starts. Python 3.15 adds explicit lazy imports (PEP 810) on largely anecdotal evidence. We measure the import cost of the 500 most-downloaded PyPI packages, every quarterly release since mid-2021, under six CPython versions (3.9-3.14) on two platforms (Apple M5/macOS and Intel Xeon/Linux): all 66,816 package-version-interpreter
cells, plus the release histories of 65 distributions beneath shared namespaces, and targeted experiments that isolate individual effects. Import cost is heavily skewed (median 12.3 ms, 99th percentile 400 ms on macOS). For the median package, 68-79% of import time is spent importing the standard library, while third-party dependencies are the largest component in the slowest decile. The cold first import is almost entirely bytecode compilation on Linux, but on macOS 67% of the cold penalty remains when bytecode is precompiled: we trace it to the operating system's security assessment of each new native library, which every fresh environment pays again. A package's own import code grows by a median 4.0-5.0% per year; resolving dependencies as of each release raises the median trend of the total from 1.8% to 3.0-3.4% per year. CPython 3.14 executes package code faster than 3.9 but imports more and slower standard-library modules, a net 1.16x slowdown on macOS and 1.07x on Linux. What dependents actually import from a package costs a median 1.0x its top-level import but 10.9-13.4x at the 90th percentile. Global lazy mode makes import statements essentially free and breaks 10 packages. Harness, analysis code, and
dataset are public.
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Computer Vision
Multi-Tool Image Editing Attribution in Facial Forgery
Sheng Liu, Qiang Sheng, Danding Wang et al. (+3 more)
As generative AI tools become increasingly powerful and easy to use, people can easily edit portrait images with a
prompt, necessitating the task of image editing attribution, which predicts the involved editing tools from the given image. Existing attribution methods hold the single-tool assumption and can only attribute a specific editing tool, but struggle to handle the more complex and increasingly common multi-tool editing scenarios, where artifacts left by different editing tools are composite and overlapped. To address this gap, we explore Multi-Tool Image Editing Attribution (MIEA), which aims to identify multiple editing tools involved in a multi-tool edited facial image. To simulate the real-life editing operations on facial images, we then construct a new
dataset, MultiEdit, which contains 500k+ edited facial images and covers six types of editing tools that support face swapping (Deepfake) and various facial enhancements. Inspired by the findings from data analysis, we design DPEC, a multi-tool attribution method that can capture distinguishable, locality-aware editing tool traces from both spatial and frequency domains with the support of an error-based curriculum learning strategy. Experiments show \Method\ outperforms nine methods for facial images edited in at most five steps.
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All authors Sheng Liu, Qiang Sheng, Danding Wang, Yu Li, Chenming Zhou, Juan Cao
Artificial Intelligence
Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems
Yihang Chen, Yuxiang Chen, Yuxuan Huang et al. (+3 more)
Multi-
agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these systems lack a unified account of coordination,
memory improvement, and the role of external verification. We model orchestrator-worker interaction as a bilevel coordination game: under bounded coupling, the workers' local-update game is an approximate potential game whose
equilibrium slack is controlled by decomposition quality. We then analyse reflection as
stochastic movement over semantic
memory states. For free-form reflection, we derive a finite-time upper bound, prove worst-case tightness, and give a positive lower bound under a falsifiable persistent-harm condition. We further prove an information-theoretic impossibility result: no gate that observes only the generated transcript can improve uniformly over text-indistinguishable environments, whereas an environment-grounded gate can. Motivated by this separation, we introduce
Stochastic Reflective
Memory Ascent (SRMA), which accepts a candidate
memory only after a grounded evaluation risk strictly decreases. Under calibration and non-degenerate corrective mass, SRMA converges exactly, geometrically or polynomially; matching constructions show that both rate regimes are order-tight. We also provide confidence gating for
stochastic evaluation and re-anchoring guarantees for piecewise-stationary environments. Experiments instantiate these objects with environment-grounded metrics and test the predicted coordination and drift laws. On 500 SWE-bench instances, the complete Kimi-based system resolves 72.2% versus a 70.8% public mini-SWE-
agent reference. Code: https://github.com/YihangChen9/Bilevel-Coordinated-Reflection
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All authors Yihang Chen, Yuxiang Chen, Yuxuan Huang, Meng Fang, Weilin Luo, Jun Wang
Artificial Intelligence
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Jianlyu Chen, Yuyang Hu, Hongjin Qian et al. (+8 more)
Autonomous
agents are beginning to carry out machine-learning (ML) research end to end. These
agents combine a model backbone with a harness for planning, execution,
memory, and verification, but this architecture still leaves domain-specific know-how outside the
agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. That knowledge is not absent from the field. It appears in repositories and papers, but in forms written for human readers and too large to load during a task. Once distilled into compact, verified skills, this knowledge can be reused across tasks rather than rediscovered during each run. We present DisCo, a skill-powered research
agent that creates skills and uses them during research. Its
distillation runs in two complementary forms: task-agnostic, condensing the field's widely used repositories into reusable skills, and task-oriented, producing the skills a concrete task calls for. The former, applied across the open ecosystem, yields the AREX-Skill Library, with 5,000+ verified skills distilled from 1,000 widely used ML repositories and organized into 20 areas and 178 capability families. With the GPT-5.5 backbone, research harness, and downstream execution budget held fixed, the skill-equipped research
agent scores 134.3% higher on MLE-bench, 34.4% higher on PaperBench, 9.2% higher on FrontierCS, and 14.0% higher on PassNet than the same
agent without skills. These gains come from adding distilled operating context under that fixed setup.
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All authors Jianlyu Chen, Yuyang Hu, Hongjin Qian, Jiawei Liu, Wenqing Wei, Xiaolong Chen, Defu Lian, Zhicheng Dou, Chaozhuo Li, Qiwei Ye, Zheng Liu
Computer Vision
Balancing Frequencies and Pixels in Flow Matching
Lucas Degeorge, Paul Couairon, Arijit Ghosh et al. (+3 more)
Natural images follow a $1/f^2$ spectral distribution: most signal energy lies in the low spatial frequencies, while the perceptually important structures such as textures and edges occupy sparse high-frequency bands. Pixel-space reconstruction objectives, however, treat all spatial errors uniformly, causing low frequencies to dominate the optimization signal and delaying the learning of fine-scale details. In this work, we identify this objective-level spectral imbalance as a key inefficiency in training pixel-space flow models. To address it, we propose a Focal Log-Frequency Loss (f-loss), a spectrally balanced objective that equalizes the learning signal across frequencies, emphasizing high-frequency components that are otherwise underrepresented in pixel-space objectives. Building on this, we introduce a simple training strategy that combines frequency and pixel supervision: we first emphasize frequency-domain learning early to capture all frequencies, and then transition to standard pixel-space v-loss for spatial refinement. This balancing mitigates the low-frequency bias of pixel losses and aligns the training signal with the evolving needs of the model. Our approach is conceptually simple, requires no architectural changes, and acts as a drop-in replacement for flow matching losses. Across multiple model scales, it accelerates convergence by up to 40% while consistently improving FID and perceptual fidelity.
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All authors Lucas Degeorge, Paul Couairon, Arijit Ghosh, Alexei A. Efros, David Picard, Vicky Kalogeiton
Computer Vision
InceptionGS: Generative Bootstrapping for Large-Scale Gaussian Splatting under Unstructured View Sampling
Tianheng Lu, Guangyu Wang, Ruqi Huang et al. (+1 more)
Achieving truly immersive large-scale scene digitization necessitates consistent and visually pleasing rendering across all possible viewing perspectives. However, collecting multi-view images covering every fine detail of a large-scale scene is prohibitive due to scene complexity, capture cost, negligence, or accessibility constraints. As a result, the sampled views tend to be highly unstructured – the majority of the scene is well covered yet certain regions inevitably lack sufficient observations. Existing reconstruction based methods are vulnerable to view scarcity while generation based approaches suffer from generalization, controllability, and 3D consistency issues. To address this challenge, we propose InceptionGS, which bootstraps Gaussian splatting by subtly balancing reconstruction and generation. Starting from an initial Gaussian splatting, InceptionGS reasonably rethinks and repairs problematic regions caused by view scarcity while preserving the quality elsewhere, by softly incorporating scene- and view-adaptive generative priors. Extensive experiments on real-world large-scale scenes demonstrate the superiority and broad applicability of our approach in handling unstructured imagery and boosting high-fidelity Gaussian splatting. Please refer to the supplementary
video for better visual demonstrations.
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All authors Tianheng Lu, Guangyu Wang, Ruqi Huang, Lu Fang
Artificial Intelligence · Cross-listed from physics.chem-ph
HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design
Ge Sun, Gervasio Zaldivar, Yuan Tian et al. (+5 more)
Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made significant advances in materials discovery, the hierarchical structure of
polymers across multiple length scales makes them inherently difficult to represent in a unified and physically meaningful way. Here we introduce HiPoly, a
polymer-native AI framework that processes complete
polymer descriptions through a three-level hierarchical graph architecture built on the G2RINS representation. HiPoly encodes
stochastic inter-monomer connectivity, composition, and molecular weight directly within its architecture, using physically motivated design principles that mirror the multi-scale nature of polymeric systems. The framework establishes an end-to-end AI-driven workflow from experimental formulation data to property prediction, generative molecular design, and physics-based validation through molecular simulations, all unified by a single
polymer representation. We demonstrate state-of-the-art prediction accuracy for thermophysical properties of multi-component
polymer systems, with ablation studies confirming that each hierarchical design choice contributes independently to model performance. As an example, the generative design pathway is applied here to the discovery of sustainable alternatives to persistent fluorinated
polymers, where it is possible to identify and independently validate PFAS-free candidates with target surface-energy properties. This work demonstrates how
polymer-native AI can accelerate discovery by linking representation, prediction, and design across complex
polymer chemistries.
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All authors Ge Sun, Gervasio Zaldivar, Yuan Tian, Gustavo Perez Lemus, Juhae Park, Daryna Safarian, Ming Han, Juan J. de Pablo
Information Retrieval
Incremental Pooled LLM Evaluation for Cost-Effective Retrieval Model Selection
Max Nelson, Hanoz Bhathena, Aviral Joshi et al. (+1 more)
Selecting a
retrieval model for a production RAG system requires reliable comparative evaluation, but obtaining relevance judgments at scale is expensive and difficult to repeat as new candidate systems arrive. We study pooled LLM evaluation, in which an LLM judges the union of documents retrieved by the current set of candidate systems, and the pool is then expanded incrementally as new systems are introduced by judging only the new documents they contribute. These judgments are reused to evaluate all systems on a common basis. We validate this approach on four
retrieval benchmarks with 11 systems spanning dense, sparse, and hybrid configurations, and deploy it to compare 62
retrieval configurations for a financial news QA system. Pooled LLM rankings correlate strongly with gold-standard evaluation across
datasets, and 97% of pairwise system orderings are preserved once bootstrap uncertainty in the qrels is taken into account. In production, document overlap yields 65-80% judgment reuse and up to 4.9x lower evaluation cost, allowing teams to
benchmark new
retrieval candidates without re-judging previously assessed documents. These results suggest pooled LLM evaluation is a practical and cost-effective workflow for incremental
retrieval model selection in deployed systems.
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All authors Max Nelson, Hanoz Bhathena, Aviral Joshi, Saket Sharma
Cryptography and Security
SPADE: SPaT Attack Detection from the Connected Vehicle's Perspective
James Di Novo, Hany Ragab and Sylvain P. Leblanc
Signal Phase and Timing (SPaT) messages are a cornerstone of connected vehicle (CV) safety, enabling CVs to perceive and respond to intersection state through Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. The integrity of these messages is threatened by a range of application-layer attacks that can bypass conventional authentication when a roadside unit or peer vehicle is compromised. Existing intrusion detection research either defends the infrastructure side or targets V2V Basic Safety Message (BSM) / Cooperative Awareness Message (CAM) misbehavior, leaving the onboard CV perspective on SPaT integrity unaddressed. To close this gap, we introduce SPADE — the SPaT Attack Detection and Evaluation
dataset — a labelled, multi-modal, simulation-based
dataset designed specifically for deep learning IDS research in this space. SPADE is generated through Eclipse MOSAIC using runtime attack injection at the SAE J2735 application layer across six attack classes and one benign class. By combining four intersection geometries, six operating conditions, and five independent random-seed repetitions, SPADE comprises 180 unique base scenario runs, yielding $\sim$1,890,000 labelled timestep records (270,000 per class). Each record fuses SPaT message fields, onboard camera confidence scores, and cooperative V2V peer data across 40 features, reflecting the multi-modal signal space required to distinguish deliberate attacks from environmental degradation. The
dataset, generation code, and scenario configurations are released publicly to support reproducible and comparative IDS research in C-V2X security. The developed toolbox, instructions, and
dataset link are publicly available on GitHub: https://github.com/jdinovo/SPADE.
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Computation and Language
Namgyu Ho, Huzama Ahmad, Woosung Koh et al. (+3 more)
Language models spend most of their
attention on a small fraction of context, yet they read the entire KV cache to find the few
tokens that matter. If the user asks about a previous detail in a 1M-
token conversation, global
attention layers must scan the full context to generate each
token of the reply. A prominent approach pre-selects relevant
tokens via lightweight proxy scores, but this extrinsic scoring still incurs O(N) per step. We take an intrinsic approach motivated by the simple question: wouldn't the model already know which parts of the context are relevant? We introduce Declarative
Attention (DA), a protocol that elicits the model to declare where it needs to attend within its chain-of-thought, partitioning generation into three modes: <global> (full context), <focus> (a specific region), and <local> (recent output only). The inference engine parses these declarations like tool calls and skips most of the KV cache read. Under zero-shot evaluation across 15 long-context tasks, DA on off-the-shelf models (Gemma-4-31B, Qwen-3.6-27B) reduces total attended
tokens per response (45.8%, 19.6%), despite extra decoding steps, with modest accuracy drops (1.40pp, 2.10pp) that shrink with model scale. DA unlocks a new axis of sparse
attention, with further potential under training-based methods. Code is available at https://github.com/itsnamgyu/declarative-
attention.
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All authors Namgyu Ho, Huzama Ahmad, Woosung Koh, Se-Young Yun, Tal Schuster, Cicero Nogueira dos Santos
Distributed Computing · Cross-listed from cond-mat.mes-hall
QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays
Pranav Vaidhyanathan, Barnaby van Straaten, Alice Petrillo et al. (+6 more)
Semiconductor quantum-dot arrays are a compelling platform for scalable
quantum technologies, yet their practical operation is hindered by the complexity of tuning large-scale devices. Existing automation tools rely on simplified physical models—such as constant-capacitance approximations and
equilibrium Hubbard models—which assume instantaneous relaxation to a steady state. These frameworks fail in experimentally critical regimes where measurement rates exceed tunneling dynamics, necessitating more sophisticated non-
equilibrium control strategies. To bridge this gap, we introduce QArray+, an extension of the QArray framework that incorporates gate-dependent tunnel coupling and a
quantum open-system description of dissipative processes. This approach enables the unified simulation of coherent interdot charge-state hybridization and the non-
equilibrium latching dynamics essential for training robust machine-learning models for automated device operation. Implemented in JAX with GPU acceleration, QArray+ scales across GPUs and multi-node systems. For example, a charge stability diagram for a 100X100 grid of gate voltages over 64 dots can be computed in $\sim0.17\,\mathrm{s}$ on multiple GPUs. Since interdot interactions are short-ranged and the corresponding tuning corrections are local, simulations at these scales capture the physics relevant to even larger devices. These capabilities support high-throughput
dataset generation for automated device tuning.
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All authors Pranav Vaidhyanathan, Barnaby van Straaten, Alice Petrillo, Rahul Marchand, Edwin De Nicolo, Menno Veldhorst, Brucek Khailany, Taylor L. Patti, Natalia Ares
Computation and Language
Choosing a PEFT Variant for Per-Patient Dysarthric ASR: A Single-Speaker Case Study on Two ASR Bases
Bernard Muller, László Tóth and LaVonne Roberts
Per-patient adapters are the preferred production architecture for dysarthric automatic
speech recognition (ASR), yet parameter-efficient fine-tuning (PEFT) variants have not been compared in the speaker-dependent, per-patient regime. We present a single-speaker case study comparing seven LoRA-family methods (LoRA, QLoRA, AdaLoRA, DoRA, LoHA, VeRA, VB-LoRA) on two production bases (Whisper-large-v3 with Hungarian fine-tuning, and a
multilingual Qwen3-ASR-1.7B checkpoint) for one post-stroke Hungarian male speaker (S1, 409 utterances; severe dysarthria on auditory-perceptual clinical assessment).
Attention-projection adapters substantially improve CER on both bases. Across three seeds, a paired bootstrap detects no significant LoRA-DoRA difference (p>0.5; 13.86/13.90 % CER on Whisper, 28.10/28.33 % on Qwen3-ASR), so we adopt the simpler, cheaper LoRA. Real 4-bit (NF4) QLoRA is worse on every seed and both bases (14.56/30.09 % CER) with no
memory saving at this scale, and LoHA, VeRA, VB-LoRA and AdaLoRA do not reach the LoRA family, though LoHA still gives an 18.6 % relative CER reduction on Whisper. On the same base, full fine-tuning is more accurate (11.43 % CER), but a 115 MB LoRA that also adapts the feed-forward blocks reaches within 0.66 pp of it at approximately 3.7 % of the per-patient storage. A 6-point enrollment grid shows about 5 min of patient audio captures 45.6 % of the zero-shot-to-30-min CER reduction, with further gains at 10 and 30 min (caveat: one speaker, one
language, severe post-stroke dysarthria). Training scripts and recipes will be released, source-available under a research-use licence, on publication.
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Machine Learning
LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
Dmitrii Andriianov, Andrey Veprikov and Aleksandr Beznosikov
Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that treats every LoRA step as a tangent vector of the fixed-rank
matrix manifold and takes the spectral-norm steepest-descent step of
Muon inside that tangent space, mapping the result back to the factors through a retraction native to the LoRA parametrization. The step avoids expensive operations on full weight
matrices, and its retraction is up to $2.8\times$ cheaper than the truncated-SVD retraction used by prior
manifold methods. We prove that the Frobenius-norm version of our surrogate recovers LoRA-Pro, and we identify the tangent-projected gradient, the Riemannian gradient of the
manifold, as the stationarity measure natural to LoRA training and computable from the factor gradients alone. Under this measure we give the first global convergence guarantees for both LoRA-Pro and LoRA-TSD, with rates that drive the factor-gradient norms to zero. Across six commonsense and natural-
language-inference
benchmarks with Llama-3.2-1B, Llama-3.1-8B and Qwen3-32B, LoRA-TSD outperforms every competing LoRA optimizer and stays robust to the adapter rank. Code is available at https://github.com/
brain-lab-research/LoRA-TSD.
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Computer Vision
Canjie Liu, Jiawen Kang, Jinbo Wen et al. (+1 more)
Large
vision-
language models have achieved remarkable success in
vision-
language tasks. However, they remain prone to Visual
Hallucinations (VHs), undermining their reliability in real-world applications. Existing solutions typically require curated
datasets, additional training, or multi-round decoding, resulting in considerable computational overhead. In this paper, we propose RVSD (\underline{R}etrieval \underline{V}ision \underline{S}parse \underline{D}ecoding), a training-free and plug-and-play decoding framework that, for the first time, unifies
token sparsification and Semantic-Space Visual
Retrieval (SSVR) within a single decoding pass. Within RVSD, we introduce a semantics-directed
token selection strategy that selectively sparsifies redundant
tokens while preserving critical visual information. We further propose the SSVR mechanism, which reformulates visual compensation as an on-demand cross-modal
retrieval process within a shared semantic space. Extensive experiments demonstrate that RVSD achieves state-of-the-art performance in mitigating VHs while maintaining robust suppression capabilities under long-context generation settings. Our code is available here.\footnote{https://github.com/canjie-liu/RVSD}
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All authors Canjie Liu, Jiawen Kang, Jinbo Wen, Zishao Zhong
Computation and Language
CORAL: An LLM-Native Harness for Production Recommender Systems
Muhammad Rafay Azhar, Yuhang Zhou, Gilbert Jiang et al. (+7 more)
Production recommender systems shape what billions of people see, and sustaining their performance requires continual optimization: as content, user behavior, and upstream models shift, the choices governing
retrieval, ranking, and serving must be revisited. Traditionally, human engineers test such changes through online experiments–a slow, reactive process limited by engineering effort, leaving parts of the system unrevised as conditions change. Although large
language models have been applied to ranking, user modeling, and offline model development, few systems place an
agent in a continual closed loop that acts on a live recommender and learns from the measured effects of its decisions. We present CORAL (Constraint-Optimized Recommender via an Agentic Loop), an LLM-native harness that closes this loop: each cycle, the
agent observes operating signals, reasons over a
memory of past decisions and outcomes, and invokes tools–including a numerical optimizer that keeps changes within a fixed operating budget–to reconfigure the recommender, with measured outcomes informing the next cycle. We formulate this as a partially observed, non-stationary, constrained optimization problem in which the policy improves in context, without parameter updates, from its prior actions. Across two large-scale social platforms, evaluated with A/B experiments, the same harness improves engagement at no additional serving cost on one and reduces serving cost without degrading engagement on the other, spanning the engagement-efficiency frontier. Performance improves as the loop iterates, suggesting that a single agentic loop can automate continual optimization work traditionally performed by human
algorithm engineers under explicit guardrails.
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All authors Muhammad Rafay Azhar, Yuhang Zhou, Gilbert Jiang, Yuchen Wang, Rahul Sharma, Matthew DeSousa, Jiayi Liu, Xin Guo, Lizhu Zhang, Xiangjun Fan
Human-Computer Interaction
BuildOcc: A Large Language Model Occupant Agent Platform for Building Energy Research
Wooyoung Jung
Occupants are a primary source of uncertainty in building energy consumption and management, yet existing occupant behavior models cannot capture adaptive and
reasoning responses considering the occupant's personal history, current context, and the type of energy signal being delivered. This study presents BuildOcc, an open-source Python platform that grounds large
language model
agents in the American Time Use Survey (ATUS), a nationally representative diary
dataset covering 16,684 respondents. Through BuildOcc, each simulated occupant
agent can be instantiated with a demographic persona drawn from ATUS population statistics, a
memory stream that accumulates and reflects on timestep-level observations, and an activity scheduler that samples empirically from ATUS time-at-activity distributions. The platform exposes a three-layer interface - Python library, REST API, and Model Context Protocol server - so that any building energy tool (EnergyPlus, Home Assistant) can integrate behavioral intelligence without bespoke coupling code. A plugin registry lets the community add new occupant strata, custom schedulers, and alternative
memory backends as separate installable packages. Two validation tiers show that ATUS-grounded sampling reproduces empirically calibrated activity distributions and that demographic priors propagate into persona-consistent
agent reasoning across timesteps, establishing internal consistency across strata. BuildOcc provides the building energy community with a reusable, openly available implementation of the occupant behavioral layer. BuildOcc is openly released at https://doi.org/10.5281/zenodo.21192895 under the Apache License 2.0 and installable via pip install buildocc.
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Machine Learning · Cross-listed from stat.ML
Momentum in large-batch training: Polyak enlarges the critical batch size, Nesterov improves data efficiency
Jia-Nan Wang, Zixun Huang, Kairui Li et al. (+2 more)
We study when and how momentum improves large-batch training in the one-pass regime, using power-law kernel regression as a tractable setting. We first characterize
stochastic stability through the critical learning rate, defined as the largest learning rate for stable training, and obtain $\eta_{\mathrm{SGD}}^{\mathrm{crit}}\eqsim 1$, $\eta_{\mathrm{Polyak}}^{\mathrm{crit}}\eqsim \min\{1,B(1-\beta)\}$, and $\eta_{\mathrm{Nesterov}}^{\mathrm{crit}}\eqsim \min\{1,B^q(1-\beta)\}$, where $B$ is the batch size, $\beta\in[0,1)$ is the momentum factor, and \(q>1\) is the capacity exponent governing spectral decay. Within the stable region, we derive scaling laws for the full risk dynamics, capturing the progression from an early transient through power-law decay to a noise floor. We then optimize the final-step risk under a fixed data budget, obtaining a three-regime phase diagram that reveals how the benefits of momentum change with batch size. Notably, Polyak enlarges the critical batch size, the largest batch size preserving the optimal small-batch data-scaling exponent, thereby allowing fewer sequential updates without sacrificing data efficiency. In contrast, Nesterov improves the optimal data-scaling exponent in the large-batch regime through look-ahead-induced suppression of noise amplification. Experiments on PLK regression quantitatively support the theoretical scaling predictions, while neural-network experiments provide qualitative evidence for the predicted batch-size dependence and stability behavior.
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All authors Jia-Nan Wang, Zixun Huang, Kairui Li, Zilin Wang, Lei Wu
Numerical Analysis
Neural operators approximate strongly continuous convex monotone semigroups
Jonas Blessing, Philipp Schmocker and Alessandro Sgarabottolo
We approximate strongly continuous convex monotone
semigroups by learning their Chernoff-type one-step operators with neural operators. First, we introduce the general class of so-called Chernoff-neural operators and show in a universal approximation
theorem that they can approximate the Chernoff one-step operators arbitrarily well. By using stability estimates between weighted Hölder spaces, the one-step approximation error can be propagated through the iterations which yields universal approximation of the corresponding
semigroup. Second, we introduce the more specialized class of envelope-neural operators for envelope
semigroups which allows us to derive quantitative approximation rates. Finally, we illustrate the effectiveness of these neural operators in several numerical examples arising from non-linear partial differential equations,
stochastic optimal control and
stochastic processes under model uncertainty.
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Game Theory
Almost Envy-Freeness for Additive Mixed Manna with Entitlements: Deterministic and Randomized Guarantees
Zehan Lin, Shengxin Liu, Biaoshuai Tao et al. (+1 more)
We investigate the fair allocation of indivisible items among
agents with asymmetric entitlements in mixed manna settings, where the items consist of both goods and chores. For additive valuations, we establish that weighted envy-free up to one item (WEF1) allocations always exist and can be computed in
polynomial time. We also study fair and efficient allocation and show that weighted envy-freeness up to one transfer (WEF1T) is compatible with fractional Pareto optimality (fPO) for every mixed-manna instance. This relaxation from WEF1 to WEF1T is tight, as demonstrated by our impossibility result. We further show a best-of-both-worlds result via a finite lottery that guarantees weighted envy-freeness (WEF) in expectation, with every realized allocation satisfying WEF1T and achieving the tight characterization complemented by the existing impossibility result.
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All authors Zehan Lin, Shengxin Liu, Biaoshuai Tao, Shengwei Zhou
Numerical Analysis
A stable rank-adaptive step-and-truncate finite volume method for Vlasov transport on domains with piecewise linear boundaries
André Uschmajew and Andreas Zeiser
We consider the numerical solution of the linear Vlasov transport equation on bounded spatial domains with inflow boundary conditions based on low-rank approximation. We combine a finite volume discretization with a rank-adaptive step-and-truncate scheme for the resulting
matrix ODE. The spatial and velocity meshes may be unstructured, while suitable numerical fluxes retain a separated space-velocity representation. For homogeneous inflow, we show that the low-rank scheme inherits the $L_2$ stability and CFL restriction of the underlying full finite volume forward Euler method. In addition, the low-rank approximation error is bounded explicitly in terms of the truncation tolerances, avoiding the modeling error associated with tangent-space projections in dynamical low-rank approximation. Numerical experiments in 1d1v and 2d2v confirm the predicted error behavior. In 2d2v, the method handles an unstructured spatial mesh with nonzero inflow and a full tensor-product discretization of approximately $5.8\cdot10^{10}$ phase-space
cells while the numerical rank is at most twelve.
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Human-Computer Interaction
Large Language Model-Driven Context-Aware Eco-Feedback Generation and Evaluation
Wooyoung Jung and Prosper Babon-Ayeng
The objective of this study is to demonstrate the potential of generating context-aware eco-feedback - eco-feedback that reflects a household's contextual characteristics alongside its energy use patterns - through a large
language model-integrated framework. Previous studies have introduced personalized eco-feedback, mostly relying on household energy use patterns; however, they frequently did not reflect distinct household characteristics, including their persona or non-negotiable routines, leaving eco-feedback ineffective and sometimes superficial. To address these limitations, we introduce a contextual engineering framework that generates eco-feedback using a self-consistency with chain-of-thought
prompt, leveraging household energy analysis data, utility rate structures, and household characteristic information. We conducted a rigorous empirical validation and a combinatorial evaluation analysis to assess this framework systematically. The former tested the framework's ability to generate accurate and contextually grounded eco-feedback for three households by comparing its output against reference interventions independently derived from the same household data. The latter examined the framework's adaptability across 400 scenarios spanning 50 households, two utility rate structures, and four behavioral personas. Our framework generated eco-feedback that aligned with reference interventions at a mean rate of 92.0% and grounded its recommendations in the provided household data with 95.7% citation accuracy. It also proved highly adaptive, shifting both the appliances targeted and the energy-saving strategies recommended in response to rate structure and household context. Ultimately, this study contributes to realizing the next level of context-aware interactions between occupants and buildings which paves the way for higher occupant living quality and sustainability.
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