Systems and Control · Cross-listed from math.OC
Communication-efficient ADMM over Hierarchical Networks
Binh Nguyen, Shuangqing Wei and Truong X. Nghiem
This paper develops a novel distributed optimization
algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve hierarchical optimization problems over tree-structured networks, termed hierarchical ADMM (hADMM), with a particular focus on enhancing communication efficiency across the network. By rearranging the augmented Lagrangian to establish a query-response communication mechanism between nodes that explicitly exploits the hierarchical tree structure, the proposed
algorithm significantly reduces communication costs compared to existing ADMM-based methods for hierarchical optimization. Furthermore, hADMM guarantees asymptotic convergence under convexity assumptions. We also present a convergence rate analysis based on linear
matrix inequalities to characterize the maximum theoretically achievable convergence rates across different network
topologies, showing that the proposed hADMM attains linear convergence under mild conditions. Three numerical experiments demonstrate that hADMM is compatible with arbitrary tree network structures and outperforms existing approaches in terms of communication efficiency.
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Computer Vision
DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision
Param Sangani, Ahmad Moori, Erik Blasch et al. (+2 more)
Polarimetric
vision is gaining increasing
attention because it provides physical cues about scene shape, material, and reflection that are difficult to recover from RGB alone. Recent work has therefore explored predicting polarization directly from conventional RGB images; however, the fidelity of these methods strongly depends on the polarization supervision used for training. Most existing
datasets rely on Division-of-Focal-Plane (DoFP) cameras with four spatially interleaved analyzer orientations, which provide limited angular redundancy and introduce interpolation and instantaneous-field-of-view errors. We introduce DensePol, a high-redundancy RGB–polarization
dataset based on Division-of-Time (DoT) acquisition, capturing 180 full-resolution analyzer orientations at $1^\circ$ intervals. DensePol contains 2,018 paired RGB–polarization images with the angular measurements and fitting residuals retained. Dense angular sampling substantially improves polarization stability, reducing AoLP deviation from $13.36^\circ$ to $2.21^\circ$. We further introduce a deterministic
diffusion-based RGB-to-polarization framework with cyclic AoLP representation and a local DoLP refiner. Experiments demonstrate improved polarization prediction and downstream surface-normal estimation. The
dataset and code will be publicly available.
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All authors Param Sangani, Ahmad Moori, Erik Blasch, Guna Seetharaman, Hadi Aliakbarpour
Information Theory
On the Sequential Test and Distributed Detection
Earnest Akofor
We present a simple definition of stopping time and its role in the formulation of sequential tests for both centralized and distributed detection, providing a straightforward procedure for obtaining optimal decision rules. Upper bounds for optimal stopping time are derived and numerically shown to possess certain qualitative features expected of the optimal stopping time. The results are extended to any distributed detection network in the form of an acyclic directed graph.
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Cryptography and Security
Encrypt What Matters: When Selective Homomorphic Inference Is Efficient
Ali Backour, Juan Reyes, Jaime Punyed et al. (+1 more)
Fully homomorphic encryption (FHE) enables inference on private data without revealing it to the server, but evaluating an entire input under FHE is expensive. We study selective homomorphic inference, where only a sensitive region of interest (ROI) is encrypted, and computations independent of that region are performed in plaintext. Selective evaluation produces the same output as full FHE on the same model, without retraining. Its efficiency depends on how quickly encrypted dependencies spread through the network. For small encrypted ROIs, locality-preserving architectures can achieve order-of-magnitude homomorphic-evaluation speedups, whereas architectures with early global mixing provide essentially no speedup. These results identify locality as the key architectural property governing the benefit of selective homomorphic inference.
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All authors Ali Backour, Juan Reyes, Jaime Punyed, Ana Onoprishvili
Computation and Language
Auditable Emergency Triage for Maternal and Newborn Care in India
Shobhit Jagga, Aman Dalmia, Niharika Priyadarshini et al. (+7 more)
At Noora Health, our nurses answer more than 50,000 medical queries per month on our WhatsApp-based service that provides caregivers with on-demand support. Their most time-critical task is emergency triage: deciding which queries need immediate in-person
attention. To support them, we built a system that uses a large
language model (LLM) to classify whether a message is an emergency and provide a rationale for interpretability. But the system was opaque: analyzing mistakes meant reading
reasoning chains for each message, which is infeasible at our scale.
Prompt changes meant re-running a full evaluation to prevent regressions, which was both costly and operationally challenging. Clinicians follow a decision tree to make this call, but it was never documented or passed to the model, which relied on a flat list of danger signs. To address these issues, we decomposed triage into two steps: an LLM extracts canonical symptoms and patient context from the query using a clinician-authored vocabulary, and a deterministic rule engine captures the scenarios that indicate an emergency. We show that the new system raised recall from 0.565 to 0.810 and F1 from 0.606 to 0.702, with structured rules driving most of the accuracy gains while the decomposition provides auditability: clinical experts can inspect each stage of the new system to see whether the query was mistranslated, symptoms were incorrectly extracted, patient context was wrongly inferred, or the necessary rules were missing. They can add new rules independently without causing regressions and avoid running costly evaluations. Since deployment, the new system has triaged 152,421 patient queries and flagged 28,535 (18.7%) as emergencies. The over-escalation rate has been 17.8%, without any increase in missed emergencies. Clinicians have also added 48 new rules since deployment, evidence of the faster correction loop we set out to build.
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All authors Shobhit Jagga, Aman Dalmia, Niharika Priyadarshini, Neelima Devadas, Amrita K Prasen, Nikhil Nalin, Santhosh SJ, Sreeram Nurani Ramasubramanian, Muhammed Afeer K, Anubhav Arora
Computation and Language
SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection
Sanghyeok Park, Minji Kang, Hosung Kwak et al. (+1 more)
Modern LLMs demonstrate impressive
multilingual performance, yet standard
benchmarks primarily reward selecting correct answers rather than evaluating genuine factual understanding. We introduce Systematic Wikidata-based Object-Relation Distortion (SWORD), a
benchmark that evaluates whether models consistently reject factual errors across
languages. SWORD generates syntactically well-formed but factually incorrect statements in eight widely spoken
languages through controlled perturbations of Wikidata triples, ranging from random entity substitutions to semantically plausible property-based selections. Our distortion-based evaluation surfaces two critical insights that remain entirely obscured by conventional
benchmarks. First, models counterintuitively achieve higher accuracy on semantically plausible distortions than on nonsensical random substitutions, suggesting reliance on distributional familiarity rather than genuine factual verification. Second, models exhibiting comparable baseline accuracy across
languages show substantial performance degradation specifically on (East) Asian
languages when presented with distorted statements, with cross-lingual performance gaps reaching up to 28 percentage points (49% relative reduction) in some models. These findings demonstrate that
multilingual factual
reasoning involves asymmetric capabilities that aggregate accuracy metrics systematically obscure.
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All authors Sanghyeok Park, Minji Kang, Hosung Kwak, Jinhyuk Yun
Distributed Computing
Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management
Antonino Vaccarella, Lanpei Li, Vincenzo Lomonaco et al. (+1 more)
Managing resources across IoT, edge, and cloud layers calls for continuous, context-aware decisions under constraints that rarely stay fixed. Deep reinforcement learning (DRL) handles this class of problems well, and large
language models (LLMs) are increasingly used to augment DRL pipelines, yet the architectural relationship between the two is seldom made explicit. We build on Wang et al.'s taxonomy of Continuum Orchestration Systems employing DRL techniques and extend it with two further dimensions. The AI Augmentation Paradigm measures how LLMs are exploited, while the Feedback channel captures whether and through which system path the execution feedback returns to the LLM in order to close the MAPE control loop at the LLM Orchestration layer. We apply this taxonomy to six recent system architectures and find a common gap, as none combines full LLM orchestration with full
agent-layer feedback in a Cloud Continuum setting. We relate this gap to a missing cross-tier feedback abstraction, bridging the incommensurable per-tier signals and the LLM Orchestrator.
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All authors Antonino Vaccarella, Lanpei Li, Vincenzo Lomonaco, Massimo Coppola
Computational Engineering
Fixed-mesh based approach for modeling of superconducting magnetic bearings
Elias Paakkunainen, Bárbara Maria Oliveira Santos, Gabriel dos Santos et al. (+3 more)
Numerical simulations of superconducting
magnetic bearings (SMBs) are complicated by the relative motion between high temperature
superconductors and the
magnetic guideway, which commonly requires repeated updates of the geometry and mesh. We present a fixed-mesh based approach for modeling the motion induced effects in which a coordinate transformation is applied only to the air region between the moving regions. The transformation can be included in a time-dependent anisotropic material tensor, leaving the mesh during the simulation and the rest of the formulation unchanged. To provide numerical examples, the approach is implemented in 2D using the finite element method with the $j$-$a$ formulation and is combined with homogenization and
circuit coupling for double-crossed loops of coated conductors. The open-source model implementations are made available. The proposed approach is verified and validated against existing modeling approaches and experimental results from the literature for SMBs. Two simulation examples are presented, and in both of them the levitation force predicted by the fixed-mesh model agrees with the experimental results, yielding $R^2$ values above 0.99. For a verification problem, it is shown that the fixed-mesh model converges towards the remeshed reference solution under mesh refinement as expected. For an SMB undergoing vertical and horizontal translations, the homogenized fixed-mesh model reproduces the lateral-force response of a fully resolved moving-geometry reference model. Additionally, the homogenized fixed-mesh model is computationally more efficient than the reference model, indicating a significant reduction in the number of degrees of freedom and the computation time. The results demonstrate accurate and computationally efficient simulation of translational motion with the fixed-mesh based approach.
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All authors Elias Paakkunainen, Bárbara Maria Oliveira Santos, Gabriel dos Santos, Timo Tarhasaari, Paavo Rasilo, Sebastian Schöps
Cryptography and Security
Cross User/App Network Attacks - Hijacking TCP Connections and DNS Cache Poisoning via a Malicious User/App (Extended Version)
Tamir Shahar and Amit Klein
Off-path network attacks against TCP and DNS (over UDP) client-server connections are generally considered impractical nowadays, due to built-in security features in these protocols, e.g. randomized TCP (initial) sequence numbers and randomized UDP source ports, respectively. In this work, we refute this presumption by demonstrating that an unprivileged malicious application running on the client (but practically off-path), when combined with a remote off-path adversary, can enable powerful network attacks against such connections. We show how such a local–remote collaboration between the malicious application and a remote adversary allows inference of sensitive connection state, including TCP sequence numbers and DNS stub-resolver UDP source ports. Our attacks exploit standard socket API calls such as bind(), protocol mechanisms such as IP options, and operating system features such as cBPF and procfs to infer the TCP initial sequence number (ISN) and the UDP source port in use by the connection of interest. Specifically, we take advantage of certain properties of the ISN generation
algorithm as implemented in major operating systems. We demonstrate TCP connection hijacking in Linux, Android, Windows, macOS and iOS, and DNS cache poisoning against Windows, Android and the popular systemd-resolved DNS stub resolver in Linux. We evaluate our techniques across multiple operating systems and realistic deployment settings, including environments behind port-preserving NAT-integrated routers. We disclosed our techniques to Microsoft, Apple, Linux and Google, which led to the release of several patches.
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Hardware Architecture
Academia x Industry: The Role of Fundamentals for Silicon in an AI Native Era
Vincent T. Lee, Armin Alaghi, Carole-Jean Wu et al. (+5 more)
Agentic AI is set to become one of the most transformational technologies in generations and materially change how we approach silicon design and engineering. The impact is being felt in real time amid a rapidly changing landscape, which can make it overwhelming for both silicon practitioners and academics to adapt to the AI native silicon design era. To add structure to how we navigate this transition, we provide a joint view from academia and industry silicon practitioners of the challenges, opportunities, and considerations we expect will catalyze how the community transitions into an AI native silicon future. In particular, we reemphasize the importance of core silicon design fundamentals in academic training and why they have renewed importance in research and industry practice for AI native silicon design. It is our hope that the views provided here will offer valuable and complementary perspectives to those in academia and industry to interpret, inform, and catalyze the transition to the AI native era. We expect that many similar and overlapping views will emerge, but the precise technical details will differ across stakeholders, so it is valuable for the community to amass a diversity of viewpoints.
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All authors Vincent T. Lee, Armin Alaghi, Carole-Jean Wu, Sai Zhang, Brandon Reagen, Thierry Tambe, Jean Boufarhat, Matheus Trevisan Moreira
Computation and Language
Osprey: Target-agnostic Pre-training Makes Stronger Drafters in Speculative Decoding
Fengxiang Bie, Yuqing Jian, Yifan Yu et al. (+7 more)
Speculative decoding is critical for accelerating LLM inference. However, the speedup is fragile: drafters are typically trained against a narrow distribution for a single target model, and their acceptance rate collapses under workload shifts. This is a striking inversion of modern LLM development, where target models are valued precisely for the broad generalization they acquire through large-scale pretraining. We argue that the natural remedy, pretraining, has been hard to apply to drafters because existing recipes are target-specific: the drafter consumes the target's hidden states and is distilled on the target's logits, so pretraining must be repeated for each target. We introduce Osprey, which instead bootstraps drafters from off-the-shelf pretrained small
language models, treating broad pretraining as a reusable, target-agnostic asset and reducing per-target work to a lightweight adaptation step. Realizing this requires overcoming two challenges: small LMs are far deeper than a latency-bound drafter can afford, and their pretrained computation must remain intact while the drafter learns to ingest target hidden states and emit
tokens in the target's vocabulary. Osprey addresses both by
pruning to a shallow backbone, restoring its
language-modeling capability with target-agnostic next-
token pretraining, and adapting it to each target through vocabulary
alignment, zero-initialized QKV expansion, and
distillation from the target model's output distribution. Empirically, a single pretrained Osprey backbone transfers across targets and improves mean acceptance length by 16.1% for Qwen3-8B, 21.2% for Llama-3.3-70B-Instruct, and 22.7% for the 229B MiniMax-M2.5 (with 17.5% higher
tokens per second), with the largest gains on out-of-domain and
multilingual data. Our code is available at https://github.com/LeanModels/Osprey.
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All authors Fengxiang Bie, Yuqing Jian, Yifan Yu, Zhongzhu Zhou, Zelei Shao, Ben Athiwaratkun, Shuaiwen Leon Song, Chenfeng Xu, Xiaoxia Wu, Tianyi Zhang
Cryptography and Security · Cross-listed from quant-ph
Execution-transcript privacy for fault-tolerant surface-code memories
Jiachen Shen and Hui Zhong
A fault-tolerant
quantum computer runs behind a telemetry stream logging syndromes, decoder actions, resets and timing separately from the answer. Can it reveal the logical input? For a distance-$d$ rotated surface-code
memory on a fixed schedule of $T=\Theta(d)$ rounds, under three stated hypotheses (sector-scalar honest backbone, transcript locality, Kotecky-Preiss smallness), the channel from logical
qubit to transcript is $e^{-\Theta(d)}$-close in diamond norm to one that ignores the input. A statement of this kind follows generically from correctability-
privacy duality. Anisotropy does not. Each logical axis pays the distance of its own coset, so under amplitude damping the computational-basis label is governed by the code's $Z$-distance $d_Z\ge d_{\min}$ and not by the code distance. Two codes of
quantum distance $1$ make the gap concrete. A phase-flip code's $X$-syndrome transcript is exactly input-independent under unobserved damping, while a repetition code leaks at first order. A matched converse identifies the records that do expose it, among them a
lattice-surgery parity readout. On a 156-
qubit superconducting processor our sufficient certificate misses by $21.5\times$, so the
theorem cannot be invoked there. Measured directly, a $d_Z=1$
memory's record identifies its input with total variation $\ge 0.927$ under randomised, label-balanced acquisition. Holding the code fixed and varying the damping exposure reproduces the parameter-free law, with exponent $0.85\pm0.03$ against a predicted $0.86$. Randomized encoding returns the statistic to the floor at no two-
qubit-gate cost. Fault tolerance does not grant transcript
privacy. It relocates it, and only to the logical state, not to the
circuit's identity.
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Human-Computer Interaction
Where Does the Human End? Creative Agency with Generative AI across Five Years of Chinese Digital Painting
Yibo Meng, Ruiqi Chen, Shuheng Cao et al. (+2 more)
As generative AI enters creative work, practitioners must decide where AI assistance ends and human authorship begins. Human-
agent interaction (HAI) research has examined AI as a tool, collaborator, consultant, and competitor. The longitudinal problem is how these roles are revised as systems become more capable, public, and economically embedded. We report a five-year interview study with 17 Chinese digital painters, based on annual semi-structured interviews from 2021 to 2025. Participants described recurring but non-uniform patterns of protective resistance, pragmatic task delegation, and, for some, reflective agency repartitioning. Early resistance protected observation, originality, signature, and ownership from AI. Later delegation placed AI in bounded tasks such as references, backgrounds, rough sketches, and client-facing drafts. By 2025, some participants built hybrid workflows around human-only zones, while others described fatigue, precarity, or difficulty locating a remaining human role. Peer norms, emotional climates, and production pressures shaped which delegations felt useful, acceptable, or exhausting. Copyright, authorship, and creative labor remained recurring limits on what participants were willing to delegate. We frame these accounts as longitudinal agency partitioning, the situated work of deciding which stages, responsibilities, values, and claims remain human in creative human-
agent interaction. We discuss design implications for revisable agency-boundary controls, provenance scaffolds, and community-facing authorship norms.
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All authors Yibo Meng, Ruiqi Chen, Shuheng Cao, Weijia Zhang, Chengxi Zang
Human-Computer Interaction
Early Epistemic Settlement in AI-Assisted Writing
Han-yu Wang
In trying to complete a passage, an author can make connections among her materials that change what she can argue and the demands the argument must meet. A
language model can supply a passage that does the work required at that point in her argument. Accepting it can end her own attempts before those connections have developed. I call this interruption early epistemic settlement. It can occur even when she fully understands the response and correctly judges it adequate for the passage's present role in the argument. The answer can satisfy the desire for resolution that kept her at work. Returning to her unfinished attempt would take more effort, and she may be unable to anticipate what she could achieve by continuing it. With repeated assistance, accepted answers shape what the writer asks next and which relations she goes on to develop. Useful answers can thus sustain inquiry while cutting short the work through which an author could form arguments that accommodate demands she has yet to recognize.
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Human-Computer Interaction
Ephemeral Feeds and Enduring Rituals: RushTok and the Formation of Event-Based Algorithmic Communities
Emelia Hughes and Tim Weninger
Each August, TikTok's For You page turns the University of Alabama's sorority recruitment into RushTok. We examine RushTok as an event-based algorithmic community: a collective assembled around a bounded offline ritual and sustained by recommendation. Using a mixed-methods survey (n=71) and a reflexive account of creator outreach, we ask who participates, how, and with what stakes. Findings show an ambiguous and entertainment based throughline; many called it a community (51/71) but few claimed membership (11/71). Affiliation centered on creators rather than shared practices, with parasocial
attention clustering around a small set of potential new members (PNMs) and returning figures. Higher content exposure tracked with self-identification as a community member; those members commented, followed creators, and engaged across
videos. Attempts to interview creators were met with silence or refusals, reflecting community boundary-work despite viral visibility. We outline implications for platform governance, including time-bounded context, graduated visibility, and aftercare.
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Machine Learning · Cross-listed from stat.ME
Real-time and adaptive anomaly detection algorithm for cyclostationary models
Justyna Witulska, Tomasz Barszcz, Ireneusz Jabłoński et al. (+1 more)
This article introduces PeriodicCALM, an effective real-time anomaly detection framework designed for cyclostationary data streams. While classical cyclostationary processes feature periodically time-varying statistical properties, real-world signals often contain recurring impulsive components that conceal abnormal behavior. Existing real-time methods for struggle with these dynamics, frequently misinterpreting phase-dependent variability as non-cyclic anomalies and causing excessive false alarms. To address this, PeriodicCALM incorporates cycle-dependent variability to systematically ignore regular cyclic impulses while accurately isolating genuine anomalies. Operating in real time with continuous retraining capabilities, the method adapts dynamically to evolving signal characteristics. Comparative evaluations against the baseline CALM framework using simulated data demonstrate significant improvements in detection accuracy and training efficiency, alongside a reduction in prediction latency. Furthermore, the practical utility of PeriodicCALM is validated on real-world vibration signals collected from a compressor monitoring system.
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All authors Justyna Witulska, Tomasz Barszcz, Ireneusz Jabłoński, Agnieszka Wyłomańska
Networking
Improving 5G AI-RAN MCS Selection by Predicting Retransmissions
Tamerlan Aghayev, Maxime Elkael, Michele Polese et al. (+5 more)
Link Adaptation (LA) in 5G NR is inherently reactive, relying on channel measurements and HARQ feedback that may become quickly obsolete when the channel changes quickly. This data is also noisy, making it hard to track accurately, and has to be fed to real-time controllers with feedback-loop effects which are hard to troubleshoot. This explains why most practical deployments select simple but robust
algorithms, which accept that the lag can leave the scheduler operating at overly aggressive or unnecessarily conservative rates, trading spectrum efficiency for predictable performance. In this paper, we improve on this status-quo with NOSTRAdAMUS, a predictive LA framework which adds foresight to existing
algorithms without replacing or redesigning them. NOSTRAdAMUS predicts whether a retransmission will occur in the next
radio frame from recent HARQ history, and applies corrections to the Modulation and Coding Scheme (MCS) selected by the underlying policy. We
benchmark several ML models and show that Gradient Boosting achieves 82.9% accuracy overall with high-confidence interventions that are correct 94.2% of the time, and an inference latency of 5.5 {\mu}s. We train the model based on data collected Over-the-Air (OTA) on the X5G testbed, using the open-source OpenAirInterface (OAI) 5G stack, NVIDIA Aerial, and COTS O-RAN
Radio Units and User Equipments. The model is then deployed as a dApp, which we evaluate OTA as well as on various channels with hardware-in-the-loop channel emulators. This includes 3GPP TDL and CDL channels, SISO and
MIMO configurations, and pedestrian and vehicular mobility. Our evaluation shows that without retraining, and across this variety of scenarios, the dApp augments two SOTA LA
algorithms, and increases goodput by up to 71.5% while reducing retransmissions by up to 71.8%. This demonstrates the robustness and generalization capabilities of our approach.
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All authors Tamerlan Aghayev, Maxime Elkael, Michele Polese, Reshma Prasad, Salvatore D'Oro, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
Social and Information Networks
Endorsement Without New Evidence: How Sequential Voting Inflates Mandates in Online Community Governance
Zihan Chen, Lei Nico Zheng and Di Zhu
Online communities often treat large support margins in public elections as strong mandates. We argue that such margins can overstate the independent scrutiny behind a decision. Using 198,275 free-text rationales from Wikipedia admin elections, we introduce vote-text divergence, a measure that flags a decisive vote paired with a thin, deferential rationale. Divergence rises as voters arrive later, even after controlling for voter and election fixed effects. The pattern is consistent with information saturation: once prior text is accounted for, arrival order no longer predicts divergence, while accumulated prior evidence does. The effect is strongest among peripheral voters in the co-voting network. Yet divergence does not predict worse post-promotion outcomes, such as administrative activity or survival. Public tallies can therefore weaken the scrutiny signal even while selecting capable administrators: a margin may appear to reflect more consensus and support than it actually contains.
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Computers and Society
Playing Whack-a-Mole with misconceptions about memorization, extraction, and copyright
A. Feder Cooper
After careful review, I'm confident the headline fine-tuning memorization results in
Alignment Whack-a-Mole use an invalid measurement procedure. The book memorization coverage metric these headline results depend on counts sequence matches far shorter than what field standards consider valid evidence of memorization, and the prompting procedure used to elicit memorization runs the risk of leaking the text being "extracted" in the
prompt. The paper doesn't include the negative-control experiments needed to see how much the results are inflated by false positives: claiming extraction success (and therefore memorization of training data) when matches between generations and training data may be due to other factors. Given these validity issues, the paper's claims that fine-tuning lets users extract substantial portions of copyrighted books, in a form that could substitute for the originals, aren't supported by the reported results. The failure to report the experiments' cost (an important component of the threat model) further compromises the copyright claims. I'm writing this note because, in the last month, (prospective) plaintiffs have reached out to me to ask about this paper. They're looking to cite this work as valid evidence in support of claims in ongoing and potential future copyright litigation.
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Software Engineering
How effective are traditional test criteria at detecting bugs in large language models generated code?
Asma Hamidi, Michael Konstantinou, Renzo Degiovanni et al. (+1 more)
Test adequacy criteria are widely used to evaluate and guide software testing. Although prior research has extensively examined these criteria using human-written programs, faults, and tests, the increasing adoption of Large
Language Models (LLMs) for code generation raises important questions about their effectiveness in detecting LLM-induced faults. To investigate this, we conduct an empirical study involving 5 LLMs and 4
benchmarks, simulating end-to-end workflows in which both code and tests are automatically generated. We collect 6,000+ faulty program instances and evaluate the effectiveness and efficiency of 3 widely used adequacy criteria: statement coverage, branch coverage, and mutation testing. Our findings reveal several key insights. First, most faults introduced by LLMs are relatively trivial to catch. Second, the challenging faults are difficult to trigger using either traditional coverage-based or mutation-based criteria. Third, actual fault detection rates remain extremely low, often near zero, because test oracles fail to capture faulty behavior triggered by the generated test prefixes, exposing a critical limitation of automated test generation. Fourth,
prompt-aware oracles can improve fault detection, but their overall effectiveness remains limited, highlighting the need for users to manually reason about test assertions. We further observe that mutation testing only marginally outperforms traditional coverage criteria in both triggering and detecting faults, raising questions about whether its significantly higher application cost is justified in this context.
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All authors Asma Hamidi, Michael Konstantinou, Renzo Degiovanni, Mike Papadakis
Systems and Control
From Learning to Control: Data-Driven Multi-Agent Reinforcement Learning for Multivariable Control in a Microalgae Bioprocess
Juan D. Gil, Ehecatl Antonio Del Rio Chanona, José Luis Guzmán et al. (+1 more)
Effective control of bioprocesses is particularly challenging due to the intrinsic nonlinearity and dynamic variability of living-
cell systems. In microalgae-based photobioreactors (PBRs), maintaining stable pH and dissolved oxygen (DO) levels is critical for optimal growth and productivity, yet their strong coupling and sensitivity to environmental fluctuations make multivariable control difficult. This study proposes a novel hybrid offline-online Multi-
Agent Reinforcement Learning (MARL) framework for simultaneous pH and DO regulation, leveraging Deep Deterministic Policy Gradient (DDPG)
agents to achieve a fully data-driven and model-free control solution. The
agents are trained using historical data generated by an expert system, eliminating the need for direct experimentation with the environment. After deployment, the
agents operate autonomously, continuously fine-tuning their policies daily to adapt to evolving process dynamics and reject fast transient disturbances. Experimental validation in an open, industrial-scale PBR at the University of Almeria demonstrated the framework's capability to maintain stable operation under realistic conditions. The results confirm that model-free MARL control provides a robust and adaptive alternative for complex bioprocess environments.
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All authors Juan D. Gil, Ehecatl Antonio Del Rio Chanona, José Luis Guzmán, Manuel Berenguel
Artificial Intelligence
Differential Functionalism: Jacobians Shape the Structure of Phenomenal Experience
David Balduzzi
This paper investigates the hypothesis that the first-order structure of physical interactions (Jacobians) shapes the structure of phenomenal experience. Two measures of Jacobian structure are introduced: transparency, the effective rank, which counts the independent differences that make a difference; and integrity, the normalised algebraic connectivity, which measures how hard a system is to split. Applied to a series of worked examples, the framework yields correlates of: (1) experiences that overlap without systems merging; (2) the boundaries of a system, which are selected by focus and limited by noise; (3) temporal thickness, the persistence of the past into the present, lengthened by a spread of timescales; (4) spatial structure under convolution, where unity at scale needs long-range connections; (5) the distinction between what is vivid and what is obscure, for errors and
attention heads; (6) the incoherence of a randomly initialised network, as a structural model of James's "blooming, buzzing confusion"; (7) distinct versus confused ideas; (8) learning as accumulated experience; and, conjecturally, (9) the contrast between
memory as duration in
brains and as storage in computer hardware. The measures can be computed at scale in artificial networks and estimated from perturbational data in
brains.
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Computer Vision
Video-MOPD: Multi-Teacher On-Policy Distillation for Video Understanding
Zhenxin Qin, Peng Shi, Cong Han et al. (+3 more)
Video understanding demands a convergence of complementary capabilities across perception, temporal understanding, and complex
reasoning, which are difficult to jointly optimize within a single model. We introduce
Video-MOPD-8B, an open-weight model dedicated to
video understanding tasks. To fundamentally enhance its capabilities, we conduct targeted reinforcement learning (RL) optimization across three core domains:
video temporal grounding (VTG), general
video comprehension, and
video STEM
reasoning. We then unify their complementary capabilities via Multi-Teacher On-Policy
Distillation (MOPD), which consolidates expert knowledge by supervising student-generated trajectories with routed teacher feedback. We further introduce Reliability-Aware Informative Sampling (RAIS), which selects examples with consistently reliable teacher supervision and large teacher-student performance gaps. Together, these components enable
Video-MOPD-8B to achieve coordinated and comprehensive performance gains across diverse
video understanding tasks. Extensive experiments on comprehensive
benchmarks covering general
video understanding, temporal grounding,
video reasoning, and
video STEM tasks demonstrate that
Video-MOPD-8B achieves state-of-the-art performance among existing models at a comparable scale. The trained model weights are available at https://huggingface.co/LandH/
Video-MOPD-8B.
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All authors Zhenxin Qin, Peng Shi, Cong Han, Yinlong Qian, Zequn Jie, Lin Ma
Machine Learning
Literati: Towards Anytime Optimal Shape Generalized Trees via AO*
Nakul Upadhya and Eldan Cohen
Decision trees are prized for their interpretability and strong performance on tabular data, but popular greedy top-down induction
algorithms can yield suboptimal and unnecessarily complex structures. Optimal decision tree methods address this through global optimization, yet remain restricted to axis-aligned threshold splits, which limit the expressivity of each node and often force deep, complex trees to capture non-linear feature effects. Shape Generalized Trees (SGTs) generalize threshold splits to learnable univariate shape functions, improving expressivity and enabling more compact trees. However, existing SGT induction
algorithms are greedy and offer no optimality guarantees. In this work, we introduce Literati, the first
algorithm for optimal SGT induction. We propose a novel AND/OR graph formulation of the problem that jointly optimizes tree structure and shape function complexity. To solve this AND/OR graph, we develop an AO*-based
algorithm with two enhancements that improve anytime performance while preserving optimality: a secondary heuristic for OR-node selection and a round-robin policy for AND-node exploration. Across 24 real-world
datasets, Literati achieves higher training and test accuracy than state-of-the-art tree approaches.
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Artificial Intelligence · Cross-listed from math.OC
Support Discovery With Iteratively Reweighted Least Squares for Fixed-Charge Network Flow
Sindura Saraswathi and Christian Kümmerle
The fixed-charge network flow problem (FCNFP) couples continuous flow allocation with discrete arc-activation decisions, making it a canonical but computationally challenging model for a variety of network design and resource allocation problems. Exact mixed-integer linear programming formulations capture the fixed-charge structure faithfully, but often become difficult to solve on large networks. We propose a scalable continuous-optimization
algorithm for large-scale single-commodity FCNFP based on an iteratively reweighted least-squares (IRLS) framework. The method replaces the discontinuous fixed-charge and linear arc cost objective with a smooth nonconvex Lasry–Lions surrogate and solves a sequence of weighted quadratic flow subproblems. Each subproblem is solved by a warm-started dual semismooth Newton method whose Newton systems have weighted graph-
Laplacian structure, enabling the use of modern
Laplacian solvers. To further improve the discovered arc supports of the challenging underlying combinatorial problem, we also develop an algorithmic variant that incorporates objective-driven perturbation restarts and an anchor-union restricted search that jointly leverages supports discovered by IRLS and by complementary FCNFP heuristics. Computational experiments on 410
benchmark, synthetic, and large-scale instances show that our method obtains the best objective quality among the evaluated scalable FCNFP
algorithms, with a mean gap of $1.316\%$ to a time-limited MILP reference and a win-or-tie rate of $90.0\%$ among the non-MILP methods. The results indicate that combining smooth continuous optimization with support-level search is an effective strategy for producing high-quality feasible solutions to large-scale FCNFP.
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Game Theory · Cross-listed from math.DS
Stable Coexistence in Ecologies and Games
Türkü Özlüm Çelik, Vincenzo Antonio Isoldi, Irem Portakal et al. (+1 more)
We study feasible stable
equilibria of Lotka-Volterra systems and their higher-order extensions. We complete the classification of impossible ecological interaction networks with at most four species and extend several of these impossibility results to families with arbitrarily many species. We then show that these sign-pattern obstructions are specific to the pairwise Lotka-Volterra model: arbitrary prescribed growth rates and pairwise coefficients can be supplemented by higher-order interactions so as to admit a feasible asymptotically stable
equilibrium. Through the correspondence with replicator dynamics, we interpret feasible
equilibria of higher-order Lotka-Volterra systems as totally mixed symmetric Nash
equilibria of symmetric multiplayer games, derive bounds on their number, and study their robustness under perturbations of the payoff tensors. We conclude by showing that every impossible ecology determines a nonempty open class of symmetric two-player games with no totally mixed evolutionarily stable strategy.
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All authors Türkü Özlüm Çelik, Vincenzo Antonio Isoldi, Irem Portakal, Giulio Zucal
Machine Learning · Cross-listed from hep-ex
"Transforming" LHCb: self-supervised maps of heavy-flavour decays
Marko Stamenkovic and Greg Landsberg
Decays of beauty and charm
hadrons provide sensitive probes of physics beyond the standard model, including decays with invisible particles, in which part of the final state leaves no reconstructed detector signature. The large heavy-flavour data samples recorded by the LHCb experiment at the CERN LHC, together with its precise tracking, displaced vertex reconstruction, and particle identification, make it particularly well suited to learning a map of reconstructed heavy-
hadron decay environments directly from data. We propose to bring recent advances in jet flavour tagging at ATLAS and CMS to significantly improve on the performance of the current LHCb taggers and extend them to the reconstruction of heavy-flavour decays with several invisible particles in the final state. To achieve this, we introduce a self-supervised
transformer architecture that learns the decay maps without flavour or exclusive-decay labels by inferring masked particle identification information and completing jets from which constituents have been removed. Across five classification tasks in simulated LHCb Open Data, the self-supervised model outperforms an otherwise identical
transformer with random weights, and performs comparably to a fully supervised
transformer. We achieve a tagging power of about 10%. In addition, removing constituents from reconstructed exclusive decays also systematically increases the model anomaly score relative to random removals from the same heavy
hadrons. We confirm this behaviour directly in 2017 LHCb
proton-
proton collision Open Data: the score increases for all eight studied heavy-flavour channels, and the signal region response exceeds that in the adjacent sidebands. These studies provide a proof of principle that mapping heavy-flavour decay environments through jets can transform flavour tagging in LHCb and extend the discovery reach for incomplete or otherwise unusual decays.
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Distributed Computing · Cross-listed from quant-ph
Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs
Joseph K. L. Lee, Mehrdad Malekmohammadi, Hong-Sheng Zheng et al. (+10 more)
Moving from
quantum research and development to production-grade, fault-tolerant
quantum workload execution remains one of the most significant challenges facing
quantum platform builders. While Python frameworks have enabled an easy entry point for
quantum algorithm design, the low-latency requirements for real-time
quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level
languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale
quantum systems. For this, we introduce $\textit{Backline}$, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build
quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several
quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of $2.305~\mu$s to an AMD Ryzen Threadripper PRO CPU and $4.5~\mu$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.
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All authors Joseph K. L. Lee, Mehrdad Malekmohammadi, Hong-Sheng Zheng, Shuli Shu, Cheick Doumbia, Kalman Szenes, Mehran Zamani Abnili, Thomas Ainsworth, Matthew Seymour, Thomas Germain, Leonhard Neuhaus, Josh Izaac, Lee J. O'Riordan
Computation and Language
StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean
Idan Davidovich, Debargha Ganguly, Vikash Singh et al. (+1 more)
Leading
benchmarks for formal
theorem proving with large
language models are small collections drawn from competition math, such as the IMO and Putnam, that poorly represent field-specific applications. We introduce StochBench, a Lean 4
benchmark of 450 graduate
stochastic-processes problems at varying abstraction levels, each paired with its natural-
language source. Addressing a field underrepresented in Mathlib, it covers finite and countable Markov chains, renewal processes, random walks, martingales, stopping times, queues, Brownian motion,
stochastic calculus, weak convergence, and Poisson and continuous-time Markov processes. Our Opus 4.8-based
agent achieves a 34.9% proof rate (157/450) under a 15-minute per-problem limit. StochBench better represents domain-specific applied mathematics while remaining challenging for advanced provers.
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All authors Idan Davidovich, Debargha Ganguly, Vikash Singh, Vipin Chaudhary
Sound
Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Models
Xiaoqun Liu, Tanu Mitra, Harshit Rajgarhia et al. (+1 more)
Speech-to-
speech (S2S) models now run inside dubbing, translation, and voice
agents. Unlike text models, they hear the speaker's voice, which carries the speaker's gender. A faithful system should treat a speaker as who they sound like, not as whoever usually says what they said. Testing this is harder than it looks, since most S2S models answer in a single, fixed output voice, hard-coded so it cannot drift toward a stereotype. Checking the output voice comes back clean even when the model is biased. We therefore ask two questions. When a model re-speaks the input, does the stereotype in the words shift the perceived gender of the output voice (voice rendering)? And when the model states the speaker's gender, does it follow the voice or the content (gender attribution)? We answer both with one controlled experiment crossing male and female voices with masculine-, neutral-, and feminine-stereotyped passages, on five open- and closed-source models in English, Spanish, and Mandarin. The rendered voice shows no stereotype drift. But every model decides the speaker's gender from the content, not the voice. Making the content one step more feminine (masculine -> neutral -> feminine) multiplies the odds of a "female" judgment by 1.7-24. When the content clashes with the voice, the worst model misgenders the speaker in 90% of cases. When they agree, it misgenders in only 2%. The bias thus hides in gender attribution, where fixed-voice evaluation cannot see, and where audits must look as S2S systems increasingly speak for real people.
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All authors Xiaoqun Liu, Tanu Mitra, Harshit Rajgarhia, Abhishek Mukherji