Peer Revenue Impact factor: volatile

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

All of arXiv

Monday 12 October

Astrophysics of Galaxies

Intermittency in the Multiphase ISM — II. Non-Gaussian Dust Polarization and Foreground Variance

Yue Hu

Interstellar turbulence produces non-Gaussian polarized Galactic dust emission whose higher-order statistics depend on thermal phase and turbulent forcing. We synthesize optically thin dust polarization from three thermally multiphase diffuse interstellar medium (ISM) simulations with compressive, mixed, and solenoidal forcing. We characterize the resulting non-Gaussian structure in the warm, unstable, and cold phases using $E$- and $B$-mode spectra, one-point distributions, scale-dependent increments, and Minkowski functionals. Both $E$ and $B$ modes are intermittent. Warm-phase emission remains closest to Gaussian, whereas cold gas shows flatter intermediate-scale spectra, broader non-Gaussian tails, and the strongest departures from Gaussian morphology. Compressive forcing generally produces the strongest projected intermittency, particularly in the cold phase and total emission, while mixed and solenoidal forcing show weaker signatures. These higher-order structures also enhance spatial fluctuations in polarized dust power: Gaussian fields matched to the same two-point covariance underestimate the region-to-region scatter by factors of up to $\sim2.6$. Our results show that thermal phase and turbulent forcing regulate not only the mean polarized dust spectrum but also the field-to-field distribution of foreground power, with implications for modeling Galactic foreground in CMB polarization measurements.
Read the full abstract

Category Theory

A characterization of central extensions relative to n-nilpotent objects

Marino Gran and James R. A. Gray

We characterize the central extensions, in the sense of the categorical theory of central extensions of Janelidze and Kelly, relative to the Birkhoff subcategory of $n$-nilpotent objects of a semi-abelian and algebraically coherent category. An extension $f \colon A \to B$ with kernel $K$ turns out to be central precisely when the iterated Huq commutator $[A,[A,\dots,[A,K]\dots]]$, with $n$ occurrences of $A$, vanishes. For $n=2$ this condition reads $[A,[K,A]] = 0$, that is, $[K,A]$ is a central subobject of $A$. In the special case of groups this description was obtained by Everaert and the first author. The present proof is categorical, and applies in particular to Lie algebras, to crossed modules, to cocommutative Hopf algebras over a field, and to any category of interest.
Read the full abstract

Category Theory

Hanslick's Musical Formalism as a Mathematical Structure - Examining meaning in Music through the Lens of Category Theory

Ilyas Khan

We investigate Eduard Hanslicks formalist aesthetics through the prism of Category Theory

Machine Learning

Cross-Market Meta-Learning for Electricity Price Forecasting

Delaram Pirhayatifard and Terrence Alsup

Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy: statistical and tree-based baselines saturate, while higher-capacity sequence models need more training data than a single market's few years of hourly prices can supply. Such methods also treat each node as an independent series, discarding the spatial dependencies induced by network topology and congestion. Because price formation across independent system operators (ISOs) is driven by shared physics and largely common market design, we instead treat cross-market history as a pretraining corpus. We present MetaGrid, a topology-aware transformer, meta-trained across markets so that a single zone-count-independent parameter set adapts to an unseen grid. On a leak-free benchmark of five U.S. ISOs, where every model receives the same prices, DA load forecasts and calendar features, MetaGrid attains the lowest average MAE, improving on the best baseline by 3.1% and on the same architecture trained from scratch by 7.7%.
Read the full abstract

Optimization and Control

Beyond periodicity: Fourier methods for optimal control

Milena Ispiryan, Vahagn Makaryan, Lusine Poghosyan et al. (+1 more)

Fourier-based spectral methods provide compact global approximations, permit simple differentiation and integration, and achieve spectral convergence for smooth periodic functions. Their use in finite-horizon optimal control problems (OCPs) is nevertheless challenged by the generally nonperiodic state and control trajectories: endpoint mismatch induces artificial discontinuities and can destroy spectral convergence. Leveraging Eckhoff-type endpoint-correction techniques, we develop a Fourier-based spectral framework for OCPs with possibly nonlinear dynamics and constraints. The correction improves convergence without enlarging the set of decision variables, yielding an efficient finite-dimensional numerical method. Furthermore, we formulate selected dynamics and obstacle-avoidance constraints through Sobolev residual bounds, which incorporate information about the residuals over the entire time horizon and improve constraint satisfaction. Numerical experiments on benchmark problems demonstrate competitive computational performance, lower residual errors, and improved solution accuracy.
Read the full abstract

All authors Milena Ispiryan, Vahagn Makaryan, Lusine Poghosyan, Tigran Bakaryan

Systems and Control

Direct Electromechanical Actuation of the Shift Drum in Non-Synchronized Motorcycle Transmissions: From Actuator Sizing and Electronics Design to Closed-Loop Position Control

Johannes Stockhammer

This thesis presents the direct electromechanical actuation of the shift drum in a non-synchronized motorcycle transmission. A DC motor drives the shift drum through a self-locking worm gear, so the entire foot shift mechanism is eliminated. As a reference, 84 manual gear changes of a large-displacement series-production motorcycle were measured on a chassis dynamometer; the mean shift time is 80 ms for upshifts and 92 ms for downshifts. The system design covers the complete chain from the mechanics to the software. The actuator motor is sized from inertia, friction and gear ratio. A dedicated shift controller is developed with supply filtering, signal conditioning for the position sensor and the incremental encoder, an H-bridge motor driver and a custom circuit board based on a SAMD21 microcontroller. The embedded firmware implements position control of the shift drum with a proportional controller, without inner speed or current loops. On a gearbox test rig, a gear change is completed in less than 80 ms without overshoot in a single measurement, below the mean of the manual reference shifts. The analysis reveals a trade-off in the choice of the worm gear ratio: at a ratio of 30, about 93% of the motor torque is needed to accelerate the motor shaft and the worm. A ratio of about 8 halves the required torque, but the worm gear then loses its self-locking. The measured shift times are published as an open dataset.
Read the full abstract

2610.17385 · 12 Oct · Original German title (2019): Entwickeln eines automatisierten Schaltgetriebes fuer Motorraeder. Diploma thesis, Westsaechsische Hochschule Zwickau, 2019; revised English edition 2026. 73 pages, 30 figures, 15 tables. Firmware and shift time dataset: https://doi.org/10.5281/zenodo.23002655)

Abstract PDF $CRCT ×1$DATA ×1

Machine Learning

Measure Neural Mappings

Ricardo Baptista, Edoardo Calvello, Bohan Chen et al. (+2 more)

We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operators on probability measures. Ensemble consistency requires outputs from growing ensembles sampled from an input law to converge to the specified population output in probability. We establish quantitative continuum limits for attention on noncompact laws under strong sub-Gaussian and tail-integrability assumptions. These laws accommodate unbounded state spaces in scientific machine learning, including Bayesian inference and uncertainty quantification. For fixed self-attention transformers, the expected output Wasserstein error is bounded by the empirical input Wasserstein error plus an additional $N^{-1/2}$ attention term. These self-attention transformers are MNMs under the stated assumptions: pre-norm architectures follow from compact stability after normalization, while post-norm architectures are covered under the stated tail conditions. We prove corresponding cross-attention estimates and extend the results to multihead attention and grouped transformer evaluation, with convergence as the group size grows. For Set Transformers viewed as measure-to-vector mappings, we obtain an $N^{-1/2}$ bound on the expected Euclidean output error under the stated assumptions. Experiments on seven operators in dimensions $d=1,2,3,5,10$ support the predicted particle-count dependence, using one-dimensional Wasserstein computations and Sinkhorn approximations in higher dimensions. The observed Wasserstein and sliced Wasserstein decay rates are close to their respective theoretical sampling benchmarks.
Read the full abstract

All authors Ricardo Baptista, Edoardo Calvello, Bohan Chen, Andrew Stuart, Urbain Vaes

Machine Learning

Variable Input Masked Autoencoders for Wrist-Worn Accelerometry

Harish Haresamudram, Wanting Mao, Aditya Radhakrishnan et al. (+2 more)

Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing models are pretrained using fixed input durations and sampling rates, despite substantial heterogeneity in recording conditions, downstream tasks, and deployment constraints. We introduce VarMAE, a variable input masked autoencoder that learns a single representation across sampling rates and temporal contexts. During pretraining we construct two views of the same signal: a long duration high frequency view, and a short duration lower frequency view, obtained via random cropping and downsampling. We perform masked reconstruction within each view and introduce a cross-resolution masked reconstruction objective that uses visible tokens from the long view to predict masked tokens from the short view. Cropping and downsampling impose complementary information bottlenecks, encouraging the model to learn representations that remain effective across input durations and sampling rate changes. We pretrain VarMAE on the large-scale NHANES dataset, producing a single pretrained encoder matching or outperforming controlled foundation model baselines on eight benchmark datasets. With only 3.82M parameters, VarMAE is parameter efficient and shows that a single foundation model can achieve broad transfer capabilities across sensing configurations.
Read the full abstract

All authors Harish Haresamudram, Wanting Mao, Aditya Radhakrishnan, Maxwell A. Xu, James M. Rehg

Machine Learning

Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion

Ali Javeri, Javad Hezareh, Radin Shahdaei et al. (+1 more)

Score-based black-box adversarial attacks pose a serious threat to deployed machine learning systems by exploiting repeated model queries to craft adversarial examples, requiring neither access to model parameters nor architecture. Existing defenses predominantly rely on stochastic output perturbations, which fail to prevent determined attackers from recovering stable gradient estimates by increasing query budgets or averaging over randomness. In this work, we propose the Gradient Confusion Defense (GCD), a retraining-free, deterministic test-time wrapper that actively shapes the probability vector returned to querying adversaries. By solving a constrained optimization problem grounded in the model's Jacobian, GCD aligns the exposed logit-gradient with a target confusion direction that is maximally misaligned with the true adversarial loss gradient—increasing the query complexity of attacks without sacrificing predictive utility or top-1 accuracy. We provide rigorous theoretical analysis demonstrating that GCD provably slows gradient-estimation-based attacks and induces systematic accept/reject errors in random search-based attacks, with the confusion effect amplifying as the fraction of defended queries increases. Extensive experiments on CIFAR-10 and ImageNet across multiple architectures (ResNet-18, ResNet-50, ViT-B/16) and four state-of-the-art attacks confirm these predictions: GCD achieves state-of-the-art robustness, while incurring zero clean accuracy degradation. %Crucially, unlike randomization-based defenses, GCD's deterministic structure provides resilience against adaptive attacks that exploit stochasticity, making it well-suited for security-critical deployment settings.
Read the full abstract

All authors Ali Javeri, Javad Hezareh, Radin Shahdaei, AmirMahdi Sadeghzadeh

Machine Learning

Robustly Reliable Learning for Regression

Avrim Blum and Anh Tuan Nguyen

We introduce the problem of robustly reliable learning for the regression setting in adversarial environments. Given a test point $x_{test}$, a training dataset $S$ of $n$ data points, a corruption budget $b \ll n$, and a real-valued hypothesis class $\mathcal H$, our goal is to output the optimal prediction range $I^*_{x_{test}}(S, b)$, such that any $L$-Lipschitz predictor consistent with all but at most $b$ points in $S$ must predict a value $h(x_{test}) \in I$. We then instantiate the analysis for $L$-Lipschitz hypothesis classes, a natural and widely studied class in the regression setting, focusing on two key scenarios. First, for the restricted case where the corrupted dataset is guaranteed to be $L$-Lipschitz consistent, we propose a simple and efficient $b^{th}$-best heuristic algorithm and prove its correctness in computing the tightest possible range $I^*_{x_{test}}(S, b)$. We then establish its computational complexity of $O(n)$ and show that it is computationally optimal in this scenario. Building on this foundation, we address the general scenario where the dataset $S$ is no longer $L$-Lipschitz consistent. We demonstrate that computing the optimal range in this setting reduces to analyzing a violation graph, which models the pairwise $L$-Lipschitz violations among data points. By leveraging the structural properties of this graph's kernel, we propose a Fixed-Parameter Tractable (FPT) algorithm that computes the exact optimal range in $O(n^2 + b^4)$ time. Furthermore, under the Strong Exponential Time Hypothesis (SETH), we establish a computational lower bound of $\Omega(n^{2 - o(1)})$, proving that our algorithm is computationally optimal. Finally, we discuss open directions for the problem of robustly reliable learning for regression.
Read the full abstract

Networking

What Is the Cost of a Handover Interruption? Rethinking 5G Mobility for Aerial Vehicles

André Melo, Yueqi Chen, Igor Kadota et al. (+1 more)

Low-altitude aviation is moving toward large-scale operations increasingly dependent on 5G cellular connectivity. However, 5G mobility remains blind to the operational risk of its decisions: the same interruption may be harmless in cruise over an empty field but critical near obstacles, crowds or other aircraft. Although 5G already provides mechanisms to obtain location, population density, and radio conditions, mobility decisions do not translate this information into an operational risk metric. We propose Risk-Aware Mobility (RAM), which maps predicted communication disruptions to an operational-risk proxy using information staleness, position uncertainty, and operational context, and exposes this risk to existing mobility mechanisms. In a dense São Paulo deployment with 188 licensed 5G sectors, risk-aware handover timing reduces the mean operational-risk proxy by 45% relative to conventional A3 without degrading serving-link quality. These results motivate shifting 5G mobility from optimizing handover performance to managing the operational risk of mobility decisions. Beyond handover timing, this perspective raises a broader question: which cells should serve aerial operations when connectivity is evaluated by operational risk rather than radio performance alone?
Read the full abstract

All authors André Melo, Yueqi Chen, Igor Kadota, Lourenço Alves Pereira Jr

Artificial Intelligence

EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design

Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong et al. (+1 more)

LLM-based automated heuristic design (AHD) discovers heuristics for combinatorial optimization problems (COPs), where an LLM generates candidates in an evolutionary search. Existing frameworks freeze the generator or fine-tune it on a scalar fitness signal hiding which instances each heuristic covers. Reflection-based frameworks improve heuristics with natural-language feedback, but it stays in the prompt and never reaches the generator's weights. We propose EPOCH, which makes instance coverage the objective of the evolutionary search and fine-tunes the generator to internalize reflection. Parent selection, operator allocation, and stagnation recovery all optimize how well the heuristics jointly cover the training instances, so the search returns a complementary heuristic ensemble. The generator learns from its own search by experiential preference optimization. A teacher LLM returns a natural-language insight on the generated heuristics, and the generator produces new heuristics with this insight. These improvements join the preference pairs for direct preference optimization (DPO). Since DPO only teaches the generator to rank these improvements, a reflective self-distillation (RSD) term makes it more likely to generate them on its own, and the gains accumulate over the long horizon of the search. Across COPs and open-source LLM backbones, EPOCH's heuristic ensembles outperform inference-only and fine-tuned baselines and generalize across problem scales.
Read the full abstract

All authors Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong, Faramarz Fekri

Artificial Intelligence

Get In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce

Adrian de Valois-Franklin, Alex Bogdan, Anas Ibrahim et al. (+2 more)

AI agents now browse and buy on people's behalf, and benchmarks measure how well the agents perform. Far less is known about the other side of the transaction: whether a merchant's website lets an agent in, publishes terms precise enough to decide a purchase, and leaves evidence of what was agreed. This paper presents a merchant-side benchmark built on those three questions and reports a pilot of it. The central measurement, decision sufficiency, rates each criterion of a concrete buyer mandate (total cost, cancellation or return terms, service date, variant identity) as determinate, indeterminate, or unknown from the merchant's own published evidence. Readiness scores carry their evidence coverage and missingness bounds; access is reported separately for a declared bot, a declared headless browser, and a user-present browser; and each observed term is mapped to the RAILS admissibility class of the evidence that could support it in a dispute. Four large merchants received the full assessment: three refused the declared profiles at their offer pages, and no mandate could be confirmed as met, because each merchant left a total or a refund term undetermined. An access census of 555 sites of merchants that two payment processors publicly name as clients found the home page unreadable to both declared profiles at 105 sites (18.9%). A declared assessment of 330 merchants, scored by deterministic rules without a rater, found that evidence coverage rather than the score decides who can be ranked: 115 merchants (35%) reached the ranking threshold, rankable profiles scored a median of 89 to 90, and material terms was the one pillar well below the others, because the all-in total was decidable at 18 merchants. The pilot shows that the measurements can be taken; it does not validate them or rank merchants, and the weights and the projected clearing band await validation against outcomes.
Read the full abstract

All authors Adrian de Valois-Franklin, Alex Bogdan, Anas Ibrahim, Ibrahim Shaer, Tamaz Andguladze

Earth and Planetary Astrophysics

Ultraviolet Reflected-Light Spectroscopy of Modern Earth Analogs with Starshade Observations

Zahra Ahmed, Stuart Shaklan, Mario Damiano et al. (+3 more)

Characterizing rocky, habitable zone planets in reflected light is a key science objective for future direct imaging missions, and the ultraviolet (UV) is uniquely suited for supporting the characterization of Earth-like atmospheres. The Hartley-Huggins ozone band at $0.25 ~\mu\mathrm{m}$ remains detectable at low spectral resolution down to $0.1\%$ PAL $\mathrm{O}_2$, providing a sensitive proxy for oxygen-bearing chemistry even for Proterozoic-like atmospheres where the visible and near-infrared $\mathrm{O}_2$ features become inaccessible. This work uses an end-to-end framework for UV starshade spectroscopy of modern Earth analogs that integrates high-fidelity observation simulation, conservative post-processing, and Bayesian spectral retrieval to quantify ozone detection significance. The framework is applied across 108 simulated scenes spanning a range of planet phase angles, system inclinations, and exozodiacal dust densities. The results establish that robust ozone detection with UV-only, $R=7$ reflected light spectroscopy requires a photometric $\mathrm{SNR} \sim 30$. UV starshade observations with the Habitable Worlds Observatory achieve this threshold within 3-day integrations for solar-type systems at 10 parsecs with inclinations up to $60^\circ$ at favorable phases ($\leq 90^\circ$). Above this SNR threshold, ozone detections are robust to systematic bias from imperfect exozodi calibration within $\sim30\%$, and the median retrieved ozone abundance remains accurate to within one order of magnitude of the true value.
Read the full abstract

All authors Zahra Ahmed, Stuart Shaklan, Mario Damiano, Armen Tokadjian, Edward W. Schwieterman, Simone D'Amico

Artificial Intelligence

What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs

Iago Brito, Walcy Rios, Lucas Fernandes et al. (+2 more)

A shared latent space is often assumed to make representations from different schemas directly interchangeable, yet sharing parameters, dimensionality, or a prior does not determine how corresponding instances are expressed in latent coordinates. We show that this assumption conflates recoverable correspondence, direct compatibility, and decoder reuse. In a controlled multi-schema motion VAE on HumanML3D, even identical inputs yield only $5.9$–$17.7\%$ raw Recall@1 and $0.221$–$0.484$ paired-token cosine, despite correspondence being almost perfectly recoverable after linear calibration. Cycle consistency strengthens recoverability but still leaves direct retrieval and token agreement near zero. In contrast, only $64$ paired clips raise raw Recall@1 to $88.4$–$90.3\%$ and token cosine to approximately $0.82$, making most post-hoc alignment unnecessary, although crossed decoding remains substantially worse than native reconstruction. We further find that reducing measured marginal discrepancy does not imply schema invariance. These results show that a shared backbone or prior does not ensure directly compatible latent coordinates, while limited paired correspondence can substantially improve direct compatibility in the tested setting.
Read the full abstract

All authors Iago Brito, Walcy Rios, Lucas Fernandes, Kaiki de Oliveira, Arlindo Galvão Filho

Computation and Language

PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models

Quentin Fruytier, Anton Xue, Sanjay Shakkottai et al. (+1 more)

Diffusion language models accelerate generation by decoding multiple masked positions per forward pass. However, accuracy is known to drop when parallel decoding trajectories deviate from their sequential counterparts, which are slow and expensive "oracles" that adhere to the joint distribution of language. In this paper, we propose Parallel Inconsistency Guidance (PIG), a new paradigm that uses a trained auxiliary model to directly estimate the inconsistency between parallel and sequential generation from a single model forward pass. During inference, masked positions are traversed by decreasing confidence, and tokens are added to the parallel decode set one-at-a-time until the total estimated inconsistency between the parallel and sequential generation exceeds a threshold. Surprisingly, it suffices to train a lightweight ~2M parameter auxiliary model on a small mixture drawn from OpenCoder, GSM8K-train, MATH-train, and MBPP-train. For LLaDA-8B Instruct and NemotronDiffusion-3B Instruct, PIG extends the accuracy-TPF frontier on GSM8K, MATH500, MBPP, HumanEval, and MMLU, when compared to the industry standard Fast-dLLM and measures favorably on LLaDA-8B Instruct against DAPD, KLASS, EB-sampler, and other learned unmasking policies.
Read the full abstract

All authors Quentin Fruytier, Anton Xue, Sanjay Shakkottai, Sujay Sanghavi

Software Engineering

VulnBump: A Benchmark for Fixing Vulnerable Dependencies in Real Projects While Keeping the Build and Tests Green

Gabija Pasiunaite, Ryan McMorrow, Brian Clark et al. (+2 more)

Software composition analysis (SCA) tools report far more vulnerable dependencies than teams fix: in Snyk Open Source production data, 94% of vulnerability findings have a known fixed version, yet only about 10% are resolved. Agentic coding tools could close this gap, but no benchmark evaluates them on vulnerability-driven upgrades in real, runnable projects across ecosystems, verified independently of the agent. We present VulnBump: 307 upgrade tasks covering 2,066 fixable vulnerabilities in 43 repositories across six package managers, matched to Snyk Open Source production vulnerability distributions. An upgrade passes only if, in a fresh container, dependencies resolve, a rescan shows the targeted vulnerabilities fixed, the project builds, and every previously passing test still passes. A non-agentic baseline that only applies Snyk's recommended version passes 46.9% of upgrades; with the same model (Claude Sonnet 5.5), Snyk's Evo Remediation Agent passes 90.2%, Claude Code + Snyk scan 77.5% and Claude Code unaided 59.6%. The agents fail in opposite ways: Claude Code mostly leaves the vulnerability in place, whereas most of Evo Remediation Agent's failures break the build or tests. An SCA scanner is a key tool for agents: Snyk's guidance raises Claude Code's PASS rate by 17.9 percentage points; without it, Claude Code often settles on a still-vulnerable version. Yet knowing the target is not enough: given the same Snyk findings, Evo Remediation Agent's purpose-built harness passes 12.7 points more than Claude Code + Snyk scan. Major framework migrations remain hard for all harnesses. Because the agents keep projects working at similar rates, evaluating remediation quality in dependency upgrades requires verifying the fix, the build and the tests together.
Read the full abstract

All authors Gabija Pasiunaite, Ryan McMorrow, Brian Clark, Liran Tal, Luke Tu

Instrumentation and Detectors

FINESSE: A Versatile Beamline for Fundamental Interactions with Neutrons at the European Spallation SourcE

J. Amaral, J. Barrow, M. Bartis et al. (+71 more)

FINESSE (A Versatile Beamline for Fundamental Interactions with Neutrons at the European Spallation SourcE) is a proposed versatile beamline for fundamental interactions with neutrons at the European Spallation Source. Building on the HIBEAM design studies, FINESSE extends the original concept into a dual-guide beamline capable of operating either in a high-flux configuration or in a low-background configuration within a single ESS beam port. The high-flux branch is optimized for rare-process searches, including neutron–antineutron oscillations, neutron–sterile-neutron transitions, axion-like-particle searches, neutron charge measurements, and rare neutron decays. The low-background branch is designed for precision measurements requiring strong suppression of prompt and line-of-sight backgrounds, with a cryogenic neutron electric-dipole-moment experiment used as a benchmark case. A baseline exchangeable-guide concept has been developed in which only short guide sections inside the ESS bunker are exchanged, while the upstream extraction system and chopper positions remain fixed. A staged implementation, Short FINESSE, would use the upstream section of the high-flux beamline together with a compact magnetically controlled experimental region, enabling an early physics and R&D programme before completion of the full facility. This paper summarizes the scientific case, beamline concept, guide simulations, chopper and timing system, magnetic-field control, detector developments, and staged implementation strategy. FINESSE would establish a long-term particle-physics infrastructure at ESS, providing a beamline for both discovery-driven searches and precision neutron measurements throughout the lifetime of the facility.
Read the full abstract

All authors J. Amaral, J. Barrow, M. Bartis, D. H. Beck, P. M. Bentley, J. Bijnens, G. Brooijmans, L. J. Broussard, A. Burgman, G. Cibinetto, G. Croci, D. D. Di Julio, P. Fierlinger, B. W. Filippone, C. Gatto, H. Gisbert, H. Gogoi, G. Gorini, A. Gutierrez-Rodriguez, A. Koberinski, M. Kaczmarek, M. Silarski, M. O'Riordan, A. Heinz, J. Henderson, M. Holl, H. T. Johansson, B. Jonsson, H. Henriksson, L. Jonsson, Y. Kamyshkov, M. P. Kuhl, P. Kinhult, R. Kolevatov, A. Kozela, W. T. Lee, K. K. H. Leung, T.-J. Liang, G. Luzin, B. Meirose, D. Milstead, R. Mohapatra, G. Muhrer, A. Muraro, A. Nepomuceno, N. Okada, A. Oskarsson, T. Palasz, R. Pasechnik, V. Larsson, L. B. Persson, O. Putignano, J. Ramsey, C. Snow, L. Werselius, B. Rataj, A. Rodriguez-Sanchez, A. Saunders, V. Santoro, F. Scioscioli, R. Shrock, D. Silvermyr, H. Sina, W. M. Snow, Y. V. Stadnik, L. Tchelidze, N. Tsapatsaris, A. Tureanu, A. Vilela Pereira, M. Wolke, J. Womersley, S. Xu, A. R. Young, K. Osterberg

Networking

Computing All-Pairs Shortest Communication Time Paths for Periodically Varying Satellite Networks

Koushik Sinha, Dibakar Saha, Sai Puppala et al. (+1 more)

We address the problem of designing an efficient algorithm for computing the all-pairs shortest-time paths in time-varying networks, specifically targeting the dynamic topological characteristics of low earth orbit (LEO) satellite constellations. We model such temporal graphs as $G = (V, E_t)$, where $V=\{1, 2,\ldots , n\}$ is the set of $n$ vertices in the graph, and $E_t$ is the set of $M$ time-varying edges considered over a total time window of size $T = M\tau$, $\tau$ being the duration of each interval in that time window. For such a network, our algorithm finds the all-pair shortest paths in $O(M^2n^3)$-time. A key advantage of our approach is its computational structure, which mirrors two matrix multiplication steps, first in the forward direction and then in the backward direction. This structure of the algorithm makes it exceptionally suitable for parallelization on SIMD architectures like GPUs, thereby offering a significant speedup over traditional shortest-path algorithms and enabling efficient, network-wide message routing in rapidly changing LEO satellite constellations.
Read the full abstract

All authors Koushik Sinha, Dibakar Saha, Sai Puppala, Bhabani P. Sinha

Optimization and Control

Global Extendability of Flat Cliques in Sparse Moment Relaxations

Haoran Sun

Moment relaxations provide lower bounds for polynomial optimization problems, while the moment matrix carries more information than the bound itself. When the moment matrix satisfies the flat truncation condition, the bound is tight and global minimizers can be extracted from the matrix. In correlatively sparse relaxations, it is common that only some blocks are flat. We study whether such partial flatness still reveals part of a global minimizer. On a unicycle trajectory optimization problem, repeatedly extracting the atoms of flat cliques and fixing them recovers feasible, near-optimal trajectories. We then construct a counterexample whose relaxation is tight and has a rank-one flat clique, yet the unique extracted atom cannot be extended to a global minimizer. Finally, we give sufficient conditions for global extendability that involve only the boundary of a clique. Every extracted atom minimizes its clique objective given its boundary value, and when the bound is tight and the boundary moment block has rank at most one, the extracted atom of the clique is globally extendable.
Read the full abstract

Machine Learning

HarnessTTT: Learn to Optimize Harness at Test-Time for Scientific Discovery

Yingzi Ma, Yulong Cao, Nanxi Li et al. (+5 more)

Scientific discovery with language models requires finding the best valid solution within a finite computational budget. We study how feedback from this search can improve the procedure used to conduct it. We introduce HarnessTTT, a test-time learning framework that trains a harness proposer while keeping the executor frozen. The proposer generates executable harnesses comprising system prompts, reusable skills, tools, middleware, and execution controls. Each harness guides a budgeted discovery rollout, whose outcome provides feedback for updating the proposer. A discovery-oriented policy-gradient objective combines gap-normalized rewards, leave-one-out advantages (RLOO), and max-sharpening to favor high-performing proposals. Generated components are validated before execution, and the search retains the best valid solution and selected harnesses across rounds. Across fourteen discovery tasks in mathematics, systems, GPU kernels, heuristic programming, astrodynamics, and biology, HarnessTTT with a frozen 9B executor improves on OpenEvolve with the same executor on all eleven benchmark tasks (e.g., from 1.17 to 2.64 on circle packing), improves on the discovery-fine-tuned Finch-9B by up to 92%, holds the best open-weight sub-10B result on nine, and reaches 0.380919 on Erdős minimum overlap, better than the best human reference of 0.380927. Scaling the proposer and frozen executor to 27B improves on eleven of fourteen tasks, and transferring the learned harnesses to GPT-5.5 matches or exceeds the strongest reported system of any backbone on nine of fourteen tasks. Together, these results show that harness search can itself be learned at test time: a trained proposer lets a small frozen executor search effectively, and the harnesses it finds carry over to stronger executors.
Read the full abstract

All authors Yingzi Ma, Yulong Cao, Nanxi Li, Fang Wu, Yizhe Zhang, James Zou, Yejin Choi, Chaowei Xiao

Probability

Large Network limit for Latent Variable Process Models with Endogeneously Evolving Interactions among Multiple Types

Ankan Ganguly and Konstantinos Spiliopoulos

Motivated by the opinion and social dynamics literature, we rigorously study dynamically co-evolving latent space network models, where agent opinions evolve based on a latent state space model influenced by and influencing an underlying dynamically evolving graph determining their interactions. In contrast to earlier work, see [18], agents are allowed to be heterogeneous, i.e., of different types, and their dynamic evolution can capture behaviors like attraction and repulsion between and within groups. In addition, the model encodes bidirectional feedback between the latent process and the underlying interaction network, persistence effects are included, and the interactions are localized, i.e., individual agents do not have global information. We produce weak and empirical limit theorems that encode the asymptotic joint distribution of agent opinion trajectories and their interaction network. In doing so, we describe and exploit the complex dependence structure of the model.
Read the full abstract

Information Retrieval

HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation

Tripti Shukla, Shwetha Somasundaram, Akshat Gupta et al. (+3 more)

Generative recommendation models have shown promising results by representing items as semantic IDs and generating these IDs autoregressively. We introduce an approach showing that explicitly incorporating hierarchical and attribute-level supervision into generative recommendation can simultaneously improve training efficiency and long-tail recommendation performance. Existing approaches typically optimize semantic ID prediction with a standard causal cross-entropy objective, which does not explicitly exploit the hierarchical structure of semantic IDs and can require substantial training to learn effective item representations. Moreover, data-sparse tail items often suffer from poor representations due to limited supervision, resulting in weaker long-tail recommendation performance. We address these challenges with HASTE (Hierarchical and Attribute-aware Supervision for generaTive rEcommendation), a unified training framework that introduces hierarchical and attribute-specific supervision. HASTE's Hierarchical Loss explicitly exploits the multi-level structure of semantic IDs, providing supervision at each level of the item hierarchy and substantially accelerating training convergence. Its Attribute-Specific Losses encourage the model to preserve fine-grained item attributes, leveraging the language model's inherent semantic knowledge to improve representations and recommendations for tail items. We extensively evaluate HASTE on a fashion recommendation benchmark and across multiple domains of the Amazon dataset. HASTE improves recommendation performance across datasets and item-frequency regimes. Notably, the Hierarchical Loss achieves approximately 2x faster convergence than the standard causal cross-entropy objective, substantially improving training efficiency while maintaining strong recommendation quality.
Read the full abstract

All authors Tripti Shukla, Shwetha Somasundaram, Akshat Gupta, Rohit Garg, Paul Duff, Ian Anderson

Quantum Physics

Periodic-Pulse Control of Collective Emission and Photon Statistics in Interacting Quantum Emitters

Kavalambramalil George Paulson, Fu Li and Hanna Terletska

We investigate the collective emission spectra and photon correlations of two identical dipole-dipole-coupled emitters under continuous-wave (CW) and periodic pulsed driving. Our results show that a periodic train of instantaneous $\pi_x$ pulses can generate emission spectra whose dominant features closely resemble those obtained under CW excitation. We find that the structure of the collective emission spectra reflects the interplay among the resonance conditions, dipole-dipole coupling, and the inter-pulse delay $\tau_D$. We further demonstrate that finite-duration $\pi_x$ pulses with sufficiently high Rabi frequencies can closely reproduce the ideal instantaneous-pulse limit. In contrast to the emission spectra, the photon correlations $g^{(2)}(t,\tau)$ are governed primarily by $\tau_D$ and, unlike under CW driving, become increasingly insensitive to the resonance conditions in the short-pulse limit. These results establish periodic pulsed driving as a versatile means of tailoring both the emission spectra and the photon statistics of coupled two-emitter systems.
Read the full abstract

Computational Physics

Wisevolve Potential Engine: Machine-Learning Molecular Dynamics at 100 Million Atom-Steps per Second on a Single GPU

Hekai Bu and Zheyong Fan

General-purpose molecular dynamics software supports diverse interatomic potentials and simulation requirements. Within a simulation stage, the loaded potential model and the required calculations typically remain fixed as the atomic configuration evolves. We present the Wisevolve Potential Engine (WPE), which uses this information from GPUMD to select and reuse a GPU execution policy tailored to the model and simulation stage. GPUMD retains simulation control and ownership of the simulation data. On a single NVIDIA RTX 5090, WPE-enabled GPUMD (GPUMD-WPE) achieves a median throughput of 109 million atom-step/s across 106 neuroevolution potential (NEP) workloads. For a 15.1-million-atom diamond Si system, it sustains 142 million atom-step/s. Relative to GPUMD, geometric-mean speedups are 3.49x across these NEP workloads, 3.61x across 16 workloads using NEP with dynamic charges (qNEP), 1.24x across 28 classical-potential workloads, and 10.5x across 56 path-integral molecular dynamics (PIMD) configurations. End-to-end calculations of water structure, lattice thermal conductivity, and PIMD thermal expansion show close agreement with corresponding GPUMD results and published simulation results. A separate 12.5-million-atom Cu shock-compression simulation demonstrates its applicability to large-scale nonequilibrium dynamics. These results support model- and stage-specific specialization as a practical way to accelerate GPU potential evaluation in general-purpose molecular dynamics workflows.
Read the full abstract

Artificial Intelligence

When Goals Leak Across Relationships: How Cached Goal Representations Shape Cross-Client Decisions

Saloni Gupta, Sébastien Krier and Glenn Matlin

As LLM agents take on more autonomous roles, they may interact with multiple clients and counterparties whose goals and incentives conflict. If an agent maintains shared inference state across these interactions, information associated with one relationship may influence decisions made for another. We test OLMo-3-7B-Instruct in a controlled two-client environment with 20 market scenarios, where each client is assigned an opposing objective. With shared conversational memory, the model deviates from the current client's objective in 22% of eligible decisions. We then investigate how these goals are represented in the KV cache. Linear probes show that client-goal ownership becomes highly recoverable in middle layers and is concentrated in particular attention heads. We use these results to identify where to intervene in the cache and test whether these representations affect the model's decisions. Ablating the competitor's cached goal representations at these locations shifts the model toward the current client. The effect is larger for heads identified by probing than for randomly selected heads, and larger when ablating goal tokens than matched non-goal tokens from the same client. Our results show that client-specific goal representations persist in the KV cache and can causally influence decisions made for other clients, highlighting the importance of relationship-specific state isolation in persistent LLM agents.
Read the full abstract

Optics

Zeptosecond-level timing-noise floor in optical-to-terahertz photomixing

William F. McGrew, James Greenberg, Scott C. Egbert et al. (+6 more)

Optical generation of terahertz (0.1 to 10\,THz) carriers can, in principle, preserve the spectral purity of an optical reference without the noise penalty associated with electronic frequency multiplication. Here, we identify and suppress the dominant excess-noise mechanisms that limit this conversion in high-power uni-travelling-carrier photodiodes. We characterize amplitude-to-phase conversion as functions of photocurrent and reverse bias and identify operating parameters to minimize the effect. We quantify the white phase noise introduced by optical amplification and show that this contribution can be eliminated by directly driving the photodiode with a high-power optical source. With the excess noise mitigated, we use cross-correlation phase-noise measurements to observe a white phase-noise floor of $-161.0$~dBc/Hz at 263.2~GHz, corresponding to a timing-noise floor of 7.6~zs/$\sqrt{\mathrm{Hz}}$. The remaining floor follows the photocurrent dependence expected from shot noise. Extending the measurement across the full WR-3.4 band, from 220 to 330~GHz, yields timing-noise floors below 10~zs/$\sqrt{\mathrm{Hz}}$ throughout, with a minimum of 6.0~zs/$\sqrt{\mathrm{Hz}}$. These results establish direct photomixing as a route to sub-10-zs/$\sqrt{\mathrm{Hz}}$ white timing-noise floors for continuous-wave sources at terahertz frequencies and identify the noise processes that must be controlled to reach this regime.
Read the full abstract

All authors William F. McGrew, James Greenberg, Scott C. Egbert, Brendan M. Heffernan, Y. Lange Simmons, Tatsuya Tanigawa, Kazutoshi Kato, Tadao Nagatsuma, Antoine Rolland

Computation and Language

UMCR: Universal Medical Concept Recognition through Guideline Integration and Agentic Linking

Fabrice Lamarche and Amal Zouaq

Medical Concept Recognition is the task of extracting and grounding spans in medical text to entries in a standard ontology. Current methods are generally developed towards a single ontology and source text type (clinical notes, drug labels, etc.) The central challenge is therefore to build methods that adapt to any target ontology without requiring ontology-specific retraining. More recent approaches like REAL and AutoPCR make direct use of Large Language Models and can theoretically adapt to any ontology choice but suffer from a lack of generalization. We propose UMCR, a pipeline that allows extraction and linking to any target ontology using any source text variety, utilizing direct guideline integration and an agentic linking module. We evaluate our method on the recent MedPath benchmark, covering a wide variety of medical text types and vocabularies, and show that our method achieves strong performance across these tasks. This ability unlocks interoperability in the healthcare domain, allowing for extraction and alignment between different medical standards. Our method also reduces dependence on parametric LLM memory, thanks to improved interaction with the target ontology compared to other proposed approaches.
Read the full abstract

High Energy Physics (Theory)

Yang-Baxter deformed sigma models on $\mathrm{SU}(N)/\mathrm{SO}(N)$

Danila Alexandrov, Alexey Bychkov, Ivan Kalinichenko et al. (+1 more)

We study Yang–Baxter deformations of two-dimensional sigma models on compact symmetric spaces $\mathrm{SU}(N)/\mathrm{SO}(N)$, with particular emphasis on their ultraviolet behavior. We claim that the ultraviolet fixed point is described by the $\mathrm{SO}(N,\mathbb C)/\mathrm{SO}(N,\mathbb R)$ non-compact WZW model together with $N-1$ compact free bosons. We determine the leading correction to this fixed point and identify it with the perturbation generated by the corresponding screening currents. We also extend our construction to the broader class of symmetric spaces of maximal rank.
Read the full abstract

All authors Danila Alexandrov, Alexey Bychkov, Ivan Kalinichenko, Alexey Litvinov

Materials Science

Collision Cascade Morphology and Defect Nucleation in Pure Tungsten and an Equimolar Refractory W-Ta-Cr-V Alloy

Mark Fedorov, Francisco Javier Dominguez-Gutierrez, Damian Kalita et al. (+1 more)

Collision cascades and defect-formation mechanisms in pure W and the WTaCrV refractory high-entropy alloy were investigated using molecular dynamics simulations over primary knock-on atom (PKA) energies of 0.1-100 keV. Machine-learning tabulated Gaussian approximation potentials (tabGAP), complemented by short-range Ziegler-Biersack-Littmark (ZBL) interactions, were employed. Analysis of the heat spike lifetime, affected region, atomic relocations and surviving defects reveals an importance of subcascade formation and presence of light elements in the alloy. Cascade splitting into subcascades contributes to the distribution of the PKA energy over a larger region in the material, nucleating less dislocations and smaller voids - latter being present only in W and absent in WTaCrV. Since cascade splitting is more frequently observed in WTaCrV, the alloy shows better resistance to primary irradiation damage compared to pure W. Apart from cascade splitting, analysis of interstitials shows that Cr and to lesser extent V, being lighter and smaller elements, accommodate the damage in form of point defects, contributing to the mitigation of formation of the dislocation loops.
Read the full abstract

All authors Mark Fedorov, Francisco Javier Dominguez-Gutierrez, Damian Kalita, Łukasz Kurpaska