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All of arXiv

Tuesday 6 October

Astrophysics of Galaxies

(Monty Python and the) HoliGRALE: A Hybrid GRALE Lens Inversion Methodology Diagnostically Evaluated with Synthetic and Real Data

Derek Perera, Jori Liesenborgs, John H. Miller Jr et al. (+4 more)

We present HoliGRALE (Hybrid, only light included, GRAvitational LEnsing), a hybrid lens modeling extension of the freeform method GRALE. HoliGRALE incorporates physically motivated parametric components within the freeform genetic algorithm GRALE. Using three synthetic clusters from IllustrisTNG, each of varying complexity, we evaluate its performance according to its accuracy in reconstructing the density distribution, image positions, magnifications, and time delays. We find that HoliGRALE generally outperforms GRALE on all fronts, and shows greater accuracy in regions containing a high image number density. We estimate the error budget in projected density to be $<5\%$ for simple, few component clusters, and $\sim5-10\%$ for complex, multi-component clusters in the regions constrained by observed images. We then use HoliGRALE to model three lenses: RX J2129.7$+$0005, MACS J0138.0$-$2155, and Abell S1063. In each case, HoliGRALE is able to accurately reconstruct observed magnifications and time delays. In its predictions, HoliGRALE is found to be more precise than GRALE. We particularly highlight the predicted time delay for the next arriving image of SN Requiem to be $4346.61^{+490.27}_{-818.36}$ days ($\approx$ April 2026 – October 2029), consistent with previous work, albeit with less precision. In Abell S1063, we report the reconstruction of a main dark matter halo core radius of $89.9\pm33.2$ kpc, in excellent agreement with past work and useful in testing theories of dark matter. Our results emphasize the expected error budget of HoliGRALE, which should be mentioned and scrutinized in future applications of HoliGRALE for other lens systems.
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All authors Derek Perera, Jori Liesenborgs, John H. Miller Jr, Ashley Francis, Liliya L. R. Williams, Birendra Dhanasingham, Jose M. Diego

Artificial Intelligence

Enabling Dynamic Computation in Looped LMs

Aayush Mishra, Arnau Padrés Masdemont, Victor Conchello Vendrell et al. (+3 more)

Looped LMs are parameter efficient and promise dynamic computation (saving memory and FLOPs on easy tokens). However, state-of-the-art open Looped LMs trained with this dynamic computation capability (Ouro models) do not realize it in practice as each loop iteration (depth) requires its own level of KV-cache, necessitating all loop computations. Moreover, Ouro's early-exit prior is enforced on each token equally, which results in static lower-depth like processing of all tokens regardless of difficulty. In this work, we propose a simple "best-available" KV caching strategy that works out-of-the-box, creating a new frontier in the performance vs depth space. Our approach enables up to 30% reduction in FLOPs and KV memory while retaining full-depth performance, showing the true flexibility of Looped LMs. Furthermore, training looped LMs with awareness about this KV caching strategy improves performance and efficiency. Finally, we apply a small but effective fix to the early-exit prior enforcement objective that makes tokens exit at truly heterogeneous depths based on effort. Our findings are validated on Ouro models as well as smaller looped LMs pre-trained from scratch.
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All authors Aayush Mishra, Arnau Padrés Masdemont, Victor Conchello Vendrell, Jordi Ros Giralt, Arash Behboodi, Fabio Valerio Massoli

Cryptography and Security

TwinGuard-Lite: A Rule-Based State-Admission Gateway for Generative Patient Digital Twins

Wenhui Chu and Sheikh Rabiul Islam

Future generative patient digital twins may combine longitudinal health records with language-model agents and keep information across sessions. The wording of a proposed update does not reveal whether it comes from an allowed source, conflicts with the patient's record, or belongs to someone else. We present TwinGuard-Lite, a rule-based gateway that admits an update to a twin's persistent state only if it passes checkable approximations of two integrity properties. Grounded state consistency (GSC) is approximated by a provenance check and a contradiction check; cross-patient noninterference (CPN) is approximated by a namespace check against trusted transport metadata. We evaluate three attack families over 20 seeded person-level splits of a semi-synthetic stream built from the openly licensed eICU database demo, with 29,131 $\pm$ 2706 candidate updates per seed. A keyword filter and an anomaly detector detect 16% and 24% of attacks, respectively; a provenance-only ablation detects 59.9% but misses every cross-patient attack. Combining GSC and CPN yields 99.1% precision, 90.5% recall, a 94.6% F1 score, and a 0.033% false-positive rate. Every attack the full gateway admits falsely claims a trusted source; when the retrieval channel does so, 92.9% of retrieval attacks are admitted. The results hold under stated trust assumptions, and no language model, agent, or retriever is executed: TwinGuard-Lite is a mechanism-level proof of concept that motivates authenticated, patient-bound ingestion, not a clinical safeguard.
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Information Theory

On Optimal Encodings and Systematicity in Function-Correcting Codes

Charul Rajput, Kanchana Lokshmii Jagatti and B. Sundar Rajan

Function-correcting codes (FCCs) protect the value of a function $f$ of the message against $t$ errors. In the original formulation of Lenz et al. (2023), the encoding is systematic, and the lower bound of $2t$ on the redundancy relies on this form. Recently, D. Ho (arXiv, 2026) showed, for linear functions and linear encodings, that systematicity can cost redundancy. We begin with the OR function to show that the cost is not confined to the linear setting: a non-systematic encoding attains redundancy $1$ while every systematic encoding needs $2t$. For a linear function $f$ and a fixed linear code $C$, let $d_f$ denote the minimum distance between codewords of messages with different function values. Different generator matrices of $C$ assign different codewords to the messages and can give different values of $d_f$. We study which generator matrix of $C$ gives the largest $d_f$. We give an algorithm that constructs an optimal generator matrix and determines the optimal value as the weight of a codeword in a greedy basis of $C$. We then characterize, in terms of information sets, when a generator matrix in systematic form attains this optimum, and give a necessary condition that is checked on the low-weight codewords of $C$ alone. For two-valued functions, we drop both linearity and systematicity. Using the minimality of initial segments of the simplicial order with respect to Hamming neighbourhoods, we show that a non-systematic $(f,t)$-FCC of length $n$ exists if and only if a condition depending on $f$ only through the size of its smaller preimage holds. For $t=1$, redundancy $1$ is sufficient for every nonconstant two-valued function on $\mathbb{F}_2^k$ with $k\ge 10$. For general $t$, redundancy $1$ suffices for all two-valued functions once $k$ is large enough, and for each $s<2t$ we give an upper bound on the threshold in $k$ beyond which redundancy $s$ suffices.
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Signal Processing

Hierarchical RIS Association for FR3 Space-Air-Ground Integrated Networks

Muddasir Rahim, Irfan Azam and Soumaya Cherkaoui

The growing demand for diverse sixth-generation (6G) applications requires efficient connectivity, seamless service continuity, and extended network coverage. Although terrestrial infrastructures can effectively serve most wireless user equipment (UE), communication reliability may be significantly degraded in remote or blocked environments. To address this challenge, this paper proposes a reconfigurable intelligent surface (RIS)-assisted space-air-ground integrated network (SAGIN) operating in the upper mid-band (FR3) spectrum. The considered network consists of a terrestrial base station (BS), a low Earth orbit (LEO) satellite, multiple terrestrial RISs (TRISs), and unmanned aerial vehicle-mounted RISs (ARISs), where the BS serves as the primary access node, while the LEO satellite provides complementary connectivity when terrestrial links are unavailable or cannot satisfy the required quality-of-service (QoS) requirements. Within this framework, we formulate a hierarchical RIS association problem to maximize the achievable network sum rate under RIS capacity constraints. The resulting optimization problem is a mixed-integer nonconvex problem due to the binary association variables and the coupling among UEs competing for limited RIS resources. To efficiently solve the problem, we develop a hierarchical many-to-one matching-based RIS association framework. Specifically, UEs with satisfactory terrestrial connectivity are first associated with TRISs, whereas UEs that cannot be reliably served by the terrestrial network are subsequently associated with ARISs through the non-terrestrial network. Simulation results demonstrate that the proposed hierarchical association framework significantly outperforms greedy and random association schemes in terms of the achievable network sum rate.
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Analysis of PDEs

Li-Yau and Harnack estimates for nonlinear parabolic equations

Thalia Jeffres and Xiaolong Li

We establish global Harnack inequalities for positive viscosity solutions of several classes of nonlinear parabolic equations. The equations include positively homogeneous uniformly parabolic flows on Euclidean space and flat tori, normalized weighted $p$-Laplacian heat flows, and homogeneous isotropic diffusions on closed smooth metric measure spaces. For the normalized infinity-Laplacian heat flow, no curvature assumption is needed. For the ordinary weighted $p$-Laplacian, we obtain pressure estimates under nonnegative Bakry-Émery Ricci lower bounds. The proofs combine comparisons at two independent space-time points using the maximum principle for semicontinuous functions with weighted geodesic variations. The corresponding Li-Yau inequalities then follow in the first-order viscosity sense.
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Artificial Intelligence

Whose Memory Is It? Scope-Aware Commit Rules for Long-Term LLM Memory

Hongyu Gu and Xinchang Li

Persistent memory allows an LLM agent to carry experience across conversations, but it also turns a local reasoning mistake into a durable one. During deliberation, an agent may consider a plan, simulate a tool result, report another speaker's belief, and then reject all of them. If memory retains only the resulting sentences, those once-useful possibilities can later return as facts. The record is neither fabricated nor irrelevant; it has simply been detached from the context in which it was valid. We identify this missing context as discourse ownership: the world, branch, or speaker that licenses a proposition. Our first finding is counterintuitive. Language models already carry a causally active signal for ownership, yet conventional memory interfaces discard it when they convert reasoning into records. We introduce CASK (Causally Anchored Scoping Keys), a commit rule that preserves this signal so that shared-world facts enter durable memory while provisional content remains available only within its original scope. Our second finding is that the most obvious way to preserve the signal—storing the discovered internal coordinates—is unreliable because equivalent representations need not keep the same coordinates. CASK instead preserves the stable relations that express ownership. Controlled long-conversation conflicts and tool-agent traces show that this design improves memory admission and prevents provisional content from contaminating later answers while complementing runtime provenance. The resulting commit boundary lets agents explore more possibilities without granting every intermediate sentence authority over future behavior.
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Artificial Intelligence

Sigma-Hunter: A Domain-Specific Language Model for Threat Hunting and Detection Engineering

Kemal Davaslioglu and Sastry Kompella

Detection engineers must translate threat reports, forensic observations, and hunt hypotheses into precise, testable rules. General-purpose large language models (LLMs) can draft such rules, but often produce invalid YAML, incorrect log sources, unsupported fields, or overly broad detection logic. This paper presents Sigma-Hunter, a domain-adapted LLM for analyst-assistive Sigma rule generation and threat hunting. We build an instruction-tuning dataset from 3,635 validated open-source Sigma rules, expanded into 7,663 question-answer and analyst-reasoning examples. Each source rule is assigned to a single train, validation, or test partition before this expansion, so no rule leaks across splits. We fine-tune a 7B Mistral model and a Phi-4 model with LoRA and score held-out rule generations on syntax, approximate field consistency, and a semantic judgment of detection logic, completeness, selectivity, and log-source alignment. Sigma-Hunter-Mistral scores 8.17 overall, against 7.88 for the strongest general-purpose baseline and 4.61 for untuned Mistral. Two findings stand out: domain adaptation enables a compact 7B model to perform competitively with larger general-purpose models on this structured task, and syntactic validity is a weak proxy for semantic rule quality, as several baselines emit well-formed YAML carrying weak detection logic. The adapted models run locally, which suits detection engineering in disconnected environments where analysts cannot reach hosted model services.
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Systems and Control

Electric Racing Kart with BLDC Drive, LiFePO4 Battery System, Traction Control and Regenerative Braking

Johannes Stockhammer, Tom Rettenwander and Philipp Huber

This diploma thesis documents the conversion of a petrol-powered go-kart into a battery-electric kart with a 10 kW BLDC drive, a LiFePO$_4$ traction battery with battery management system, a slip-based traction control and regenerative braking. The traction control estimates the slip of the driven rear axle from three Hall-effect wheel-speed sensors as $\lambda = (v_R - v_F)/v_R$ and reduces the torque command of the throttle pedal when the axle slips. It runs on a 32-bit ARM Cortex-M3 microcontroller (SAM3X8E) in the signal path between pedal and motor controller, with galvanic isolation and an emergency stop. Regenerative braking uses the generator mode of the motor controller in three stages: motor brake on throttle release, light braking via a steering-wheel button, and blended braking with the hydraulic brake. Drivetrain calculations show that the kart is traction-limited. At rated torque, the drive force at standstill exceeds the transmissible traction force by a factor of 1.7 to 2.0, up to approx. 27 to 43 km/h. Traction control is therefore a functional requirement of the drivetrain. The calculated top speed is approx. 65 km/h. Regenerative braking reaches approx. 0.45 g and could cover up to about 30 % of the energy demand on a winding track. With 80 % of the nominal battery energy, approx. 44 min of driving at 50 % motor load result, so the target of one hour is missed. The initial charging measurement quantifies the flat LiFePO$_4$ voltage plateau at 0.7 to 1.8 mV per percent state of charge, which makes voltage-based state-of-charge estimation unreliable. Cold cells showed a voltage sag under load that triggered the low-voltage alarm. Validation of the control functions by instrumented driving tests is the main open point.
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Programming Languages

When Double Rounding is Correct

Brett Saiki, Bill Zorn, Cynthia Richey et al. (+1 more)

Number libraries, which implement number formats and their arithmetic in software, are used to simulate hardware, making hardware behavior reproducible and thus verifiable and easier to design. However, modern machine learning accelerators are difficult to simulate, as they increasingly use specialized number formats that existing number libraries do not support. It is costly to extend these libraries using traditional methods: each combination of operation, format, and rounding mode typically requires a bespoke implementation. An easier way is to double round - compute at higher precision, then re-round - so that one high-precision kernel serves many formats, but double rounding is only known to be correct in select cases. We give a precise, efficiently checkable characterization of when double rounding is correct across many formats and rounding modes. Our result rests on a novel abstract number format that unifies fixed- and floating-point representations, reducing the correctness of double rounding to format containment: whether every value of one format is representable in another. We mechanized these results in Lean 4. In addition, we introduce a format inference algorithm that, given a sequence of operations, bounds each expression by a number format guaranteed to contain its result. We apply these insights in two ways. First, we present MPFX, a correctly-rounded, multi-precision number library that exploits correct double rounding to efficiently simulate many number formats. MPFX's correctly-rounded operations are up to 11x (mean: 6.05x) faster than MPFR's, and competitive with SoftFloat's - between 0.46x and 1.63x as fast (mean: 0.94x) - at the same precision. Second, in a case study, we implement a software simulation of a hardware specification using both SoftFloat and MPFX's primitives; the MPFX version is up to 13.5x faster.
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All authors Brett Saiki, Bill Zorn, Cynthia Richey, Zachary Tatlock

Machine Learning

Removing Information Content Does Not Certify Tamper Resistance in Open-Weight Models

Domenic Rosati, Alessa Carbo, Ali Dadsetan et al. (+5 more)

Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot universally certify slow recovery. Function-preserving reparameterizations leave information unchanged while altering gradient-descent geometry, so an invariant certificate is bounded by the fastest reachable parameterization. We apply this principle to weight–data mutual information under training-data filtering and label–representation mutual information under capability removal. Training order can change recovery time at fixed weight–data information, while exact representation-level independence can preserve the entire parameter Jacobian. An explicit construction has both information quantities equal to zero and recovers in one gradient step. Controlled experiments illustrate order-dependent recovery and parameterization-dependent attack speed. These results identify the missing requirement for certification: constraints on attack dynamics beyond mutual information at release.
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All authors Domenic Rosati, Alessa Carbo, Ali Dadsetan, Hong Huang, Matthew Young, Subhabrata Majumdar, Frank Rudzicz, Hassan Sajjad

Machine Learning

Algorithmic Scratchpads and Curriculum Staging for Arithmetic Reasoning in Tiny Transformers

Sourabh Kasliwal

Autoregressive Large Language Models (LLMs) frequently struggle with deterministic multi-step algorithmic tasks such as multi-digit multiplication and long division. In this paper, we investigate the mechanics of multi-step arithmetic in compact "Tiny" Transformers (~10.6M non-embedding parameters, 49.3M total) trained on synthetic data across four basic operations (+, -, *, /) unrolled as step-by-step scratchpads. First, we establish the necessary training foundations: (1) dataloader sequence padding creates an 83% gradient starvation artifact that collapses accuracy from 40% to 1%, remediated via continuous sequence packing; (2) linguistic pretraining is an essential prerequisite (<= 2.0% without it); and (3) modern architectural primitives (RoPE, RMSNorm, SwiGLU) and Sparse Mixture of Experts (MoE) substantially improve additive reasoning over baseline GPT-2. Second, we demonstrate that algorithmic scratchpad formulation directly dictates success. Introducing a deterministic Digit-by-Digit Long Division scratchpad within a 4-stage Hierarchical Developmental Curriculum dramatically elevates single-digit division from 4.0% to 86.7% accuracy on a 4,000-problem held-out benchmark. In contrast, multi-digit multiplication remained challenging: detailed error analysis revealed that while the model correctly computed single-digit sub-products and place-value zeros, our FOIL scratchpad failed because it forced a simultaneous summation of up to nine multi-digit terms in a single step without pairwise intermediate accumulation. Finally, we identify two key boundaries: performance collapses to 0.00% on unseen 4-digit operands, and unbuffered training induces catastrophic forgetting, collapsing division accuracy from 86.7% down to 0.00%.
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Artificial Intelligence

Learning to Report Unsafe Tasks in a Multi-Agent Game

Avyay M. Casheekar and Hariganesh Tangirala

When a team of agents shares a reward for completed tasks, reporting and stopping unsafe work reduces the team's reward. Random audits that penalize silence can make universal reporting optimal without ensuring that a silent team learns to report. We study this problem in a game where any witness can stop a task by reporting. When a task's k witnesses share a policy and draw actions independently, the derivative of expected reward with respect to the shared silence probability at universal silence counts its benefit k times, while the comparison between universal reporting and silence counts it once. A fully shared policy can therefore be locally attracted to silence even when reporting is optimal. For any grouping into shared policies, we give an audit condition sufficient for exact policy-gradient updates to reach universal reporting and, outside boundary cases, necessary from near universal silence. In a balanced family, meeting the condition with prescribed margins costs k times as much for a fully shared policy as for one policy per role. We test these predictions with PPO on 24 witness graphs, starting from learned silence. Witness groups, which place co-witnesses in separate networks, reduce unsafe task completion by 33.59 percentage points relative to shuffled groups of the same sizes under the same audits (95% graph-bootstrap interval: 21.03-45.13). Only 9 of 48 witness-group runs achieve below 1% unsafe completion while retaining at least 90% legitimate completion. At the same audit budget, a fully shared network meets both thresholds in 0/48 runs with independent action draws and 48/48 with a common draw. On 24 further graphs with witness groups fixed, audits allocated by the condition reduce unsafe completion by 42.22 points relative to audits maximizing a static coalition deterrence ratio at the same budget.
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Analysis of PDEs

Critical Sobolev Inequalities and Extremals for a Partially Fractional Grushin Operator

S. Malhotra, L. Sharma and K. Sreenadh

We study the partially fractional Grushin-type operator \begin{equation} \mathcal L_{\alpha,\beta}u=(-\Delta_x)^\alpha u-(1+\beta)^2|x|^{2\beta}\Delta_y u, \qquad (x,y)\in\mathbb{R}^n\times\mathbb{R}^m,\quad 0<\alpha<1,\ \beta\ge 0, \end{equation} which couples fractional diffusion in $x$ with degenerate local diffusion in $y$. Working with the anisotropic dilations $(x,y)\mapsto(\rho x,\rho^{\alpha+\beta}y)$ and an adapted quasi-distance, we prove a uniform fractional Grushin-Poincaré inequality on anisotropic cylinders and, via a Riesz-type potential built from a telescoping sum over dyadic cylinders, a global Sobolev inequality at the sharp critical exponent $2^*_{\alpha,\beta}=2Q_{\alpha,\beta}/(Q_{\alpha,\beta}-2\alpha)$, where $Q_{\alpha,\beta}=n+(\alpha+\beta)m$. We then establish a concentration-compactness principle for the associated energy space. For $\beta>0$ the weight $|x|^{2\beta}$ destroys translation invariance in $x$, and we show that concentration is confined to the degenerate hyperplane $ \Sigma= \{(0,y):y\in\mathbb{R}^m\}$; this localization yields the existence of extremals for the sharp constant $S_{\alpha,\beta}(n,m)$.
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Artificial Intelligence

How Fragile Is On-Device Language Model Safety? Localizing Safety-Critical Parameters for Sparse Fault Analysis

Muhammad Zeeshan Karamat and Christiana Chamon Garcia

As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of locally stored model parameters becomes an important safety concern. We investigate whether safety-sensitive behavior in LLaMA-2-7B-Chat is concentrated within a sparse subset of parameters, creating a reduced fault surface for targeted analysis. We study two complementary localization methods: low-rank safety-associated subspace analysis and parameter-level safety–utility importance filtering. Both approaches reveal highly non-uniform safety sensitivity across the network, with the MLP down_proj consistently emerging as a prominent safety-sensitive component and o_proj providing a smaller contribution. Using parameter-level localization, modifying only 0.19% of model weights in down_proj yields 53% Basic ASR and 56% GCG ASR, while tinyBenchmarks accuracy remains at 51.6% compared with a 52.2% unmodified baseline. These results motivate targeted fault analysis and selective integrity protection for language models deployed in resource-constrained, on-device, and agentic settings.
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Astrophysics of Galaxies

Euclid Quick Data Release (Q1). Exploring the complexity of quenching processes across time, environment, and mass through recently quenched galaxies

Euclid Collaboration: Z. Mao, L. Pozzetti, A. Enia et al. (+307 more)

Recently quenched galaxies (RQGs) provide insights into galaxy quenching. We present a photometric and spectroscopic analysis of RQGs using the first Euclid Quick Data Release (Q1). Using rest-frame UVJ colours, we classify star-forming (SFGs), quiescent (QGs), and recently quenched galaxies at 0.2<z<3.0, separating RQGs into fast- and slow-quenching types (fRQGs and sRQGs) following model evolutionary tracks. The relative fractions of RQGs and their quenching efficiencies both decrease with cosmic time. At early epochs, fast quenching is more efficient in massive galaxies, whereas it becomes efficient only at low masses at later epochs. Slow quenching becomes more efficient at z<1.5, progressing from high to intermediate masses with cosmic time. Most RQGs at z<0.5 reside in low-density environments, while they also exist in high-density environments at z>0.5, suggesting earlier quenching in dense regions. RQGs show intermediate properties between SFGs and QGs in stellar mass, star-formation rate, specific star-formation rate, Sersic index, and radius. Most RQGs exhibit regular morphologies, excluding merger as a primary mechanism. We find that fRQGs retain disc-like morphologies, whereas sRQGs are more concentrated, suggesting different stages of morphological transformation. Our analysis suggests that multiple mechanisms drive quenching. Stacked spectra of galaxies with spectroscopic redshifts confirm old, passive populations in QGs, while sRQG spectrum shows weak Ha emission, indicating residual star-formation activity or AGN contribution. The recently-quenched nature of RQGs is confirmed by strong Balmer absorption lines in stack DESI spectra. The stacked Euclid QG spectrum agrees with previous templates and stellar population models older than 5Gyr at z<1. These results demonstrate Euclid's potential for studying galaxy quenching, to be further enhanced by future releases.[Abrg.]
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All authors Euclid Collaboration: Z. Mao, L. Pozzetti, A. Enia, S. Quai, W. G. Hartley, M. Bolzonella, F. Gentile, G. De Lucia, M. Moresco, N. Borghi, C. Saulder, C. Nipoti, M. Talia, P. Corcho-Caballero, L. Gabarra, Abdurro'uf, L. Wang, L. Bisigello, E. Daddi, M. Siudek, C. D'Eugenio, G. Zamorani, F. Calura, H. Fu, R. Ragusa, F. Shankar, M. Hirschmann, O. Cucciati, T. Kodama, S. Belli, L. Spinoglio, J. R. Weaver, B. Altieri, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, C. Dolding, H. Dole, F. Dubath, X. Dupac, M. Farina, R. Farinelli, S. Ferriol, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, S. Hemmati, J. Hoar, W. Holmes, A. Hornstrup, M. Huertas-Company, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, V. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, M. Magliocchetti, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, N. Mauri, S. Maurogordato, E. Medinaceli, S. Mei, M. Meneghetti, E. Merlin, G. Meylan, P. Monaco, A. Mora, C. Moretti, L. Moscardini, R. Nakajima, C. Neissner, S.-M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, A. Pezzotta, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, I. Risso, E. Romelli, M. Roncarelli, H. J. A. Rottgering, B. Rusholme, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, E. Bozzo, C. Burigana, A. Cappi, T. Castro, J. A. Escartin Vigo, R. Maoli, J. Martín-Fleitas, R. B. Metcalf, A. A. Nucita, M. Pöntinen, V. Scottez, M. Sereno, M. Tenti, M. Tucci, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, L. Bazzanini, D. Bertacca, M. Bethermin, F. Beutler, L. Blot, H. Böhringer, M. Bonici, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, F. Cogato, T. E. Collett, T. Contini, A. R. Cooray, S. Davini, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, P.-A. Duc, V. Duret, M. Y. Elkhashab, Y. Fang, A. Finoguenov, F. Fontanot, A. Franco, K. Ganga, T. Gasparetto, R. Gavazzi, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, L. K. Hunt, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, J. Kim, C. C. Kirkpatrick, S. Kruk, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, S. J. Liu, X. Lopez Lopez, A. Loureiro, G. Maggio, E. A. Magnier, C. Mancini, F. Mannucci, C. J. A. P. Martins, L. Maurin, M. Miluzio, G. Morgante, K. Naidoo, A. Navarro-Alsina, S. Nesseris, D. Paoletti, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, G. W. Pratt, M. Radovich, G. Rodighiero, W. Roster, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, L. C. Smith, J. G. Sorce, K. Tanidis, F. Tarsitano, G. Testera, R. Teyssier, A. Troja, A. Venhola, D. Vergani, G. Verza, S. Vinciguerra, N. A. Walton, A. H. Wright

Number Theory

Automorphisms of general modular curves

Valerio Dose, Guido Maria Lido and Pietro Mercuri

We prove that for a geometrically connected modular curve $X_H$ of prime level larger than $311$, all automorphisms are modular, i.e., they arise from the moduli interpretation of $X_H$. The proof relies on a general criterion involving the gonality of $X_H$ and the dimension of the CM part of the Jacobian of $X_H$. For arbitrary level, we give algorithms to compute the modular automorphisms and, in many cases, to prove that there are no other automorphisms. Finally, we apply these algorithms to the curves in the database LMFDB.
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Soft Condensed Matter

Phase-Field Fracture Simulation of Highly Deformable Thin Structures via a Discrete Differential Geometry Framework

Bohan Zhang, Bo Wang, Huajiang Ouyang et al. (+3 more)

Thin elastic flexible structures exploit large geometric deformation to achieve mechanical functionality, making their fracture behavior strongly coupled with the evolving structural configuration. This work presents a unified discrete differential geometry (DDG) phase-field framework for fracture in highly deformable thin structures. Membrane and bending elasticity are formulated from discrete geometric measures on a triangulated midsurface, while the phase field is defined on the same discrete surface to describe onset of crack growth and subsequent propagation. The coupled problem is solved using a staggered scheme, with phase-field irreversibility enforced by an active-set method. A degradation-deletion procedure removes nearly fully failed elements and reconstructs the DDG topology, enabling complete crack opening and substantial post-fracture reconfiguration. For two-dimensional in-plane fracture problems, the DDG predictions agree closely with geometrically nonlinear finite element results in both crack evolution and mechanical response. In three-dimensional tearing, the framework resolves the interaction between out-of-plane deformation and crack propagation, including the experimentally observed convergence and coalescence of initially parallel cracks. Its engineering applicability is further demonstrated using an island–bridge structure representative of flexible electronics. The simulations capture direction-dependent deformation modes, instability and snap-through during loading, and the coupled evolution of structural configuration and fracture. These results establish the proposed framework as an effective tool for investigating geometry-dependent fracture and supporting the damage-tolerant design of flexible thin structures.
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All authors Bohan Zhang, Bo Wang, Huajiang Ouyang, Zhigang Wu, Yu Zhou, Weicheng Huang

Computational Physics

Convergence of Roberts flow dynamos in Eulerian and Lagrangian codes

Nikyta Shchutskyi, Axel Brandenburg and Matthieu Schaller

Standard benchmark tests for numerical magnetohydrodynamics codes focus on one- and two-dimensional problems that ignore the possibility of dynamos, i.e., an exponential instability converting kinetic energy into magnetic energy. They also tend to ignore magnetic helicity conservation that can have dramatic effects in periodic domains. The existence of magnetic diffusion is crucial for dynamos, as was exemplified by the impossibility of finding dynamo solutions when using Euler potentials, which is a perfectly valid approach in the absence of magnetic diffusion. The Roberts flows~I, II, III, and IV are two-dimensional flows that support three-dimensional dynamos, all of which are also large-scale dynamos in the sense that their planar averages are of significant strengths, even though flow~II is pointwise non-helical. Here we demonstrate the numerical convergence properties of solutions obtained using discretisation schemes of second, sixth, and tenth order in the mesh width in the Pencil Code, compare the results with the SPH-based SWIFT code, and present a list of averaged quantities that facilitate their use in characterising the solutions as benchmarks. In addition, we present a procedure to obtain two-dimensional time-independent magnetic field visualisation for all Roberts flows, and perform per-pixel comparison between the Pencil and SWIFT codes. The rich combination of features makes Roberts flows an excellent dynamo benchmark for the direct numerical simulations of magnetohydrodynamics equations.
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Robotics

PhysEvo: Astra Can Act, Let It

Wenqing Tian, Zeyu Zhang, Zhaocheng Liu et al. (+3 more)

Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world. We introduce PhysEvo, a framework for physical recursive self-improvement (RSI) around a single frozen model. A task agent executes robot tasks; a meta-agent uses the resulting trajectories to diagnose failures, revise tools and skills, and test corrections. The meta-agent can also improve its own diagnostic tools, so retained revisions support both later action and later self-improvement. This process develops joint-level control, evidence-seeking observation, and reusable manipulation skills without model-weight updates or a separately trained action policy. Across 42 RoboDojo tasks, held-out-layout evaluation of retained task-specific deployment versions yields a five-dimension average score of 68.14/100 and 62.00% success, compared with 47.17% for RoboDawn's one-shot Astra agent, the strongest published reference in our comparison. On eight manipulation tasks challenging direct Astra, PhysEvo achieves 55.00% success, compared with 1.25% for the direct-Astra reference. Deploying the simulation-evolved harness on AgileX PiPER and continuing skill revision yields 90.60/100 average score and 84.00% success across 25 trials on five real-world tasks. PhysEvo turns the consequences of action into persistent, testable changes to how a frozen model acts and improves.
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All authors Wenqing Tian, Zeyu Zhang, Zhaocheng Liu, Fengwei Liu, Qiang Liu, Liang Wang

Audio and Speech Processing

Phoneme-Guided Initialization for LLM-based Speech Recognition

Ryo Magoshi, Shinsuke Sakai and Tatsuya Kawahara

Speech large language models (speech LLMs) perform well on automatic speech recognition (ASR) when sufficient paired speech-text data is available, but their performance degrades in low-resource settings. A cascaded pipeline that performs speech-to-phoneme (S2P) conversion followed by phoneme-to-grapheme (P2G) conversion has been shown to outperform end-to-end speech LLMs in this regime, suggesting that phoneme-mediated processing is beneficial when paired data is scarce. We propose phoneme-guided initialization, a simple method that uses this insight within an end-to-end framework: we pre-train the audio encoder on S2P and the LLM on P2G tasks, then connect them and fine-tune the full model end-to-end on the target ASR task. Experiments on Japanese (CSJ), Chinese (AISHELL-1), and two low-resource languages from Common Voice 25.0 (Tatar and Urdu) show that our method matches or outperforms both the cascaded S2P-P2G baseline and the end-to-end model without P2G initialization.
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Artificial Intelligence

Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents

Jiamu Bai, Lizhu Zhang, Xin Yu et al. (+9 more)

As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirical verification is available, it often requires computationally costly model training and evaluation, limiting the speed and scale of agent evolution. Yet verification efficiency remains under-explored, and frontier models provide only limited gains when used directly as idea selectors. We address this gap with specialized idea-level critic models that predict whether a proposed ML modification will improve upon the current solution, allowing agents to screen ideas and concentrate verification resources on the most promising candidates. We train the critic models through supervised fine-tuning on high-quality critiques synthesized by Gemini-3.1-Pro, followed by GRPO to further improve their predictive accuracy. Empirically, our critic models outperform Gemini-3.1-Pro in static idea evaluation, and these gains extend to agent inference, continual learning, and policy training. During inference-time evolution, they improve final solution quality under the same verification budget by selecting more promising ideas, with further gains from continual learning. During policy training, they serve as learned reward models, reserving empirical verification for uncertain cases and enabling substantially more policy updates with the same verification resources. Together, these results show that idea-level critic models help ML agents discover better solutions and learn stronger proposal policies under limited verification budgets.
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All authors Jiamu Bai, Lizhu Zhang, Xin Yu, Yanhong Wu, Zellux Wang, Serena Li, Weiwei Li, Zhuokai Zhao, Lingzhou Xue, Kiwan Maeng, Xiangjun Fan, Bo Peng

Signal Processing

Geometry-Informed Neural Network for Pilot-Limited Channel Estimation via Sensing from Uplink Traffic

Wenyu Huang, Alireza Javid and Nuria González-Prelcic

Reliable wireless systems depend on accurate uplink channel estimation for precoding, scheduling, and link adaptation at the base station. In the upper mid-band, wide bandwidths and large antenna arrays increase the channel dimension and make pilot-efficient estimation particularly challenging. While uplink data is available as every user transmits it, sounding resources are shared across all users in the cell, leaving only a few pilots for each user. Thus, channel estimation heavily depends on prior information. Existing approaches obtain such priors in different ways, each with limitations. Ray tracing requires a site model, channel knowledge maps rely on measurements at or near the locations they represent, and learned channel distributions do not explicitly capture propagation geometry. We therefore propose TRaffic-Aided Channel Estimation (TRACE), which builds the prior from uplink traffic without additional sounding. TRACE learns a geometry-aware channel prior from routine multi-UE uplink traffic and combines it with the available pilots for channel estimation. In a ray-traced street canyon at 15 GHz, TRACE achieves -7.48 dB NMSE with 8 pilots, outperforming three state-of-the-art priors and two geometric ablations by more than 3 dB, while remaining effective with only 2 pilots. Within a deployment, TRACE remains robust across SNRs, carrier frequencies, and reduced mapping traffic without retraining.
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Machine Learning

REFIT: Recognize, Fix, and Test Wearable Sensor Placement Shifts without Labels

Bangxun Tang

We present REFIT, an input calibration for frozen activity-recognition models whose inertial sensors are worn differently at deployment than in training. When users move a watch to the other wrist or put a strap sensor back on turned, the model sees the same motion on changed axes. REFIT undoes such shifts without labels or retraining. It describes them by families of axis transforms, such as reflections and rotations, and fits each family to the user's data so that simple statistics match those of the training data. The family that removes most of the mismatch names the shift. REFIT fixes the shift by applying the best member of that family before the frozen model and re-estimating its normalization statistics. It tests the fixed model with a label-free accuracy estimate and asks the user to re-wear the sensor when it is low. Experiments on real left/right sensor pairs and on real and simulated re-attachment show that REFIT outperforms label-free test-time adaptation methods on every dataset and restores most of the accuracy lost to re-attachment. It names injected shifts far more reliably than a confidence-based selector. After a correction over all signed permutations of the axes, the estimate separates successful from failed corrections.
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Databases

RiftANN: Efficient Graph Traversal for Vector Search with RDMA-Based Memory Disaggregation

Qi Lin, Zhenyu Zhang, Jun Kong et al. (+1 more)

Graph indexes for billion-scale vector collections can exceed a single server's DRAM capacity. Passive RDMA-based disaggregated memory provides scalable capacity without memory-node computation, but conventional best-first search performs poorly in this setting. Fine-grained and unnecessary remote reads increase communication cost, while dependencies between candidate evaluation and subsequent expansion serialize traversal and leave CPUs idle. We present RiftANN, a graph-based approximate nearest neighbor search (ANNS) system for passive RDMA-based disaggregated memory. RiftANN navigates using compact vectors at compute nodes and selectively verifies exact vectors using a threshold calibrated from observed approximation errors. A bounded one-sided RDMA pipeline batches and overlaps the remaining remote accesses with local processing. RiftANN evaluates returned neighbors in parallel and incrementally incorporates completed results. An impact gate estimates whether unfinished evaluations may change the candidate set, allowing traversal to continue when their expected influence is small while preventing unsafe advancement from incomplete search state. A lightweight feedback controller coordinates RDMA concurrency with background evaluation capacity as search configurations and query loads change. We evaluate RiftANN on SIFT, DEEP, and SPACEV at 100M scale and SIFT1B. At matched recall, RiftANN achieves 1.6x to 4.3x latency speedups and 1.1x to 2.2x higher throughput than DistVS, and 1.6x to 5.6x latency speedups over SSD-based DiskANN and PipeANN. These results show that passive disaggregated memory can support low-latency graph-based vector retrieval without query-time computation at memory nodes.
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All authors Qi Lin, Zhenyu Zhang, Jun Kong, Zhichao Cao

Machine Learning

LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL

Songyuan Zhang, Oswin So, Eric Yang Yu et al. (+3 more)

While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by the risk of executing out-of-distribution (OOD) actions. Recent approaches mitigate this by learning a behavior-cloning policy through flow matching and then performing RL within its constrained latent space. However, naively optimizing the latent policy can easily cause the policy to collapse into a brittle mode or exploit sharp artifacts of the learned critic. In this work, we find that entropy regularization is essential in latent-space RL for addressing these challenges. We introduce LASER, a novel offline RL algorithm that applies latent-space adjoint matching to achieve entropy-regularized latent-space RL with expressive flow policies while avoiding backpropagation through time. Through comprehensive experiments on 40 challenging OGBench tasks with varying dataset qualities, we show that LASER achieves state-of-the-art performance. Notably, LASER uses fixed method-specific hyperparameters across all tasks and outperforms the evaluated baselines, including those with task- and dataset-specific tuning, which highlights the robust applicability of LASER. Project website: https://mit-realm.github.io/laser/.
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All authors Songyuan Zhang, Oswin So, Eric Yang Yu, Matthew Cleaveland, Peter Crowley-Dolen, Chuchu Fan

Computational Complexity

Associativity of Multiplication Is Hard for Resolution

Vincent Liew

SAT solvers are empirically known to perform poorly when reasoning about multiplication. Yet for over a decade we have lacked a theoretical explanation for this phenomenon. CDCL SAT solvers implicitly search for resolution proofs, and no lower bound on proof size has ruled out the existence of short proofs that solvers simply fail to find. We give the first lower bound of this kind by showing that general resolution proofs of the associativity of $n$-bit multiplication require size $2^{\Omega((n/\log n)^{1/4})}$. This lower bound holds for a broad class of multiplier encodings based on partial product summation, including the standard array and Wallace-tree multipliers used to bit-blast multiplication in SMT solvers. This result resolves an open problem of Beame and Liew. The proof constructs a reduction from a perfect-matching principle on bounded-degree bipartite expander graphs to multiplier associativity. Itsykson, Slabodkin, and Sokolov proved that this principle is hard for resolution. The lower bound for multiplier associativity follows. The same reduction, when combined with Håstad's recent lower bound for the perfect-matching principle of the odd grid, yields an exponential lower bound for multiplier associativity in the stronger bounded-depth Frege proof systems.
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Systems and Control

The internal model principle in reservoir computing

Danyal Saqib and Alberto Padoan

The paper studies learning in reservoir computing systems through the lens of the internal model principle. The key idea is to model training signals as outputs of an exogenous signal generator, thus connecting reservoir computing to output regulation, observer theory, and frequency-domain interpolation. For linear signal generators, we characterize the steady-state response of a linear reservoir through invariant subspaces, establishing connections with Luenberger observers and classical frequency-domain interpolation. For nonlinear signal generators, we obtain analogous results using invariant manifolds, connecting synchronization to Kazantzis-Kravaris/Luenberger observers and readout training to nonlinear moment matching. Case studies illustrate how our perspective explains possible instability of trained predictors and informs reservoir design.
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Logic

Finite index extensions of treeable equivalence relations are treeable

Ruiyuan Chen, Anush Tserunyan and Robin Tucker-Drob

We prove that every countable Borel equivalence relation containing a finite index Borel treeable subequivalence relation is Borel treeable. This answers a long-standing question of Jackson–Kechris–Louveau. Our proof is based on a median subalgebra filtration construction on products of trees, together with an extension of the median graph treeing algorithm of Chen–Poulin–Tao–Tserunyan to certain well-behaved increasing unions.
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Instrumentation and Methods for Astrophysics

A method for multimodal analysis of TAIGA experiment data using essential features

Alexander Kryukov, Julia Dubenskaya, Elena Fedotova et al. (+6 more)

The aim of processing and analyzing experimental data from physical experiments is to obtain physically significant information about the phenomenon under study. This goal is achieved by multi-stage processing of experimental data, during which noise associated with measurements is suppressed and the dimensionality of the input data is reduced. In this paper, we propose a new method based on the use of neural networks such as autoencoders to extract essential features. The special value of the proposed approach lies in the possibility of its application to the analysis of multimodal data received simultaneously from several installations. We will apply this approach to a multimodal data (MMD) of the experiment TAIGA. Currently, the analysis of the MMD is carried out independently for each installation separately. Therefore, the development of methods for the joint analysis of MMD from TAIGA-type installations is an urgent task in cosmic ray physics and gamma-ray astronomy. Based on Monte Carlo simulation, it is shown that the proposed method allows for effective MMD analysis. It can also be used for MMD analysis at other experimental complexes.
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All authors Alexander Kryukov, Julia Dubenskaya, Elena Fedotova, Elizaveta Gres, Stanislav Polyakov, Eugene Postnikov, Alexander Razumov, Pavel Volchugov, Dmitry Zhurov