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Tuesday 13 October

Robotics

Breaking the Average: Overcoming Convergent Defaults in Robotic Manipulation via Diagnostic Chain-of-Thought and Structured Guidance

Mohammed Daba and Waleed Abdulrahman Saleh AlAkwa

Recent Vision-Language-Action (VLA) models, such as $\pi_{0.5}$, have demonstrated impressive generalization capabilities by scaling behavior cloning across massive datasets. However, these models frequently suffer from distributional convergence, a phenomenon where the policy regresses to "safe" but suboptimal mean behaviors due to mode averaging in the training data. This often results in generic grasp strategies, Convergent Defaults, that fail in constrained or long-tail scenarios. In this work, we propose a novel framework to identify and correct these behaviors without parameter updates. We introduce a three-stage methodology: (1) Probing for Defaults, identifying task-specific mode collapse; (2) Diagnostic Root Cause Analysis, utilizing the VLA's latent reasoning capabilities to verbalize the logic behind suboptimal actions; and (3) Structured Guidance Injection, where concrete, distinct alternatives are encoded into reusable "Lens Prompts" that shift the sampling distribution. We validate this approach using $\pi_{0.5}$ in ManiSkill3 simulation environments. We hypothesize that our method outperforms vanilla $\pi_{0.5}$ and standard Chain-of-Thought prompting by effectively breaking convergent defaults in complex manipulation tasks.
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High Energy Astrophysical Phenomena

A0538-66: A transient ultraluminous X-ray source with a relativistic outflow, 69ms pulsations, and anomalous optical activity

Lorenzo Ducci, Sandro Mereghetti, Ciro Pinto et al. (+8 more)

A0538-66 is a peculiar Be/X-ray binary in the Large Magellanic Cloud, hosting the fastest spinning neutron star of its class (~69ms) and showing the brightest optical bursts of any system of this type. We report four XMM-Newton observations obtained near periastron in 2025, together with MAXI and REM monitoring, and a reanalysis of archival RXTE data. On 30 September 2025 the source reached L_X~1.5E39 erg/s (0.2-10 keV), the highest level since 1980, and in February, May, and August 2026, MAXI detected similarly bright outbursts. During the XMM-Newton observation taken on 30 September 2025, we detected the pulsations, measuring P_spin=69.25878(2) ms and dP_spin/dt=6.58(9)E-10 s/s; the pulsed fraction was low (~3% in 1-10 keV), increased with energy, and the soft and hard pulse profiles were shifted by half a cycle. A new detection in RXTE data of 3 December 1998 gives a faster spin period of P_spin=68.88813(28) ms. The XMM-Newton/RGS spectra show emission lines from photoionised plasma at rest and blueshifted absorption lines that imply an outflow at ~0.12c. This indicates a highly super-Eddington accretion rate and an intrinsic L_X~1.5E39 erg/s, and it makes A0538-66 a transient ultra-luminous X-ray pulsar, a class of which only a few objects are known so far. Since 2023 the optical bursts observed by REM have become longer and brighter, resembling those of the 1980s, but most of them lack a luminous X-ray counterpart (L_X>1E37 erg/s). We interpret these results in terms of a radiation pressure dominated accretion disc whose geometrically thick inner regions intermittently screen the innermost emitting regions.
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All authors Lorenzo Ducci, Sandro Mereghetti, Ciro Pinto, Matteo Bachetti, Motoki Nakajima, Rahul Sharma, Stefano Covino, Michela Rigoselli, Caterina Tresoldi, Tatehiro Mihara, Mutsumi Sugizaki

Earth and Planetary Astrophysics

Physical characterization of Earth's quasi-satellite 2025 PN7

Grigori Fedorets, Zuri Gray, Mikael Granvik et al. (+1 more)

A hypothetical origin as lunar ejecta for Earth's quasi-satellites (QS) has gained initial support in recent years, but there is scarce photometric and/or spectroscopic data to support the premise due to the rare observational windows to obtain such data for these small objects. One of the key pieces of evidence supporting the lunar-origin hypothesis, the lunar-like, very red reflectance spectrum of QS (469219) Kamo'oalewa, was recently found to be flawed. We present reflectance spectroscopy of Earth's QS 2025~PN$_7$ obtained with FORS2 at UT1-Antu of the ESO Very Large Telescope. This is only the second QS that has ever been characterized, the first being (469219) Kamo'oalewa, the sample-return target of the Tianwen-2 mission. Our results indicate that 2025~PN$_7$ exhibits a near-solar, flat visual spectrum, most consistent with the B-type in the Bus-DeMeo taxonomy. Our results therefore still support an asteroid-belt origin for most of Earth's QSs. The lunar-ejecta hypothesis can only be formally rejected with a larger sample of spectroscopically characterized QSs.
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All authors Grigori Fedorets, Zuri Gray, Mikael Granvik, Eric MacLennan

Machine Learning

Exact power indices for plurality-voting ensembles

Ilie Sarpe, Theofanis Georgakopoulos and Aristides Gionis

Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random. Despite the simple aggregation rule, attributing the impact of each base classifier to the ensemble's predictions is far from straightforward. That is, a single base classifier's impact depends on how it interacts with the votes of all other classifiers in the ensemble, for example, in resolving ties, and not only on its own predictions. We address the attribution problem using established tools from cooperative game theory, studying the Banzhaf and Shapley power indices for new games modeling plurality voting and random tie-breaking, for binary and multiclass classification settings. Computing power indices is, in general, intractable. Surprisingly, our methods, collectively named ENPOWER, enable the exact computation of Banzhaf and Shapley values in linear time for the binary classification setting and in low-degree polynomial time for the multiclass setting. The algorithms in ENPOWER rely on new combinatorial formulations of the Banzhaf and Shapley indices, yielding closed-form solutions for the binary case and ordinary generating function representations for the multiclass case. We validate empirically the methods in ENPOWER demonstrating their high applicability, providing attribution values not captured by existing methods. We apply the attribution of ENPOWER to: ensemble pruning and prompt-based ensembling of vision-language models.
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Materials Science

Field-free Magnetoacoustic Nonreciprocity via Exchange-bias Engineering

Weiqi Du, Mingyuan Ma, Zheng Sun et al. (+8 more)

Nonreciprocal wave propagation is essential for directional signal processing and isolation. Magnetoacoustic systems are attractive platforms for nonreciprocal devices because of their small footprint and passive operation. However, pronounced nonreciprocity typically requires an external bias field,limiting device miniaturization and integration. Here, we demonstrate field-free magnetoacoustic nonreciprocity in an IrMn/FeGaB/Cu/FeGaB multilayer integrated with a shear-horizontal-wave delay line. Exchange-bias engineering stabilizes a remanent magnetic state with broken time-reversal and spatial-inversion symmetries while tailoring the spin-wave dispersion to achieve resonant magnetoacoustic coupling at zero field. The normalized isolation reaches 21 dB/mm, with its sign programmable by the field-sweep history. Further numerical optimization predicts a zero-field normalized isolation of 91 dB/mm by increasing the dynamical magnetic asymmetry. Our work establishes exchange-bias engineering as a promising route toward compact RF isolators and circulators and as a general strategy for realizing magnetoacoustic devices without external magnetic fields.
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All authors Weiqi Du, Mingyuan Ma, Zheng Sun, Chong Chen, Shixuan Liang, Deqi Tao, Shihai An, Yifan Fu, Sulei Fu, Feng Pan, Cheng Song

Networking

NetCoFi: A Budget-Aware Coarse-Fine Dual-Branch Model for Encrypted Traffic Classification

Zhengyi Ma, Qiming Yu, Chungang Lin et al. (+3 more)

Encrypted traffic classification is critical for network management and security, but practical deployment must balance classification accuracy and inference cost. Existing models typically process fixed flow windows with limited control over computation after training, which can limit the accuracy-efficiency trade-off under constrained resources. To address this problem, we present NetCoFi, a budget-aware classifier that allocates computation at coarse and fine granularities. Using byte representations and observable packet metadata, NetCoFi selects token neighborhoods for fine-grained encoding while compressing the full window into complementary coarse context. Multi-budget training enables the same model to operate at different routing budgets without retraining. Evaluation on six public datasets shows that NetCoFi achieves state-of-the-art classification performance when trained from scratch. Under low-budget settings, NetCoFi improves average Macro-F1 by 7.86 percentage points across three datasets, compared with a same-depth static baseline. In a DPDK-GPU traffic processing pipeline, it increases mean batch throughput from 26.42 to 43.42 Mb/s, a 1.64x improvement over NetMamba.
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All authors Zhengyi Ma, Qiming Yu, Chungang Lin, Wentao Cui, Zijia Song, Guozheng Yang

Robotics

ReEnvision: Future-Guided Selective Action Repair for Long-Horizon Manipulation

Xiyin Zeng, Xuewei Huang, Yi Lu et al. (+2 more)

Long-horizon robotic manipulation requires robust execution over sequences of interdependent actions. Existing methods typically handle execution deviations by replanning the remaining action chunk or correcting actions step by step as new observations arrive. However, a local deviation may affect only a few actions under distribution shift, yet replanning regenerates the entire chunk from an unfamiliar state, potentially introducing errors into otherwise valid actions. Step-wise correction may react too late, as it does not explicitly anticipate how the deviation affects subsequent actions. We propose ReEnvision, a future-guided selective action repair framework that updates a joint future-action belief using execution feedback. It then learns how the resulting belief revision affects each unexecuted action. The resulting belief revision enables selective repair of only the affected actions within a cached action chunk, preserving valid actions and reserving full replanning for severe deviations. On CALVIN, L-CALVIN, and RLBench, ReEnvision achieves 86.6% 5-step success, improves 10-step success from 56% to 63%, and reaches an average success rate of 88.8%, respectively. On five real-world manipulation tasks, it achieves an average success rate of 82.8%. Controlled repair experiments further demonstrate the benefits of localizing affected actions and preserving valid ones during execution.
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All authors Xiyin Zeng, Xuewei Huang, Yi Lu, Weihao Yuan, Hao Wang

Artificial Intelligence

Seeing Depth but Choosing Cues in Vision-Language Models

Zhengtao Zou and Pekka Marttinen

Do vision-language models use depth information to judge which object is closer, or rely on image-plane cues? We investigate this question through controlled depth–cue conflicts, representation analysis, and interventions. Among the many image-plane cues relevant to depth perception, we focus on apparent size and vertical position. We compare depth judgments under cue-consistent and cue-conflict conditions in both rendered scenes and real-world photographs. Across model families and scales, depth judgments systematically shift toward misleading cues. Yet the correct depth order remains decodable from the queried objects' visual representations, including when the model answers incorrectly. Image and category controls support interpreting this signal as image-dependent depth information beyond the two target cues. Strengthening the model's own depth readout through object-level write-back can correct conflict decisions without updating model parameters or using ground-truth depth annotations. Interventions show that the effect of this correction depends on the direction and target tokens of the written signal, while its strength varies across architectures and domains. Together, these findings reveal a mismatch between a model's ability to represent depth and use this information when deciding the answer. VLMs can retain useful depth information while allowing competing image-plane cues to exert greater influence on their answers.
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Chemical Physics

All-electron machine-learned orbital-free density functional for periodic CHNO systems

Marc K. Ickler, Andreas Hermann and Fred A. Hamprecht

We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems. Electrostatics are evaluated analytically in an atom-centered Gaussian basis, while an equivariant network learns the kinetic and exchange-correlation energy from energies and gradients of non-ground-state densities. Minimizing the functional from a superposition of atomic densities recovers the Kohn-Sham ground state: on held-out crystals, the density error falls from 6.7% to 0.4% of the electron count and the energy error reaches 0.25 mHa per atom, half that of a same-architecture interatomic potential. A single functional can be trained for both isolated molecules and periodic systems, and the model generalizes to larger unit cells. It largely preserves relative-energy rankings and reproduces convex-hull vertices on the tested H-N composition slice. Together, these results demonstrate the viability of machine-learned all-electron orbital-free DFT for variational density optimization and energy evaluation in fixed-geometry solids.
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Plasma Physics

Validation of geometric proxies using negative and positive triangularity plasmas with varying elongation on TCV and multi-machine gyrokinetic simulations

Alessandro Balestri, Justin Ball, Stefano Coda et al. (+7 more)

Negative Triangularity (NT) tokamak plasmas can achieve H-mode-like confinement while avoiding Edge Localized Modes, making them an attractive reactor scenario. Predicting in which configuration this benefit occurs requires understanding how magnetic geometry controls turbulent transport. Using a set of geometric proxies based on the poloidally averaged magnetic drift velocity, its poloidal variation, and Finite Larmor Radius (FLR) effects, this work validates their predictive power against gyrokinetic simulations and experiments. First, the proxies are used to predict how the relative benefit of NT over Positive Triangularity (PT) evolves with elongation $\kappa$, forecasting a detrimental effect of NT below $\kappa\sim0.75$–$0.8$. This prediction is confirmed by linear and nonlinear GENE simulations in both ITG- and TEM-dominated regimes. Additionally, a dedicated experimental elongation scan on TCV shows that NT plasmas at $\kappa^{\rm LCFS}=1.5$ have $\beta_N$ and $H_{98y2}$ roughly 1.5 times larger than PT counterparts, while no difference is observed at $\kappa^{\rm LCFS}=0.95$. The analysis is then extended to realistic ITG-dominated scenarios from DIII-D, AUG, and JET, including up-down asymmetric equilibria where conventional triangularity-based descriptions break down. In all three devices, simulations of NT equilibria exhibit substantially reduced nonlinear heat fluxes relative to PT (factors of 1.8, 1.6, and 2.1, respectively). A systematic swap of geometric coefficients between NT and PT equilibria shows that magnetic drifts and FLR effects are the dominant stabilizing mechanisms across all machines, with parallel dynamics playing a secondary role. These results establish the geometric proxies as reliable, computationally inexpensive predictors of NT transport benefits across diverse shapes and turbulence regimes.
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All authors Alessandro Balestri, Justin Ball, Stefano Coda, Olivier Sauter, Paola Mantica, Alberto Mariani, Benoit Labit, Branka Vanovac, Joerg Hobirk, the TCV team

Numerical Analysis

Simultaneous energy and entropy stability of volume-penalisation immersed-boundary methods for the compressible Navier–Stokes equations

Abbas Ballout, Albert Jiménez-Ramos, Gonzalo Rubio et al. (+1 more)

Immersed-boundary methods avoid body-fitted meshes, but the energy or entropy stability of the underlying flow discretisation does not automatically extend to the immersed treatment. This manuscript analyses the volume-penalised compressible Navier–Stokes equations for both energy and entropy stability, on the continuous and semi-discrete levels, and integrates the isolated source exactly in time. The source is formulated as a one-parameter family indexed by $\theta$, whose momentum component relaxes the fluid velocity towards the prescribed solid velocity; $\theta$ sets how much of the resulting kinetic-energy loss leaves the domain rather than being converted to internal energy. The two stability requirements impose opposing bounds on $\theta$, with dissipation of the relative total energy requiring $\theta\geq0$ and stability of the mathematical entropy requiring $\theta\leq1$. Where penalisation acts, both conditions are therefore satisfied if and only if $0\leq\theta\leq1$, and the symmetric value $\theta=1/2$ divides the kinetic-energy loss equally between the domain and the internal energy. We conclude that any entropy-stable split-form discontinuous Galerkin spectral element method remains semi-discretely entropy stable once the source is added at the collocation nodes, for stationary and prescribed moving bodies alike.
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All authors Abbas Ballout, Albert Jiménez-Ramos, Gonzalo Rubio, Esteban Ferrer

Solar and Stellar Astrophysics

Revisiting the Role of Global Magnetic Variables in Coronal Heating of Solar Active Regions

David H. Brooks, Harry P. Warren and Johnathan R. Stauffer

Reconnection-based models of coronal heating predict that magnetic complexity—non-potential configurations, currents, shear—should enhance coronal emission independently of total magnetic flux. Previous work, however, found no evidence that complexity contributes independently to coronal heating (Fisher et al. 1998). We revisit this question using SDO/HMI vector magnetograms and SDO/AIA EUV imaging. HMI Space-weather Active Region Patches (SHARPs) provide 16 magnetic parameters characterizing field strength, gradients, currents, helicity, and non-potentiality. We measure spatially-integrated transition region and coronal emission using AIA 304A, 193A, 94A, and isolated Fe XVIII emission for 137 active regions, sampling plasma at ~0.09MK, ~1.4MK, and ~7.1MK. Partial correlation analysis isolates the contribution of each complexity parameter after controlling for total unsigned vertical magnetic flux. Unsigned flux (Phi_M) explains 72–82% of variance in emission, following power laws (I ~ Phi_M^alpha, where alpha=0.61-1.19) consistent with prior soft X-ray results. Partial correlations reveal weak positive contributions (r = 0.2-0.3) to transition region (304A) emission from some vertical current and field parameters, but little evidence that other active-region-averaged measures of magnetic complexity enhance coronal emission. The exception is total photospheric magnetic free energy density (rho_tot), which shows a positive partial correlation (r = 0.49, p < 0.001) with Fe XVIII emission, but negative, or non-existent, partial correlations with other AIA filters. These results suggest quasi-steady coronal heating scales with magnetic flux, while rho_tot may contribute to high temperature coronal heating. At lower temperatures, regions with more stored energy than expected for their flux produce less EUV emission than expected.
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Information Theory

Rarity Determines Minimax Rates of Semantic Entropy From Weak Pairwise Judgments With a Known Channel

Xiaoli Yu and Jiamiao Liu

The semantic entropy of a language model is the entropy of the meanings of its sampled answers. The meanings are never observed, and the entropy is estimated from the verdicts of a pairwise equivalence judge. Given the meanings of $n$ answers, a known channel declares each pair equivalent independently, with probability $q+\Delta$ within a meaning and $q$ across meanings. At most $K$ meanings occur, and $q$ is bounded away from $0$ and $1$. For a weak judge, with $n\Delta^2\to0$, the global minimax rate is set by distributions near a vertex of the simplex and overstates the difficulty elsewhere. We show that the difficulty is set by how many meanings can be rare. Over distributions with at most $s$ meanings below a fixed probability $\tau<1/(K-s)$, the minimax squared risk is of order $N^{-1/s}$ for $s\ge2$, $N^{-1}\log^2(e+N)$ for $s=1$ and $N^{-1}$ for $s=0$, capped at one, where $N=n^K\Delta^{2K-2}$. The matching lower bounds hold on every fixed polynomial window $n\Delta^2\le n^{-\kappa}$. One estimator attains these rates without knowing $s$ or $\tau$: it averages products of centered verdicts along all spanning trees of every $k$-set of answers, projects the resulting power sums onto the valid moment set and inverts them. The case $s=K-1$ gives the global rate $N^{-1/(K-1)}$ and the consistency threshold: with $\Delta=n^{-\gamma}$ the entropy is uniformly consistently estimable exactly when $\gamma<K/(2K-2)$. On polynomial windows, the same estimator attains the sharp local minimax constant on shrinking neighborhoods of interior points with distinct probabilities. Restricting the average to stars multiplies the constant by $K^{K-3}$.
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Performance

AlignPred: Error-Controlled KV Cache Compression via Alignment and Predictive Coding

Xu Yang, Jiapeng Zhang, Zhiwei Zuo et al. (+5 more)

Long-running agents retain KV caches across turns and sessions for hours or days. Compressing these persistent caches requires reducing storage while controlling reconstruction error. We present AlignPred, a codec that combines local alignment with predictive coding under a configurable KV reconstruction-error budget. Local orthogonal transforms align related heads or layers into a common coordinate frame, allowing AlignPred to combine token-wise prediction with same-token prediction across heads or layers. Residuals are quantized on an error-bounded lattice and entropy-coded, without learned predictors, encoder-side mode search, or large joint transforms. Across three attention mechanisms, AlignPred requires less storage than the evaluated baselines at comparable measured reconstruction error. Against the evaluated CPU codec implementations, it decodes at least 1.6 times faster while matching or improving encode time. AlignPred achieves near-native task scores at substantially compressed operating points on LongBench-Pro at 128K context. AlignPred's SGLang HiCache integration reduces persistent target-KV storage by 42-72% relative to native HiCache, with comparable latency.
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All authors Xu Yang, Jiapeng Zhang, Zhiwei Zuo, Yuxin Chen, Feiqiang Sun, Chengguang Xu, Feng Jin, Zhuo Tang

Machine Learning

Neural Dimensional Analysis: Foundations of Dimensionless and Scale-Consistent Computation

Bum Jun Kim, Makoto Kawano, Yusuke Iwasawa et al. (+1 more)

Modern Transformers rely on many scale-control mechanisms, including normalization, residual scaling, attention temperatures, loss weighting, and adaptive updates. To explain how these mechanisms maintain scale consistency, we introduce Neural Dimensional Analysis (NDA), a unified framework based on dimensional consistency. We define neural units that specify how tensors rescale and which quantities can be added, just as physical units do in physics. Our rules match residual units, nondimensionalize attention and gate inputs, combine losses across units, and convert gradient units into parameter units. We implement NDA through dimensionless Unit Violation Indices (UVI) for diagnosing scale mismatches within Transformers, a unit-consistent multi-loss objective, and the Gauge-Canonical Transformer (GCT) with normalized coordinates and orthogonalized residual additions. In language-model experiments spanning 51M to 1B parameters, GCT remains stable during training across a broad sixteenfold learning-rate range, outperforming Pre-LN, LLaMA-style, and QK-Norm in 124M validation loss at tuned learning rates. GCT's normalized residual stream also shows improved robustness to quantization. At 1B parameters, calibrated per-tensor int8 quantization increases GCT's validation loss by only 0.009 nats, compared with up to 5.51 nats for the baselines. Vision and physics-informed neural network experiments further confirm NDA's benefits beyond language modeling. These results establish dimensional consistency as a neural network design principle.
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All authors Bum Jun Kim, Makoto Kawano, Yusuke Iwasawa, Yutaka Matsuo

Computer Vision

dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions

Xi Zhu, Yijie Li, Xiaofan Wang et al. (+4 more)

Diffusion MRI (dMRI) probes water diffusion within the brain, providing unique information about tissue microstructure and white matter connectivity. High-quality dMRI enables advanced and accurate analyses but demands dense acquisitions, resulting in long scan times and hardware constraints that are often infeasible. Further, dMRI requires dedicated computational methods to support diverse analysis tasks (e.g., parcellation, registration, and synthesis) for both neuroscientific and clinical applications. Here, we present dMRI-F, a foundation model that unifies dMRI data enhancement and downstream tasks across diverse acquisitions and populations, while requiring only minimal acquisition input. dMRI-F is pre-trained with a q-space masked reconstruction task: predicting densely sampled multi-shell data from only six volumes. It learns generalizable latent feature representations of dMRI data, which serve as a core foundation for adapting to diverse downstream tasks, including brain parcellation, brain registration, and cross-modal synthesis. Across nine independently acquired datasets, we demonstrate that dMRI-F outperforms state-of-the-art methods with robust generalization across acquisitions, populations, and tasks. Our results demonstrate dMRI-F's versatility as a general-purpose framework that enables diverse dMRI applications from low-cost, sparse acquisitions.
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All authors Xi Zhu, Yijie Li, Xiaofan Wang, Wei Zhang, Huafu Chen, Ce Zhu, Fan Zhang

High Energy Physics (Experiment)

Amplitude analysis of $\psi(3686)$ radiative decays into $\pi^{0} \pi^{0}$ system

BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson et al. (+767 more)

A model-independent amplitude analysis of the $\pi^{0} \pi^{0}$ system produced in radiative $\psi(3686)$ decays is presented. The analysis is based on the unique dataset of $(2712.4\pm14.3)\times10^6$ $\psi(3686)$ decays collected by the BESIII detector. Scalar and tensor partial-wave contributions are obtained as piecewise functions of $M_{\text{inv}}(\pi^{0} \pi^{0})$ with minimal model assumptions. The observed contribution of the scalar partial wave exhibits similar behavior to the one reported in the study of radiative $J/\psi$ decays, while the tensor contributions provide no evidence for resonance-like structures except for the clearly visible $f_2(1270)$. The results are presented in the form of log-likelihood scans in $M_{\text{inv}}(\pi^{0} \pi^{0})$ bins, which preserve all the statistical information and account for relevant systematic uncertainties. The model-independent description provided in this paper allows to combine the presented results with other related data in the development of phenomenological models.
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All authors BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, D. Cabiati, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, T. T. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, W. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Z. K. Chen, J. Cheng, L. N. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denisenko, M. Destefanis, F. De Mori, E. Di Fiore, X. X. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, Z. J. Dong, M. C. Du, S. X. Du, Shaoxu Du, X. L. Du, Y. Q. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, Jin Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Xu Gao, Y. Gao, Y. N. Gao, Y. Y. Gao, Yunong Gao, Z. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, A. Gilman, K. Goetzen, J. Gollub, J. B. Gong, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, M. Greco, M. D. Gu, M. H. Gu, C. Y. Guan, A. Q. Guo, H. Guo, J. N. Guo, L. B. Guo, M. J. Guo, R. P. Guo, X. Guo, Y. P. Guo, Z. Guo, A. Guskov, J. Gutierrez, J. Y. Han, T. T. Han, X. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, C. Z. He, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Y. X. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, L. K. Jia, X. Q. Jia, D. Jiang, S. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, L. C. L. Jin, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, X. L. Kang, X. S. Kang, B. C. Ke, V. Khachatryan, A. Khoukaz, O. B. Kolcu, B. Kopf, L. Kröger, L. Krümmel, Y. Y. Kuang, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, C. K. Li, Chunkai Li, Cong Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. L. Li, H. N. Li, H. P. Li, Hui Li, J. N. Li, J. S. Li, J. W. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, M. R. Li, M. T. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. Li, S. X. Li, S. Y. Li, Shanshan Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. K. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. H. Li, Y. B. Li, Y. C. Li, Y. G. Li, Y. P. Li, Z. H. Li, Z. J. Li, Z. L. Li, Z. X. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, Z. Z. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Y. Liu, Ke Liu, Kun Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. M. Liu, W. T. Liu, X. Liu, X. K. Liu, X. L. Liu, X. P. Liu, X. T. Liu, X. Y. Liu, Y. Liu, Y. B. Liu, Yi Liu, Z. A. Liu, Z. D. Liu, Z. L. Liu, Z. Q. Liu, Z. X. Liu, Z. Y. Liu, X. C. Lou, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Maity, S. Malde, L. M. Mansur, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, H. Neuwirth, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, C. Normand, S. L. Olsen, Q. Ouyang, I. V. Ovtin, S. Pacetti, Y. Pan, C. Y. Pang, A. Pathak, Y. P. Pei, M. Pelizaeus, G. L. Peng, H. P. Peng, X. J. Peng, Y. Y. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, L. Pöpping, F. Z. Qi, H. R. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, K. Ravindran, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, W. R. Ruangyoo, N. Salone, A. Sarantsev, Y. Schelhaas, M. Schernau, K. Schoenning, M. Scodeggio, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, Ch. Y. Shi, H. Shi, J. L. Shi, J. Y. Shi, M. H. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, M. H. Song, T. Z. Song, W. M. Song, Y. X. Song, Zirong Song, S. Sosio, S. Spataro, S. Stansilaus, F. Stieler, M. Stolte, S. S Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, R. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, H. Tabaharizato, N. T. Tagsinsit, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, Z. H. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, K. Yu. Todyshev, I. Uman, E. van der Smagt, B. Wang, Bin Wang, Bo Wang, C. Wang, Chao Wang, Cong Wang, D. Y. Wang, F. K. Wang, H. J. Wang, H. R. Wang, J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, Mi Wang, N. Y. Wang, P. Wang, S. Wang, Shun Wang, T. Wang, W. Wang, W. P. Wang, X. F. Wang, X. L. Wang, X. N. Wang, Xin Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Yanning Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, Zhi Wang, Ziyi Wang, D. Wei, D. H. Wei, D. J. Wei, H. R. Wei, F. Weidner, H. R. Wen, S. P. Wen, U. Wiedner, G. Wilkinson, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. W. Wu, Z. Wu, H. L. Xia, L. Xia, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, D. B. Xiong, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, Y. Y. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, X. Y. Yang, Y. Yang, Y. G. Yang, Y. H. Yang, Y. M. Yang, Y. Q. Yang, Y. Z. Yang, Youhua Yang, Z. Y. Yang, W. J. Yao, Z. P. Yao, M. Ye, M. H. Ye, Z. J. Ye, K. Yi, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. W. Yu, T. Yu, X. D. Yu, Y. C. Yu, Yongchao Yu, C. Z. Yuan, H. Yuan, J. Yuan, Jie Yuan, L. Yuan, M. K. Yuan, S. H. Yuan, Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. J. Zeng, Yujie Zeng, Y. C. Zhai, Y. H. Zhan, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, Gengyuan Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. L. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, Han Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jin Zhang, Jiyuan Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, Q. Z. Zhang, R. Y. Zhang, S. H. Zhang, S. N. Zhang, Shulei Zhang, X. M. Zhang, X. Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. P. Zhang, Yao Zhang, Yu Zhang, Yu Zhang, Z. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Zh. Zh. Zhang, Zhilong Zhang, Ziyang Zhang, Ziyu Zhang, G. Zhao, J.-P. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. P. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, W. Q. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, X. Zhong, H. Zhou, J. Q. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. X. Zhu, Lin Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, M. Zhuge, J. H. Zou, J. Zu

Machine Learning

BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation

Yaoyang Liu, Shuwei Shen, Jincheng Tang et al. (+5 more)

Long-context inference makes the key-value (KV) cache a major memory bottleneck for large language models. We propose BELT, a low-rank KV cache compression framework. Its key-side objective targets attention distributions rather than raw keys or pre-softmax logits, yielding a quadratic surrogate with a closed-form low-rank solution. A learned global rank allocator uses differentiable soft selection during training and hard selection at deployment to meet a user-specified component budget. Experiments demonstrate improved model quality under aggressive compression and increased decoding throughput at comparable accuracy. Evaluated across three LLMs and multiple benchmarks, BELT achieves up to 70% low-rank KV cache compression while outperforming the strongest baseline by up to 23.3 points on LongBench, and up to 17.8x overall KV cache reduction when combined with quantization. With Triton-based GPU kernels and a quantized cache, BELT delivers up to 2.7x speedup for the attention module and 3.3x end-to-end decoding throughput.
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All authors Yaoyang Liu, Shuwei Shen, Jincheng Tang, Jinguang Liu, Jinbao Xue, Hailong Yang, Ke Zhang, Yinjun Wu

Optimization and Control

Control Realization of Schrödinger Bridges over Discrete-Time Nonlinear Systems via Maximum-Entropy Distribution Steering

Kaito Ito and Kenji Kashima

This paper shows how a Schrodinger bridge (SB) structure emerges from maximum-entropy (MaxEnt) distribution steering of discrete-time nonlinear systems. We also establish that the resulting bridge can be realized by an optimal control policy. We consider general state and input costs, non-Gaussian endpoint distributions, and input constraints. Assuming the existence of a feasible policy with finite objective value, we show that the state-path measure under a MaxEnt optimal control policy is the unique SB. The reference measure of this SB is induced by an optimal policy of the corresponding terminal-unconstrained problem. Moreover, we give an equivalent characterization of the optimal state-path measure using a killed reference and its normalized Feynman–Kac measure, thereby interpreting the state cost as killing. We then characterize the finite-cost feasibility by the existence of an endpoint coupling having finite Kullback–Leibler divergence from the reference endpoint distribution. For compactly supported marginals with bounded target density, we derive reachability-based sufficient conditions guaranteeing this feasibility. We further provide specializations to fully actuated control-affine and reachable linear systems.
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Algebraic Geometry

Two-Dimensional Torus Action on the Quadric Threefold and GW-DT-GV Correspondence

Kiryong Chung and Sanghyeon Lee

We compute Gromov–Witten (GW) and Donaldson-Thomas (DT) invariants (including descendant invariants) for local CY $4$-folds over a smooth quadric threefold $Q$, up to degree $4$. We use the virtual localization formula with respect to a two-dimensional torus action to compute DT invariants. Combining these results with the computation of the J-function of the variety $Q$, we confirm the correspondence between DT and Gopakumar–Vafa invariants suggested by Cao–Maulik–Toda in genus zero. Based on these results, we predict a genus one BPS invariant on $Q$.
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Machine Learning

Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling

Jules Désir, Vincent Auriau, Martin Možina et al. (+1 more)

Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences. In particular, assortment optimization - proposing the ideal mix of products - and promotion planning hold a pivotal role in their strategy. By leveraging sales data, retailers can make informed decisions on which products to sell and how to manage inventory, based on customer preferences as well as regional and seasonal trends. It is especially crucial to capture interactions between products, in order to minimize the number of items that cannibalize each other's sales and to ensure that complementary products, which are often purchased together, are conjointly available and sold across all stores. In this paper, we propose a model of shopping basket that learns embeddings to represent interactions between products, prices and stores. Our model is built to uncover sales patterns from very large transaction datasets. In particular, the optimization loss is computed with random negative samples in order to overcome the computational bottlenecks that arise with large number of items. Our experiments on synthetic data show the efficiency of drawing such negative samples based on the actual assortment of available products, with better results than approaches from the literature. We also validate our approach by training and evaluating our model on a dataset composed of millions of transactions from Fortenova Group, one of the leading retail company in Southeast Europe. Our model showcases promising applications in the sector of retail, with enriched interfaces to efficiently support category managers.
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All authors Jules Désir, Vincent Auriau, Martin Možina, Emmanuel Malherbe

Networking

CALM-5G: Event-Level Conformal Risk Control for Predictive Service-Assurance Alarms

Ömer Faruk İşcanl{\i} and Yağmur Sabucu Sandal

Predictive service assurance converts forecasts of key performance indicators into alarms, and that conversion decides whether the assurance function is deployable. We present CALM-5G, an assurance layer that operates above any forecasting backbone and issues alarms with distribution-free, finite-sample guarantees. It calibrates on service-level violation events, scores violation onset directly, selects its calibration scheme out of sample, and reports the alarm duty cycle with every certified miss rate. Four corpora are evaluated separately, namely a production cellular network, commercial 5G traces, an open radio access network testbed and our 5G standalone testbed. Risk control on the regression-derived score yields duty cycles of 0.24 to 0.91. Onset targeting with cross-validated calibration reduces time-in-alarm by 56.3% to 89.7% on three corpora, with no significant change on the fourth. The realised miss rate stays within a two-standard-error tolerance on three corpora and within one standard error of it on the fourth. Under label shift, pooled batch calibration exceeds that tolerance structurally, by 10.9 standard errors on the production corpus. Online calibration restores compliance. Calibration schemes cannot be ranked on the split that fits their thresholds. Miss rate and duty cycle are certified jointly where within-entity dependence leaves sufficient calibration units.
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Networking

Shortcut in Generalization: Rethinking Evaluation Protocol for Encrypted Traffic Classification

Qiming Yu, Chungang Lin, Zhengyi Ma et al. (+4 more)

Encrypted traffic classification is commonly evaluated under a range of experimental protocols, with classification performance reported as the primary measure. However, the reported performance may be influenced by shortcuts left uncontrolled in the evaluation protocol, such as shortcut features and cross-partition sharing, rather than reflect generalizable traffic patterns. And test-only feature transformations may further introduce train-test mismatch. In this paper, we study how current evaluation protocols are affected by shortcut-related factors, including field-level shortcuts, cross-partition sharing, and train-test processing. We compare feature importance, retrained feature removal, and field-only split sensitivity. We also vary Source-IP overlap and packets per flow, compare test-only IP transformations with matched train-test transformations. Experiments on three public datasets show that the performance of existing methods are affected by both cross-partition sharing and the consistency of train-test transformations. Specifically, increasing Source-IP overlap from 0% to 100% raises accuracy by about 32-33 points before identifier masking, compared with only about 8 points after masking. For two state-of-the-art pretrained models (i.e., YaTC and NetMamba), the 11-13 points loss caused by test-only IP zeroing is almost fully recovered when the same transformation is applied during both training and testing.
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All authors Qiming Yu, Chungang Lin, Zhengyi Ma, Zijia Song, Yuliang Lu, Guozheng Yang, Dongzhen Qi

Differential Geometry

Equivariant analytic torsion forms

Yuxiang Li

In this paper, we first construct infinitesimal equivariant analytic torsion forms for Kähler fibrations. We then compare them with Ma's equivariant analytic torsion forms, modulo $\partial$- and $\overline{\partial}$-exact forms, in terms of equivariant Bott–Chern currents and the $I$-genus. Our comparison formula extends that of Bismut and Goette to families.
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Computation and Language

Virtual Head Attention

Xibo Ding, Guoxia Wang, Jinle Zeng et al. (+4 more)

The design of attention mechanisms in long-context large language models faces an “impossible triangle” among KV cache size, training and prefill FLOPs, and model quality. GQA achieves strong quality but incurs high KV cache overhead; alternative approaches such as MLA and MFA can compress the cache, but MLA suffers from quality loss while MFA substantially increases training and prefill compute. We propose Virtual Head Attention (VHA), which simultaneously improves all three vertices through two lightweight linear operations: Q Premix applies a near-identity transformation to queries within each KV group to recover virtual head diversity from halved physical query heads, and Linear Postmix fuses inter-head features via a low-rank $I + AB^\top$ residual structure. Since VHA introduces no nonlinear operations, Premix can be folded into the query projection and Postmix into the output projection at inference time, reducing the model to a standard GQA-2 architecture that directly reuses all existing GQA inference optimizations. With only two KV heads, the group size in large models readily reaches 64, fully exploiting Tensor Core parallelism during decoding. Across four scales with 4K-context 50B-token pretraining, VHA consistently surpasses the standard GQA baseline at every scale ($+$0.05% to $+$0.81%), while reducing KV cache by $4\times$ vs.\ GQA-8 at dense scales and $2\times$ vs.\ GQA-4 at the MoE scale, and lowering training and prefill FLOPs by 7.9% (1.7B, $S{=}4096$). On an industrial 30B-A3B MoE model trained on 500B tokens at 8K context, VHA outperforms both MLA and GQA-4 by $+$0.70\,/\,$+$2.60\,% on a 12-benchmark base-model evaluation suite; in a separate matched 10B-A1B MoE comparison after long-context mid-training, VHA attains an average RULER score of 84.4 over 8K–128K versus 73.0 for GQA, while using two rather than four KV heads in the full-attention layers.
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All authors Xibo Ding, Guoxia Wang, Jinle Zeng, Jiabin Yang, Sirui Tao, Chao Yang, Dianhai Yu

Data Structures and Algorithms

Mining Correlated Motifs with Wildcards in PPI Networks: Exact and Optimal

Lucas P. Oliveira, Ben Bals, Aalt D. J. van Dijk et al. (+1 more)

A protein-protein interaction (PPI) network is a graph whose nodes are protein sequences and whose edges represent physical interactions between proteins. Correlated Motif Mining (CMM) seeks pairs of sequence motifs whose co-occurrence in interacting proteins reveals binding sites and interaction mechanisms. Formally, in the $(\ell,d)$-CMM problem, we are given a graph $G=(V,E)$, with $V\subseteq\Sigma^*$ for an alphabet $\Sigma$, and integers $0\leq d<\ell$, and we seek a pair $\{X,Y\}$ of motifs, each of length $\ell$ and containing $d$ wildcards, that maximizes a support measure $f(G,\{X,Y\})$; e.g., the count $f_E$ of edges $\{u,v\}\in E$ such that $X$ matches a substring of $u$ and $Y$ matches a substring of $v$, or vice versa. Although existing approaches have produced biologically meaningful results, they are either inefficient or heuristic-based, as the underlying optimization problem is NP-hard. Here, we revisit $(\ell,d)$-CMM. For $d=0$, we give an exact $\mathcal{O}(N^2)$-time and $\mathcal{O}(N)$-space algorithm, where $N$ is the total length of the node strings, and prove that this time is conditionally optimal under the Strong Exponential-Time Hypothesis. For any $d>0$, we give an exact $\mathcal{O}\!\left(\binom{\ell}{d}^2N^2\right)$-time and $\mathcal{O}(N)$-space algorithm, and observe that the problem is W[1]-hard parameterized by $d$. The latter algorithm improves the state of the art by exponentially large factors in both time and space. We further demonstrate experimentally that our algorithms are practical on real-world PPI networks and find significantly higher-scoring motif pairs than the state of the art. Moreover, we show that our algorithms find many top-scoring motif pairs that the state of the art misses entirely. Taken together, our results suggest that exact CMM with wildcards is more tractable in practice than previously assumed.
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All authors Lucas P. Oliveira, Ben Bals, Aalt D. J. van Dijk, Solon P. Pissis

Number Theory

A generating function identity for finite and symmetric multiple zeta values via Dougall's bilateral ${}_2H_2$ summation

Takumi Maesaka and Masataka Ono

We establish a generating function identity for finite and symmetric multiple zeta values using Dougall's bilateral ${}_2H_2$ summation formula. As an application, we prove a restricted sum formula in which the parity of each component of the index is prescribed. We also derive a generating function identity for finite multiple zeta values $\zeta^{}_{\mathcal{A}}(\{1,2\}^n)$ and symmetric multiple zeta values $\zeta^{}_{\mathcal{S}}(\{1,2\}^n)$, thereby proving Kaneko's conjecture for finite multiple zeta values and its symmetric analogue.
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Machine Learning (Statistics)

Learning Distributions from Samples and Qualities

Marc Jourdan and Nicolas Flammarion

We study parameter estimation when each sample from a continuous parametric distribution is accompanied by a binary quality label governed by the same unknown parameter. By imposing hard feasibility constraints, deterministic labels can accelerate estimation from the classical $n^{-1/2}$ rate of smooth quality-aware M-estimators to $n^{-1}$. Combining PAC guarantees with local error-to-parameter conversions, we establish this rate for both identifiable and finitely ambiguous quality classifiers, with likelihood information resolving the latter ambiguity. When the quality model is known only up to an additive constant, we construct threshold-free pairwise preferences from oppositely labeled samples and retain the accelerated rate despite the unknown threshold. We further show that pointwise qualities and pairwise preferences are incomparable: either can be strictly more informative than the other. Our assumptions hold for Gaussian, Laplace, and exponential families under thresholded log-likelihood rewards. Finally, a minimax lower bound establishes the optimal $n^{-1}$ dependence on sample size, up to logarithmic and dimension-dependent factors, while experiments confirm the benefits of constraint-based estimation.
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Materials Science

Thermal stability and structural ordering of thick Co$_2$MnAl films grown on MgO and Si substrates

David Robles-Cuenca, Juan Antonio González, Juan Pedro Andrés et al. (+3 more)

While most studies on Co$2$MnAl (CMA) focus on the ultrathin regime for spintronic junctions, applications scaling with magnetic volume–such as Anomalous Nernst Effect (ANE) energy harvesters, integrated power inductors, and magnetic MEMS–require significantly thicker films. In this work, 750 nm thick CMA films were grown on MgO (001) and Si substrates to establish a roadmap for their thermal integration. Structural analysis reveals that B2 ordering is optimized on MgO at 600 $^\circ$C, which is important for functional devices. In contrast, samples on Si substrates exhibit a disorder transition toward the A2 phase, closely linked to diffusion-driven degradation processes. Above 500 $^\circ$C, Si interdiffusion into the Heusler layer triggers the formation of interfacial silicides and Kirkendall voids, leading to microcracking, electrical resistivity jumps, and a final fully disordered A2 phase. Despite the Mn-poor stoichiometry (Co$2.25$Mn$0.7$Al$1.05$), the films maintain a robust saturation magnetization of ~600 emu/cm$^3$, consistent with recent theoretical models for off-stoichiometric Heusler alloys. These results define the processing windows for integrating thick CMA layers into multifunctional CMOS-compatible platforms.
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All authors David Robles-Cuenca, Juan Antonio González, Juan Pedro Andrés, Miguel Ángel Arranz, Mohamed Salaheldeen, Ricardo López Antón

Group Theory

$\mathrm{CS}\mathfrak{X}$-groups and free constructions

Omar Al-Raisi and Mohammad Shahryari

Let $\X$ be a variety of groups. A group $G$ is $\CSX$ if its maximal $\X$-subgroups are malnormal. We prove that the amalgamated free product of two $\CSX$-groups with a malnormal amalgam and without any involutions is again $\CSX$. Using this, we describe conditions under which the HNN-extension of a $\CSX$-group is $\CSX$.
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