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All of arXiv

Friday 18 September

Probability

Interval-Constrained Brownian Paths: Exact Interpolation and Extrapolation

Radu Herbei and Kumar Somnath

We study Brownian motion and Brownian bridge processes conditioned to remain in a fixed interval $[0,a]$, focusing on the conditional distribution at a single time. For a Brownian motion in $[0,a]$, conditioned on survival up to time $t$ we recall (and present in a self-contained form) the conditional density of its position at $t$ (exact extrapolation). For a Brownian bridge conditioned to remain in $[0,a]$ on $[0,T]$ we express the probability density at an interior time as a normalized product of killed transition densities (exact interpolation). These densities admit dual complementary series representations, which are linked via the Jacobi theta identity. Our main contribution is a unified exact sampling suite for both extrapolation and interpolation that (i) includes boundary endpoints and (ii) automatically switches between density representations to keep acceptance rates efficient across regimes. To that end, we derive simple proposal families which cover both small- and large-time regimes, with an automatic rule selecting the tighter envelope. Our procedures extend to exact simulation of discrete skeleton paths at multiple times via the Markov property.
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Robotics

When Should a Failing Robot Ask? Initiating Corrective Human-Robot Dialogue from Audited Sensor Evidence

Eshika Pathak and Leela Krishna

A robot that fails at a task faces the first decision in corrective dialogue: act on its own diagnosis, consult another onboard sensor, or interrupt a person. Choosing well requires knowing how much the robot's sensors reveal about the cause and how reliable the robot's own diagnosis is. We build a simulated benchmark in which every failure's true cause is known, because we injected it, and measure what each sensor reveals, with explicit checks against data leakage. Some failures are diagnosable from camera images; others only from the robot's force data (0.99 from force data, no image method above 0.55). We then test six open vision-language models. Their behavior tracks the surface of the prompt, not the evidence: moving the refusal option from last to first in the answer list collapses refusal rates from 78-100% to 0-6% in three of the six swept model-and-family pairs. Accuracy from frames stays at or below a majority-class baseline under every prompt variant, with or without worked examples, and stated confidence carries no information about correctness. Handing the same models the force data as ten lines of text produces the first above-baseline diagnoses, in four of the six models: much of the failure reflects missing sensor data, not missing ability. We pose the choice as a three-action decision problem, act, consult your own sensors, or ask a human, whose optimal policy follows from measured accuracy. The models do not follow it, and their ask rates ignore a fourfold change in question cost. One question to a human still lifts them from that baseline to roughly the answerer's own reliability (0.70-0.81 when they ask). The decision to ask should be tied to measured accuracy and stated costs, not to the model's confidence.
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Cryptography and Security

End-to-End Hard-Label Cryptanalytic Model Extraction Using Efficient Sign Recovery

Akira Ito, Takayuki Miura and Yosuke Todo

The importance of deep neural networks (DNNs) is widely recognized, and the parameters obtained through training are regarded as valuable assets. Recently, attacks that extract these parameters using only oracle queries to a DNN have been actively studied at IACR conferences. The hard-label setting is the most challenging setting for model extraction, where an adversary can observe only the final output label, such as "dog" or "cat." At Eurocrypt 2025, Carlini et al. proposed polynomial-time hard-label extraction of ReLU-based MLPs. However, one step of this attack process, i.e., sign recovery, requires a large number of queries and substantial computation. Implementing this step in a black-box setting remains difficult. Consequently, a fully black-box end-to-end demonstration on trained deep ReLU MLPs has remained a challenge. In this paper, we propose a new sign-recovery algorithm based on a completely different principle from the existing method. Our method requires no dedicated queries for sign recovery. In our experiments, it achieves higher sign-recovery accuracy than the existing method. Consequently, it enables efficient sign recovery even for trained models. With our sign-recovery algorithm, all steps of hard-label model extraction can be implemented in a black-box setting. By combining these implementations, we demonstrate end-to-end model extraction from models trained on MNIST and Fashion-MNIST, with width 16 and 4 or 6 hidden layers, achieving over 98% label agreement.
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Artificial Intelligence

AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory

Zijie Cao, Xijun Qu, Zhicheng Gu et al. (+7 more)

Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions. Existing memory systems typically rely on fixed granularities or static schemas, but these designs struggle when heterogeneous information, such as preferences, events, constraints, and temporal updates, is embedded in a single mixed representation. The resulting semantic interference makes top-K retrieval sensitive to noise and often leaves relevant evidence poorly ranked. We present AutoViewMem, a data-driven framework that organizes long-term conversational memory into self-configuring, low-overlap semantic views before indexing. AutoViewMem discovers candidate views from interaction traces, selects a compact complementary view set, and uses these views to guide write-time structured extraction of provenance-grounded memories. This representation-first design moves semantic disentanglement from retrieval time to write time, allowing standard top-K similarity search to retrieve focused evidence without explicit routing or iterative retrieval. We further apply offline consolidation to improve memory compactness and consistency. Experiments on the LoCoMo and PersonaMem benchmarks, under both Qwen3-8B and Qwen3-14B backbones, show that AutoViewMem improves long-horizon question answering and personalization over strong memory baselines while preserving a simple inference pipeline.
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All authors Zijie Cao, Xijun Qu, Zhicheng Gu, Xiaoshu Chen, Duanyang Yuan, Yanning Hou, Sihang Zhou, Jianxing Gong, Jian Huang, Yang Mei

Astrophysics of Galaxies

Stellar Yields of Rapidly Rotating Population III Stars for the High Redshift Universe

Devesh Nandal, Konstantinos Topalakis, Vasilisa Sergienko et al. (+6 more)

JWST has revealed rapid nitrogen enrichment in high redshift galaxies, renewing the need for stellar yields that follow metal free stars beyond the main sequence and across their final fates. We present Geneva stellar evolution models of rapidly rotating Pop~III stars with $5 \leq M_{\rm ini}/M_\odot \leq 200$ and $\upsilon_{\rm ini}/\upsilon_{\rm crit}=0.7$, evolved to the end of core O-burning. We calculate gross adopted ejecta masses using fate dependent remnant prescriptions spanning core collapse, pulsational pair instability, and complete pair instability. Our quantitative results focus on hydrostatically produced CNO material, while heavier species are treated as final-model reservoirs. Rotation produces primary CNO material by mixing newly synthesized C and O into H-burning layers, where CNO cycling generates nitrogen. The resulting abundance signature depends strongly on which stellar layers escape. Models leaving compact remnants can reach $\log({\rm N/O})=-0.49$ while remaining poor in Si/S/Ar/Ca-rich material. PISN models eject the largest nitrogen masses, $M_{\rm N}=1.8$–$2.5,M_\odot$, but also release $57$–$65,M_\odot$ of oxygen, lowering $\log({\rm N/O})$ to $-1.44$ to $-1.29$. Thus, high nitrogen yields do not imply high N/O ratios. This trend persists after IMF weighting, as increasingly top-heavy populations add oxygen and deep alpha material faster than they build a nitrogen dominated mixture. Our grid provides a new CNO-focused prompt-enrichment dataset for interpreting nitrogen-rich systems including GN-z11, CEERS-1019, and GS~3073.
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All authors Devesh Nandal, Konstantinos Topalakis, Vasilisa Sergienko, Doga Eker, Efe K. Celep, Danielle A. Berg, Daniel J. Whalen, Sophie Tsiatsiou, Raphael Hirschi

Computer Vision

Info3R: Information-Adaptive Test-Time Training for 3D Reconstruction

Sunghyun Baek, Hanna Bae, Minchan Kwon et al. (+1 more)

Transformer-based models have recently achieved strong performance on 3D reconstruction from images, and recent works extend them to process video streams in an online manner for real-world deployment. However, existing methods overlook two key signals when handling long image streams: the importance of each incoming frame and the information saturation of the model's internal state. In this paper, we propose Info3R, a novel information-adaptive test-time training method for the online 3D reconstruction. We introduce an information-aware state update that modulates the state update strength based on the redundancy and informativeness of each incoming frame. To restore the state's plasticity – its capacity to incorporate new observations – we propose a dynamic state reset, triggered by the cumulative magnitude of state updates and the model's prediction confidence and accompanied by an anchor-to-world alignment. Our method achieves consistent improvements on camera pose estimation, video depth estimation, and 3D reconstruction, while substantially mitigating the performance degradation in the long sequence evaluation. Notably, on KITTI Odometry, our method achieves on average 1.68x lower ATE than LongStream, demonstrating its robustness on extended outdoor sequences.
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All authors Sunghyun Baek, Hanna Bae, Minchan Kwon, Junmo Kim

Probability

Independence Threshold for Collision Times of Many Planar Random Walks

Ziyang Liu

We study collision times of many independent simple random walks on $\mathbb Z^2$ through the joint moment generating function of their pairwise collision local times. For a fixed number of walks, these collision times are known to be asymptotically independent after a suitable logarithmic normalisation. We investigate the extent to which this independence persists when the number of walks grows. For $N$ being the walk length, our results identify a threshold $\asymp (\log N)^{\frac{1}{3}}$, on which the transition from independence to dependence happens. The proofs combine chaos expansion techniques and a correlation inequality, which is the result of a local limit theorem.
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Data Structures and Algorithms

Sampling Matchings in Near-linear Time

Tianshun Miao and Yitong Yin

For every fixed activity $\lambda>0$, we establish three results for the monomer–dimer model on an $n$-vertex simple graph $G$ with $m\ge1$ edges and maximum degree $\Delta$. 1. Near-linear mixing and sampling. Single-edge Glauber dynamics has mixing time $O_\lambda(m[\log^2 n+\log(1/\varepsilon)])$, giving a near-linear-time approximate sampler. 2. Work-efficient parallel sampling. We simulate the same Glauber dynamics in parallel using $\tilde{O}_\lambda(m+n)$ work and $\tilde{O}_\lambda(\min\{\Delta,m^{1/3},\sqrt n\})$ depth with high probability. 3. Fast approximate counting. We estimate the partition function within relative error $\varepsilon$ in $\tilde{O}_\lambda(n^2/\varepsilon^2)$ work. For dense graphs with $m=\Theta(n^2)$, this is near-linear in the input size. For the mixing theorem, we establish a general log–Sobolev criterion based on field-dynamics spectral stability, with only logarithmic dependence on the inverse occupied-marginal lower bound. Parallelism uses a matching-specific analysis of occupation-interval dependencies. Counting uses monomer-preconditioned Jerrum–Sinclair dynamics, whose parameters are learned efficiently by Glauber dynamics.
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Chemical Physics

fix uvt and fix pimd/uvt: A Unified LAMMPS Framework for Constant-Potential Constant-Temperature Molecular Dynamics

Li Fu, Yifan Li and Shenzhen Xu

Accurate simulations of electrochemical interfaces require the simultaneous treatment of constant-potential conditions, nuclear quantum effects, and sufficient configurational sampling. Integrating these capabilities within a general and efficient molecular dynamics (MD) framework remains challenging. In this work, we implement fix uvt and fix pimd/uvt in LAMMPS for constant-potential classical MD and path integral molecular dynamics (PIMD), respectively. We organize the class hierarchy to reuse LAMMPS's existing Nosé–Hoover chain thermostat routines and share nuclear propagation routines across PIMD integrators. A common interface connects these integrators to models that provide the electron-number derivative of the potential energy. We present three examples with accompanying input commands to guide users through constant-potential classical MD, thermostatted PIMD, and constant-potential PIMD, covering analytical models, liquid water, and electrochemical interfaces described by machine learning potentials. This work provides practical tools and guidance for large-scale constant-potential simulations incorporating nuclear quantum effects.
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Systems and Control

Interconnections of Dissipative Networks Through a Dynamic Scattering Controller

Ilia G. Polushin

The problem of modular scattering-based design of dissipative networks is addressed. Under basic assumptions imposed on subsystems, design of a dynamic scattering-based interconnection controller is proposed which guarantees that the resulting interconnection is internally stable and possesses the same set of basic properties as the individual subsystems. This enables iterative application of the developed technique, which allows for building of large-scale dissipative networks in a modular fashion. The proposed method provides substantial flexibility in the controller design as well as in the choice of input and output signals used for interconnections.
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Analysis of PDEs

Homogenization and averaging in the Bourgain-Brezis-Mironescu limit

Andrea Braides, Stefano Mannella and Alec Jacopo Almo Schiavoni Piazza

We study the simultaneous asymptotic behaviour of homogenization and the Bourgain–Brezis–Mironescu limit for quadratic fractional energies with periodic coefficients. The functionals depend on two small parameters: the period of the coefficients and the fractional exponent approaching the local limit. We prove that the $\Gamma$-limit is completely determined by the relative scaling of these parameters. In the two extreme regimes, one recovers the expected separation of scales, yielding either the homogenized Dirichlet energy or the energy with averaged coefficients. At the critical logarithmic scaling, however, the two mechanisms coexist, and the limit is a convex combination of the homogenized and averaged energies, with weights depending explicitly on the relative scaling of the two asymptotic processes. The proof combines the homogenization analysis of the short-range interactions with a blow-up argument showing that long-range interactions are simply averaged using a Riemann–Lebesgue-type argument.
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Machine Learning

Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data

Khoa Tran, Ho-Si-Hung Nguyen, Phone Wai Yan Moe et al. (+2 more)

Joint remaining useful life (RUL) prediction and capacity estimation require representations of both gradual degradation and recent battery behavior. This paper presents a cross-expert framework using partial-charging measurements without requiring measured historical full-cycle capacity as an input. The RUL Expert captures long-term degradation from nominal 10-min segments sampled across a 30-cycle history, while the Capacity Expert characterizes recent battery behavior from statistical descriptors of nominal 40-min segments over ten consecutive cycles. Their complementary representations are integrated through feature-wise linear modulation for joint RUL and capacity prediction. A key contribution is a three-stage training strategy that progressively controls frozen and trainable components: supervised representation pretraining, independent expert pretraining, and final fusion training with both experts frozen. This staged optimization preserves expert-specific degradation knowledge while improving the balance between the two prediction tasks, with RUL treated as the primary prognostic objective. On two public battery-aging datasets, the reference configuration achieves mean RUL root-mean-square errors of 143.69 and 161.10 cycles and capacity errors of 12.36 and 7.28 mAh, respectively. On Dataset I, cross-expert fusion reduces both mean errors relative to either standalone expert. The proposed framework achieves the lowest reported RUL RMSE among the compared methods on both datasets while maintaining competitive capacity-estimation accuracy.
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All authors Khoa Tran, Ho-Si-Hung Nguyen, Phone Wai Yan Moe, Hung-Cuong Trinh, Thi-Hoang-Giang Tran

Symplectic Geometry

Periodic Magnetic Geodesics with Every Low Energy: Existence and Localization

Valerio Assenza, Gabriele Benedetti and Leonardo Macarini

Let $(Q,g)$ be a Riemannian manifold equipped with a non-identically zero magnetic field represented by a closed $2$-form $\beta$. We allow $Q$ to be non-compact and do not assume that the metric $g$ is complete. We prove that if the magnetic strength, defined as the pointwise norm of $\beta$, attains a strict local maximum on a non-empty compact set $K$, then every sufficiently low energy level carries a contractible periodic magnetic geodesic of the pair $(g,\beta)$ localized near $K$. More precisely, such orbits exist in every neighborhood of $K$, and their lengths converge to zero with the energy. In particular, if $Q$ is compact, then every sufficiently small energy level carries a contractible periodic magnetic geodesic. We also show that, in general, neither the non-emptiness nor the compactness of $K$ can be omitted. Our proof relies on the calculus of variations of the Lagrangian action functional, and uses several new ideas. More precisely, we overcome: (i) the non-exactness of $\beta$ by restricting the minimax to the set of short loops; (ii) the non-completeness of $g$ by combining a compactification of $Q$ with Thom's Jet Transversality and a blow-up for sequences of magnetic geodesics with energy tending to zero; (iii) the possible non-compactness of Palais–Smale sequences by the positivity of the Ricci magnetic curvature for low energy established by the first-named author. Unlike previous work, Struwe's monotonicity argument cannot be used for our purposes, and we rely on a two-Lyapunov-function argument due to Abbondandolo and Majer.
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High Energy Astrophysical Phenomena

Euclid: Quick Data Release (Q1) – Optical hotspots in powerful radio galaxies

M. J. Hardcastle, L. Bisigello, M. Bondi et al. (+154 more)

The hotspots of powerful radio galaxies are the sites of high-energy particle acceleration, but radio observations give us limited information on the electron energy distributions that they generate, which affects our overall understanding of their physics and energetics. Optical synchrotron counterparts to radio galaxy hotspots, which probe high electron energies, have been known for many years, but have only been studied in a small number of objects selected as being very bright in the radio. By combining Low-Frequency Array (LOFAR) observations with the Euclid Q1 data release in the Euclid Deep Field North, we identify 77 powerful radio galaxies with hotspots and show that a small but significant fraction of them, 7–15%, may have detected infrared and optical counterparts to their hotspots. The hotspot spectral energy distributions in our clearest cases are consistent with particle acceleration up to (but not beyond) 1\,TeV in energy on the assumption of field strengths close to equipartition, whereas non-detected hotspots either do not reach such high electron energies or would have to have very different electron energy spectra or significantly sub-equipartition magnetic field strengths. Two new candidate optical hotspots are clearly detected matching high-resolution LOFAR data; one is remarkable as both the highest-redshift optical hotspot known to date ($z \approx 1.6$) and also the first optical detection of the hotspot of the inner lobe of a restarting source. We discuss approaches to performing a large-scale study of hotspots with the full wide-area Euclid surveys, particularly when combined with the international baselines of LOFAR, enabling for the first time a statistical analysis of high-energy particle acceleration in hotspots.
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All authors M. J. Hardcastle, L. Bisigello, M. Bondi, M. Magliocchetti, L. K. Morabito, J. Petley, F. Ricci, H. J. A. Rottgering, K. Rubinur, D. J. B. Smith, D. Stern, B. Altieri, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, F. Courbin, H. M. Courtois, M. Cropper, J.-G. Cuby, A. Da Silva, H. Degaudenzi, G. De Lucia, 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. V. H. Haugan, J. Hoar, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, 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, S. Pires, G. Polenta, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, B. Rusholme, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, A. Secroun, G. Seidel, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, 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, O. R. Williams, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, J. García-Bellido, V. Scottez, F. Tarsitano, J. H. Knapen

Robotics

MAAP: Multi-Agent Active Perception for Collaborative Manipulation

Bruno N.Y. Chen, Li Kang, Heng Zhou et al. (+4 more)

Multi-agent manipulation naturally produces multiple task-driven viewpoints: every arm carries a wrist camera and moves through the scene while acting. Yet these observations are typically underutilized, and active perception in manipulation is still often treated as requiring a dedicated sensing agent. We introduce MAAP (Multi-Agent Active Perception), in which every arm is dual-purpose: it executes manipulation actions and, through the wrist camera it carries, simultaneously serves as a moving viewpoint for the team. We pair this with RAIL (Role-Aware Imitation Learning), a controller that predicts each arm's current role alongside its action chunk and conditions action generation on it, representing role-dependent actions within one network. Across four simulated tasks, widening the perception regime lifts average success from 56.5% with a fixed camera to 62.5% with one active wrist view and 70.0% with all of them, while MAAP+RAIL reaches 79.2%. RAIL's additional gain is concentrated on the three-arm Microwave task, where success rises from 47% to 82% on identical multi-wrist inputs. On a dual-arm platform, MAAP+RAIL succeeds in 14 of 20 placement trials compared with 0 of 20 for fixed-view ACT. Collaborative manipulation can thus serve as an active perception mechanism in its own right.
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All authors Bruno N.Y. Chen, Li Kang, Heng Zhou, Xiufeng Song, Zhemeng Zhang, Jiahua Ma, Yiran Qin

Astrophysics of Galaxies

Stellar ages from [C/N] in giant stars: applicability and limitations

Giada Casali, Luca Casagrande, Fiorenzo Vincenzo et al. (+5 more)

The [C/N] abundance ratio is a powerful age indicator for giant stars, enabling age estimates for spectroscopic samples where traditional methods are limited. We calibrate a multivariate [C/N]-age-[Fe/H] relationship using 9,122 giant stars observed by Kepler, with asteroseismic ages and APOGEE DR17 abundances. The calibration is performed separately for lower red giant branch (LRGB), upper RGB (URGB), and red clump (RC) stars. We validate the relationships using independent samples from K2, TESS, and open clusters, finding good agreement with reference ages, particularly for LRGB stars, with typical precision of ~30% for ages between ~2 - 10 Gyr. The performance degrades for URGB and RC stars and low metallicities, likely due to extra mixing processes, for young (<2 Gyr) and old (>10 Gyr) stars, where the [C/N]-age correlation weakens. We show that the [C/N]-based ages reproduce known Milky Way features, including the age distribution across the [alpha/Fe]-[Fe/H] plane and the flaring of the Galactic disc. Compared to other chemical clocks, [C/N] provides more robust and precise age estimates within its domain of validity. Our results demonstrate that [C/N], combined with metallicity, is an effective empirical tool for deriving stellar ages of giant stars from spectroscopic surveys, particularly for LRGB stars with [Fe/H] > -1, enabling Galactic archaeology studies beyond the reach of current asteroseismic samples.
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All authors Giada Casali, Luca Casagrande, Fiorenzo Vincenzo, Andrea Miglio, Melissa Ness, Grazina Tautvaišiene, Laura Magrini, Sven Buder

Combinatorics

A colorful Steinitz theorem with different centers

G. Ivanov

We prove a colorful quantitative Steinitz theorem in which the color classes may have different centers. If the convex hull of each of $2d$ sets in $\R^d$ contains a translate of the Euclidean unit ball, then a rainbow convex hull contains a ball of radius $(3d)^{-2d^2}$ whose center belongs to the convex hull of the given centers. The main ingredient is an exact result for translates of a segment, proved by a lifting and a topological colorful Helly theorem. The same idea also yields a sharp colorful Helly theorem for translated cones: for $1 \le k \le d-1$, if every rainbow selection from $2d-k+1$ finite families of convex sets in $\mathbb{R}^d$ has a $k$-dimensional cone in its intersection, then the intersection of one of the families contains such a cone.
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Machine Learning

Kinks vs. Smoothness: Identifiability of Real Analytic nICA for Laplace-like Sources

Isaac Manring and Kejun Huang

Many machine learning systems try to explain complex data - like images or financial time series - in terms of hidden, independent factors that generated them. Recovering the true underlying factors, rather than some scrambled version of them, is the central challenge of nonlinear Independent Component Analysis (nICA). We prove identifiability (exact recovery) up to trivial ambiguities for real analytic generating functions when source probability density functions have a finite number of discontinuities in the first derivative. The Laplace distribution is the most prominent example satisfying this assumption. Our proof relies on the contrast between kinks in the source distribution and the smoothness of real analytic functions. Real analytic functions comprise a broad class of generating mechanisms, and can be approximated with Normalizing Flows or Variational Autoencoders with standard activation functions (e.g., tanh, softplus, GELU), so our result applies with minimal changes to existing training pipelines. We perform experiments on real and synthetic data with both Normalizing Flows and Variational Auto-Encoders demonstrating their identifiability properties. In experiments on CelebA data we recover several interpretable latent factors controlling unique attributes across the dataset.
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Human-Computer Interaction

Can I Trust My Body? A Three-Year Autoethnography of ChatGPT's Place in My Support System for Panic Attacks

Dongyijie Primo Pan, Pan Hui and Mirjana Prpa

People increasingly seek mental health support from large language models, yet little is known about their use across years of recurrent panic. We present a three-year analytic autoethnography of the first author's ChatGPT use while living with panic disorder, drawing on conversations, personal records, and accounts from friends or family members and professionals. Narrative analysis traces how my questions shaped ChatGPT's roles and how earlier experiences influenced later responses to symptoms. Familiar explanations could make sensations less frightening, while changed symptoms renewed fears of serious illness. During sudden panic, advice could be difficult to follow, and some replies prompted further checking. Conversations could end while symptoms, checking, or help-seeking continued. We propose trajectory-level safety during and after panic: usable advice (Fit), a stopping point for repeated checking and reassurance seeking (Closure), and useful understanding and human support that remain available over time (Continuity).
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Artificial Intelligence

What Should We Ask Next? Retrieval-Aware Question Learning for Interactive ReID

Lyucheng Qian, John Yuehan Zhang and Pingyu Wang

Interactive retrieval with partial evidence constitutes a sequential information-acquisition problem: an agent must choose questions that acquire useful evidence for the next retrieval update. Existing systems train this decision by imitating an offline ordering of candidate QA pairs. However, a question's value depends on the response it elicits and its downstream effect on retrieval. We establish that candidate discriminativeness and perceived usefulness provide weak supervision for this objective, then introduce RAVEL, a retrieval-aware online reinforcement learning framework for interactive person re-identification. RAVEL initializes from supervised question generation, observes the current Top-4 candidates directly, and optimizes the question policy with rank feedback from the full question-answer-retrieval loop. Experiments on Interactive-PEDES show that RAVEL delivers progressively stronger retrieval performance across five interaction rounds. Further analysis shows that RAVEL allocates more of its interaction budget to localized open-ended prompts targeting specific attributes.
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High Energy Astrophysical Phenomena

Nonlinearly Causal General-Relativistic Two-Fluid Dissipative Magnetohydrodynamics

Elias R. Most and Samuel J. Dunham

We present a formulation of general-relativistic (GR) 19-moment dissipative magnetohydrodynamics (MHD), capable of handling all first-order dissipative terms (viscosity, heat conductivity, resistivity and Hall terms), as well as all ideal electron degrees of freedom (number density, momentum and energy) in a GR astrophysical two-fluid plasma. We derive necessary and sufficient nonlinear causality conditions for the constrained first-order 19-moment system, as well as both necessary and sufficient conditions for strong hyperbolicity. To assess the formulation numerically, we develop a high-resolution shock-capturing scheme that solves these equations in a performance-portable fashion. The scheme expresses all evolution equations, including the dissipative and electron sectors, in flux-divergence form, allowing us to model systems with scales separated by orders of magnitude without resolving kinetic scales everywhere on the grid. In addition, we use implicit integration methods to systematically overstep kinetic scales in MHD regions, such as cyclotron and plasma frequencies. To make the scheme as robust as GRMHD codes, we construct necessary and sufficient conditions for a conserved state to be physically admissible, and based on these construct a new physicality-enforcement scheme. As an exact validation comparison, we formulate and derive a full solution to dissipative two-fluid Bondi accretion in general relativity. We then validate the equations against a series of results from kinetic particle-in-cell models of black hole accretion and magnetospheric dynamics, demonstrating that our formulation and scheme can correctly capture major features relevant for feedback on global scales of these solutions, including dimensionless reconnection rates of order $0.1$ and Braginskii-like anisotropic pressures and heat fluxes.
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Combinatorics

On the Injectivity of Elementary Symmetric Partitions and the Multiset Recovery Problem

Ziyao Sun

The elementary symmetric partition map $\pre_s$, introduced by Ballantine, Beck, and Merca, sends an integer partition to the summands in the evaluation of the $s$-th elementary symmetric polynomial at its parts. By encoding partition parts as prime-exponent valuation vectors, we connect $\pre_s$ to Leo Moser's additive Multiset Recovery Problem (1957) and prove that $\pre_s$ is unconditionally injective on partitions of length $n$ whenever $n$ lies outside the Moser root set $\mathcal{Z}_s$, with no size restrictions. Furthermore, under the equal-size constraint $|\lambda| = |\mu| = N$, we prove that $\pre_4$ is injective at the isolated singular length $n = 12$, and that every fiber of $\pre_3$ on $\Part_6(N)$ has cardinality at most $2$, completely excluding both triplets and quartets.
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High Energy Physics (Experiment)

Observation of the doubly charmed baryon $\varOmega^+_{cc}$

LHCb collaboration: R. Aaij, M. Abdelfatah, A.S.W. Abdelmotteleb et al. (+1177 more)

A search for the doubly charmed baryon $\varOmega^+_{cc}$ in the $\varOmega^0_c\pi^+$ decay channel is performed using proton-proton collision data corresponding to an integrated luminosity of $6.3\text{fb}^{-1}$, collected with the upgraded LHCb detector in 2024 at a center-of-mass energy of 13.6$\text{TeV}$. A peaking structure with a global significance of $8.7\sigma$ is observed in the $\varOmega^0_c\pi^+$ mass spectrum, where the $\varOmega^0_c$ baryon is reconstructed in the $pK^-K^-\pi^+$ final state. The structure is consistent with originating from a weakly decaying particle and is identified as the doubly charmed baryon $\varOmega^+_{cc}$. Its mass is determined to be $3725.9 \pm 1.0 \,(\mathrm{stat}) \pm 0.2 \,(\mathrm{syst}) \pm 0.4 \,(\mathrm{lifetime}) \pm 0.6 \,(\mathrm{ext})\,\text{MeV/}c^2$, where the third uncertainty arises from the dependence of the selection-induced bias on the unknown $\varOmega^+_{cc}$ lifetime, and the fourth is due to the uncertainties on the masses of the $\varOmega^0_c$, $\varXi^+_c$, and $\varXi^{++}_{cc}$ baryons.
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All authors LHCb collaboration: R. Aaij, M. Abdelfatah, A.S.W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A.A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C.A. Aidala, S. Akar, K. Akiba, H. Al Saleh, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J.L. Amey, Y. Amhis, Z. Amos, L. An, L. Anderlini, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J.A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J.J. Back, M. Bai, Z.B. Bai, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I.R. Barbosa, W. Barker, R.J. Barlow, M. Barnyakov, S. Baron, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P.B. Battista, A. Bavarchee, A. Bay, A. Beck, M. Becker, F. Bedeschi, I.B. Bediaga, N.A. Behling, S. Belin, A. Bellavista, I. Belyaev, G. Bencivenni, E. Ben-Haim, J.L.M. Berkey, R. Bernet, A. Bertolin, L. Bertsch, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, M.S. Bieker, N.V. Biesuz, A. Biolchini, M. Birch, F.C.R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J.E. Blank, S. Blusk, J.A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, C. Bolognani, R.B. Bonacci, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J.T. Borsuk, E. Bottalico, S.A. Bouchiba, M. Bovill, T.J.V. Bowcock, A. Boyer, C. Bozzi, J.D. Brandenburg, A. Brea Rodriguez, N. Breer, C. Breitfeld, J. Brodzicka, J. Brown, E. Buchanan, M. Burgos Marcos, C. Burr, C. Buti, J.S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, Y. Cai, A. Caillet, R. Calabrese, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J.I. Cambon Bouzas, P. Campana, A. Campomagnani, A.C. Campos, A.F. Campoverde Quezada, Y. Cao, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A.J. Chadwick, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, S. Chernyshenko, X. Chiotopoulos, G. Chizhik, V. Chobanova, A. Christakakis, M. Chrzaszcz, Y. Chu, V. Chulikov, P. Ciambrone, X. Cid Vidal, P. Cifra, P.E.L. Clarke, M. Clemencic, H.V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, A. Codovini, C. Codovini, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, G.C. Costantino, C. Cotirlan, J. Cottee Meldrum, B. Couturier, D.C. Craik, N. Crepet, M. Cruz Torres, M. Cubero Campos, E. Curras Rivera, R. Currie, C.L. Da Silva, X. Dai, J. Dalseno, C. D'Ambrosio, G. Darze, A. Davidson, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, F. De Gregorio, E. De Lucia, J.M. De Miranda, L. De Paula, A. De Robertis, E. De Santis, M. De Serio, P. De Simone, F. De Vellis, J.A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, B. Demaire-Lepape, J. Deng, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, S. Ding, Y. Ding, L. Dittmann, J.F. Diverchy, A.D. Docheva, A. Doheny, C. Dong, F. Dordei, J. Dorta Moreno, A.C. dos Reis, J. Dos Santos Oliveira, A.D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M.M. Duras, J.M. Durham, O.D. Durmus, K. Duwe, A. Dziurda, S. Easo, E. Eckstein, U. Egede, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, T. Evans, F. Fabiano, S. Faghih, L.N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, C. Feng, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, B. Fernandez Rodino, J. Fernandez-John, A.D. Fernez, F. Ferrari, F. Ferreira Rodrigues, R.A. Fini, R. Fiorenza, M. Fiorini, M. Firlej, D.S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, M. Fontes Vaz, L.A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D.A. Friday, J. Fu, Y. Fu, Q. Führing, T. Fulghesu, G. Galati, M.D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T.Q. Gao, Y. Gao, Y. Gao, Y. Gao, L.M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F.I. Giasemis, V. Gibson, H.K. Giemza, A.L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M.A. Giza, F.C. Glaser, V.V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, A. Golutvin, S. Gomez Fernandez, A.G. Gomez Mongui, W. Gomulka, F. Goncalves Abrantes, I. Gonçales Vaz, M. Goncerz, G. Gong, S. Gong, J.A. Gooding, C. Gotti, E. Govorkova, J.P. Grabowski, L.A. Granado Cardoso, R. Grande Quartieri, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A.T. Grecu, N.A. Grieser, L. Grillo, C. Gu, M. Guarise, L. Guerry, A.-K. Guseinov, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P.M. Hamilton, Q. Han, S. Han, X. Han, S. Hansmann-Menzemer, N. Harnew, T.J. Harris, L. Hartman, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R.D.L. Henderson, A.M. Hennequin, K. Hennessy, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D.C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R.J. Hunter, D. Hutchcroft, M. Idzik, P. Ilten, A. Iohner, S. Jacevicius, H. Jage, S.J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, A. Jawahery, C. Jayaweera, A. Jelavic, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y.J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C.R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J.W. Kautz, M.K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, S. Kholodenko, V. Kholoimov, G. Khreich, F. Kiraz, T. Kirn, V.S. Kirsebom, N. Kleijne, A. Kleimenova, D. Klekots, K. Klimaszewski, M.R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, O. Kravcov, M. Kreps, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, A. Kupsc, A. Kurzina, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, G. Lanfranchi, C. Langenbruch, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E.D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, L. Li, P. Li, P.-R. Li, Q. Li, T. Li, T. Li, W. Li, Y. Li, Y. Li, Y. Li, Z. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, J. Lin, S. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F.L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, X. Liu, Y. Liu, Y. Liu, Y.L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F.C.L. Lopes, J.H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, M. Lupberger, E. Luppi, K. Lynch, J. Lyu, S. Lyu, X.-R. Lyu, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L.M. Mackey, L.R. Madhan Mohan, M.J. Madurai, D. Magdalinski, J.J. Malczewski, S. Malde, L. Malentacca, G. Manca, C. Mancuso, R. 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Numerical Analysis

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Human-Computer Interaction

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All authors Yajing Wang, Emilia Marchese, Talayeh Aledavood, Juhi Kulshrestha

Sound

Investigating the Performance and Energy Costs of Replicating Band-Split RNN for Music Source Separation

Paul Magron, Romain Serizel and Constance Douwes

Band-split recurrent neural network (BSRNN) is a popular music source separation model that yields close to state-of-the-art results using reasonable computational resources and public datasets. It is therefore interesting from a reproducible research perspective, but achieving its performance is not straightforward since its full code is not available. In this paper, we conduct a replication of BSRNN via implementing the full pipeline. We extend the original paper's analysis by experimentally studying various design choices about data preprocessing, the optimization protocol, and architectural parameters. We report and discuss this project's energy cost, and we underline how its footprint could have been substantial lower upon availability of the full pipeline, which advocates for more reproducible research practices. To comply with this objective, we publicly release our code and pre-trained models.
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Optics

Quantized Transport of Gap Solitons in the Harper-Hofstadter Model

Oliver Ashfield, Henry Davenport, Tom Sheppard et al. (+6 more)

We investigate the dynamics of gap solitons found in the Harper-Hofstadter model with an additional cubic nonlinearity, such as that describing weakly-interacting cold atoms or a Kerr nonlinearity in an optical platform. We find that certain solitons with frequencies deep in the band gap travel with a robust, quantized velocity perpendicular to the applied force and maintain their spatial profile over long timescales. Crucially, this velocity is linearly proportional to the applied force, allowing for arbitrary steering of the nonlinear wavepacket, without breakdown, using only a tunable external force. This quantized transport persists despite a highly non-uniform occupation of the linear Bloch states, and contrasts with solitons near the band edge, which deform and delocalize under the same force. These results point to a robust and highly controllable mechanism for steering nonlinear solitons in experimentally accessible platforms.
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All authors Oliver Ashfield, Henry Davenport, Tom Sheppard, David G. Reid, Holly A. J. Middleton-Spencer, A. C. Berceanu, Frank Schindler, Iacopo Carusotto, Hannah M. Price

Quantum Physics

Asymptotics for Frequency Redundancies in Quantum Machine Learning Models

Felix Paul, Bhilahari Jeevanesan and Peter Jung

The redundancy distribution of the frequency spectrum has been shown in the literature to impact the expressivity and trainability of Quantum Fourier Models (QFMs). In this work, we address the question of how this redundancy spectrum is shaped by the choice of eigenvalues of the data re-uploading Hamiltonians, using a simple mathematical formalism based on generating functions. We derive exact and asymptotic redundancy profiles for several structured eigenvalue choices identical for every layer, including arithmetic progressions and single-qubit Pauli encodings, and show that both approach a Gaussian profile as the number of layers grows. We then show that this Gaussian limit is not specific to these constructions but is a generic feature of QFMs built from integer eigenvalues that are identical in every layer. These results can be tied to the central limit theorem for random walks. These results clarify why generic or unstructured encoding choices give rise to a redundancy bias that favours low frequencies, highlighting the importance of well-thought-out encoding strategies when constructing a QFM.
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Functional Analysis

Reconstruction of solutions to abstract Schrodinger, diffusion, and wave-type equations from snapshots

Isaac Z. Pesenson

It is shown that if one knows "sufficiently many" snapshots (samples) from an individual solution of a Schrödinger-type equation-which is bandlimited in an appropriate sense-then the solution can be reconstructed by a simple formula similar to the Shannon formula for bandlimited signals. Furthermore, results demonstrate that by using snapshots of bandlimited solutions of a Schrödinger-type equation, one can reconstruct bandlimited solutions of some relevant diffusion- and wave-type equations. Interestingly, the reconstruction formulas are essentially independent of the operators defining the equations.
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Algebraic Geometry

On the atomic decomposition of complete intersection in flag varieties

Boris Alexeev, Leonardo F. Cavenaghi, Giovane Galindo et al. (+3 more)

We develop a root-theoretic localization formalism for genus-zero Gromov–Witten invariants of flag varieties and of smooth zero loci of globally generated homogeneous vector bundles. The resulting invariants are expressed as finite sums over decorated trees whose contributions are determined by the root system of the ambient flag variety and by the torus weights of the defining bundle. We connect these computations with the Theory of Hodge Atoms of Katzarkov–Kontsevich–Pantev–Yu via the matrix of small quantum multiplication by first Chern class. Let $X$ be a Fano fourfold with $h^{3,1}(X)=1$ arising as a hyperplane section of a suitable Fano fivefold. Its cohomology decomposes into a monodromy-fixed part and the middle vanishing cohomology. This decomposition is preserved by quantum multiplication by the first Chern class. Moreover, it acts by a scalar on the vanishing summand. We show, for the monodromy-fixed block, that (a) if every eigenvalue has algebraic multiplicity at most two and $X$ is Hodge general, then $X$ is irrational; (b) if every eigenvalue has Jordan defect at most one and $X$ is rational, then every weak factorization contains a smooth surface center whose minimal model is a projective K3 surface. Many applications are presented. Using the recently introduced quantum connection approach to irrationality, we obtain from the computations here presented the irrationality of the very general smooth complete intersection of a hyperplane and a quadric in $\operatorname{LG}(3,6)$; the very general smooth quadric section in $G_2/P_2$; and the very general smooth complete intersection of five hyperplanes and a quadric in the spinor 10-fold.
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All authors Boris Alexeev, Leonardo F. Cavenaghi, Giovane Galindo, Bogdan Georgiev, Ludmil Katzarkov, Pedro Antonio Muniz Martins