Combinatorics
Schrijver-Delsarte rigidity in association schemes and undecidability of quantum graph homomorphism
Lorenzo Ciardo, Iris Hebbeker, Gideo Joubert et al. (+2 more)
We prove RE-completeness of the
quantum homomorphism problem parameterised by families of graphs derived from the classic metric association schemes. These include Kneser graphs, $q$-Kneser graphs, and the complements of Johnson, Grassmann, and Hamming graphs. Our proof develops a spectral method for establishing non-contextuality of
quantum polymorphisms. It combines an equality analysis of Roberson's bound on the projective packing number in terms of Schrijver's theta with a structural argument inspired by Erdős-Ko-Rado theory.
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All authors Lorenzo Ciardo, Iris Hebbeker, Gideo Joubert, Jana Kreiß, Antoine Mottet
Machine Learning
RISC-V and machine learning: a survey
Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo et al. (+2 more)
The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware. This survey examines the state of the RISC-V ISA in machine learning applications, analyzing current capabilities, challenges, and future directions based on recent research. The analysis covers academic and commercial implementations, software frameworks, and real-world applications. The RISC-V machine learning ecosystem is evaluated, from instruction set extensions and core implementations to compiler optimizations and deployment strategies. Key contributions include a unified taxonomy of RISC-V ML implementations, a comparative analysis of performance and design trade-offs, an evaluation of software toolchain maturity, and the identification of emerging trends in instruction set extensions and specialized accelerators. Findings reveal progress in energy efficiency, specialized instruction development, and framework integration, while highlighting challenges in standardization, verification complexity, and ecosystem fragmentation. The analysis proposes four research directions to address current limitations: specialized neural processing extensions, adaptive and modular processor architectures, security frameworks, and energy-efficient multi-domain architectures. These directions provide a roadmap for advancing RISC-V as a foundational platform for next-generation machine learning systems.
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All authors Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo, Shreya Adya, Subhankar Mishra
Machine Learning
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sambit Mishra, Yingying Wang, Christine K. Johnson et al. (+1 more)
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed
datasets containing ordinal scales, counts, and continuous measurements. This paper investigates causal discovery in Directed Acyclic Graphs (DAGs) where nodes follow either an ordinal distribution (via an ordered logit model) or a regular one-parameter exponential family distribution. We prove that the edge direction between an ordinal and an exponential family node is distributionally identifiable for generic parameter values. Our findings generalize previous Ordinal-Poisson results to the broader exponential family. Computationally, we introduce a score-based exhaustive search and a masked continuous optimization framework using DAGMA for larger graphs. Numerical results validate the theory, recovering edge orientations within a Markov equivalence class that are unidentifiable under classical structural equation models.
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All authors Sambit Mishra, Yingying Wang, Christine K. Johnson, Urbashi Mitra
Complex Variables
Limit theorems for Coulomb gases on a Jordan curve in an external potential
Kurt Johansson and Thomas Wolfs
We consider a Coulomb gas on a Jordan curve $\gamma$ in an external potential $V$ at inverse temperature $\beta>0$ and obtain an asymptotic expansion of the free energy up to $o(1)$ and a central limit
theorem for linear statistics. We focus on the one-cut regime, where the density of the weighted
equilibrium measure of $\gamma$ in $V$ is strictly positive on $\gamma$. The constant term in the (normalized) expansion consists of two parts: the Fredholm determinant of a generalized Grunsky operator and the Dirichlet energy of the logarithm of the density of the weighted
equilibrium measure of $\gamma$. The coefficient of the latter vanishes for $\beta=2$. The variance of the fluctuations of the linear statistics only depends on the Dirichlet energy of the test function and is therefore independent of $V$. Essential in our approach is that the generalized Grunsky operator and the accompanying
equilibrium parametrization allow us to transport the particles on the curve in the external potential to a reference object in a way that preserves the
equilibrium measure. In our setting, the unit circle is the natural reference object.
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Analysis of PDEs
Dense orbits for scale-invariant rotationally symmetric solutions of the 2D Euler equations
Ibrahim Suleiman
Let $m \ge 4$. We prove that there is a forward orbit of the 2D Euler system for scale-invariant $m$-fold symmetric vorticities, namely \begin{equation*} \partial_tg + 2G\partial_\theta g = 0, \quad 4G + \partial_{\theta\theta}G = g, \end{equation*} which is dense in $S :=\{g \in L_{m,\mathrm{odd}}^\infty(\mathbb{S}^1): g\sin(m\theta) \ge 0 \text{ and }\|g\|_{L^\infty} \le 1\}$ equipped with the
topology of weak$^*$ convergence on $L^\infty(\mathbb{S}^1)$, where $L_{m,\mathrm{odd}}^\infty(\mathbb{S}^1)$ denotes the space of odd and $m$-fold symmetric functions in $L^\infty(\mathbb{S}^1)$. The system above was first derived by Elgindi and Jeong, who showed well-posedness in the $m$-fold symmetric class. In subsequent work by Elgindi, Murray and Said, it was shown that solutions with regulated vorticity relax to piece-wise constant steady states with finitely many jumps; our construction shows that this regularity assumption cannot be relaxed. The proof starts from a simple linear mechanism: by evolving prescribed step functions backwards from chosen times and superimposing the resulting data, one can arrange for a single linear orbit to approximate a countable dense family. We show that this mechanism persists for the nonlinear Euler coupling. The key stability estimates imply that the error introduced at each stage becomes arbitrarily small when the next approximation time is taken sufficiently far in the future, allowing the construction to be iterated indefinitely.
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Computer Vision
FunArt: Decoding Functional Structure and Articulation from Generative 3D Latents
Dennis Rotondi, Abdelrhman Werby and Kai O. Arras
To operate effectively in human environments,
robots must identify articulated objects, segment their movable and interactive parts, and estimate their kinematic models. Existing articulated scene representations typically recover kinematics from observed interactions, while methods operating on static scans often decouple articulation from functional interactive elements. We present FunArt, a framework that constructs articulation-aware functional 3D scene graphs from posed RGB-D observations captured in a single static configuration. FunArt reconstructs object instances, converts their fused geometry directly into the O-Voxel representation of TRELLIS.2, and exploits its frozen, sparse-compression VAE as a structural prior. A lightweight query-based decoder combines compact object-level latents with dense, surface-aligned features to jointly segment movable parts and functional interactive elements while estimating motion type, axis, origin, and range. On the Articulate3D
dataset, FunArt achieves state-of-the-art performance across movable-part
segmentation, articulation estimation, and functional-element
segmentation, both with and without ground-truth object input. In the end-to-end setting, it outperforms the strongest baselines by 1.5 AP_{50} points for movable parts, 2.8 AP_{50} points under joint origin-and-axis constraints, and 6.7 AP_{50} points for functional elements. These results demonstrate that generative 3D latents encode actionable structural cues that can initialize robotic perception and planning before physical interaction.
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Functional Analysis
Every compact operator is a commutator of compact operators
Zhichao Liu
We prove that every compact operator $T$ on a separable infinite-dimensional complex
Hilbert space is a commutator of two compact operators, thereby answering a question of Pearcy and Topping. Moreover, the compact factors $A$ and $B$ can be chosen such that $[A, B] = T$ and $\max\{\Vert{}A\Vert{},\Vert{}B\Vert{}\}\leq c\Vert{}T\Vert{}^{1/2}$ for a universal constant $c$.
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Astrophysics of Galaxies
Local constraints on alcohol–thioalcohol analogs in G35.2N
Xuefang Xu, Shanghuo Li, Qian Gou et al. (+6 more)
The chemistry of sulfur-bearing complex organic molecules in dense
star-forming environments remains uncertain, partly because the dominant sulfur reservoirs in dense gas and ices are poorly identified. Alcohol–thioalcohol pairs allow direct comparisons of structurally related O- and S-bearing molecules. We present ALMA Band 6 observations of CH$_3$OH/CH$_3$SH and C$_2$H$_5$OH/C$_2$H$_5$SH toward three spectral-extraction positions (MM3-pos, MM4-pos, and MM5-pos) in the MM3–MM5 region of G35.2N. Local thermodynamic
equilibrium modeling yields column densities and abundance ratios. CH$_3$OH column densities were inferred from $^{13}$CH$_3$OH assuming $^{12}$C/$^{13}$C = 50 to mitigate optical-depth effects. CH$_3$OH, CH$_3$SH, and C$_2$H$_5$OH are robustly constrained at all three positions; C$_2$H$_5$SH is robustly constrained at MM3-pos and MM5-pos but remains tentative at MM4-pos. The corresponding adopted column-density ranges are $(1.8-2.7)\times10^{18}$, $(1.7-2.6)\times10^{16}$, $(5.7-8.5)\times10^{16}$, and $(2.4-4.6)\times10^{15}$ cm$^{-2}$. The CH$_3$OH/C$_2$H$_5$OH and CH$_3$OH/CH$_3$SH ratios are consistent across the positions within uncertainties, with nominal values of 31–32 and 100–110, respectively. Comparisons with chemically rich sources and warm-up chemical models show that CH$_3$OH/C$_2$H$_5$OH lies within the range measured elsewhere, whereas CH$_3$OH/CH$_3$SH exhibits greater source-to-source variation. The ethyl-level O/S comparison remains less certain because many literature C$_2$H$_5$SH measurements provide only lower limits. CH$_3$OH/CH$_3$SH is thus the best-constrained O/S alcohol–thioalcohol ratio in these data and provides an empirical probe of source-dependent sulfur-bearing organic chemistry. More sensitive C$_2$H$_5$SH observations are needed to test C$_2$H$_5$OH/C$_2$H$_5$SH.
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All authors Xuefang Xu, Shanghuo Li, Qian Gou, Chunguo Duan, Laurent Pagani, Di Li, Jun Kang, Jiaxin Du, Jiaxiang Jiao
Robotics
MAGNETAR: Multipath-Guided Spatial Posteriors for Transmitter Pose Inference in the Upper Mid-Band
Haozhe Lei, Ruibin Chen, Yuhan Jiang et al. (+3 more)
Robots that localize a
radio transmitter need more than a point estimate: in cluttered rooms, one measurement is often consistent with several transmitter locations and, because upper-mid-band
antennas are directional, several headings. We present MAGNETAR, which infers a joint posterior over planar transmitter position and heading from a single asynchronous
radio-frequency (RF) multipath snapshot, represented by angle-of-arrival and signal-to-noise-ratio estimates, given the room layout and receiver pose. Among our five neural scorers, MAGNETAR adopts a shared 2D U-Net conditioned on each candidate heading, jointly normalizing scores over a discretized position-heading grid. Training uses real-to-sim-calibrated 10 GHz simulations and a small measured subset. Grid-based joint posteriors outperform parametric ones on held-out simulations, the heading-conditioned scorer transfers best to robotic measurements, and fusing joint posteriors improves on fusing position-only marginals.
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All authors Haozhe Lei, Ruibin Chen, Yuhan Jiang, Ali Rasteh, Aditya Dhananjay, Sundeep Rangan
Robotics
Learning Foresight without Explicit Trajectories for 3D Diffusion Policies
Zhongbo Zhang, Zaibin Zhang, Yifan Wang et al. (+3 more)
3D
diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only knowing what motion is feasible now, but also anticipating where the interaction is heading. Existing policies largely leave such foresight to emerge implicitly from action learning. We introduce Movement Trend Guidance, a simple but effective way to provide this foresight without introducing an explicit plan. From a short observation history, the policy learns a compact latent representation of interaction
evolution. During training, sparse future gripper states supervise this representation; at inference, only the latent is retained as future-oriented conditioning alongside the current observation. The latent provides global conditioning for action generation, while an additional gated FiLM branch is used only at the UNet bottleneck. Despite adding only 3.52% more parameters to DP3, our method preserves the original dense-action and receding-horizon formulation and consistently improves upon DP3 across RoboTwin2.0, LIBERO-40, and DexArt. It reaches 62.8% vs. 56.1% in 50-task RoboTwin2.0 mixed training, 71.93% vs. 37.08% on LIBERO-40, and 72.0% vs. 49.0% on five real-
robot tasks. These results show that a
diffusion policy can benefit substantially from knowing where an interaction is heading, without being told exactly where to move.
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All authors Zhongbo Zhang, Zaibin Zhang, Yifan Wang, Changbo Yan, Lijun Wang, Huchuan Lu
Logic
Logarithmic–exponential preparation in sharply o-minimal structures
Gal Binyamini, Oded Carmon and Dmitry Novikov
We develop a complex-analytic approach to the model theory of the (real) unrestricted exponential. As a consequence we derive sharp forms of many of the foundational results for the structure ${\mathbb R}^\text{RE}_{\exp}$ (and more general structures). In particular we establish sharp o-minimality, a sharp form of Wilkie's
theorem of the complement, a sharp form of Wilkie's
conjecture and a sharp form of piecewise definability by terms. Our approach is based on a complexification of the LE-preparation
theorem of Lion–Rolin. We also develop a parallel complex theory for ${\mathbb R}_\text{an,exp}$, proving for example that the rational points of height $H$ on a nowhere-dense definable set can be interpolated by an algebraic hypersurface of degree $\text{poly}(\log H)$. This generalizes a
theorem of Cluckers–Pila–Wilkie who proved the same statement for ${\mathbb R}_\text{an}^\text{pow}$.
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High Energy Physics (Theory)
Exploring multi-parameter optimization in FRG
A. Codello, G. P. Vacca and D. Zarrilli
The use of nonperturbative Wilsonian functional renormalization group (FRG) equations within an unavoidable approximation scheme produces physical results that depend on the choice of the coarse-graining regulator. This dependence of physical observables on the regulator can be minimized according to the principle of minimal sensitivity (PMS). For the 1PI formulation based on the Wetterich-Morris equation, we propose to investigate the optimal choice within a space of compactly supported
polynomial regulators parametrized by a set of variables. We test this approach using the derivative expansion of the effective average action up to fourth order, applying it to the scalar field theory representing the three-dimensional Ising universality class and minimizing the dependence of the anomalous dimension on the regulator. At fourth order, we find appreciable corrections to the critical exponents that improve the predictions, compared to previous one parameter analyses. We also observe families of compact regulators that rapidly converge towards an optimal functional form as a small number of parameters is progressively introduced.
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Chemical Physics
Human Language Shapes the Emerging Physical Reasoning in Large Language Models
Gregorii A. Semakin, Timofey V. Losev, Ilya V. Prolomov et al. (+3 more)
Whether large
language models (LLMs) acquire adequate representations of physical reality by predicting the next
token remains unresolved. Molecular conformations provide a quantitative test: we asked 28 LLMs to rank 810 geometries of 27 molecules from atomic coordinates alone. Frontier LLMs approach the accuracy of specialized force fields, but their ability is bound to well-defined chemical concepts. They rank conformers with more hydrogen bonds as more stable, inverting the true order when the hydrogen-bonded conformer is less stable. On pairs with equal hydrogen-bond counts, they are most accurate for molecules whose explanations invoke hydrogen bonding, whereas molecules governed by ring strain remain difficult. Thus, the physical understanding emerging in LLMs inherits the structure and blind spots of human scientific discourse, a machine analogue of linguistic relativity.
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All authors Gregorii A. Semakin, Timofey V. Losev, Ilya V. Prolomov, Stepan N. Ostarkov, Igor V. Alabugin, Michael G. Medvedev
Logic
Stoic Logic and Natural Term Logic
Clarence Lewis Protin
In this paper we propose a reconstruction of the theory of multiple generality in Stoic Logic using the Natural Term Logic (NTL) developped by the author in \cite{ntl}. Contrary to a frequent misconception, it can be shown conclusively, based on the available evidence, that Stoic logic was far more than a mere propositional logic (we further argue that even identifying the Stoic conditional with any type of propositional connective is misleading). Its more general and complex logical theory - and notable the treatment of multiple generality - was rather patterned after the logico-syntactic mechanisms of natural
language (subject to a regimentation which recalls Polish notation) rather than the variable-based quantifier logic of the modern Fregean tradition. NTL is likewise is a variable-free formal framework which captures the core syntactic and logic mechanisms of natural
language (and notably a large array of features involving intensionality and anaphoric constructions). Bobzien and Shogry \cite{bobzienshogry} have presented abundant evidence and arguments for the case that quantified conditionals and multiple generality were treated by the Stoics through a disambiguifying regimentation of syntax using indefinite pronouns and anaphoric constructions involving such pronouns. There are however some patent difficulties involving how exactly the equivalent of universal quantification was treated. In this paper we explore some possible building blocks for a formal reconstruction of the Stoic logic of multiple generality based on a variant of NTL. We focus on formalizing the more general form of Stoic modus ponens involving quantified conditions which was clearly attested by ancient authors. We also sketch, as a preliminary study for formalizing the grammatical theory deployed in Stoic logic, how NTL can be used to formalize adjectival and adverbial constructions.
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Earth and Planetary Astrophysics
Water Cloud and Chemical Modulations in the Coldest Brown Dwarf
Brittany E. Miles, James Mang, Caroline V. Morley et al. (+24 more)
We present high signal-to-noise, medium resolution (R ~ 1000), time-series JWST/NIRSpec spectra of WISE 0855 (265K), the coldest known brown
dwarf. Medium resolution, time-series spectroscopy gives us the power to disentangle the effects of chemistry, temperature, and condensates on this cool world. Our observations span 11 hours with a 15 minute cadence covering 2.87 - 5.27 $\mu$m. The strongest time variable spectroscopic feature is carbon monoxide gas absorption producing modulations with a peak-to-peak amplitude up to 10% at some wavelengths. Using principal component analysis, we show that the variations in carbon monoxide and phosphine correlate with one another. By comparing our data to atmospheric and structure models we present evidence of patchy water clouds within the atmosphere of WISE 0855. We find that variations in CO and PH$_{3}$ abundances must originate from quenched atmospheric pressures while variations in water cloud thickness occurs at lower pressures.
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All authors Brittany E. Miles, James Mang, Caroline V. Morley, Andrew J. I. Skemer, Melanie Rowland, Brianna Lacy, Genaro Suarez, Channon Visscher, Dániel Apai, Gordon Bjoraker, Aarynn L. Carter, Jacqueline K. Faherty, Jonathan J. Fortney, Nguyen Fuda, Thomas Geballe, Harshil Kothari, Mary Anne Limbach, Mark Marley, Allison M. McCarthy, Adam Schneider, Johanna Vos, Michael Cushing, Richard Freedman, Michael Line, Roxana Lupu, Emily Martin, Mikayla J. Wilson
General Relativity and Quantum Cosmology
Entropy, area, and the choice of regulator during gravitational collapse
Jana N. Guenther, Christian Hoelbling, Sophie Mutzel et al. (+1 more)
We present a real time formalism to numerically describe the gravitational collapse of a scalar
quantum field in the spherically symmetric case. We employ a Pauli-Villars regulator that is specifically designed to cancel the ultraviolet divergences in the energy-momentum tensor identified by covariant point splitting, including the logarithmic ones. Using this regulator, we find that the leading term of the
entanglement entropy, which is proportional to the surface area, vanishes for a spherical region of flat spacetime. First numerical results for the dynamical case indicate that for a collapsing shell of a massless scalar field, the area normalized
entropy is concentrated on the shell and has an approximately constant maximum value during time
evolution. This maximum value appears to be finite in the continuum limit and only mildly dependent on the regulator mass.
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All authors Jana N. Guenther, Christian Hoelbling, Sophie Mutzel, Lukas Varnhorst
Computer Vision
Earth Surface Immune System for Rapid Monitoring of Unknown Anomalies
Jingtao Li, Qian Zhu, Xinyu Wang et al. (+3 more)
Earth surface anomalies, driven by escalating
climate change, and expanding human activities, are increasing in both frequency and diversity, yet their limited historical data and unpredictability make them fundamentally different from conventional remote sensing targets. Existing methods address specific anomaly categories or stop at localization, leaving a gap between detection and actionable information. Here we present ESIA, an Earth Surface Immune System whose architecture is constrained by three principles from the biological immune system, refined over millions of years against equally diverse and uncertain threats. A non-specific innate immune stage treats anomalies as unobserved changes in time-series satellite imagery, generating binary localization maps at 14.51 km2/s without assuming any anomaly category, surpassing the strongest general baseline by 37% in F1. A specific adaptive immune stage applies negative selection to filter text
prompts and matches surviving
prompts with localized image patches through a multi-modal foundation model, enabling open-vocabulary recognition of unknown anomaly attributes including category, affected area, and damage severity, with recognition F1 exceeding 80%. A mutation mechanism tunes minimal
embeddings at test time, adapting to each scene in 3.26s using a single reference image pair. We validate ESIA on a global-scale
dataset covering 19,801.60 km2 across six anomaly categories, comparing against 22 models, and further apply it to quantify degraded farmland in the Dnipro Delta following the Kakhovka Dam collapse and assess burn severity from 2025 Palisades Fire in Los Angeles. This unprecedented flexibility in handling unknown anomalies opens new avenues for real-time disaster response and environmental surveillance.
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All authors Jingtao Li, Qian Zhu, Xinyu Wang, Deren Li, Liangpei Zhang, Yanfei Zhong
Computers and Society
When Does the Public Become Suspicious of Bots? Demand-Side Evidence from Botometer Query Logs
Tuğrulcan Elmas
We study private bot-checking behavior from the demand side: when people suspect an account is automated, whom they suspect, and what follows. Using Botometer's server-side query logs, the most widely used bot-detection service, we treat each query as a behavioural trace of suspicion. We analyze over 1 million public checks of Twitter accounts from 2020 to 2023, enriched with 3.2 billion tweets from the contemporaneous 1% public stream. Collective suspicion spikes with platform crises, most sharply around the 2022 Musk-Twitter bot dispute. Checked accounts are older and more prolific, have more followers, and post promotional, political, and crypto content. Accounts that draw collective suspicion have higher bot scores and are more likely to be suspended. Bot-related public
attention and Botometer activity are elevated during the same broad periods, although their short-run fluctuations are largely uncoupled. Public feedback focuses on first-person identity claims for humans, and evidence-based arguments citing posting rate, political content, and cross-account coordination for bots and cyborgs. Bot suspicion thus constitutes a mass, distributed form of platform
auditing that tracks meaningful signals of automation, establishing audit-tool query logs as a novel lens on public responses to platform manipulation.
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Analysis of PDEs
Stochastic Two-Dimensional Anisotropic Magnetohydrodynamics System with Multiplicative Noise
Mohammad Mahabubur Rahman
In this paper, we investigate the
stochastic two-dimensional anisotropic magnetohydrodynamics system on $\mathbb T^2$ with velocity dissipation acting only in the horizontal direction. Under multiplicative noise, we prove the existence of global martingale solutions and global probabilistically weak solutions, together with pathwise uniqueness.
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Robotics
HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface
Zimu Han, Yiming Zeng, Jiyao Zhang et al. (+9 more)
Large-scale
vision-
language-action (VLA) models provide powerful priors for
robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuning (SFT) on task-specific demonstrations provides a step toward deployment, but faces two persistent limitations: static data provide limited coverage of out-of-distribution states, and standard imitation objectives do not distinguish progressing behavior from less useful data. Interactive post-training can address these limitations, but typically requires repeated policy execution and human intervention on a physical
robot. We introduce HIL-UMI, a policy-guided Universal Manipulation Interface (UMI) framework for
robot-free human-in-the-loop VLA post-training. During handheld UMI demonstrations, HIL-UMI queries the current policy on the same observation stream without executing its predictions. The Energy Score compares the human action trajectory with policy inference and triggers collection when their discrepancy indicates an out-of-distribution region. In a separate feedback loop, low online advantage predictions identify essential segments for refining a progress-based advantage
estimator. The updated
estimator then guides advantage-conditioned behavioral cloning using a balanced mixture of base demonstrations and new policy data. This design preserves the iterative and policy-aware nature of human-in-the-loop learning while decoupling data collection from
robot deployment. Experiments on four real-world tasks spanning long-horizon and precise manipulation show that HIL-UMI achieves consistent improvement over SFT and benefits from both targeted collection and advantage refinement. Moreover, HIL-UMI outperforms HG-DAgger on Clean Up Table with lower per-frame collection time, suggesting a scalable path for VLA post-training across operators and locations.
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All authors Zimu Han, Yiming Zeng, Jiyao Zhang, Zihao Zhao, Yuanfei Wang, Yixiang Jin, Shiqi Li, Shuangben Chen, Wei Huang, Ruodai Li, Hui Shen, Hao Dong
Artificial Intelligence
Ownership in AI-Assisted Everyday Tasks
Megan Wei, Melanie Subbiah, Audrey Lee et al. (+4 more)
When does work done with AI still feel like ours? As AI becomes woven into everyday tasks, we must examine what happens to our sense of ownership and contribution when a machine shares in producing what we make. We report an exploratory qualitative survey in which participants were asked to describe two recent, self-selected tasks completed with AI: one that felt like their own and one that did not. We find that felt ownership depends on the process of collaboration: people disown work when they merely approve AI's suggestions, but retain ownership when they lead, iterate, or rewrite. Ownership can also extend to settings where people own the
vision for a project but not the execution; respondents reported high ownership on tasks they could not have completed without AI. Loss of personal voice and a lack of comprehension of the output both erode ownership. Finally, willingness to disclose AI use is often decoupled from actual pride or ownership, and instead shaped by community norms and fear of credit erasure. We propose several research directions as a result of these findings to promote AI development that supports people's sense of authorship over their own lives.
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All authors Megan Wei, Melanie Subbiah, Audrey Lee, Annya Dahmani, Dave Edwards, Helen Edwards, Ellie Pavlick
Probability
The skeleton-blocks decomposition of Bienaymé trees, and applications to their local convergence
Marc Bernard and Robin Stephenson
We study multi-type critical Bienaymé trees through the lens of the skeleton-blocks structure, seeing them as a monotype skeleton on which we have added multi-type blocks. This decomposition enjoys a remarkably elegant compatibility with the concept of local convergence, allowing us to find simple proofs of traditional local limit
theorems.
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Combinatorics
Framingtopes
Sergio Alejandro Fernandez de soto Guerrero, Cesar Ceballos and Matias von Bell
Framing
lattices arise from the dual graphs of framed (or DKK) triangulations of flow polytopes and provide a common framework encompassing classical
lattices such as the Boolean, Tamari, and weak-order
lattices, as well as $\tau$-tilting posets of certain gentle
algebras. In this paper, we introduce the framingtope, a polytopal complex that provides a
geometric counterpart to a framing
lattice: its edge graph is the Hasse diagram of the framing
lattice. We prove that the framingtope admits three equivalent descriptions, in terms of interior faces of the framed triangulation, sets of pairwise coherent routes covering the graph, and pure intervals of the framing
lattice. We further construct a tropical realization of the framingtope as the bounded-
cell complex of an arrangement of tropical hypersurfaces associated with an admissible height function. This construction yields explicit vertex coordinates for broad classes of framed graphs, including plane framed graphs and multioruga graphs. In the multioruga case, these coordinates give tropical realizations of weak orders on multipermutations and, in the ordinary oruga case, recover the classical permutahedron.
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Econometrics
Using machine learning metrics to provide deeper insights into the performance of choice models
Lorenzo Muñoz, Stephane Hess, Thomas O. Hancock et al. (+1 more)
Machine learning (ML) techniques are increasingly drawing interest in the choice modelling (CM) field. The focus has primarily been on comparing the performance of these contrasting approaches or on improving behavioural insights for ML techniques, rather than translating ideas from one field into the other. In the present paper, we specifically focus on knowledge transfer from ML into CM in the context of model performance evaluation. In CM, model performance is typically evaluated using log-likelihood and related indicators, which are aggregate fit metrics that focus on overall fit. Conversely, in ML, the focus is on alternative-level misclassifications and correct classifications, which provide a more nuanced view of the results. To bridge these approaches, we explore the use of a probabilistic version of the confusion
matrix, which reports the average probability of the model predicting each alternative, conditional on which alternative was observed to be chosen, across all choice tasks. This enables the computation of probabilistic ML metrics for both classic choice models and ML
algorithms. We analyse model performance jointly in terms of overall fit and alternative-level predictions. Our findings demonstrate that models with similar log-likelihood can exhibit substantially different confusion
matrices, revealing different probability patterns that aggregate metrics cannot capture. This framework identifies where models systematically `confuse' alternatives, highlighting trade-offs between alternatives, and potentially guiding model specification. Furthermore, evaluating these
matrices and metrics out-of-sample reveals alternative-level prediction shifts that significantly impact
forecasting performance.
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All authors Lorenzo Muñoz, Stephane Hess, Thomas O. Hancock, Georges Sfeir
High Energy Physics (Phenomenology)
Line shapes of $\Omega(2012)$ production in the $\Xi \bar K $ and $\Xi \pi \bar K$ decay channels
Natsumi Ikeno and Eulogio Oset
We theoretically study the line shape of the experiment for $\Omega(2012)$ production in the $\Xi^- \pi^+ K^-$ and $\Xi^0 K^-$ decay modes, reflecting the $\Xi^* \bar K$ and $\Xi \bar K$ decay modes, from the perspective that the $\Omega(2012)$ is a molecular state dynamically generated from the interaction of the $\Xi^* \bar K $ and $\Omega \eta$, coupled channels with $\Xi \bar K$ as a decay channel. We show the consistency of the picture with the experimental mass distributions, giving support to the molecular picture for the $\Omega(2012)$ state. We also call the
attention to the sensitivity of the results to the cut imposed on the $\pi \Xi$ invariant mass, and suggest a different method to obtain the ratio $R^{\Xi \pi \bar K}_{\Xi \bar K}$ of the three-body to two-body decay widths used so far as a test of the molecular picture. We stress that the direct comparison with the experimental mass distributions done here is a more stringent test of the molecular nature of the $\Omega(2012)$ state than the comparison of the ratio $R^{\Xi \pi \bar K}_{\Xi \bar K}$ which has been so far obtained using different criteria.
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Combinatorics
Infinite log-concavity of the Boros–Moll sequences
Matthew H. Y. Xie and Philip B. Zhang
Let $(d_i(n))_{i=0}^n$ be the Boros–Moll coefficient sequence. We prove that, for every integer $n\ge1$, the
polynomial \[ M_n(x)=\sum_{i=0}^n \bigl(d_i(n)^2-d_{i-1}(n)d_{i+1}(n)\bigr)x^i \] has only simple negative zeros, which strictly interlace those of the Narayana
polynomial of the same degree. This proves a
conjecture of Chen, Yang, and Zhang and, by Brändén's preservation
theorem, settles the infinite log-concavity
conjecture of Boros and Moll. The proof uses an expansion of the reversed and normalized form of $M_n(x)$ in derivatives of the Narayana
polynomial, together with estimates for the weights and partial sums of the normalized derivatives.
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Operator Algebras
Toeplitz $C^*$-algebras on radially weighted Fock spaces: commutativity and spectral representation
Khalid Bdarneh
We study Toeplitz operators acting on radial weighted Fock spaces. We use tools from representation theory to construct commutative families of $C^*$-
algebras that are generated by Toeplitz operators whose symbols are invariant under the action of $\U(n)$. For a partition $m=(b_1,...,b_k)$ of an integer $n$, we realize \[\mathbf{\U_m}:=\U(b_1)\times...\times\U(b_k)\] as a block diagonally subgroup of $\U(n)$ and describe the decomposition of the weighted Fock space into irreducible $\mathbf{U_m}$-modules. This allows us to study Toeplitz operators with $\mathbf{U_m}$-invariant, equivalently $k$-quasi-radial, symbols. Also, we provide an explicit integral representation of their
eigenvalues. More generally, for an arbitrary compact subgroup $H\subseteq\U(n)$, we characterize the commutativity of the $C^*$-
algebra generated by $H$-invariant Toeplitz operators in terms of the multiplicity-free property of the representation $\pi|_H$. Finally, for a logarithmically growing radial weight, we construct a bounded radial symbol for which the corresponding
eigenvalue sequence is not uniformly continuous with respect to the square-root metric. Consequently, the uniform closure of the set of
eigenvalue sequences does not coincide with the $C^*$-
algebra of bounded sequences that are uniformly continuous with respect to the square-root metric.
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Quantum Physics
Hardness of Pathfinding in a Welded Tree
David Miloschewsky and Supartha Podder
Starting from the entrance of a welded tree, a
quantum walk
algorithm can find its exit vertex exponentially faster than any classical
algorithm. However, it has been an open question whether any
quantum algorithm is able to efficiently find a path from the entrance to the exit. We answer this by proving an exponential
quantum query lower bound for finding such path. Specifically, for trees of height $n$, any
quantum query
algorithm requires at least $\Omega(2^{n/24})$ queries in order to succeed with constant probability. Our proof uses the compressed permutation oracle technique in order to construct databases which track the graph information an
algorithm has learned and forgotten, and show that no efficient
quantum algorithm can build an entrance-to-exit path in these records.
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Machine Learning
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
Simon Süwer, Julian Klemm, Elisa Acitelli et al. (+39 more)
Federated learning enables collaborative training without sharing patient-level data, but most studies remain simulations. Based on five requirements derived from the literature, we analyzed 14 FL frameworks and found that none fully satisfied these requirements. We present FL-Net, a novel
federated clinical research framework to fulfill all requirements. It integrates modular data harmonization, data discovery, disclosure control, securely built versioned FL-Net-Tools and containerized
federated workflow execution into a persistent network. It enables the re-use of harmonized data and workflows across studies. FL-Net's end-to-end capabilities were evaluated through harmonization, cross-study patient discovery across MIMIC and US-130, and reproducible, audited
federated workflows with up to 50 concurrent clients. FL-Net is being developed within the dAIbetes and Microb-AI-ome EU projects and will cover over 800,000 patients across 10 hospitals in 9 countries covering longitudinal and single point in time data, FL-Net provides a practical foundation for interoperable, reproducible, and
privacy-preserving multicenter clinical research.
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All authors Simon Süwer, Julian Klemm, Elisa Acitelli, Mathieu Almeida, Lucia Altucci, Zsolt Bagyura, Michelangela Barbieri, Zsolt-Zoltán Bedő, Rosaria Benedetti, Béla Bihari, Csongor Csalóka, Lucia Dicunta, Stanislav Ehrlich, Bjoern M. Eskofier, Sándor-József Fejér, Georg Fröwis, Walter Hötzendorfer, Alexandra Kautzky-Willer, Jens Johann Georg Lohmann, Marianna Maranghi, Lorenzo Marconi, Rudolf Mayer, Wouter Leonard Megchelenbrink, Monika Moga, Adham Mottalib, Sanjeev Mehta, Madeleine Müller, Thomas Nyström, Balázs-Attila Orbán, Micheal O'Riordain, Paul O'Toole, Giuseppe Paolisso, Paolo Parini, Matteo Pedrelli, Enrico Petrillo, Philipp Poindl, Niklas Probul, Anastasia Pustozerova, Tanja Šarčević, Lukas Weilguny, Jan Baumbach, Andreas Maier
Robotics
DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation
Yan Qin, Yue Chen, Wenwei Lin et al. (+9 more)
Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real
robots and remain tied to embodiment-specific sensors. We introduce DexTouch-WM, an action-conditioned world model that learns from scalable human touch to jointly predict future RGB observations and bilateral tactile dynamics. Our insight is that human and
robot manipulation share transferable contact dynamics when their tactile observations and action spaces are made compatible. We deploy flexible piezoresistive arrays with a shared sensing layout on both human and dexterous
robot hands, and retarget human motion into the
robot action space so that human interaction can supervise the same dynamics model used for real-
robot prediction. DexTouch-WM couples a pretrained
video expert with a lightweight tactile expert using anatomy-aware tactile
tokens and aligned action conditioning. In human-to-
robot scaling experiments, we keep five hours of real-
robot supervision fixed while increasing human interaction from 0 to 100 hours, and observe substantial improvements in held-out
robot-domain visual,
geometric, and contact prediction despite disjoint human and
robot task sets. Beyond prediction, we evaluate the world models as surrogate environments for policy evaluation and as generators of synthetic trajectories for real-
robot policy learning, showing that scalable human interaction provides a complementary data axis for learning dexterous
robot world models.
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All authors Yan Qin, Yue Chen, Wenwei Lin, Shujia Liu, Chuqiao Lyu, Kailun Su, Weiyang Jin, Chenze Yu, Ping Luo, Wenbo Ding, Tianxing Chen, Renjing Xu