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Friday 9 October

Digital Libraries

Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis

Daniel J. Evans, J. P. Naiman, JooYoung Seo et al. (+1 more)

Digital libraries increasingly host scientific publications whose figures encode primary research findings, yet making this visual content searchable, accessible, and reusable remains an open challenge. Large language and multimodal models (LLMs/LMMs) provide the possibility to automate many figure parsing tasks such as determining data trends or extracting data from plots. In this work, we consider if these models can reliably support task-oriented visual data analysis when presented with synthetic, but realistic, scientific figures that contain multiple charts. Our Visual Question Answering (VQA) process scaffolds our tasks along a four-level description model from basic conceptual answers to interpretive and analytical understanding. Additionally, we outline our process for synthetically generating our single and multi-panel (charts in which there are multiple panels present) plots and testing the data with various LMMs using quantitative analysis. In general, we find LMMs become less accurate with increasing figure complexity as the number of panels, number of plot elements, and difficulty of questions increase. Finally, because LMM accuracy is expected to change rapidly, our analysis also considers cost relative to performance as a metric to inform future user decisions. We provide the detailed question-answer pairs used in this analysis, along with the code used to generate random plots, question-answer pairs and detailed bounding-box metadata for chart elements. The code and data are available at https://github.com/ReadingTimeMachine/LLM_VQA_MultiPanel and https://huggingface.co/datasets/ReadingTimeMachine/visual\_qa\_multipanel
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All authors Daniel J. Evans, J. P. Naiman, JooYoung Seo, J. Stephen Downie

Astrophysics of Galaxies

A first view of interstellar Fe-bearing dust grains with XRISM

I. Psaradaki, L. Corrales, E. Costantini et al. (+8 more)

Understanding the reservoirs of iron (Fe) in our Galaxy is crucial for probing the mechanisms that drive dust grain growth in the interstellar medium (ISM). High-resolution X-ray absorption spectroscopy offers a powerful tool to investigate the chemistry of dust grains, particularly those containing Fe, through spectroscopic features near photoabsorption edges known as X-ray Absorption Fine Structures (XAFS). With the Resolve spectrometer aboard XRISM, we can now examine the Fe K-edge (7.1 keV) owing to its improved spectral resolution in this energy range. This enabled us to probe the dust grain chemistry in high Galactic column densities. In this work, we present a pilot study on dust grain chemistry through the Fe K-edge of the XRISM Performance Verification target GX 340+0. This source is a persistently bright low-mass X-ray binary with a hydrogen column density of 6.6x10^22 cm^-2 along the line of sight, providing sufficient optical depth at the Fe K-edge. By fitting combined dust and gas models, we find that Fe is depleted by 95% into dust along the line of sight towards the source. Moreover, we find evidence that 38% of the solid-phase Fe is in metallic iron, 47% in iron sulfides, and the remainder in silicates. The large contribution from metallic iron and iron sulfides differs from what is typically inferred from X-ray studies of diffuse ISM and may suggest that that these compounds become more significant reservoirs of Fe in higher column density Galactic sightlines.
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All authors I. Psaradaki, L. Corrales, E. Costantini, M. Mehdipour, D. Rogantini, H. Takahasi, R. Ballhausen, P. Chakraborty, T. Nakagawa, R. K. Smith, S. Zeegers

Quantum Gases

Experimental observation of Higgs oscillations and their power-law decay across pairing regimes in a cavity QED quantum simulator

Chitose Maruko, Haoqing Zhang, Eliot A. Bohr et al. (+6 more)

Understanding how pairing correlations in superconductors evolve out of equilibrium is a central challenge in quantum many-body physics. In systems with $s$-wave pairing, the character of Cooper pairing evolves continuously from a BCS regime of weakly bound, overlapping pairs to a BEC regime of tightly bound molecules, each with distinct excitation spectra. Here we realize and probe pairing dynamics across these regimes using cold, non-degenerate $^{87}$Rb atoms coupled to an optical cavity with Floquet dressing. Our cavity-QED platform emulates fermionic superfluids through a mapping onto Anderson pseudospins, while optical and microwave control tunes the ratio of interactions to single-particle energies, providing a flexible approach complementary to ultracold Feshbach and ultrafast solid-state platforms. Following a quench, we observe persistent pairing and transient oscillations of the order parameter associated with a collective amplitude (Higgs) mode. We find that the decay of these oscillations sharply distinguishes weak- and strong-pairing regimes: in the weak-pairing regime, oscillations decay with a power-law exponent $|\alpha|=0.65(8)$, whereas in the strong-pairing regime the decay is substantially faster, with $|\alpha|>1$. The oscillation frequency is set by the minimum quasiparticle excitation energy, directly linking nonequilibrium dynamics to the underlying excitation spectrum. Our results establish nonequilibrium dynamics as a direct probe of pairing character and collective excitations in interacting quantum systems.
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All authors Chitose Maruko, Haoqing Zhang, Eliot A. Bohr, Leah Huzjak, Anjun Chu, Victor Gurarie, Chengyi Luo, Ana Maria Rey, James K. Thompson

High Energy Physics (Phenomenology)

Solutions of Roy equations for variable pion mass

Gilberto Colangelo

The Roy equations for the $S$ and $P$ waves are solved for pion masses below the physical value in the region between threshold and $s_A=0.84\GeV^2$. The analysis is based on the assumption that the phenomenological input above $s_A$ can be used essentially unchanged. Moreover, for $M_\pi \le M_{\pi,\mathrm{phys}}$, the chiral perturbation theory predictions for the two $S$-wave scattering lengths provide a very reliable additional input to the Roy equations. The outcome is an accurate description of the $S$ and $P$ partial waves as the light-quark masses decrease towards the chiral limit. As a test of the approach, we evaluate the first few threshold parameters of the $S$, $P$, and $D$ waves via Wanders' sum rules and find excellent agreement with chiral perturbation theory across the entire range $0 \le M_\pi \le M_{\pi,\mathrm{phys}}$. We further solve the Roy equations for pion masses above the physical value (up to $240\MeV$) and show that the resulting solutions agree well with existing lattice data at $M_\pi=239\MeV$. The present work paves the way for analyses of lattice data that fully incorporate the constraints of analyticity, unitarity, and crossing symmetry, and provides the basis for an accurate study of the dependence of the $\sigma$ and $\rho$ resonances on the light-quark masses, both of which will be addressed in future work.
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Astrophysics of Galaxies

Cluster Ages to Reconstruct the Milky Way Assembly (CARMA). VI. The age-metallicity relation of in-situ clusters and the age of the Universe

Chiara Zerbinati, Davide Massari, Fernando Aguado-Agelet et al. (+11 more)

Within the homogeneous framework for the derivation of GC ages defined by the CARMA collaboration, we focus on the clusters born in-situ in the MW. As they are the oldest among the old GCs, their absolute age provides constraints about the age of the Universe. Moreover, the shape of their AMR is expected to show the imprints of past merger events, in case these were able to trigger GC formation in the Galaxy. We derive new homogeneous ages for 18 GCs dynamically and/or chemically classified as in-situ, and build the MW AMR from a total sample of 34 GCs. We find that all the selected GCs follow a well defined AMR, with the exception of NGC6642 and Pal1. While the former is likely a member of the LKH merger event, thus putting chronological evidence for the existence of this progenitor on even more robust ground, for the latter we propose an alternative scenario according to which it formed in-situ, but in a metal-depleted region of the Galaxy, possibly due to the interaction with an unspecified merger event. To find evidence of merger-driven GC formation, we use a GMM for the observed age distribution. This is statistically best-fit by 4 components, which we associate with significant events of the MW merger history. Cluster ages were finally calibrated to a more accurate absolute age scale based on asteroseismic measurements of the mass of red giant stars in NGC6791, and the resulting AMR was fit to compute the look-back time for the beginning of cluster formation in the MW. The fit yields t_GC,i=13.59+0.33-0.32 Gyr for initial cluster formation, and tU=13.79+/-0.34 Gyr for the age of the Universe. This value translates to H0=68.72+3.95-3.49(stat.)+2.65-2.32(sys.) km s-1 Mpc-1, where the systematic error comes from a realistic estimate of the zero point uncertainty on the GC absolute ages derived from stellar models.
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All authors Chiara Zerbinati, Davide Massari, Fernando Aguado-Agelet, Cristiano Fanelli, Massimiliano Matteuzzi, Santi Cassisi, Matteo Monelli, Cristiana Spingola, Carme Gallart, Maurizio Salaris, Roger E. Cohen, Edoardo Ceccarelli, Sara Saracino, Elena Pancino

High Energy Physics (Phenomenology)

A Witten effect for global monopoles

Pablo Quílez Lasanta

Global monopoles can carry electric charge even when the scalar field that forms them is neutral. We show that a CP-odd operator in the Goldstone effective theory, typically discarded as a total derivative, gives them an electric charge $Q=-Cn$, with $C$ its Wilson coefficient and $n$ the winding number. This is a Witten effect on a global defect, generated by a mixed $\theta$-term between electromagnetism and a composite connection built from the Goldstone fields. The same group theory condition that allows the operator also guarantees the existence of monopoles. We also give a fermionic UV completion and determine the coefficient by matching the Goldstone-Wilczek current.
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Cosmology and Nongalactic Astrophysics

Where Cluster Galaxies Reside and How Strongly They Lens

Isaque Dutra, Priyamvada Natarajan and Cian Roche

The efficiency of galaxy-galaxy strong lensing (GGSL) in clusters depends jointly on the inner mass distributions of member galaxies and their locations within the cluster potential. Observed GGSL probabilities exceed $\Lambda$CDM predictions by nearly an order of magnitude, but the relative roles of internal structure and environment remain unclear. We disentangle these effects by comparing HST strong-lensing clusters with TNG-Cluster analogs at similar lens redshifts, matched by their primary Einstein radius. For each member, we measure its lensing strength in isolation and when embedded in the cluster field. Members in cluster-critical regions are classified as Major or Minor Perturbers depending on whether they strongly lens in isolation; those in subcritical regions are classified as Intrinsic Lenses, Boosted Lenses, or Inert Members according to whether they lens independently, only with environmental assistance, or not at all. Observed clusters contain 33.7% of selected members in cluster-critical regions, compared with 21.6% in TNG-Cluster, while their Major-to-Minor ratios are similar. Outside these regions, observations contain substantially more Intrinsic and Boosted Lenses and fewer Inert Members, with Boosted Lens fractions of 25.8% and 13.5%, respectively. The discrepancy persists at fixed subhalo mass and after including line-of-sight structure. A mass-conserving contraction mimicking core-collapsed self-interacting dark matter largely recovers the conditional lensing fractions but not cluster-critical occupancy. The GGSL discrepancy therefore reflects both insufficient central concentration in simulated members and differences in their locations within the cluster lensing field.
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Solar and Stellar Astrophysics

A Spectroscopic Census of Cluster White Dwarfs from DESI, LAMOST, and SDSS

David R. Miller, Jeremy Heyl, Huahui Yan et al. (+2 more)

Gaia has substantially expanded the sample of candidate white dwarfs (WDs) in open clusters, but many remain without spectroscopic characterization. We leverage the increasingly extensive archives of large public spectroscopic surveys to identify 62 candidate cluster WDs with available spectra from DESI, LAMOST, and SDSS. After discarding sources with very low S/N and unreliable host clusters, 44 candidates remain, including 37 non-magnetic DA WDs, of which 15 had not previously been characterized in the literature for cluster studies. We find that nine are consistent with single-star origins in their proposed host clusters, increasing the combined cluster DA initial–final mass relation (IFMR) sample to 132 WDs. All lie within $2\sigma$ of the best-fit IFMR from the most comprehensive recent cluster WD sample, suggesting these additions do not require recalibrating the empirical IFMR. One is a likely Hyades tidal-tail WD, providing further evidence that dynamical dispersal helps explain the long-standing WD deficit in the nearest cluster. We further compare WDs with spectra from multiple surveys. Among seven Praesepe WDs observed by both DESI and SDSS, DESI gives higher WD masses in six cases, with a mean difference of $0.067\,\mathrm{M}_\odot$, while the inferred progenitor masses differ by as much as $0.459\,\mathrm{M}_\odot$. More broadly, nine of ten single-star cluster DAs with DESI spectra lie above the empirical IFMR, with a mean final-mass residual of $0.06\pm0.04\,\mathrm{M}_\odot$. These results show that survey-dependent differences can strongly affect cluster WD parameters, and should be considered when combining heterogeneous spectroscopy in empirical IFMR studies.
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All authors David R. Miller, Jeremy Heyl, Huahui Yan, Chengyuan Li, Pier-Emmanuel Tremblay

Mathematical Physics

Categorifying Quantum Spin Systems

Matthew Yu

We develop a homotopical framework for understanding the space of quantum spin systems based on topological $n$-nets, which assign associative unital algebras to local regions in an $n$-dimensional space in a manner that captures the locality structure of the underlying lattice theories. The net-theoretic perspective admits a natural categorification, in which the category of topological $n$-nets carries the structure of a higher Morita category. Moreover, the nets of greatest physical interest form a Karoubi-complete subcategory that shares many of the characteristic structural features of the category of finite TQFTs. Our main result identifies the group of locality-preserving symmetries of (3+1)-dimensional quantum spin systems (i.e. quantum cellular automata modulo stabilization, finite-depth quantum circuits, and translations) with the full Witt group of nondegenerate braided fusion categories, thereby answering a question posed by Haah.
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Optimization and Control

Intrinsic Contraction Theory and Stabilization of Probability Measures on the Wasserstein Space

Min Kim, Joshua D. Ibrahim and Soon-Jo Chung

Deterministic flows can contract Wasserstein distances between probability laws without being contractive in point-state space. We develop a differential contraction theory for pushforward flows on forward-invariant subsets of the 2-Wasserstein space that quotients out the gauge freedom of the continuity equation. Weighted Helmholtz-Hodge decomposition and the Wasserstein contingent cone define the weighted horizontal tangent cone (whtc). Requiring the contraction quadratic form to be nonpositive only on the whtc yields the Intrinsic Wasserstein Contraction Condition (IWCC), which generalizes the classical contraction LMI and recovers it on Dirac measures. An almost-everywhere Euler approximation based on the superposition principle and Banach-valued Lebesgue differentiation theorem proves that IWCC is sufficient for intrinsic Wasserstein contraction. Law contraction without point-state contraction is illustrated by a rotationally symmetric shear example. Finally, a new comparison lemma yields an incremental Wasserstein ISS theorem; for Brockett's integrator, we prove exponential stabilization of a class of axisymmetric laws to the uniform law on a circle via a smooth feedback.
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Software Engineering

AutoEnvScaling: Automating the Data Flywheel with Terminal Agents

Simon Yu, Baolin Peng, Bo Liu et al. (+8 more)

Terminal agents solve tasks in software engineering and scientific research by executing commands in a terminal. Training them via reinforcement learning (RL) requires diverse and verifiable environments, but these environments are still mostly designed by human researchers, so their supply grows only as fast as researchers can work. We introduce AutoEnvScaling, which treats environment design as a terminal task and uses agents to automate the data flywheel. A proposer agent builds new RL environments from the rollouts of the solver being trained, and the solver's new rollouts guide the next round. By turning environment generation into a terminal task, AutoEnvScaling increases the valid-environment rate by up to 25 times compared to prompt-only generation, at up to 75% lower cost per valid environment. We show that training Qwen3.5-35B-A3B on AutoEnvScaling environments from GPT-5.6-sol improves its Terminal-Bench 2.1 score by 4.3 points over the matched Tmax baseline. The broader goal of AutoEnvScaling is to enable recursive self-improvement (RSI): by making both environment generation and solving terminal tasks, it lets one model co-evolve in both roles. We show that Qwen3.6-35B-A3B, acting in both roles, gains 12.2 points on Terminal-Bench 2.1 and raises its Terminal-Bench 4.0 score from 1.6 to 3.2. The pipeline also generalizes across agentic domains, generating environments for software engineering, computer use, professional work, and GPU kernel programming. Overall, these results show a path toward agents building their own training environments, reducing human reliance and taking a core step toward RSI.
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All authors Simon Yu, Baolin Peng, Bo Liu, Zhengyan Shi, Zhaoyang Wang, Hao Cheng, Qianhui Wu, Wenlin Yao, Natasha Jaques, Weiyan Shi, Jianfeng Gao

Computation and Language

Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation

Hermann Kumbong, Ishan S. Khare, Iddah Mlauzi et al. (+6 more)

Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as inference becomes a dominant computational workload. However, DLMs degrade quickly with fewer sampling steps, process the full sequence at every step, and require many passes over the dataset to reach good quality. We introduce Next-Concept Modeling (NCM), a two-stage, coarse-to-fine framework: a multiscale residual VQ-VAE encodes text into a hierarchy of discrete codes, and a transformer generates one level at a time, predicting all codes within a level in parallel. At matched parameter scales, NCM achieves better generative perplexity than the strongest DLM baseline on LM1B while running $21\times$ faster. On OpenWebText, NCM is competitive with the strongest DLM while running $10\times$ faster; on XSum, it matches the strongest DLM's ROUGE-L while being $31\times$ faster. On the reasoning task of Sudoku, NCM outperforms autoregressive and diffusion baselines across all difficulty levels, using fewer function evaluations. On the reasoning task of Sudoku, NCM outperforms autoregressive and diffusion baselines across all difficulty levels, using fewer function evaluations.
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All authors Hermann Kumbong, Ishan S. Khare, Iddah Mlauzi, Andrew Woen, Charles Ding, E. Kelly Buchanan, Simran Arora, Azalia Mirhoseini, Christopher Ré

Software Engineering

Agentic Evolution of SQL Test Generators

Suyang Zhong and Manuel Rigger

Automated testing has uncovered many logic bugs in Database Management Systems (DBMSs). Existing tools are typically limited to a few SQL dialects and a subset of these dialects' features. Coding agents based on Large Language Models (LLMs) could encode the semantics of additional SQL features into reusable test generators, requiring no additional LLM calls during testing and thus enabling high efficiency and low cost. However, a key challenge is validating the added capabilities in the presence of potential hallucinations. In this paper, we propose Gungnir, an approach for evolving DBMS test generators using coding agents. Our core mechanism is witness replay: the coding agent provides a generation witness recording the database setup and semantic choices needed to generate a target statement. A validator replays these choices through the modified generator and checks that it reproduces the target statement. Historical witnesses serve as regression tests for subsequent modifications. We evaluated Gungnir on SQLite, DuckDB, and PostgreSQL and found 44 bugs confirmed by developers, of which 34 have been fixed. Beyond DBMSs, witness replay could generalize to other systems to guide agents in extending test generators.
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Classical Analysis and ODEs

Uniformly Bounded Orthonormal Bases of Homogeneous Polynomials on the Complex Sphere

Felipe Gonçalves, João P. Ferreira and Maksym Radziwiłł

We resolve the long-standing problem of Rudin concerning uniformly bounded orthonormal bases of homogeneous polynomials on complex unit spheres. Specifically, we show that for every $K \geq 2$ and $N \geq 0$ there exists a basis of degree $N$ homogeneous polynomials in $\mathbb{C}^K$ that is orthonormal with respect to the surface measure of the unit sphere of $\mathbb{C}^K$ and uniformly bounded by a constant depending only on $K$ on the unit ball of $\mathbb{C}^K$. Bourgain resolved the case $K = 2$ in 1985 and the case $K = 3$ in 2016. No cases $K \geq 4$ were previously known.
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Robotics

Disentangling Task-Relevant Latent Dynamics for Reward Modeling

Dayou Li, Jionghao Wang, Peihao Wang et al. (+9 more)

Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited guidance provided by sparse rewards, but require substantial task-specific reward engineering. Learning rewards from videos provides a scalable, data-driven alternative to manual reward design. However, existing video-based rewards are often computed in entangled visual spaces where task-relevant state changes are confounded with extraneous visual variation. As a result, the learned reward may respond to visual distractors instead of actual task execution. To address this limitation, we propose DiTaR (Disentangling Task-Relevant Latent Dynamics for Reward Modeling), a framework for learning dense rewards from heterogeneous videos. DiTaR learns a continuous task-relevant representation from multi-frame video latents through an asymmetric two-stage procedure that suppresses task-irrelevant visual variation. In the task-relevant latent space, we train a history- and language-conditioned rectified-flow model to capture task dynamics and derive a directional dense reward from temporal changes in dynamics compatibility. Together, these components enable reward modeling directly over task-relevant dynamics rather than generic visual variation. DiTaR substantially improves policy learning on MetaWorld-v3 and ManiSkill over video-based reward baselines, and transfers to real-world offline RL through reward relabeling. Reward analyses further show that DiTaR provides more reliable rewards.
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All authors Dayou Li, Jionghao Wang, Peihao Wang, Jiuzhou Lei, Hao Wang, Jichen Chai, Hezhen Hu, Minghui Zheng, Zhangyang Wang, Xin Li, Wenping Wang, Zhiwen Fan

Computer Vision

Video-Arena: A Scalable Agentic Video Benchmark for Spatiotemporal Capability

Kuan Zhang, Yukun Chen, Zhihao Yang et al. (+7 more)

Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models can maintain a coherent understanding of evolving world state. Building such a benchmark from real-world videos is challenging because the underlying world state is partially observable, making annotations hard to obtain. To close this gap, we introduce Video-Arena, a benchmark that turns competitive games into scalable, verifiable tests of how well models understand evolving world states from videos. Specifically, inspired by how humans understand dynamic events, we evaluate spatiotemporal world state understanding and tracking through three task categories: perceiving critical events, interpreting state updates, and tracking the resulting states. Our pipeline can generate videos and aligned reference labels from game records at scale. To keep evaluation affordable and reproducible, we curate a golden set of 180 tasks across 9 games and run evaluations under two protocols: native visual understanding and tool-assisted agentic analysis. The best-performing systems achieve F1 scores of and under the two protocols, respectively. The result highlights that native models struggle to perceive events reliably, while tool-assisted systems improve event recovery but still struggle to interpret state updates and track the resulting states consistently over time. Our project page is in https://videoarena-bench.github.io
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All authors Kuan Zhang, Yukun Chen, Zhihao Yang, Yue Su, Xiangnan Wu, Zirong Chen, Run Luo, Tao Tan, Yinhe Zheng, Yiming Li

Software Engineering

OpenMobile-2: Building Versatile Mobile Agents with Scalable Environments and App-Native Tools

Chenyang Yan, Yingying Zhang, Kanzhi Cheng et al. (+21 more)

We introduce OpenMobile-2, a near-frontier mobile agent with fully open training environments and recipes. We make three key advances: (1) Diverse environments with simulated commercial apps: We build MobileGym++, featuring 35 realistic, functionally rich commercial-style apps with cross-app workflows, while preserving full controllability for reset and verification. Together with newly configured apps in Android emulators, this yields a diverse playground spanning over 110 apps. (2) Open training data at scale: Building on this foundation, we curate nearly 12K mobile interaction trajectories for supervised fine-tuning and the largest open collection of verifiable RL training data for mobile agents, comprising over 2K executable tasks with automatic rewards. (3) Hybrid GUI and app-native tool use: We explore an experimental mobile-use setting where apps expose selected functionalities as app-native tools alongside their GUIs, allowing agents to interleave GUI actions and tool calls within a task. We implement this setting in MobileGym++ with over 300 carefully scoped tools across 50+ apps. Additionally, we introduce MobileGym++ Bench for complex, long-horizon commercial mobile scenarios, supporting both GUI-only and hybrid GUI-tool evaluation on a shared task suite. OpenMobile-2 performs competitively across established benchmarks, including AndroidWorld (79.9) and MobileWorld (50.4), while showing promising transfer to real-device mobile use, nearly doubling SPA-Bench performance from 31.9 to 59.6. All resources are available at https://os-copilot.github.io/OpenMobile2-Home/.
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All authors Chenyang Yan, Yingying Zhang, Kanzhi Cheng, Qiushi Sun, Zhengyuan Pan, Zheng Ma, Hang Yan, Yian Wang, Nuo Chen, Jialin Cao, Xingdong Gong, Wenpo Song, Han Chen, Zichen Ding, Fangzhi Xu, Shujian Huang, Xinyu Dai, Yichen Liu, Tiankuo Yao, Bo Wang, Ben Kao, Jianbing Zhang, Lewei Lu, Dahua Lin

High Energy Physics (Experiment)

Branching fraction measurement of the $B^0\to K^+\pi^-\mu^+\mu^-$ decay at high dihadron mass

LHCb collaboration: R. Aaij, M. Abdelfatah, A.S.W. Abdelmotteleb et al. (+1125 more)

This Letter presents the first measurement of the $B^{0} \to K^{+}\pi^{-}\mu^{+}\mu^{-}$ differential branching fraction in the region of the dihadron $K^+\pi^-$ mass, $m_{K\pi}$, $1000< m_{K\pi} < 1835\,\text{MeV}/c^{2}$. This decay mode is mediated by the quark-level transition $b \to s\ell^{+}\ell^{-}$, for which the measured effective couplings show tensions with Standard Model predictions. The analysis is performed using proton-proton collision data collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of $8.4\,\text{fb}^{-1}$. The differential branching fraction is determined across different regions of dimuon mass squared, $q^2$, in the interval $0.1 < q^2 < 18.0\,\text{GeV}^2/c^{4}$, as well as in different regions of $ m_{K\pi}$ in the interval $1.1 < q^2 < 6.0\,\text{GeV}^2/c^{4}$. The results represent the first differential measurement of the branching fraction across $m_{K\pi}$ within the selected dihadron-mass region, and the first observation of the decay within this region above the $J/\psi$ resonance.
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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, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J.L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, 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, 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, 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. Belov, I. Belyaev, G. Bencivenni, E. Ben-Haim, R. Bernet, A. Bertolin, 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. Bolzonella, 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, D. Brundu, E. Buchanan, M. Burgos Marcos, C. Burr, C. Buti, J.S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J.I. Cambon Bouzas, P. Campana, 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, I. Chahrour, 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, M. Chrzaszcz, 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, 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, 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, J.E. Davies, 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, E. De Lucia, J.M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J.A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, S. Ding, Y. Ding, L. Dittmann, A.D. Docheva, A. Doheny, C. Dong, F. Dordei, A.C. dos Reis, 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, J. Eschle, 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, A.D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, R.A. Fini, M. Fiorini, M. Firlej, K.L. Fischer, D.S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, 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, 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, W. Gomulka, F. Goncalves Abrantes, I. Gonçales Vaz, M. Goncerz, G. Gong, J.A. Gooding, C. Gotti, E. Govorkova, J.P. Grabowski, L.A. Granado Cardoso, 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, X. Han, S. Hansmann-Menzemer, N. Harnew, T.J. Harris, 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, 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, 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. Kooij, 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, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J.J. Lane, 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, P. Li, P.-R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F.L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. 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, 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, G. Mancinelli, C. Mancuso, R. Manera Escalero, A. Mangalasseri, F.M. Manganella, D. Manuzzi, S. Mao, D. Marangotto, J.F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A.M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, C. Martinez, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, C. Matteuzzi, K.R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, D. Mazzanti Tarancon, M. McCann, N.T. McHugh, A. McNab, R. McNulty, B. Meadows, S.E.R. Medaer, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F.M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D.A. Milanes, A. Minotti, E. Minucci, B. Mitreska, D.S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R.D. Moise, E.F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M.J. Morello, M.P. Morgenthaler, A. Moro, J. Moron, W. Morren, A.B. Morris, A.G. Morris, R. Mountain, Z. Mu, N. Muangkod, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, G. Napoletano, I. Nasteva, M. Needham, N. Neri, S. Neubert, N. Neufeld, J. Nicolini, D. Nicotra, E.M. Niel, L. Nisi, Q. Niu, B.K. Njoki, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, H.N. Nur, A. Oblakowska-Mucha, T. Oeser, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R.H. O'Neil, J.S. Ordonez Soto, D. Osthues, J.M. Otalora Goicochea, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K.O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, L. Paolucci, A. Papanestis, M. Pappagallo, L.L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C.J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M.J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J.P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, R.N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V.M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J.R. Pybus, Q. Qian, W. Qian, N. Qin, R. Quagliani, R.I. Rabadan Trejo, B. Rachwal, R. Racz, J.H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M.S. Rangel, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P.K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J.A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogovskiy, D.L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, M.S. Rudolph, M. Ruiz Diaz, J. Ruiz Vidal, J.J. Saavedra-Arias, J.J. Saborido Silva, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M.H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, B. Sevilla Sanjuan, Y. Shang, D.M. Shangase, R.S. Sharma, L. Shchutska, T. Shears, J. Shen, Z. Shen, S. Sheng, B. Shi, J. Shi, Q. Shi, W.S. Shi, E. Shmanin, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, I. Siral, N. Skidmore, T. Skwarnicki, M.W. Slater, E. Smith, M. Smith, L. Soares Lavra, M.D. Sokoloff, F.J.P. Soler, A. Solomin, K. Solovieva, N.S. Sommerfeld, R. Song, Y. Song, Y. Song, Y.S. Song, F.L. Souza De Almeida, B. Souza De Paula, K.M. Sowa, E. Spadaro Norella, E. Spedicato, J.G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, O. Steinkamp, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y.T. Tang, M.D. Tat, J.A. Teijeiro Jimenez, F. Terzuoli, F. Teubert, E. Thomas, D.J.D. Thompson, A.R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T.T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, L. Toscano, D.Y. Tou, C. Trippl, G. Tuci, N. Tuning, L.H. Uecker, A. Ukleja, A. Upadhyay, B. Urbach, A. Usachov, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C.B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J.J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F.C. Volle, D. vom Bruch, K. Vos, C. Vrahas, J. Wagner, J. Walsh, N. Walter, E.J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N.W. Wang, R. Wang, X. Wang, X. Wang, X. Wang, X.W. Wang, Y. Wang, Y. Wang, Y.H. Wang, Z. Wang, Z. Wang, J.A. Ward, M. Waterlaat, N.K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B.D.C. Westhenry, C. White, M. Whitehead, E. Whiter, A.R. Wiederhold, D. Wiedner, M.A. Wiegertjes, C. Wild, G. Wilkinson, M.K. Wilkinson, M. Williams, M.J. Williams, M.R.J. Williams, R. Williams, S. Williams, Z. Williams, F.F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S.A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T.X. Xing, A. Xu, L. Xu, M. Xu, R. Xu, Z. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, Z. Yang, H. Yeung, H. Yin, X. Yin, C.Y. Yu, J. Yu, X. Yuan, Y Yuan, J.A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, S.H Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S.L. Zhang, Y. Zhang, Z. Zhang, J. Zhao, Y. Zhao, A. Zhelezov, S.Z. Zheng, X.Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, V. Zhovkovska, L.Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo Gao, D. Zuliani, G. Zunica

Robotics

Axiom-0: Hierarchical Self-Refinement for Vision-Language-Action Policies

Renbin Shu, Jing Yang, Xiangyuan You et al. (+7 more)

Reliable manipulation requires policies that generalize across tasks, layouts, and object interactions. Vision-language-action (VLA) models combine pretrained multimodal understanding with learned control, but their performance remains limited on unfamiliar task compositions. We present Axiom-0, a VLA policy that addresses both the quality of its representations and their use in action generation through hierarchical self-refinement. Intra-VLM Representation Refinement (IVRR) strengthens selected intermediate features through consistency supervision with learned feature projections during training. VLM-Action Interface Refinement (VAIR) recursively updates the resulting conditioning before action generation. Joint optimization of the supervised VLM layers, VAIR, and the action generator connects the two components. On RoboCasa365, Axiom-0 achieves 62.32% overall success in our evaluation, exceeding the highest published leaderboard score (Paimon-0, 58.1%) by 4.22 percentage points and the Xiaomi-Robotics-1 reference by 4.92 points. Under the evaluated profiling setup, mean action-chunk generation latency increases by 1.7% over Xiaomi-Robotics-1. These results demonstrate improved task completion with modest inference overhead.
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All authors Renbin Shu, Jing Yang, Xiangyuan You, Guolin Qiu, Yufei Xia, Yiming Zhu, Nanjun Dai, Fei Zhang, Feng Wu, Keke Tang

Probability

Geometry of semi-infinite geodesics in the Airy line ensemble

Pantelis Tassopoulos and Sourav Sarkar

We study the geometry of semi-infinite geodesics in the Airy line ensemble. We establish sharp estimates for disjointness probabilities involving geodesics with nearby directions and endpoints in the top line of the Airy line ensemble. Moreover, we obtain a power law upper bound on the coalescence depth of two semi-infinite geodesics with same asymptotic direction and establish exponential concentration estimates for geodesic jump times more generally. We also establish pathwise Hölder $1/2^-$-continuity of Busemann profiles associated to semi-infinite geodesics with common asymptotic directions (in terms of the asymptotic direction). We characterize exceptional behaviour of semi-infinite geodesics through their Busemann profiles and establish geometric properties of the associated sets of non-coalescence and non-uniqueness. Finally, we determine the Hausdorff dimensions of collections of semi-infinite geodesics: collections with a fixed top-line endpoint and arbitrary positive asymptotic direction almost surely have Hausdorff dimension $1$, while collections with a fixed positive direction and arbitrary endpoint almost surely have Hausdorff dimension $3/2$.
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Computer Vision

TrajCFG: Token-Wise Classifier-Free Guidance via Denoising Trajectory Feedback

Jingyu Liu, Hao Jiang, Yikang Ding et al. (+6 more)

Classifier-free guidance (CFG) typically applies a shared scale to all latent tokens, although their velocity predictions evolve differently during sampling. We introduce Trajectory-Adaptive Classifier-Free Guidance (TrajCFG), a training-free method for flow-matching models that adapts token-wise guidance using historical changes in guided velocity direction. TrajCFG maps this feedback to bounded scales, assigning stronger guidance to larger directional changes and weaker guidance to more consistent directions. It combines adjacent-step and interval-normalized longer-span feedback, reusing standard CFG predictions without additional model evaluations. Controlled diagnostics and ablations examine the roles of directional feedback, token-wise allocation, and history-span fusion. Experiments on text-to-video, image-to-video, text-to-audio-video, text-to-image, and image-to-3D generation show improvements over base samplers across multiple backbones. For text-to-video generation, TrajCFG improves VBench Total scores by 0.30-0.75 points over the strongest evaluated baseline on each of three backbones. These results support historical directional feedback as a broadly applicable signal for fine-grained guidance allocation. The project page is available at https://jyliu-98.github.io/trajcfg-project-page/.
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All authors Jingyu Liu, Hao Jiang, Yikang Ding, Huafeng Shi, Xu Yang, Xiaoqiang Liu, Guoxin Zhang, Pengfei Wan, Kun Gai

Image and Video Processing

Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification

Yuan Liang, Sourav Bhattacharjee and Abraham Campbell

Gate coefficients describe how a fusion layer weights intermediate features, but they do not necessarily reflect the magnitude of branch contributions at the classifier output. We investigate this distinction in a fixed 3D MedVAE–radiomics model for clear cell versus non-clear cell renal cell carcinoma classification from contrast-enhanced CT. Because the model uses a linear classification head, the binary decision logit can be decomposed exactly into a gated image term, a direct radiomics term, and a bias. We define effective radiomics dominance as the direct radiomics term's share of the summed absolute non-bias branch contributions and compare it with structural radiomics weighting. Internally, mean structural radiomics weighting was 0.823, whereas mean effective radiomics dominance was 0.148, with a modest case-wise association (h̊o} = 0.290). The gated image term retained substantially stronger discriminative ranking information than the direct radiomics term. The same structural-to-effective discrepancy was observed in exploratory external evaluation. Branch-targeted permutation, integrated-gradient, and Shapley-style analyses further identified radiomics descriptors influencing the direct radiomics term. Because radiomics also generates the gate, the gated image term remains radiomics-dependent, so additive branch contribution should not be interpreted as total input influence. These findings show that structural gate weighting and decision-level branch contribution represent distinct aspects of gated feature fusion and should be interpreted separately.
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Image and Video Processing

Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification

Yuan Liang, Morgan May, Abraham Campbell et al. (+3 more)

Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics–image fusion models for renal cell carcinoma (RCC) are commonly evaluated primarily by discrimination. Using 396 patients from the KiTS23 dataset, we evaluated whether adding radiomics to four image-model families improved probabilistic performance in addition to discrimination. Seventeen individual models and four probability-average ensembles were assessed using discrimination, probability calibration, and proper scoring measures. Across 12 matched image-only versus fusion comparisons, AUC and AP improved in all 12, while Brier score and negative log likelihood (NLL) improved in 11 of 12 and expected calibration error (ECE) in eight of 12. The 3D MedVAE gated-fusion model achieved the strongest overall individual-model profile. Probability averaging across concatenation, cross-attention, and gated fusion produced favourable point estimates across all five primary metrics relative to image-only models in all four families, although paired-bootstrap support was stronger for Brier score and NLL than for ECE. These findings show that radiomics–image fusion can improve probabilistic prediction quality beyond discrimination, while calibration behaviour varies across image architectures and fusion strategies. We show that our proposed probability-average ensemble models improve discrimination and calibration simultaneously across all architectures in the study.
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All authors Yuan Liang, Morgan May, Abraham Campbell, Sourav Bhattacharjee, Simon Caton, Pierpaolo Dondio

Game Theory

Algorithms with Smoothed Polynomial-Time Complexity for Deterministic Discounted-sum and Mean-payoff Games

Ali Asadi, Krishnendu Chatterjee and Ruichen Luo

Turn-based deterministic games on graphs are two-player zero-sum games played on finite directed graphs, where the vertex set is partitioned between the Max and the adversarial Min Player. The respective player chooses outgoing edges from their vertices. We consider these games with the classical discounted-sum and mean-payoff objectives. The goal of the Max Player is to maximize the objective against the adversarial Min Player. These games lie in NP $\cap$ coNP and are among the rare combinatorial problems that belong to this complexity class, yet the existence of a polynomial-time algorithm is a major open question. All known deterministic algorithms require exponential time in the worst case, while the classical strategy iteration (policy iteration) algorithm is remarkably efficient in practice, which motivates the study of the smoothed complexity of these games. Previous results established a smoothed polynomial-time bound only for the restricted class of ergodic games and presented only a high-probability guarantee. Our main result is a new analysis technique for a variant of the strategy iteration algorithm, and we show these algorithms achieve smoothed polynomial complexity for general game graphs with both high-probability and expected runtime guarantees. In particular, our result removes the ergodicity restriction of prior work and strengthens the high-probability guarantee to the stronger expected-runtime guarantee, thereby resolving the open question on the smoothed complexity of these games.
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Artificial Intelligence

VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation

Katherine J. Koebel, Renata Ivanek, Parminder Basran et al. (+33 more)

VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through standardized, high-quality datasets and multi-institutional collaboration across academic and non-academic sectors. This framework addresses the gap between AI advances in human medicine and the fragmented state of veterinary AI development.
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All authors Katherine J. Koebel, Renata Ivanek, Parminder Basran, Jennifer Sun, Casey Cazer, Gayatri Anil, Tommaso Banzato, Liz Barton, Dottie Cimino Brown, Leszek Bukowski, Marta Castelhano, Simon Coghlan, Christopher De Sa, Nikki Dutta, Hala Ghali, Julio Giordano, Joshua Glass, David C. Hogg, Nicholas Hollingshead, Adam Kiehl, Ziang Liu, Monica Meduri, Param Mohapatra, Kim Nayyer, Amelia Nelson, Peter-John Mantyla Noble, Christopher Pinard, Beryl Rabindran, Scott Ross, Ron Seccia, Moira Sheehan, Fei Wang, Tracy L. Webb, Kilian Q. Weinberger, Heather M. Whitney, Chen Xin

Cryptography and Security

Memory-Coupled Unlearning for Data-Absent Identity Forgetting

Chun-I Fan, Lu-En Chen, Ming-Feng Tsai et al. (+1 more)

Machine unlearning aims to reduce the influence of designated training data from a trained model while preserving its utility on retained data. Identity-level unlearning is particularly challenging when only limited target-specific information is available when a forgetting request is processed. In this work, we propose Memory-Coupled Unlearning (MCU), a framework that augments a host model with a Modern Hopfield Network (MHN) serving as a controllable external identity-prototype memory. During prototype construction, feature representations associated with each training identity are aggregated into identity prototypes and stored in the MHN. A Prototype Adaptation Network (PAN) transforms the retrieved memory representation, while a Dependency Gating Unit (DGU) controls its contribution to downstream prediction. When an identity is requested to be forgotten, MCU first removes the corresponding prototype from the external memory and subsequently readapts the model using limited support information associated with the target identities together with retained data. Importantly, in this work, “data-absent” specifically refers to the absence of the complete original forget set during request-time gradient-based readaptation; retained data may still be used to preserve downstream-task utility. Experiments based on the identity-partitioning and one-shot support construction of 1-SHUI show that MCU achieves a favorable empirical trade-off among model utility, membership distinguishability, and request-time computational efficiency compared with the evaluated baselines. These results provide empirical evidence for the potential of controllable external memory in machine unlearning, rather than a formal guarantee of complete information removal.
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All authors Chun-I Fan, Lu-En Chen, Ming-Feng Tsai, Tomohiro Morikawa

Materials Science

In-Situ TEM Observation of Solid-State Phase Evolution in High-Entropy Alloy Nanoparticles

Nirmal Kumar Katiyar, Noorbasha Bhavani Sai, Guilherme Gurian Dariani et al. (+5 more)

The emergence of new materials has gone beyond conventional design principles. Among them are high entropy alloys (HEAs), in which multiple metallic elements coexist within a single unit cell. In this case, each atomic position in a solid material has a different chemical environment, leading to configurational entropy as high as that in the fully random or melted state, which has been frozen to a solid solution. However, the behaviour of such alloy systems at pre-melting temperatures is largely unknown, where atoms gain sufficient mobility and time to rearrange, leading to an enthalpic rather than an entropic interplay. This study demonstrated the phase transformation of HEA nanoclusters (CoCrFeMnNi) under high-resolution transmission electron microscopy (HRTEM) at different temperatures, including room temperature (RT), 550 {\deg}C, and 850 {\deg}C. The clustered nanoparticles coarsen into larger clusters, accompanied by compositional heterogeneity due to surface and lattice diffusion. This experimental finding of atomic mobility was further corroborated using molecular dynamics, which provides a detailed mechanistic description.
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All authors Nirmal Kumar Katiyar, Noorbasha Bhavani Sai, Guilherme Gurian Dariani, Douglas S. Galvao, Pulickel M. Ajayan, Pedro Alves da Silva Autreto, Sriram Vijayan, Chandra Sekhar Tiwary

Artificial Intelligence

Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress

Zongxia Li, Zhongzhi Li, Yucheng Shi et al. (+14 more)

Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its final state passes a binary check. We present Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0), an extension of Long-Horizon-Terminal-Bench that preserves its 46 original tasks while correcting task and evaluation-infrastructure defects and extending 31 tasks. LHTB 2.0 contains 77 tasks across 13 domains and eight task types. The extended tasks are produced through a systematic reconstruction-task pipeline. We start from a working scientific or engineering artifact, then use a parameterized reference implementation to generate multiple scenarios, reveal six as behavioral evidence, hold out three for evaluation, and use dense progress ladders to reward partial progress while preventing memorization of public outputs. We also extend the evaluation horizon from 90 minutes to three hours and record verifier checkpoints every 30 minutes, enabling direct measurement of sustained progress within a run. Across 1,145 scored model-task runs from 15 frontier models, GPT-6-astra leads with dense mean reward 0.69 and 37 tasks at R at least 0.95, followed by GPT-5.6-sol with dense mean reward 0.67 and 32 tasks at the same threshold. On the 30 reconstruction tasks, binary verifier scores average 0.221, whereas dense ladder scores average 0.653, showing that outcome-only evaluation discards substantial intermediate progress. LHTB 2.0 provides a broader, hardened, and process-aware testbed for studying reliable agent execution over hours.
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All authors Zongxia Li, Zhongzhi Li, Yucheng Shi, Ruhan Wang, Junyao Yang, Chengsong Huang, Zhichao Liu, Xiyang Wu, Anhao Li, Yue Yu, Ergan Shang, Fuxiao Liu, Tong Zheng, Ninghao Liu, Lichao Sun, Haitao Mi, Leowei Liang

General Economics

Is the business of frontier AI companies selling tokens? Value capture and equity prices

Andrew J. Peterson

We examine business models that could support frontier AI companies' valuations and the public evidence of plans to earn income beyond selling model access. Reverse cash-flow calculations for OpenAI and Anthropic yield combined year-ten revenue requirements of \$2.79 trillion or \$733 billion, depending on financial assumptions. Public documents identify proposed payments and ownership interests that could supply additional income, such as through partnerships on pharmaceuticals, robotics, or materials science. We discuss how these arrangements relate to firm boundaries and value capture.
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High Energy Physics (Experiment)

Planck-Scale Constraints on Quantum Gravity-Induced Neutrino Decoherence with IceCube

R. Abbasi, M. Ackermann, J. Adams et al. (+415 more)

Many quantum gravity theories predict spacetime fluctuations at the Planck scale. Neutrinos, which interact only via the weak force and gravity, propagate over long distances without losing quantum coherence. Planck-scale fluctuations would spoil that coherence, damping the flavor oscillation probability over long baselines. This makes neutrinos sensitive interferometers for Planck-scale physics. We present a search for quantum decoherence using 368,071 atmospheric neutrinos detected by IceCube in the energy range 0.5-100 TeV, an order of magnitude higher in energy than any previous decoherence search. We find no evidence for decoherence and set the strongest constraints to date on this class of neutrino decoherence models, improving on previous limits by over an order of magnitude for decoherence rate scaling as $E^2$ and $E^3$. These results exclude natural Planck-suppressed decoherence for all energy scalings $E^n$ with $0\leq n \leq 3$, the first such exclusion at the Planck scale for $n=3$.
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All authors R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J.M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, J. Beise, C. Bellenghi, S. Benkel, S. BenZvi, D. Berley, E. Bernardini, D. Z. Besson, E. Blaufuss, L. Bloom, S. Blot, F. Bontempo, J. Y. Book Motzkin, C. Boscolo Meneguolo, S. Böser, O. Botner, J. Braun, B. Brinson, Z. Brisson-Tsavoussis, L. Brusa, R. T. Burley, D. Butterfield, K. Carloni, J. Carpio, N. Chau, Y. C. Chen, Z. Chen, D. Chirkin, S. Choi, A. Chubarov, B. A. Clark, D. A. Coloma Borja, A. Connolly, J. M. Conrad, D. F. Cowen, C. De Clercq, J. J. DeLaunay, D. Delgado, T. Delmeulle, S. Deng, P. Desiati, K. D. de Vries, G. de Wasseige, T. DeYoung, J. C. Díaz-Vélez, S. DiKerby, T. Ding, M. Dittmer, A. Domi, L. Draper, L. Dueser, D. Durnford, K. Dutta, M. A. DuVernois, T. Ehrhardt, L. Eidenschink, A. Eimer, C. Eldridge, P. Eller, E. Ellinger, D. Elsässer, R. Engel, H. Erpenbeck, W. Esmail, S. Eulig, J. Evans, P. A. Evenson, K. L. Fan, K. Fang, K. Farrag, A. Fattorini, A. R. Fazely, A. Fedynitch, N. Feigl, C. Finley, D. Fox, A. Franckowiak, S. Fukami, P. Fürst, J. Gallagher, A. Garcia, M. Garcia, E. Genton, L. Gerhardt, A. Ghadimi, C. Glaser, T. Glüsenkamp, J. G. Gonzalez, S. Goswami, A. Granados, D. Grant, S. J. Gray, S. Griffin, S. Griswold, K. M. Groth, D. Guevel, C. Günther, P. Gutjahr, C. Ha, A. Hallgren, F. Halzen, M. Handt, K. Hanson, J. Hardin, A. A. Harnisch, P. Hatch, A. Haungs, J. Häu\ssler, K. Helbing, J. Hellrung, B. Henke, L. Hennig, F. Henningsen, L. Heuermann, R. Hewett, N. Heyer, S. Hickford, A. Hidvegi, C. Hill, G. C. Hill, R. Hmaid, K. D. Hoffman, A. Hollnagel, D. Hooper, S. Hori, K. Hoshina, M. Hostert, W. Hou, M. Hrywniak, T. Huber, K. Hultqvist, K. Hymon, A. Ishihara, W. Iwakiri, M. Jacquart, S. Jain, O. Janik, M. Jansson, M. Jin, N. Kamp, D. Kang, W. Kang, A. Kappes, L. Kardum, T. Karg, A. Karle, A. Katil, M. Kauer, J. L. Kelley, B. Kern, M. Khanal, A. Khatee Zathul, A. Kheirandish, T. Kim, H. Kimku, F. Kirchner, J. Kiryluk, C. Klein, S. R. Klein, Y. Kobayashi, S. Koch, A. Kochocki, R. Koirala, H. Kolanoski, T. Kontrimas, L. Köpke, C. Kopper, D. J. Koskinen, P. Koundal, M. Kowalski, A. Kravka, N. Krieger, T. Krishnan, K. Kruiswijk, E. Krupczak, E. Kun, N. Kurahashi, C. Lagunas Gualda, L. Lallement Arnaud, M. J. Larson, F. Lauber, J. P. Lazar, K. Leonard DeHolton, A. Leszczyńska, C. Li, J. Liao, C. Lin, Q. R. Liu, Y. T. Liu, M. Liubarska, C. Love, L. Lu, F. Lucarelli, W. Luszczak, Y. Lyu, M. Macdonald, F. Machingal, E. Magnus, Y. Makino, E. Manao, S. Mancina, A. Mand, I. C. Mariş, S. Marka, Z. Marka, I. Martinez-Soler, R. Maruyama, J. Mauro, F. Mayhew, F. McNally, K. Meagher, A. Medina, M. Meier, Y. Merckx, L. Merten, J. M. Mitchell, S. Mondal, T. Montaruli, R. W. Moore, Y. Morii, A. Mosbrugger, D. Mousadi, E. Moyaux, T. Mukherjee, M. Nakos, U. Naumann, R. Neshat, L. Neste, M. Neumann, M. U. Nisa, K. Noda, A. Noell, A. Novikov, A. Obertacke, V. O'Dell, A. Olivas, R. Orsoe, J. Osborn, E. O'Sullivan, B. Owens, V. Palusova, H. Pandya, A. Parenti, C. Parisel, N. Park, V. Parrish, E. N. Paudel, L. Paul, C. Pérez de los Heros, T. Pernice, T. C. Petersen, J. Peterson, S. Pick, M. Plum, A. Pontén, V. Poojyam, B. Pries, R. Procter-Murphy, G. T. Przybylski, L. Pyras, C. Raab, J. Rack-Helleis, N. Rad, M. Ravn, K. Rawlins, Z. Rechav, A. Rehman, R. Reimann, I. Reistroffer, E. Resconi, C. D. Rho, W. Rhode, L. Ricca, B. Riedel, A. Rifaie, E. J. Roberts, S. Rodan, M. Rongen, A. Rosted, C. Rott, T. Ruhe, L. Ruohan, D. Ryckbosch, J. Saffer, D. Salazar-Gallegos, P. Sampathkumar, A. Sandrock, G. Sanger-Johnson, M. Santander, S. Sarkar, M. Scarnera, M. Schaufel, H. Schieler, S. Schindler, L. Schlickmann, B. Schlüter, F. Schlüter, N. Schmeisser, T. Schmidt, F. Schmitt, A. Scholz, F. G. Schröder, S. Schwirn, S. Sclafani, D. Seckel, L. Seen, M. Seikh, S. Seunarine, P. A. Sevle Myhr, R. Shah, S. Shah, A. Sharma, S. Shefali, N. Shimizu, M. Shin, R. Singh, B. Skrzypek, R. Snihur, J. Soedingrekso, D. Soldin, P. Soldin, G. Sommani, D. Song, C. Spannfellner, G. M. Spiczak, C. Spiering, J. Stachurska, M. Stamatikos, T. Stanev, T. Stezelberger, T. Stürwald, T. Stuttard, G. W. Sullivan, I. Taboada, S. Ter-Antonyan, A. Terliuk, A. Thakuri, P. Thelen, M. Thiesmeyer, W. G. Thompson, J. Thwaites, W. Tian, S. Tilav, K. Tollefson, J. A. Torres, S. Toscano, D. Tosi, K. Upshaw, A. Vaidyanathan, N. Valtonen-Mattila, J. Valverde, J. Vandenbroucke, T. Van Eeden, N. van Eijndhoven, L. Van Rootselaar, J. van Santen, J. Vara, F. Varsi, M. Velazquez, M. Venugopal, M. Vereecken, S. Vergara Carrasco, S. Verpoest, A. Vijai, J. Villarreal, C. Walck, A. Wang, E. H. S. Warrick, C. Weaver, P. Weigel, A. Weindl, J. Weldert, A. Y. Wen, C. Wendt, J. Werthebach, M. Weyrauch, N. Whitehorn, C. H. Wiebusch, D. R. Williams, L. Witthaus, J. Woodward, G. Wrede, X. W. Xu, J. P. Yanez, Y. Yao, E. Yildizci, S. Yoshida, F. Yu, S. Yu, T. Yuan, S. Yun-Cárcamo, A. Zander Jurowitzki, A. Zegarelli, S. Zhang, Z. Zhang, P. Zhelnin, P. Zilberman, C. Zilleruelo Cañas, M. Zimmermann