Published: 2026-06-10 14:51:00
Authors: Jiali Deng, Giovanni Pantuso
Categories: math.OC
Abstract:
The problem of pricing mobility services has attracted significant attention. In most studies, uncertain demand is modeled as an exogenous random variable with known distribution. This assumption overlooks the likely effect of prices on user adoption decisions. To address this dependency, we formulate the pricing problem as a stochastic program with decision-dependent demand uncertainty. Specifically, we make the non-standard assumption that the probability distribution of demand depends on pricing decisions. We show that the problem can be written as a mixed-integer linear program whose size is exponential in the input parameters. To find exact numerical solutions we specialize the L-shaped method for stochastic programs with decision-dependent uncertainty. In particular, we devise efficient separation routines by proving closed-form primal and dual solutions to the involved subproblems. In addition, we develop problem-specific valid inequalities and cut-sharing mechanisms which significantly improve convergence. We show that the method outperforms by far a commercial solver used to solve the monolithic formulation. Furthermore, in a case study based on a real-world carsharing system, we show that incorporating decision-dependent uncertainty improves expected profits by 8.39% compared to a benchmark that considers deterministic price-elastic demand, and by 8.53% compared to a benchmark that considers exogenous random demand, on average. In addition, we evaluate the performance of preventive pricing and relocation decisions under two vehicle allocation policies. The results suggest that a controlled allocation of vehicles to customers can improve service rates while only marginally affecting profits.
Published: 2026-06-10 14:48:36
Authors: Carlos Muñoz-Moncayo, David I. Ketcheson
Categories: physics.flu-dyn, math.NA
Abstract:
Accurate modeling of tsunamis (such as those generated by landslides) requires capturing both wave dispersion in the deep ocean and wave breaking near the shore. The shallow water equations are often preferred for working with tsunamis, but neglect dispersion and may be inaccurate in scenarios where dispersive effects are significant. In this work, we develop an approach that seeks to incorporate the best aspects of both hyperbolic and dispersive models by combining either of two hyperbolic reformulations of the Serre-Green-Naghdi equations away from the shore with the non-dispersive shallow water equations near the shore. The model is discretized and implemented within the GeoClaw software, and incorporates adaptive mesh refinement as well as shared-memory parallelism. We validate it through comparison with benchmarks and real tsunami data. The results and performance compare favorably with the existing dispersive water wave solvers, including a speedup of about 2x relative to GeoClaw's existing dispersive solver for a large-scale tsunami simulation.
Published: 2026-06-10 14:45:40
Authors: Meng Zhang, Qingyun Xu, Zhi Lin
Categories: cond-mat.quant-gas
Abstract:
We investigate the ground-state phase diagram of repulsively interacting bosons on a two-leg ladder threaded by a uniform artificial magnetic flux, using the cluster Gutzwiller mean-field method. In the strong-rung-coupling regime, self-consistent calculations are performed on a $2\times4$ cluster. By analyzing the superfluid order parameter, leg-resolved currents, chiral current, the current ratio on adjacent legs, and the density imbalance between the two legs, we distinguish Mott-insulating from superfluid regimes and characterize the observed states as Meissner-like, vortex-like (superfluid or Mott insulating), or biased-ladder. In regions overlapping with previous DMRG studies, our results qualitatively agree with the established phase structure, demonstrating that the cluster Gutzwiller approach balances computational efficiency and physical accuracy. We then construct the first grand-canonical $t$--$μ$ phase diagrams for this system, revealing how the magnetic flux modifies the shape, tilt, and extent of the Mott lobes. We further explore previously inaccessible regimes, including higher fillings $ρ\gtrsim1$ and the intermediate interaction window $U/t\in[7.69,9.09]$. Special attention is paid to $\varphi=π$, where the effective triangular-ladder mapping becomes singular. Owing to the equivalence of $\varphi=π$ and $-π$ modulo $2π$, a combined symmetry forbids net chiral currents, leading to a nonchiral Mott-insulating state, in contrast to the chiral-superfluid tendency expected away from $\varphi=π$. Our results offer a computationally efficient route for mapping the global phase structure of bosonic flux ladders and provide guidance for future ultracold-atom experiments in artificial gauge fields.
Published: 2026-06-10 14:43:25
Authors: Katherine E. Whitaker, Rachel Bezanson
Categories: astro-ph.GA
Abstract:
The shutdown of star formation - quenching - marks a pivotal transition in the lives of massive galaxies, which dominate the present-day stellar mass density. This review synthesizes our current understanding of the mechanisms that trigger and maintain quiescence. We discuss the nuances of how quiescent systems are identified across cosmic time and summarize the evolving physical properties of the growing massive population, including their stellar populations, chemical enrichment histories, and gas and dust reservoirs, highlighting several key results: (1) Quiescent galaxies can be identified with empirical color selections, but evolving specific star formation rate thresholds offer a more robust physical distinction from star-forming systems. (2) The earliest massive quiescent stellar populations show rapid formation histories and high metallicities, with enhanced $α$-elemental abundances often distinct from local analogs. (3) Nascent studies of gas and dust in quiescent galaxies reveal diverse multiphase reservoirs and outflows, pointing to fast ejective and slow regulatory modes of galaxy quenching. (4) In situ processes establish galaxy central density, while assembly continues via (minor) mergers post-quenching, reshaping all massive galaxies and disrupting rotation in most cases. We distill observations into two broad modes by which massive galaxies form and quench: one involves a rapid, early shutdown driven by supermassive black hole outflows on short timescales; the other proceeds gradually through gas exhaustion, virial heating, or preventative feedback, each leaving distinct observational signatures. Together, these pathways offer a testable framework for modeling the formation and evolution of massive galaxies, which will be informed by future studies of their stars, gas, dust, and dynamics.
Published: 2026-06-10 14:42:04
Authors: Qishan Liu, Kenny C. Y. Ng
Categories: hep-ph
Abstract:
One of the most important neutrino interactions is the Inverse Beta Decay (IBD). However, the IBD events typically carry no directional information in water Cherenkov detectors as the positron directions are mostly isotropic at low energies, such as those in supernova studies. As Gadolinium is being added to Super-Kamiokande, the improved neutron capture efficiency not only allows better background rejection, but the neutron capture information could potentially provide additional information that allows better event reconstruction. Due to neutron diffusion in water, event-by-event reconstruction is difficult. However, if the final neutron capture position is correlated with the initial neutrino momentum, it may be possible that neutrino directionality could be reconstructed statistically, with or without using the positron information. In this work, we use Geant4 to simulate neutron propagation in water. We show that in a wide range of neutrino energies from about 10 MeV to several hundred MeV, neutron capture information could statistically enhance the neutrino directionality, compared to positron-only inference, even with neutron diffusion considered. However, practical application of this technique depends crucially on detection effects, especially the vertex reconstruction resolutions. Our work therefore motivates developments of better reconstruction algorithms and techniques, as well as detector upgrades.
Published: 2026-06-10 14:37:49
Authors: Ruichen Qiu, Yichuan Cao, Qiao-Long Huang, Ruyong Feng, Xiao-Shan Gao
Categories: cs.SC, cs.CC
Abstract:
In this paper, we prove that output-sensitive sparse polynomial GCD computation over finite fields is NP-hard under BPP many-one reduction. More precisely, for two sparse univariate polynomials $f,g$ with finite field coefficients, there exists no randomized algorithm to compute $\mathrm{gcd}(f,g)$, which is polynomial-time in the sizes of $f,g,\gcd(f,g)$ under the standard complexity assumption $\mathrm{NP}\nsubseteq\mathrm{BPP}$. This settles the open problem posed as Challenge 5 in The Sparsity Challenges in the finite field setting. Furthermore, we show that the Roots of Unity Detection problem over finite fields is NP-hard; that is, determining whether the GCD of a sparse univariate polynomial and $x^n - 1$ has nonzero degree is NP-hard.
Published: 2026-06-10 13:51:56
Authors: Xiangyu Wang, Xinyu Wang, Minyong Guo, Hai-Qing Zhang
Categories: astro-ph.HE, gr-qc
Abstract:
In this work, we systematically study the gravitational lensing properties and observational signatures of compact boson stars. Unlike black holes, the photon effective potential of a compact boson star develops a nearly flat region, whose width increases with the compactness of the star. This flat structure significantly broadens the range of impact parameters that can produce large-angle deflections, leading to noticeably wider lensing rings of all orders. Photons constituting these rings traverse more complex paths, rendering the resulting images more sensitive to the spatial distribution of the accretion flow. Ray tracing results show that, compared to black hole models, the image topology and visibility amplitudes of compact boson stars exhibit a stronger dependence on the accretion flow structure. These results highlight qualitative differences in the observational properties of compact boson stars and black holes.
Published: 2026-06-10 13:47:31
Authors: Bassam Nima, Mingyu Fan, Xubo Wang, Sen Wang, En Fu Zhou, Andrew M. Jayich, Jiang Ming Yao, Lan Cheng, Amar Vutha
Categories: physics.atom-ph, hep-ex, nucl-ex
Abstract:
The Schiff moment of a nucleus is a symmetry-violating nuclear moment that indicates new physics beyond the Standard Model. We place the limit, $|\mathscr{S}({}^{153}$Eu)$| < 1.7 \times 10^{-8}$ $e\,$fm$^3$ (95\% confidence), on the Schiff moment of the $^{153}$Eu nucleus, using nuclear spin resonances in two ensembles of oppositely-polarized $^{153}$Eu$^{3+}$ ions in a Y${}_2$SiO${}_5$ crystal. This measurement using octupolar nuclei in a mm-scale crystal constrains new physics at the TeV energy-scale.
Published: 2026-06-10 09:18:19
Authors: Kacper Darowski, Sebastian N. Peters, Lukas Lautenschlager
Categories: cs.CR
Abstract:
European Rail Traffic Management System (ERTMS) is a widely adopted standard unifying train management in the EU. While the standard allows for use cases like fully autonomous driving, cybersecurity has been an afterthought. Risk analysis enables the systematic assessment and prioritization of threats and mitigations. To date, it remains unclear which threats are most significant in ERTMS. This study systematically models components of ERTMS and analyzes their security in light of threats identified in the underlying technologies. The results suggest a concerning state of ERTMS, despite its critical role in railway safety. The use of legacy standards like EuroBalises and GSM-Railway (GSM-R) introduces vulnerabilities that persist across minimal ERTMS implementations, deployments incorporating various optional safety measures, and prospective future evolutions of the system, e.g., adopting Future Railway Mobile Communication System (FRMCS). Fully transitioning to European Train Control System (ETCS) level 2 was identified as the most significant measure for advancing ERTMS cybersecurity. The results indicate that a shift of ERTMS toward security is required to ensure availability and safe operation. While the chosen methodology proved its feasibility and shows remaining weaknesses of ERTMS, future work is needed to develop railway-centric adaptations to improve the quantification and evaluation of the computed risks.
Published: 2026-06-10 06:37:06
Authors: Alexander Ryabchenko, Idan Attias, Daniel M. Roy
Categories: cs.LG, stat.ML
Abstract:
Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, tracking resources are finite, and feedback from untracked rounds is permanently lost. In this paper, we study delayed online convex optimization (OCO) under a hard capacity constraint, where at most $C$ pending rounds can be tracked at any time. To model delay information, we introduce a semi-clairvoyant model that refines the clairvoyant assumption from prior work: rather than requiring delays to be known at prediction time, the learner observes delay expirations online, consistent with the classical unconstrained delayed setting. Our approach proceeds via a reduction to a novel ``delayed and weighted'' OCO problem, using a scheduler that randomizes tracking decisions and importance-weights the resulting observations. For this base problem, we propose and analyze Delayed-Weighted FTRL and its bandit analogue, establishing regret bounds that explicitly characterize the interaction between time-varying weights and delayed feedback. Combining these base learners with our schedulers yields the first regret guarantees for capacity-constrained OCO under convex and strongly convex losses, for both first-order and bandit feedback. For first-order feedback, capacity $C = Ω(\log T)$ suffices to recover standard delayed OCO rates up to logarithmic factors. For bandit feedback, the regret rates are modulated by powers of $(1 + σ_{\text{max}}/C)$, where $σ_{\text{max}}$ is the maximum number of pending observations at any time. This allows the regret bound to degrade gracefully when $C < σ_{\text{max}}$, while remaining sublinear.
Published: 2026-06-10 01:04:58
Authors: Jongmin Kim, Hyesung Ji, Wonseok Choi, Hyunah Yu, Jung Ho Ahn
Categories: cs.CR
Abstract:
Fully homomorphic encryption (FHE) enables computations on encrypted data without decryption, offering strong data privacy at the expense of substantial computational and memory overheads. Prior efforts have steadily improved FHE performance through cryptographic and algorithmic enhancements or hardware acceleration, yet these two directions have progressed largely in isolation, hindering the full exploitation of available hardware capabilities. This work presents WHET, which introduces memory-centric, architecture-aware optimizations to better align cryptographic and algorithmic constructions with FHE accelerator architectures. We identify conventional FHE constructions as major sources of excessive working sets and heavy off-chip memory traffic. We propose accelerator-specific techniques, including fine-grained coefficient-to-slot transformation, plaintext compression, and intermediate modulus raising, to reduce the on-chip data footprint by minimizing temporary ciphertexts and plaintext loads. With these techniques applied, we observe additional opportunities to improve on-chip memory efficiency; hence, we introduce lightweight architectural refinements, including a special-purpose buffer and functional unit extensions. With these optimizations, WHET achieves 1.38-8.74$\times$ per-area performance improvements over state-of-the-art FHE accelerators and the first-ever sub-millisecond CKKS bootstrapping.
Published: 2026-06-10 00:40:14
Authors: Jongmin Kim, Hyesung Ji, Jean-Luc Watson, Charles Gouert, G. Edward Suh, Jung Ho Ahn
Categories: cs.CR
Abstract:
While private information retrieval (PIR) enables private database services by fully concealing access patterns, it simultaneously requires high computational throughput, large memory capacity, and substantial memory bandwidth. We introduce VIPIR, a versatile GPU framework that co-designs PIR protocols with GPU acceleration. We develop a unified analytic model showing that state-of-the-art PIR protocols fall into two categories with complementary limitations, and propose two protocols that flexibly combine techniques across these categories, overcoming the limitations of both classes. These protocols incorporate a GPU-friendly data compression method called expansion-based ring packing (ExpPack), which offers a high degree of parallelism and minimal communication cost. VIPIR applies further optimizations to core operations, including number-theoretic transforms (NTTs) and various matrix-matrix multiplications (GEMMs). Notably, we develop a tensor-core-based execution method for database multiplication by interpreting it as a mixed-integer-type GEMM. We also design memory-efficient scheduling methods that minimize intermediate buffers and enable multi-GPU scaling under memory capacity constraints. Overall, VIPIR achieves orders-of-magnitude higher throughput than prior PIR systems while reducing communication and memory overheads, making large-scale PIR practical.
Published: 2026-06-09 21:09:15
Authors: Dražen Glavan, David M. J. Vokrouhlický
Categories: gr-qc, hep-th
Abstract:
We perform a Hamiltonian constraint analysis of metric $f(R)$ gravity in the Jordan frame and show that the regular constraint classification degenerates on singular phase-space surfaces located at $f'(R)\!=\!0$ and $f''(R)\!=\!0$. We then study the perturbative implications of these surfaces. For exact backgrounds satisfying $f(R)\!=\!0$ and $f'(R)\!=\!0$, the linearized spectrum is empty; the known pure $R^2$ result is therefore a special case of a more general degeneracy in $f(R)$ gravity. We also show that FLRW trajectories in the Starobinsky model can cross the surface $f'(R)=0$, but that inhomogeneous perturbations develop a degenerate constraint structure at the crossing. The resulting crossing condition is better interpreted as a regularity condition for perturbative evolution than as an ordinary constraint within the Dirac--Bergmann algorithm. Together, these results distinguish backgrounds that lie entirely on a singular surface from backgrounds that cross one dynamically, and show that the two situations lead to different perturbative degeneracies.
Published: 2026-06-09 20:02:01
Authors: Amartya Shekhar Dubey, Mattie Ji
Categories: math.AT, math.KT
Abstract:
For a commutative ring $R$ with unity, its algebraic $K$-theory space $K(R)$ may be obtained by group-completing the symmetric monoidal category of finitely generated free $R$-modules under direct sum. A natural question is what happens when one group-completes with respect to the tensor product structure instead. In this note, we give a direct proof of the folklore theorem that the resulting group-completion is the rationalization of $K(R)$, up to $π_0$. We also discuss how a similar group-completion would give the $p$-perfection and, more generally, the localization of $K(R)$ at any non-trivial multiplicatively closed subset $S \subseteq \mathbb{Z}_{> 0}$. The localization statement can be recovered from a localization theorem of May. We give a plus-construction proof without using the full machinery of multiplicative infinite loop space theory.
Published: 2026-06-09 18:32:42
Authors: Nikolay V. Koldunov, Suvarchal K. Cheedela, Sergey Danilov, Dmitry Sidorenko, Sebastian Beyer, Thomas Jung
Categories: physics.ao-ph, cs.DC, cs.SE, physics.comp-ph
Abstract:
Large language models (LLMs) can translate and modify source code, and have been shown to do so for codes of different complexity. Whether they can port a complete, production geophysical model to a different language without degrading its physics has not been established. We demonstrate that LLM-assisted code translation can preserve the physics of a complete production ocean model while moving it into a modern performance-portable form. We report our experience using an agentic LLM coding assistant, directed by domain experts, to port the FESOM2 unstructured mesh ocean--sea-ice model (about 74000 lines of core Fortran) first to C and then to C++/Kokkos for performance portability across CPUs and GPUs. We describe the practices that proved necessary, what worked and what did not, and the failure modes that we encountered. Three practices mattered most: translating in two stages that separate reproducing the numerics (Fortran to a clean C reference) from introducing parallelism (C to Kokkos); requiring a strictly literal translation in which the assistant was not permitted to ``improve'' the source; and validating each stage against an acceptance criterion suited to it.
The C port reproduces the original Fortran at the level of long-term simulation statistics over five years. The Kokkos port is bit-for-bit identical to the C reference on CPU and statistically close on GPU over multi-year runs. On eddy-rich meshes up to 7.4 million surface vertices a single A100 GPU node runs 1.6--3.7 times faster than a CPU node, reaching the 1-2 simulated-years-per-day required for production integrations. The result is more than a single GPU port: by following a clear validation procedure, an LLM moved a full Fortran ocean model into another language and onto accelerators while preserving its physics in a matter of weeks.
Published: 2026-06-09 18:00:28
Authors: Yanou Cui, Fengyi Li, Xiaolin Qi, Ian M. Shoemaker, Yu-Dai Tsai
Categories: hep-ph
Abstract:
This work proposes a new terrestrial probe for millicharged particles (mCPs) and demonstrates promising discovery prospects. mCPs can be copiously produced in core-collapse supernovae (SNe), and a fraction may escape, travel to Earth and yield distinct signals. The mCP mass induces a time-of-flight (ToF) delay relative to the SN neutrino burst, opening a clean search window after the neutrino signal has passed. We compute the mCP-induced electron-recoil signals at XENONnT, JUNO, DUNE, and Hyper-Kamiokande for benchmark SN scenarios, and find that for $\varepsilon = 10^{-9}$ and sub-MeV to MeV-scale masses, more than 10 events per year can be detected. This search can improve upon existing SN cooling bound on $\varepsilon$ by up to an order of magnitude.
Published: 2026-06-09 17:48:41
Authors: Megan Frisella, Shubham Tiwari, Andy Ruan, Yi Pan, Parker Gustafson, Mat Jacob, Gilbert Bernstein, Stephanie Wang
Categories: cs.DC, cs.AI
Abstract:
Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together with memory-saving optimizations like ZeRO. Deployed systems for foundation model pretraining often rely on human experts to manually design a high-level parallelism strategy then implement the corresponding low-level execution strategy, making it difficult to adapt the system to new strategies. Meanwhile, many general-purpose frameworks are more flexible but their implementations are still tied to a fixed set of common parallelism strategies, making it challenging to integrate state-of-the-art strategies.
We present Piper, a user-controllable distributed training system that decouples the strategy from the runtime implementation. Piper allows users to declare a comprehensive distributed training strategy with a small set of model annotations and scheduling directives. Each directive applies a transformation on Piper's intermediate representation (IR), a unified global training DAG that represents all computation and communication. Using this IR, Piper compiles per-device execution plans and executes them with a distributed runtime agnostic to the strategy. We show that the combined system maintains performance parity on commonly available strategies such as ZeRO, while also enabling additional performance and memory efficiency gains through joint scheduling of compute and communication in composed parallelism strategies such as DeepSeek-V3's DualPipe.
Published: 2026-06-09 17:46:55
Authors: Atsumoto Ohashi, Neil Zeghidour, Alexandre Défossez, Eugene Kharitonov
Categories: cs.CL, eess.AS
Abstract:
Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, current models are trained solely with supervised learning through token-level likelihood maximization, which does not directly optimize interaction-level behaviors, causing interactivity issues such as excessive silence and ill-timed turn-taking. Recent work has applied reinforcement learning (RL) to improve interactivity, but existing methods address only a limited set of interactive behaviors in their rewards. In this work, we propose a post-training alignment method that comprehensively improves the interactivity of full-duplex spoken dialogue models through RL. We address the four canonical axes of interactivity: pause handling, turn-taking, backchanneling, and user interruption. For each axis, we extract short audio segments from human conversation corpora and optimize the model with axis-specific reward functions. An extra LLM-based reward for response quality prevents semantic degradation. We apply our method to two open-source models, Moshi and PersonaPlex, demonstrating consistent improvements in interactivity on both offline evaluation with pre-recorded audio and real-time multi-turn dialogue evaluation.
Published: 2026-06-09 17:36:34
Authors: Yikang Yang, Zhanpeng Hu, Youtian Lin, Mengqi Zhou, Jingxi Xu, Feihu Zhang, Jiaheng Liu, Yao Yao
Categories: cs.CV
Abstract:
Multimodal large language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue for 3D generation powered by their priors, world knowledge and reasoning. Yet existing benchmarks rarely evaluate 3D modeling through code. Such modeling demands more than runnable code: from a text or visual specification, a model must generate a parametric 3D program that is geometrically precise, semantically aligned and assembly-consistent. We introduce P3D-Bench, a benchmark for parametric 3D generation. Unlike a 3D mesh, a parametric 3D program exposes explicit dimensions, construction operations and part relations, revealing whether a model recovers a design's structure, not just its appearance. Under a unified protocol, P3D-Bench covers three task families (Text-to-3D, Image-to-3D and Assembly-3D) and scores each output for executability, geometric fidelity, topology, text-grounded constraints, multiview semantic alignment and part-level structure. We evaluate frontier MLLMs and text-only LLMs on 400 text cases, 400 image cases and 203 annotated assemblies, with domain-specific models as reference points. Our extensive evaluation yields three findings. First, assemblies are the hardest setting, where models still fail to compose multiple parts into a coherent structure. Second, models can often recover the global shape and semantic identity of the target object, yet fail to reproduce the precise parametric geometry specified by the input. Third, part-level modeling remains weak on assemblies, where models recover neither the geometry of each part nor the right number of parts. These results position P3D-Bench as a benchmark for evaluating precise parametric geometry and part-level structure in parametric 3D generation.
Published: 2026-06-09 16:51:13
Authors: Yichao Zhong, Yidan Lu, Yuhang Lu, Tianyang Tang, Haoguang Mai, Yixuan Pan, Tianyu Li, Li Chen, Jingbo Wang, Zhongyu Li, Peng Lu, Hongyang Li
Categories: cs.RO, cs.AI
Abstract:
Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven reinforcement learning (RL) provides stability in whole-body movement coordination, but a fixed reference makes it hard to adapt to varied ball positions and strike timings; in contrast, task reward-driven RL struggles to explore and discover valid kicks from scratch. We therefore introduce RoboNaldo, a three-stage motion-guided curriculum RL framework for high-impulse humanoid interaction. A single human-kick reference is used as a scaffold and progressively shifts optimization towards shooting performance. The curriculum first learns a stable whole-body kicking prior, then adapts the kick to free-kick settings where the ball is stationary at random positions, and finally extends it to moving-ball shooting through a locomotion-command and kick-trigger interface. A high-level heuristic planner controls this interface during training, while alternative high-level controllers can drive the same low-level policy at inference. In simulation, RoboNaldo demonstrates free-kick shot error 48.6% lower and shoot velocity 2.96x than prior work baselines. In real world on a Unitree G1 with onboard perception, RoboNaldo attains 0.73 m and 0.86 m average target shooting error from 3 m away in free-kick and moving-ball cases, accordingly. And the post-contact ball velocity reaches 13.10 m/s, which is 59-71% of reported professional open-play shot speed. Project page: https://opendrivelab.com/RoboNaldo.
Published: 2026-06-09 14:55:16
Authors: Jyrki Jauhiainen, Yassine Nabou, Tuomo Valkonen
Categories: math.OC
Abstract:
We study efficient online methods for dynamic inverse problems with infinite time horizon. We concentrate, in particular, on problems whose forward model arises from a PDE. Our motivating application is flow monitoring with Electrical Impedance Tomography (EIT). The idea of such online methods is to take single steps of of standard optimisation algorithms, on each time index; each data frame. A predictor, based on problem dynamics, is used to transfer iterates one from time index to the next one. If we monitor a fast flow with a correspondingly fast measurement modality, such as EIT, basic methods are unable to solve the PDE before new data arrives. Our idea, then, is to not solve it, and instead, on each iteration, each time index, take single or few steps of standard iterative solvers towards the solution of both the PDE and an adjoint PDE. This is what ``single loop'' refers to. To the overall problem, we apply standard online optimisation methods, at the outside developed for exact gradients $\nabla E_k(x^k)$ of the iteration-dependent data fidelity $E_k$ that incorporates the PDE. We replace the gradient by a single-loop estimate $\tilde\grad E_k(x^k)$ that satisfies standard smoothness properties with summable errors. This allows standard regret proofs to go through. Our numerical experiments on dynamic EIT validate the theoretical predictions and highlight the potential of the proposed approach for the real-time solution of PDE-constrained dynamic inverse problems.
Published: 2026-06-09 14:48:49
Authors: Tom Carroll, Clifford Gilmore
Categories: math.FA
Abstract:
The concept of $ρ$-frequent hypercyclicity is introduced in order to provide a refined form of frequent hypercyclicity. This is achieved by replacing the denominator in the definition of frequent hypercyclicity by an appropriately chosen calibration function $ρ$. A $ρ$-Frequent Hypercyclicity Criterion is determined and the $ρ$-frequent hypercyclicity of weighted backward shifts is investigated.
Published: 2026-06-09 14:46:47
Authors: Adam Nordling
Categories: cs.LG
Abstract:
Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence. Although prior work demonstrated the viability of geometric perturbation, it relied on naive random selection, leaving a critical gap in understanding which trajectories should be augmented for maximal benefit. This thesis addresses this gap by developing a systematic and scalable framework to evaluate five systematic selection strategies: Outlierness, Diversity, Representativeness, Uncertainty, and Random selection. These strategies were rigorously tested across four datasets covering animal behavior (Foxes and Starkey), maritime traffic (AIS), and urban traffic (Car) using a suite of linear and non-linear machine learning models. As part of this evaluation, an Optuna-based hyperparameter optimization loop was integrated to empirically identify the best-performing augmentation parameters for each dataset within the explored search space. The results indicate that, while systematic selection is not a universal solution, it offers distinct advantages over the random baseline. Systematic strategies, particularly Outlierness and Uncertainty, demonstrated higher stability and were less prone to performance degradation observed with random sampling in dense datasets. However, the findings also reveal that the value of augmentation is strictly conditional. Visual analysis via UMAP demonstrates that while systematic augmentation successfully repairs topological fragmentation in sparse datasets, it can act as a corrupting noise signal in high-quality, dense datasets. Furthermore, the study identified physical limitations in high-velocity domains, where standard perturbation techniques lead to divergence in feature space...
Published: 2026-06-09 14:33:34
Authors: Fiza Naseer, Javad Khan, Muhammad Yaqoob, Alexios Mylonas
Categories: cs.SE
Abstract:
Software vulnerability detection is increasingly important as modern applications combine multiple programming languages. This paper presents an early comparative evaluation of BERT, RoBERTa, and CodeBERT for binary vulnerability detection across HTML, Python, JavaScript, and PHP using the CVEFixes dataset and language-wise three-fold stratified cross-validation. The results show clear performance differences across languages, indicating that multilingual vulnerability detection requires more language-aware and robust transformer-based modelling strategies.
Published: 2026-06-09 14:30:30
Authors: Victor Daniel Reyes Dreke, Rahul Rane, Aleksandra Lekić
Categories: eess.SY
Abstract:
Multi-terminal DC (MTDC) transmission systems based on modular multilevel converters (MMCs) are a key component of the envisioned future energy sector, where sustainability and efficiency are increasingly prioritized. To ensure their reliable operation, MMC currents must be regulated safely and rapidly under a wide range of uncertain operating conditions. Consequently, the design of current controllers faces a fundamental challenge: achieving fast transient response while maintaining robustness against uncertainties. This paper addresses this challenge by proposing a linear matrix inequality (LMI)-based design framework that leverages Lyapunov stability conditions to synthesize a less conservative static state-feedback controller. The proposed design method explicitly accounts for system constraints, including input saturation and overcurrent limits. The proposed method effectiveness is assessed on the CIGRE MT-HVDC benchmark, simulated in RTDS, and compared with existing methods.
Published: 2026-06-09 14:21:45
Authors: Vojtěch Staněk, Veronika Jirmusová, Anton Firc, Kamil Malinka, Jakub Reš, Martin Perešíni
Categories: cs.SD, cs.AI, cs.CR, cs.LG
Abstract:
Deepfake speech detectors often output a single score without explaining why an audio sample is flagged, where in the signal the evidence lies, or what cues drive the decision. We propose an audio-native explainability pipeline using Integrated Gradients on time-aligned self-supervised representations to localize decision evidence over time. We apply the proposed method to three WavLM-based detectors (AASIST, CA-MHFA, SLS) on ASVspoof 5 and manually annotate the highest-attribution regions to provide a semantic meaning of the most important cues. Despite similar performance, the detectors rely on different cues: AASIST emphasizes non-speech/environment cues, CA-MHFA focuses on localized phoneme artifacts, and SLS relies on word boundaries and spectral integrity. We move beyond speculative reasoning and validate our findings by causal masking of the primary detector cues. Observed performance degradation further supports the explained detector semantics.
Published: 2026-06-09 14:13:58
Authors: Sunil Khatri, Steven Landgraf, Markus Ulrich, Simon Reiß
Categories: cs.CV
Abstract:
Visual in-Context Learning (VICL) aims at making progress towards adaptive vision models, that can -- based on a few examples -- adapt to a new task at test-time. With the history of in-context learning in natural language processing research, where large, parameter-heavy models are in use, one pathway that current VICL methods take is model- and data-scaling as key ingredients. Yet, it is not clear, whether these ingredients are the key for in-context learning to take shape in vision models. To stress-test such large models, we challenge them with an extreme counterexample: we train a tiny visual in-context model with merely $1$ million parameters and a modest amount of $70,000$ images. We compare the results of this severely capacity capped tiny model to $7,000\times$ larger VICL models in different adaptive settings, (1) on image data with small distribution shifts, (2) on unseen task encodings and (3) on a completely new task, i.e., the setting VICL envisions. With the chasm of training resources between the tiny- and large models, our experiments showcase a lack in how adaptive capabilities are measured, with respect to how tasks are encoded, which tasks were used in pre-training and the choice of metrics. These gaps in current VICL benchmarking underscore a need for innovation in evaluation of adaptive capabilities.
Published: 2026-06-09 14:13:54
Authors: Bulat Nutfullin, Vladimir Evgrafov, Dmitry Namiot
Categories: cs.CR
Abstract:
Multimodal large language models (MLLMs) now appear in safety-critical applications, but the visual channel leaves them open to adversarial attacks that predominantly text-oriented safety alignment addresses only in part. Retraining a model for each new vulnerability class is usually too expensive to be practical. We report a comparative empirical evaluation of three inference-time defense methods and their combinations, run on eight models from the InternVL and Qwen-VL families across seven safety benchmarks that span four attack classes and total 9,000 evaluation samples. Every figure below comes from the same unified proxy classifier. Five findings emerge from the evaluation. First, within the evaluated models and benchmarks, no single defense dominates across all settings: what works depends on the model's baseline safety and on the attack type. Second, combining defenses directly drives benign-query over-refusal to 97-100% across all eight evaluated models, and SmoothVLM on its own reaches 99.2-100%. Third, a simple safety prompt keeps utility largely intact (0.0-18.2% over-refusal across all eight models, five of them below 7%, although two exceeded 15%) while still yielding moderate safety gains. Fourth, different attack classes expose different weaknesses across the evaluated setup, which is why multi-benchmark evaluation matters. Fifth, in a preliminary whitebox test on two models (n=20), text-level defenses suppressed a PGD visual attack that had succeeded without any defense: the defenses act at the output stage, where gradient optimization has limited direct leverage in the tested configuration. Read together, these results argue for adaptive defense selection rather than a single fixed defense configuration.
Published: 2026-06-09 14:13:40
Authors: Xuan Han, Yihao Zhao, Mingyu You
Categories: cs.CV, cs.AI
Abstract:
Subject Customization is a foundational task in modern image generation. By providing a few reference images and a text prompt, users can generate images of a specific object in any desired scene. However, existing methods still struggle to achieve effective pose control for customized subjects. In practice, they often exhibit inaccurate poses or inconsistent cross-pose appearances. These limitations suggest that understanding objects in a volumetric manner remains a significant challenge for 2D-native backbones. To address this challenge, we propose Pose-ICL, a tuning-free framework that leverages 3D-aware In-Context Learning (ICL) to directly adapt to new subjects through multiple paired image-pose references. Its core mechanism,Surface-Anchored Position Embedding (SAPE), equips the model with explicit 3D awareness by anchoring image tokens to the surface coordinates of a volumetric bounding box. Dedicated refinements ensure its seamless compatibility with existing DiT models. Extensive evaluations on both 3D assets and real-world subjects demonstrate that Pose-ICL significantly outperforms current methods in both pose accuracy and identity consistency.
Published: 2026-06-09 14:09:13
Authors: Yinchen Tian, Huan Li, Muyao Peng, Xi Wang, Yan Wang, You Yang
Categories: cs.RO
Abstract:
Robotic manipulation has been widely applied in industrial scenarios. Compared with single-arm manipulation, bimanual manipulation is equipped with multiple cameras to capture information from different viewpoints. However, existing multi-view policies encode each view independently or fuse view features shallowly, resulting in limited sharing semantic perception and unreliable spatial awareness. In this paper, we propose \textbf{MV-Actor}, a multi-view perception framework that builds a unified semantic-spatial representation for bimanual manipulation. First, MV-Actor performs Multi-view Semantic Interaction to share semantic perception across views. Then it uses Semantic-Spatial Token Interaction to ground visual semantics with feed-forward reconstruction model features and acquire reliable spatial awareness. Finally, a Guided Metric Depth Repair module refines degraded sensor depth to provide more reliable metric anchors under consumer-grade depth noise. In simulation experiments conducted on the PerAct2 bimanual benchmark, MV-Actor achieves a state-of-the-art average success rate of 87.8\%. In real-world evaluations with more frequent viewpoint changes and unstable consumer-grade depth, MV-Actor outperforms both RGB and RGB-D baselines, further demonstrating the benefit of sharing semantic perception and reliable spatial awareness for bimanual manipulation.
Published: 2026-06-09 14:03:04
Authors: Juan Amboage, Pablo Monteagudo-Lago, Ian Colbert, Giuseppe Franco, Nicholas Fraser
Categories: cs.LG, cs.AI
Abstract:
Post-training quantization (PTQ) compresses large language models by mapping weights to low-bit representations. The scaling factor that defines the quantization grid is typically chosen using simple, data-free heuristics. In this work, we present PiSO (Piecewise Scale Optimization), an algorithm that leverages calibration data to compute the optimal channel-wise weight scales exactly and efficiently under round-to-nearest quantization. PiSO partitions the scale search space into finitely many intervals on which the objective admits a closed-form minimizer. We extend PiSO to group-wise quantization via principled heuristics and propose effective strategies for interleaving scale optimization with error correction. Experiments on Llama and Qwen models across multiple model sizes and target weight bit-widths demonstrate consistent improvements in perplexity and downstream zero-shot accuracy, both standalone and combined with error correction. In particular, we observe increased benefits as the target bit-width narrows and quantization becomes more challenging.
Published: 2026-06-09 14:02:40
Authors: Stanisław Narębski, Tomasz Komendziński, Tomasz M. Rutkowski
Categories: q-bio.NC, cs.LG
Abstract:
Early detection of neurodegeneration remains a critical clinical challenge. This study investigates whether sleep EEG signal criticality, quantified via Multifractal Detrended Fluctuation Analysis (MFDFA), serves as a non-invasive biomarker for future cognitive decline. We analyzed longitudinal data from the National Sleep Research Resource (NSRR) Study of Osteoporotic Fractures (SOF) cohort, comparing baseline sleep EEG dynamics between women who remained cognitively normal and those who later progressed to dementia-related impairment ($3MS < 78$).Our results reveal significant group-level differences in Hurst exponent $H(q)$ distributions, particularly during non-REM stages N2 and N3. Cognitively healthy individuals exhibited signal dynamics significantly closer to an optimally critical state across all electrode locations ($p \leqslant 0.001$), supporting the Brain Criticality Hypothesis. Supervised UMAP projections confirmed clear spatial separation between groups throughout the overnight sleep architecture.The dementia group demonstrated a shift in DFA exponents toward $1.0$, suggesting that a reconfiguration of scale-free neural dynamics during sleep precedes clinical symptoms. These findings highlight the potential for MFDFA-derived measures to be integrated into automated, sleep-based screening tools, enabling earlier preventative interventions during the prodromal window of dementia.
Published: 2026-06-09 14:01:49
Authors: Avi Gupta, Nilotpal Sinha, Vishnu Raj, Sambuddha Saha, Pratik Joshi, Koteswar Rao Jerripothula, Tammam Tillo
Categories: cs.CV
Abstract:
Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. While recent CIL advances have spurred significant interest across various modalities, the audio-visual setting remains underexplored. Furthermore, although foundational multimodal models like SAM-Audio encapsulate rich static priors, our empirical analysis reveals that these representations struggle in incremental settings. This work bridges this gap by integrating SAM-Audio's audio-visual priors into the CIL setting. Specifically, we leverage its dense audio and visual representations and employ a novel guided attention strategy where the audio features contextually guide the visual representations. To further mitigate catastrophic forgetting, we introduce dual-level distillation objectives at both the feature and logit levels. Extensive evaluations on audio-visual CIL benchmarks demonstrate that our approach consistently outperforms state-of-the-art methods.
Published: 2026-06-09 13:53:21
Authors: Guillaume Gay, Théo Barnouin, Marc Mongy, Guillaume Maucort, Perrine Paul-Gilloteaux, Emmanuel Faure
Categories: q-bio.OT
Abstract:
Modern biological microscopy routinely generates large and complex image datasets, including multidimensional, multimodal, and time-resolved acquisitions. While imaging technologies have rapidly evolved, data management infrastructures within microscopy facilities often remain fragmented, relying on heterogeneous local solutions that are difficult to maintain, scale, and integrate with High-Performance Computing (HPC) centers and public data repositories. To address these issues, France BioImaging (FBI), the French national infrastructure for biological imaging, has developed FBI.DATA and the associated BioImage Cloud platform. This initiative aims to provide a coordinated national infrastructure connecting microscopy facilities, centralized storage resources, HPC environments, and public bioimaging archives through interoperable and scalable workflows.The proposed architecture combines open-source technologies including OMERO for image management, iRODS for distributed data orchestration, Authentik for federated authentication, and emerging standards such as OME-Zarr and REMBI metadata recommendations. The infrastructure is designed to support the complete imaging data lifecycle, from acquisition and transfer to visualization, analysis, sharing, and long-term archiving. Beyond the technical implementation, this work presents the organizational and governance strategies required to deploy a shared national infrastructure across distributed imaging facilities. We discuss the challenges associated with interoperability, metadata standardization, sustainability, and user adoption, as well as the perspectives opened by tighter integration between imaging data and large-scale computing resources for future AI-driven bioimage analysis workflows.
Published: 2026-06-09 13:46:22
Authors: Thomas Sinclair
Categories: math.CO, math.OA
Abstract:
We give a structural account of the finite free convolutions of Marcus, Spielman, and Srivastava in terms of reproducing kernel inner products on polynomial spaces and a multilinear model over the squarefree algebra. In this model, additive convolution becomes algebra multiplication, and the nilpotent logarithm linearizes it, recovering the finite free cumulants of Arizmendi and Perales. This perspective leads to a class $\mathcal{LC}_n$ of multilinear polynomials characterized by nonpositivity of higher-order cumulants, closed under additive convolution and satisfying several key permanence properties associated with negatively dependent measures. We show that every graph Laplacian pencil belongs to this class, with higher-order cumulants given by Hamiltonian cycle counts in induced subgraphs.
Published: 2026-06-09 13:44:29
Authors: Yuanjie Zhang, Jiaojiao Li, Zhihuang Luo
Categories: quant-ph
Abstract:
High-order exceptional points (EPs) emerging in non-Hermitian systems have attracted broad interest for their significantly enhanced sensitivity to perturbations. However, quantum sensing schemes based on high-order EPs remain scarce, due to the experimental challenge of fine-tuning the system to such an extremely sensitive isolated point. Here we propose a four-channel dissipative coupling model that supports both fourth-order exceptional surfaces and second-order exceptional volumes. This non-Hermitian model can be realized in a thermal atomic system, and its complex energy spectra can be determined via electromagnetically induced transparency spectroscopy. The proposed model exhibits a characteristic fourth-order response to multiple physical quantities such as the laser detuning and the distance between optical channels, significantly surpassing the response of second-order EPs. We further reveal the sensitivity-robustness trade-off under experimental noise. Our work opens a route toward high-performance sensing leveraging higher-order EPs.
Published: 2026-06-09 13:36:55
Authors: Christopher Devik Fjeldstad, Jonas Bueie, Astrid S. de Wijn
Categories: cond-mat.stat-mech, cond-mat.soft
Abstract:
We present a new framework for extending Chapman-Enskog theory beyond the hard-sphere fluid model. Rather than relying on effective hard sphere diameters, the approach makes use of on an exchange function which can be related to the thermodynamic properties of the system. We show that two existing extensions, including modified Enskog theory (MET), fit into this new framework. Based on our approach, we propose an alternative to MET that takes into account the potential interaction energy associated with the inter-particle interactions in the fluid. The proposed expression is applied to predicting the shear viscosity of several different simulated fluid models across a wide set of densities $0.05 \leq ρ^* \leq 0.8$ and temperatures $1.5 \leq T^* \leq 4.0$ in Lennard-Jones units. The fluid models considered include both the Weeks-Chandler-Anderson (WCA) fluid and the Lennard-Jones (LJ) fluid. At low and intermediate density, here taken to be $ρ^* \leq 0.3$, we report mean relative prediction errors between $2\%$ and $4\%$ for both these. Across all densities considered, the largest mean relative errors reported are $4.4\%$ and $8.1\%$ for the WCA fluid and LJ fluid respectively. We also investigate other interaction models, including a diatomic molecular model, in order to better understand the limitations of our approach.
Published: 2026-06-09 13:36:26
Authors: Orestis Konstantaropoulos, Welf Rehberg, Mihir Kulkarni, Kostas Alexis
Categories: cs.RO, cs.LG
Abstract:
We present a generalist position control policy capable of controlling arbitrary multirotor configurations of a certain rotor count (e.g., hexarotors or quadrotors) with a single set of network weights. The policy is conditioned on a physics-grounded embodiment descriptor: a mass and inertia-normalized control allocation matrix that captures how mass-normalized motor thrusts generate linear and angular accelerations in the body-frame. To train the policy, we sample from a broad distribution of arbitrary multirotor configurations, including non-planar and asymmetric systems, and optimize a single, compact network using Proximal Policy Optimization. Training requires only five minutes on an RTX 3090 GPU using a custom NVIDIA Warp-based dynamics simulator. Through extensive simulation experiments, we show that embodiment conditioning enables robust generalist control across arbitrary morphologies. We demonstrate zero-shot real-world transfer of this generalist policy on three diverse hexarotor systems, including a planar robot, a partially symmetric non-planar system, and a random asymmetric, non-planar configuration.
Published: 2026-06-09 13:31:13
Authors: Polydoros Giannouris, Mohsinul Kabir, Sophia Ananiadou
Categories: cs.CL, cs.AI
Abstract:
LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment. However, many real-world misleading communications do not depend on false statements, rather, they arise from selective treatment of true material facts: omitting adverse evidence, softening unfavorable details, emphasizing favorable details, or replacing precise qualifications with vague language. Existing benchmarks largely miss this subtler and arguably more dangerous failure mode. We introduce JANUS, a benchmark for measuring goal-conditioned pragmatic distortion in fact-grounded LLM outputs. Each scenario in our benchmark provides a fixed pool of favorable and adverse facts and compares a neutral condition against a goal-directed condition, such as increasing adoption, enrollment, approval, or support, despite potential harm to directly affected individuals or groups. Because all outputs are constrained to use the same fact pool, JANUS isolates misleading net impressions from hallucination and fabrication. JANUS contains 160 scenarios across 8 domains, with each scenario paired with neutral and goal-conditioned prompts and annotated material facts. Extensive experiments across 12 LLMs reveal consistent goal-conditioned distortions, demonstrating that current models remain sensitive to incentive and framing objectives and lack robust safeguards against selectively misleading communication. We publicly release our corpus and code for future research.
Published: 2026-06-09 13:31:05
Authors: Pramod Shukla
Categories: hep-th, hep-ph
Abstract:
Fibre inflation, Poly-instanton inflation and (Loop) Blow-up inflation are among the most popular Kähler moduli based inflationary models realized in the standard LARGE volume scenarios (LVS). In this article, we present a unified framework in which all these three LVS inflationary models can be realized by using (different orientifolds of) a single Calabi-Yau (CY) threefold. In fact, the desired CY threefold needs to have a K3- or ${\mathbb T}^4$-fibration structure along with two diagonal del Pezzo divisors, and a so-called `Wilson' divisor which corresponds to a surface realized as a ${\mathbb P}^1$ fibration over ${\mathbb T}^2$s. For classification purpose, we perform a detailed scan of the CY geometries with $1 \leq h^{1,1}({\rm CY}) \leq 6$ that arise from the triangulation of the four-dimensional reflexive polytopes of the Kreuzer-Skarke database. In this regard, after scanning around 100,000 CY geometries and the corresponding topologies of around a million of toric divisors, we find two CY threefolds satisfying these requirements for $1 \leq h^{1,1}({\rm CY}) \leq 4$, while there are 14 and 45 candidate CY geometries for $h^{1,1}({\rm CY}) = 5$ and $h^{1,1}({\rm CY}) = 6$, respectively. We discuss the extended applications of such CY threefolds for cosmological model building in string theoretic frameworks.
Published: 2026-06-09 13:24:36
Authors: Donghwan Lee
Categories: cs.LG, cs.AI
Abstract:
Periodic hard target updates are among the most common stabilization devices in modern deep Q-learning. Recent studies suggest that target updates can improve stability in Q-learning with function approximation, including linear function approximation. We introduce and analyze the so-called $λ$-target update, obtained by averaging the $m$-periodic target update maps with $λ$-geometric weights $(1-λ)λ^{m-1}$, $λ\in [0,1]$. The endpoint $λ=0$ recovers the one-period target update, while the continuous endpoint $λ\uparrow1$ recovers projected Q-value iteration. We study this mechanism for Q-learning with linear function approximation, namely linear Q-learning, using a switching-system model and related tools. For clarity, the paper treats a deterministic version; the formulation extends to stochastic reinforcement-learning settings.
Published: 2026-06-09 13:18:32
Authors: Leandro Candido
Categories: math.FA
Abstract:
In this paper we develop an abstract theory of derivatives for Banach spaces based on objects that we call \emph{bidual assignments}. This framework encompasses both the Semadeni derivative and the recently introduced Semadeni--Pełczyński derivative. More generally, suitable ideals of subsets of the dual space give rise to a broad family of derivatives within this setting.
We establish addition and product formulas for these derivatives, showing that they behave naturally with respect to direct sums and injective tensor products. As an application, we compute iterated derivatives for a number of spaces $C(K)$ associated with scattered compacta, including scattered compact lines and compact trees.
As a further application, we establish classification results for spaces of the form $C(K^n,X)$. In particular, for uncountable ordinals $α$ and $β$, an integer $n\geq 1$, and Banach spaces $X$ satisfying suitable rigidity assumptions, we prove that \[ C([0,α]^n,X)\sim C([0,β]^n,X) \quad\text{if and only if}\quad C([0,α])\sim C([0,β]). \] This extends Kislyakov's classification of spaces $C([0,α])$ and its vector-valued extension due to Galego to finite powers of ordinal intervals.
Published: 2026-06-09 13:17:27
Authors: Yusuf Sahin, Ahmed Rockey Saikia, Volkan Cevher, Paolo Favaro
Categories: cs.CL, cs.AI
Abstract:
Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration, but this parallelism is fragile: positions that are individually confident may be unsafe to commit together when their predictions are coupled. Existing training-free samplers such as Top-\(k\), Fast-dLLM, and EB-Sampler mainly control how many tokens to reveal, while often ranking candidates by token-wise scores that ignore interactions within the selected set. We propose ADAS, a training-free reranking rule for parallel masked diffusion decoding. ADAS leaves the base sampler's stopping rule unchanged and modifies only subset construction: it greedily discounts a candidate when it attends strongly to already selected positions whose predictions remain uncertain. Unlike graph-constrained methods that turn attention into hard compatibility constraints, ADAS keeps attention continuous and uses it as a soft marginal penalty. Across LLaDA-8B-Base and Dream-7B-Base on GSM8K, MATH500, HumanEval, and MBPP, plugging ADAS into Top-\(k\), Fast-dLLM, and EB-Sampler improves low-NFE performance at matched denoiser evaluations by \(9.11\) and \(10.46\) percentage points on average, respectively, with \(3.1\%\) per-forward runtime overhead. These results show that soft attention-discounted reranking is a simple and modular way to improve quality in highly parallel decoding for masked diffusion language models.
Published: 2026-06-09 13:16:30
Authors: Wen-Jing Wei, Feng-Li Yan, Ting Gao
Categories: quant-ph
Abstract:
In this work, we investigate controlled quantum teleportation in the presence of noisy channels acting on the three-qubit resource state. We employ a series of generalized noisy channels that bridge the dephasing channels and amplitude damping channels while encompassing extensive intermediate scenarios. We provide an in-depth analysis of the degradation of the maximal average fidelity and the optimal average fidelity in controlled quantum teleportation induced by such noisy channels by deriving the analytical expression and examining several special cases. The analytical expression shows that attaining the optimal average fidelity requires Charlie's cooperation in performing a measurement at suitably chosen angles, and is also related to the initial state and the channel parameters. Our analysis reveals that the optimal average fidelity does not always decrease monotonically with the evolution parameter, instead, it first decreases and then increases. This non-monotonic behavior depends on the entanglement of the initial resource state, as well as on the parameters of the channel traversed by the first qubit.
Published: 2026-06-09 13:02:55
Authors: David Sivy, Jozef Strecka
Categories: cond-mat.stat-mech
Abstract:
The spin-1/2 Ising-Heisenberg model on the extended Lieb lattice in a magnetic field is exactly mapped onto an effective spin-1/2 Ising model on the square lattice. The ground-state phase diagram comprises the quantum antiferromagnetic (QAF), quantum monomer-dimer (MD), classical ferrimagnetic (FRI), and classical ferromagnetic phase. The MD-FRI ground-state phase boundary extends to finite temperatures as a dome-shaped surface of discontinuous thermal transitions bounded by a line of Ising critical points. The QAF phase is enclosed by a surface of continuous thermal transitions evolving from the QAF-MD and QAF-FRI ground-state phase boundaries. Monte Carlo simulations fully confirm the existence and nature of both continuous and discontinuous thermal phase transitions obtained by exact and approximate analytical calculations.
Published: 2026-06-09 13:00:56
Authors: Jiawei Gao, Chaoqi Liu, Peilin Wu, Haonan Chen, Yilun Du
Categories: cs.RO, cs.CV
Abstract:
Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically employ imitation learning policies that output target end-effector poses tracked by low-level impedance controllers. In these systems, forceful interactions are either implicitly realized through steady-state tracking errors or explicitly commanded using wrist force/torque or tactile sensors. However, implicit approaches generalize poorly across object weights, while explicit approaches require specialized hardware and increase system complexity. In this work, we propose IMPACT, a framework that decouples these forceful tasks into task-planning and internal-model-based predictive control. Extensive simulation and real-world experiments demonstrate that the proposed framework achieves higher success rates and improved generalization to unseen object weights, as well as better safety and energy efficiency.
Published: 2026-06-09 12:46:35
Authors: Yifeng Sun
Categories: cs.AI
Abstract:
Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs. Standard global evaluation approaches suffer from "context poisoning," in which superficially plausible statements mask subtle logical flaws, leading to hallucination or over-skepticism. To address this, we shift from global evaluation to strict step-level verification: our framework maintains detailed context for each deduction step and strictly constrains the sources of applied theorems. We evaluate on a carefully curated adversarial diagnostic suite of research-level proofs drawn from the FirstProof challenge. A systematic ablation study demonstrates that these deductive constraints are indispensable, as unconstrained global prompting consistently fails to localize subtle logical errors. Beyond outperforming global evaluation, our approach fundamentally alters the failure taxonomy. Error analysis reveals that, rather than exhibiting severe logical hallucinations, remaining rejections are primarily instances of "pedantic hyper-rigor" stemming from unstated domain conventions, effectively exposing implicit ambiguities within the expert benchmark itself. Our findings suggest that prompting agents to organize their verification notes in a cautious, human-mathematician-like manner can substantially improve their ability to distinguish rigorous proofs from flawed ones, with the potential to strengthen agentic reasoning on frontier mathematical concepts that the base model does not already know well, and to lay a theoretical foundation for future automated proof-review systems. Code and prompts are available at GitHub.
Published: 2026-06-09 12:44:02
Authors: Peiyang Yu
Categories: math.FA
Abstract:
In this paper, we study some refined properties of uniformly quasi-greedy bases. In particular, we characterize the stability of uniformly quasi-greediness under scalings with bounded quotient and show that unconditionality is sufficient to guarantee this property. For the "isometric" case, i.e., when the uniformly quasi-greedy constant is $1$, we prove that 1-uniformly quasi-greediness implies disjointness if the underlying norm is strictly monotone. We also introduce a stronger notion of quasi-greediness by requiring uniform order boundedness of the greedy sums over greedy orderings. The characterization and properties of such bases are derived along the lines of uniformly quasi-greedy bases.
Published: 2026-06-09 12:40:47
Authors: Bharghav Kota, Yulia Sandamirskaya
Categories: cs.CV
Abstract:
Existing hand detection algorithms work on images and the detection rate is restricted by the frame rate of the camera. In hand detection applications for moving robotic systems, conventional cameras cause motion blur, especially in darker lighting conditions. We can leverage the use of event-based cameras which possess a high dynamic range, high temporal resolution, and low power consumption. Recent work has shown that using a stereo setup of an event-based and a frame-based camera improves detection accuracy and the bandwidth-latency tradeoff. The main bottleneck in using event-based cameras in object detection and recognition tasks is a relatively low amount of training data. In this work, we propose a methodology and an exemplary synthetic event-based hand dataset from an egocentric, first-person view perspective. The data is synthesized from the existing RGB Egohands dataset with the v2e toolbox. Parameters of the v2e toolbox are varied to provide versions of the dataset with different lighting conditions and scales. Ground truth detections are generated with a fine-tuned YOLOv8 model which is applied to the RGB images in the Egohands dataset and interpolated on the high-temporal resolution events. We use the multi-modal dataset to perform hand detection with existing object detection algorithms which use a multi-modal setup of event and RGB cameras and demonstrate performance comparable to the state-of-the-art.
Published: 2026-06-09 12:36:52
Authors: S. A. Matveev
Categories: math.DS, cond-mat.stat-mech
Abstract:
In this work, we review and revisit the generating function techniques that provide exact analytical solutions for aggregation and fragmentation equations across several physical regimes including spontaneous and collisonal shattering. For discrete coagulation-fragmentation equations with size-independent rates under monodisperse initial conditions, we show the derivation of sevaral explicit closed-form solutions. We also briefly report the exact solutions for continuous, three-particle, $D$-particle collisions and two-component generalizations. Source-driven aggregation yields steady distributions featuring a universal $s^{-3/2}$ power-law decay and a cutoff mass scaling $s_{*} \sim t^{2}$.