Published: 2026-07-06 17:59:58
Authors: Shiyuan Feng, Huan-ang Gao, Haohan Chi, Hanlin Wu, Zhilong Zhang, Zheng Jiang, Bingxiang He, Wei-Ying Ma, Ya-Qin Zhang, Hao Zhou
Categories: cs.LG, cs.AI, cs.CL
Abstract:
Reinforcement learning with verifiable rewards (RLVR) is a powerful recipe for improving language-model reasoning, but it is expensive to repeat on every new strong model because the target model must generate many rollouts during training. As models scale, post-training itself becomes a bottleneck. We study a weak-to-strong alternative: run RL on a smaller model where rollouts are cheaper, then reuse what that RL run learned to improve a stronger target model. Directly distilling the post-RL weak teacher is not enough, because the teacher's final policy mixes useful RL gains with the limitations of the smaller model. We propose Direct On-Policy Distillation (Direct-OPD), which transfers the teacher's RL-induced policy shift instead. Direct-OPD compares the post-RL teacher with its own pre-RL reference and treats their log-ratio as a dense implicit reward for the student. In plain terms, the checkpoint pair tells us which actions RL made the weak model more or less likely to take, and Direct-OPD applies that signal on the stronger student's own on-policy states. This directly reuses the weak model's RL supervision signal without training an explicit reward model or running sparse-reward RL on the target model. Empirically, Direct-OPD consistently leverages weaker teachers to improve stronger target models; notably, it boosts Qwen3-1.7B from 48.3% to 62.4% on AIME 2024 in just 4 hours on 8 A100 GPUs. It outperforms step-matched direct RL and enables the sequential composition of multiple policy shifts. Our results show that RL outcomes can be reused across model scales as implicit reward signals, not merely as final models to imitate.
Published: 2026-07-06 14:37:51
Authors: Charley Schaefer, Mark Leake
Categories: physics.bio-ph
Abstract:
Phase-separated liquid droplets organize molecules in cells, but the underlying physical principles differ from abiotic mixing and quantitative rules in living systems remain poorly understood. The pyrenoid -- a liquid-like organelle that enhances photosynthetic carbon fixation in algae and hornworts -- provides an unusually tractable model system. Here, we review recent advances in our understanding of pyrenoids from the perspective of biophysics. We highlight how reaction-diffusion models connect compartment architecture to catalytic performance, how soft matter theories link molecular interactions to condensate assembly, and how modern experimental methods enable these predictions to be tested quantitatively. Recent studies suggest that pyrenoid function may be described by a small number of effective
transport and reaction processes, while condensate assembly can be understood through molecular design parameters and thermodynamic constraints. Together, these findings establish the pyrenoid as a powerful system for investigating catalytic compartmentalization, biomolecular self-organization and the emergence of effective physical descriptions in living systems.
Published: 2026-07-06 14:27:56
Authors: Fabian Göttsch, Oriol Font-Bach, Andreas Benzin, Dennis Osterland, Andre Puschmann, Felix-Christopher Lutz, Wilhelm Keusgen, Giuseppe Caire
Categories: cs.NI, eess.SP
Abstract:
In this paper, we present a modular open radio access network (O-RAN) consisting of the 5G Core, a central (O-CU) and distributed unit (O-DU) by Software Radio Systems (SRS) and an O-RAN radio unit (O-RU), MODRAD-SC, by Massive Beams (MB). OCUDU provides an open source 5G-compliant O-CU and O-DU solution developed by SRS, while MB's radio unit is a fully O-RAN compliant category A O-RU. According to O-RAN split 7.2a, OCUDU performs higher layer functions up to the high physical (PHY) layer, while the O-RU handles low PHY and RF functions. This results in an O-RAN-compliant 5G gNodeB. In an alternative configuration, OCUDU and MODRAD-SC operate in a software-defined radio fashion corresponding to split 8, facilitating non-real-time experiments among others. In both cases, the system provides full control over O-CU, O-DU, and O-RU. In addition, we will discuss the possibility to attach an analog beamformer to the O-RU, enabling hybrid digital-analog beamforming. The flexibility and modularity offered by OCUDU and MODRAD-SC enable the practical realization of a multitude of applications, ranging from 5G demonstrators to pre-6G experiments. The system addresses the requirements of academia and industry and is well-suited as an easy-to-use platform for experimental and practical deployments.
Published: 2026-07-06 14:23:18
Authors: Jianping Jiang, Sike Lang
Categories: math.PR, math-ph
Abstract:
For the Potts model on the Cayley tree $\mathbb{T}^d$ with branching factor $d\geq 2$, we consider the free state which is obtained as the limiting Gibbs measure with free boundary conditions. We prove that the free state is either extremal or glassy (i.e., whose decomposition into extremal Gibbs measures contains uncountably many components). As a corollary, the free state for the Ising model on $\mathbb{T}^d$ is glassy if and only if the inverse temperature $β>\operatorname{arctanh}(1/\sqrt{d})$; this generalizes a previous result by Gandolfo, Maes, Ruiz and Shlosman (2020) from very low temperature regime to the entire spin-glass regime.
Published: 2026-07-06 14:21:52
Authors: R. Pasolari, K. Kourtzanidis
Categories: physics.plasm-ph, cs.CE, physics.comp-ph
Abstract:
SoPlasmaFoam is an open-source, multi-region plasma-dielectric solver built on OpenFOAM, integrated with the PETSc linear-algebra suite (CPU and GPU back-ends), the blastAMR adaptive-mesh-refinement library (hexahedral and polyhedral meshes), and the ROUND family of high-resolution convective schemes. It solves drift-diffusion-reaction transport for charged species, coupled self-consistently to Poisson's equation explicitly or semi-implicitly, with plasma and dielectric regions joined by a monolithic multi-domain coupling for arbitrary curved interfaces. This work makes three contributions. First, a systematic assessment of convective schemes on a stiff scalar-advection problem and the positive-streamer benchmark shows that Scharfetter-Gummel is stable but excessively diffusive on coarse meshes, while ROUNDF outperforms all tested TVD limiters and is recommended for streamer transport. Second, an analysis of Poisson-transport coupling shows that fixed-point correction loops critically control accuracy, that a semi-implicit Poisson formulation does not remove this requirement, and that coupling must be tightened even when Courant and dielectric-relaxation numbers are well below unity. Third, a drift-robust wall boundary condition acting on discretized matrix coefficients is introduced, remaining accurate in the drift-dominated limit where conventional mixed-boundary mappings fail. The solver is validated against a low-pressure DC glow discharge and the positive-streamer benchmark, and its multi-region capability is demonstrated on a nanosecond surface dielectric barrier discharge. Performance analysis confirms memory-bound finite-volume scaling and shows that with AMR the solver is competitive with the fastest reported plasma codes. The framework provides a modular foundation for multiphysics simulations in plasma-assisted combustion, plasma processing, and plasma-based flow control.
Published: 2026-07-06 14:19:58
Authors: Shunrui Li, Yang Liu
Categories: hep-th
Abstract:
We establish a rigorous geometric correspondence between thermal partition functions of discrete-spectrum quantum systems with bounded ground energy and the Chern character of "virtual physical sheaf" over spacetime. By interpreting Hamiltonian dynamics as a $U(1)$-equivariant flow on the quantum phase space $\mathbb{CP}^n$ and pushforward to spacetime, we show that the finite-temperature partition function emerges as the integral of the Chern character of "virtual physical sheaf" over spacetime. The construction extends naturally to infinite dimensions through trace class guaranteed by Weyl's aspmtotic law. Using the Grothendieck-Riemann-Roch formalism, we prove pushforward invariance of the Chern character under thermal compactification on arbitrary manifolds, providing a topological foundation for thermal traces in quantum field theory. This framework unifies spectral theory with characteristic class theory, offering a geometric interpretation of partition functions based on operator-algebraic approach.
Published: 2026-07-06 14:19:15
Authors: Ahmed Farooq
Categories: physics.flu-dyn, hep-th
Abstract:
Fully developed isotropic turbulence exhibits a dual nature: a continuous, scale-invariant energy cascade coexists with discrete, intense vortex filaments. We show that this duality arises from a spontaneously broken SO(3) gauge symmetry. By identifying the specific angular momentum $\mathbf{L} = \mathbf{r}\times\mathbf{u}$ as a non-Abelian gauge connection and the radial velocity $u_r$ as a Higgs field, the turbulent vacuum is described by the SO(3) Georgi-Glashow model. When the radial strain condenses, the symmetry breaks SO(3) $\to$ U(1), generating a topological mass gap $M_W = gv$. This gap partitions the energy into a massless U(1) sector (the solenoidal background) that sustains the Kolmogorov cascade, and a massive SO(3)/U(1) sector that is confined to vortex filaments. Using high-resolution DNS data (JHTDB, $Re_λ\approx433$), we empirically verify three key predictions: (i) the energy spectra obey a strict 1:2 equipartition over the inertial range, with a sharp divergence at $M_W \approx 40$; (ii) the radial Higgs field extracted around isolated vortex cores follows the exact BPS monopole profile $H(r)=\coth(r/η)-η/r$ with $η= 0.0093$ domain units and the VEV $v = 0.338$, identifying the ubiquitous "worms" as macroscopic 't Hooft-Polyakov monopoles; (iii) the Wilson loop computed from the velocity field exhibits a clean area law $\langle W_C \rangle \sim e^{-σA}$ with string tension $σ= 0.303 \pm 0.009$, directly confirming the confining nature of the turbulent vacuum.
Published: 2026-07-06 14:18:30
Authors: Akshat Jani, Prathamesh Gadekar, Sakhinana Sagar Srinivas, Venkataramana Runkana
Categories: cs.LG, cs.AI
Abstract:
We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The workflow links problem specification, data generation, operator training, and checkpoint-based inference. A stateful input graph converts multi-turn natural-language input and user edits into validated problem specifications. The data-generation module then samples parameters, solves the configured governing-equation with FEniCSx finite-element backend, and stores the solutions as operator-ready tensors. The training and inference stages use a registry-based interface, allowing different neural operators to be trained and deployed without changing the surrounding pipeline. In the current implementation, we instantiate this interface with a multi-branch Bayesian DeepONet. Experiments on benchmark ODE and PDE tasks show that PDEFlow can construct valid specifications, generate solver-backed datasets, train neural operators across steady and transient problem classes, and provide solver-free predictions from saved checkpoints. The framework is designed for repeatable scientific and engineering workflows where many related physics configurations must be specified, simulated, learned, and queried with minimal manual intervention.
Published: 2026-07-06 14:17:59
Authors: Jerick Shi, Terry Jingcheng Zhang, Bernhard Schölkopf, Vincent Conitzer, Zhijing Jin
Categories: cs.CY, cs.CL
Abstract:
As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents that publicly commit to actions will honor those commitments. We place LLM agents in repeated $n$-player games with a three-stage protocol that separates private intent, public announcement, and final action, allowing us to identify whether each deviation from a stated announcement was already planned during private deliberation. Evaluating three frontier models across six games in homogeneous and heterogeneous groups over 10 rounds, we report two findings. First, when agents deviate from their announcements, the deviation is predominantly already stated in their private plan (exceeding 90% in the highest-deception conditions), yet this is not a fixed model property: the same model ranges from perfect honesty to near-total deviation across games. Second, different models interpret announcements incompatibly, some as binding commitments and others as cheap talk, producing payoff gaps that emerge in Round~0 and persist across all 10 rounds. Systems that combine models from different providers therefore cannot assume shared announcement semantics and require empirical testing of model interactions before deployment.
Published: 2026-07-06 14:17:03
Authors: Ruohan Xu, Faezeh Khodabandehlou, Christian Maes
Categories: cond-mat.stat-mech
Abstract:
We complete the phase diagram of the macroscopic Curie-Weiss magnet in a time-periodic external field, as a function of temperature and driving parameters. There is a regime (large enough driving amplitude and frequency, at low temperatures) where stable paramagnetic and ferromagnetic phases coexist. In particular, we present a new detailed analysis of the (nonequilibrium) specific heat, diverging at the same critical inverse temperature $β_c$ as the magnetic susceptibility. The new Curie temperature decreases with the driving, and we find critical exponent $α=1$ for $β\downarrow β_c$, and $α\simeq 0.86$ for $β\uparrow β_c$, even for small driving. A Floquet analysis shows the nature of the criticality, which is dynamical, with implications that remain unseen and are mostly impossible when the system is in thermal equilibrium.
Published: 2026-07-06 14:12:11
Authors: Yurui Dong, Shu Zou, Siqi Li, Nianchen Deng, Hongbin Zhou, Xuemeng Yang, Pinlong Cai, Licheng Wen, Xinyu Cai, Botian Shi
Categories: cs.AI, cs.CV
Abstract:
Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, production-ready mechanical assembly generation remains largely unsolved. Unlike single-part modeling, assemblies require coordinated reasoning over multiple components, functional interfaces, assembly relations, engineering principles, and physical consistency. Consequently, directly generating executable CAD code is insufficient for constructing mechanically valid and reusable assemblies. We present AssemCAD, an axiom-grounded framework for production-ready CAD assembly generation from natural language. Instead of representing an assembly as monolithic CAD code, AssemCAD first constructs an axiomatic Assembly Specification consisting of typed parts, geometry-backed ports, executable mates, and engineering axioms. Each assembly relation is explicitly grounded in one or more engineering principles, making the resulting specification interpretable, reusable, and verifiable. To realize this specification, AssemCAD introduces a port- and mate-based CAD assembly library that executes symbolic assembly relations through deterministic mate transformations and validates declared interfaces using concrete B-Rep geometric evidence. Built on this representation and library, AssemCAD further supports on-demand synthesis of reusable parametric component factories for both standard and open-world geometries. Experiments on AssemBench show that AssemCAD substantially improves assembly preservation and physical validity over code-centric CAD generation baselines, while generalizing across different foundation-model backbones. By combining axiom-grounded assembly reasoning with deterministic geometric execution, AssemCAD extends Text-to-CAD from isolated part generation toward production-ready mechanical assembly design.
Published: 2026-07-06 14:10:56
Authors: Amal Akli, Melissa Akli, Cedric Richter, Mike Papadakis, Yves Le Traon
Categories: cs.SE
Abstract:
Large language models are known to be sensitive to prompt formulation. Even minor variations in wording can substantially degrade performance. This sensitivity reveals an opportunity: if prompt phrasing can harm performance, can it be used to improve it? To investigate this question, we introduce a search-based approach that identifies and evolves a set of natural language transformation rules with strong downstream effects on coding performance. We then propose DUALFIX, a staged repair pipeline that combines the evolved transformation rules with execution-feedback repair, addressing both specification-level and implementation-level failures. A key strength of our approach lies in its generality: the evolved rules are error-agnostic, reusable across problems, and transferable across models. We evaluate DUALFIX against execution-feedback repair baselines across three models on two challenging benchmarks, LiveCodeBench and APPS. Our results show that the evolved transformations fix from 10-30% of failing cases, including 12-17% of failures that execution-based repair alone cannot resolve. Overall, DualFix recovers up to 30% of baseline failures and fixes 3-5 times more failing cases than Self-Fix across all evaluated settings. Furthermore, we also show that rules evolved on one model transfer zero-shot to other models, outperforming execution-feedback repair without any re-optimization.
Published: 2026-07-06 14:04:57
Authors: C. W. J. Beenakker, J. Sánchez Férnan, J. Tworzydło
Categories: quant-ph, cond-mat.mes-hall
Abstract:
It is known that a one-dimensional (1D) quantum walk gives a local space-time discretization of the massless Dirac equation with a single quasi-energy cone (no fermion doubling at low energies), keeping the fundamental symmetries (chiral and time-reversal) of the continuum theory. We show that the analogous 2D construction is fundamentally more fragile. Local two-band quantum walks can have an unpaired Dirac cone, but the protecting symmetries then cease to be ordinary on-site symmetries: they become non-symmorphic, involving half-lattice translations, and are broken by generic spatial inhomogeneities. In particular, we demonstrate that the 2D Dirac quantum walk based on the Ho-Chalker network model can be gapped by potential scattering.
Published: 2026-07-06 14:04:24
Authors: Jin Huang, Eric Nichols, Fethiye Irmak Dogan, Hatice Gunes
Categories: cs.HC, cs.RO
Abstract:
Social robots are increasingly deployed in clinical settings to support the well-being of children, where effective support must be personalized to each child. Personalization, choosing the robot action best suited to each child, can be framed as a recommendation problem, and a recently proposed recommender-system framework for social robots offers a principled approach through user profiling, ranking, and responsible computing. Instantiating it, however, is blocked not by the model but by the data, which is hard to gather. A child's state shifts within and across visits, so no fixed description of the user holds. Within a session, the few signals of whether the robot's actions helped are weak and indirect. Across sessions, children are rarely seen more than once, and anonymization breaks the identity needed to link visits. Because care cannot be randomized, existing data is observational, biased toward whatever was already done. Each is a familiar recommender-system problem, and we propose four data principles in response: an integrated profile, effectiveness signals, linkable coverage, and an exposure record logged at collection time. We identify which of these principles each capability requires, and frame them as concrete guidelines for data collection.
Published: 2026-07-06 14:00:49
Authors: Xue Yao, Zehua Zhang, Jiatong Liu, Yongqiang Tian
Categories: cs.SE
Abstract:
Empirical software engineering studies frequently build datasets from GitHub issues and pull requests. In many projects, researchers inspect pages in a browser, copy selected fields into spreadsheets, keep side notes in separate documents, and later run scripts to normalize or export the data. This workflow is flexible, but the page evidence, the research codes, and the rationale behind each decision end up spread across tabs and files, which leaves provenance, update tracking, and multi-reviewer labeling hard to audit.
RepoTrace is a browser-assisted research tool that collects GitHub issue and pull-request evidence into a local SQLite-backed workspace. It combines a Chrome side-panel extension, an Express backend, and a React dashboard to capture page snapshots, comments, labels, notes, screening and labeling decisions, refresh history, and scoped exports, keeping the source evidence and the research interpretation linked together.
A validation pass collected and checked 20 Matplotlib issues across two study projects. The resulting dataset preserves 22 snapshots, 38 comments, 20 research notes, 98 annotations, 20 screening reviews, 20 fix-evidence entries, and 4 simulated unresolved consensus conflicts. The results show that RepoTrace can support a complete local evidence-collection workflow for manually constructed GitHub issue and pull-request datasets.
Published: 2026-07-06 14:00:22
Authors: Bangwei She, Congying Wang, Jin Zhao
Categories: math.NA
Abstract:
We study a Marker-and-Cell (MAC) scheme for the barotropic Euler system. First, we apply the recently developed Lax-type convergence theorem to show the convergence of the MAC scheme to i) a dissipative weak solution unconditionally and ii) a strong solution as long as it exists. Second, We derive relative energy error estimates up to the lifespan of a strong solution, without assuming uniform boundedness of the numerical sequence. Additionally, assuming the boundedness of the numerical solutions, we obtain the optimal relative energy rate of 1, corresponding to a convergence rate of 1/2 for the numerical solutions. Finally, we corroborate our theoretical results by numerical experiments.
Published: 2026-07-06 13:52:52
Authors: Zhiyang Tan, Piet W. Brouwer
Categories: quant-ph, cond-mat.mes-hall, math-ph
Abstract:
Matrix Brownian motion provides a powerful framework for studying crossover ensembles in quantum chaos and quantum transport, as well as thermalization and information scrambling in many-body dynamics. Here, we develop a unified diagrammatic framework to characterize Brownian ensembles for orthogonal and symplectic random matrices, which describe systems with particle-hole symmetry. We compute polynomial averages up to fourth order and construct an orthogonally invariant interpolation for the disconnected $\mathrm{SO}^-(q)$ sector of the orthogonal group. We consider applications relating to the fields of quantum information, quantum chaos, and quantum transport.
Published: 2026-07-06 13:51:49
Authors: Useong Shin
Categories: q-fin.GN, q-fin.CP, q-fin.PR, q-fin.ST
Abstract:
This paper extends the cap-axis integral diagnostic to general characteristic axes and measures factor-model pricing errors as bridge-alpha curves. A predetermined characteristic order generates prefix portfolios; subtracting equal-exposure aggregate portfolios gives zero-investment bridges indexed by cutoff p. The null is not a pointwise alpha test on selected deciles, but a zero-curve restriction on the restricted subspace generated by the characteristic order. In 1967-2024 CRSP data, value, profitability, investment, and momentum axes show systematic sign reversals. HML and CMA overcorrect significantly, whereas RMW and UMD largely flatten their axes. Axis-level pricing errors are nearly orthogonal to maximum-Sharpe gains.
Published: 2026-07-06 13:49:22
Authors: Daniil Smirnov, Lutz Wisotzki, Tanya Urrutia, John Pharo, Ramona Augustin, Yucheng Guo, Daria Kozlova, Haruka Kusakabe, Jorryt Matthee, Ismael Pessa, Joop Schaye
Categories: astro-ph.GA
Abstract:
We present a comparative study of Ly$α$ haloes (LAHs) around low-luminosity (L$_{\mathrm{Ly}α}\lesssim 10^{42}$ erg s$^{-1}$) Ly$α$-emitting galaxies (LAEs) at very high redshifts $z\geq6$ and a reference sample at $z\sim 3$ covering a similar Ly$α$ luminosity and host galaxy stellar mass range. Using data from the Multi-Unit Spectroscopic Explorer (MUSE) at the ESO VLT, we extracted the samples such that at the different redshifts we obtain the same intrinsic surface brightness sensitivity, accounting for cosmological dimming. We detect extended Ly$α$ emission around 6 out of 18 high-$z$ LAEs in the MUSE eXtremely Deep Field (MXDF), more than doubling the number of known such objects at $z\geq6$. We obtain an only slightly higher individual LAH detection fraction of 40% among the lower redshift comparison sample. Yet the typical exponential scale lengths at $z\geq6$ are three times smaller than those at $z\sim3$. Stacking the LAEs with undetected haloes gives again drastically different results for the two samples, with a highly significant halo detection at $z\sim 3$ but no trace of extended Ly$α$ emission at $z\geq6$. We also find the Ly$α$ spectral line widths of the high-$z$ sample to be $\sim$2.5 smaller in comparison to the lower redshift objects. We discuss the potential mechanisms driving such strong changes. In a reionisation-driven scenario the higher neutral fraction in the intergalactic and circumgalactic media might lead to substantial scattering losses of escaping Ly$α$ radiation, leaving detectable only emission from the vicinity of the star-forming regions. In an alternative scenario the LAH properties might be linked more closely to the evolution of their host galaxies than previously thought.
Published: 2026-07-06 13:48:02
Authors: Elizabeth H. Mahood, Natália Komorníková, Tomáš Pluskal, Pranam Chatterjee
Categories: q-bio.BM
Abstract:
Artificial Intelligence has had a profound impact on the biological sciences, and in particular has accelerated research on protein form and function. Enzymes are no exception: a surge of predictive models have been recently developed to address a range of enzyme tasks. Models addressing enzyme-substrate (ES) or enzyme-reaction (ER) compatibility could be especially valuable for enzyme annotation, biosynthetic pathway elucidation, and biocatalyst retrieval, the central challenge of which is the identification of a true catalyst (or truly compatible reaction) among many similar candidates. While existing models report strong performance on alternative benchmarks, less is known about their capabilities in this regime. Herein, we benchmark four recently released ES and ER prediction models, using tasks and datasets tailored to this setting. We first show that two representative ES prediction models perform near random baselines across two enzyme families when considering enzymes and substrates not encountered during training. To evaluate additional models across a consistent dataset, we next assemble the largest cytochrome P450 (CYP) reaction dataset to date, 2,922 reactions across 768 enzymes, and construct a CYP ranking benchmark requiring the correct enzyme to be prioritized among all CYPs in its native organism. We again find that most models do not outperform sequence-based (BLAST) baselines even after fine-tuning. We finally adapt the bimolecular structure prediction model Boltz to ES prediction by training supervised classifiers on residue-ligand pair embeddings, and show that this approach consistently surpasses the BLAST baselines on our CYP ranking benchmark. Together, our results argue for more discovery-relevant benchmarking and suggest that interaction-aware representations from full biomolecular complexes may provide a promising basis for enzyme prioritization.
Published: 2026-07-06 13:39:00
Authors: Jean-Jacques Dubray
Categories: cs.SE, cs.FL
Abstract:
Formal verification is seldom applied to production software, because writing and maintaining a model has historically cost more than it returns. A companion study [1] extended SysMoBench [4] with a lower-cost alternative: specifications are graded against traces captured from the running system. It found that when large language models write the specifications, reliability is governed by the structure of the specification contract, not the language. This paper evaluates both on production software: the payment workflow of an operational restaurant point-of-sale system, which must keep the register, payment terminal, and payment processor in agreement. We report three results. First, the core protocol is correct relative to a hand-built, line-cited model under a precisely stated failure model. The audit found seven failure-handling gaps, nearly all with a common root cause; three were reproduced as real executions, and a patch closing them was re-checked with all failure gates enabled, after which a follow-up patch closed a defect the re-check itself exposed. Systematic extensions of the failure model (crash-restart, stale reads, two attempts) each found the windows they were designed to probe. Second, a single probe of the production payment sandbox exposed a response-shape divergence that makes an entire recovery ladder unreachable against the live API. The emulator-based audit could not detect it, because code and emulator share the same misreading: a correlated-oracle failure. Third, the companion study's central finding replicates across seven models from two vendors: contract structure, not language, governs what LLMs specify reliably. The replication concerns the ordering of contracts and the failure taxonomy, not the absolute level: only the strongest models reached the corpus ceiling, and the harder task restores discriminating power the benchmark had lost.
Published: 2026-07-06 13:37:42
Authors: Zhichao Tang, Xiang Fan
Categories: math.NT
Abstract:
We complete the classification of exceptional rational functions of degree 5 over any finite field $\mathbb{F}_{q}$ of characteristic different from 2 and 5, up to Möbius equivalence. Our approach employs the arithmetic and geometric monodromy groups, combined with the ramification structure of the associated cover and the Riemann-Hurwitz formula. The cyclic monodromy case yields precisely monomials and Rédei functions, while the dihedral case is resolved by analysing its inertia groups and branch points; this leads to Dickson polynomials and a non-polynomial family arising from rational 5-isogenies of elliptic curves. We also obtain partial classifications in characteristics 2 and 5: all cases with cyclic geometric monodromy and all dihedral cases admitting an $\mathbb{F}_{q}$-rational branch point are determined. The only unresolved cases are those with geometric monodromy group $D_{5}$ and no $\mathbb{F}_{q}$-rational branch point.
Published: 2026-07-06 13:31:09
Authors: Ho-Lam Chung, Yiming Chen, Hung-yi Lee
Categories: cs.SD
Abstract:
Technical lectures mix Mandarin speech with English technical terms. These terms carry the core meaning of the lecture, yet they occupy few characters. Character error rate (CER) therefore hides their recognition failures. We study whether lecture context helps recognize these terms. We build a term-rich Mandarin AI/ML lecture benchmark, and we define term-centric metrics that measure technical-term recognition directly. We then propose a two-pass, reference-free decoding method. The first pass runs segment-only ASR. We extract the most frequent technical terms from the first-pass hypotheses, and we prompt the recognizer with this self-built glossary in the second pass. Across five ASR backbones, the first-pass glossary raises term recall for every model and holds or lowers CER on all five. On Breeze-ASR-25 it lifts term recall from 52.50% to 60.13% while lowering CER, and a hybrid that adds a small external term list reaches 62.05% recall and 82.73% term precision. Lecture context, recovered from the model's own output, is a practical signal for technical-term recognition. Term-centric evaluation exposes errors that CER misses.
Published: 2026-07-06 13:29:28
Authors: Tao Jin, Li Xiao
Categories: cs.CV
Abstract:
Recently, diffusion models have achieved considerable success in the text-guided video editing domain. However, existing works often struggle to balance the trade-off between temporal consistency and editability in video editing, with consistency and editability typically being inversely related. To address this, we propose a high-quality video editing framework enhanced for consistency and editability, named EquiEdit, which improves coordinatively the temporal consistency and editability of the edited videos while achieving a balance between the two. In terms of temporal consistency, the proposed temporal Mamba module with a tailored temporal-aware scanning scans fused video sequences following four designed directions, effectively enhancing the inter-frame consistency of edited videos. For editability, we design a noise injection strategy based on the spectral transformation to increase editing flexibility, where the Fourier transform is used to preserve the hidden structure in the initial latent noise used for editing, ensuring inter-frame consistency of the edited video and fidelity to the input video. Extensive qualitative and quantitative experiments demonstrate the effectiveness of our method in terms of temporal consistency and editability, as well as its great fidelity to the input video itself.
Published: 2026-07-06 13:28:38
Authors: Joseph Najnudel, Truong Vu
Categories: math.PR, math.AP, math.DS
Abstract:
The dynamics of polynomial roots under repeated differentiation has recently been conjectured to converge to a limiting measure governed by specific nonlinear PDEs, the conjectures being shown in some particular settings. For rotationally invariant initial distributions, a deterministic structured sampling model placing roots on concentric circles was recently introduced by Galligo, Najnudel, and Vu. In this paper, the authors proved convergence under the technical growth condition $m_n / (n \log n) \to \infty$, where $n$ is the number of circles and $m_n$ is the number of points per circle. In this paper, we significantly improve this result by relaxing the growth condition to $m_n / \log n \to \infty$, thus allowing for regimes where the number of points per circle grows proportionally to the number of circles. The key innovation is a refined upper bound on the root magnitudes after differentiation. This sharper estimate prevents the rapid accumulation of errors over multiple differentiations, fully validating a recent conjecture regarding the robustness of the sampling scheme.
Published: 2026-07-06 13:24:53
Authors: Shunji Kotsuki, Kazuaki Yasunaga, Atsushi Hamada, Kazuhiro Yoshimi, Kenta Shiraishi, Akira Takeshima, Yoshiki Terashima, Kaya Kanemaru, Yusuke Hiraga, Takuya Funatomi, Hiroyuki Kubo, Kohei Suzuki, Masanao Ohashi, Tadashi Tsuyuki
Categories: physics.ao-ph
Abstract:
This study proposes a real-time remote-sensing-guided decision-support framework for cloud-seeding operations using high frequency geostationary satellite and ground weather radar observations. The framework integrates cloud assessment, human-in-the-loop decision support, and aircraft operation to translate high-frequency remote-sensing information into actionable guidance for seeding aircraft. We demonstrate the framework using 2.5-min Himawari-9 geostationary satellite observations and 60-s C-band phased-array weather radar (C-PAWR) observations during the preliminary dry-ice cloud-seeding field campaign conducted over Toyama Bay, Japan, in January 2026. In the 13 January case, the framework enabled the ground team to identify a developing cumulus cloud with a lifetime of approximately 20 min, communicate guidance to the aircraft, and conduct seeding immediately before the cloud began to dissipate naturally. Candidate seedable clouds were identified from Himawari-9 infrared indices, and their selection was supported by near-real-time C-PAWR observations of precipitation echoes. Because the released dry-ice amount was limited to 30 kg, this study does not attempt to attribute subsequent cloud evolution to seeding effects. Instead, the results demonstrate that rapid-scan satellite and ground radar observations can support real-time target selection and aircraft guidance for responsible, operationally feasible weather-intervention field experiments.
Published: 2026-07-06 13:21:13
Authors: Mohammad Hassan Erfani
Categories: physics.ao-ph
Abstract:
Modern machine-learning weather prediction (MLWP) has largely inherited the initial-value-problem (IVP) framing of numerical weather prediction (NWP). This inheritance leads to a dominant paradigm of learned autoregressive time-stepping and constrains how the learning problem is defined and architectures are favored. In this study we make the inheritance explicit, contrast two philosophical traditions: "scientific surrogate modeling," where machine learning (ML) is embedded within a physical system and must respect its structural constraints, and "free-form data-driven modeling," where atmospheric fields are treated as spatiotemporal sequences and models learn latent dynamics without explicit physical constraints. By reviewing the governing primitive equations, surveying recent literature, and analyzing concrete physical examples, we map each modeling paradigm to either a state-conditioned or evolution operator formulation. We conclude that principled model selection requires explicitly aligning architecture and training objectives with either the physical system structure or the statistical structure of the data.
Published: 2026-07-06 13:21:04
Authors: Mariam Bouhmadi-López, Hsu-Wen Chiang, Beñat Ibarra-Uriondo
Categories: astro-ph.CO
Abstract:
Sign-switching dark energy has recently been proposed as a minimal modification of the late-time expansion history aimed at alleviating tensions within the standard cosmological model. In this work, we investigate ECDM, a smooth realisation of this scenario, with the dark energy density gradually transitioning from a negative to a positive value. We develop a consistent formulation of the perturbation equations that remains well behaved even when the dark energy equation-of-state parameter diverges during the transition. We confront the model with a comprehensive set of cosmological observations, including cosmic microwave background measurements from Planck 2018, ACT DR6 and SPT-3G, baryon acoustic oscillation measurements from DESI DR2, Type Ia supernova distances from Pantheon+, and local Hubble constant measurement of SH0ES. The inclusion of perturbations allows us to assess the impact of the model on structure growth and CMB anisotropies, providing a more thorough test of sign-switching dark energy. Our results show that this class of models is fully compatible with current precision cosmological observations while alleviating the Hubble tension and providing a compelling modification of the late-time dynamics of the Universe.
Published: 2026-07-06 13:17:37
Authors: Madhumitha Krishnakumar, Marc Roth
Categories: cs.CC, cs.DM
Abstract:
Introduced by Lee, Ko, and Shin (VLDB 2020), a hypergraph motif is a connected subhypergraph consisting of three hyperedges whose intersections satisfy a prescribed pattern. Such patterns are represented by Venn diagrams $\mathcal{V}\in\{0,1\}^7$, indicating which of the seven regions determined by three sets must be empty or non-empty. Lee et al. designed and implemented exact and approximate algorithms for counting, in a hypergraph $G$, the motifs specified by $\mathcal{V}$; their algorithms run in worst-case cubic time in the number of hyperedges of $G$. This cubic worst case can occur even for hypergraphs of bounded rank, and already for $2$-uniform hypergraphs, that is, for simple graphs.
In this work, we give a complete fine-grained picture of the parameterised complexity of exact hypergraph motif counting with respect to the rank of the input hypergraph. We use $\tilde{O}$ to hide polylogarithmic factors in the input size. First, we show that every Venn diagram $\mathcal{V}$ admits an exact counting algorithm running in FPT-near-quadratic time, \[ f(\mathsf{rank}(G))\cdot \tilde{O}(|E(G)|^2), \] for some computable function $f$. Second, we precisely characterise when this can be improved to FPT-near-linear time. We prove that such an algorithm exists exactly for the degenerate Venn diagrams, namely those that force one of the three hyperedges to be fully contained in another. For all non-degenerate Venn diagrams, we show that no FPT-near-linear-time algorithm exists unless either the Triangle Hypothesis or the Hyperclique Hypothesis fails. Exact hypergraph motif counting is thus always fixed-parameter near-quadratic in the rank, and the degenerate Venn diagrams are precisely the cases admitting fixed-parameter near-linear time.
Published: 2026-07-06 13:17:28
Authors: Shimin Yu, Leming Shen, Jianing Zhang, Xin Li, Xianjin Xia, Yuanqing Zheng, Yaxiong Xie
Categories: cs.NI
Abstract:
The Open Radio Access Network (O-RAN) promises unprecedented flexibility through its reconfigurable architecture and AI-driven control. However, this agility exposes a critical fragility: AI models trained on one network configuration suffer significant performance degradation after an upgrade due to dramatic data drift. The standard solution, reactive retraining, is unacceptably slow, leaving the network in a suboptimal state for tens of minutes and undermining the core benefits of O-RAN's dynamism. This paper introduces RANPilot, the first framework to address this challenge through proactive AI adaptation. RANPilot constructs a lightweight "virtual O-RAN" (a trace-driven emulator) to synthesize high-fidelity training data representing the post-reconfiguration state before the physical change occurs, allowing AI models to be adapted in advance. Extensive experiments on a real-world 5G testbed demonstrate that RANPilot achieves near interruption-free AI services upon reconfiguration, reducing AI downtime by 85% to 94% against reactive baselines. By shifting the AI evolution paradigm from reactive redevelopment to proactive preparation, RANPilot explores a digital-leadoff approach to enable robust AI in reconfigurable O-RAN deployments.
Published: 2026-07-06 13:16:51
Authors: Dongyi He, Xiangkai Wang, Binbing Xu, Bin Jiang, Hongjie Yan, Weixiang Liu, Wai Ting Siok, Nizhuan Wang
Categories: cs.CV, cs.AI
Abstract:
Few-shot brain tumor segmentation remains challenging due to noisy support masks, inter-patient variations between support and query images, and the lack of pixel-wise confidence estimation. This study proposes RUFNet, a Hybrid Mamba-based few-shot framework that combines support mask refinement with uncertainty-aware posterior fusion. To preserve support-query dependencies with manageable cost, RUFNet adopts a Hybrid Mamba interaction backbone with linear complexity. To reduce support-mask noise, an Attention-Guided Mask Refinement module (AGMR) uses query features to recalibrate support masks and improve prototype consistency. To handle ambiguous predictions, an Uncertainty-Aware Posterior Fusion module (UAPF) estimates pixel-wise variance and adaptively balances few-shot predictions with query-aligned priors. On the Brain Tumor Segmentation Challenge (BraTS) 2020 dataset, RUFNet achieves Dice coefficients of 84.3% and 86.1% in the 1-way 1-shot and 1-way 5-shot settings, respectively, outperforming the compared state-of-the-art methods. These results suggest that Hybrid Mamba interaction, mask refinement and uncertainty modelling can improve the robustness of few-shot medical image segmentation. The official implementation code is available at https://github.com/hdy6438/RUFNet.
Published: 2026-07-06 13:10:13
Authors: Neeraj Karamchandani, Piyush Nagasubramaniam, Sencun Zhu, Dinghao Wu
Categories: cs.CR, cs.AI
Abstract:
Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage information, and context. While this has improved the agent's functionality and continuity across tasks, it has also introduced a new attack surface: the agent's own reasoning history. In this paper, we introduce the Forged Amplifying Rationale Memory Attack (FARMA), which poisons an agent's remembered reasoning rather than its factual knowledge. It inserts forged reasoning traces using evasive language that bypasses keyword-based defenses, then amplifies them through self-referential reinforcement that defeats consensus-based defenses. To address FARMA, we introduce SENTINEL, a layered defense pipeline to detect forged reasoning entries. Its central component is the Reasoning Guard that structurally analyzes candidate entries for forgery using five weighted signals.
We evaluate FARMA and SENTINEL across multiple agents and different LLM models with 50 trials and show that FARMA achieves an attack success rate of up to 100% under baseline conditions and is capable of defeating defense mechanisms like keyword filter and A-MemGuard. Our evaluation also shows that SENTINEL reduces FARMA's attack success rate to as low as 0% with no false positives observed across 326 benign agent traces. Our work demonstrates the need to protect not only an agent's retrieved content but also the integrity of its reasoning history.
Published: 2026-07-06 13:00:08
Authors: Irina Devyatkova
Categories: math.RT
Abstract:
Let $S(\infty)$ denote the group of finitary permutations of the set $\mathbb N:=\{1,2,3,\dots\}$. It is a countable group admitting a lot of different topologies compatible with the group structure. In particular, such topologies arise from partitions of the set $\mathbb N$ into blocks of infinite size. The corresponding categories of continuous unitary representations of $S(\infty)$ were studied by Nessonov (Sbornik: Mathematics, 2012). We propose a different approach to his classification results based on the so-called semigroup method. Some additional information is also obtained.
Published: 2026-07-06 12:56:11
Authors: Masashi Yamashita, Shu Tanaka
Categories: cond-mat.stat-mech, cond-mat.dis-nn, quant-ph
Abstract:
Large-scale quadratic unconstrained binary optimization (QUBO) formulations of constrained combinatorial optimization problems often exceed the input-size limit of present Ising machines or suffer from degraded solution quality as the number of binary variables increases. Large neighborhood search (LNS) mitigates this difficulty by sequentially optimizing restricted subproblems, but the structural factors that distinguish subproblems beyond the number of binary variables remain insufficiently characterized. In this study, we examine vehicle routing problems and compare a construction based on the vehicle routes of the current solution, denoted by LNS-K, with a construction based on QUBO variables and constraint relations, denoted by LNS-Q, while controlling the number of binary variables in the subproblems. Under the tested conditions, LNS-K obtained shorter total distances than LNS-Q in the matched-size comparisons, and the position variance, a measure of the spatial spread of the selected customers, decreased during the iterations in LNS-K. These observations suggest that subproblem design for sequential optimization with Ising machines should consider not only subproblem size but also semantic and geometric structures inherited from the current solution.
Published: 2026-07-06 12:55:21
Authors: Nikolaos Xiros, Maria-Eleni Zoumpoulidi, Georgios Paraskevopoulos
Categories: cs.CL, cs.LG
Abstract:
Although LLMs have made significant progress in mathematical reasoning, determining whether a mathematical problem is solvable remains a fundamental yet challenging capability. While recent studies have probed internal representations of model solvability beliefs, verbalization has primarily been studied behaviorally rather than as an internal representation, limiting its analysis and manipulation. We address this gap by separately probing representations of solvability knowledge and verbalization, allowing us to disentangle the two within model hidden states. Across multiple LLMs, we show that knowledge and verbalization are encoded as distinct, linearly decodable representations and that fabrication is primarily associated with changes in verbalization rather than the underlying knowledge. Prompting with unsolvability cues reduces fabrication primarily by shifting verbalization, while activation steering demonstrates that these representations can be echanistically manipulated to improve model abstention.
Published: 2026-07-06 12:52:37
Authors: Chris Angstmann, Tim Gebbie
Categories: q-fin.PR, q-fin.MF, q-fin.TR
Abstract:
We derive an operational-time variance kernel for a latent-order-book reaction boundary and use it to separate three objects usually collapsed in calendar-time volatility models: a structural boundary cumulant, a clock projection, and a pricing-measure choice. The reaction boundary is the zero of a bid--ask imbalance field. For a locally linear book, signed order-flow perturbations displace this zero through a damped Abel response kernel, so the variance of boundary increments is obtained as a finite-scale Green-function cumulant rather than introduced as a primitive diffusion coefficient. For long-memory forcing with exponent $0<γ<1$, the operational variance has a closed asymptotic form involving effective signed-forcing intensity, liquidity slope, resilience, memory, and operational coarse-graining scale. A deterministic activity clock gives the benchmark local-volatility projection. More general, non-unique clocks generate candidate calendar-time pricing systems. We argue that such projections are admissible only when the induced forward density operator and backward valuation operator remain adjoint on the same state space. Adjoint consistency is therefore a reality constraint on operational-to-calendar time projection: it disciplines non-unique time and identifies where incompleteness enters.
Published: 2026-07-06 12:52:15
Authors: Mariano Simone, Frasca Paolo
Categories: eess.SY, math.DS, math.OC
Abstract:
This paper addresses the problem of designing recommendation systems for social networks and e-commerce platforms from a control-theoretic perspective. We formulate recommendation design as an infinite-horizon state-feedback optimal control problem whose performance index rewards alignment/engagement while penalizing polarization, large deviations from an uncontrolled baseline, recommendation mismatch, control effort, and exposure disagreement across neighboring users. We derive explicit spectral conditions under which the reduced quadratic stage cost is strictly positive-definite, and we show that the failure of these conditions makes the resulting recommendation design exhibit pathological behaviors, such as unstable free modes, non-attainment of the infimum, or failure of the stationary affine synthesis.
Published: 2026-07-06 12:46:15
Authors: Fang Gao, Jiongkai Qin, Jiabao Wang, Jingfeng Tang, Ming Cheng, Hanbo Zheng, Qingbao Huang, Cheng Wu
Categories: cs.CV
Abstract:
Low-light degradation reduces image visibility and weakens structural cues that are important for visual representation and scene understanding. Existing low-light image enhancement methods mainly focus on appearance restoration, while insufficiently exploiting scene geometry to preserve structural consistency. To address this limitation, this paper proposes a Depth-guided Multi-scale Attention Network (DMSA-Net) for geometry-aware low-light image enhancement. DMSA-Net introduces depth-related structural priors into low-light representation learning through reflectance-geometry interaction. A Retinex-based decomposition module is first used to obtain illumination-invariant reflectance representations, from which depth cues are inferred to characterize scene structure under degraded illumination. A multi-scale depth-guided fusion strategy is then embedded into a hierarchical encoder-decoder architecture, where depth-aware attention adaptively integrates geometric and appearance features. Experiments on several benchmark datasets show that DMSA-Net achieves effective low-light restoration while improving structural preservation. Moreover, we construct LOL-D, a depth-augmented low-light dataset, to facilitate research on geometry-aware low-light vision.
Published: 2026-07-06 12:42:29
Authors: Alex Thomas, Bugao Zou, Shreya Sutariya, Yuhan Wang, Gabriele Coppi, Samuel Day-Weiss, Nicholas Galitzki, Kathleen Harrington, Erin Healy, Claire Lessler, Aashrita Mangu, Jeffrey McMahon, Michael D. Niemack, Edward J. Wollack
Categories: astro-ph.IM, astro-ph.CO
Abstract:
Expanded closed-cell polymer foams are widely used as thermal infrared (IR) blocking filters in millimeter-wave cameras, particularly for Cosmic Microwave Background observations. Precise knowledge of their millimeter-wave properties is essential for optimizing sensitivity. We present broadband (150 GHz - 2 THz) transmittance spectroscopy of Styroace-II and several Zotefoam filters, fitting their spectra with a radiative transfer model incorporating dielectric absorption and Rayleigh, Mie, and higher-order scattering. For a typical 5~cm thick filter stack at 280~GHz, Styroace-II exhibits ${\sim}10\%$ scattering with absorption estimated as ${\lesssim}5\%$ by effective-medium theory, while Zotefoam HD30 offers superior performance at ${\sim}3\%$ scattering and absorption likewise bounded to ${{\lesssim}0.3\%}$. Each model component is constrained at the ${\sim}0.1\%$ transmittance level for millimeter wavelengths. We observe batch-to-batch scattering variability of up to 2 percentage points in foams with multiple tested batches. Less commonly used Zotefoam formulations (LD15 and LD24) can further reduce in-band scattering to ${<}1\%$ while maintaining negligible in-band absorption and likely comparable IR blocking due to shared polyethylene absorption features and similar cell sizes. Based on this work, a filter constructed from the best measured LD24 batch has replaced the Styroace-II filter in a Simons Observatory 220/280 GHz Small Aperture Telescope.
Published: 2026-07-06 12:39:06
Authors: Alexander He, Nana Liu, Mark M. Wilde
Categories: quant-ph, cond-mat.stat-mech, cs.LG
Abstract:
Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians. Here, we apply this principle to a fundamental computational primitive of machine learning: the neuron. Specifically, by viewing a neuron as a composition of an energy function and an activation function, we quantize this model by replacing the energy function with a quantum Hamiltonian and applying the activation function to it through matrix functional calculus. This results in an activation observable that can be measured on an input quantum state. We investigate the use of these quantized neurons for function approximation, where the objective is to learn an unknown observable from labeled quantum data. For this purpose, we develop hybrid quantum-classical algorithms for training and evaluation, including procedures for measuring the activation observable and estimating gradients of the squared loss error. Our algorithms for gradient estimation rely on basic primitives like classical random sampling, the Hadamard test, and Hamiltonian simulation, and those for measuring an activation observable rely on quantum algorithms known as the power of one qumode and Schroedingerization. Numerical experiments demonstrate that our quantized neurons exhibit enhanced expressive capabilities relative to corresponding classical neurons on representative learning tasks. Our work establishes canonical quantization as a principled framework for constructing quantum machine learning primitives and provides a foundation for developing neural architectures tailored to quantum data.
Published: 2026-07-06 12:36:36
Authors: Matthieu Josuat-Vergès
Categories: math.CO
Abstract:
In a previous work, we defined the complex of cluster parking functions. On one side, they encode the type-refined enumeration of faces of the cluster complex, and on the other side, they have a reduced homology which is isomorphic to (ungraded) diagonal coinvariants. The goal of this work is to take into account the underlying dihedral symmetry. We thus have a product of a dihedral group and a symmetric group (there is a precise conjecture in the case of other finite Coxeter groups, but we focus on symmetric groups because of technicalities about diagonal coinvariants beyond this case). Under the action of the product group, the reduced homology of cluster parking functions is conjecturally isomorphic to diagonal coinvariants up to tensoring by a sign character of the dihedral group. This isomorphism can be reformulated as a dihedral sieving phenomenon. The main technical contribution is the definition of the dihedral automorphism group of cluster parking functions, and we discuss various features of the reduced homology character and its conjectural connection with diagonal coinvariants.
Published: 2026-07-06 12:36:18
Authors: Melvyn Tyloo
Categories: nlin.AO, physics.soc-ph
Abstract:
Many real-world coupled dynamical systems have the interaction structure and strength that evolve or adapt over time. Here, we investigate how one can control the state of a system by tuning its temporal interaction network. We present a framework based on nonlinear optimal control, where one has control over the coupling matrix of a dynamical system. We show how to obtain the gradient of the Lagrangian function of the system using the adjoint method. We then focus on a linear time-variant system for which we illustrate the framework. Finally, we explore how the states at the nodes can be steered to target trajectories, by controlling the coupling matrix, imposing various constraint on its structure. The workflow presented here can be leveraged to steer the dynamics of systems with artificial or engineered interaction that is tunable.
Published: 2026-07-06 12:24:58
Authors: Ashraf Naji, Mohammed Q. Shormani
Categories: cs.CL
Abstract:
This article tackles an important phenomenon in the syntax of Yemeni Ibbi Arabic (YIA), viz., wh-agreement, a phenomenon common to several languages including Greek, Indonesian, Lubukusu, Irish, etc. In YIA, wh-agreement manifests itself via agreement inflections on the Wh-Op, C, T/V, v. To account for this phenomenon, we propose an Agree across phases (AAP) approach anchored in the mechanism of Feature Inheritance (FI) in which Agree as MATCHING (AM) is a bit separated from feature valuation (FV). AM concerns Cs/vs, but FV Ts/Vs. Analyzing the agreement patterns observed between Wh-Op(erators), functional heads (precisely C, (T), v), and verbal complexes, we argue that the suffixes -eh, -uh, -nen, -um, having undergone grammaticalization process from Stannard Arabic (SA) third person pronouns, function as morphological marking of wh-agreement. Findings indicate that YIA data offer a unique empirical contribution to generative syntax, specifically concerning wh-agreement in this dialect operating via MATCHING mechanism. Our proposal straightforwardly accounts for wh-agreement cross-linguistically. This study provides further evidence that incorporating under-investigated typology provides further support for the universality of Universal Grammar (UG) by revealing how specific I-language operations reflect deeper, invariant principles of human language architecture. It concludes that the wh-agreement mechanism in YIA is more morphosyntactically robust than in languages such as Greek, Indonesian, Palauan, and Irish, providing compelling evidence for AAP as a UG approach to long-distance dependencies.
Published: 2026-07-06 12:18:13
Authors: Francesco Brandolin, Tariq Alkhalifah
Categories: physics.geo-ph, cs.AI, cs.LG
Abstract:
High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface recorded data limits resolution. Utilizing well measurements to enhance the resolution of our subsurface models is an important objective. To this end, we present a diffusion-guided framework for structurally preconditioned velocity-model reconstruction from sparse well-log information. The proposed approach combines plane-wave PDE regularization, structurally preconditioned inversion, and measurement-guided diffusion posterior sampling within a unified formulation. Local structural slopes estimated through plane-wave destruction are used both to propagate well information along geological dip directions and to guide the diffusion sampling process through a joint velocity--slope generative prior. Numerical experiments on the Volve synthetic model and the Viking Graben field dataset demonstrate that the proposed framework improves structural continuity, lateral consistency, and geological realism compared with conventional structurally preconditioned inversion approaches while maintaining computationally practical inference through DDIM sampling.
Published: 2026-07-06 12:14:04
Authors: Francisco Piñero, Cristian Franco, Hernán I. de la Cruz, Fernando L. Pelayo, Vicente Pascual, Mauro Mezzini, Jose Javier Paulet, Fernando Cuartero
Categories: quant-ph
Abstract:
The Bloch sphere is an elegant tool for representing single-qubit states. However, a widely accepted generalization for multi-qubit systems with entanglement remains absent. We propose a novel geometric model extending the Bloch sphere representation to arbitrary $N$-qubit systems using $2^N-1$ spheres. We demonstrate that any pure 2-qubit state is uniquely described by three spheres: two for individual qubits and a third encapsulating bipartite entanglement. Generalizing this, we establish an $N$-qubit parameterization through the hierarchical application of controlled rotation gates along the $Z$ and $Y$ axes. We formally prove a strict bijection between the standard state vector representation and our model's angular parameters. This framework provides an intuitive visualization of multiple entanglement, offering potential computational advantages for quantum simulators and new analytical perspectives on quantum gates.
Published: 2026-07-06 12:06:24
Authors: Takeshi Hayashida, Shunsuke Izumikawa, Kenta Kimura, Carl S. Davies, Andrei Kirilyuk
Categories: cond-mat.str-el, physics.optics
Abstract:
What distinguishes front from back? In physics, such directionality emerges only when an underlying symmetry is broken. Antiferromagnets that inherently break both space-inversion and time-reversal symmetries provide a striking example, exhibiting nonreciprocal optical responses that depend on the direction of light propagation. Beyond distinguishing antiferromagnetic domains, we show that this nonreciprocity can deterministically create them. Using mid-infrared light, we demonstrate deterministic switching of antiferromagnetic domains in the magnetoelectric antiferromagnet LiFePO4, where illumination from opposite sides selectively stabilizes opposite domain states. Remarkably, the switching persists over a broad wavelength range rather than being confined to a narrow transition-specific spectral region, overcoming the spectral and material constraints of resonance-based optical switching schemes. The broadband switching originates from the material's intrinsic nonreciprocity through optically generated heat currents. Our results establish nonreciprocity as a general principle for deterministically controlling symmetry-broken phases with light.
Published: 2026-07-06 12:02:02
Authors: Yang Li, Feng Xue, Fan Mo, Yunhao Liu, Jianhong Wang, Ying Wen, Qingrui Zhang, Shaoshuai Mou, Wei Pan
Categories: cs.RO, cs.AI
Abstract:
Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates. We formalize this as open adaptive multi-robot teaming and propose a hypergraphic-form game formulation that captures team-level cooperative relationships beyond pairwise interactions, providing a principled foundation for coordination structure inference when team composition changes dynamically within episodes. Unlike graph neural network architectures, this is a game-theoretic construct for modeling strategic interactions and payoff structures among agents. Building on this formulation, we develop the Hypergraphic Open-ended Learning Algorithm (HOLA), which progressively expands partner and environment diversity during training rather than optimizing for fixed configurations. Evaluated on cooperative pursuit with multi-drone and multi-quadruped platforms, HOLA outperforms all baselines across all three adaptability dimensions. Learned policies transfer directly to physical hardware without fine-tuning, with successful deployments on Crazyflie and Zsibot L1 platforms confirming robust real-world coordination in novel environments with unseen teammates.
Published: 2026-07-06 12:01:17
Authors: Habib Ammari, Silvo Barandun, Jiayu Qiu, Alexander Uhlmann
Categories: math-ph, math.AP
Abstract:
Spectral and transport properties of high-contrast resonator systems can be described in the subwavelength regime in terms of the so-called capacitance operator. In this paper, we consider an infinitely periodic chain of high-contrast resonators in three dimensions. The first result is a precise estimate of the off-diagonal decay rate of the capacitance operator $C$. Importantly, we demonstrate that the decay rate is long-range and critical: as $|n-m|\to\infty$, \begin{equation*} C(n,m)\sim \frac{1}{|n-m|\log^2|n-m|}, \end{equation*} which is $\ell^1$ summable but slower than quadratic. This borderline decay of the off-diagonal entries makes the present proof of Anderson localisation with arbitrary disorder, which is observed numerically in this paper, out of reach; we hope that this physical example of classical wave systems with critical long-range interactions provides new insight in the field of Anderson localisation. As the second main result, based on the off-diagonal decay estimate, we prove a strong convergence of the finite capacitance operator, which corresponds to a truncated chain, to the capacitance operator as the size of the truncated chain grows to infinity. Using this strong convergence, we improve the results of [Ammari et al., SIAM J. Math. Anal., 2023 and Bull. London Math Soc., 2025] by presenting a rigorous estimate of the convergence rate of the spectrum.
Published: 2026-07-06 12:00:01
Authors: Sopheak Sorn
Categories: cond-mat.mes-hall, cond-mat.mtrl-sci
Abstract:
We investigate surfaces of a $g$-wave altermagnet(AM) and show that they provide a platform for realizing $d$-wave altermagnetism and the associated spin-splitter functionality. Using the Kubo formalism applied to a minimal slab model, we evaluate the spin-splitter effect(SSE) by computing the spin conductivity corresponding to a transverse spin current induced by a longitudinal electric field. We find a finite SSE, absent in the bulk, that emerges from surface-induced $d$-wave altermagnetism. Strikingly, the sign pattern of the $d$-wave altermagnetism on both surfaces of the slab geometry is identical to each other, leading to additive contributions to SSE from the two surfaces, with a spin-splitter angle reaching up to 15 degrees. In addition, this response is intrinsically linked to an accompanying surface-induced weak ferromagnetism, which potentially enables control of altermagnetic domains via an external magnetic field and provides a route to optimize the SSE functionality. These results can be understood in terms of a bulk-boundary correspondence between surface states and bulk altermagnetic order parameters, where the magnetic multipolar character of the latter plays a central role. Our findings strongly suggest thin-film engineering as a viable strategy to functionalize non-$d$-wave AMs.
Published: 2026-07-06 11:53:15
Authors: Matt Himsworth, Chester Camm, Max Carey, Jack Saywell, Jonathan Woods, Vilius Atkoucius, Florence Concepcion, Konstantinos Karakostas, Hannah Brady, Doruk Tan Atila, Ellie Heywood, Alex Jantzen, Andrei Dragomir, Christopher Morley, James Bateman
Categories: physics.atom-ph, quant-ph
Abstract:
Laser cooled and trapped atoms have been the workhorse of atomic physics for the past four decades. The predominant method has been the highly versatile Magneto-Optical Trap. We describe an alternative laser trap involving a simple geometry of collimated laser beams that provides both a velocity and position dependent restoring force such that a dense cloud of cold atoms is formed. This technique produces similar atom number ($>10^6$) and density ($10^{10}$\,atoms/cm$^{3}$) to the Magneto-Optical Trap, albeit with \emph{no magnetic field}. The beam geometry is compatible with conventional sub-Doppler cooling techniques, allowing the trapped cloud to be cooled to $< 10~μ$K. We demonstrate the validity and robustness of the trap by capturing $^{87}$Rb atoms directly from the background vapor and provide a theoretical discussion of the underlying principles. This trap has many unique properties that make it highly suitable for quantum sensing, timing, and computing applications as well as a new tool in fundamental science and metrology.