Computer Vision
Audio for Sports Highlight Detection: A Comparative Empirical Study
Hao Xu, Meenakshi Sarkar, Vishnu Raj et al. (+1 more)
Sports highlight detection aims to identify the most exciting and meaningful moments from long sports
videos. While existing methods often emphasize visual or visual-
language representations, sports
videos contain rich audio cues, including commentator
speech, crowd reactions, whistles, ball impacts, and referee calls. In this work, we revisit the role of audio in sports highlight detection and ask a simple question: how far can audio alone go? We construct lightweight audio-only baselines using pretrained audio representations and compare them with visual-only and audio-visual methods on the SV-Highlights
benchmark. Surprisingly, our audio-only GRU baseline achieves strong performance and outperforms several existing audio-visual methods under our supervised evaluation setting. Furthermore, a simple audio-visual fusion baseline achieves the best performance across all metrics, indicating that audio and visual cues provide complementary information. To better understand the contribution of audio, we conduct source-separated analysis and show that vocal/commentary audio is more informative than background-only audio, while their combination performs best. We also analyze interpretable audio cues and find that highlight clips exhibit higher RMS loudness, peak loudness, and mid-frequency energy than non-highlight clips, although substantial distribution overlap indicates that loudness alone is insufficient. Our findings suggest that audio is an underexplored but highly informative modality for sports highlight detection and should be treated as a primary signal rather than merely an auxiliary cue.
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All authors Hao Xu, Meenakshi Sarkar, Vishnu Raj, David Gunawan
Hardware Architecture
Demystifying Gate-Level Localization of RTL Trojans
Navid Nader Tehrani, Azadeh Davoodi and Rasit Onur Topaloglu
Hardware Trojans are malicious modifications that compromise functionality or leak sensitive data. They pose a severe threat, particularly when inserted at the Register Transfer Level (RTL). After synthesis, these Trojans are often concealed by optimizations in gate-level netlists. Recent efforts, including the ICCAD 2025 contest, emphasize golden-chip-free detection using machine learning (ML) on labeled netlists. In this work, we show that RTL Trojans exhibit stable structural and signal-flow patterns post-synthesis, enabling effective detection through targeted heuristics rather than generic ML feature learning. We propose LoRD, a lightweight heuristic-based approach that exploits these distinctive subgraph signatures, achieving near-perfect detection and localization on the contest testcases. Com- pared to a
transformer-based ML baseline and top five teams, LoRD achieves on-average a score of 2.957 (out of 3) for Trojan- implanted designs without the data and tuning overhead.
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Artificial Intelligence
Collaborative Memory for Multi-Agent VLM Systems
Huixin Zhang, Shao-Jun Xia, Di Wang et al. (+3 more)
Vision-
language model (VLM)
agents combine specialized perception, tools, and
reasoning to address complex visual tasks. In multi-
agent settings, different
agents inspect different image regions,
video frames, or visual representations, so collaboration extends beyond distributed
reasoning to distributed perception. This makes shared visual context a central problem in VLM
agent collaboration. In this paper, we frame
memory hierarchy, cross-
agent sharing, and consistency mechanisms around the need to reconcile interpretations and update dependent
reasoning. Effective collaboration requires
agents to build on contributions from other
agents, recover missing visual context, and reconcile differing interpretations as new evidence emerges. Shared visual
memory preserves not only images or textual summaries but also the dependencies among observations,
agent interpretations, and subsequent
reasoning. Together, these design considerations shape how information flows and evolves across VLM
agents. The proposed framework provides a foundation for building reliable and resource-efficient
agent teams.
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All authors Huixin Zhang, Shao-Jun Xia, Di Wang, Liangxi Liu, Hainan Xiong, Zihao Wang
Quantum Physics
Classical, quantum, and general probabilistic state-discrimination profiles
Mihály Weiner
For a collection of states indexed by $[n]={1,\ldots,n}$, let $F(H)$ denote the optimal unnormalized minimum discrimination error for each nonempty subfamily $H\subseteq[n]$. We call $F$ the discrimination profile and characterize exactly which profiles can arise in a general probabilistic theory (GPT). For fixed $n$, the GPT region $\mathfrak{G}_n$ is a bounded rational polytope, giving a hierarchy $\mathfrak{C}_n\subseteq\mathfrak{Q}_n\subseteq\mathfrak{G}_n$ of classical,
quantum, and GPT profiles, analogous to the local–
quantum–no-signalling hierarchy in Bell theory. For $n=3$ we determine the classical and GPT polytopes explicitly. Within $\mathfrak{G}_3$, the classical region is characterized by $\beta:=F(123)-F(12)-F(13)-F(23)\le0$, while the GPT maximum is $1$. For
qubit triples, the maximal value is attained by the trine, $\beta_{\mathrm{tr}}=3\sqrt3/2-2$, and we prove the dimension-independent
quantum bound $\beta_{\mathrm Q}\le2/3$, with a slight further improvement. Hence $0=\beta_{\mathrm C}<3\sqrt3/2-2\le\beta_{\mathrm Q}\le2/3<1=\beta_{\mathrm{GPT}}$. We
conjecture $\beta_{\mathrm Q}=\beta_{\mathrm{tr}}$ and prove this for arbitrary pure-state triples, together with further supporting evidence. For arbitrary $n$, every classical facet admitting a GPT violation already admits a
quantum violation in dimension at most $3$. A four-state
qubit example shows that
quantum success profiles need not be submodular, unlike for $n=3$. Finally, we exhibit a four-dimensional
quantum triple whose discrete profile is classical for all subfamilies but becomes nonclassical when unequal priors are allowed.
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Game Theory
Estimate then Predict: Convex Formulation for Travel Demand Forecasting
Youngseo Kim, Gioele Zardini, Samitha Samaranayake et al. (+1 more)
Travel demand
forecasting is essential for evaluating large-scale infrastructure projects, yet the traditional sequential four-step process can produce inconsistencies across trip distribution, mode choice, and traffic assignment. Although combined models address these inconsistencies, their practical use has been limited by simplified behavioral assumptions, computational burden, and the lack of a unified parameter-estimation framework. We propose a convex programming approach that integrates destination, mode, and route choices within a hierarchical extended logit model. The optimal primal solution characterizes the joint travel-demand
equilibrium, while the optimal dual variables recover taste coefficients, alternative-specific constants, and destination- and mode-level scale parameters. The model captures mode correlations through nested logit and route overlap through path-size logit. Its convex structure provides global optimality guarantees and enables efficient solution using off-the-shelf conic solvers. Observed travel patterns are incorporated through moment and conditional-
entropy constraints, while the route-level dispersion parameter is calibrated separately using partial link counts. Numerical experiments on eight
benchmark networks demonstrate the scalability and computational efficiency of the formulation.
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All authors Youngseo Kim, Gioele Zardini, Samitha Samaranayake, Soroosh Shafiee
Statistical Methodology
Coherent Hierarchical Forecasting for Proportion and Discrete Time Series
Hannah Comiskey
Hierarchical and grouped time series arise when a multivariate time series is forced to satisfy a set of aggregation constraints, motivating forecast reconciliation methods that ensure coherent forecasts of such hierarchical structures. Many real-world applications involve discrete or bounded supports, introducing additional challenges that are not addressed Gaussian-based reconciliation methods. We develop a post-hoc hierarchical
forecasting approach to construct coherent forecast hierarchies for discrete and bounded time series. The method constructs coherent forecasts by convolution and exponential tilting, preserving the distributional properties and the underlying support throughout the hierarchy. We evaluate the proposed approach against state-of-the-art reconciliation methods for both discrete and continuous settings, demonstrating strong performance across a range of experiments. Through simulation studies and empirical applications in epidemiological and demographic data, we show that the method provides reliable and coherent distributional forecasts in challenging scenarios.
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High Energy Physics (Theory)
Smooth cutoffs and analytic continuation in Casimir physics
Lucía N. Helou and Francisco D. Mazzitelli
Divergent series arising in the computation of Casimir energies admit two seemingly different treatments: a physically motivated regularization and subtraction procedure, and formal analytic continuation methods that assign finite values directly to divergent expressions. The agreement between these approaches for the Casimir energy between parallel plates is well known, but its origin is often left implicit. In this work, we provide a unified framework that makes this equivalence transparent. Building on Tao's theory of smoothed sums, we show that the introduction of a smooth cutoff leads to an asymptotic expansion whose finite, regulator-independent part is universally determined by the analytic continuation of the associated Dirichlet series. This identification follows from a Mellin-transform representation, in which divergences and finite contributions are encoded in the pole structure of the integrand. We extend this approach to a class of series relevant to Casimir problems. In this setting, we show that logarithmic divergences arise from pole coincidences, and we obtain a natural decomposition of the result into universal and cutoff-dependent terms, mirroring the structure of renormalization in
quantum field theory.
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Machine Learning
EdgeReMIND: A Scalable, Top-Ranked Memorization Baseline for Temporal Multi-Relational Link Prediction
Bryant Pollard
Temporal link prediction on the Temporal Graph
Benchmark 2.0 (TGB 2.0) faces a scalability ceiling: on the
benchmark's three largest
datasets, every existing
embedding method runs out of
memory or exceeds the time budget. These large-scale graphs are the ones nearest real deployment scale, so failing on them is a real production limitation. EdgeReMIND sets the highest reported test mean reciprocal rank (MRR) on six of eight TGB 2.0
datasets and is the only relation-aware method that runs on all of them. This linear memorization model, with learned per-relation weights over data-calibrated features, is therefore not merely a fallback where
embeddings fail but a practical state-of-the-art baseline across the
benchmark.
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Atomic Physics
Electrostatic quadrupole lens for focusing a cryogenic $^{205}$TlF molecular beam
Pengyu Zhou, Olivier Grasdijk, David DeMille et al. (+7 more)
Precision measurements with molecules require molecular beams with high flux and narrow divergence. Here we present the design, implementation, and characterization of an electrostatic quadrupole lens (EQL) for focusing a cryogenic beam of $^{205}$TlF molecules. The EQL consists of four electrodes operated at spatially alternating potentials up to $\pm 30$ kV. This configuration generates a transverse restoring force that focuses molecules prepared in selected
manifold of hyperfine states. At the optimal applied voltage, the EQL increases the detected molecular signal by a factor of $14.4(4)$ at the current detection position. The measured lens gain, transverse Doppler spectra, and transverse spatial profiles are all reproduced by trajectory simulations using independently measured beam parameters as input. Based on this agreement, we project a gain of $16.5(8)$ for the full $7.9~\mathrm{m}$ CeNTREX beamline. These results establish the EQL as a key component for increasing sensitivity in precision experiments using cryogenic beams of polar molecules.
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All authors Pengyu Zhou, Olivier Grasdijk, David DeMille, Jakob Kastelic, David Kawall, Jianhui Li, Oskari Timgren, Konrad Wenz, Yuanhang Yang, Tanya Zelevinsky
Computational Complexity
Separating Non-redundancy and Chain Length
Joshua Brakensiek, Venkatesan Guruswami and Aaron Putterman
For a constraint satisfaction problem defined by a relation $R$, its non-redundancy $\text{NRD}(R,n)$ is the size of largest instance (as a function of the number $n$ of variables) for which no constraint is implied by the rest. Its chain length $\text{CL}(R,n)$ is the largest such instance where the constraints can be ordered so that no constraint is implied by the preceding ones. Clearly $\text{CL}(R,n) \ge \text{NRD}(R,n)$ but so far no asymptotic separation was known between these quantities. We exhibit an explicit arity $4$ relation for which $\text{CL}(R,n) \ge \omega(\text{NRD}(R,n))$.
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Computer Vision
Face-voice Association across LAnguages and Gender (FLAG) 2027 Challenge Evaluation Plan
Marta Moscati, Swapnil Khandoker, Muhammad Saad Saeed et al. (+8 more)
Face–voice association models may rely on
language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead to a performance deterioration when the model has to identify a
multilingual speaker or distinguis same-gender speakers. To investigate these issues, we introduce the Face-voice Association across
LAnguages and Gender (FLAG) 2027 Challenge. The challenge formulates face–voice association as a cross-modal verification task: given a voice, identify the speaker's face from a “gallery” of faces consisting of the speaker's face and a set of negative samples. Models are evaluated on identities not present in the training data (“unseen”) and both for
languages present or absent from the training data (“heard” and “unheard”). Two evaluation settings are used to test models' reliance on gender: a standard, unconstrained and a gender-constrained one, where the latter uses a same-gender gallery. The performance of existing, baseline models in these settings reveals that models performance degrades under
language shifts and in gender-constrained settings, highlighting the need to foster the development of models that capture identity-specific aspects beyond
language and gender. The challenge provides a
benchmark dataset, pretrained baseline models, and an evaluation framework to advance face–voice association.
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All authors Marta Moscati, Swapnil Khandoker, Muhammad Saad Saeed, Shah Nawaz, Fatima Noor, Rohan Kumar Das, Mubashir Noman, Junaid Mir, Muhammad Haroon Yousaf, Khalid Malik, Markus Schedl
General Relativity and Quantum Cosmology
Stochastic Thermodynamics of Dynamical Thin-Shell Wormholes: Effective Temperatures, Fluctuations and Conditional Quasi-Horizon Unification
Francisco S. N. Lobo and Manuel E. Rodrigues
We develop an effective
stochastic and semiclassical framework for dynamical thin-shell wormholes connecting two asymptotic spacetime regions. The Israel junction geometry is separated from the phenomenological open-system description:
stochastic dynamics is formulated in the radial phase space $(a,\dot a)$, whereas $\sigma$, $P$, and the effective temperature scales are induced observables. A one-dimensional Langevin–Fokker–Planck model is obtained only after an explicit overdamped/adiabatic approximation. Local acceleration scales are interpreted as reservoir temperatures only under additional KMS and detailed-balance assumptions. For a symmetric Schwarzschild–Schwarzschild wormhole, the transparent conservative shell defines the reference potential and restoring frequency, while dissipation and noise are introduced as open-system corrections. The stable linear response is closed by the constitutive slope $\beta_0^2=(dP/d\sigma)_0$. For $2M<a_0<3M$, stability requires a lower bound on $\beta_0^2$ that diverges as $a_0\to2M^+$. Moreover, the derivative along the family of static configurations equals the marginal-stability slope and must not be identified with the constitutive derivative of a stable
stochastic shell. The reduced model determines stationary distributions, fluctuations, correlations, Markovian and non-Markovian spectra, and
entropy production. Near the horizon, the junction conditions yield $T_{\rm stress}=T_{\rm acc}+O(\hbar\gamma/a)$, with equality at leading order under a specified dominance condition. A peeling temperature participates only if the ray-tracing map satisfies generalized Unruh/peeling and adiabaticity conditions. Thus, the three-temperature correspondence is conditional, while the
stochastic coefficients remain phenomenological pending a microscopic shell–reservoir model.
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Solar and Stellar Astrophysics
Host-star metallicities and kinematics of directly imaged brown-dwarf companions
C. Swastik, R. K. Banyal, M. Muduli et al. (+14 more)
Brown
dwarfs are common as free-floating objects but rare as close companions to Sun-like
stars, a disparity known as the "brown-
dwarf desert". Host-
star metallicity can constrain whether these companions form mainly through metal-sensitive core
accretion or through less metal-dependent disc or cloud fragmentation. We extend our homogeneous spectroscopic analysis of directly imaged
planet hosts into the brown-
dwarf regime and compare their
metallicities with those of
planet hosts and close-in brown-
dwarf hosts. We compiled 54 unique directly imaged brown-
dwarf systems selected over an inclusive 13-80 M_Jup interval and projected separations from about 5 au to several thousand au. Objects near the model-dependent 70-75 M_Jup hydrogen-burning boundary may instead be very-low-mass
stars. For 31 hosts with archival high-resolution spectra, we derived atmospheric parameters and
metallicities using
Bayesian spectral synthesis. Literature companion masses and projected separations are heterogeneous and are used only for demographic context.
Galactic velocities were calculated for 46 hosts solely to characterise the youth-biased imaging sample. The host
stars have a broadly
solar metallicity distribution, with a median [Fe/H] of +0.06 dex and a median absolute deviation of 0.11 dex, and show no strong metal-rich bias. No statistically significant
metallicity difference is detected between the lower- and higher-mass directly imaged subsamples. The hosts are kinematically cold, as expected from the youth-biased selection of direct-imaging surveys. The absence of a strong metal-rich bias suggests that classical core
accretion does not dominate the wide-orbit brown-
dwarf population. Disc instability and cloud fragmentation remain plausible, but the current sample and heterogeneous companion properties do not permit object-by-object discrimination between these channels.
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All authors C. Swastik, R. K. Banyal, M. Muduli, S. Soni, A. Jino, S. Facchini, Z. Wahhaj, G. Lodato, P. Saraf, A. Choudhary, A. K. Bhavya, M. P. Navaneeth, B. Banerjee, S. Biswas, T. Sivarani, G. Maheswar, A. Surya
Robotics
Map2Route: Benchmarking Compositional Language-Grounded Route Planning over Semantic Maps
Muyi Bao, Hang Xu, Jingfan Tang et al. (+5 more)
We introduce Map2Route, a human-curated
benchmark for compositional
language-grounded route planning over pre-built semantic maps. Map2Route contains 1,000 episodes across 40 scenes, where instructions use relational, comparative, and nested descriptions to identify route-relevant objects and regions, while specifying ordered must-pass regions, must-avoid requirements, five categories of soft preferences, and spatial and route-stage scopes, which is partially tested by existing works. Alongside Map2Route, we propose Grounding2Route, which combines executable code-as-grounding with verification-guided repair and scope-aware planning.Across seven representative adapted baselines, Grounding2Route substantially outperforms existing methods in all metrics. Despite these gains, a substantial gap to human demonstrations remains, highlighting the difficulty of Map2Route and the considerable headroom for future progress. Additional qualitative results and resources are available on https://anonymous.4open.science/w/Map2Route-F05F/.
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All authors Muyi Bao, Hang Xu, Jingfan Tang, Zihan Liu, Yuxin Cai, Chen Lv, Wenshan Wang, Ji Zhang
Computer Vision
Zing-0.5: Toward Playable Worlds with Real-Time Joint Action and Text Control
Mingyang Chen, Shengdong Chen, Xiaoxiao Fu et al. (+13 more)
We introduce Zing-0.5, a 5B autoregressive world model designed for playability: users can explore generated worlds, influence unfolding events, and respond to the resulting feedback through joint keyboard and online text control. Our approach brings together three technical contributions: (1) Unified action and text conditioning, combining magnitude-aware keyboard inputs with temporally aligned text instructions and jointly annotated
videos to learn
navigation and event control within the same sequence; (2) Event-scale supervision for incremental generation, using a segment-level teacher trained on connected multi-
prompt videos to supervise a block-level causal student through distribution-matching
distillation; and (3) Low-cost real-time interaction, combining four-step generation with context-preserving streaming to support 832 x 480 inference at 24 FPS at an estimated server rental cost of approximately USD 0.009 per stream-minute. Zing-0.5 achieves an overall score of 81.0 and a consistency score of 88.5 across 158 WBench
Navigation cases. A joint-control demonstration shows a text-directed event change during continued
navigation without restarting generation. We release the model weights, inference code, and Zing-SGLang serving implementation to support further work on playable generated worlds.
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All authors Mingyang Chen, Shengdong Chen, Xiaoxiao Fu, Bosheng Gong, Haoyuan Guo, Bowen Li, Jiawen Li, Kejun Li, Tianpeng Li, Yin Liu, Haoze Sun, Zeyang Tian, Meng Wang, Xinmiao Wu, Jiangqiao Yan, Zining Zhao
Statistical Methodology
Cross Validation for the log Gaussian Cox Process
Hans Montcho, Håvard Rue, Finn Lindgren et al. (+4 more)
The log Gaussian Cox Process (LGCP) is one of the most widely used models for the analysis of spatial point patterns. Although
Bayesian methods and software for fitting LGCPs are now well established, practical tools for model criticism, predictive assessment, and model comparison remain comparatively underdeveloped. This paper develops a practical
Bayesian cross-validation framework for LGCPs while placing it within a broader framework for
Bayesian model assessment. Our approach is motivated by the conceptual and computational challenges that arise when defining predictive validation for point processes, including the choice of holdout data, prediction task, scoring rule, and computational strategy. We propose a leave-region-out cross-validation framework in which the fundamental unit of prediction is a bounded spatial region rather than an individual event. Predictive performance is assessed through the logarithmic scoring rule for joint probabilistic forecasts, while computational efficiency is achieved by combining a new likelihood approximation with a
Bayesian unlearning strategy and Laplace approximations to obtain leave-region-out predictive distributions without repeated model refitting. We validate the proposed approximations against Monte Carlo estimates obtained via brute-force refitting, demonstrate the methodology on simulated and real spatial point pattern
datasets defined on Euclidean domains and networks, and provide implementations in the R-INLA, inlabru, and MetricGraph packages.
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All authors Hans Montcho, Håvard Rue, Finn Lindgren, David Bolin, Elias T Krainski, Daniela Castro-Camilo, Sara Martino
Differential Geometry
Sasaki-Einstein rational homology spheres, rational varieties and the Berglund-Hübsch rule
Jaime Cuadros Valle and Joe Lope Vicente
We find Sasaki-Einstein metrics on rational homology $(4n-1)$-spheres for $n>1$ built from cyclic
polynomials of index 1 cutting out rational varieties. The Einstein metrics found here are inequivalent to the ones found by Boyer and Galicki in arXiv:math/0311355. Our findings are consequence of an improvement, for hypersurfaces defined by cycle
polynomials, on the estimate given by Johnson and Kollár to determine Kähler-Einstein orbifold metrics. We also construct weighted hypersurfaces that contain the rational varieties described above as codimension two subvarieties and, due to the refined estimate for cyclic
polynomials, we find conditions on the weights and degrees of these hypersurfaces so their corresponding smooth links admit Sasaki-Einstein metrics. Finally we study the effect of the Berglund-Hübsch transpose rule on the
topology and on the existence of Sasaki-Einstein metrics on the links studied and generalize all the results given in arXiv:2311.15998 for rational homology 7-spheres to rational homology $(4n-1)$-spheres, that is, we show invariance of these two features under the transpose rule.
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Quantitative Methods
FlowLOT: Linearized Optimal Transport for Flow Cytometry Analysis
Naqib Sad Pathan, Mohammad Shifat-E-Rabbi, Kristofor E. Pas et al. (+3 more)
Multiparameter flow cytometry generates high-dimensional, unordered single-
cell mea- surement data for disease diagnosis and monitoring, yet analysis often remains dependent on manual gating, limiting scalability and reproducibility. Existing machine-learning ap- proaches can reduce annotation burden but frequently require large training cohorts and offer limited interpretability. To address these challenges, we introduce FlowLOT , an optimal-transport-based framework that models the single-
cell measurement data of a pa- tient sample as an empirical cellular distribution and maps it directly into a fixed-length feature vector. Within a single transparent architecture, FlowLOT unifies high-dimensional classification, interpretable visualization, and continuous quantitative inference. In few-shot regimes, using as few as 16 patients per class on FlowCAP-II and 8 patients per class on BLAST110, it accurately distinguishes healthy from acute myeloid leukemia (AML) sam- ples, reaching 94.3% and 98.0% balanced accuracy, respectively. The underlying
embedding exposes marker-level variation driving disease-associated population shifts and enables quantitative measurable residual disease (MRD) estimation, achieving a Pearson correlation of 0.82 on held-out samples and 0.79 under cross-
dataset transfer. Furthermore, at the clinically relevant 0.1% threshold for leukemia-associated immunophenotype (LAIP) residual disease, FlowLOT detects positivity with 72% sensitivity at 100% specificity. By replacing subjective manual gating and black-box deep learning with a distribution-aware framework, FlowLOT offers a sample-efficient, scalable, and interpretable solution for high- dimensional cytometry under realistic clinical and experimental constraints.
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All authors Naqib Sad Pathan, Mohammad Shifat-E-Rabbi, Kristofor E. Pas, Ivan Medri, Bartek Rajwa, Gustavo K. Rohde
Emerging Technologies
Co-occurrence-Aware Quadratic Assignment for Local Feature Matching in Simultaneous Localization and Mapping
Yutaka Yamada, Yohei Hamakawa, Yutaro Ishigaki et al. (+2 more)
Local feature matching, which associates keypoints in two images as keypoint pairs, is fundamental to Visual Simultaneous Localization and Mapping (Visual SLAM). Nearest Neighbor (NN) search is commonly used for keypoint matching, but it has difficulty selecting correct keypoint pairs when multiple candidates have similar costs. To improve matching accuracy, this paper proposes a keypoint matching method that considers the pairwise co-occurrence of two keypoint pairs. The keypoint matching is formulated as a quadratic assignment problem, which is an NP-hard combinatorial optimization problem, making it difficult to solve quickly on conventional computers. Recently, Ising machines have been developed as computing devices capable of solving hard combinatorial optimization problems. Using a simulated bifurcation based Ising machine, the proposed method improved matching accuracy by approximately 8 percentage points over a conventional method on the HPatches
dataset. Furthermore, we integrated the proposed method into ORB-SLAM3, a representative academic Visual SLAM system, and achieved a 3.78-fold improvement in absolute pose error (APE) and a 2.85-fold improvement in relative pose error (RPE) on the KITTI
dataset scenes where multiple same shape objects are repeatedly arranged, which are challenging for accurate self-pose estimation by the original ORB-SLAM3.
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All authors Yutaka Yamada, Yohei Hamakawa, Yutaro Ishigaki, Masaya Yamasaki, Kosuke Tatsumura
Robotics
Timely Activation of Safety Filters via One-Step Reachability Expansion
Javier Borquez
Least-restrictive safety filters based on Hamilton-Jacobi reachability provide strong safety guarantees by overriding a nominal controller only when the system reaches the boundary of the set of unsafe states defined as a Backward Reachable Tube (BRT). These guarantees, however, rely on the continuous-time nature of the underlying formulation. In practice, robotic systems apply control at discrete sampling intervals, which creates a mismatch where the system may jump into the unsafe BRT between updates, allowing failures that are theoretically avoidable. This work introduces a principled solution based on a one-step expanded BRT that predicts all states capable of reaching the true BRT within a single timestep. By using this expanded boundary as the activation condition for the safety filter, safety interventions occur early enough to ensure correctness under discrete-time execution. We formulate this expanded set as a modified reachability problem and compute it using standard continuous-time solvers.
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Emerging Technologies
Composability rather than computation sets the cost of an analog EML hardware fabric
C. Teuscher
The operator eml(x, y) = exp(x) - ln(y) with the constant 1 generates the elementary functions, a continuous counterpart to NAND. Whether it yields a useful fabric had not been asked of hardware. We ask in network models,
circuit simulation and SkyWater 130 nm layout. Four bipolar junctions evaluate the operator for 13 fJ, beating a width-matched digital datapath by 4-134x. The fabric assembled from them is not cheap: it loses to resource-matched baselines, and over the reals its grammar excludes trigonometry. Amplifiers holding those junctions' operating points take 74.5% of a
cell's current, so a
cell costs 3000 times what they spend. Extracted non-idealities cost 2.6x when a
cell must hold a value and nothing when it need only be repeatable. Sharing them across
cells recovers two of the three orders. The premise was that a universal primitive licenses a uniform machine. It survives in the primitive and fails in the machine.
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Cryptography and Security
Ghost-Filled Orders: Detecting and Testing Atomicity Violations in Non-Custodial Prediction Markets
Zhiyang Chen, Fan Long and Zhendong Su
Blockchain based prediction
markets combine offchain order management with onchain settlement. This architecture supports user controlled custody, since users keep funds in wallets or smart contracts while submitting signed orders to an offchain order book. However, it creates an atomicity gap. An order may be valid when accepted or matched offchain, but become invalid before the corresponding onchain settlement transaction is executed. This behavior, often called ghost filled orders by the community, can cause trades that appear filled offchain to fail onchain. This paper studies this atomicity gap through a case study of Polymarket. We show how the delay between offchain order acceptance and onchain settlement allows adversaries to invalidate unfavorable orders after observing
market outcomes or price movements. We then quantify the scale and financial impact of this behavior over a nine-month period from August 12, 2025, to May 22, 2026, using 1.8 million reverted transactions involving Polymarket official smart contracts. Our analysis separates attacker profit from
market and user loss, and develops conservative measurement rules to avoid overclaiming impact. We further develop a methodology for testing other blockchain based prediction
markets and apply it to three additional
markets. We find that all three are vulnerable to the same class of attack, and that one design enables direct attacker profit. We have reported the findings to all three projects; one project had acknowledged the issue at the time of the study.
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Machine Learning
Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds
Jan Tauberschmidt, Brian B. Moser, Stanislav Frolov et al. (+3 more)
Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in the training data. We instead frame generation as continuous-time
evolution on a learned data
manifold. To this end, we leverage pretrained score-based models as
geometric priors and learn a vector field that evolves data along score-induced interpolation paths. Because these dynamics follow transitions that respect the geometry learned by the score model, they support generation at arbitrary timestamps and temporal super-resolution beyond the discretization of the training data. Moreover, this
geometric formulation allows us to train the vector field simulation-free through a regression objective. To improve long-horizon rollout robustness, we introduce an objective that promotes path-relative transverse exponential stability. While motivated by stability theory, it admits a practical interpretation as denoising score matching transverse to the interpolation path. Further, we extend the framework to a probabilistic setting that models a distribution over plausible future trajectories. We demonstrate the method on natural
video and scientific dynamical data, including temporal super-resolution, PDE-based spatiotemporal fields, and molecular dynamics. Our results show that score-based priors provide a strong foundation for learning
stochastic continuous-time generative dynamics.
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All authors Jan Tauberschmidt, Brian B. Moser, Stanislav Frolov, Andreas Dengel, Andrew B. Duncan, Sebastian J. Vollmer
Mesoscale and Nanoscale Physics
A semiconductor photon-pair source based on a polariton cascade
Karol Kawa
We present a theoretical study of a
semiconductor photon-pair source based on a radiative cascade enabled by a permanent
exciton dipole. The source consists of a GaAs
quantum dot placed between a metal nanoparticle and a mirror. The dot
exciton and the localized electromagnetic mode mix to form upper and lower polaritons. Without a permanent dipole, a
symmetry separating states with even and odd excitation numbers forbids
photon emission between the polaritons. Separation of the mean electron and hole positions gives the
exciton a permanent dipole and breaks this
symmetry. The upper polariton can then emit an idler
photon as it decays to the lower polariton. The lower polariton can emit a signal
photon as the system returns to its ground state. Using an effective model, we calculate the probability that a prepared upper polariton emits both
photons through the metal
antenna. We also analyze fluctuations in the emitted
photon counts and correlations between the two emission channels. The calculation establishes an operating principle for assumed values of the coupling strength, emitter properties and optical loss rates. Whether the device can be built and its
photon correlations measured remains open.
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Machine Learning (Statistics)
Generalized DCCQ: From Binary Quotients to Multinomial Simplex Geometry and Critical-Strip Coordinates
Y. Kenan Y{\i}lmaz
We extend the discrete complex complement quotient (DCCQ) framework from binary Bernoulli counts to multinomial count compositions. For m+1 categories, m is the number of independent probability degrees of freedom. Integer count vectors modulo common scaling determine rational points of the m-dimensional probability simplex. Building on standard simplex and log-ratio coordinate geometry, for m >= 2 we define the full multinomial DCCQ coordinate map and show that it is a real-analytic diffeomorphism The previously established binary baseline m=1 gives a critical-line coordinate, while the ternary case m=2 gives the full open critical strip; higher multinomial models retain m-2 additional real contrasts. We also give a one-versus-rest specialization, an exact integer-
lattice realization of the ternary coordinate, and a
hyperbolic representation of its log-ratio. No zero-location
theorem or proof of the Riemann Hypothesis is claimed.
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Algebraic Geometry
Toric vector bundles with trivial Chern class and flag decorations
Sergio Cristancho
We study toric vector bundles on complete toric varieties whose total equivariant Chern classes are trivial. Our approach is tropical, using the notion of tropical toric vector bundles as piecewise linear maps introduced by Kaveh and Manon. We prove that any toric vector bundle of rank $r$ with trivial Chern class and affinely independent equivariant Chern roots is equivariantly isomorphic to a toric vector bundle pulled back from one of a finite set of varieties with dimension at most $r-1$ after twisting by a character. This extends a
theorem of Payne about toric vector bundles of rank $r\leq 3$ with trivial Chern class. As an application, we construct examples of complete toric varieties of dimension $n$ that admit no nontrivial toric vector bundles of rank $r\leq n+1$ with the aforementioned properties. We also introduce combinatorial gadgets we call flag decorations of permutohedra, whose convexity properties are key for our results.
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Cryptography and Security
When AI Agents Meet MEV: Cross-Chain Arbitrage in the Agentic Economy
Wei Ye, Jingyan Xu and Yuanhong Wu
We study cross-chain arbitrage when autonomous AI
agents, rather than humans or bots, are the searchers. We model
agents as both arbitrage extractors and Maximal Extractable Value targets, derive the optimal trade size for a risk-averse
agent under mean-variance utility with
stochastic bridge delays, and formalize multi-chain path selection as a belief-weighted online learning problem whose belief estimates converge under a Robbins-Monro schedule. Using 23,000 Uniswap V3 swap events across Ethereum, Arbitrum, and Base, we find that Ethereum-Arbitrum price gaps average 0.044% at 10-second resolution and Arbitrum–Base gaps average 0.013%, so $10,000 trades clear in 63% of L2-L2 windows via CCTP while L1-L2 routes require $50,000 or more for comparable viability. Our adaptive path-selection
algorithm outperforms standard baselines by 11% on average, and moderate randomization cuts MEV exposure by over 50% with only modest profit loss.
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Quantum Physics
QEMScore: How Much Does the Measurement Add to Learned Quantum Error Mitigation?
Yue Zhao, Huayue Gu, Yushun Dong et al. (+1 more)
How much does the noisy measurement add to learned
quantum error mitigation? An accuracy table cannot say, because a model handed
circuit structure can score well without reading the measurement at all. QEMScore adds the comparison that can. Each simulated
circuit carries an exact ideal answer. The learned mitigator is scored beside a capacity-matched control, a model just as flexible that reads the same
circuit description but never the measurement. Each method's measurement spend is accounted and not equalized. We run a controlled campaign on simulated
circuits and reanalyze two published learned mitigators, Q-LEAR and QRAFT, from their released hardware data. Three findings stand out. First, under familiar within-family conditions (S0) evaluated across two
spin-chain families and three seeds, continuous couplings identify the target, and the control that never reads the measurement matches 87.7 to 100.5 percent of the mitigator's gain over an affine fit to the
circuit description. A plain
polynomial in the coupling parameters, fitted after the campaign, beats the mitigator on all six evaluations, reflecting the selected learners' capacity. Second, for these selected learners, matching most of the gain is not matching the accuracy: on five of six evaluations the mitigator removes 19.5 to 74.5 percent of the error the capacity-matched control leaves, a learner-specific gap rather than a measurement requirement. Third, on released hardware data where descriptors only partially identify queries, the findings differ: flexible models of the descriptors show negligible mean gain over affine fits in Q-LEAR, and measurement inputs carry predictive gains in both Q-LEAR and QRAFT. These comparisons reflect representation- and protocol-specific behavior rather than an isolated cross-regime difference. A learned mitigator's accuracy should therefore be reported beside such controls.
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All authors Yue Zhao, Huayue Gu, Yushun Dong, Xiyang Hu
Machine Learning
TabPFN-3.5: Technical Report
Benjamin Jäger, Nick Erickson, Léo Grinsztajn et al. (+44 more)
We introduce TabPFN-3.5, our new flagship Tabular Foundation Model. It significantly outperforms its predecessor, TabPFN-3, and all existing baselines across a broad range of tabular problems. TabPFN-3.5 sets a new state of the art on standard tabular prediction in TabArena, and extends it to the data practitioners encounter in practice: non-i.i.d. data with temporal or grouped splits, tables with strings, text and images, high-cardinality categorical features, and wide tables with many features. These gains carry over to our task-specific harnesses: state of the art on relational data and stronger time-series
forecasting. For faster inference, our variant TabPFN-3.5-Fast runs up to 3x faster than TabPFN-3 while keeping most of the accuracy gains. In addition, we upgrade TabPFN-3.5-Plus, expanding our
multimodal capabilities with advanced text and date handling alongside proprietary inference optimizations. Finally, we release a new version of our Thinking mode, TabPFN-3.5-Thinking, which scales inference-time computation to push the state of the art further. It benefits from our stronger base model and from inference-time improvements that make it up to 12x faster than TabPFN-3-Thinking.
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All authors Benjamin Jäger, Nick Erickson, Léo Grinsztajn, Felix Birkel, Klemens Flöge, Oscar Key, Kürşat Kaya, Jonas Kübler, Adèle Frankel, Tobias Schröder, Anurag Garg, Jan Hendrik Metzen, David Salinas, Simon Bing, Kristina Collins, Tuana Çelik, Vahid Balazadeh, Lydia Sidhoum, Tomás Pereda, Brendan Roof, Andrej Tschalzev, Siyuan Guo, Philipp Singer, Lennart Purucker, Jake Robertson, Marie Salmon, Philipp Jund, Jerry Chen, Diana Kriuchkova, Arthur Cahu, Eliott Kalfon, Adrian Hayler, Georg Grab, Vitor Monteiro, Lilly Wehrhahn, Dominik Safaric, Clara Cornu, Alan Arazi, Rylee Grace, Simone Alessi, Mihir Manium, Bernhard Schölkopf, Yann LeCun, Madelon Hulsebos, Sauraj Gambhir, Noah Hollmann, Frank Hutter
Instrumentation and Methods for Astrophysics
Graph neural networks for exoplanet atmospheres
Antonia Vojtekova, Kai Hou Yip, Ingo P. Waldmann et al. (+4 more)
Calculating disequilibrium chemistry in
exoplanet atmospheres remains a significant computational bottleneck in atmospheric
retrievals. The increasing observational precision from facilities such as JWST and the Ariel mission requires including disequilibrium chemistry in these analyses. Previous studies have demonstrated that neural networks can emulate kinetic chemistry, although their spatial inductive bias does not align with the
topology of chemical reaction networks. This study introduces a graph neural network surrogate that represents chemical species as nodes and temperature-dependent reaction rates as edges, thereby enabling information propagation along physically meaningful chemical pathways. The model is trained on atmospheres generated using the Venot+2020 chemical scheme and Guillot temperature-pressure profiles. The GNN accurately reconstructs disequilibrium abundances across the sampled parameter space and reduces the mean abundance error by a factor of approximately 3 compared to the previous U-Net model. When applied to transmission spectra, most predictions fall within the observational precision expected for JWST and Ariel, with only about 7% of test atmospheres exceeding a 20 ppm mean spectral error. Performance variations are primarily observed in chemically transitional regimes near a carbon-to-oxygen ratio of one and at low temperatures. An evaluation of the boundary-case
planet WASP-39b demonstrates effective performance under a moderate domain shift. Perturbation analysis indicates that disturbances propagate along chemical connectivity rather than spatial adjacency, confirming that the architecture captures the structure of reaction networks. These results suggest that GNN surrogates provide accurate, computationally efficient predictions of disequilibrium chemistry, facilitating integration into the atmospheric
retrieval pipeline.
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All authors Antonia Vojtekova, Kai Hou Yip, Ingo P. Waldmann, Nikolaos Nikolaou, Olivia Venot, Ahmed Faris Al-Refaie, Bruno Merín