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Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

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arXiv2026

Classical elliptic ${\rm BC}_1$ Ruijsenaars-van Diejen model: relation to Zhukovsky-Volterra gyrostat and 1-site classical XYZ model with boundaries

We present a description of the classical elliptic ${\rm BC}_1$ Ruijsenaars-van Diejen model with 8 independent coupling constants through a pair of ${\rm BC}_1$ type classical Sklyanin algebras generated by the (classical) quadratic reflection equation with non-dynamical XYZ $r$-matrix. For this purpose, we consider the classical version of the $L$-operator for the Ruijsenaars-van Diejen model proposed by O. Chalykh. In ${\rm BC}_1$ case it is factorized to the product of two Lax matrices depending on 4 constants. Then we apply an IRF-Vertex type gauge transformation and obtain a product of the Lax matrices for the Zhukovsky-Volterra gyrostats. Each of them is described by the ${\rm BC}_1$ version of the classical Sklyanin algebra. In particular case, when 4 pairs of constants coincide, the ${\rm BC}_1$ Ruijsenaars-van Diejen model exactly coincides with the relativistic Zhukovsky-Volterra gyrostat. Explicit change of variables is obtained. We also consider another special case of the ${\rm BC}_1$ Ruijsenaars-van Diejen model with 7 independent constants. We show that it can be reproduced by considering the transfer matrix of the classical 1-site XYZ chain with boundaries. In the end of the paper, using another gauge transformation we represent the Chalykh's Lax matrix in a form depending on the Sklyanin's generators.

math-ph↗
arXiv2026

Long-Lived Oscillons as Closed Domain Walls in the $\mathbb Z_2$-Symmetric Two-Higgs-Doublet Model

Domain walls arising from spontaneous symmetry breaking in the early universe are typically unstable and decay rapidly. However, numerical simulations of the Z2-symmetric two-Higgs-doublet model (2HDM) reveal an unexpected long-lived closed domain wall structure that emerges at late stages of decay, which we identify as a high-amplitude oscillon. Their longevity stems from a potential with two distinct vacua: an interior oscillating in one vacuum and an exterior static in the other. Within a specific parameter regime, radiation is highly suppressed and the lifetime can exceed cosmological timescales. To gain analytical insight into the mechanism, we first consider reduced models with fewer fields (2 and 4 real scalars). These are not approximations to the 2HDM but rather toy models that capture the essential feature of a Z2-symmetric potential with two vacua. Floquet analysis further confirms the stability of these structures under perturbations in both the 4-field toy model and the full 2HDM.

hep-ph↗
arXiv2026

Hierarchical GTV Estimation: Bridging the Gap between Container-Level Ranking and Item-Level Conversion

Emerging e-commerce platforms, such as live-streaming and Online-to-Offline (O2O) services, inherently operate under a "Container-based" display paradigm. In this setting, a critical granularity mismatch exists: the platform performs ranking and estimation at the container level (e.g., livestream rooms or stores), whereas the actual transactions occur at the item level within these containers. This structural discrepancy complicates Gross Transaction Value (GTV) estimation, introducing unique challenges including high label variance, severe label sparsity, and dynamic item heterogeneity. To address these challenges, we propose a novel framework named Hierarchical GTV Estimation (HGE). HGE employs a Hybrid Set-Aware Encoder (HSAE) to model the dynamic composition of items and feature interactions. To mitigate label variance, we introduce a Proxy-Label Learning (PLL) strategy that decomposes post-click GTV into purchase probability, quantity, and unit price. Furthermore, a Residual Correction and Ensemble Fusion (RCEF) module is designed to handle label sparsity by effectively combining item-level aggregation with container-level inference. Extensive experiments on large-scale industrial datasets demonstrate that HGE significantly outperforms state-of-the-art baselines, achieving a +0.63% lift in XAUC offline and a +1.42% increase in Revenue Per Search (RPS) in online A/B tests. HGE framework has been fully deployed into our main traffic.

cs.IR↗
arXiv2026

Trident: Efficient Load Verification for Yoked Surface Codes using a Three-Level Memory Hierarchy

Fault-Tolerant Quantum Computers (FTQCs) commonly use the surface code to encode program qubits as error-corrected logical qubits. Unfortunately, the surface code has a poor encoding rate, so recent work has proposed Yoked Surface Codes (YSCs), which concatenate the surface code with a high-rate quantum code. The surface code qubits in YSCs are protected by the checks, or yokes, of the high-rate code. YSCs have 1D and 2D variants, which are $1.8\times$ and $4.5\times$ more efficient than the surface code, respectively. Modern FTQC architectures use a two-level hierarchy: surface codes perform logical operations (compute) while 2D YSCs store idle program qubits (memory). To load a qubit from memory, the FTQC first performs retrieval, which logically moves a program qubit from a 2D YSC into a surface code (8 cycles). However, retrieval is not fault-tolerant, so the FTQC must verify the load by measuring all the yokes of the 2D YSC (178 cycles). As verification dominates memory access latency, eliminating it can provide a speedup of $5.7\times$. Our goal is to reduce the verification overhead of 2D YSCs while retaining their storage efficiency. We exploit the fact that 1D YSCs, though less storage-efficient, incur negligible verification latency. We propose Trident, which expands the two-level hierarchy into a three-level hierarchy by adding a small intermediate level containing a 1D YSC (L1). If most loads hit L1 rather than L2 (2D YSC tier), it can significantly reduce verification overhead. We present two variants: Trident-D (demand-driven) and Trident-P (prefetch-driven), which achieve speedups of $2.8\times$ and $4.2\times$, respectively, while increasing the physical qubit cost of memory by only 4.9%.

quant-ph↗
arXiv2026

GeoLEON-CLIP: Graph-Anchored Multimodal Contrastive Learning for Geographic Region Representation

Geographic regions are multimodal: satellite imagery captures visual morphology, text conveys human-interpretable semantics, and map structures describe urban context. Existing geospatial contrastive methods typically align image-text or image-location pairs, underusing structured map data. We introduce GeoLEON-CLIP, a graph-anchored contrastive framework that aligns three views of each H3-indexed region: an OpenStreetMap-derived graph embedding, a satellite image crop, and a textual description. Frozen pretrained LEON graph embeddings provide a semantic anchor between vision and language. Frozen graph, image, and text encoders are connected by trainable residual projection heads and trained with a symmetric tri-modal contrastive objective. Experiments show strong graph-image and graph-text alignment; graph-image retrieval achieves the best cross-modal performance in a mixed-resolution pool. To evaluate the anchoring hypothesis, we propose chained cross-modal recall $\mathrm{R}@A@B$, which routes retrieval through the graph modality and compares it with direct retrieval under a matched candidate budget. At $\mathrm{R}@1@1$, image$\rightarrow$graph$\rightarrow$text and text$\rightarrow$graph$\rightarrow$image outperform direct image-text retrieval by 4.8 and 4.4 points, respectively, despite using a single intermediate cell. Downstream probes show task-dependent gains, with the largest improvements on Airbnb and Beijing housing. These results indicate that OpenStreetMap graph structure can anchor multimodal representations combining maps, imagery, and language.

cs.CV↗
arXiv2026

PoKER: A Probability of Kill Estimation Rate Model for Air-to-Air Missiles Using Machine Learning on Stochastic Targets

This work introduces PoKER, a novel probabilistic model engineered to optimize missile launch effectiveness in air-to-air scenarios, specifically within Beyond Visual Range (BVR) air combat. Unlike conventional Weapon Engagement Zone (WEZ) models that delineate zones based on static distances such as maximum, minimum, and no-escape ranges, PoKER applies machine learning to predict kill probabilities more accurately by integrating the stochastic behaviors of targets and missile miss distances. This model dynamically evaluates target behavior, greatly expanding the predictive capabilities of engagement analysis. By factoring in elements such as warhead lethality, target and shooter orientations, and the specific conditions of engagement, PoKER provides important insights into engagement dynamics and quantifies success probabilities. Consequently, it can potentially be an important tool for BVR air combat pilots, improving operational decision-making within this specialized combat domain.

cs.LG↗
arXiv2026

Tile-Based Decompression in Compiled GPU Engines

Modern GPUs reach record analytical performance only when a query's working set fits within its GPU memory budget, and that budget is under growing pressure: GPUs are partitioned and shared, working sets are outgrowing high-bandwidth memory (HBM), and HBM is a fixed on-device resource whose per-dollar capacity has stagnated across generations. Lightweight compression can relieve this pressure by shrinking both footprint and bandwidth demand, but existing GPU compressed-execution techniques either bind decompression to a specific execution model or materialize decompressed columns in HBM before query execution. We present a compression subsystem for compiled GPU query engines that treats decompression as a first-class physical operator. Compression-specific logic is isolated behind a narrow backend interface, so new GPU-oriented encodings can be added without rewriting downstream operators. We implement the design in Proteus and study where decompressed values should be materialized in the GPU memory hierarchy. Our compiler-integrated shared-memory strategy fuses cooperative tile decompression with tuple-at-a-time query execution inside a single generated GPU kernel, avoiding decompressed-column materialization in HBM. Compared with HeavyDB and Sirius on GPU-resident data, fused on-chip decompression achieves competitive query times while reducing the resident memory footprint by up to 4x and memory-bandwidth consumption by up to 6x.

cs.DB↗
arXiv2026

Expressivity Results for Structured Argumentation Frameworks with Uncertain Rules and Premises

Modelling qualitative uncertainty in formal argumentation is essential both for practical applications and theoretical understanding. Yet, most existing work focuses on abstract models of argumentation under uncertainty. Following a recent trend in the literature, we tackle the open question of studying plausible instantiations of these abstract models. To do so, we root the uncertainty of arguments in their components in structured models: rules and premises. Our main conceptual contribution is to provide an intuitive notion of comparative expressivity suitable for both abstract and structured formalisms. Roughly, one formalism for arguing with qualitative uncertainty is at least as expressive as another if the former can generate the same set of abstract argumentation frameworks (completions) as the latter. On the technical side, we prove negative and positive results comparing the expressivity of abstract and structured models of argumentation with uncertainty. These results concern incomplete abstract argumentation frameworks and their extension with dependencies on the abstract side, and ASPIC$^+$, assumption-based argumentation (ABA) and deductive argumentation on the structured side.

cs.AI↗
arXiv2026

A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT

Diversity has been gaining interest in the NLP community in recent years. At the same time, state-of-the-art transformer models such as ModernBERT use very large pre-training datasets, which are driven by size rather than by diversity. This summons to investigate the impact of diversity on pre-training. We do so in this study, with the express intent of reducing pre-training dataset size, while retaining at least comparable performance. We compare diversity-driven sampling algorithms, and we use the best one to pre-train several ModernBERT models on French with a fixed compute budget. We fine-tune and evaluate them on a variety of French benchmarks. We compare them with models pre-trained on randomly sampled data of commensurate size, with the same compute budget. We find that both random and diversity-driven sampling may reduce the pre-training dataset by up to 94% and the pre-training time by up to 73% while maintaining performance. Moreover, in some tasks, the inherent quality of models, estimated via head-only fine-tuning, is up to 10 points higher with diversity sampling than with random sampling.

cs.CL↗
arXiv2026

Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner

Pre-trained foundation models have recently made significant progress in table-related tasks such as table understanding and reasoning. However, recognizing the structure and content of unstructured tables using Vision Large Language Models (VLLMs) remains under-explored. To bridge this gap, we propose a benchmark based on a hierarchical design philosophy to evaluate the recognition capabilities of VLLMs in training-free scenarios. Through in-depth evaluations, we find that low-quality image input is a significant bottleneck in the recognition process. Drawing inspiration from this, we propose the Neighbor-Guided Toolchain Reasoner (NGTR) framework, which is characterized by integrating diverse lightweight tools for visual operations aimed at mitigating issues with low-quality images. Specifically, we transfer a tool selection experience from a similar neighbor to the input and design a reflection module to supervise the tool invocation process. Extensive experiments on public datasets demonstrate that our approach significantly enhances the recognition capabilities of the vanilla VLLMs. We believe that the benchmark and framework could provide an alternative solution to table recognition.

cs.CV↗
arXiv2026

Cartographing Gravity: Operational Maps, the Conatus of Proper Time, and the Potentia of Matter

Classical natural philosophy sought the natural law governing the motion of free bodies long before daring to construct the appropriate mathematical formalism. Following this longstanding methodological perspective, in the present work General Relativity is reconstructed from operational and dynamical principles, rather than taking spacetime geometry as its starting point. The \textit{conatus} of a massive free particle is identified with its natural tendency to follow a worldline of stationary proper time, which, under the appropriate local conditions, corresponds to a maximum of its proper time, whereas gravitation is interpreted as the \textit{potentia} of matter to modify the local rate of proper time. By requiring the exact validity of Galilean free fall, the weak-field static invariant is determined. The gravitational field of moving sources is subsequently obtained by Lorentz-transforming the elementary static solution and causally superposing the retarded contributions of infinitesimal source elements. The resulting linearized field equations coincide with those of General Relativity in the harmonic gauge. Finally, imposing general energy-momentum conservation together with the requirement of second-order field equations yields--- within the class of theories satisfying the stated assumptions and by virtue of Lovelock's theorem---the unique covariant completion represented by Einstein's field equations. Within this construction, geometry is not postulated from the outset; instead, it emerges as the final mathematical synthesis of operational dynamics based on proper time, inertia, and free fall.

physics.hist-ph↗
arXiv2026

Magnetic field effect on neutral color superconductivity as a mechanism to boost the inner magnetic field of magnetars

In this paper, we review the impact of a strong magnetic field on the neutral two-flavor color superconducting (2SC) phase of dense quark matter. Using a Nambu-Jona-Lasinio (NJL) model approach in the mean-field approximation, we first solve the gap equations for the diquark condensate $Δ$ and the electric chemical potential $μ_e$ under a uniform external magnetic field, and discuss the coupled evolution of the chiral and diquark order parameters. We then evaluate the field-dependent Meissner masses of the rotationally charged gluons for a wide range of densities and moderate diquark coupling strengths. Combining the "zero-mode problem" mechanism with the neutrality condition, we show that the gluon masses become tachyonic even at moderate magnetic-field values, signaling a chromomagnetic instability in density regions where the system was stable at zero field. This instability can be resolved through the restructuring of the ground state with the formation of a gluon vortex condensate. A remarkable consequence of the vortex configuration is the paramagnetic amplification of the in-medium magnetic field: seed fields of the order of $10^{15}$ G can be enhanced by up to two orders of magnitude, reaching $\sim 10^{17}$ G, precisely the range predicted for magnetar cores. We compare this microscopic mechanism with the conventional dynamo explanation and discuss how it offers a plausible explanation for the extremely high magnetic fields expected to be realized in magnetar interiors.

hep-ph↗
arXiv2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization Using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

We present a Quality by Design (QbD) styled approach for optimizing lipid nanoparticle (LNP) formulations, aiming to offer scientists an accessible workflow. The inherent restriction in these studies, where the molar ratios of ionizable, helper, and PEG lipids must add up to 100%, requires specialized design and analysis methods to accommodate this mixture constraint. Focusing on lipid and process factors that are commonly used in LNP design optimization, we provide steps that avoid many of the difficulties that traditionally arise in the design and analysis of mixture-process experiments by employing space-filling designs and utilizing the recently developed statistical framework of self-validated ensemble models (SVEM). In addition to producing candidate optimal formulations, the workflow also builds graphical summaries of the fitted statistical models that simplify the interpretation of the results. The newly identified candidate formulations are assessed with confirmation runs and optionally can be conducted in the context of a more comprehensive second-phase study.

stat.AP↗
arXiv2026

The Best Guess: Testing new and old formalisms for the common envelope against observations

We present a systematic test of formalisms for common envelope evolution by forward-modelling observable post-common envelope binaries. We compare predictions from the $α$-formalism, and the Two-stage and SCATTER formalisms against observed post-common envelope binaries, including wide binaries with ultra-massive white dwarfs and central binaries of planetary nebulae. The angular momentum-based SCATTER formalism does not predict populations which match the complete observed population, even with adjustments to its parameters. We take this as indicative of fundamental challenges with using the orbital angular momentum balance to predict common envelope outcomes. The energy-based $α$ and hybrid Two-stage formalisms both well-replicate the observed population. $α_{\rm CE} \sim 0.2\text{--}0.3$ can match current observations, in agreement with previous works. Recombination energy is necessary, but only a fraction of it ($\sim\! 10\text{--}40\%$) can contribute in order to predict IK Peg-like binaries with ultra-massive white dwarfs at the correct orbital periods. Our work suggests energy-based formalisms remain the most accurate for predicting common envelope outcomes, but more observations can constrain the recombination contribution and how these outcomes systematically vary with the donor mass.

astro-ph.SR↗
arXiv2026

Binary Dynamics and Mergers in a Stellar Disk and the Formation of Dusty Objects in the Galactic Center

For more than a decade, observers have been tracking a population of enigmatic dusty objects orbiting the central supermassive black hole of the Milky Way, Sagittarius A$^\star$ (SgrA$^\star$). In one interpretation, these objects are products of stellar mergers enshrouded in gaseous-dusty envelopes, which account for their infrared excess, emission lines, and stability against the tidal forces of SgrA$^\star$. Recent near-infrared observations of D9, one of the dusty objects, revealed that it embeds a spectroscopic stellar binary. In this work, we present full-fledged N-body simulations of an eccentric disk of binary stars similar to the young stellar disk observed around SgrA$^\star$. We find that some of the binaries in the disk are naturally drawn towards SgrA$^\star$ and merge there due to Kozai-Lidov (KL) oscillations in their orbits. Together with the observed nature of D9, these findings represent a self-consistent picture of how the population of the observed dusty objects could have been brought into existence. The binary fraction in our N-body model evolves to increase with distance from SgrA$^\star$, which is also in agreement with the observational constraints. Our simulations also suggest that beside the dusty objects, stellar mergers are present within the young stellar disk, which could be identified as chemically peculiar or fast-rotating stars. Our results show that dynamical evolution of binary stars in the young stellar disk plays a crucial role in shaping the stellar content in the Galactic Center.

astro-ph.GA↗
arXiv2026

Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization

Calibrating agent-based models (ABMs) is computationally demanding, as traditional black-box optimization typically requires hundreds or thousands of independent simulation runs. To reduce this burden, we introduce an online calibration framework that estimates parameters within a single simulation run. The approach combines black-box optimization with stochastic batch optimization, a method widely applied in machine learning but not yet explored in ABM calibration. Using MATSim as a case study, we treat each simulation iteration as a stochastic batch, enabling incremental parameter updates as agents make decisions. This online calibration paradigm allows model parameters and agent behaviors to co-evolve, achieving convergence without restarting the simulation. We demonstrate the effectiveness of our method by calibrating utility function parameters in MATSim for a large-scale transportation scenario of Switzerland. Results show that mode shares align closely with observed data within a typical simulation timeframe, confirming the feasibility and efficiency of the approach. By embedding calibration directly into the simulation loop, our method opens new possibilities for real-time, scalable, and efficient calibration of complex ABMs.

cs.MA↗
arXiv2026

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution

The interest in leveraging physics-based inductive bias in deep learning has resulted in recent development of hybrid deep generative models (hybrid-DGMs) that integrate known physics-based mathematical expressions in neural generative models. To identify these hybrid-DGMs requires inferring parameters of the physics-based component along with their neural component. The identifiability of these hybrid-DGMs, however, has not yet been theoretically probed or established. How does the existing theory of the un-identifiability of general DGMs apply to hybrid-DGMs? What may be an effective approach to construct a hybrid-DGM with theoretically-proven identifiability? This paper provides the first theoretical probe into the identifiability of hybrid-DGMs, and presents meta-learning as a novel solution to construct identifiable hybrid-DGMs. On synthetic and real-data benchmarks, we provide strong empirical evidence for the un-identifiability of existing hybrid-DGMs using unconditional priors, and strong identifiability results of the presented meta-formulations of hybrid-DGMs. Code is available at https://github.com/GoForIt-YYB/Meta-Hybrid-VAE.

cs.LG↗
arXiv2026

Forging Emotions: a deep learning experiment on emotions and art

Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media artworks, but they have focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that have recently been driven by the use of Generative Adversarial Networks (GANs), a deep learning method. The experiment presented in this paper, Forging Emotions, explores the use of visual emotion datasets and the working processes of GANs for visual affect generation, that is, for generating images that can convey or trigger specified emotions. This experiment concludes that the methodology used so far by computer science researchers to build image datasets for describing high-level concepts such as emotions is insufficient and proposes utilizing emotional networks of associations according to psychology research. Forging Emotions also concludes that to generate affect visually, merely corresponding to basic psychology findings, such as bright or dark colours, does not seem adequate. Therefore, research efforts should aim to understand the structure of trained GANs and compositional GANs in order to produce genuinely novel compositions that can convey or trigger emotions through the subject matter of generated images.

cs.CV↗