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ARXIV · 2026 · arXiv

Generative World Renderer

Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic dataset curated from visually complex AAA games. Using a novel dual-screen stitched capture method, we extracted 4M continuous frames (720p/30 FPS) of synchronized RGB and five G-buffer channels across diverse scenes, visual effects, and environments, including adverse weather and motion-blur variants. This dataset uniquely advances bidirectional rendering: enabling robust in-the-wild geometry and material decomposition, and facilitating high-fidelity G-buffer-guided video generation. Furthermore, to evaluate the real-world performance of inverse rendering without ground truth, we propose a novel VLM-based assessment protocol measuring semantic, spatial, and temporal consistency. Experiments demonstrate that inverse renderers fine-tuned on our data achieve superior cross-dataset generalization and controllable generation, while our VLM evaluation strongly correlates with human judgment. Combined with our toolkit, our forward renderer enables users to edit styles of AAA games from G-buffers using text prompts.

Authors: Zheng-Hui Huang, Zhixiang Wang, Jiaming Tan, Ruihan Yu, Yidan Zhang, Bo Zheng, Yu-Lun Liu, Yung-Yu Chuang
Citations: N/A
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ARXIV · 2026 · arXiv

Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning

Large Language Models employing Chain-of-Thought reasoning achieve strong performance but suffer from excessive token consumption that inflates inference costs. Existing efficiency methods such as explicit length penalties, difficulty estimators, or multi-stage curricula either degrade reasoning quality or require complex training pipelines. We introduce Batched Contextual Reinforcement, a minimalist, single-stage training paradigm that unlocks efficient reasoning through a simple structural modification: training the model to solve N problems simultaneously within a shared context window, rewarded purely by per-instance accuracy. This formulation creates an implicit token budget that yields several key findings: (1) We identify a novel task-scaling law: as the number of concurrent problems N increases during inference, per-problem token usage decreases monotonically while accuracy degrades far more gracefully than baselines, establishing N as a controllable throughput dimension. (2) BCR challenges the traditional accuracy-efficiency trade-off by demonstrating a "free lunch" phenomenon at standard single-problem inference. Across both 1.5B and 4B model families, BCR reduces token usage by 15.8% to 62.6% while consistently maintaining or improving accuracy across five major mathematical benchmarks. (3) Qualitative analyses reveal emergent self-regulated efficiency, where models autonomously eliminate redundant metacognitive loops without explicit length supervision. (4) Crucially, we empirically demonstrate that implicit budget constraints successfully circumvent the adversarial gradients and catastrophic optimization collapse inherent to explicit length penalties, offering a highly stable, constraint-based alternative for length control. These results prove BCR practical, showing simple structural incentives unlock latent high-density reasoning in LLMs.

Authors: Bangji Yang, Hongbo Ma, Jiajun Fan, Ge Liu
Citations: N/A
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ARXIV · 2026 · arXiv

Robust Correlation-Induced Localization Under Time-Reversal Symmetry Breaking

We study Anderson localization in a one-dimensional disordered system with long-range correlated hopping decaying as $1/r^{a}$ with complex hopping amplitudes that break time-reversal symmetry in a tunable fashion by varying their argument. We find analytically a corelation-induced algebraic localization that is robust to a finite strength of the time-reversal-symmetry-breaking parameter, beyond which all states delocalize. This establishes a localization--delocalization transition driven by the interplay between long-ranged correlated hopping and time-reversal symmetry breaking. In addition to obtaining the static localization phase diagram, we also investigate the dynamical phase diagram through the lens of wavepacket spreading. We find that the growth in time of the mean-squared displacement of a wavepacket, which is subdiffusive for the time-reversal symmetric case, becomes diffusive for any finite value of the time-reversal-symmetry-breaking parameter.

Authors: Bikram Pain, Sthitadhi Roy, Jens H. Bardarson, Ivan M. Khaymovich
Citations: N/A
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ARXIV · 2026 · arXiv

Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models

Standard LLM benchmarks evaluate the assistant turn: the model generates a response to an input, a verifier scores correctness, and the analysis ends. This paradigm leaves unmeasured whether the LLM encodes any awareness of what follows the assistant response. We propose user-turn generation as a probe of this gap: given a conversation context of user query and assistant response, we let a model generate under the user role. If the model's weights encode interaction awareness, the generated user turn will be a grounded follow-up that reacts to the preceding context. Through experiments across $11$ open-weight LLMs (Qwen3.5, gpt-oss, GLM) and $5$ datasets (math reasoning, instruction following, conversation), we show that interaction awareness is decoupled from task accuracy. In particular, within the Qwen3.5 family, GSM8K accuracy scales from $41\%$ ($0.8$B) to $96.8\%$ ($397$B-A$17$B), yet genuine follow-up rates under deterministic generation remain near zero. In contrast, higher temperature sampling reveals interaction awareness is latent with follow up rates reaching $22\%$. Controlled perturbations validate that the proposed probe measures a real property of the model, and collaboration-oriented post-training on Qwen3.5-2B demonstrates an increase in follow-up rates. Our results show that user-turn generation captures a dimension of LLM behavior, interaction awareness, that is unexplored and invisible with current assistant-only benchmarks.

Authors: Sarath Shekkizhar, Romain Cosentino, Adam Earle
Citations: N/A
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ARXIV · 2026 · arXiv

Detecting Symmetry-Resolved Entanglement: A Quantum Monte Carlo Approach

Symmetry and entanglement are two fundamental concepts in quantum many-body physics. Their interplay is captured by symmetry-resolved entanglement, which decomposes the total entanglement into contributions from different symmetry sectors. Computing symmetry-resolved entanglement in strongly interacting higher-dimensional quantum systems remains challenging. Here, we introduce a quantum Monte Carlo (QMC) approach for computing symmetry-resolved Rényi entropies (SRRE) in large-scale interacting systems by measuring disorder (symmetry-twisted) operators on replica manifolds and reconstructing SRRE from the corresponding charged moments. We apply this method to the transverse-field Ising model (TFIM) in one and two dimensions. In one dimension, we recover the conformal-field-theory prediction for the logarithmic scaling of the disorder operator and observe the expected approach to entanglement equipartition. In two dimensions, our data provide numerical evidence consistent with entanglement equipartition at the (2+1)D Ising critical point. We further apply the framework to the 1D Heisenberg chain and obtain results consistent with the expected asymptotic scaling and finite-size corrections in the U(1)-resolved sectors. Our work establishes a practical numerical route to symmetry-resolved entanglement in interacting lattice models and provides a useful framework for future studies beyond one dimension.

Authors: Kuangjie Chen, Weizhen Jia, Xiaopeng Li, René Meyer, Jiarui Zhao
Citations: N/A
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ARXIV · 2026 · arXiv

The Retraction Epidemic in Science Across Publishers, Fields, and Countries

Retractions serve as an indicator of failures in research integrity, yet most analyses focus on absolute counts rather than risk per paper. We use one of the largest open bibliographic databases to develop incidence metrics normalized by population: retractions per publication and per active author annually. Applying an epidemiological framework that models counts with exposure, we find evidence of exponential growth in retraction incidence, with approximately a 5-year doubling time at both the paper and author levels. These patterns vary significantly across fields, publishers, and countries. While scientific output is becoming more democratized globally, retractions are concentrated in fewer countries, creating a "concentration" paradox that calls for targeted monitoring. Despite exponential growth, the absolute incidence remains low (0.12% in 2021), allowing for corrective intervention. Incidence-based monitoring provides a framework for evaluating policies that safeguard research integrity at scale.

Authors: Sara Venturini, Alessandra Urbinati, Paola Gallo, Jessica T. Davis, Alessandro Vespignani
Citations: N/A
macromonetary policy asset pricingglobal liquidity financial markets
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ARXIV · 2026 · arXiv

Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation

Recent multimodal large language models have achieved strong performance in unified text and image understanding and generation, yet extending such native capability to 3D remains challenging due to limited data. Compared to abundant 2D imagery, high-quality 3D assets are scarce, making 3D synthesis under-constrained. Existing methods often rely on indirect pipelines that edit in 2D and lift results into 3D via optimization, sacrificing geometric consistency. We present Omni123, a 3D-native foundation model that unifies text-to-2D and text-to-3D generation within a single autoregressive framework. Our key insight is that cross-modal consistency between images and 3D can serve as an implicit structural constraint. By representing text, images, and 3D as discrete tokens in a shared sequence space, the model leverages abundant 2D data as a geometric prior to improve 3D representations. We introduce an interleaved X-to-X training paradigm that coordinates diverse cross-modal tasks over heterogeneous paired datasets without requiring fully aligned text-image-3D triplets. By traversing semantic-visual-geometric cycles (e.g., text to image to 3D to image) within autoregressive sequences, the model jointly enforces semantic alignment, appearance fidelity, and multi-view geometric consistency. Experiments show that Omni123 significantly improves text-guided 3D generation and editing, demonstrating a scalable path toward multimodal 3D world models.

Authors: Chongjie Ye, Cheng Cao, Chuanyu Pan, Yiming Hao, Yihao Zhi, Yuanming Hu, Xiaoguang Han
Citations: N/A
macromonetary policy asset pricing
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ARXIV · 2026 · arXiv

CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse

The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic resource sharing. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC's resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.

Authors: Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Salem
Citations: N/A
creditcredit default swap indexcredit spread decompositiondistressed debt credit markets
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ARXIV · 2026 · arXiv

SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely follows. We ask whether skills can instead be internalized into model parameters, enabling zero-shot autonomous behavior without any runtime skill retrieval. We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization. SKILL0 introduces a training-time curriculum that begins with full skill context and progressively withdraws it. Skills are grouped offline by category and rendered with interaction history into a compact visual context, teaching he model tool invocation and multi-turn task completion. A Dynamic Curriculum then evaluates each skill file's on-policy helpfulness, retaining only those from which the current policy still benefits within a linearly decaying budget, until the agent operates in a fully zero-shot setting. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7\% for ALFWorld and +6.6\% for Search-QA), while maintaining a highly efficient context of fewer than 0.5k tokens per step. Our code is available at https://github.com/ZJU-REAL/SkillZero.

Authors: Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Chengcheng Han, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao
Citations: N/A
macromonetary policy asset pricing
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ARXIV · 2026 · arXiv

Crossovers from nonlinear wave-packet acceleration to wave-mixing and self-trapping in the Hatano-Nelson model

We demonstrate that wave amplification enables even weak nonlinearities to reshape linear wave-packet transport in nonreciprocal systems. We study the dynamics of bulk Gaussian wave packets in the Hatano--Nelson model with onsite cubic nonlinearity. We show that the interplay between nonlinearity and amplification generates growing frequency shifts that drive the wave packet through three successive dynamical regimes: an early nonlinear-skin regime with coherent propagation, an intermediate wave-mixing regime driven by mode resonances, and a self-trapping regime in which part of the packet localizes while the remainder ballistically spreads along the system favored direction. The crossover time scales are set by the width and average spacing of the eigen-frequency spectrum. Crucially, within the nonlinear-skin regime, we derive analytical predictions for the wave-packet dynamics and show that nonlinearity couples amplification, dispersion, and nonreciprocity, thereby modifying the magnitude of the wave-packet acceleration and introducing an explicit time dependence into its evolution. Focusing nonlinearities suppress the acceleration and cause it to decrease in time, whereas defocusing nonlinearities enhance it and cause it to increase. We further show that nonlinear interactions typically break down the wave packet before the non-Hermitian jump can occur. Our results provide a route toward accurate control of waves in nonreciprocal metamaterials.

Authors: Bertin Many Manda, Vassos Achilleos
Citations: N/A
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ARXIV · 2026 · arXiv

Plücker degrees of Quot schemes

We study the degree of the Plücker embedding $\varpi$ of the Quot scheme of length $l$ quotients of a locally free sheaf on a smooth projective scheme $\mathrm{S}$ of dimension $d\geqslant 1$. This degree is determined by classes in the Chow ring of the symmetric product $\mathrm{S}^{(l)}$, which are given by the pushforward of the powers of $c_{1}(\mathcal{O}^{[l]})$ with respect to the canonical morphism from the Quot scheme to $\mathrm{S}^{(l)}$. We describe a decomposition of these classes, allowing us to compute the (in a certain sense) leading term of $\mathrm{deg} \ \varpi$. We also obtain a higher-dimensional analogue of a classical result of Schubert.

Authors: Samuel Stark
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ARXIV · 2026 · arXiv

BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy

Multimodal time-to-event prediction often requires integrating sensitive data distributed across multiple parties, making centralized model training impractical due to privacy constraints. At the same time, most existing multimodal survival models produce single deterministic predictions without indicating how confident the model is in its estimates, which can limit their reliability in real-world decision making. To address these challenges, we propose BVFLMSP, a Bayesian Vertical Federated Learning (VFL) framework for multimodal time-to-event analysis based on a Split Neural Network architecture. In BVFLMSP, each client independently models a specific data modality using a Bayesian neural network, while a central server aggregates intermediate representations to perform survival risk prediction. To enhance privacy, we integrate differential privacy mechanisms by perturbing client side representations before transmission, providing formal privacy guarantees against information leakage during federated training. We first evaluate our Bayesian multimodal survival model against widely used single modality survival baselines and the centralized multimodal baseline MultiSurv. Across multimodal settings, the proposed method shows consistent improvements in discrimination performance, with up to 0.02 higher C-index compared to MultiSurv. We then compare federated and centralized learning under varying privacy budgets across different modality combinations, highlighting the tradeoff between predictive performance and privacy. Experimental results show that BVFLMSP effectively includes multimodal data, improves survival prediction over existing baselines, and remains robust under strict privacy constraints while providing uncertainty estimates.

Authors: Abhilash Kar, Basisth Saha, Tanmay Sen, Biswabrata Pradhan
Citations: N/A
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ARXIV · 2026 · arXiv

Evaluation of gNB Monostatic Sensing for UAV Use Case

3GPP Release 19 has initiated the standardization of integrated sensing and communications (ISAC), including a channel model for monostatic sensing, evaluation scenarios, and performance assessment methodologies. These common assumptions provide an important basis for ISAC evaluation, but reproducible end-to-end studies still require a transparent sensing implementation. This paper evaluates 5G New Radio (NR) base station (gNB)-based monostatic sensing for the Unmanned Aerial Vehicle (UAV) use case using a 5G NR downlink Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) waveform and positioning reference signals (PRS), following 3GPP Urban Macro-Aerial Vehicle (UMa-AV) scenario assumptions. We present an end-to-end processing chain for multi-target detection and 3D localization, achieving more than 70% detection probability with less than 5% false alarm rate, in the considered scenario. For correctly detected targets, localization errors are on the order of a few meters, with a 90th-percentile error of 4m and 6m in the vertical and horizontal directions, respectively. To support reproducible baseline studies and further research, we release the simulator 5GNRad, which reproduces our evaluation

Authors: Steve Blandino, Neeraj Varshney, Jian Wang, Jack Chuang, Camillo Gentile, Nada Golmie
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macromacro finance
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ARXIV · 2026 · arXiv

Jahn-Teller distortion on strained La$_3$Ni$_2$O$_7$ thin films

We present a systematic study of the electronic structure of strained La$_3$Ni$_2$O$_7$ thin films. We show that biaxial compressive strain mainly elongates the outer apical Ni-O bond while leaving the inner apical Ni-O bond nearly unchanged. As a result, the Jahn-Teller splitting $Δ_{JT}$ is strongly enhanced, whereas the interlayer $d_{z^2}$ hopping $t_\perp^z$ changes only weakly. Since superconductivity is widely believed to emerge only below a critical in-plane lattice constant, our results identify the strain-enhanced $Δ_{JT}$ as the relevant microscopic tuning parameter. Consistently, the calculated Fermi surfaces and Hall response for LaAlO$_3$ and SrLaAlO$_4$ substrates agree with ARPES and Hall measurements. Our results identify Jahn-Teller distortion as a key tuning parameter in strained La$_3$Ni$_2$O$_7$ and support its central role in optimizing superconductivity in bilayer nickelates.

Authors: Yuxin Wang, Zhan Wang, Fu-Chun Zhang, Kun Jiang
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ARXIV · 2026 · arXiv

AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection

As TLS 1.3 encryption limits traditional Deep Packet Inspection (DPI), the security community has pivoted to Euclidean Transformer-based classifiers (e.g., ET-BERT) for encrypted traffic analysis. However, these models remain vulnerable to byte-level adversarial morphing -- recent pre-padding attacks reduced ET-BERT accuracy to 25.68%, while VLESS Reality bypasses certificate-based detection entirely. We introduce AEGIS: an Adversarial Entropy-Guided Immune System powered by a Thermodynamic Variance-Guided Hyperbolic Liquid State Space Model (TVD-HL-SSM). Rather than competing in the Euclidean payload-reading domain, AEGIS discards payload bytes in favor of 6-dimensional continuous-time flow physics projected into a non-Euclidean Poincare manifold. Liquid Time-Constants measure microsecond IAT decay, and a Thermodynamic Variance Detector computes sequence-wide Shannon Entropy to expose automated C2 tunnel anomalies. A pure C++ eBPF Harvester with zero-copy IPC bypasses the Python GIL, enabling a linear-time O(N) Mamba-3 core to process 64,000-packet swarms at line-rate. Evaluated on a 400GB, 4-tier adversarial corpus spanning backbone traffic, IoT botnets, zero-days, and proprietary VLESS Reality tunnels, AEGIS achieves an F1-score of 0.9952 and 99.50% True Positive Rate at 262 us inference latency on an RTX 4090, establishing a new state-of-the-art for physics-based adversarial network defense.

Authors: Vickson Ferrel
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ARXIV · 2026 · arXiv

Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors

Magnetohydrodynamic (MHD) phenomena play a pivotal role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten salts employed in reactor blankets) interact with magnetic fields of varying intensity and orientation, influencing the resulting flow dynamics. The numerical solution of MHD models entails the resolution of highly nonlinear, multiphysics systems of equations, which can become computationally demanding, particularly in multi-query, parametric, or real-time contexts. This study investigates a fully data-driven framework for MHD state reconstruction that integrates dimensionality reduction through Singular Value Decomposition (SVD) with the SHallow REcurrent Decoder (SHRED), a neural network architecture designed to reconstruct the full spatio-temporal state from sparse time-series measurements of selected observables, including previously unseen parametric configurations. The SHRED methodology is applied to a three-dimensional geometry representative of a portion of a WCLL blanket cell, in which lead-lithium flows around a water-cooled tube. Multiple magnetic field configurations are examined, including constant toroidal fields, combined toroidal-poloidal fields, and time-dependent magnetic fields. Across all considered scenarios, SHRED achieves high reconstruction accuracy, robustness, and generalization to magnetic field intensities, orientations, and temporal evolutions not seen during training. Notably, in the presence of time-varying magnetic fields, the model accurately infers the temporal evolution of the magnetic field itself using temperature measurements alone. Overall, the findings identify SHRED as a computationally efficient, data-driven, and flexible approach for MHD state reconstruction, with significant potential for real-time monitoring, diagnostics and control in fusion reactor systems.

Authors: M. Lo Verso, C. Introini, E. Cervi, L. Savoldi, J. N. Kutz, A. Cammi
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ARXIV · 2026 · arXiv

Towards Chemically Accurate and Scalable Quantum Simulations on IQM Quantum Hardware: A Quantum-HPC Hybrid Approach

We present a large-scale experimental study of quantum-computing-based molecular simulation carried out on IQM's Sirius 24-qubit superconducting processor, utilizing up to 16 operational qubits. The work employs Sample-based Quantum Diagonalization (SQD) together with the Local Unitary Cluster Jastrow (LUCJ) ansatz to estimate ground-state energies for a set of benchmark molecules, including H$_2$, LiH, BeH$_2$, H$_2$O, and NH$_3$. In addition, we introduce a Linear-CNOT variant of the Unitary Coupled-Cluster Singles and Doubles (LCNot-UCCSD) ansatz within the SQD workflow, trading higher circuit depth for reduced classical preprocessing. A comparison between these ansätze is provided, clarifying their respective strengths, limitations, and suitability for near-term quantum hardware. We further explore potential energy landscapes through 1D scans for H$_2$ and HeH$^+$ using both STO-3G and 6-31G basis sets, and for LiH and BeH$_2$ in STO-3G. Extending beyond this, we demonstrate the experimental construction of a full 2D potential energy surface for the water molecule on quantum hardware, mapped over a 32 $\times$ 32 grid in bond length and bond angle. To move beyond small benchmark systems, we combine SQD(LUCJ) with Density Matrix Embedding Theory (DMET) to compute active-space energies for a set of ligand-like molecules, as well as the pharmacologically relevant amantadine system. Across all studies, the majority of quantum-computed energies agree with reference FCI results, as well as with DMET-CASCI energies for embedded systems, to within chemical accuracy for the chosen basis sets. These results demonstrate the reliability of sample-based diagonalization approaches and underscore the potential of hybrid embedding strategies for extending quantum simulations to increasingly complex molecular systems, while also highlighting their practicality on current IQM quantum hardware.

Authors: Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Alok Shukla, Sai Shankar P., Ruchika Bhat, Radhika T. S. L., Jaiganesh G
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ARXIV · 2026 · arXiv

Rotational Fluorescence Recovery after Orientational Photobleaching via surface electromagnetic waves on dielectric stacks

Protein rotational kinetics are essential for understanding macromolecular behavior in crowded environments, yet measuring these dynamics at solid-liquid interfaces remains a significant challenge due to low signal strengths. Here, we experimentally demonstrate a label-based optical technique for measuring rotational diffusion kinetics using an all-dielectric multilayer stack that sustains both transverse electric and transverse magnetic polarized surface electromagnetic waves. We introduce the concept of Fluorescence Recovery after Orientational Photobleaching, a rotational analogue to the standard translatory fluorescence recovery after photobleaching technique, which utilizes anisotropic photobleaching via resonant transverse electric excitation followed by real-time monitoring of the orientational relaxation towards isotropy. Our ratiometric analysis of the transverse electric and magnetic polarized fluorescence components allows for a distance-independent estimation of the rotational friction coefficient. Applying this method to covalently bound neutravidin, we observe a rotational friction coefficient (about 5.8E-18 J s) significantly higher than in bulk solutions, highlighting the impact of surface anchoring and molecular crowding. The proposed approach provides a robust, high-sensitivity platform for resolving biomolecular dynamics in complex interfacial environments.

Authors: Francesco Michelotti, Elisabetta Sepe, Agostino Occhicone, Norbert Danz, Alberto Sinibaldi
Citations: N/A
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OPENALEX · 2026 · Journal of Materials Science

POM-activated gCN intermediate as a new composite precursor for organic–inorganic materials: introducing a new composite as a starting material toward graphitic carbon nitride-based photocatalysts

No abstract available.

Authors: Simon Yves Tameu Djoko, Michael Schwarze, Daniel Brüggemann, Philipp Isbrücker, Billy Toussie Tchegnitegni, Arne Thomas, Harvey Arellano-Garcia, Reinhard Schomäcker
Citations: 0
Advanced Photocatalysis TechniquesMagnesium Oxide Properties and ApplicationsLayered Double Hydroxides Synthesis and ApplicationsGraphitic carbon nitrideMaterials scienceComposite number
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ARXIV · 2026 · arXiv

ELT-Bench-Verified: Benchmark Quality Issues Underestimate AI Agent Capabilities

Constructing Extract-Load-Transform (ELT) pipelines is a labor-intensive data engineering task and a high-impact target for AI automation. On ELT-Bench, the first benchmark for end-to-end ELT pipeline construction, AI agents initially showed low success rates, suggesting they lacked practical utility. We revisit these results and identify two factors causing a substantial underestimation of agent capabilities. First, re-evaluating ELT-Bench with upgraded large language models reveals that the extraction and loading stage is largely solved, while transformation performance improves significantly. Second, we develop an Auditor-Corrector methodology that combines scalable LLM-driven root-cause analysis with rigorous human validation (inter-annotator agreement Fleiss' kappa = 0.85) to audit benchmark quality. Applying this to ELT-Bench uncovers that most failed transformation tasks contain benchmark-attributable errors -- including rigid evaluation scripts, ambiguous specifications, and incorrect ground truth -- that penalize correct agent outputs. Based on these findings, we construct ELT-Bench-Verified, a revised benchmark with refined evaluation logic and corrected ground truth. Re-evaluating on this version yields significant improvement attributable entirely to benchmark correction. Our results show that both rapid model improvement and benchmark quality issues contributed to underestimating agent capabilities. More broadly, our findings echo observations of pervasive annotation errors in text-to-SQL benchmarks, suggesting quality issues are systemic in data engineering evaluation. Systematic quality auditing should be standard practice for complex agentic tasks. We release ELT-Bench-Verified to provide a more reliable foundation for progress in AI-driven data engineering automation.

Authors: Christopher Zanoli, Andrea Giovannini, Tengjun Jin, Ana Klimovic, Yotam Perlitz
Citations: N/A
macroinflation expectations rates
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OPENALEX · 2026 · JOM

Influence of Indium on Cu6(Sn,In)5 Formation and Microstructure Refinement in Sn-10wt.%Cu Solder Alloy

No abstract available.

Authors: Muhammad Fadlin Hazim Baser, Mohd Arif Anuar Mohd Salleh, Rita Mohd Said, Muhammad Fadlan Zakaria, Nur Syahirah Mohamad Zaimi, Suttipong Wannapaiboon, Krongthong Kamonsuangkasem, S. Tancharakorn
Citations: 0
Electronic Packaging and Soldering TechnologiesNanomaterials and Printing TechnologiesMetallurgical and Alloy ProcessesMaterials scienceMicrostructureMetallurgy
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ARXIV · 2026 · arXiv

Stabilizing Rubric Integration Training via Decoupled Advantage Normalization

We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, treating all correct responses identically regardless of reasoning quality, and gradually lose the advantage signal as groups become uniformly correct. Process reward models (PRM) offer richer supervision, but directly using PRM scores causes reward hacking, where models exploit verbosity to inflate scores while accuracy collapses. PAPO resolves both by composing the advantage from an outcome component Aout, derived from ORM and normalized over all responses, and a process component Aproc, derived from a rubric-based PRM and normalized exclusively among correct responses. This decoupled design ensures that Aout anchors training on correctness while Aproc differentiates reasoning quality without distorting the outcome signal. Experiments across multiple model scales and six benchmarks demonstrate that PAPO consistently outperforms ORM, reaching 51.3% vs.\ 46.3% on OlympiadBench while continuing to improve as ORM plateaus and declines.

Authors: Zelin Tan, Zhouliang Yu, Bohan Lin, Zijie Geng, Hejia Geng, Yudong Zhang, Mulei Zhang, Yang Chen
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macromonetary policy asset pricing
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OPENALEX · 2026 · Journal of Economics Finance and Accounting Studies

Clustering Capital Management Approaches: Evidence from Banking Firms during Economic Disruptions

No abstract available.

Authors: Enrico Aurelio L. Torres, Emmanuel P. Paulino, Ronald Romero, Paulo Noel Mazo, Karina Clara C. Romero, Reynaldo J. Villegas
Citations: 0
Supply Chain Resilience and Risk ManagementSustainable Finance and Green BondsFinancial Distress and Bankruptcy PredictionBusinessFinancial capitalCapital (architecture)
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OPENALEX · 2026 · Scientific Reports

Multi-scale performance, life-cycle and economic assessment of blended concrete using recycled coarse aggregates

No abstract available.

Authors: Bypaneni Krishna Chaitanya, Yellinedi Madhavi, Chava Venkatesh, Chereddy Sonali Sri Durga, Sarath C. Gowd, Bahiru Bewket Mitikie
Citations: 0
Recycled Aggregate Concrete PerformanceInnovative concrete reinforcement materialsRecycling and utilization of industrial and municipal waste in materials productionEnvironmental scienceWaste managementEconomic analysis
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ARXIV · 2026 · arXiv

Primordial Non-Gaussianity and the Field-Level Cramer-Rao Bound

Primordial non-Gaussianity is one of the most powerful probes of the inflationary epoch. The particle spectrum relevant to inflation, including masses and spins, is encoded in the precise form of statistical correlations of the adiabatic modes. Yet, in the presence of nonlinear structure formation, the optimal approach to measuring these signals remains unclear. Accurate modeling becomes crucial as late-time non-Gaussianty can become degenerate with primordial physics. Moreover, scale-dependent bias shows that information can move from non-Gaussian initial conditions to the amplitude of the Gaussian fluctuations. In this paper, we aim to clarify how primordial information is encoded in maps of galaxies. We use the field-level Cramer-Rao bound to investigate the ultimate limit of what can be extracted from realistic maps of the Universe. For local non-Gaussianity, we show that multi-tracer scale-dependent bias can exceed the sensitivity of conservative higher-point analyses. However, as expected, the multi-tracer analysis falls short of the optimal constraint when all the modes at the scale of the dark matter halos are included. We then forecast the potential reach of future surveys for equilateral and local non-Gaussianity. Equilateral in particular is highly sensitive to priors and modeling assumptions and can benefit dramatically from theoretical input such as the redshift evolution of the bias.

Authors: Eugene Chen, Daniel Green, Vincent S. H. Lee
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macroinflation expectations rates
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ARXIV · 2026 · arXiv

Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Approach with Jump-Diffusion

Generating synthetic financial time series that preserve the statistical properties of real market data is essential for stress testing, risk model validation, and scenario design. Existing approaches struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation, and persistent volatility clustering. We developed a hybrid hidden Markov framework that discretized excess growth rates into Laplace quantile-defined states and augmented regime switching with a Poisson jump-duration mechanism to enforce realistic tail-state dwell times. Parameters were estimated by direct transition counting, bypassing the Baum-Welch EM algorithm and scaling to a 424-asset pipeline. Applied to ten years of daily equity data, the framework achieved high distributional pass rates both in-sample and out-of-sample while partially reproducing the volatility clustering that standard regime-switching models miss. No single model was best at everything: GARCH(1,1) better reproduced volatility clustering but failed distributional tests, while the standard HMM without jumps passed more distributional tests but could not generate volatility clustering. The proposed framework delivered the most balanced performance overall. For multi-asset generation, copula-based dependence models that preserved each asset's marginal HMM distribution substantially outperformed a Single-Index Model factor baseline on both per-asset distributional accuracy and correlation reproduction.

Authors: Abdulrahman Alswaidan, Jeffrey D. Varner
Citations: N/A
creditcredit default swap indexdistressed debt credit marketsmacromacro financemonetary policy asset pricing
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ARXIV · 2026 · arXiv

Sculpting of Martian brain terrain reveals the drying of ancient Mars

The Martian brain terrain (MBT), characterized by its unique brain-like morphology, is a potential geological archive for finding hints of paleoclimatic conditions during its formation period. The morphological similarity of MBT to self-organized patterned ground on Earth suggests a shared formation mechanism. However, the lack of quantitative descriptions and robust physical modeling of self-organized stone transport jointly limits the study of the thermal and aqueous conditions governing MBT's formation. Here we established a specialized quantitative system for extracting the morphological features of MBT, taking a typical region located in the northern Arabia Terra as an example, and then employed a numerical model to investigate its formation mechanisms. Our simulation results accurately replicate the observed morphology of MBT, matching its key geometric metrics with deviations <15%. Crucially, however, we find that the self-organized transport can solely produce relief <0.5 m, insufficient to explain the formation of MBT with average relief of 3.29 \pm 0.65 m. We attribute this discrepancy to sculpting driven by late-stage sublimation, constraining cumulative subsurface ice loss in this region to ~3 meters over the past ~3 Ma. These findings demonstrate that MBT's formation is a multi-stage process: initial patterning driven by freeze-thaw cycles implying liquid water followed by vertical sculpting via sublimation requiring a dry environment. This evolution provides physical evidence for the transition of the ancient Martian climate from a wetter period to a colder hyper-arid state.

Authors: Shenyi Zhang, Lei Zhang, Yutian Ke, Jinhai Zhang
Citations: N/A
creditcorporate bond liquidity
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SEMANTIC SCHOLAR · 2025 · The Journal of Financial Data Science

Graph-Based Factor Models for Interpretable Credit Spread Decomposition

Factor models are essential tools for understanding asset returns. Statistical factor models such as principal component analysis (PCA) and autoencoders have been widely used to reduce the high-dimensional panels of returns into a lower-dimensional latent space. Although effective at retaining much of the original variance, these models often lack inherent economic interpretation and rely solely on historical data, failing to incorporate contextual features such as asset characteristics into factor construction. Consequently, ad hoc analyses are often required to assign real-world meaning to latent factors. To address these limitations, this article introduces a novel graph factor model (GFM) that integrates domain-informed sparsity, explicitly connecting factors to financially validated features to enable interpretable and robust factor extraction. Extensive experiments on modeling corporate spread returns demonstrate that the GFM captures more variance, is more robust to missing data, and provides clearer economic insights than PCA, autoencoders, and instrumented PCA. By bridging the gap between statistical performance and economic interpretability, this new framework supports tasks such as performance attribution and offers valuable insights for portfolio management.

Authors: Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, M. Vazirgiannis
Citations: 0
creditcredit spread decomposition
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SEMANTIC SCHOLAR · 2024 · Financial Innovation

Impact of implicit government guarantee on the credit spread of urban construction investment bonds

Financing sources for urban construction have garnered significant attention globally. Among various financing methods, the urban construction investment bond (UCIB) is unique to China. The UCIB credit spread, which represents the compensation for credit risk, has become a focal point for researchers. However, owing to shortcomings of previous approaches, few scholars have accurately assessed the impact of implicit government guarantees on credit spreads. This study introduces an innovative approach that uses orthogonal decomposition to extract proprietary information from credit ratings, reflecting implicit government guarantees. After accounting for bond factors, local government financing vehicle factors, and macroeconomic conditions, the implicit government guarantee substantially reduces the UCIB's credit spread. This conclusion remains robust when controlling for investor attention, regional factors, or duration.

Authors: Rongda Chen, Han Li, Xuhui Tang, Chenglu Jin, Shuonan Zhang, Xinyu Zhang
Citations: 1
creditcredit spread decomposition
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OPENALEX · 2022 · Applied Finance Letters

REFLECTIONS ON EDITING THE JOURNAL OF FUTURES MARKETS AND FACTORS INFLUENCING DERIVATIVES MARKETS RESEARCH

The Journal of Futures Markets is the leading academic journal specializing in publishing scholarly research on derivative securities and markets. I had the privilege of serving as Editor of the Journal of Futures Markets for 24 years. That position provided me with a catbird seat with which to view the evolution of the financial-economic literature on derivative securities and markets. It also provided me with an opportunity to reflect on how changes in derivative securities and markets, have influenced research. These include the influence of technological advances; changes in market microstructure; financial crises; the growth of derivatives markets in emerging economies; the introduction of credit default swaps, VIX derivatives, cryptocurrency derivatives; and other new products; among others. Although my reflections on the impact of market events on derivatives research is the principal focus of this paper, I want to preface that discussion with some comments on the editing process and the influence of my education, research, and experience on my role as an Editor.

Authors: Robert I. Webb
Citations: 1
FinTech, Crowdfunding, Digital FinancePrivate Equity and Venture CapitalBanking stability, regulation, efficiency
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SEMANTIC SCHOLAR · 2020 · Journal of international financial markets, institutions, and money

No-arbitrage determinants of credit spread curves under the unconventional monetary policy regime in Japan

Abstract We introduce an affine term structure model with observed macroeconomic factors for credit spread curves under the unconventional monetary policy regime in Japan. Empirical results based on the model selection using Japanese data demonstrate that the credit spread curves are dominated by the monetary policy and suggest that global economic forces, such as the U.S. Treasury yield and Baa-Aaa credit spread, play a major role in the dynamics of credit spread curves, complementing a growing body of literature explaining what drives credit spread curves. Our contemporaneous response and historical decomposition analyses find that monetary policy and global economic and financial forces have large impacts on credit spread curves at all maturities and rating classes.

Authors: Tatsuyoshi Okimoto, Sumiko Takaoka
Citations: 6
creditcredit spread decomposition
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OPENALEX · 2017 · Journal of Financial Markets

Price discovery in equity and CDS markets

No abstract available.

Authors: Lawrence Kryzanowski, Stylianos Perrakis, Rui Zhong
Citations: 25
Credit Risk and Financial RegulationsFinancial Markets and Investment StrategiesBanking stability, regulation, efficiency
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OPENALEX · 2015 · Review of Financial Studies

… and the Cross-Section of Expected Returns

Hundreds of papers and factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make sense to use the usual criteria for establishing significance. Which hurdle should be used for current research? Our paper introduces a new multiple testing framework and provides historical cutoffs from the first empirical tests in 1967 to today. A new factor needs to clear a much higher hurdle, with a t-statistic greater than 3.0. We argue that most claimed research findings in financial economics are likely false. (JEL C12, C52, G12)

Authors: Campbell R. Harvey, Yan Liu, Caroline Zhu
Citations: 1978
Financial Markets and Investment StrategiesStochastic processes and financial applicationsFinancial Risk and Volatility Modeling
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OPENALEX · 2009 · Review of Financial Studies

Explaining Credit Default Swap Spreads with the Equity Volatility and Jump Risks of Individual Firms

This paper attempts to explain the credit default swap (CDS) premium, using a novel approach to identify the volatility and jump risks of individual firms from high-frequency equity prices. Our empirical results suggest that the volatility risk alone predicts 48% of the variation in CDS spread levels, whereas the jump risk alone forecasts 19%. After controlling for credit ratings, macroeconomic conditions, and firms' balance sheet information, we can explain 73% of the total variation. We calibrate a Merton-type structural model with stochastic volatility and jumps, which can help to match credit spreads after controlling for the historical default rates. Simulation evidence suggests that the high-frequency-based volatility measures can help to explain the credit spreads, above and beyond what is already captured by the true leverage ratio.

Authors: Benjamin Yibin Zhang, Hao Zhou, Haibin Zhu
Citations: 608
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyFinancial Distress and Bankruptcy Prediction
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OPENALEX · 2008 · Applied Economics Letters

An empirical analysis of the CDX index and its tranches

The desire of market participants to go long or short a portfolio of corporate credits led to the introduction of various types of indices of credit default swaps. In this article, we empirically investigate the relationships between the spreads of the North America CDX index and its tranches and their theoretical determinants. We find (1) support for a number of results predicted by the structural models used in credit risk modelling, such as the Merton model and (2) that CDX spreads are highly responsive to microstructure variables but not to macroeconomic variables.

Authors: Frank J. Fabozzi, Yichen Wang, Shih‐Kuo Yeh, Ren‐Raw Chen
Citations: 2
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyMonetary Policy and Economic Impact
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OPENALEX · 2007 · The Journal of Finance

Corporate Yield Spreads and Bond Liquidity

ABSTRACT We find that liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4,000 corporate bonds and spanning both investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads, and an improvement in liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond‐specific, firm‐specific, and macroeconomic variables, and are robust to issuers' fixed effect and potential endogeneity bias. Our findings justify the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants.

Authors: Long Chen, David A. Lesmond, Jason Zhanshun Wei
Citations: 1130
Credit Risk and Financial RegulationsBanking stability, regulation, efficiencyFinancial Markets and Investment Strategies
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OPENALEX · 2004 · Econstor (Econstor)

Measuring financial integration in the euro area

In 2004 all ECB publications will feature a motif taken from the €100 banknote. This paper can be downloaded from the ECB’s website

Authors: Lieven Baele, Annalisa Ferrando, Peter Hördahl, Elizaveta Krylova, Cyril Monnet
Citations: 535
Monetary Policy and Economic ImpactGlobal Financial Crisis and PoliciesEconomic Policies and Impacts
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