Tootfinder

Eatman: Complementary football not always a good thing https://www.dallascowboys.com/news/eatman-complementary-football-not-always-a-good-thing

Eatman: Complementary football not always a good thing https://www.dallascowboys.com/news/eatman-complementary-football-not-always-a-good-thing

The current AI strategies of China and the US are complementary, as unlike the US, China isn't "AGI-pilled" yet, focusing on embodied AI and open source models (Dean W. Ball/Hyperdimensional)
https://www.hyperdimensional.co/p/the-bitter-lessons

The Bitter Lessons
Thoughts on US-China Competition

Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
Blessing Agyei Kyem, Neema Jakisa Owor, Andrews Danyo, Joshua Kofi Asamoah, Eugene Denteh, Tanner Muturi, Anthony Dontoh, Yaw Adu-Gyamfi, Armstrong Aboah
https://arxiv.org/abs/2510.11907

Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
Traffic safety analysis requires complex video understanding to capture fine-grained behavioral patterns and generate comprehensive descriptions for accident prevention. In this work, we present a unique dual-model framework that strategically utilizes the complementary strengths of VideoLLaMA and Qwen2.5-VL through task-specific optimization to address this issue. The core insight behind our approach is that separating training for captioning and visual question answering (VQA) tasks minimizes…

KoALA: KL-L0 Adversarial Detector via Label Agreement
Siqi Li, Yasser Shoukry
https://arxiv.org/abs/2510.12752 https://arxiv.org/pdf/2510.12752

KoALA: KL-L0 Adversarial Detector via Label Agreement
Deep neural networks are highly susceptible to adversarial attacks, which pose significant risks to security- and safety-critical applications. We present KoALA (KL-L0 Adversarial detection via Label Agreement), a novel, semantics-free adversarial detector that requires no architectural changes or adversarial retraining. KoALA operates on a simple principle: it detects an adversarial attack when class predictions from two complementary similarity metrics disagree. These metrics-KL divergence an…

Informative Keyboard and its Application to Raise Awareness of Smartphone Use
Jaroslaw Domaszewicz, Damian Sienicki, Michal Obirek
https://arxiv.org/abs/2510.10710 https://

Informative Keyboard and its Application to Raise Awareness of Smartphone Use
Excessive smartphone use is now widely considered a personal and societal problem. It is recognized by application and smartphone makers, who provide tools to track the amount of use, set limits, or block certain services at predefined times. These tools, while powerful, may require significant cognitive effort to operate: configuration parameters need to be set, and captured statistics need to be analyzed. To offer a complementary solution, we propose a radically different approach. We employ …

Distributed clustering in partially overlapping feature spaces
Alessio Maritan, Luca Schenato
https://arxiv.org/abs/2510.09799 https://arxiv.org/pdf/2510.0…

Distributed clustering in partially overlapping feature spaces
We introduce and address a novel distributed clustering problem where each participant has a private dataset containing only a subset of all available features, and some features are included in multiple datasets. This scenario occurs in many real-world applications, such as in healthcare, where different institutions have complementary data on similar patients. We propose two different algorithms suitable for solving distributed clustering problems that exhibit this type of feature space heter…

VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents
Jiliang Hu, Wenfu Wang, Zuchao Li, Chenxing Li, Yiyang Zhao, Hanzhao Li, Liqiang Zhang, Meng Yu, Dong Yu
https://arxiv.org/abs/2510.11098

VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents
Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks remain limited -- they are mainly English-centric, rely on synthetic speech, and lack comprehensive, discriminative evaluation across multiple dimensions. To address these gaps, we present Voice Chat Bot Bench (VCB Bench) -- a high-quality Chinese benchmark built entirely on real human speech. VCB Bench evaluates LALMs from three complementary perspectives…

Complementarity of gravitational wave analyses and di-Higgs production in the exploration of the Electroweak Phase Transition dynamics in the RxSM
Johannes Braathen, Sven Heinemeyer, Carlos Pulido Boatella, Alain Verduras Schaeidt
https://arxiv.org/abs/2510.12569

Complementarity of gravitational wave analyses and di-Higgs production in the exploration of the Electroweak Phase Transition dynamics in the RxSM
The real singlet extension of the Standard Model (SM), RxSM, is one of the simplest Beyond-the-Standard Model (BSM) theories that can accommodate a strong first-order electroweak phase transition (SFOEWPT). We survey the possible thermal histories of the early Universe in the RxSM, and find that a SFOEWPT can occur in this model as single- or two-step phase transitions. We investigate complementary approaches to probe such scenarios experimentally: either via searches for a stochastic backgroun…

Ambient-Stable Transfer-Free Graphdiyne Wafers with Superhigh Hole Mobility at Room Temperature
Beining Ma, Jianyuan Qi, Xinghai Shen
https://arxiv.org/abs/2510.09998 https://…

Ambient-Stable Transfer-Free Graphdiyne Wafers with Superhigh Hole Mobility at Room Temperature
Graphdiyne (GDY) is recognized as a compelling candidate for the fabrication of next-generation high-speed low-energy electronic devices due to its inherent p-type semiconductor characteristics. However, the development of GDY for applications in field-effect transistors (FETs), complementary metal-oxide-semiconductor (CMOS), and logic devices remains constrained by the relatively low carrier mobility reported in current experimental studies. Herein, the synthesis of layer-controlled hydrogen-s…

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Antonio Montieri, Alfredo Nascita, Antonio Pescap\`e
https://arxiv.org/abs/2510.11269 https://…

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Generative AI (GenAI) chatbots are now pervasive in digital ecosystems, yet their network traffic remains largely underexplored. This study presents an in-depth investigation of traffic generated by three leading chatbots (ChatGPT, Copilot, and Gemini) when accessed via Android mobile apps for both text and image generation. Using a dedicated capture architecture, we collect and label two complementary workloads: a 60-hour generic dataset with unconstrained prompts, and a controlled dataset bui…

From Primordial Stars to Early Galaxies: A Semi-Analytic Model Calibrated with Aeos and Renaissance
Ryan Hazlett, Jennifer Mead, Eli Visbal, Greg L. Bryan, Mordecai-Mark Mac Low, Mihir Kulkarni, Eric P. Andersson, Kaley Brauer, John H. Wise
https://arxiv.org/abs/2510.11629

From Primordial Stars to Early Galaxies: A Semi-Analytic Model Calibrated with Aeos and Renaissance
We present an extension of our semi-analytic model that follows the formation of Population III stars and their metal-enriched descendants, incorporating dark matter halo merger trees from cosmological $N$-body simulations and feedback from reionization. Our extended model is calibrated using two complementary cosmological hydrodynamical simulations: Aeos, which resolves individual Population III and II stars to $z\sim14.6$, and Renaissance, which is lower resolution but follows large-scale met…

Ideal Optical Antimatter using Passive Lossy Materials under Complex Frequency Excitation
Olivia Y. Long, Peter B. Catrysse, Seunghoon Han, Shanhui Fan
https://arxiv.org/abs/2510.11796

Ideal Optical Antimatter using Passive Lossy Materials under Complex Frequency Excitation
The original concept of left-handed material has inspired the possibility of optical antimatter, where the effect of light propagation through a medium can be completely cancelled by its complementary medium. Despite recent progress in the development of negative-index metamaterials, losses continue to be a significant barrier to realizing optical antimatter. In this work, we show that passive, lossy materials can be used to realize optical antimatter when illuminated by light at a complex freq…

Science ouverte et collaborative pour l'\'elaboration d'un banc automatis\'e de caract\'erisation de pertes en commutation par opposition
Nicolas Rouger, Luiz Villa, Matthieu Masson, Pauline Kergus, Joseph Kemdeg, Lorenzo Leijnen, Jean Alinei, Adrien Colomb, Ayoub Farah-Hassan, Arnauld Biganzoli
https://arxiv.org/abs/25…

Science ouverte et collaborative pour l'élaboration d'un banc automatisé de caractérisation de pertes en commutation par opposition
The switching losses of power transistors are generally measured using the so-called double pulse method. Measuring the opposition of two switching cells is a complementary method that is more accurate but indirect. However, implementing this method can be more complex and requires calibration steps and comprehensive control, with the added issue of thermal management. In this context, we proposed to address this topic through open and collaborative science, first in the form of a two-day hacka…

DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction
Jinxin Shi, Zongsheng Cao, Runmin Ma, Yusong Hu, Jie Zhou, Xin Li, Lei Bai, Liang He, Bo Zhang
https://arxiv.org/abs/2510.08959

DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction
The deep-research framework orchestrates external tools to perform complex, multi-step scientific reasoning that exceeds the native limits of a single large language model. However, it still suffers from context pollution, weak evidentiary support, and brittle execution paths. To address these issues, we propose DualResearch, a retrieval and fusion framework that matches the epistemic structure of tool-intensive reasoning by jointly modeling two complementary graphs: a breadth semantic graph th…

Time domain braiding of anyons revealed through a nonequilibrium fluctuation dissipation theorem
Ines Safi
https://arxiv.org/abs/2510.10525 https://arxiv.o…

Time domain braiding of anyons revealed through a nonequilibrium fluctuation dissipation theorem
We derive a novel fluctuation--dissipation theorem (FDT) valid for nonequilibrium initial states that imprint the braiding of anyons in the time domain. The derivation is carried out within the Unifying Nonequilibrium Perturbative Theory (UNEPT), which applies to both standard reservoir geometries and configurations with one or two quantum point contacts (QPCs) injecting dilute anyonic fluxes. Based on this FDT, we propose complementary methods to determine the time-domain braiding phase. The f…

Generation and annotation of item usage scenarios in e-commerce using large language models
Madoka Hagiri, Kazushi Okamoto, Koki Karube, Kei Harada, Atsushi Shibata
https://arxiv.org/abs/2510.07885

Generation and annotation of item usage scenarios in e-commerce using large language models
Complementary recommendations suggest combinations of useful items that play important roles in e-commerce. However, complementary relationships are often subjective and vary among individuals, making them difficult to infer from historical data. Unlike conventional history-based methods that rely on statistical co-occurrence, we focus on the underlying usage context that motivates item combinations. We hypothesized that people select complementary items by imagining specific usage scenarios an…

Geometry-aware Policy Imitation
Yiming Li, Nael Darwiche, Amirreza Razmjoo, Sichao Liu, Yilun Du, Auke Ijspeert, Sylvain Calinon
https://arxiv.org/abs/2510.08787 https://…

Geometry-aware Policy Imitation
We propose a Geometry-aware Policy Imitation (GPI) approach that rethinks imitation learning by treating demonstrations as geometric curves rather than collections of state-action samples. From these curves, GPI derives distance fields that give rise to two complementary control primitives: a progression flow that advances along expert trajectories and an attraction flow that corrects deviations. Their combination defines a controllable, non-parametric vector field that directly guides robot be…

A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis
Valentin Biller, Lucas Zimmer, Can Erdur, Sandeep Nagar, Daniel R\"uckert, Niklas Bubeck, Jonas Weidner
https://arxiv.org/abs/2510.09365

A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis
Magnetic resonance imaging (MRI) inpainting supports numerous clinical and research applications. We introduce the first generative model that conditions on voxel-level, continuous tumor concentrations to synthesize high-fidelity brain tumor MRIs. For the BraTS 2025 Inpainting Challenge, we adapt this architecture to the complementary task of healthy tissue restoration by setting the tumor concentrations to zero. Our latent diffusion model conditioned on both tissue segmentations and the tumor …

Extending CSST Emulator to post-DESI era
Zhao Chen, Yu Yu
https://arxiv.org/abs/2510.09503 https://arxiv.org/pdf/2510.09503

Extending CSST Emulator to post-DESI era
The recent DESI BAO measurements have revealed a potential deviation from a cosmological constant, suggesting a dynamic nature of dark energy. To rigorously test this result, complementary probes such as weak gravitational lensing are crucial, demanding highly accurate and efficient predictions of the nonlinear matter power spectrum within the $w_0w_a$CDM framework. However, most existing emulators fail to cover the full parameter posterior from DESI DR2+CMB constraints in the $w_0\mbox{-}w_a$ …

Are you a purpose-driven exec hitting a wall? Will you be at SustainableBrands next week? I’m offering a complementary ontological coaching session for 3 of you. No cost. No obligation. No net. Interested? DM me: What’s your wall? What do you think—or hope—might be on the other side of it?

Articulation-Informed ASR: Integrating Articulatory Features into ASR via Auxiliary Speech Inversion and Cross-Attention Fusion
Ahmed Adel Attia, Jing Liu, Carol Espy Wilson
https://arxiv.org/abs/2510.08585

Articulation-Informed ASR: Integrating Articulatory Features into ASR via Auxiliary Speech Inversion and Cross-Attention Fusion
Prior works have investigated the use of articulatory features as complementary representations for automatic speech recognition (ASR), but their use was largely confined to shallow acoustic models. In this work, we revisit articulatory information in the era of deep learning and propose a framework that leverages articulatory representations both as an auxiliary task and as a pseudo-input to the recognition model. Specifically, we employ speech inversion as an auxiliary prediction task, and th…

Homomorphism Problems in Graph Databases and Automatic Structures
R\'emi Morvan
https://arxiv.org/abs/2510.07422 https://arxiv.org/pdf/2510.07422

Homomorphism Problems in Graph Databases and Automatic Structures
This thesis investigates the central role of homomorphism problems (structure-preserving maps) in two complementary domains: database querying over finite, graph-shaped data, and constraint solving over (potentially infinite) structures. Building on the well-known equivalence between conjunctive query evaluation and homomorphism existence, the first part focuses on conjunctive regular path queries, a standard extension of conjunctive queries that incorporates regular-path predicates. We study t…

The assembly of intermediate black holes with complementary approaches: Dragon II and BPop
Cristiano Ugolini
https://arxiv.org/abs/2510.07389 https://arxiv…

The assembly of intermediate black holes with complementary approaches: Dragon II and BPop
Intermediate-mass black holes (IMBHs) occupy the $ 10^2 - 10^5\,M_\odot $ range, but their existence remains poorly constrained. Only a few candidates have been suggested in dwarf galaxies, globular clusters, and LIGO-Virgo-Kagra detections. To investigate their formation and demographics, we adopt two complementary approaches. We first analyze the \textsc{dragonii} direct $N$-body simulations, which follow clusters with up to $ 10^6 $ stars, capture IMBHs growth. We then employ the semi-analyt…

Single-Shot Universality in Quantum LDPC Codes via Code-Switching
Shi Jie Samuel Tan, Yifan Hong, Ting-Chun Lin, Michael J. Gullans, Min-Hsiu Hsieh
https://arxiv.org/abs/2510.08552

Single-Shot Universality in Quantum LDPC Codes via Code-Switching
Code-switching is a powerful technique in quantum error correction that allows one to leverage the complementary strengths of different codes to achieve fault-tolerant universal quantum computation. However, existing code-switching protocols that encapsulate recent generalized lattice surgery approaches often either require many rounds of measurements to ensure fault-tolerance or suffer from low code rates. We present a single-shot, universal protocol that uses code-switching between high-rate …

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Lei Xu, Pierre Beckmann, Marco Valentino, Andr\'e Freitas
https://arxiv.org/abs/2510.06774 https://

Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an adaptive, multi-paradigm, neuro-symbolic inference framework that: (1) automatically identifies formal reasoning strategies from problems expr…

Crosslisted article(s) found for astro-ph.SR. https://arxiv.org/list/astro-ph.SR/new
[1/1]:
- The assembly of intermediate black holes with complementary approaches: Dragon II and BPop
Cristiano Ugolini

Bootstrapping Yang-Mills matrix integrals
Wenliang Li, Xinran Su
https://arxiv.org/abs/2510.06704 https://arxiv.org/pdf/2510.06704

Bootstrapping Yang-Mills matrix integrals
We revisit the large $N$ limit of bosonic $D$-matrix Yang-Mills integrals using two complementary bootstrap methods. In the positivity bootstrap, we obtain bounds for $\langle \text{tr}\, XX \rangle$ and $\langle \text{tr}\, XXXX \rangle$ at various length cutoff $L_{\max}$. For $D=3$, we do not find an isolated region until $L_{\max}=12$. For larger $D$, the allowed regions become islands at $L_{\max}=8$ and shrink rapidly as $L_{\max}$ increases. The precision of some $L_{\max}=12$ islands is…

A bijection between symmetric plane partitions and quasi transpose complementary plane partitions
Takuya Inoue
https://arxiv.org/abs/2509.17064 https://arx…

A bijection between symmetric plane partitions and quasi transpose complementary plane partitions
We resolve the explicit bijection problem between symmetric plane partitions (SPPs) and quasi transpose complementary plane partitions (QTCPPs), introduced by Schreier-Aigner, who proved their equinumerosity. First, we relate this problem to Proctor's parallel equinumerosities for SPPs, even SPPs, staircase plane partitions, and parity staircase plane partitions, by constructing several bijections, one of which is based on the jeu de taquin algorithm. As a result, we reduce the task to construc…

Multidimensional Uncertainty Quantification via Optimal Transport
Nikita Kotelevskii, Maiya Goloburda, Vladimir Kondratyev, Alexander Fishkov, Mohsen Guizani, Eric Moulines, Maxim Panov
https://arxiv.org/abs/2509.22380

Multidimensional Uncertainty Quantification via Optimal Transport
Most uncertainty quantification (UQ) approaches provide a single scalar value as a measure of model reliability. However, different uncertainty measures could provide complementary information on the prediction confidence. Even measures targeting the same type of uncertainty (e.g., ensemble-based and density-based measures of epistemic uncertainty) may capture different failure modes. We take a multidimensional view on UQ by stacking complementary UQ measures into a vector. Such vectors are a…

Intra-Cluster Mixup: An Effective Data Augmentation Technique for Complementary-Label Learning
Tan-Ha Mai, Hsuan-Tien Lin
https://arxiv.org/abs/2509.17971 https://

Intra-Cluster Mixup: An Effective Data Augmentation Technique for Complementary-Label Learning
In this paper, we investigate the challenges of complementary-label learning (CLL), a specialized form of weakly-supervised learning (WSL) where models are trained with labels indicating classes to which instances do not belong, rather than standard ordinary labels. This alternative supervision is appealing because collecting complementary labels is generally cheaper and less labor-intensive. Although most existing research in CLL emphasizes the development of novel loss functions, the potentia…

Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair
Jos\'e Cambronero, Michele Tufano, Sherry Shi, Renyao Wei, Grant Uy, Runxiang Cheng, Chin-Jung Liu, Shiying Pan, Satish Chandra, Pat Rondon
https://arxiv.org/abs/2510.03217

Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair
Agentic Automated Program Repair (APR) is increasingly tackling complex, repository-level bugs in industry, but ultimately agent-generated patches still need to be reviewed by a human before committing them to ensure they address the bug. Showing unlikely patches to developers can lead to substantial noise, wasting valuable developer time and eroding trust in automated code changes. We introduce two complementary LLM-based policies to reduce such noise: bug abstention and patch validation polic…

Semantic Segmentation Algorithm Based on Light Field and LiDAR Fusion
Jie Luo, Yuxuan Jiang, Xin Jin, Mingyu Liu, Yihui Fan
https://arxiv.org/abs/2510.06687 https://

Semantic Segmentation Algorithm Based on Light Field and LiDAR Fusion
Semantic segmentation serves as a cornerstone of scene understanding in autonomous driving but continues to face significant challenges under complex conditions such as occlusion. Light field and LiDAR modalities provide complementary visual and spatial cues that are beneficial for robust perception; how- ever, their effective integration is hindered by limited viewpoint diversity and inherent modality discrepancies. To address these challenges, the first multimodal semantic segmentation datase…

Flow Regimes in Hot Jupiter Atmospheres: Insights from Anelastic Models
W. Dietrich, J. Wicht
https://arxiv.org/abs/2510.05964 https://arxiv.org/pdf/2510.0…

Flow Regimes in Hot Jupiter Atmospheres: Insights from Anelastic Models
Hot Jupiters are Jupiter-sized exoplanets with close-in orbits, characterized by extreme day-night temperature contrasts due to synchronous rotation. These planets offer unique observational opportunities through transit photometry, transmission spectroscopy, and infrared (IR) phase curve analysis, which reveal information about heat redistribution and atmospheric dynamics. Complementary to common generalized circulation models (GCMs), we introduce a more comprehensive approach using the anel…

Integrating Domain Knowledge into Process Discovery Using Large Language Models
Ali Norouzifar, Humam Kourani, Marcus Dees, Wil van der Aalst
https://arxiv.org/abs/2510.07161 ht…

Integrating Domain Knowledge into Process Discovery Using Large Language Models
Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvement. However, models derived solely from event data may not accurately reflect the real process, as event logs are often incomplete or affected by noise, and domain knowledge, an important complementary resource, is typically disregarded. As a result, the discovered models may lack reliability for downstream tasks. We…

Uniqueness of Complementary Recovery in Holographic Error-Correcting Codes
Julia Jones, Jason Pollack
https://arxiv.org/abs/2509.19299 https://arxiv.org/pd…

Quantum super Manin matrices
Naihuan Jing, Yinlong Liu, Jian Zhang
https://arxiv.org/abs/2510.04119 https://arxiv.org/pdf/2510.04119

Quantum super Manin matrices
Manin matrices are quantum linear transformations of general quantum spaces. In this paper, we study the $q$-analogue of super Manin matrices and obtain several quantum versions of classical identities, such as Jacobi's ratio theorem, Schur's complement theorem, Cayley's complementary theorem, Muir's law, Sylvester's theorem, MacMahon Master Theorem and Newton's identities.

Surface Wave Solutions in 1D and 2D for the Broer-Kaup-Boussinesq-Kupershmidt (BKBK) System
Darryl D. Holm, Ruiao Hu, Hanchun Wang
https://arxiv.org/abs/2510.02577 https://

Surface Wave Solutions in 1D and 2D for the Broer-Kaup-Boussinesq-Kupershmidt (BKBK) System
The BKBK system is a singular perturbation of the classical shallow water equations which modifies their transport velocity to depend on wave elevation slope. This modification introduces backward diffusion terms proportional to a real parameter $κ$. These terms also make BKBK completely integrable as a Hamiltonian system. Remarkably, when $κ=i/2$ the BKBK system may be transformed into the focusing nonlinear Schrödinger (NLS). Thus, the BKBK system with its real parameter $κ$ is complement…

SatFusion: A Unified Framework for Enhancing Satellite IoT Images via Multi-Temporal and Multi-Source Data Fusion
Yufei Tong, Guanjie Cheng, Peihan Wu, Yicheng Zhu, Kexu Lu, Feiyi Chen, Meng Xi, Junqin Huang, Shuiguang Deng
https://arxiv.org/abs/2510.07905

SatFusion: A Unified Framework for Enhancing Satellite IoT Images via Multi-Temporal and Multi-Source Data Fusion
With the rapid advancement of the digital society, the proliferation of satellites in the Satellite Internet of Things (Sat-IoT) has led to the continuous accumulation of large-scale multi-temporal and multi-source images across diverse application scenarios. However, existing methods fail to fully exploit the complementary information embedded in both temporal and source dimensions. For example, Multi-Image Super-Resolution (MISR) enhances reconstruction quality by leveraging temporal compleme…

The H.E.S.S. Gravitational Wave and Gamma-Ray Burst Follow-Up Programs
Bernardo Cornejo (on behalf of the H.E.S.S. Collaboration), Halim Ashkar (on behalf of the H.E.S.S. Collaboration), Matteo Cerruti (on behalf of the H.E.S.S. Collaboration), Ilja Jaroschewski (on behalf of the H.E.S.S. Collaboration), Pierre Pichard (on behalf of the H.E.S.S. Collaboration), Santiago Pita (on behalf of the H.E.S.S. Collaboration), Fabian Schussler (on behalf of the H.E.S.S. Collaboration)

The H.E.S.S. Gravitational Wave and Gamma-Ray Burst Follow-Up Programs
Multi-wavelength and multi-messenger astrophysics have experienced rapid growth over the past decade, seeking a complete picture of different cosmic phenomena. Transient sources, in particular, benefit from the input of multi-messenger observations, offering complementary perspectives on the same event while maximizing the detection probability of a rapidly fading signal. In this context, Gravitational Wave (GW) detections serve as perfect triggers for potential counterpart detections. Notably,…

Low-energy Cross Section Measurements of ${}^{\mathsf{12}}\mathsf{C}(\mathsf{p},\gamma)$ Deep Underground at LUNA
Jakub Skowronski, Axel Boeltzig
https://arxiv.org/abs/2510.05918

Low-energy Cross Section Measurements of ${}^{\mathsf{12}}\mathsf{C}(\mathsf{p},γ)$ Deep Underground at LUNA
The ${}^{\mathsf{12}}\mathsf{C}(\mathsf{p},γ)$ reaction cross section is currently under investigation in the low-background environment of the Laboratory for Underground Nuclear Astrophysics (LUNA). It is being studied using different types of solid targets, and employing two complementary detection techniques: HPGe spectroscopy and activation counting. To reduce systematic uncertainties, targets have been accurately characterized and their degradation under the intense beam of the LUNA-400 a…

Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Series
Ross Koval, Nicholas Andrews, Xifeng Yan
https://arxiv.org/abs/2509.19628

Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Series
Text and time series data offer complementary views of financial markets: news articles provide narrative context about company events, while stock prices reflect how markets react to those events. However, despite their complementary nature, effectively integrating these interleaved modalities for improved forecasting remains challenging. In this work, we propose a unified neural architecture that models these interleaved sequences using modality-specific experts, allowing the model to learn u…

Emergent evaluation hubs in a decentralizing large language model ecosystem
Manuel Cebrian, Tomomi Kito, Raul Castro Fernandez
https://arxiv.org/abs/2510.01286 https://

Emergent evaluation hubs in a decentralizing large language model ecosystem
Large language models are proliferating, and so are the benchmarks that serve as their common yardsticks. We ask how the agglomeration patterns of these two layers compare: do they evolve in tandem or diverge? Drawing on two curated proxies for the ecosystem, the Stanford Foundation-Model Ecosystem Graph and the Evidently AI benchmark registry, we find complementary but contrasting dynamics. Model creation has broadened across countries and organizations and diversified in modality, licensing, …

Spectral peak picking improves tactile speech perception https://www.nature.com/articles/s41598-025-28930-6 "The algorithm is suitable for real-time use in wearable sensory substitution devices and could aid the development of effective haptic hearing aids."

CompAir: Synergizing Complementary PIMs and In-Transit NoC Computation for Efficient LLM Acceleration
Hongyi Li, Songchen Ma, Huanyu Qu, Weihao Zhang, Jia Chen, Junfeng Lin, Fengbin Tu, Rong Zhao
https://arxiv.org/abs/2509.13710

CompAir: Synergizing Complementary PIMs and In-Transit NoC Computation for Efficient LLM Acceleration
The rapid advancement of Large Language Models (LLMs) has revolutionized various aspects of human life, yet their immense computational and energy demands pose significant challenges for efficient inference. The memory wall, the growing processor-memory speed disparity, remains a critical bottleneck for LLM. Process-In-Memory (PIM) architectures overcome limitations by co-locating compute units with memory, leveraging 5-20$\times$ higher internal bandwidth and enabling greater energy efficiency…

Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Tianyu Fu, Zihan Min, Hanling Zhang, Jichao Yan, Guohao Dai, Wanli Ouyang, Yu Wang
https://arxiv.org/abs/2510.03215

Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Multi-LLM systems harness the complementary strengths of diverse Large Language Models, achieving performance and efficiency gains unattainable by a single model. In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences. This process both loses rich semantic information and incurs token-by-token generation latency. Motivated by these limitations, we ask: Can LLMs communicate beyond text? Oracle experiments show that enric…

Crosslisted article(s) found for astro-ph.SR. https://arxiv.org/list/astro-ph.SR/new
[1/1]:
- The assembly of intermediate black holes with complementary approaches: Dragon II and BPop
Cristiano Ugolini

A half-shift reflection identity for the digamma function
Nikita Kalinin
https://arxiv.org/abs/2510.00012 https://arxiv.org/pdf/2510.00012

A half-shift reflection identity for the digamma function
We prove the identity \[ 2W_1(x) + \log 4 + ψ\left(\tfrac{1}{2} + x\right) + ψ\left(\tfrac{3}{2} - x\right) = 0, \] where $ψ$ is the digamma function and \[ W_1(x) = 2\int_0^\infty \Re\left( \frac{y}{(y^2+1)(e^{π(y+2ix)} - 1)} \right) dy. \] The identity was first conjectured while studying class number $h(D)$ for $D=m^2$ from two complementary perspectives. Our proof, however, is purely analytic: we compute cosine-series expansions of both sides, expressed in terms of the cosine integral C…

Valence $1s-0d$ proton vacancy of the $^{32}$Si ground state
N. Watwood, C. R. Hoffman, B. P. Kay, I. A. Tolstukhin, J. Chen, T. L. Tang, D. Bazin, Y. Ayyad, S. Beceiro-Novo, S. J. Freeman, L. P. Gaffney, R. Garg, H. Jayatissa, A. N. Kuchera, P. T. MacGregor, A. J. Mitchell, A. Mu\~noz-Ramos, C. M\"uller-Gatermann, F. Recchia, C. Santamaria, M. Z. Serikow, D. K. Sharp, G. L. Wilson, A. H. Wuosmaa, J. C. Zamora

Valence $1s-0d$ proton vacancy of the $^{32}$Si ground state
The $^{32}$Si($^3$He,$d$)$^{33}$P reaction was studied in inverse kinematics at 6.3~MeV/$u$. States in $^{33}$P corresponding to the proton $1s-0d$ single-particle orbitals were identified up to $\sim$4.5 MeV in excitation energy. The ($^{3}$He,$d$) spectroscopic factors were determined from Distorted Wave Born Approximation calculations. When combined with complementary neutron-adding data, the $1s-0d$ proton vacancies in the $^{32}$Si ground state were extracted. In conjunction with a re-anal…

Wearable and Ultra-Low-Power Fusion of EMG and A-Mode US for Hand-Wrist Kinematic Tracking
Giusy Spacone, Sebastian Frey, Mattia Orlandi, Pierangelo Maria Rapa, Victor Kartsch, Simone Benatti, Luca Benini, Andrea Cossettini
https://arxiv.org/abs/2510.02000

Wearable and Ultra-Low-Power Fusion of EMG and A-Mode US for Hand-Wrist Kinematic Tracking
Hand gesture recognition based on biosignals has shown strong potential for developing intuitive human-machine interaction strategies that closely mimic natural human behavior. In particular, sensor fusion approaches have gained attention for combining complementary information and overcoming the limitations of individual sensing modalities, thereby enabling more robust and reliable systems. Among them, the fusion of surface electromyography (EMG) and A-mode ultrasound (US) is very promising. H…

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
Riccardo Mereu, Aidan Scannell, Yuxin Hou, Yi Zhao, Aditya Jitta, Antonio Dominguez, Luigi Acerbi, Amos Storkey, Paul Chang
https://arxiv.org/abs/2510.07092

Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report
World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model Challenge introduces an open-source benchmark of real-world humanoid interaction, with two complementary tracks: sampling, focused on forecasting future image frames, and compression, focused on predicting future discrete latent codes. For the sampling track, we adapt the video generation foundation model Wan-2.2 TI2V-…

Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs
Bang Chen, Lijun Guo, Houli Fan, Wentao He, Rong Zhang
https://arxiv.org/abs/2510.06290

Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs
Identifying cancer driver genes (CDGs) is essential for understanding cancer mechanisms and developing targeted therapies. Graph neural networks (GNNs) have recently been employed to identify CDGs by capturing patterns in biological interaction networks. However, most GNN-based approaches rely on a single protein-protein interaction (PPI) network, ignoring complementary information from other biological networks. Some studies integrate multiple networks by aligning features with consistency con…

VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models
Geonung Kim, Janghyeok Han, Sunghyun Cho
https://arxiv.org/abs/2509.17985 https://

VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models
In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling flexible design exploration and rapid production of deliverables. A straightforward approach to synthesizing a video from coarse geometry might condition a video diffusion model on geometric structure. However, existing video diffusion models struggle to generate …

Revisiting the Uniform Information Density Hypothesis in LLM Reasoning Traces
Minju Gwak, Guijin Son, Jaehyung Kim
https://arxiv.org/abs/2510.06953 https://

Revisiting the Uniform Information Density Hypothesis in LLM Reasoning Traces
The Uniform Information Density (UID) hypothesis suggests that effective communication maintains a stable flow of information. In this work, we revisit this principle in the context of large language model (LLM) reasoning traces, asking whether step-level uniformity reflects reasoning quality. To this end, we propose an entropy-based stepwise information density metric and introduce two complementary measures of uniformity, local and global uniformity scores. Across the experiments on six diffe…

REMIND-PyPSA-Eur: Integrating power system flexibility into sector-coupled energy transition pathways
Adrian Odenweller, Falko Ueckerdt, Johannes Hampp, Ivan Ramirez, Felix Schreyer, Robin Hasse, Jarusch Muessel, Chen Chris Gong, Robert Pietzcker, Tom Brown, Gunnar Luderer
https://arxiv.org/abs/2510.04388

REMIND-PyPSA-Eur: Integrating power system flexibility into sector-coupled energy transition pathways
The rapid expansion of low-cost renewable electricity combined with end-use electrification in transport, industry, and buildings offers a promising path to deep decarbonisation. However, aligning variable supply with demand requires strategies for daily and seasonal balancing. Existing models either lack the wide scope required for long-term transition pathways or the spatio-temporal detail to capture power system variability and flexibility. Here, we combine the complementary strengths of REM…

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Eyal Cohen (LPSM), Christophe Denis (SAMM), Mohamed Hebiri (LAMA)
https://arxiv.org/abs/2510.04926 h…

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Set-valued classification is used in multiclass settings where confusion between classes can occur and lead to misleading predictions. However, its application may amplify discriminatory bias motivating the development of set-valued approaches under fairness constraints. In this paper, we address the problem of set-valued classification under demographic parity and expected size constraints. We propose two complementary strategies: an oracle-based method that minimizes classification risk while…

PAST: Pilot and Adaptive Orchestration for Timely and Resilient Service Delivery in Edge-Assisted UAV Networks under Spatio-Temporal Dynamics
Houyi Qi, Minghui Liwang, Liqun Fu, Sai Zou, Xinlei Yi, Wei Ni, Huaiyu Dai
https://arxiv.org/abs/2509.25700

PAST: Pilot and Adaptive Orchestration for Timely and Resilient Service Delivery in Edge-Assisted UAV Networks under Spatio-Temporal Dynamics
Incentive-driven resource trading is essential for UAV applications with intensive, time-sensitive computing demands. Traditional spot trading suffers from negotiation delays and high energy costs, while conventional futures trading struggles to adapt to the dynamic, uncertain UAV-edge environment. To address these challenges, we propose PAST (pilot-and-adaptive stable trading), a novel framework for edge-assisted UAV networks with spatio-temporal dynamism. PAST integrates two complementary mec…

Capacity-Achieving Codes for Noisy Insertion Channels
Hengfeng Liu, Chunming Tang, Cuiling Fan
https://arxiv.org/abs/2509.24161 https://arxiv.org/pdf/2509.…

Capacity-Achieving Codes for Noisy Insertion Channels
DNA storage has emerged as a promising solution for large-scale and long-term data preservation. Among various error types, insertions are the most frequent errors occurring in DNA sequences, where the inserted symbol is often identical or complementary to the original, and in practical implementations, noise can further cause the inserted symbol to mutate into a random one, which creates significant challenges to reliable data recovery. In this paper, we investigate a new noisy insertion chann…

Feebly Interacting Particles: FIPs at LHCb
J. Alimena, J. Boyd, G. Cacciapaglia, A. Casais Vidal, X. Cid Vidal, S. Collaviti, A. De Oyanguren Campos, G. Dalla Valle Garcia, G. Elor, G. Ferretti, D. Gorbunov, E. Goudzovski, J. Hajer, J. Jerhot, B. Kishor Jashal, V. Kholoimov, J. Klaric, F. Kling, E. Kriukova, Y. Kyselov, G. Lanfranchi, C. Langenbruch, A. Merli, M. Ovchinnykov, J. Pfaller, G. Perez, P. Reimitz, I. Sanderswood, L. Shchutska, E. Torro Pastor, Y. Tsai, A. Usachov, L. Vale S…

Feebly Interacting Particles: FIPs at LHCb
With the establishment and maturation of the experimental programs searching for new physics with sizeable couplings at the LHC, there is an increasing interest in the broader particle and astrophysics community for exploring the physics of light and feebly-interacting particles as a paradigm complementary to a New Physics sector at the TeV scale and beyond. FIPs@LHCb continues the successful series of the FIPs workshops, FIPs 2020 and FIPs 2022. The main focus of the workshop was to explore th…

Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning
Yilun Hao, Yongchao Chen, Chuchu Fan, Yang Zhang
https://arxiv.org/abs/2510.03182 https://

Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning
Vision Language Models (VLMs) show strong potential for visual planning but struggle with precise spatial and long-horizon reasoning. In contrast, Planning Domain Definition Language (PDDL) planners excel at long-horizon formal planning, but cannot interpret visual inputs. Recent works combine these complementary advantages by enabling VLMs to turn visual planning problems into PDDL files for formal planning. However, while VLMs can generate PDDL problem files satisfactorily, they struggle to a…

Bidirectional Mammogram View Translation with Column-Aware and Implicit 3D Conditional Diffusion
Xin Li, Kaixiang Yang, Qiang Li, Zhiwei Wang
https://arxiv.org/abs/2510.04947 ht…

Bidirectional Mammogram View Translation with Column-Aware and Implicit 3D Conditional Diffusion
Dual-view mammography, including craniocaudal (CC) and mediolateral oblique (MLO) projections, offers complementary anatomical views crucial for breast cancer diagnosis. However, in real-world clinical workflows, one view may be missing, corrupted, or degraded due to acquisition errors or compression artifacts, limiting the effectiveness of downstream analysis. View-to-view translation can help recover missing views and improve lesion alignment. Unlike natural images, this task in mammography i…

A Greedy PDE Router for Blending Neural Operators and Classical Methods
Sahana Rayan, Yash Patel, Ambuj Tewari
https://arxiv.org/abs/2509.24814 https://arx…

A Greedy PDE Router for Blending Neural Operators and Classical Methods
When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency components. Designing an optimal hybrid iterative solver--where, at each iteration, a solver is selected from an ensemble of solvers to leverage their complementary strengths--poses a challenging combinatorial problem. While the greedy selection strategy is desirable for its constant-factor approximation guarantee to …

Cross-Model Verification of Wall-Bounded Flows using Finite-JAX
Arturo Rodriguez, Avinash Potluri, Aryan Singh, Vyom Kumar, Kate Reza, Francisco O. Aguirre Ortega, Vineeth Vijaya Kumar, Noah L. Estrada
https://arxiv.org/abs/2509.25569

Cross-Model Verification of Wall-Bounded Flows using Finite-JAX
Accurate prediction of wall-bounded flows remains central to advancing both theoretical understanding and computational methods in fluid mechanics. In this study, we perform a numerical simulation of channel flow using a complementary approach: a high-performance, differentiable finite-difference solver developed in JAX (Finite-JAX) and an analytical solution derived from the Navier-Stokes Equations, also referred to as the Hagen-Poiseuille equation. The solver is applied to the incompressible …

Oops

Tracer diffusion coefficients in a sheared granular gas. Exact results
David Gonz\'alez M\'endez, Vicente Garz\'o
https://arxiv.org/abs/2509.26115 https://

Tracer diffusion coefficients in a sheared granular gas. Exact results
The diffusion of tracer particles immersed in a granular gas under uniform shear flow (USF) is analyzed within the framework of the inelastic Boltzmann equation. Two different but complementary approaches are followed to achieve exact results. First, we maintain the structure of the Boltzmann collision operator but consider inelastic Maxwell models (IMM). Using IMM allows us to compute the collisional moments of the Boltzmann operator without knowing the velocity distribution functions of the g…

Far-from-equilibrium thermodynamics of non-Abelian thermal states
Franklin L. S. Rodrigues, Eric Lutz
https://arxiv.org/abs/2510.04788 https://arxiv.org/pd…

Far-from-equilibrium thermodynamics of non-Abelian thermal states
Noncommutativity of observables is a central feature of quantum physics. It plays a fundamental role in the formulation of the uncertainty principle for complementary variables and strongly affects the laws of thermodynamics for systems with noncommuting, that is, non-Abelian, conserved quantities. We here derive nonequilibrium generalizations of the second law of thermodynamics in the form of fluctuation relations, both for mechanically and thermally driven quantum systems. We identify a non-A…

Unveiling the Bright Spot with Mie Scattering
Vin\'icius dos Passos de Souza, Antonio Alvaro Ranha Neves
https://arxiv.org/abs/2510.05948 https://arxiv…

Unveiling the Bright Spot with Mie Scattering
This work applies Mie scattering theory to provide a new perspective on the propagation of light through a spherical obstacle, offering a novel explanation for the formation of the Poisson spot (also known as the Arago or Fresnel spot). We demonstrate that the diffraction patterns generated by a sphere and by a circular disk can be understood as complementary outcomes of the same underlying scattering process. Our analysis highlights the constructive interference responsible for the bright cent…

LATIS: Galaxy-Environment Relations at Cosmic Noon and the Role of Sample Selection
Nima Chartab, Andrew B. Newman, Gwen C. Rudie, Guillermo Blanc, Daniel D. Kelson, Mahdi Qezlou, Simeon Bird, Brian C. Lemaux, Olga Cucciati
https://arxiv.org/abs/2510.07445

LATIS: Galaxy-Environment Relations at Cosmic Noon and the Role of Sample Selection
We investigate the environmental dependence of galaxy properties at $z\sim2.5$ using the Ly$α$ Tomography IMACS Survey (LATIS), which provides high-resolution three-dimensional maps of intergalactic medium (IGM) overdensity via Ly$α$ forest tomography. Our analysis focuses on a UV-selected spectroscopic sample of 2185 galaxies from LATIS and a complementary set of 1157 galaxies from heterogeneous spectroscopic surveys in the COSMOS field. We compare these datasets to forward-modeled mock cata…

AI-Enhanced Kinematic Modeling of Flexible Manipulators Using Multi-IMU Sensor Fusion
Amir Hossein Barjini, Jouni Mattila
https://arxiv.org/abs/2510.02975 https://

AI-Enhanced Kinematic Modeling of Flexible Manipulators Using Multi-IMU Sensor Fusion
This paper presents a novel framework for estimating the position and orientation of flexible manipulators undergoing vertical motion using multiple inertial measurement units (IMUs), optimized and calibrated with ground truth data. The flexible links are modeled as a series of rigid segments, with joint angles estimated from accelerometer and gyroscope measurements acquired by cost-effective IMUs. A complementary filter is employed to fuse the measurements, with its parameters optimized throug…

A comprehensive study of $\Lambda_c^- \to \Lambda (\to p \pi) \mu^- \bar \nu_{\mu}$ incorporating SMEFT implications and right-handed neutrino
Priyanka Boora, Siddhartha Karmakar, Dinesh Kumar, Kavita Lalwani
https://arxiv.org/abs/2510.05050

A comprehensive study of $Λ_c^- \to Λ(\to p π) μ^- \bar ν_μ$ incorporating SMEFT implications and right-handed neutrino
Charm baryon decays provide a complementary probe of new physics beyond the Standard Model. We study the decay $Λ_c^- \to Λ(p π) μ^- \bar ν_μ$ in a model-independent effective field theory framework. This study covers both the left-handed and right-handed neutrino interactions in the $c \to s μν_μ$ transition. For the left-handed neutrino operators, we incorporate the implications of the Standard Model effective field theory and do a global fit considering several observables sensitive…

Reward Model Routing in Alignment
Xinle Wu, Yao Lu
https://arxiv.org/abs/2510.02850 https://arxiv.org/pdf/2510.02850

Reward Model Routing in Alignment
Reinforcement learning from human or AI feedback (RLHF / RLAIF) has become the standard paradigm for aligning large language models (LLMs). However, most pipelines rely on a single reward model (RM), limiting alignment quality and risking overfitting. Recent work explores RM routing--dynamically selecting an RM from a candidate pool to exploit complementary strengths while maintaining $O(1)$ RM calls--but existing methods suffer from cold-start and insufficient exploration. We propose BayesianR…

Shadow Ringing of Black Holes from Photon Sphere Quasinormal Modes
Reggie C. Pantig
https://arxiv.org/abs/2509.24479 https://arxiv.org/pdf/2509.24479

Shadow Ringing of Black Holes from Photon Sphere Quasinormal Modes
The recent convergence of gravitational-wave (GW) observations and black hole imaging provides complementary probes of strong-gravity dynamics. While the black hole shadow is typically modeled as a static feature, a dynamically perturbed spacetime in its ringdown phase must induce temporal modulations in the shadow's apparent size and shape. We develop a theoretical framework within linear perturbation theory to investigate this shadow ringing effect for a Schwarzschild black hole. By modeling …

Complementarity Reveals Entanglement Sharing in Sequential Quantum Measurements
Zinuo Cai, Changliang Ren
https://arxiv.org/abs/2510.02710 https://arxiv.or…

Complementarity Reveals Entanglement Sharing in Sequential Quantum Measurements
We investigate entanglement sharing in a two-qubit sequential measurement scenario using three complementary classical correlation metrics: mutual information (I), sum of conditional probabilities (S), and the Pearson correlation coefficient (C). By investigating both weak measurement and probabilistic projective measurement (PPM) strategies in unilateral and bilateral scenarios, the phenomenon of entanglement sharing is conclusively certified when multiple pairs of classical correlation metric…

Differentially Private Learning of Exponential Distributions: Adaptive Algorithms and Tight Bounds
Bar Mahpud, Or Sheffet
https://arxiv.org/abs/2510.00790 https://

Differentially Private Learning of Exponential Distributions: Adaptive Algorithms and Tight Bounds
We study the problem of learning exponential distributions under differential privacy. Given $n$ i.i.d.\ samples from $\mathrm{Exp}(λ)$, the goal is to privately estimate $λ$ so that the learned distribution is close in total variation distance to the truth. We present two complementary pure DP algorithms: one adapts the classical maximum likelihood estimator via clipping and Laplace noise, while the other leverages the fact that the $(1-1/e)$-quantile equals $1/λ$. Each method excels in a d…

WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities
Ziyi Zeng, Zhenyang Cai, Yixi Cai, Xidong Wang, Junying Chen, Rongsheng Wang, Yipeng Liu, Siqi Cai, Benyou Wang, Zhiguo Zhang, Haizhou Li
https://arxiv.org/abs/2510.00032

WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities
Electroencephalography (EEG) interpretation using multimodal large language models (MLLMs) offers a novel approach for analyzing brain signals. However, the complex nature of brain activity introduces critical challenges: EEG signals simultaneously encode both cognitive processes and intrinsic neural states, creating a mismatch in EEG paired-data modality that hinders effective cross-modal representation learning. Through a pivot investigation, we uncover complementary relationships between the…

Vanishing Acts: Quantifying Black Hole Formation with the DSNB Signal
Tim Chariss\'e, David Maksimovi\'c, George A. Parker, Michael Wurm
https://arxiv.org/abs/2510.01177

Vanishing Acts: Quantifying Black Hole Formation with the DSNB Signal
The diffuse supernova neutrino background (DSNB) created by stellar core-collapses throughout cosmic history is on the verge of discovery, with SK-Gd showing early deviations from the background expectation and JUNO starting to take data. However, the interpretation of early DSNB data will face significant challenges due to degeneracies between astrophysical parameters and uncertainties in supernova neutrino modeling. We explore how complementary astronomical observations can break these degene…

The Secret Agenda: LLMs Strategically Lie and Our Current Safety Tools Are Blind
Caleb DeLeeuw, Gaurav Chawla, Aniket Sharma, Vanessa Dietze
https://arxiv.org/abs/2509.20393 htt…

The Secret Agenda: LLMs Strategically Lie and Our Current Safety Tools Are Blind
We investigate strategic deception in large language models using two complementary testbeds: Secret Agenda (across 38 models) and Insider Trading compliance (via SAE architectures). Secret Agenda reliably induced lying when deception advantaged goal achievement across all model families. Analysis revealed that autolabeled SAE features for "deception" rarely activated during strategic dishonesty, and feature steering experiments across 100+ deception-related features failed to prevent lying. Co…

From Coarse to Fine: Recursive Audio-Visual Semantic Enhancement for Speech Separation
Ke Xue, Rongfei Fan, Lixin, Dawei Zhao, Chao Zhu, Han Hu
https://arxiv.org/abs/2509.22425

From Coarse to Fine: Recursive Audio-Visual Semantic Enhancement for Speech Separation
Audio-visual speech separation aims to isolate each speaker's clean voice from mixtures by leveraging visual cues such as lip movements and facial features. While visual information provides complementary semantic guidance, existing methods often underexploit its potential by relying on static visual representations. In this paper, we propose CSFNet, a Coarse-to-Separate-Fine Network that introduces a recursive semantic enhancement paradigm for more effective separation. CSFNet operates in two …

An Inductive Proof that Lights Out Configurations are Invertible
Keivan Mirzaei
https://arxiv.org/abs/2509.18223 https://arxiv.org/pdf/2509.18223

BroRL: Scaling Reinforcement Learning via Broadened Exploration
Jian Hu, Mingjie Liu, Ximing Lu, Fang Wu, Zaid Harchaoui, Shizhe Diao, Yejin Choi, Pavlo Molchanov, Jun Yang, Jan Kautz, Yi Dong
https://arxiv.org/abs/2510.01180

BroRL: Scaling Reinforcement Learning via Broadened Exploration
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key ingredient for unlocking complex reasoning capabilities in large language models. Recent work ProRL has shown promise in scaling RL by increasing the number of training steps. However, performance plateaus after thousands of steps, with clear diminishing returns from allocating more computation to additional training. In this work, we investigate a complementary paradigm for scaling RL, BroR-Lincreasing the number of rol…

IndiSeek learns information-guided disentangled representations
Yu Gui, Cong Ma, Zongming Ma
https://arxiv.org/abs/2509.21584 https://arxiv.org/pdf/2509.21…

IndiSeek learns information-guided disentangled representations
Learning disentangled representations is a fundamental task in multi-modal learning. In modern applications such as single-cell multi-omics, both shared and modality-specific features are critical for characterizing cell states and supporting downstream analyses. Ideally, modality-specific features should be independent of shared ones while also capturing all complementary information within each modality. This tradeoff is naturally expressed through information-theoretic criteria, but mutual-i…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Ran Liu, Yuan Fang, Xiaoli Li
https://arxiv.org/abs/2510.00894 https://arxiv.or…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we pro…

Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization
Weixuan Wang, Minghao Wu, Barry Haddow, Alexandra Birch
https://arxiv.org/abs/2509.20900

Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization
Long document summarization remains a significant challenge for current large language models (LLMs), as existing approaches commonly struggle with information loss, factual inconsistencies, and coherence issues when processing excessively long documents. We propose SummQ, a novel adversarial multi-agent framework that addresses these limitations through collaborative intelligence between specialized agents operating in two complementary domains: summarization and quizzing. Our approach employs…

The (PXP)$^2$ model: long-range quantum scars in optical cavities
Hossein Hosseinabadi, Riccardo J. Valencia-Tortora, Aleksandr N. Mikheev, Darrick E. Chang, Johannes Zeiher, Roderich Moessner, Jamir Marino
https://arxiv.org/abs/2510.02246

The (PXP)$^2$ model: long-range quantum scars in optical cavities
Rydberg-cavity systems are emerging as promising platforms for quantum simulation and quantum information processing. These hybrid architectures combine two complementary interaction mechanisms: cavity photons mediate collective long-range couplings, while Rydberg excitations generate strong short-range interactions. Together, they offer a setting for engineering many-body phases characterized by a hierarchy of interactions across widely different length scales. In this work, we introduce a min…

FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah
https://arxiv.org/abs/2509.22412

FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data. In this paper, we identify a new type of model bias in the frequency domain, termed spectral bias, where detectors overly rely on specific frequency bands, restricting their ability to generalize across unseen forgeries. To address this, we propose FreqDebias, a frequency debiasing framework that mitigates spectral bias through two complementary strategies. First, we introduc…

Uncertainty-Aware Multi-Robot Task Allocation With Strongly Coupled Inter-Robot Rewards
Ben Rossano, Jaein Lim, Jonathan P. How
https://arxiv.org/abs/2509.22469 https://

Uncertainty-Aware Multi-Robot Task Allocation With Strongly Coupled Inter-Robot Rewards
This paper proposes a task allocation algorithm for teams of heterogeneous robots in environments with uncertain task requirements. We model these requirements as probability distributions over capabilities and use this model to allocate tasks such that robots with complementary skills naturally position near uncertain tasks, proactively mitigating task failures without wasting resources. We introduce a market-based approach that optimizes the joint team objective while explicitly capturing cou…

EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Yuchen Tian, Ruiyuan Huang, Xuanwu Wang, Jing Ma, Zengfeng Huang, Ziyang Luo, Hongzhan Lin, Da Zheng, Lun Du
https://arxiv.org/abs/2510.00732

EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of problem statements. To address this limitation, we introduce a novel data augmentation pipeline designed to enhance model robustness from two perspectives: symmetry and difficulty. From the symmetry perspective, we propose two complementary methods: EvolAST, an Abstract Syntax Tree (AST) based approach that targets syntacti…

Enhancing Credit Risk Prediction: A Meta-Learning Framework Integrating Baseline Models, LASSO, and ECOC for Superior Accuracy
Haibo Wang, Lutfu S. Sua, Jun Huang, Figen Balo, Burak Dolar
https://arxiv.org/abs/2509.22381

Enhancing Credit Risk Prediction: A Meta-Learning Framework Integrating Baseline Models, LASSO, and ECOC for Superior Accuracy
Effective credit risk management is fundamental to financial decision-making, necessitating robust models for default probability prediction and financial entity classification. Traditional machine learning approaches face significant challenges when confronted with high-dimensional data, limited interpretability, rare event detection, and multi-class imbalance problems in risk assessment. This research proposes a comprehensive meta-learning framework that synthesizes multiple complementary mod…

Multi-Hierarchical Feature Detection for Large Language Model Generated Text
Luyan Zhang, Xinyu Xie
https://arxiv.org/abs/2509.18862 https://arxiv.org/pdf/…

High-Dimensional Statistical Process Control via Manifold Fitting and Learning
Burak I. Tas, Enrique del Castillo
https://arxiv.org/abs/2509.19820 https://…

High-Dimensional Statistical Process Control via Manifold Fitting and Learning
We address the Statistical Process Control (SPC) of high-dimensional, dynamic industrial processes from two complementary perspectives: manifold fitting and manifold learning, both of which assume data lies on an underlying nonlinear, lower dimensional space. We propose two distinct monitoring frameworks for online or 'phase II' Statistical Process Control (SPC). The first method leverages state-of-the-art techniques in manifold fitting to accurately approximate the manifold where the data resi…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Ran Liu, Yuan Fang, Xiaoli Li
https://arxiv.org/abs/2510.00894 https://arxiv.or…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we pro…

Photonic Hybrid Quantum Computing
Jaehak Lee, Srikrishna Omkar, Yong Siah Teo, Seok-Hyung Lee, Hyukjoon Kwon, M. S. Kim, Hyunseok Jeong
https://arxiv.org/abs/2510.00534 https://…

Photonic Hybrid Quantum Computing
Photons are a ubiquitous carrier of quantum information: they are fast, suffer minimal decoherence, and do not require huge cryogenic facilities. Nevertheless, their intrinsically weak photon-photon interactions remain a key obstacle to scalable quantum computing. This review surveys hybrid photonic quantum computing, which exploits multiple photonic degrees of freedom to combine the complementary strengths of discrete and bosonic encodings, thereby significantly mitigating the challenge of wea…

When backgrounds become signals: neutrino interactions in xenon-based dark matter detectors
M. Atzori Corona, M. Cadeddu, N. Cargioli, F. Dordei, M. Sestu
https://arxiv.org/abs/2509.22178

When backgrounds become signals: neutrino interactions in xenon-based dark matter detectors
Direct detection dark matter experiments have proven to be compelling probes for studying low-energy neutrino interactions with both nuclei and atomic electrons, offering complementary information to accelerator and reactor-based neutrino experiments. Recently, the XENONnT and PandaX-4T collaborations reported the first evidence of coherent elastic neutrino-nucleus scattering from $^8\mathrm{B}$ solar neutrinos. Thanks to their excellent background rejection capabilities and distinctive signal …

Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification
Woo-Jin Cho Kim, Jorge Oliveira, Arian Beqiri, Alex Thorley, Jordan Strom, Jamie O'Driscoll, Rajan Sharma, Jeremy Slivnick, Roberto Lang, Alberto Gomez, Agisilaos Chartsias
https://arxiv.org/abs/2509.19694

Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification
Guidelines for transthoracic echocardiographic examination recommend the acquisition of multiple video clips from different views of the heart, resulting in a large number of clips. Typically, automated methods, for instance disease classifiers, either use one clip or average predictions from all clips. Relying on one clip ignores complementary information available from other clips, while using all clips is computationally expensive and may be prohibitive for clinical adoption. To select the…

Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say
Jacob Fein-Ashley, Dhruv Parikh, Rajgopal Kannan, Viktor Prasanna
https://arxiv.org/abs/2509.21164

Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say
Open-source Large Language Models (LLMs) increasingly specialize by domain (e.g., math, code, general reasoning), motivating systems that leverage complementary strengths across models. Prior multi-LLM approaches either (i) route a query to one or a few experts and generate independently, (ii) aggregate outputs from each model via costly multi-turn exchanges, or (iii) fuse weights into a single model-typically requiring architectural homogeneity. We introduce Mixture of Thoughts (MoT), a simple…

Finite-Size Security Bounds in Semi-Quantum Key Distribution: Spectral, Operator-Theoretic, and Entropic Perspectives
Zahidur Rezwan Ratul
https://arxiv.org/abs/2509.25078 https…

Finite-Size Security Bounds in Semi-Quantum Key Distribution: Spectral, Operator-Theoretic, and Entropic Perspectives
We study Semi-Quantum Key Distribution (SQKD) with a focus on finite-size security bounds, developed through three complementary perspectives. (i) Spectral disturbance: wrong-basis Lüders updates produce closed-form spectra and purity loss, which serve as basis-independent indicators of disturbance. (ii) Operator-theoretic reduction: in Z/Z-sifted rounds, intercept-resend attacks can be represented as an effective depolarizing channel, characterized by a fidelity-QBER relation. (iii) Entropic …

CoLLM-NAS: Collaborative Large Language Models for Efficient Knowledge-Guided Neural Architecture Search
Zhe Li, Zhiwei Lin, Yongtao Wang
https://arxiv.org/abs/2509.26037 https:…

CoLLM-NAS: Collaborative Large Language Models for Efficient Knowledge-Guided Neural Architecture Search
The integration of Large Language Models (LLMs) with Neural Architecture Search (NAS) has introduced new possibilities for automating the design of neural architectures. However, most existing methods face critical limitations, including architectural invalidity, computational inefficiency, and inferior performance compared to traditional NAS. In this work, we present Collaborative LLM-based NAS (CoLLM-NAS), a two-stage NAS framework with knowledge-guided search driven by two complementary LLMs…

PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction
Manahil Raza, Ayesha Azam, Talha Qaiser, Nasir Rajpoot
https://arxiv.org/abs/2509.20022

PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction
Current multimodal fusion approaches in computational oncology primarily focus on integrating multi-gigapixel histology whole slide images (WSIs) with genomic or transcriptomic data, demonstrating improved survival prediction. We hypothesize that incorporating pathology reports can further enhance prognostic performance. Pathology reports, as essential components of clinical workflows, offer readily available complementary information by summarizing histopathological findings and integrating ex…

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
Yixuan Han, Fan Ma, Ruijie Quan, Yi Yang
https://arxiv.org/abs/2509.22572 https://

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
Test-Time Scaling (TTS) enhances the reasoning ability of large language models (LLMs) by allocating additional computation during inference. However, existing approaches primarily rely on output-level sampling while overlooking the role of model architecture. In mainstream Mixture-of-Experts (MoE) LLMs, we observe that varying the number of activated experts yields complementary solution sets with stable accuracy, revealing a new and underexplored source of diversity. Motivated by this observa…

MTS-DMAE: Dual-Masked Autoencoder for Unsupervised Multivariate Time Series Representation Learning
Yi Xu, Yitian Zhang, Yun Fu
https://arxiv.org/abs/2509.16078 https://

MTS-DMAE: Dual-Masked Autoencoder for Unsupervised Multivariate Time Series Representation Learning
Unsupervised multivariate time series (MTS) representation learning aims to extract compact and informative representations from raw sequences without relying on labels, enabling efficient transfer to diverse downstream tasks. In this paper, we propose Dual-Masked Autoencoder (DMAE), a novel masked time-series modeling framework for unsupervised MTS representation learning. DMAE formulates two complementary pretext tasks: (1) reconstructing masked values based on visible attributes, and (2) est…