Tootfinder

Analyzing the Impact of Strategic Bidding on the Reserve Capacity via a Bi-Level Model
Yun Xu, Yunxiao Bai, Yunyong Zhang, Peng Wang, Xuelin Wang, Jiqun Guo, Kaijun Xie, Rusheng Zhao
https://arxiv.org/abs/2506.20493

Analyzing the Impact of Strategic Bidding on the Reserve Capacity via a Bi-Level Model
The growing integration of renewable energy sources necessitates adequate reserve capacity to maintain power balance. However, in market clearing, power companies with flexible resources may submit strategic bids to maximize profits, potentially compromising system reserves. This paper examines the effects of such strategic behavior by modeling the market as a bi-level problem. The upper level represents a strategic company aiming to maximize profit, while the lower level simulates the system o…

Meta's AI Model 'Memorized' Huge Chunks of Books, Including 'Harry Potter' and '1984' https://www.404media.co/meta-ai-model-memorized-harry-potter-books/

An Extended Model of Fractional-Dimensional Space for Anisotropic Solids with Deformed Derivatives
Jos\'e Weberszpil
https://arxiv.org/abs/2506.18127 h…

An Extended Model of Fractional-Dimensional Space for Anisotropic Solids with Deformed Derivatives
In this work, we extend a fractional-dimensional space model for anisotropic solids by incorporating a q-deformed derivative operator, inspired by Tsallis' nonadditive entropy framework. This generalization provides an analytical framework for exploring anisotropic thermal properties, within a unified and flexible mathematical formalism. We derive modified expressions for the phonon density of states and specific heat capacity, highlighting the impact of the deformation parameters on thermodyna…

Overfitting in Histopathology Model Training: The Need for Customized Architectures
Saghir Alfasly, Ghazal Alabtah, H. R. Tizhoosh
https://arxiv.org/abs/2506.16631

Overfitting in Histopathology Model Training: The Need for Customized Architectures
This study investigates the critical problem of overfitting in deep learning models applied to histopathology image analysis. We show that simply adopting and fine-tuning large-scale models designed for natural image analysis often leads to suboptimal performance and significant overfitting when applied to histopathology tasks. Through extensive experiments with various model architectures, including ResNet variants and Vision Transformers (ViT), we show that increasing model capacity does not …

Theoretical Analysis of Near-Field MIMO Channel Capacity and Mid-Band Experimental Validation
Haiyang Miao, Jianhua Zhang, Pan Tang, Heng Wang, Lei Tian, Guangyi Liu
https://arxiv.org/abs/2506.15972

Theoretical Analysis of Near-Field MIMO Channel Capacity and Mid-Band Experimental Validation
With the increase of multiple-input-multiple-output (MIMO) array size and carrier frequency, near-field MIMO communications will become crucial in 6G wireless networks. Due to the increase of MIMO near-field range, the research of near-field MIMO capacity has aroused wide interest. In this paper, we focus on the theoretical analysis and empirical study of near-field MIMO capacity. First, the near-field channel model is characterized from the electromagnetic information perspective. Second, with…

Hidden Bose-Einstein Singularities in Correlated Electron Systems: III. Thermodynamic Signals
Takafumi Kita
https://arxiv.org/abs/2506.19211 https://

Hidden Bose-Einstein Singularities in Correlated Electron Systems: III. Thermodynamic Signals
We study thermodynamic consequences of the hidden Bose-Einstein singularities, which have been predicted to cause a pseudogap phase, based on quantum field theory of ordered phases. Starting from the Luttinger-Ward functional for the grand thermodynamic potential, we derive expressions of the Helmholtz free energy, internal energy, entropy, and heat capacity in a form suitable for numerical studies. They are applied to the weakly attractive Hubbard model in three dimensions to calculate the the…

Rack-Aware MSR Codes with Linear Field Size and Smaller Sub-Packetization for Tolerating Multiple Erasures
Hengming Zhao, Dianhua Wu, Minquan Cheng
https://arxiv.org/abs/2506.18367

Rack-Aware MSR Codes with Linear Field Size and Smaller Sub-Packetization for Tolerating Multiple Erasures
In an $(n,k,d)$ rack-aware storage model, the system consists of $n$ nodes uniformly distributed across $\bar{n}$ successive racks, such that each rack contains $u$ nodes of equal capacity and the reconstructive degree satisfies $k=\bar{k}u+v$ where $0\leq v\leq u-1$. Suppose there are $h\geq1$ failed nodes in a rack (called the host rack). Then together with its surviving nodes, the host rack downloads recovery data from $\bar{d}$ helper racks and repairs its failed nodes. In this paper, we fo…

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data
Anton Changalidis, Aki H\"arm\"a
https://arxiv.org/abs/2506.14704

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data
This paper studies how the model architecture and data configurations influence the empirical memorization capacity of generative transformers. The models are trained using synthetic text datasets derived from the Systematized Nomenclature of Medicine (SNOMED) knowledge graph: triplets, representing static connections, and sequences, simulating complex relation patterns. The results show that embedding size is the primary determinant of learning speed and capacity, while additional layers provi…

Capacity Based Design of Slot Array Antennas
Volodymyr Shyianov, Bamelak Tadele, Vladimir I. Okhmatovski, Faouzi Bellili, Amine Mezghani
https://arxiv.org/abs/2506.17845

Capacity Based Design of Slot Array Antennas
Historically, the design of antenna arrays has evolved separately from Shannon theory. Shannon theory adopts a probabilistic approach in the design of communication systems, while antenna design approaches have relied on deterministic Maxwell theory alone. In this paper, we introduce a new approach to the design of antenna arrays based on information theoretic metrics. To this end, we develop a statistical model suitable for the numerical optimization of antenna systems. The model is utilized t…

This https://arxiv.org/abs/2505.15030 has been replaced.
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Harnessing On-Device Large Language Model: Empirical Results and Implications for AI PC
The increasing deployment of Large Language Models (LLMs) on edge devices, driven by model advancements and hardware improvements, offers significant privacy benefits. However, these on-device LLMs inherently face performance limitations due to reduced model capacity and necessary compression techniques. To address this, we introduce a systematic methodology -- encompassing model capability, development efficiency, and system resources -- for evaluating on-device LLMs. Our comprehensive evaluat…

Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
Xingyu Liu, Yubin Li, Guozhang Chen
https://arxiv.org/abs/2506.11062

Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, t…

Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model
Chong Li, Yingzhuo Deng, Jiajun Zhang, Chengqing Zong
https://arxiv.org/abs/2506.12388

Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model
The curse of multilinguality phenomenon is a fundamental problem of multilingual Large Language Models (LLMs), where the competition between massive languages results in inferior performance. It mainly comes from limited capacity and negative transfer between dissimilar languages. To address this issue, we propose a method to dynamically group and scale up the parameters of multilingual LLM while boosting positive transfer among similar languages. Specifically, the model is first tuned on monol…

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Ren Xin, Hongji Liu, Xiaodong Mei, Wenru Liu, Maosheng Ye, Zhili Chen, Jun Ma
https://arxiv.org/abs/2506.14589

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Integrating General Models (GMs) such as Large Language Models (LLMs), with Specialized Models (SMs) in autonomous driving tasks presents a promising approach to mitigating challenges in data diversity and model capacity of existing specialized driving models. However, this integration leads to problems of asynchronous systems, which arise from the distinct characteristics inherent in GMs and SMs. To tackle this challenge, we propose NetRoller, an adapter that incorporates a set of novel mechan…

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
Tao Yu, Simin Wang, Shunqing Zhang, Xiaojing Chen, Zi Xu, Xin Wang, Jiandong Li, Junyu Liu, Sihai Zhang
https://arxiv.org/abs/2506.03508

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or dat…

Improving Outbreak Forecasts Through Model Augmentation
Graham C. Gibson, Spencer J. Fox, Emily Javan, Susan E. Ptak, Oluwasegun M. Ibrahim, Michael Lachmann, Lauren Ancel Meyers
https://arxiv.org/abs/2506.16410

Improving Outbreak Forecasts Through Model Augmentation
Accurate forecasts of disease outbreaks are critical for effective public health responses, management of healthcare surge capacity, and communication of public risk. There are a growing number of powerful forecasting methods that fall into two broad categories -- empirical models that extrapolate from historical data, and mechanistic models based on fixed epidemiological assumptions. However, these methods often underperform precisely when reliable predictions are most urgently needed -- durin…

This https://arxiv.org/abs/2505.12538 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

On long-duration storage, weather uncertainty and limited foresight
Long-duration energy storage (LDES) is a key component for fully renewable, sector-coupled energy systems based on wind and solar. While capacity expansion planning has begun to take into account interannual weather variability, it often ignores weather uncertainty and limited foresight in capacity and operational decisions. We build a stochastic capacity expansion model for fully decarbonized energy systems with LDES in Europe accounting for weather uncertainty - isolating the effect of limite…

Powers of Magnetic Graph Matrix: Fourier Spectrum, Walk Compression, and Applications
Yinan Huang, David F. Gleich, Pan Li
https://arxiv.org/abs/2506.07343

Powers of Magnetic Graph Matrix: Fourier Spectrum, Walk Compression, and Applications
Magnetic graphs, originally developed to model quantum systems under magnetic fields, have recently emerged as a powerful framework for analyzing complex directed networks. Existing research has primarily used the spectral properties of the magnetic graph matrix to study global and stationary network features. However, their capacity to model local, non-equilibrium behaviors, often described by matrix powers, remains largely unexplored. We present a novel combinatorial interpretation of the mag…

This https://arxiv.org/abs/2505.16865 has been replaced.
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LARES: Latent Reasoning for Sequential Recommendation
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequential recommendation methods predominantly rely on non-reasoning paradigms, which may limit the model's computational capacity and result in suboptimal recommendation performance. To address these limitations, we present LARES, a novel and scalable LAtent REasoning framework for Sequential recommendation that enhance…

This https://arxiv.org/abs/2503.19217 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages
The rapid evolution of large language models (LLMs) has opened new possibilities for automating various tasks in software development. This paper evaluates the capabilities of the Llama 2-70B model in automating these tasks for scientific applications written in commonly used programming languages. Using representative test problems, we assess the model's capacity to generate code, documentation, and unit tests, as well as its ability to translate existing code between commonly used programming…

IGSM: Improved Geometric and Sensitivity Matching for Finetuning Pruned Diffusion Models
Caleb Zheng, Eli Shlizerman
https://arxiv.org/abs/2506.05398 https…

IGSM: Improved Geometric and Sensitivity Matching for Finetuning Pruned Diffusion Models
Diffusion models achieve realistic outcomes across a wide range of generative tasks, but their high computational cost remains a major barrier to deployment. Model pruning has emerged as a promising strategy to reduce inference cost and enable lightweight diffusion models. While effective, pruned diffusion models are proned to quality reduction due to limited capacity. A key limitation of current pruning approaches is that pruned models are finetuned using the same objective as the dense model,…

This https://arxiv.org/abs/2505.23337 has been replaced.
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Matryoshka Model Learning for Improved Elastic Student Models
Industry-grade ML models are carefully designed to meet rapidly evolving serving constraints, which requires significant resources for model development. In this paper, we propose MatTA, a framework for training multiple accurate Student models using a novel Teacher-TA-Student recipe. TA models are larger versions of the Student models with higher capacity, and thus allow Student models to better relate to the Teacher model and also bring in more domain-specific expertise. Furthermore, multiple…

Grids Often Outperform Implicit Neural Representations
Namhoon Kim, Sara Fridovich-Keil
https://arxiv.org/abs/2506.11139 https://arxi…

Grids Often Outperform Implicit Neural Representations
Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood. We investigate the performance of diverse INRs across a suite of 2D and 3D real and synthetic signals with varying effective bandwidth, as well as both overfitting and generalization tasks including tomography, super-resolution, and denoising. By stratifying performance according to model size as well as signal type and ba…

Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources?
Houyi Li, Ka Man Lo, Ziqi Wang, Zili Wang, Wenzhen Zheng, Shuigeng Zhou, Xiangyu Zhang, Daxin Jiang
https://arxiv.org/abs/2506.12119

Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources?
Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token compute. However, can MoEs surpass dense architectures under strictly equal resource constraints - that is, when the total parameter count, training compute, and data budget are identical? This question remains under-explored despite its significant practical value and potential. In this paper, we propose a novel perspective and methodological framework to …

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
Guibin Zhang, Muxin Fu, Guancheng Wan, Miao Yu, Kun Wang, Shuicheng Yan
https://arxiv.org/abs/2506.07398

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems
Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to discover that prevailing MAS memory mechanisms (1) are overly simplistic, completely disregarding the nuanced inter-agent collaboration trajectories, and (2) lack cross-trial and agent-specific customiza…

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
Ziye Jia, Can Cui, Chao Dong, Qihui Wu, Zhuang Ling, Dusit Niyato, Zhu Han
https://arxiv.org/abs/2506.01972

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
With an extensive increment of computation demands, the aerial multi-access edge computing (MEC), mainly based on unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), plays significant roles in future network scenarios. In detail, UAVs can be flexibly deployed, while HAPs are characterized with large capacity and stability. Hence, in this paper, we provide a hierarchical model composed of an HAP and multi-UAVs, to provide aerial MEC services. Moreover, considering the errors of c…

Continuous-time multifarious systems -- Part II: non-reciprocal multifarious self-organization
Jakob Metson, Saeed Osat, Ramin Golestanian
https://arxiv.org/abs/2506.07649

Continuous-time multifarious systems -- Part II: non-reciprocal multifarious self-organization
In the context of self-assembly, where complex structures can be assembled from smaller units, it is desirable to devise strategies towards disassembly and reassembly processes that reuse the constituent parts. A non-reciprocal multifarious self-organization strategy has been recently introduced, and shown to have the capacity to exhibit this complex property. In this work, we study the model using continuous-time Gillespie simulations, and compare the results against discrete-time Monte Carlo …

This https://arxiv.org/abs/2506.04168 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Horizon Reduction Makes RL Scalable
In this work, we study the scalability of offline reinforcement learning (RL) algorithms. In principle, a truly scalable offline RL algorithm should be able to solve any given problem, regardless of its complexity, given sufficient data, compute, and model capacity. We investigate if and how current offline RL algorithms match up to this promise on diverse, challenging, previously unsolved tasks, using datasets up to 1000x larger than typical offline RL datasets. We observe that despite scaling…

Clustering scientific publications: lessons learned through experiments with a real citation network
Vu Thi Huong, Thorsten Koch
https://arxiv.org/abs/2505.18180

Clustering scientific publications: lessons learned through experiments with a real citation network
Clustering scientific publications can reveal underlying research structures within bibliographic databases. Graph-based clustering methods, such as spectral, Louvain, and Leiden algorithms, are frequently utilized due to their capacity to effectively model citation networks. However, their performance may degrade when applied to real-world data. This study evaluates the performance of these clustering algorithms on a citation graph comprising approx. 700,000 papers and 4.6 million citations ex…

This https://arxiv.org/abs/2412.03970 has been replaced.
initial toot: https://mastoxiv.page/@ar…

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems
In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order interactions. This study introduces a stepwise data-driven framework for discovering fractional differential equations (FDEs) directly from data. FDEs, known for their capacity to model non-local dynamics with fewer parameters than integer-order derivatives, can represent complex systems with long-range interactions.…

This https://arxiv.org/abs/2505.08944 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
Mixture-of-Experts (MoE) architectures offer the promise of larger model capacity without the prohibitive costs of fully dense designs. However, in real-world inference serving, load skew across experts often leads to suboptimal device utilization and excessive synchronization overheads. This paper introduces Asynchronous Expert Parallelism (AEP), a new paradigm that decouples layer execution from barrier-style synchronization. By dynamically queuing tokens at each layer (referred to as $μ$-qu…