id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.17076 | DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training
for Roadside LiDAR Applications | [
"cs.CV"
] | Recent advancements in deep-learning methods for object detection in point-cloud data have enabled numerous roadside applications, fostering improvements in transportation safety and management. However, the intricate nature of point-cloud data poses significant challenges for human-supervised labeling, resulting in su... | {
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2501.17077 | Induced Modularity and Community Detection for Functionally
Interpretable Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Interpretability in reinforcement learning is crucial for ensuring AI systems align with human values and fulfill the diverse related requirements including safety, robustness and fairness. Building on recent approaches to encouraging sparsity and locality in neural networks, we demonstrate how the penalisation of non-... | {
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2501.17079 | Learning Mean Field Control on Sparse Graphs | [
"cs.MA",
"cs.AI",
"cs.GT",
"cs.LG"
] | Large agent networks are abundant in applications and nature and pose difficult challenges in the field of multi-agent reinforcement learning (MARL) due to their computational and theoretical complexity. While graphon mean field games and their extensions provide efficient learning algorithms for dense and moderately s... | {
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2501.17081 | Graph Transformers for inverse physics: reconstructing flows around
arbitrary 2D airfoils | [
"cs.LG",
"cs.AI",
"cs.CE"
] | We introduce a Graph Transformer framework that serves as a general inverse physics engine on meshes, demonstrated through the challenging task of reconstructing aerodynamic flow fields from sparse surface measurements. While deep learning has shown promising results in forward physics simulation, inverse problems rema... | {
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2501.17084 | Token-by-Token Regeneration and Domain Biases: A Benchmark of LLMs on
Advanced Mathematical Problem-Solving | [
"cs.LG"
] | Large language models (LLMs) excel in many natural language tasks, yet they struggle with complex mathemat-ical problem-solving, particularly in symbolic reasoning and maintaining consistent output. This study evalu-ates 10 LLMs with 7 to 8 billion parameters using 945 competition-level problems from the MATH dataset. ... | {
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2501.17085 | Evaluating CrowdSplat: Perceived Level of Detail for Gaussian Crowds | [
"cs.CV"
] | Efficient and realistic crowd rendering is an important element of many real-time graphics applications such as Virtual Reality (VR) and games. To this end, Levels of Detail (LOD) avatar representations such as polygonal meshes, image-based impostors, and point clouds have been proposed and evaluated. More recently, 3D... | {
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2501.17086 | Accelerated Training through Iterative Gradient Propagation Along the
Residual Path | [
"cs.LG"
] | Despite being the cornerstone of deep learning, backpropagation is criticized for its inherent sequentiality, which can limit the scalability of very deep models. Such models faced convergence issues due to vanishing gradient, later resolved using residual connections. Variants of these are now widely used in modern ar... | {
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2501.17088 | Mamba-Shedder: Post-Transformer Compression for Efficient Selective
Structured State Space Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large pre-trained models have achieved outstanding results in sequence modeling. The Transformer block and its attention mechanism have been the main drivers of the success of these models. Recently, alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address the ine... | {
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2501.17096 | Why is the estimation of metaorder impact with public market data so
challenging? | [
"q-fin.TR",
"cs.AI",
"econ.EM",
"physics.soc-ph"
] | Estimating market impact and transaction costs of large trades (metaorders) is a very important topic in finance. However, using models of price and trade based on public market data provide average price trajectories which are qualitatively different from what is observed during real metaorder executions: the price in... | {
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2501.17099 | Text-to-Image Generation for Vocabulary Learning Using the Keyword
Method | [
"cs.HC",
"cs.CV",
"cs.GR",
"cs.LG"
] | The 'keyword method' is an effective technique for learning vocabulary of a foreign language. It involves creating a memorable visual link between what a word means and what its pronunciation in a foreign language sounds like in the learner's native language. However, these memorable visual links remain implicit in the... | {
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2501.17104 | COS(M+O)S: Curiosity and RL-Enhanced MCTS for Exploring Story Space via
Language Models | [
"cs.CL",
"cs.AI"
] | We present COS(M+O)S, a System 2-inspired framework for open-ended plot development that systematically explores the vast space of possible story expansions, enabling a 3B-parameter language model to approach the plot quality of a 70B model on select short-story tasks. The method accomplishes this by combining Monte Ca... | {
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2501.17105 | Optimal control over Markovian wireless communication channels under
generalized packet dropout compensation | [
"eess.SY",
"cs.SY",
"math.OC"
] | Control loops closed over wireless links greatly benefit from accurate estimates of the communication channel condition. To this end, the finite-state Markov channel model allows for reliable channel state estimation. This paper develops a Markov jump linear system representation for wireless networked control with per... | {
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2501.17110 | Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods
and Neural Networks | [
"math.NA",
"cs.LG",
"cs.NA"
] | We consider the use of Gaussian Processes (GPs) or Neural Networks (NNs) to numerically approximate the solutions to nonlinear partial differential equations (PDEs) with rough forcing or source terms, which commonly arise as pathwise solutions to stochastic PDEs. Kernel methods have recently been generalized to solve n... | {
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2501.17112 | Unlocking Transparent Alignment Through Enhanced Inverse Constitutional
AI for Principle Extraction | [
"cs.LG"
] | Traditional methods for aligning Large Language Models (LLMs), such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on implicit principles, limiting interpretability. Constitutional AI (CAI) offers an explicit, rule-based framework for guiding model outputs. Building ... | {
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2501.17115 | Evidence on the Regularisation Properties of Maximum-Entropy
Reinforcement Learning | [
"cs.LG"
] | The generalisation and robustness properties of policies learnt through Maximum-Entropy Reinforcement Learning are investigated on chaotic dynamical systems with Gaussian noise on the observable. First, the robustness under noise contamination of the agent's observation of entropy regularised policies is observed. Seco... | {
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2501.17116 | Optimizing Large Language Model Training Using FP4 Quantization | [
"cs.LG",
"cs.CL"
] | The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce these costs. While FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge due to sig... | {
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2501.17117 | Histoires Morales: A French Dataset for Assessing Moral Alignment | [
"cs.CL",
"cs.AI"
] | Aligning language models with human values is crucial, especially as they become more integrated into everyday life. While models are often adapted to user preferences, it is equally important to ensure they align with moral norms and behaviours in real-world social situations. Despite significant progress in languages... | {
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2501.17122 | Convergence of two-timescale gradient descent ascent dynamics:
finite-dimensional and mean-field perspectives | [
"math.OC",
"cs.LG",
"cs.NA",
"math.NA"
] | The two-timescale gradient descent-ascent (GDA) is a canonical gradient algorithm designed to find Nash equilibria in min-max games. We analyze the two-timescale GDA by investigating the effects of learning rate ratios on convergence behavior in both finite-dimensional and mean-field settings. In particular, for finite... | {
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2501.17123 | Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks
Detection: A Comparative Analysis | [
"cs.CR",
"cs.NE"
] | Cache side channel attacks are a sophisticated and persistent threat that exploit vulnerabilities in modern processors to extract sensitive information. These attacks leverage weaknesses in shared computational resources, particularly the last level cache, to infer patterns in data access and execution flows, often byp... | {
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2501.17124 | The Asymptotic Capacity of Byzantine Symmetric Private Information
Retrieval and Its Consequences | [
"cs.IT",
"cs.CR",
"cs.NI",
"eess.SP",
"math.IT"
] | We consider the problem of finding the asymptotic capacity of symmetric private information retrieval (SPIR) with $B$ Byzantine servers. Prior to finding the capacity, a definition for the Byzantine servers is needed since in the literature there are two different definitions. In \cite{byzantine_tpir}, where it was fir... | {
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2501.17125 | CoRe-Net: Co-Operational Regressor Network with Progressive Transfer
Learning for Blind Radar Signal Restoration | [
"cs.LG"
] | Real-world radar signals are frequently corrupted by various artifacts, including sensor noise, echoes, interference, and intentional jamming, differing in type, severity, and duration. This pilot study introduces a novel model, called Co-Operational Regressor Network (CoRe-Net) for blind radar signal restoration, desi... | {
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2501.17131 | Scenario Understanding of Traffic Scenes Through Large Visual Language
Models | [
"cs.CV"
] | Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalization often suffers due to domain-specific data distributions, making an effective scene-based categorization of samples necessary to improve th... | {
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2501.17132 | ASTRAL: Automated Safety Testing of Large Language Models | [
"cs.SE",
"cs.CL"
] | Large Language Models (LLMs) have recently gained attention due to their ability to understand and generate sophisticated human-like content. However, ensuring their safety is paramount as they might provide harmful and unsafe responses. Existing LLM testing frameworks address various safety-related concerns (e.g., dru... | {
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2501.17144 | FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data | [
"cs.CL",
"cs.AI"
] | Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. However, conventional NLI datasets are not well-suited for document-level reasoning, which is critical for detectin... | {
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2501.17148 | AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse
Autoencoders | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Fine-grained steering of language model outputs is essential for safety and reliability. Prompting and finetuning are widely used to achieve these goals, but interpretability researchers have proposed a variety of representation-based techniques as well, including sparse autoencoders (SAEs), linear artificial tomograph... | {
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2501.17151 | Scanning Trojaned Models Using Out-of-Distribution Samples | [
"cs.LG"
] | Scanning for trojan (backdoor) in deep neural networks is crucial due to their significant real-world applications. There has been an increasing focus on developing effective general trojan scanning methods across various trojan attacks. Despite advancements, there remains a shortage of methods that perform effectively... | {
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2501.17152 | Three-Dimensional Diffusion-Weighted Multi-Slab MRI With Slice Profile
Compensation Using Deep Energy Model | [
"eess.IV",
"cs.AI",
"physics.med-ph"
] | Three-dimensional (3D) multi-slab acquisition is a technique frequently employed in high-resolution diffusion-weighted MRI in order to achieve the best signal-to-noise ratio (SNR) efficiency. However, this technique is limited by slab boundary artifacts that cause intensity fluctuations and aliasing between slabs which... | {
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2501.17159 | IC-Portrait: In-Context Matching for View-Consistent Personalized
Portrait | [
"cs.CV"
] | Existing diffusion models show great potential for identity-preserving generation. However, personalized portrait generation remains challenging due to the diversity in user profiles, including variations in appearance and lighting conditions. To address these challenges, we propose IC-Portrait, a novel framework desig... | {
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2501.17160 | A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from
Computed Tomography (CT) Scan Images | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Early detection of COVID-19 is crucial for effective treatment and controlling its spread. This study proposes a novel hybrid deep learning model for detecting COVID-19 from CT scan images, designed to assist overburdened medical professionals. Our proposed model leverages the strengths of VGG16, DenseNet121, and Mobil... | {
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2501.17161 | SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model
Post-training | [
"cs.AI",
"cs.CV",
"cs.LG"
] | Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization capabilities remain unclear. This paper studies the difference between SFT and RL on generalization and memorization, focusing on text-based... | {
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2501.17162 | CubeDiff: Repurposing Diffusion-Based Image Models for Panorama
Generation | [
"cs.CV",
"cs.LG"
] | We introduce a novel method for generating 360{\deg} panoramas from text prompts or images. Our approach leverages recent advances in 3D generation by employing multi-view diffusion models to jointly synthesize the six faces of a cubemap. Unlike previous methods that rely on processing equirectangular projections or au... | {
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2501.17164 | Split Knowledge Distillation for Large Models in IoT: Architecture,
Challenges, and Solutions | [
"cs.LG",
"cs.AI"
] | Large models (LMs) have immense potential in Internet of Things (IoT) systems, enabling applications such as intelligent voice assistants, predictive maintenance, and healthcare monitoring. However, training LMs on edge servers raises data privacy concerns, while deploying them directly on IoT devices is constrained by... | {
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2501.17166 | Optimizing Carbon Footprint in ICT through Swarm Intelligence with
Algorithmic Complexity | [
"cs.NE",
"physics.comp-ph"
] | Global emissions from fossil fuel combustion and cement production were recorded in 2022, signaling a resurgence to pre-pandemic levels and providing an apodictic indication that emission peaks have not yet been achieved. Significant contributions to this upward trend are made by the Information and Communication Techn... | {
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2501.17167 | QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM
Quality Checks | [
"cs.SE",
"cs.AI"
] | We introduce QualityFlow, a dynamic agentic workflow for program synthesis. Given the English description of a programming problem and a set of unit tests, the model's goal is to synthesize the correct program that solves the problem and passes the tests. QualityFlow consists of multiple large language model (LLM) agen... | {
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2501.17168 | EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming | [
"cs.NE",
"cs.AI"
] | Tree-based Genetic Programming (TGP) is a key evolutionary algorithm widely used in symbolic regression, feature engineering, and scientific modeling. Its high computational demands make GPU acceleration essential for scalable and high-performance evolutionary computation. However, GPU acceleration of TGP faces three k... | {
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2501.17170 | Benchmarking Randomized Optimization Algorithms on Binary, Permutation,
and Combinatorial Problem Landscapes | [
"cs.NE",
"cs.AI",
"cs.CL",
"cs.LG"
] | In this paper, we evaluate the performance of four randomized optimization algorithms: Randomized Hill Climbing (RHC), Simulated Annealing (SA), Genetic Algorithms (GA), and MIMIC (Mutual Information Maximizing Input Clustering), across three distinct types of problems: binary, permutation, and combinatorial. We system... | {
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2501.17171 | Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot
Learning | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | Compositional Zero-Shot Learning (CZSL) aims to recognize subtle differences in meaning or the combination of states and objects through the use of known and unknown concepts during training. Existing methods either focused on prompt configuration or on using prompts to tune the pre-trained Vision-Language model. Howev... | {
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2501.17172 | Towards spiking analog hardware implementation of a trajectory
interpolation mechanism for smooth closed-loop control of a spiking robot arm | [
"cs.NE",
"cs.RO"
] | Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propose a closed-loop neuromorphic control system for an event-based robotic arm. The proposed system consists of a shifted Winner-Take-All spikin... | {
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2501.17173 | Model Evaluation of a Transformable CubeSat for Nonholonomic Attitude
Reorientation Using a Drop Tower | [
"astro-ph.IM",
"cs.RO"
] | This paper presents a design for a drop tower test to evaluate a numerical model for a structurally reconfigurable spacecraft with actuatable joints, referred to as a transformable spacecraft. A mock-up robot for a 3U-sized transformable spacecraft is designed to fit in a limited time and space of the microgravity envi... | {
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2501.17174 | Extractive Schema Linking for Text-to-SQL | [
"cs.DB",
"cs.AI",
"cs.CL"
] | Text-to-SQL is emerging as a practical interface for real world databases. The dominant paradigm for Text-to-SQL is cross-database or schema-independent, supporting application schemas unseen during training. The schema of a database defines the tables, columns, column types and foreign key connections between tables. ... | {
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2501.17175 | Document-Level Sentiment Analysis of Urdu Text Using Deep Learning
Techniques | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Document level Urdu Sentiment Analysis (SA) is a challenging Natural Language Processing (NLP) task as it deals with large documents in a resource-poor language. In large documents, there are ample amounts of words that exhibit different viewpoints. Deep learning (DL) models comprise of complex neural network architect... | {
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2501.17176 | Prompt-Based Cost-Effective Evaluation and Operation of ChatGPT as a
Computer Programming Teaching Assistant | [
"cs.CY",
"cs.AI",
"cs.CL"
] | The dream of achieving a student-teacher ratio of 1:1 is closer than ever thanks to the emergence of large language models (LLMs). One potential application of these models in the educational field would be to provide feedback to students in university introductory programming courses, so that a student struggling to s... | {
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2501.17178 | Tuning LLM Judge Design Decisions for 1/1000 of the Cost | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Evaluating Large Language Models (LLMs) often requires costly human annotations. To address this, LLM-based judges have been proposed, which compare the outputs of two LLMs enabling the ranking of models without human intervention. While several approaches have been proposed, many confounding factors are present betwee... | {
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2501.17181 | An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome:
Minimizing Research Waste and Advancing Evidence Synthesis | [
"cs.AI",
"cs.CL",
"cs.DL",
"cs.IR"
] | The Brain-Heart Interconnectome (BHI) combines neurology and cardiology but is hindered by inefficiencies in evidence synthesis, poor adherence to quality standards, and research waste. To address these challenges, we developed an AI-driven system to enhance systematic reviews in the BHI domain. The system integrates a... | {
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2501.17182 | Dialogue Systems for Emotional Support via Value Reinforcement | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.HC"
] | Emotional support dialogue systems aim to reduce help-seekers' distress and help them overcome challenges. While human values$\unicode{x2013}$core beliefs that shape an individual's priorities$\unicode{x2013}$are increasingly emphasized in contemporary psychological therapy for their role in fostering internal transfor... | {
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2501.17183 | LLM Evaluation Based on Aerospace Manufacturing Expertise: Automated
Generation and Multi-Model Question Answering | [
"cs.CL",
"cs.AI"
] | Aerospace manufacturing demands exceptionally high precision in technical parameters. The remarkable performance of Large Language Models (LLMs), such as GPT-4 and QWen, in Natural Language Processing has sparked industry interest in their application to tasks including process design, material selection, and tool info... | {
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2501.17184 | Deep Learning in Wireless Communication Receiver: A Survey | [
"cs.IT",
"cs.LG",
"cs.NI",
"math.IT"
] | The design of wireless communication receivers to enhance signal processing in complex and dynamic environments is going through a transformation by leveraging deep neural networks (DNNs). Traditional wireless receivers depend on mathematical models and algorithms, which do not have the ability to adapt or learn from d... | {
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2501.17186 | Complete Chess Games Enable LLM Become A Chess Master | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Large language models (LLM) have shown remarkable abilities in text generation, question answering, language translation, reasoning and many other tasks. It continues to advance rapidly and is becoming increasingly influential in various fields, from technology and business to education and entertainment. Despite LLM's... | {
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2501.17187 | Visualizing Uncertainty in Translation Tasks: An Evaluation of LLM
Performance and Confidence Metrics | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) are increasingly utilized for machine translation, yet their predictions often exhibit uncertainties that hinder interpretability and user trust. Effectively visualizing these uncertainties can enhance the usability of LLM outputs, particularly in contexts where translation accuracy is crit... | {
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2501.17188 | Letters, Colors, and Words: Constructing the Ideal Building Blocks Set | [
"cs.AI",
"cs.NE"
] | Define a building blocks set to be a collection of n cubes (each with six sides) where each side is assigned one letter and one color from a palette of m colors. We propose a novel problem of assigning letters and colors to each face so as to maximize the number of words one can spell from a chosen dataset that are eit... | {
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2501.17190 | A Comprehensive Study on Fine-Tuning Large Language Models for Medical
Question Answering Using Classification Models and Comparative Analysis | [
"cs.CL"
] | This paper presents the overview of the development and fine-tuning of large language models (LLMs) designed specifically for answering medical questions. We are mainly improving the accuracy and efficiency of providing reliable answers to medical queries. In our approach, we have two stages, prediction of a specific l... | {
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2501.17191 | Aspect-Aware Decomposition for Opinion Summarization | [
"cs.CL",
"cs.IR"
] | Opinion summarization plays a key role in deriving meaningful insights from large-scale online reviews. To make this process more explainable and grounded, we propose a modular approach guided by review aspects which separates the tasks of aspect identification, opinion consolidation, and meta-review synthesis, enablin... | {
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2501.17194 | AI-assisted German Employment Contract Review: A Benchmark Dataset | [
"cs.CL"
] | Employment contracts are used to agree upon the working conditions between employers and employees all over the world. Understanding and reviewing contracts for void or unfair clauses requires extensive knowledge of the legal system and terminology. Recent advances in Natural Language Processing (NLP) hold promise for ... | {
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2501.17195 | Atla Selene Mini: A General Purpose Evaluation Model | [
"cs.CL",
"cs.AI"
] | We introduce Atla Selene Mini, a state-of-the-art small language model-as-a-judge (SLMJ). Selene Mini is a general-purpose evaluator that outperforms the best SLMJs and GPT-4o-mini on overall performance across 11 out-of-distribution benchmarks, spanning absolute scoring, classification, and pairwise preference tasks. ... | {
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2501.17200 | Improving LLM Leaderboards with Psychometrical Methodology | [
"cs.CL",
"cs.AI",
"stat.AP"
] | The rapid development of large language models (LLMs) has necessitated the creation of benchmarks to evaluate their performance. These benchmarks resemble human tests and surveys, as they consist of sets of questions designed to measure emergent properties in the cognitive behavior of these systems. However, unlike the... | {
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2501.17201 | Smart Cubing for Graph Search: A Comparative Study | [
"cs.AI"
] | Parallel solving via cube-and-conquer is a key method for scaling SAT solvers to hard instances. While cube-and-conquer has proven successful for pure SAT problems, notably the Pythagorean triples conjecture, its application to SAT solvers extended with propagators presents unique challenges, as these propagators learn... | {
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2501.17202 | Audio Large Language Models Can Be Descriptive Speech Quality Evaluators | [
"cs.SD",
"cs.CL",
"eess.AS"
] | An ideal multimodal agent should be aware of the quality of its input modalities. Recent advances have enabled large language models (LLMs) to incorporate auditory systems for handling various speech-related tasks. However, most audio LLMs remain unaware of the quality of the speech they process. This limitation arises... | {
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2501.17205 | Near-Optimal Algorithms for Omniprediction | [
"stat.ML",
"cs.DS",
"cs.LG"
] | Omnipredictors are simple prediction functions that encode loss-minimizing predictions with respect to a hypothesis class $\mathcal{H}$, simultaneously for every loss function within a class of losses $\mathcal{L}$. In this work, we give near-optimal learning algorithms for omniprediction, in both the online and offlin... | {
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2501.17206 | Integrating Reinforcement Learning and AI Agents for Adaptive Robotic
Interaction and Assistance in Dementia Care | [
"cs.AI",
"cs.RO"
] | This study explores a novel approach to advancing dementia care by integrating socially assistive robotics, reinforcement learning (RL), large language models (LLMs), and clinical domain expertise within a simulated environment. This integration addresses the critical challenge of limited experimental data in socially ... | {
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2501.17207 | Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning
Models Help? | [
"cs.NE",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | Functional brain connectome is crucial for deciphering the neural mechanisms underlying cognitive functions and neurological disorders. Graph deep learning models have recently gained tremendous popularity in this field. However, their actual effectiveness in modeling the brain connectome remains unclear. In this study... | {
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2501.17211 | MR imaging in the low-field: Leveraging the power of machine learning | [
"eess.IV",
"cs.LG"
] | Recent innovations in Magnetic Resonance Imaging (MRI) hardware and software have reignited interest in low-field ($<1\,\mathrm{T}$) and ultra-low-field MRI ($<0.1\,\mathrm{T}$). These technologies offer advantages such as lower power consumption, reduced specific absorption rate, reduced field-inhomogeneities, and cos... | {
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2501.17216 | Amplifier: Bringing Attention to Neglected Low-Energy Components in Time
Series Forecasting | [
"cs.LG"
] | We propose an energy amplification technique to address the issue that existing models easily overlook low-energy components in time series forecasting. This technique comprises an energy amplification block and an energy restoration block. The energy amplification block enhances the energy of low-energy components to ... | {
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2501.17256 | Increasing Information for Model Predictive Control with Semi-Markov
Decision Processes | [
"cs.LG"
] | Recent works in Learning-Based Model Predictive Control of dynamical systems show impressive sample complexity performances using criteria from Information Theory to accelerate the learning procedure. However, the sequential exploration opportunities are limited by the system local state, restraining the amount of info... | {
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2501.17260 | ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised
Pretraining Networks for Retinal OCT Classification | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Optical Coherence Tomography (OCT) is a non-invasive imaging modality essential for diagnosing various eye diseases. Despite its clinical significance, developing OCT-based diagnostic tools faces challenges, such as limited public datasets, sparse annotations, and privacy concerns. Although deep learning has made progr... | {
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2501.17261 | NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal
Emotion-Cause Analysis in Conversations | [
"cs.CL"
] | This paper describes the architecture of our system developed for Task 3 of SemEval-2024: Multimodal Emotion-Cause Analysis in Conversations. Our project targets the challenges of subtask 2, dedicated to Multimodal Emotion-Cause Pair Extraction with Emotion Category (MECPE-Cat), and constructs a dual-component system t... | {
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2501.17265 | Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained
Decoding for Automatic Post-Editing | [
"cs.CL"
] | Automatic Post-Editing (APE) systems often struggle with over-correction, where unnecessary modifications are made to a translation, diverging from the principle of minimal editing. In this paper, we propose a novel technique to mitigate over-correction by incorporating word-level Quality Estimation (QE) information du... | {
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2501.17266 | Advancing the Biological Plausibility and Efficacy of Hebbian
Convolutional Neural Networks | [
"cs.NE",
"cs.CV"
] | The research presented in this paper advances the integration of Hebbian learning into Convolutional Neural Networks (CNNs) for image processing, systematically exploring different architectures to build an optimal configuration, adhering to biological tenability. Hebbian learning operates on local unsupervised neural ... | {
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2501.17269 | A 1-D CNN inference engine for constrained platforms | [
"cs.LG"
] | 1D-CNNs are used for time series classification in various domains with a high degree of accuracy. Most implementations collect the incoming data samples in a buffer before performing inference on it. On edge devices, which are typically constrained and single-threaded, such an implementation may interfere with time-cr... | {
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2501.17270 | Comprehensive Evaluation for a Large Scale Knowledge Graph Question
Answering Service | [
"cs.CL",
"cs.DB"
] | Question answering systems for knowledge graph (KGQA), answer factoid questions based on the data in the knowledge graph. KGQA systems are complex because the system has to understand the relations and entities in the knowledge-seeking natural language queries and map them to structured queries against the KG to answer... | {
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2501.17273 | Tailored Truths: Optimizing LLM Persuasion with Personalization and
Fabricated Statistics | [
"cs.CL"
] | Large Language Models (LLMs) are becoming increasingly persuasive, demonstrating the ability to personalize arguments in conversation with humans by leveraging their personal data. This may have serious impacts on the scale and effectiveness of disinformation campaigns. We studied the persuasiveness of LLMs in a debate... | {
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2501.17275 | Dual-Lagrange Encoding for Storage and Download in Elastic Computing for
Resilience | [
"cs.IT",
"cs.DC",
"math.IT"
] | Coded elastic computing enables virtual machines to be preempted for high-priority tasks while allowing new virtual machines to join ongoing computation seamlessly. This paper addresses coded elastic computing for matrix-matrix multiplications with straggler tolerance by encoding both storage and download using Lagrang... | {
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2501.17281 | Stiff Transfer Learning for Physics-Informed Neural Networks | [
"cs.LG",
"math.AP"
] | Stiff differential equations are prevalent in various scientific domains, posing significant challenges due to the disparate time scales of their components. As computational power grows, physics-informed neural networks (PINNs) have led to significant improvements in modeling physical processes described by differenti... | {
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2501.17282 | From Natural Language to Extensive-Form Game Representations | [
"cs.AI",
"cs.CL",
"cs.GT",
"cs.MA"
] | We introduce a framework for translating game descriptions in natural language into extensive-form representations in game theory, leveraging Large Language Models (LLMs) and in-context learning. Given the varying levels of strategic complexity in games, such as perfect versus imperfect information, directly applying i... | {
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2501.17284 | Nonlinear dynamics of localization in neural receptive fields | [
"cs.LG"
] | Localized receptive fields -- neurons that are selective for certain contiguous spatiotemporal features of their input -- populate early sensory regions of the mammalian brain. Unsupervised learning algorithms that optimize explicit sparsity or independence criteria replicate features of these localized receptive field... | {
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2501.17286 | Fine-Tuning Open-Source Large Language Models to Improve Their
Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate
Their Potential Clinical Applications in Radiation Oncology | [
"physics.med-ph",
"cs.AI",
"cs.CL"
] | Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplo... | {
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2501.17289 | A Contrastive Teacher-Student Framework for Novelty Detection under
Style Shifts | [
"cs.CV"
] | There have been several efforts to improve Novelty Detection (ND) performance. However, ND methods often suffer significant performance drops under minor distribution shifts caused by changes in the environment, known as style shifts. This challenge arises from the ND setup, where the absence of out-of-distribution (OO... | {
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2501.17295 | Mitigating Hallucinated Translations in Large Language Models with
Hallucination-focused Preference Optimization | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Machine Translation (MT) is undergoing a paradigm shift, with systems based on fine-tuned large language models (LLM) becoming increasingly competitive with traditional encoder-decoder models trained specifically for translation tasks. However, LLM-based systems are at a higher risk of generating hallucinations, which ... | {
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2501.17296 | Multi-Physics Simulations via Coupled Fourier Neural Operator | [
"cs.LG",
"cs.AI"
] | Physical simulations are essential tools across critical fields such as mechanical and aerospace engineering, chemistry, meteorology, etc. While neural operators, particularly the Fourier Neural Operator (FNO), have shown promise in predicting simulation results with impressive performance and efficiency, they face lim... | {
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2501.17299 | "Ownership, Not Just Happy Talk": Co-Designing a Participatory Large
Language Model for Journalism | [
"cs.HC",
"cs.CL",
"cs.CY"
] | Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face divergent financial incentives: LLMs already permeate newswork processes within financially constrained organizations, even as ongoing legal challe... | {
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2501.17300 | Dilemmas and trade-offs in the diffusion of conventions | [
"physics.soc-ph",
"cs.SI",
"stat.AP"
] | Outside ideal settings, conventions are shaped by heterogeneous competing processes that can challenge the emergence of universal norms. This paper identifies three trade-offs challenging the diffusion of conventions and explores each of them empirically using observational behavioral data. The first trade-off (I) conc... | {
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2501.17303 | Measurement-Based Modeling and Analysis of UAV Air-Ground Channels at 1
and 4 GHz | [
"eess.SP",
"cs.IT",
"math.IT",
"physics.ins-det"
] | In the design of unmanned aerial vehicle (UAV) wireless communications, a better understanding of propagation characteristics and an accurate channel model are required. Measurements and comprehensive analysis for the UAV-based air-ground (AG) propagation channel in the vertical dimension are presented in this letter. ... | {
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2501.17304 | Summary of the NOTSOFAR-1 Challenge: Highlights and Learnings | [
"cs.SD",
"cs.LG",
"eess.AS"
] | The first Natural Office Talkers in Settings of Far-field Audio Recordings (NOTSOFAR-1) Challenge is a pivotal initiative that sets new benchmarks by offering datasets more representative of the needs of real-world business applications than those previously available. The challenge provides a unique combination of 280... | {
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2501.17310 | Probing LLM World Models: Enhancing Guesstimation with Wisdom of Crowds
Decoding | [
"cs.AI",
"cs.HC"
] | Guesstimation, the task of making approximate quantity estimates, is a common real-world challenge. However, it has been largely overlooked in large language models (LLMs) and vision language models (VLMs) research. We introduce a novel guesstimation dataset, MARBLES. This dataset requires one to estimate how many item... | {
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2501.17311 | RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on
Scaled Platforms | [
"cs.RO",
"cs.LG"
] | Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tire models are reliable, they often demand extensive tuning or system identification. Reinforcement Learning (RL) methods offer significant po... | {
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2501.17313 | Surena-V: A Humanoid Robot for Human-Robot Collaboration with
Optimization-based Control Architecture | [
"cs.RO"
] | This paper presents Surena-V, a humanoid robot designed to enhance human-robot collaboration capabilities. The robot features a range of sensors, including barometric tactile sensors in its hands, to facilitate precise environmental interaction. This is demonstrated through an experiment showcasing the robot's ability ... | {
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2501.17315 | A sketch of an AI control safety case | [
"cs.AI",
"cs.CR",
"cs.SE"
] | As LLM agents gain a greater capacity to cause harm, AI developers might increasingly rely on control measures such as monitoring to justify that they are safe. We sketch how developers could construct a "control safety case", which is a structured argument that models are incapable of subverting control measures in or... | {
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2501.17318 | Floodgates up to contain the DeePC and limit extrapolation | [
"eess.SY",
"cs.SY"
] | Behavioral data-enabled control approaches typically assume data-generating systems of linear dynamics. This may result in false generalization if the newly designed closed-loop system results in input-output distributional shifts beyond learning data. These shifts may compromise safety by activating harmful nonlineari... | {
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2501.17319 | MDDM: A Molecular Dynamics Diffusion Model to Predict Particle
Self-Assembly | [
"cs.LG",
"physics.comp-ph"
] | The discovery and study of new material systems relies on molecular simulations that often come with significant computational expense. We propose MDDM, a Molecular Dynamics Diffusion Model, which is capable of predicting a valid output conformation for a given input pair potential function. After training MDDM on a la... | {
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2501.17322 | Influence of field of view in visual prostheses design: Analysis with a
VR system | [
"cs.HC",
"cs.CV"
] | Visual prostheses are designed to restore partial functional vision in patients with total vision loss. Retinal visual prostheses provide limited capabilities as a result of low resolution, limited field of view and poor dynamic range. Understanding the influence of these parameters in the perception results can guide ... | {
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2501.17323 | Exploring Non-Convex Discrete Energy Landscapes: A Langevin-Like Sampler
with Replica Exchange | [
"cs.LG",
"stat.ML"
] | Gradient-based Discrete Samplers (GDSs) are effective for sampling discrete energy landscapes. However, they often stagnate in complex, non-convex settings. To improve exploration, we introduce the Discrete Replica EXchangE Langevin (DREXEL) sampler and its variant with Adjusted Metropolis (DREAM). These samplers use t... | {
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} |
2501.17324 | CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data | [
"cs.LG",
"stat.ML"
] | High-cardinality categorical features are a common characteristic of mixed-type tabular datasets. Existing generative model architectures struggle to learn the complexities of such data at scale, primarily due to the difficulty of parameterizing the categorical features. In this paper, we present a general variational ... | {
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} |
2501.17325 | Connecting Federated ADMM to Bayes | [
"cs.LG",
"cs.AI",
"stat.ML"
] | We provide new connections between two distinct federated learning approaches based on (i) ADMM and (ii) Variational Bayes (VB), and propose new variants by combining their complementary strengths. Specifically, we show that the dual variables in ADMM naturally emerge through the 'site' parameters used in VB with isotr... | {
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} |
2501.17326 | Memorize and Rank: Elevating Large Language Models for Clinical
Diagnosis Prediction | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Clinical diagnosis prediction models, when provided with a patient's medical history, aim to detect potential diseases early, facilitating timely intervention and improving prognostic outcomes. However, the inherent scarcity of patient data and large disease candidate space often pose challenges in developing satisfact... | {
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"cs.SY": 0
} |
2501.17328 | WASUP: Interpretable Classification with Weight-Input Alignment and
Class-Discriminative SUPports Vectors | [
"cs.CV",
"cs.LG"
] | The deployment of deep learning models in critical domains necessitates a balance between high accuracy and interpretability. We introduce WASUP, an inherently interpretable neural network that provides local and global explanations of its decision-making process. We prove that these explanations are faithful by fulfil... | {
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} |
2501.17329 | Anomaly Detection in Cooperative Vehicle Perception Systems under
Imperfect Communication | [
"cs.MA",
"cs.AI",
"cs.LG"
] | Anomaly detection is a critical requirement for ensuring safety in autonomous driving. In this work, we leverage Cooperative Perception to share information across nearby vehicles, enabling more accurate identification and consensus of anomalous behaviors in complex traffic scenarios. To account for the real-world chal... | {
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} |
2501.17330 | Attribution analysis of legal language as used by LLM | [
"cs.LG",
"cs.CL"
] | Three publicly-available LLM specifically designed for legal tasks have been implemented and shown that classification accuracy can benefit from training over legal corpora, but why and how? Here we use two publicly-available legal datasets, a simpler binary classification task of ``overruling'' texts, and a more elabo... | {
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} |
2501.17332 | Compact Neural TTS Voices for Accessibility | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Contemporary text-to-speech solutions for accessibility applications can typically be classified into two categories: (i) device-based statistical parametric speech synthesis (SPSS) or unit selection (USEL) and (ii) cloud-based neural TTS. SPSS and USEL offer low latency and low disk footprint at the expense of natural... | {
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} |
2501.17333 | A Guaranteed-Stable Neural Network Approach for Optimal Control of
Nonlinear Systems | [
"math.OC",
"cs.LG"
] | A promising approach to optimal control of nonlinear systems involves iteratively linearizing the system and solving an optimization problem at each time instant to determine the optimal control input. Since this approach relies on online optimization, it can be computationally expensive, and thus unrealistic for syste... | {
"Other": 0,
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"cs.SD": 0,
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} |
2501.17335 | Pandora's Box: Cross-Chain Arbitrages in the Realm of Blockchain
Interoperability | [
"cs.CR",
"cs.CE"
] | Over recent years, the blockchain ecosystem has grown significantly with the emergence of new Layer-1 (L1) and Layer-2 (L2) networks. These blockchains typically host Decentralized Exchanges (DEXes) for trading assets such as native currencies and stablecoins. While this diversity enriches the ecosystem, it also fragme... | {
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"cs.SD": 0,
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} |
2501.17338 | Inferring from Logits: Exploring Best Practices for Decoding-Free
Generative Candidate Selection | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Generative Language Models rely on autoregressive decoding to produce the output sequence token by token. Many tasks such as preference optimization, require the model to produce task-level output consisting of multiple tokens directly by selecting candidates from a pool as predictions. Determining a task-level predict... | {
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"cs.SY": 0
} |
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