arXiv:2408.12150v2 Announce Type: replace-cross Abstract: Unlike fixed- or variable-rate image coding, progressive image coding (PIC) aims to compress various qualities of images into a single bitstream, increasing the versatility of bitstream utilization and providing high compression efficiency compared to simulcast compression. Research on neural network (NN)-based PIC is in its early stages, mainly focusing on […]
GTAlign: Game-Theoretic Alignment of LLM Assistants for Social Welfare
arXiv:2510.08872v3 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable progress in reasoning, yet sometimes produce responses that are suboptimal for users in tasks such as writing, information seeking, or providing practical guidance. Conventional alignment practices typically assume that maximizing model reward also maximizes user welfare, but this assumption frequently fails in practice: […]
AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence
arXiv:2511.01144v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language reasoning, yet their application to Cyber Threat Intelligence (CTI) remains limited. CTI analysis involves distilling large volumes of unstructured reports into actionable knowledge, a process where LLMs could substantially reduce analyst workload. CTIBench introduced a comprehensive benchmark for evaluating […]
Pharmacovigilance Analysis of Drug-Induced Rhabdomyolysis Based on the FDA Adverse Event Reporting System (FAERS)
arXiv:2511.00093v1 Announce Type: new Abstract: This study aimed to systematically identify and quantify risks for drug-induced rhabdomyolysis (DIR) using real-world data and to propose an evidence-based risk mitigation framework. We conducted a retrospective pharmacovigilance study using the FDA Adverse Event Reporting System (FAERS) database from Q1 2005 to Q1 2025. A two-stage analysis involved initial […]
When, What, and How: Rethinking Retrieval-Enhanced Speculative Decoding
arXiv:2511.01282v1 Announce Type: cross Abstract: Speculative decoding (SD) has emerged as an effective technique to accelerate large language model (LLM) inference without compromising output quality. However, the achievable speedup largely depends on the effectiveness of the drafting model. While model-based methods like EAGLE-2 are accurate but costly, retrieval-enhanced methods like SAM-Decoding rely on heuristic switching […]
AI-derived layer-specific OCT biomarkers for classification of geographic atrophy
arXiv:2511.00057v1 Announce Type: new Abstract: Geographic atrophy (GA) is a key biomarker of dry age-related macular degeneration (AMD) traditionally identified through color fundus photography. Hyper-transmission defects (hyperTDs), a feature highly correlated with GA, have recently gained prominence in optical coherence tomography (OCT) research. OCT offers cross-sectional imaging of the retina, leading to the development of […]
Scalable Processing-Near-Memory for 1M-Token LLM Inference: CXL-Enabled KV-Cache Management Beyond GPU Limits
arXiv:2511.00321v1 Announce Type: cross Abstract: The expansion of context windows in large language models (LLMs) to multi-million tokens introduces severe memory and compute bottlenecks, particularly in managing the growing Key-Value (KV) cache. While Compute Express Link (CXL) enables non-eviction frameworks that offload the full KV-cache to scalable external memory, these frameworks still suffer from costly […]
GEPOC Parameters – Open Source Parametrisation and Validation for Austria, Version 2.0
arXiv:2511.00048v1 Announce Type: new Abstract: GEPOC, short for Generic Population Concept, is a collection of models and methods for analysing population-level research questions. For the valid application of the models for a specific country or region, stable and reproducible data processes are necessary, which provide valid and ready-to-use model parameters. This work contains a complete […]
Enhancing Adversarial Transferability by Balancing Exploration and Exploitation with Gradient-Guided Sampling
arXiv:2511.00411v1 Announce Type: cross Abstract: Adversarial attacks present a critical challenge to deep neural networks’ robustness, particularly in transfer scenarios across different model architectures. However, the transferability of adversarial attacks faces a fundamental dilemma between Exploitation (maximizing attack potency) and Exploration (enhancing cross-model generalization). Traditional momentum-based methods over-prioritize Exploitation, i.e., higher loss maxima for attack […]
Graph-Attentive MAPPO for Dynamic Retail Pricing
arXiv:2511.00039v1 Announce Type: new Abstract: Dynamic pricing in retail requires policies that adapt to shifting demand while coordinating decisions across related products. We present a systematic empirical study of multi-agent reinforcement learning for retail price optimization, comparing a strong MAPPO baseline with a graph-attention-augmented variant (MAPPO+GAT) that leverages learned interactions among products. Using a simulated […]