large language models

LLM-Assisted Emotional Expressive Behavior Generation for Engagement Enhancements in Human-Robot Interaction

Social robots are increasingly being integrated into human environments, whether in homes, workplaces, or public spaces. Enhancing user engagement is crucial for effective human-robot interaction, as it determines the quality and sustainability of these interactions across various applications. However, current researc...

Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders

Previous work in group recommender systems has demonstrated a sensitivity to the distribution of preferences within a group. Specifically, the selection of the preference aggregation strategy benefits from considering such group configurations. In this paper, we study whether LLMs are able to mimic this sensitivity and...

LLM-Based User Personas for Recommendations at Scale

Large Language Models (LLMs) offer unprecedented potential for enhancing recommendation systems through their world knowledge and reasoning capabilities. However, existing approaches often rely on structured IDs or offline processing, limiting semantic richness, real-time adaptability, and user-facing interpretability....

CheXanatomy: Anatomy–Aware Vision–Language Modeling for Chest Radiographs

Building digital societies as ecosystems: how recognition and repeat relationships sustain cross-community work in open source

Abstract We measure cross-boundary collaboration in an open-source software (OSS) ecosystem by reconstructing the bipartite contributor–repository graph of 464 cybersecurity-focused projects and $11{,}372$ 11 , 372 contributor identities active over October 2001–May 2022, seeded from the curated Rawsec Cybersecurity In...

Tacit Knowledge and Hermeneutics: Critical Reflections on Automated Text Interpretation with Large Language Models

Overview This study explores the interplay between tacit knowledge and hermeneutical interpretation within the domain of large language models (LLMs). In this study, we examine the question of whether automated systems can approximate interpretive practices that depend on implicit understanding, situated experience, an...

Automated Conjecture Resolution with Formal Verification

Recent advances in large language models (LLMs) have significantly improved their ability to perform mathematical reasoning, extending from elementary problem solving to increasingly capable performance on research-level problems. However, reliably solving and verifying such problems remains challenging due to the inhe...

Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models

Unleashing Video Language Models for Fine-Grained HRCT Report Generation

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents

Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more critically, designing sophisticated optimizers, architectures, and reward functions to capture nuanced user behaviors. Achieving substantial impr...

TotalFM: An Organ-Separated 3D-CT Foundation Model Leveraging Large-Scale Routine Clinical Radiology Data

Disaggregated KV-Cache Fabric: Hardware-Agnostic RAM-Tiering, PCIe DMA Eviction, and Fine-Grained Prefix Deduplication for Autonomous Agent Swarms

Modern Large Language Model (LLM) serving systems face an acute memory wall dominated by Key-Value (KV) cache allocation rather than model weight storage. In multi-agent autonomous swarms—where dozens of concurrent agents share extensive system prompts, tool schemas, and branching conversational trajectories—redundant...