Papers
Research papers from arXiv and related sources
Operationalization of Machine Learning with Serverless Architecture: An Industrial Operationalization of Machine Learning with Serverless Architecture: An Industrial Implementation for Harmonized System Code Prediction
This paper presents a serverless MLOps framework orchestrating the complete ML lifecycle from data ingestion, training, deployment, monitoring, and retraining to using event-driven pipelines and ma...
Sai Vineeth Kandappareddigari, Santhoshkumar Jagadish, Gauri Verma, Ilhuicamina Contreras, Christ...
AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation
Large language model(LLM)-driven multi-agent systems(MAS) coordinate specialized agents through predefined interaction topologies and have shown promise for complex tasks such as competition-level ...
Siyu Wang, Ruotian Lu, Zhihao Yang, Yuchao Wang, Yanzhou Zhang, Lei Xu, Qimin Xu, Guojun Yin, Cai...
AudioChat: Unified Audio Storytelling, Editing, and Understanding with Transfusion Forcing
Despite recent breakthroughs, audio foundation models struggle in processing complex multi-source acoustic scenes. We refer to this challenging domain as audio stories, which can have multiple spea...
William Chen, Prem Seetharaman, Rithesh Kumar, Oriol Nieto, Shinji Watanabe, Justin Salamon, Zeyu...
Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence
As 6G wireless systems evolve, growing functional complexity and diverse service demands are driving a shift from rule-based control to intent-driven autonomous intelligence. User requirements are ...
Zhaoyang Li, Xingzhi Jin, Junyu Pan, Qianqian Yang, Zhiguo Shi
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
Parameter-efficient fine-tuning techniques such as low-rank adaptation (LoRA) enable large language models (LLMs) to adapt to downstream tasks efficiently. Federated learning (FL) further facilitat...
Chuiyang Meng, Ming Tang, Vincent W. S. Wong
A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. Howeve...
Aadi Joshi, Kavya Bhand
What to Cut? Predicting Unnecessary Methods in Agentic Code Generation
Agentic Coding, powered by autonomous agents such as GitHub Copilot and Cursor, enables developers to generate code, tests, and pull requests from natural language instructions alone. While this ac...
Kan Watanabe, Tatsuya Shirai, Yutaro Kashiwa, Hajimu Iida
Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling
Diffusion models recently developed for generative AI tasks can produce high-quality samples while still maintaining diversity among samples to promote mode coverage, providing a promising path for...
Xinghao Dong, Huchen Yang, Jin-long Wu
How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request des...
Kan Watanabe, Rikuto Tsuchida, Takahiro Monno, Bin Huang, Kazuma Yamasaki, Youmei Fan, Kazumasa S...
Rememo: A Research-through-Design Inquiry Towards an AI-in-the-loop Therapist's Tool for Dementia Reminiscence
Reminiscence therapy (RT) is a common non-pharmacological intervention in dementia care. Recent technology-mediated interventions have largely focused on people with dementia through solutions that...
Celeste Seah, Yoke Chuan Lee, Jung-Joo Lee, Ching-Chiuan Yen, Clement Zheng
Environmental policy in the context of complex systems: Statistical optimization and sensitivity analysis for ABMs
Coupled human-environment systems are increasingly being understood as complex adaptive systems (CAS), in which micro-level interactions between components lead to emergent behavior. Agent-based mo...
Dylan Munson, Arijit Dey, Simon Mak
BankMathBench: A Benchmark for Numerical Reasoning in Banking Scenarios
Large language models (LLMs)-based chatbots are increasingly being adopted in the financial domain, particularly in digital banking, to handle customer inquiries about products such as deposits, sa...
Yunseung Lee, Subin Kim, Youngjun Kwak, Jaegul Choo
A Long-term Value Prediction Framework In Video Ranking
Accurately modeling long-term value (LTV) at the ranking stage of short-video recommendation remains challenging. While delayed feedback and extended engagement have been explored, fine-grained att...
Huabin Chen, Xinao Wang, Huiping Chu, Keqin Xu, Chenhao Zhai, Chenyi Wang, Kai Meng, Yuning Jiang
ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning
While recent Arabic NLP benchmarks focus on scale, they often rely on synthetic or translated data which may benefit from deeper linguistic verification. We introduce ALPS (Arabic Linguistic & Prag...
Hussein S. Al-Olimat, Ahmad Alshareef
RFEval: Benchmarking Reasoning Faithfulness under Counterfactual Reasoning Intervention in Large Reasoning Models
Large Reasoning Models (LRMs) exhibit strong performance, yet often produce rationales that sound plausible but fail to reflect their true decision process, undermining reliability and trust. We in...
Yunseok Han, Yejoon Lee, Jaeyoung Do
Large Language Models Persuade Without Planning Theory of Mind
A growing body of work attempts to evaluate the theory of mind (ToM) abilities of humans and large language models (LLMs) using static, non-interactive question-and-answer benchmarks. However, theo...
Jared Moore, Rasmus Overmark, Ned Cooper, Beba Cibralic, Nick Haber, Cameron R. Jones
Quantifying the limits of human athletic performance: A Bayesian analysis of elite decathletes
Because the decathlon tests many facets of athleticism, including sprinting, throwing, jumping, and endurance, many consider it to be the ultimate test of athletic ability. On this view, estimating...
Paul-Hieu V. Nguyen, James M. Smoliga, Benton Lindaman, Sameer K. Deshpande
Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing \emph{simplicity ...
Shengtian Yang, Yu Li, Shuo He, Yewen Li, Qingpeng Cai, Peng Jiang, Lei Feng
Wink: Recovering from Misbehaviors in Coding Agents
Autonomous coding agents, powered by large language models (LLMs), are increasingly being adopted in the software industry to automate complex engineering tasks. However, these agents are prone to ...
Rahul Nanda, Chandra Maddila, Smriti Jha, Euna Mehnaz Khan, Matteo Paltenghi, Satish Chandra
Product Hardy Spaces on Spaces of Homogeneous Type: Discrete Product Calderón-Type Reproducing Formula, Atomic Characterization, and Product Calderón--Zygmund Operators
Let $i\in\{1,2\}$ and $X_i$ be a space of homogeneous type in the sense of Coifman and Weiss with the upper dimension $ω_i$. Also let $η_i$ be the smoothness index of the Auscher--Hytönen wavelet f...
Ziyi He, Dachun Yang, Taotao Zheng