2 citations · 2 across the 7 of their papers we have counts for
9 papers
AECP: Artifact-Exclusive Communication Protocol for Multi-Agent Code Generation
Jiaqi Xue, Yanjun Wang, Xiangci Li +5
As AI agents increasingly tackle complex repository-level coding tasks, distributing work across multiple agents is a natural way to scale beyond the capabilities of a single agent…
Teaching Agents to Code Reliably
Muhammad Ahmed Mohsin, Myeongsoo Kim, Kangrui Ruan +3
Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra inference-time compute yields gains only when it pr…
CODESTRUCT: Code Agents over Structured Action Spaces
Myeongsoo Kim, Joe Hsu, Dingmin Wang +3
LLM-based code agents treat repositories as unstructured text, applying edits through brittle string matching that frequently fails due to formatting drift or ambiguous patterns. W…
CodeScout: Contextual Problem Statement Enhancement for Software Agents
Manan Suri, Xiangci Li, Mehdi Shojaie +5
Current AI-powered code assistance tools often struggle with poorly-defined problem statements that lack sufficient task context and requirements specification. Recent analysis of…
Coherence Collapse: Diagnosing Why Code Agents Fail After Reaching the Right Code
Myeongsoo Kim, Dingmin Wang, Siwei Cui +6
Code agents resolve 65-70% of SWE-bench Verified issues, but Pass@1 cannot tell us why the rest fail, and, as we show, capable-model failures are systematically misdiagnosed withou…
Textual Gradients are a Flawed Metaphor for Automatic Prompt Optimization
Daniel Melcer, Qi Chen, Wen-Hao Chiang +3
A well-engineered prompt can increase the performance of large language models; automatic prompt optimization techniques aim to increase performance without requiring human effort…
CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance
Myeongsoo Kim, Shweta Garg, Baishakhi Ray +2
Programming assistants powered by large language models have improved dramatically, yet existing benchmarks still evaluate them in narrow code-generation settings. Recent efforts s…
Early prediction of onset of sepsis in Clinical Setting
Fahim Mohammad, Lakshmi Arunachalam, Samanway Sadhu +7
This study proposes the use of Machine Learning models to predict the early onset of sepsis using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A sup…
MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information
Yu Zhang, Shweta Garg, Yu Meng +2
We study the problem of weakly supervised text classification, which aims to classify text documents into a set of pre-defined categories with category surface names only and witho…