activity
20232026
most citedLow-code LLM: Graphical User Interface over Large Language Models

23 citations · 31 across the 10 of their papers we have counts for

collaborators

10 papers

cs.AI2026

ExecCritic: Learn to Test, Test to Improve for Coding Agents

Leitian Tao, Baolin Peng, Haorui Wang +7

Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agent-generated tests can encode…

cs.LG2026

TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents

Leitian Tao, Baolin Peng, Wenlin Yao +5

Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-…

cs.IR2026

LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval

Mike Hang Wang, Utkarsh Garg, Reza Davari +5

We introduce LEDGER to tackle the novel context engineering challenge of agentic document editing, where localized edits to long, structured documents must be applied efficiently w…

cs.AI2026

Orchard: An Open-Source Agentic Modeling Framework

Baolin Peng, Wenlin Yao, Qianhui Wu +11

Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with external envi…

cs.AI2026

Synthetic Computers at Scale for Long-Horizon Productivity Simulation

Tao Ge, Baolin Peng, Hao Cheng +1

Realistic long-horizon productivity work is strongly conditioned on user-specific computer environments, where much of the work context is stored and organized through directory st…

cs.CL2024

Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction

Dingyao Yu, Yang An, Wei Ye +4

Chinese Spelling Correction (CSC) commonly lacks large-scale high-quality corpora, due to the labor-intensive labeling of spelling errors in real-life human writing or typing scena…

cs.CL2024★ 1 cited

Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning

Yadong Zhang, Shaoguang Mao, Wenshan Wu +4

This paper introduces BI-Directional DEliberation Reasoning (BIDDER), a novel reasoning approach to enhance the decision rationality of language models. Traditional reasoning metho…

cs.CL2024★ 6 cited

LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models

Yadong Zhang, Shaoguang Mao, Tao Ge +7

This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that n…

cs.CL2024★ 1 cited

K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning

Yadong Zhang, Shaoguang Mao, Tao Ge +4

Strategic reasoning is a complex yet essential capability for intelligent agents. It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent…

cs.CL2023★ 23 cited

Low-code LLM: Graphical User Interface over Large Language Models

Yuzhe Cai, Shaoguang Mao, Wenshan Wu +10

Utilizing Large Language Models (LLMs) for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a nov…