Papers by Valerie Chen
Coding Agents with Multimodal Browsing are Generalist Problem Solvers (2026.findings-eacl)
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| Challenge: | specialized AI agents with task-specific tools or architectures fail to generalize beyond their intended scope. |
| Approach: | They propose a single-agent system with a modest number of general tools . they propose to generalize across software engineering, deep research and web browsing . |
| Outcome: | The proposed system achieves superior or competitive performance over specialized agents on three benchmarks. |
When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback (2025.findings-acl)
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| Challenge: | Existing static benchmarks that measure task performance often rely on a simple input-output configuration. |
| Approach: | They propose an evaluation pipeline that evaluates code models with different feedback types in an interactive setting. |
| Outcome: | The proposed evaluation pipeline compares model-user collaboration with static benchmarks by obfuscating inputs to a simulated user. |
RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions (2026.acl-demo)
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| Challenge: | Deployed in a university software engineering course, RECAP captured 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. |
| Approach: | They propose an open-source platform that passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer’s workflow. |
| Outcome: | The open-source platform captures 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. |
Scaling Collaborative Effort with Agents (2026.findings-acl)
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Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J Shen, Ameet Talwalkar, Tongshuang Wu, David Sontag
| Challenge: | Current evaluations of agents focus on producing high-quality, final outputs in one shot, failing to account for the inherently iterative nature of many real-world problems. |
| Approach: | They propose a framework that captures how an agent’s utility grows with increasing user involvement. |
| Outcome: | The proposed framework captures how an agent’s utility grows with increasing user involvement, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. |