Wayne Chi
2026
Scaling Collaborative Effort with Agents
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
Findings of the Association for Computational Linguistics: ACL 2026
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
Findings of the Association for Computational Linguistics: ACL 2026
Current evaluations of agents remain centered around one-shot task completion, failing to account for the inherently iterative and collaborative nature of many real-world problems, where human goals are often underspecified and evolve. We argue for a shift from building and assessing task completion agents to developing collaborative agents, assessed not only by the quality of their final outputs but by how well they engage with and enhance human effort throughout the problem-solving process. To support this shift, we introduce collaborative effort scaling, a framework that captures how an agent’s utility grows with increasing user involvement. Through case studies and simulated evaluations, we show that state-of-the-art agents often underperform in multi-turn, real-world scenarios, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. Collaborative effort scaling offers a lens for diagnosing agent behavior and guiding development toward more effective interactions.
RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions
Keyu He | Qianou Ma | Valerie Chen | Wayne Chi | Tongshuang Wu
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Keyu He | Qianou Ma | Valerie Chen | Wayne Chi | Tongshuang Wu
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Understanding how developers interact with AI coding assistants requires more than chat logs or git histories in isolation; it requires reconstructing the full context: which prompt led to which edit, what the developer tried and discarded, and how their strategy evolved over time. We present RECAP (Replay and Examine Captured AI Programming), an open-source platform that (1) passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer’s workflow, (2) merges them into a unified timeline for interactive session replay, and (3) exposes an extensible analysis layer, with example modules for behavioral classification and AI reliance measurement. 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. We demonstrate how the platform’s linked data and replay capabilities enable analyses of developer-AI interaction patterns that no single data source could support.