# When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback

arXiv: https://arxiv.org/abs/2502.18413 · HTML: https://mlsanity.com/paper/2502.18413/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2025.

## Built on by (most cited first, 16 of 16)

- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [SWE-chat: Coding Agent Interactions From Real Users in the Wild](https://mlsanity.com/paper/2604.20779/) (2026)
- [Goal Alignment in LLM-Based User Simulators for Conversational AI](https://mlsanity.com/paper/2507.20152/) (2025)
- [SWE-Together: Evaluating Coding Agents in Interactive User Sessions](https://mlsanity.com/paper/2606.29957/) (2026)
- [FLAWS: A Benchmark for Error Identification and Localization in Scientific Papers](https://mlsanity.com/paper/2511.21843/) (2025)
- [Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey](https://mlsanity.com/paper/2506.11102/) (2025)
- [Deceive, Detect, and Disclose: Large Language Models Play Mini-Mafia](https://mlsanity.com/paper/2509.23023/) (2025)
- [Dialogue SWE-Bench: A Benchmark for Dialogue-Driven Coding Agents](https://mlsanity.com/paper/2606.13995/) (2026)
- [LHAW: Controllable Underspecification for Long-Horizon Tasks](https://mlsanity.com/paper/2602.10525/) (2026)
- [ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders](https://mlsanity.com/paper/2607.21217/) (2026)
- [How can we assess human-agent interactions? Case studies in software agent design](https://mlsanity.com/paper/2510.09801/) (2025)
- [Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks](https://mlsanity.com/paper/2508.18905/) (2025)
- [Failing to Explore: Language Models on Interactive Tasks](https://mlsanity.com/paper/2601.22345/) (2026)
- [XYEval: Agents say yes to bad advice](https://mlsanity.com/paper/2609.23939/) (2026)
- [CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking](https://mlsanity.com/paper/2610.02460/) (2026)
- [FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests](https://mlsanity.com/paper/2610.14539/) (2026)

## Abstract (arXiv)

Programming is a fundamentally interactive process, yet coding assistants are often evaluated using static benchmarks that fail to measure how well models collaborate with users. We introduce an interactive evaluation pipeline to examine how LLMs incorporate different types of feedback in a collaborative setting. Specifically, we perturb static coding benchmarks so that the code model must interact with a simulated user to retrieve key information about the problem. We find that interaction significantly affects model performance, as the relative rankings of 10 models across 3 datasets often vary between static and interactive settings, despite models being fairly robust to feedback that contains errors. We also observe that even when different feedback types are equally effective with respect to performance, they can impact model behaviors such as (1) how models respond to higher- vs. lower-quality feedback and (2) whether models prioritize aesthetic vs. functional edits. Our work aims to "re-evaluate" model coding capabilities through an interactive lens toward bridging the gap between existing evaluations and real-world usage.