mlsanityData to 14 Oct 2026

EvoWeb: A Benchmark for Coding Agents on Evolving Web Applications

arXiv 2610.16402 ↗First posted 12 Oct 2026cs.AI cs.SEcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Web application development often proceeds through successive requests that extend an existing implementation. Yet web-generation benchmarks primarily evaluate development toward fixed specifications. We introduce EvoWeb, a novel benchmark comprising 320 cumulative requests over 32 web applications, grounded in real-world product features and structured by functional dependency graphs. Agents iteratively modify a persistent codebase, while a multimodal web agent tests each intermediate application in a live browser, assessing newly requested functionality, cross-request integration, and previously requested functionality. To characterize reliability throughout development, we integrate trajectory performance across user-tolerance thresholds rather than impose a fixed stopping criterion. Experiments across open- and closed-source frontier models show that EvoWeb reveals clear performance differences between models, with cross-request integration emerging as a key challenge. Further analysis shows that later requests with deeper or more numerous dependencies are solved less accurately, while runtime errors become more frequent over successive requests. EvoWeb provides a testbed for evaluating and improving coding agents in long-horizon, multi-request development.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 29 of its 32 references that are in the map. First posted on arXiv 12 Oct 2026.

Builds on

29 of 32 references.

Closest ancestors · 9

Same direction, newest first

  1. 2026ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders23 Jul
  2. 2026SWE-Together: Evaluating Coding Agents in Interactive User Sessions29 Jun
  3. 2026StaminaBench: Stress-Testing Coding Agents over 100 Interaction Turns17 Jun
  4. 2026Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement4 Jun
  5. 2026EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions22 May
  6. 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks25 Mar
  7. 2025SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios20 Dec
  8. 2025Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworks12 May
  9. 2025WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch6 May

Other references · 20

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  3. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  4. 2017Selective Classification for Deep Neural Networkscited by 852
  5. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  6. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 491
  7. 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 438
  8. 2024Self-Preference Bias in LLM-as-a-Judgecited by 258
  9. 2025AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectoriescited by 108
  10. 2023LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scorescited by 90
  11. 2024TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinementcited by 39
  12. 2024Overcoming Common Flaws in the Evaluation of Selective Classification Systemscited by 37
8 more
  1. 2026SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?cited by 23
  2. 2026WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metricscited by 18
  3. 2024A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimatorscited by 15
  4. 2025WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learningcited by 15
  5. 2026Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Developmentcited by 9
  6. 2025From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Groundingcited by 5
  7. 2025AgentBalance: Backbone-then-Topology Design for Cost-Effective Multi-Agent Systems under Budget Constraintscited by 4
  8. 2026SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamicscited by 1

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