MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding

arXiv:2609.14992 · cs.CL · Submitted 2026-09-14 · Read on arXiv

cs.CL

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 23 pages, 7 figures

Code: https://github.com/abelperry/AgentProbe

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 28× in this paper · explained in 2 episodes
• Verification — 26× in this paper · explained in 2 episodes
• Agentic Coding — 22× in this paper · explained in 1 episode
• Trace — 16× in this paper · explained in 2 episodes
• Benchmarking — 9× in this paper · explained in 3 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Core — 6× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• LLM-as-a-Judge — 4× in this paper · explained in 3 episodes
• Agent Harness — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• F1 Score — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Agentic AI — 1× in this paper · explained in 1 episode
• Build Success Rate — 1× in this paper
• Function Success Rate — 1× in this paper
• Function-Instruction Success Rate — 1× in this paper
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Task Planning — 1× in this paper · explained in 2 episodes

Sources

• Program Synthesis with Large Language Models
• AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios
• Evaluating Large Language Models Trained on Code
• SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
• OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding
• ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
• Language Models for Code Optimization: Survey, Challenges and Future Directions
• Measuring Coding Challenge Competence With APPS
• Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
• Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
• AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios
• SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
• Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
• Gemini: A Family of Highly Capable Multimodal Models
• AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities
• CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
• IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation
• FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedback
• SWE-Together: Evaluating Coding Agents in Interactive User Sessions
• IFEvalCode: Controlled Code Generation

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