CCTU: A Benchmark for Tool Use under Complex Constraints

• URL: http://arxiv.org/abs/2603.15309v1
• Date: Mon, 16 Mar 2026 14:05:13 GMT
• Title: CCTU: A Benchmark for Tool Use under Complex Constraints
• Abstract summary: We introduce CCTU, a benchmark for evaluating large language models (LLMs) under complex constraints.<n>The benchmark comprises 200 carefully curated and challenging test cases across diverse tool-use scenarios.<n>We develop an executable constraint validation module that performs step-level validation and enforces compliance.
• Score: 66.87622847854337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Solving problems through tool use under explicit constraints constitutes a highly challenging yet unavoidable scenario for large language models (LLMs), requiring capabilities such as function calling, instruction following, and self-refinement. However, progress has been hindered by the absence of dedicated evaluations. To address this, we introduce CCTU, a benchmark for evaluating LLM tool use under complex constraints. CCTU is grounded in a taxonomy of 12 constraint categories spanning four dimensions (i.e., resource, behavior, toolset, and response). The benchmark comprises 200 carefully curated and challenging test cases across diverse tool-use scenarios, each involving an average of seven constraint types and an average prompt length exceeding 4,700 tokens. To enable reliable evaluation, we develop an executable constraint validation module that performs step-level validation and enforces compliance during multi-turn interactions between models and their environments. We evaluate nine state-of-the-art LLMs in both thinking and non-thinking modes. Results indicate that when strict adherence to all constraints is required, no model achieves a task completion rate above 20%. Further analysis reveals that models violate constraints in over 50% of cases, particularly in the resource and response dimensions. Moreover, LLMs demonstrate limited capacity for self-refinement even after receiving detailed feedback on constraint violations, highlighting a critical bottleneck in the development of robust tool-use agents. To facilitate future research, we release the data and code.

Related papers

• MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding [83.0805548642958]
  MTAC-IFBench is a comprehensive benchmark for instruction-following in software development.<n>It features multi-turn progressive software development instructions with diverse constraints spanning 6 primary and 18 secondary categories.<n>MTAC-IFBench identifies significant deficiencies in existing code agents in multi-turn instruction-following, with their performance degrading rapidly as the interaction session grows longer.
  arXiv  Detail & Related papers  (2026-09-14T03:57:37Z)
• IFHierBench: Hierarchical Instruction Following for Large Language Models [9.572252219147096]
  We introduce IFHierBench, a hierarchical instruction-following benchmark of 600 prompts stratified across four constraint-tree depths and 35 distinct constraints.<n>We find that even the strongest model only marginally exceeds 50% prompt-level accuracy and that accuracy degrades sharply as constraint depth grows.
  arXiv  Detail & Related papers  (2026-07-30T09:26:06Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• A Hierarchical and Evolvable Benchmark for Fine-Grained Code Instruction Following with Multi-Turn Feedback [30.446511584123492]
  Large language models (LLMs) have advanced significantly in code generation, yet their ability to follow complex programming instructions with layered and diverse constraints remains underexplored.<n>We introduce MultiCodeIF, a comprehensive benchmark designed to evaluate instruction-following in code generation across multiple dimensions.<n>We synthesize and evolve 2,021 code tasks sourced from 14 programming languages, supporting multi-turn evaluation through feedback-driven task variants.
  arXiv  Detail & Related papers  (2025-07-01T11:51:40Z)
• RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data [47.19854998380304]
  RECAST is an efficient framework for synthesizing datasets with far more constraints than those in existing benchmarks.<n>We construct RECAST-30K, a large-scale, high-quality dataset comprising 30k instances spanning 19 constraint types.<n> Experimental results demonstrate that models finetuned on RECAST-30K substantially improve in following complex instructions.
  arXiv  Detail & Related papers  (2025-05-25T08:31:08Z)
• AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios [51.46347732659174]
  Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications.<n>AgentIF is the first benchmark for systematically evaluating LLM instruction following ability in agentic scenarios.
  arXiv  Detail & Related papers  (2025-05-22T17:31:10Z)
• A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models [48.361839372110246]
  We develop an automated instruction generation pipeline that performs constraint expansion, conflict detection, and instruction rewriting.<n>We evaluate 19 large language models and uncover substantial variation in performance across constraint forms.<n>In-depth analysis indicates that these gains stem primarily from modifications in the model's attention modules parameters.
  arXiv  Detail & Related papers  (2025-05-12T14:16:55Z)
• The Ability of Large Language Models to Evaluate Constraint-satisfaction in Agent Responses to Open-ended Requests [0.6249768559720121]
  We develop and release a novel Arithmetic Constraint-Satisfaction (ACS) benchmarking dataset. This dataset consists of complex user requests with corresponding constraints, agent responses and human labels indicating each constraint's satisfaction level in the response. We show that most models still have a significant headroom for improvement, and that errors primarily stem from reasoning issues.
  arXiv  Detail & Related papers  (2024-09-22T09:27:42Z)
• From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification [70.08146540745877]
  We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments. We propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints.
  arXiv  Detail & Related papers  (2024-03-10T22:14:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.