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ARXIV:2603.15309 · BENCHMARKING LLMS · SUBMITTED 18 MAR · 22:54 UTC · FRESHNESS STALE
ARXIV:2603.15309BENCHMARKING LLMSSUBMITTED 18 MAR · 22:54 UTCFRESHNESS STALEarXiv
CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance.
Opportunity summary
Pain CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance. However, progress has been hindered by the absence of dedicated evaluations.
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Benchmarking LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance.
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10.48550/arXiv.2603.15309CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance.
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.
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Proof status
partial0 refs; 0 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance. However, progress has been hindered by the absence of dedicated evaluations.
METHOD
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 hinder...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 env...
WHY NOW
Benchmarking LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance. However, progress has been hindered by the absence of dedicated evaluations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Benchmarking LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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CCTU is a benchmark designed to evaluate large language models' tool use under complex constraints, revealing critical limitations in their performance.
Segment
Benchmarking LLMs
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Public code linked for build inspection
Commercial read
7.0/10 public viability
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status
missing
reason
passport_row_missing
proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Buyer clarity
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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