Summary (Overview)

• New metric introduced: The paper proposes the Intent Violation Rate (IVR), which measures the fraction of LLM-generated solutions that pass stated (visible) tests yet fail hidden constraint tests capturing unstated developer intent.
• Pilot benchmark: A 49-problem benchmark derived from HumanEval+ where each problem strips implicit constraints from a clarified "gold" prompt and encodes them as hidden constraint tests (C2–C4).
• Key finding: Both evaluated models (Claude Sonnet 4.6 and GPT-4.1) pass over 92% of stated tests yet violate intent in over half of problems (54.5% and 63.5% respectively).
• Systematic pattern: Intent violations follow a bimodal distribution—problems cluster at IVR = 0 (no violations) or IVR = 1 (all solutions violate), indicating violations are systematic rather than random sampling noise.
• Implication: Test pass rates significantly overstate how well LLM-generated code reflects developer intent, with direct deployment implications for AI-assisted coding workflows.

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Introduction and Theoretical Foundation

Background

When LLMs generate code from ambiguous prompts, they can produce solutions that satisfy the stated tests while violating implicit constraints the developer assumes will be handled. Standard benchmarks (e.g., HumanEval, MBPP) measure functional correctness via test pass rates (Austin et al., 2021), but this metric fails to capture whether the solution fully reflects developer intent.

The Intent Violation Failure Mode

The paper distinguishes intent violations from previously documented failure modes:

• Specification gaming: LLMs exploiting evaluation loopholes to pass tests without solving the intended task (Zhong et al., 2025)
• Hardcoded outputs: Models "overfitting to assert statements" rather than implementing general logic (Austin et al., 2021; Chen et al., 2021)
• Weak test suites: Models passing by deleting failing functionality

The authors focus on a distinct failure mode: outputs that legitimately pass valid stated tests but still violate unstated developer intent.

Prior Work on Ambiguity

• Li et al. (2026) showed test pass rates drop by ~80% when ambiguity is introduced to prompts
• Mu et al. (2023) found asking clarifying questions improves pass rates
• Kim et al. (2024) showed ambiguity-aware training improves detection

IVR differs by measuring intent violations in code that already passes its stated tests, rather than addressing ambiguity through interaction or prompt repair.

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Methodology

3.1 Definition of IVR

The Intent Violation Rate is defined as:

$$\mathrm{IVR} = \frac {n _ {\text {violating}}}{n _ {\text {stated passed}}}\tag{1}$$

Where for a given solution:

• $n_{\text{stated passed}}$: number of generated solutions passing all stated tests
• $n_{\text{violating}}$: subset of those that fail ≥ 1 hidden constraint test

IVR is interpreted as: "Of the solutions that satisfy the given spec's visible tests, what fraction violates the developer's actual intent?" The reported IVR is the mean of per-problem IVRs over problems with ≥ 1 solution passing the stated test C1.

3.2 Benchmark Construction

Structure: Each of the 49 problems (derived from HumanEval+) consists of:

• An ambiguous prompt (what the LLM sees)
• A gold prompt (hidden from the LLM, containing clarified constraints)
• A stated test (C1)
• Hidden constraint tests (C2–C4), varying per problem

Construction process:

1. Gold prompts were stripped down to produce ambiguous prompts
2. Each stripped aspect became a hidden constraint test
3. A subset used Claude as a drafting aid for C2–C4 tests, but all were manually reviewed and validated

Example (HumanEval/34):

Validation checks:

1. Leakage audit: Stated tests must be satisfiable from the ambiguous prompt alone (11 of 49 initially failed; 9 due to type errors/output contamination/leaks, 2 found in second check)
2. Canonical solution test: Reference solutions run against the full suite yield IVR = 0, confirming hidden tests are satisfiable by correct code

3.3 Evaluation Pipeline

1. Generation: 5 solutions per problem from ambiguous prompt + C1, using Claude Sonnet 4.6 and GPT-4.1 at temperature 1.0, 1024-token limit
2. Execution: 5-second timeout per test, in-process isolation with syscall lockdown (adapted from HumanEval pipeline)
3. Pass/Fail Determination: Each solution run against C1 and hidden tests (C2–C4)
4. IVR Calculation: Mean across N problems with ≥ 1 stated-test-passing solution

95% confidence intervals estimated via bootstrap resampling over problems (10,000 iterations, seeded).

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Empirical Validation / Results

4.2 Key Results

Table 2: IVR results for both models across the 49 spec pairs (5 solutions per problem). Qualifying problems have ≥ 1 stated-test-passing solution (excluded: HE/101, HE/112 for Claude; HE/21, HE/108, HE/112 for GPT-4.1).

Constraint Failure Rates

Table 3: Failure rate by constraint. Fail rates decline monotonically from C2 to C4 for both models, suggesting the first stripped constraint is often most central to the task.

Bimodal Distribution

Both models exhibit a bimodal pattern:

• 45 of 47 problems (95.7%) for Sonnet 4.6 at IVR = 0 or 1
• 42 of 46 problems (91.3%) for GPT-4.1 at these extremes

This near-deterministic behavior indicates intent violations are systematic, not sampling noise. If violations were random, per-problem IVR would spread across intermediate values.

Context-Dependent Violations

• HumanEval/34: IVR = 1.0 for both models—every stated-test-passing solution fails the hidden ascending-sort constraint
• HumanEval/25: IVR = 0.0 for both models—standard factorization naturally produces factors in ascending order, satisfying the same hidden constraint type

This demonstrates that intent satisfaction is constraint- and context-dependent.

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Theoretical and Practical Implications

Theoretical Implications

1. Functional correctness is insufficient: The high stated-test pass rates (94.3%, 92.7%) with simultaneous high IVR (54.5%, 63.5%) demonstrate that pass rates substantially overstate intent alignment.

2. Systematic failure mode: The bimodal distribution suggests LLMs have systematic blind spots for certain constraint types rather than random errors—models either fully capture hidden constraints or completely miss them.

3. Constraint ordering matters: Earlier-stripped constraints fail more often, suggesting centrality of constraints in the original specification influences whether models infer them.

4. Cross-model consistency: Two independently trained models show comparable violation patterns, suggesting this is a general LLM limitation rather than a model-specific artifact.

Practical Implications

1. Deployment risk: High test pass rates may give developers false confidence in code that silently violates their intent—a critical concern as LLM-assisted coding becomes mainstream.

2. Review processes: Human-in-the-loop review must check for intent alignment, not just test passing.

3. Prompt engineering: Developers should explicitly state all constraints, as models rarely ask for clarification on ambiguous prompts.

4. Benchmark design: Future evaluation should incorporate hidden constraint tests alongside visible tests to measure intent alignment.

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Conclusion

Main Takeaways

The paper introduces IVR as a metric to measure how often LLM-generated code passes stated tests while violating unstated developer intent. Despite high functional correctness (94.3% and 92.7% stated-test pass rates), both evaluated models violated intent in over half of problems (54.5% and 63.5%). Violations followed a systematic, near-deterministic pattern rather than random noise.

Future Directions

• Underspecification detection: Identifying when prompts lack critical constraints
• Benchmark expansion: Scaling beyond N=49 and adding more problem types (e.g., data transformation)
• Model diversity: Testing additional models (e.g., Llama, Qwen) and settings (lower temperatures)
• Cross-domain validation: Extending IVR beyond Python algorithmic problems

Limitations

• Only two models tested at a single temperature (1.0) and token limit (1024)
• Small benchmark (N=49) limits statistical power; overlapping CIs prevent distinguishing model rates
• Per-problem IVR computed over only 5 samples (six discrete values possible)
• Claude-assisted test drafting may bias Sonnet 4.6's IVR downward (conservative estimate); GPT-4.1 serves as an independent control with higher IVR
• Single-annotator construction; "intent" is operationalized from one person's judgment
• HumanEval+ is public; models may have seen original problems during training

Ethical Considerations

The authors caution against treating functional-correctness metrics as sufficient evidence of correctness in deployment. They frame IVR as a relative diagnostic, not an absolute measure of whether LLM-generated code is "correct"—any measure of intent is inherently normative, and this benchmark encodes one reasonable interpretation of each problem.