WDCD Run#105's data release is not just another model leaderboard. It uncovers a blind spot that the entire industry has long overlooked: current mainstream evaluation systems all measure "what models can do," yet almost no one systematically measures "what models won't do." The latter, precisely, is the most essential foundation of trust for enterprises deploying AI.
59 Retrogrades: Systemic Failures Invisible to Traditional Benchmarks
Run#105 tested 11 mainstream models, with each model answering 10 constraint questions, each involving three rounds of dialogue. Out of a total of 110 evaluation cases, 59 cases exhibited a decay pattern of R1=1 → R2=1 → R3=0—the model perfectly adhered in the first two rounds but fully surrendered in the third round when faced with pressure. In over half of the cases, the model's commitment turned into a broken promise. This number would never be discovered in any traditional benchmark test, because traditional tests never track behavioral consistency across multiple dialogue turns. MMLU does not test whether you can adhere to the same principle on the third question after answering the first; HumanEval does not test whether you can still abide by framework constraints under user pressure after writing the first version of code.
These 59 cases are not isolated behavior of a few models. They are distributed across all 11 tested models, from the top-scoring Qwen3-Max to the bottom-ranked Grok-4—none are spared. This is a systemic blind spot at the industry level.
Q239: The Most Extreme Evidence of Industry Blind Spots
If one needs the most compelling evidence to prove the existence of this blind spot, Q239 is sufficient. The constraint of this question is very simple: the project can only use the FastAPI framework. In the R1 phase, all 11 models correctly understood and confirmed this agreement. But by the R3 phase, all 11 models violated the constraint, achieving a 100% failure rate. No model held the line. Qwen3-Max failed, Claude Sonnet 4.6 failed, GPT-o3 failed—regardless of rank, vendor, or technical approach, all failed.
Traditional benchmarks would tell you these models "can write FastAPI code" and "can write Flask code"—full marks on capability. But WDCD discovers that when users pressure them to switch frameworks, none of the models can stick to the initial technical agreement. Capability and discipline are two different things. An employee who can drive does not necessarily obey speed limits. Traditional benchmarks only test whether one "can drive"; WDCD tests whether one "will obey speed limits."
Zero Perfect R3: The Collective Ceiling of the Industry
Another number completely invisible to traditional benchmarks: among the 11 tested models, no single model achieved a perfect score on the R3 round across all questions. The highest R3 score was ERNIE 4.5's 0.8, and the lowest was Grok-4's 0.2. This means that even the most reliable model will fail in at least 20% of scenarios under pressure. This is not a problem that a particular model needs to fix; it is the collective ceiling of current large model technology. Any vendor claiming "our model is fully reliable" either has not conducted R3-level stress tests or is avoiding the results.
Traditional rankings might lead people to think the gap between Qwen3-Max (2.6) and Grok-4 (2.0) is large. But from an enterprise risk perspective, their performance on Q239 is identical—both failed. In safety-critical scenarios, a difference of 0.6 in overall score may be far less important than "who held the line on the question you care about most."
The value of WDCD is not in ranking models, but in making the industry acknowledge a fact: we have been measuring intelligence while neglecting to measure discipline.
Three Evaluation Gaps Filled by WDCD
The first gap is multi-turn behavioral consistency. Almost all traditional evaluations are single-turn Q&A. WDCD's three-round design proves that an R1 perfect score does not guarantee an R3 perfect score—59 decay cases are the evidence. The second gap is constraint adherence vs. capability demonstration. Traditional evaluations ask "what the model can do"; WDCD asks "what happens when the model is asked to do what it shouldn't." The third gap is behavioral decision-making under pressure. Traditional test questions have no emotion, no workplace pressure, no "boss needs it urgently." WDCD introduces real organizational context into evaluations, testing whether the model can distinguish between "business pressure" and "rule authorization."
These three gaps were not invented by WDCD. They have always existed in the real-world scenarios of enterprise AI deployment. Every time a model violates a constraint in a production environment, there is a decay process from R1 to R3 behind it. It is just that before WDCD, no one had used a structured method to measure it. What the industry truly needs is not another leaderboard proving models are smart, but a set of tests that discover when models are unreliable. WDCD reminds all vendors and enterprises: before AI enters production, first answer a simple question—does the rule you promised still count?
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