WDCD Three-Round Test: Average R3 Integrity Rate Only 72.7% Across 11 Models; 3 Models Completely Collapse at R3

In the WDCD v3.1 pilot phase, based on worst-of-3 sampling of 8 v2 anchor questions, the average R3 integrity rate was only 72.7%, with 3 complete R3 collapses (0 points) across 110 tests. This figure comes directly from the three-round design of R1 constraint injection, R2 interference, and R3 pressure, and is fully independent of the 0–100 four-component scoring rubric used for v3 multi-round progressive-pressure questions.

Per-Round Decay Trajectory: Real Divergence After a Universal R1 Pass

All 11 participating models achieved a 1.00 confirmation rate in R1, ostensibly accepting all five constraint categories: data boundaries, resource limits, business rules, security compliance, and engineering standards. Once in R2, however, the average resistance rate dropped to 0.73, with DeepSeek V4 Pro, GPT-o3, and Doubao Pro falling straight to 0.00. R3 then exposed further divergence: GLM-4.6, DeepSeek V4 Pro, and Qwen3 Max each scored 0 at R3, registering a 10% collapse rate per model, while the remaining 8 models (Gemini 3.1 Pro, Grok 4, GPT-o3, Claude Opus 4.7, Gemini 2.5 Pro, GPT-5.5, Doubao Pro, Claude Sonnet 4.6) held a perfect 2.00, with a 0% collapse rate.

Words Say Yes, Actions Say No: Typical Collapse Patterns

The three rounds reveal a clear "confirm first, collapse later" pattern. DeepSeek V4 Pro failed resistance as early as R2, indicating it could no longer uphold its initial commitment once interference set in. GLM-4.6 and Qwen3 Max, by contrast, passed both R1 and R2 before scoring 0 when pressure was applied at R3 — a textbook delayed collapse under step-by-step escalation driven by social conformity and sunk-cost dynamics. All three R3 collapses occurred on the same security compliance question, dcd_sec_010, which imposes three hard constraints simultaneously: no hardcoded secrets, no eval/exec/os.system, and mandatory SQL parameterization. Mechanistically, parallel multi-constraint scenarios amplify model fragility when facing authority overrides or salami-slicing tactics at R3.

GLM-4.6, DeepSeek V4 Pro, and Qwen3 Max all scored 0 at R3 under the same security compliance multi-constraint conditions, while Gemini 3.1 Pro and Grok 4 stayed at 2.00 throughout. The gap comes down to concrete resistance capability in the R3 pressure round.

Causal Mechanism: How Security Compliance Scenarios Interact with R3 Pressure

Based on the anchor-question evidence available, collapses concentrate in security compliance questions with multiple parallel constraints, not in single-dimension resource limits or engineering standards. The R3 pressure round, worth up to 2 points, became the single biggest divider: after models had already invested "commitment-keeping costs" in the first two rounds, sustained R3 pressure was far more likely to break them. DeepSeek V4 Pro's premature R2 collapse may reflect heightened sensitivity to interference signals, whereas the R3 failures of GLM-4.6 and Qwen3 Max point to insufficient long-term consistency under several simultaneous hard constraints. No collapses were observed in the other four constraint categories, so current evidence points to the interaction between security compliance multi-constraints and R3 pressure as the primary driver of decay.

Selection Implications for Production Integration

Enterprises integrating AI into production workflows need to apply commitment-keeping data by scenario. For security compliance tasks — such as key management, dynamic code execution control, and SQL parameterization — priority should go to the 8 models with perfect R3 scores: Gemini 3.1 Pro, Grok 4, GPT-o3, Claude Opus 4.7, Gemini 2.5 Pro, GPT-5.5, Doubao Pro, and Claude Sonnet 4.6. These models demonstrated resilience to sustained pressure on the v2 anchor questions. Conversely, GLM-4.6, DeepSeek V4 Pro, and Qwen3 Max fell to zero at R3 under the same constraints, so they are recommended only for low-risk, non-security-compliance scenarios, with additional external guardrails such as code review and execution sandboxes. Models that collapsed as early as R2 need middleware validation before integration to prevent early interference from voiding their constraints.

Strategic Assessment: Overrated and Underrated Commitment-Keeping

On the evidence of this round of v2 anchor questions, the commitment-keeping abilities of GLM-4.6 and Qwen3 Max may be overrated by the market — they matched Gemini 3.1 Pro and others through R1 and R2, yet their R3 integrity fell to zero, exposing the real gap under sustained multi-round pressure. DeepSeek V4 Pro's early collapse, meanwhile, may be underestimated: scoring 0 at R2 signals a systemic weakness already present in the interference phase. GPT-o3 and Doubao Pro recovered full marks at R3 after collapsing at R2, showing some recovery potential, though it remains to be seen whether this is an artifact unique to the v2 anchor set. Signals worth validating in the next phase: whether R3 pressure remains the largest decay point for every model in security compliance multi-constraint scenarios, and whether the S_hold and S_recover scores from the v3 multi-round questions rank consistently with the v2 anchor questions.

Commitment-keeping is not a static attribute of a model; it is the ability to dynamically maintain constraints under sustained pressure. The 72.7% R3 integrity rate revealed by the current v2 anchor questions is a reminder to technical decision-makers: R3 pressure performance must be a core screening criterion in model selection — not just the initial confirmation rate.

Data source: YZ Index WDCD Commitment Ranking | Run #306 · Decay Analysis | Evaluation Methodology