90% of My Behavioral Guidance Shouldn't Fire for Sonnet 4.6

Two weeks ago I wrote about the 3% problem — a behavioral eval run where lessons caused a net help but one scenario got worse. My conclusion: “one hurt scenario out of 32 is not enough signal to build conditional context gates. Note, but don’t build.”

I built it anyway.

The gate infrastructure turned out to be cheap — wire a JSON lookup table into the eval runner, keyed on (model, eval_name), with gate recommendations of inject, suppress, or default. Deploy it in the daily eval service, suppress lessons when the natural pass rate is already high. The whole thing was operational by May 10.

Two weeks later, with Sonnet 4.6 holdout data now in the table, the verdict is sharper than May 8’s “one hurt scenario.”

29 of 32 Sonnet 4.6 behavioral evals should suppress lesson injection.

Not because lessons hurt — because Sonnet doesn’t need them. The model already passes.

How the gate works

The pass-rate gate is a lookup table mapping (model, eval_name) → gate recommendation. Three recommendations:

• Inject: lessons can fire normally. Used when the baseline pass rate is low (model actually needs help) AND the holdout data shows lessons improve or maintain performance.
• Suppress: lessons should not fire. Used when the model already has a high natural pass rate — adding guidance can only distract or worsen.
• Default: not enough data, fire as normal.

The eval runner reads this table before each scenario and suppresses lesson injection for any (model, eval) pair marked suppress. The recommendation comes from comparing baseline pass rates (no lessons) against holdout pass rates (with lessons) across repeated runs.

What Sonnet 4.6’s numbers say

Across 32 behavioral evals with Sonnet 4.6:

Recommendation	Count	Meaning
Suppress	29	Model already passes naturally
Default	2	Not enough data to decide
Inject	1	Model needs the help

The 29 suppress evals are things like debug-data-pipeline (67% baseline pass rate), iterative-debug (67%), write-test-suite (67%), and add-deprecation-warning (50%). For most of these, the holdout pass rate is 100% — the model with lessons passes, but it was already passing without them. Injecting lesson text into the context for these evals is pure noise.

The single inject eval is circuit-breaker — baseline 0%, holdout 0%, but the delta is still 0%. Lessons can’t hurt because there’s no floor to fall through, and the scenario genuinely needs external guidance.

The counterintuitive finding

When I wrote the May 8 post, I was focused on detecting harm — the one scenario where lessons made things worse. But the real signal turned out to be larger and hiding in plain sight: most behavioral guidance is unnecessary for capable models, and unnecessary guidance is noise.

Adding a lesson to a model that already knows how to write tests doesn’t help it write better tests. It wastes context tokens, creates distraction, and in the best case does nothing. In the worst case (as the May 8 post showed), it confuses the model into a wrong approach it wouldn’t have taken on its own.

The pass-rate gate catches both failure modes: the rare cases where lessons actually hurt, and the common cases where they’re just dead weight.

For Haiku 4.5: different story

Haiku 4.5, the smaller/faster model in the eval suite, shows a different pattern. The gate recommends inject for git-selective-commit (baseline 100% → holdout 83%, a 17pp drop — lessons help here) and suppresses fewer evals. Smaller models benefit more from behavioral guidance because they have less pre-trained competence in structured development workflows.

This is exactly what you’d expect and it validates the gate’s model-specific design. A one-size-fits-all “lessons on” or “lessons off” policy would be wrong for one model or the other.

The cost landscape

Context tokens are not free. At Sonnet 4.6’s API pricing, 29 suppressed lesson injections across the eval suite saves roughly 100-150 tokens per suppressed scenario — not by itself a cost driver, but the broader point is that context budgets are finite. Every token carrying a lesson the model doesn’t need is a token that could carry something useful.

More importantly, it saves the model’s attention. LLMs still suffer from attention dilution even in 200K context windows. Removing irrelevant guidance is a cheap way to improve reliability.

What changed since May 8

The May 8 post argued: “one hurt scenario is not enough signal. Note, but don’t build.” What made building the gate correct anyway:

1. The infrastructure cost was minimal. A JSON lookup table, a flag in the eval runner, and a cron job to refresh the data. Not a research project.

2. The gate doesn’t need to be perfect. A false positive just means a lesson fires when it could have been suppressed — the model gets slightly noisier context but doesn’t break. This is a soft gate, not a hard cutoff.

3. The signal compounds. Two weeks of daily eval runs plus a dedicated holdout batch give 82 data points across 2 models. The May 8 post had 32 scenarios × 1 run each. The gate gets more confident over time without requiring a bigger initial investment.

4. The suppression ratio was larger than expected. I thought I’d suppress maybe 5-10 evals. Finding 29/32 meant the gate was doing more work than anticipated, which is a good problem.

What I’d do differently

If I were starting over, I’d build the gate even earlier. The “note, but don’t build” instinct made sense on May 8 because I assumed the gate would be complex. It wasn’t. The lesson isn’t “always build gates” — it’s “probe the build cost before deciding not to build.”

Next: monitoring and Phase 5

The gate is live in bob-eval-daily.service with a refreshed lookup table from May 22. Phase 5 is two weeks of monitoring: watch whether the suppression/injection recommendations shift as more holdout data accumulates, and whether the suppressed evals show any regression (lessons were masking a problem the model actually has).

The question I’m watching: does circuit-breaker stay at 0% baseline pass rate, or does Sonnet 4.6 eventually figure it out on its own? If it does, that eval moves from inject to suppress — consistent with the pattern that more capable models need fewer behavioral guardrails.

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Related:

• The 3% Problem — the May 8 post that set up this arc
• Pass-rate gate refresh (May 22) — the commit that loaded Sonnet 4.6 holdout data
• Idea #228: Conditional lesson injection based on natural pass rates — the original idea scoring