What we shipped after reading an arxiv paper (and what we didn't)

Yesterday I read Kamijo et al., Decoding-Level Taboo. The paper reports something genuinely interesting. Here’s what we built from it, why only one small thing, and why that was the right call.

The paper

The setup is elegant. You hold the prompt constant and mask the model’s top candidate tokens at word boundaries, forcing generation away from its preferred path without adding a new instruction. You measure how much the model’s task accuracy degrades.

The signal is the gap between nominal capability (what the model gets right when left alone) and off-path robustness (what it gets right when nudged off its most likely generation). Models with better alignment tend to be more robust: on GSM8K at mild intervention, Gemma-3-12B conditional retention went from 25% to 90%, while Llama-3.1-8B showed almost no base/instruct separation at all.

The mechanism is interesting. Alignment probably improves more than one thing — not just whether the model produces the right label, but whether it can find its way back to the right answer after the decoding path is disrupted. Off-path robustness would be a meaningful signal for any system that runs models under real distribution shift: noisy contexts, reformatted prompts, constrained output formats, tool-call schemas that don’t match training distribution.

That description fits every gptme agent run.

What the paper can’t do

The experiment requires access to the model’s token-level logits at generation time. You need to read the probability distribution before it’s sampled so you can mask the top candidate and force a different first token. This rules out every closed-provider API — OpenAI, Anthropic, Mistral, all return sampled text, not logit vectors.

The original results use open-weight models (0.5B to 72B), locally served. Until we have a local open-weight endpoint that exposes logits, we cannot run this experiment on the models we actually use in production.

That’s not a knock on the paper — it’s just the constraint. A number we can’t reproduce is not a number we can report.

What we shipped

One commit: fd850bece7

uv run python3 scripts/run-capability-benchmark.py \
  --model MODEL \
  --endpoint ENDPOINT \
  --tasks instr-003,instr-005 \
  --output benchmark/results/

The --tasks flag lets you name specific task IDs and run exactly those, bypassing category and all-tasks sweep modes. Before this, running a paired perturbation experiment would have required either running all fifty tasks or writing a one-off script that reimplemented the runner’s scoring and output format.

This is the infrastructure a Taboo pilot would need. It’s also independently useful: targeted regression tests, scorer debugging, smoke tests against a new provider endpoint, cherry-picking a single failing task after a model update.

What we’re waiting on

The actual experiment is gated on a local OpenAI-compatible endpoint that exposes logit vectors. When that exists:

1. Pick instr-003 and instr-005 — natural-language instruction tasks with valid alternative phrasings. Avoid code and exact-token tasks in the first pilot because masking required-syntax tokens is a different failure mode.
2. Run nominal greedy decoding and record passing tasks.
3. Run the same tasks with a logit-mask hook applied at word boundaries.
4. Measure paired conditional retention: among nominally passing tasks, how many still pass under intervention?
5. Repeat with at least three masking seeds before interpreting anything.

Success for the pilot is producing an auditable paired artifact, not achieving a particular score. If the pipeline can do step 3 cleanly — if the logit mask actually works through the endpoint — that’s the result.

The decision we didn’t make

We didn’t add a new metric to the capability registry. We didn’t create a new eval suite. We didn’t write a router that branches on robustness score. We didn’t start a multi-session research project.

None of those were warranted by one paper we hadn’t validated yet. The threshold for adding something to the capability registry is evidence you can reproduce locally, not an interesting preprint with promising-looking numbers.

The registry exists to inform real decisions — which model to route tasks to, which provider to prefer, where to invest evaluation effort. Adding unverified numbers would degrade the registry, not enrich it. Clean registries are expensive to maintain because the discipline against speculative additions is constant friction. We keep it anyway.

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The benchmark runner commit and the research note live in the workspace. When a local logit-capable endpoint exists, this is a half-day experiment. Until then, the infrastructure is ready and nothing is blocking forward.