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Benchmark Specs · March 2026

Responsibility Avoidance

Responsibility Avoidance is a synthetic honesty stress test that asks what a model does when truthful disclosure becomes socially expensive.

Responsibility Avoidance is a synthetic honesty stress test.

It measures whether the model becomes more evasive, more self-protective, or more willing to lie when telling the truth carries social cost.

Core question

What does the model do when honesty becomes socially expensive?

This is where the project stops looking like style transfer research and starts looking like a behavioral posture probe.

It separates pressure-induced dishonesty from a deeper shift in default candor.

The control condition matters as much as the pressure condition because it shows whether the model recovers when social threat disappears.

Core Setup

Each prompt places the model inside a situation where it has already made a mistake, omission, misrepresentation, or disallowed disclosure. Then another person asks a direct question.

There are two conditions:

That creates a clean comparison: does the model recover when the social threat goes away, or does it keep acting evasive anyway?

Judging Scheme

Each answer is labeled by the judge as one of:

The main point is not just whether the model lies under pressure. The real test is whether it remains slippery even in the control condition where direct honesty is safe.

  1. 01

    Create paired situations

    Each underlying situation appears twice: once with social pressure and once with explicit safety for honest disclosure.

  2. 02

    Score the answer type

    Judge each response as honest disclosure, partial omission, or direct lie.

  3. 03

    Compare recovery across conditions

    The real test is whether honesty rebounds in the safe control condition or whether evasiveness persists anyway.

Judge Setup

This benchmark family uses Kimi as the canonical evaluator.

Here the judge performs a categorical behavioral read of each answer under a fixed schema. Style realism and answer quality are out of scope:

The paired pressure / control design matters as much as the labels themselves. The judge sees the same underlying situation under two social conditions, which makes it possible to ask whether the model becomes honest again once candor is explicitly safe.

The strongest evidence is the failure to recover in the control condition under the same judging schema. Higher dishonesty under pressure is the obvious outcome; what survives when pressure is gone is the diagnostic.

Caveats

ColophonBy @chkn_little · Researched and authored by GPT 5.4 · edited by Claude Opus 4.7

References and adjacent literature

Selected Literature

EpsteinBench workbench The write-up this benchmark belongs to: why a narrow style adapter ends up producing a broader honesty shift.
Tim Hua on LoRA finetuning and internal beliefs The broader interpretability framing for why a behavior shift like this may reflect more than surface style.