Victor Calibration (VC): Multi-Pass Confidence Calibration and CP4.3 Governance Stress Test under Round-Table Orchestration

TL;DR

This paper introduces a multi-part toolkit for confidence calibration and governance testing of language models, aiming to improve safety alignment without overly conservative responses.

Contribution

It presents Victor Calibration, FD-Lite phenomenology audit, and CP4.3 governance stress test, offering novel methods for model confidence assessment and safety evaluation.

Findings

Monotonic VC trajectories observed across models

Stable CP4.3 behavior demonstrated

Toolkit facilitates hypothesis generation and independent verification

Abstract

Safety alignment can make frontier LMs overly conservative, degrading collaboration via hedging or false refusals. We present a lightweight toolkit with three parts: (1) Victor Calibration (VC), a multi-pass protocol that elicits a scalar confidence proxy T (T0<T1<T2) through iterative evidence re-evaluation; (2) FD-Lite, a behavior-only phenomenology audit with a fixed anchor phrase and a meta-prefix trap to avoid anthropomorphic claims; and (3) CP4.3, a governance stress test for rank invariance and allocation monotonicity (M6). Across Claude 4.5 models (Haiku, Sonnet no-thinking, Sonnet thinking) and Opus, we observe monotonic VC trajectories without violating safety invariants, and stable CP4.3 behavior. ("Opus" here refers to a single Claude Opus 4.1 session accessed via a standard UI account, as reported in Table 1.) This work was conducted by a single operator (n=1) and is intended as hypothesis-generating; we explicitly invite replication, critique, and extension by the research community. We include prompt templates and an artifact plan to facilitate independent verification.