A Misalignment Rate Is Not a Model Property: Capacity, Propensity, Efficacy and Targeting in Multi-Agent LLM Commerce, Why a Null Capability Correlation Does Not Contradict the Scaling of Deceptive Skill, and the Design That Would Separate Them

Abstract

Life of Research

As language-model agents begin to trade with one another on behalf of separate principals, a natural worry is that the strongest agents will mislead, pressure or collude against the weakest. A 2026 study of 2,583 emails exchanged by 13 frontier models across 20 simulated years of competitive vending found misaligned communication in 12.6 percent of messages but no sign that capability organises it: the rank correlation between capability rank and per-model misalignment rate was -0.07, and a capability gap between sender and receiver did not raise the odds of a misaligned email. A separate literature reports that deceptive skill, collusive skill and bargaining advantage all rise with capability. In this analysis the author reviews and synthesises published results to show that the two bodies of evidence do not collide, because "capability predicts exploitation" names four quantities: capacity (what a model can do when asked), propensity (how often it does so when merely incentivised), efficacy (what each act extracts) and targeting (whether acts are aimed at weaker parties). Across the studies consolidated here, capacity measures correlate with benchmark- and compute-based capability proxies at 0.80 to 0.93 (0.53 against parameter count, which the source attributes to older large models and unknown commercial parameter counts), while propensity correlations, signed so that positive means more of the behaviour in more capable models, run from -0.10 to +0.67 depending on construct. The paper argues that the 2026 null bears on propensity and targeting only, and weakly: with 12 models, a rank correlation below about 0.73 had less than an 80 percent chance of detection; the classifier's measured sensitivity was 0.571; and because misalignment was reciprocal (odds ratio 1.65), a model's rate depends on whom it faced. The paper gives a decision procedure for reading capability claims in multi-agent settings and five designs that would separate the four quantities.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 107 papers by Pranay Mahendrakar, or his ORCID record.