A Four-Axis Bayesian Epoch Capabilities Index with Human Baselines

This is a crosspost from the General-Purpose AI Policy Lab research blog. The Epoch Capabilities Index compresses many benchmark scores into one for each model, following the framework of the Rosetta Stone paper. In a previous post, we added human baselines to the same scale to see how the models compare to humans. But one issue is that most humans score near-perfectly on abstract reasoning benchmarks like ARC-AGI or VPCT and sit near chance on GPQA-type benchmarks, while many models show the opposite pattern. One index cannot produce both of these orderings, so in this post we move to a Bayesian setup with four skill axes instead of one, and proper uncertainty estimation. TL;DR We extended the Epoch Capabilities Index to four skill axes, in a Bayesian setup where every ability comes with its uncertainty and the human tiers are fitted inside the model as test-takers. Sparse data (6% of the test-taker-by-benchmark matrix) means the model does not settle on one answer by itself. We need two ordering priors to help identify it, a hard ordering on the human tiers, and a soft expected improvement along releases of the same model family. Main Results: The four axes that come out of the model are Fluid Intelligence, Scientific Knowledge and Reasoning, Agentic Capabilities, and Legacy QA. Models have now passed every human tier on Scientific Knowledge and Reasoning, though this arguably says more about breadth of recall than about doing actual science. On Agentic Capabilities, the frontier is reaching the Skilled Generalist baseline around now (summer 2026). On Fluid Intelligence, AI models have likely passed Average Humans, but human experts still lead. The trend, if it stays linear, will cross top tiers in 2027 and 2028. Four of the ten runs converge on a second mode that places human tiers slightly differently and move these dates by a few months to years. One-Dimensional Setup Following Alexander Barry's Bayesian version of the Epoch Capabilities Index (ECI), we rebuilt the model in Python using PyMC. In this Bayesian setup, instead of finding one single best value for each parameter, we sample a whole distribution of plausible ones, so every ability comes with uncertainty. The full setup is in the model section below. We also fit the nine human tiers as test-takers next to the models, from Average Human up to Committees of Domain Experts, plus two high-school tiers. The baselines and the benchmark table have been updated and expanded since the previous post (the full tables with sources are in the appendix), and we added a partial ordering prior on human tiers (see following sections). For the 1D fit only, we excluded the human-easy benchmarks like ARC-AGI or VPCT (in a similar fashion to the previous post; the list is in the Appendix). Here's our rebuilt index (called ECI-H with H for the human baselines) matched with the ECI scores for the state-of-the-art models: Figure 1: SOTA models: our ECI-H (90% HDI) vs Epoch's published values (CIs where published). Epoch publishes one ECI per model, repeated here across its effort variants, so for example the four Claude Fable 5 rows share one orange value. Our values differ from Epoch's for three main reasons. First, we do not fit the same table, since our benchmark set is larger and drops eight human-easy benchmarks. Second, Epoch publishes one value per model, taking its best score on each benchmark, while we fit every thinking-effort variant as its own test-taker with its own scores. Third, we include human baselines. Each benchmark also gets a difficulty on the same scale, so we can plot models, benchmarks and the human tiers together over time: Figure 2: AI capability, human baselines and benchmark difficulty on the ECI-H scale, with 80% intervals. Green points are AI models at their release date, pink points are benchmark difficulties. Dashed lines are the human tiers. The scale is pinned at Claude 3.5 Sonnet = 130 and GPT-5 (medium) = 150 to match Epoch's. However, putting AIs and humans on a single axis is arguably quite objectionable. Multidimensional extension As we mentioned earlier and discussed in the previous post, some benchmarks are trivial for humans and hard for AI models, which breaks the single difficulty axis. Epoch's Benchmark Scores = General Capability + Claudiness also points to scores carrying more than one dimension (and that's between models alone). We test this by extending the model to four skill axes using an MIRT (Multidimensional Item Response Theory) model, commonly used in psychometrics, while keeping all the benchmarks and human baselines. The intuition behind the model is that each test-taker mjx-container[jax="CHTML"] { line-height: 0; } mjx-container [space="1"] { margin-left: .111em; } mjx-container [space="2"] { margin-left: .167em; } mjx-container [space="3"] { margin-left: .222em; } mjx-container [space="4"] { margin-left: .278em; } mjx-container [space="5"] { margin-left: .333em; } mjx-containe