AI shows early scientific taste; Fable beats peers on discovery games

The two research projects reflect a shared focus on isolating and measuring prerequisites for autonomous discovery and invention in AI systems. DiG-bench frames this as the ability to infer hidden environmental rules through exploration and curiosity—a foundational skill that humans excel at but frontier models still struggle with significantly. The benchmark's seven-tier structure reveals a steep performance cliff: models that handle Tier 4 stumble dramatically on Tiers 6 and 7, with only the highest-capability models (Opus 5 and Fable 5) achieving even minimal success at the hardest level. Inherent's Faraday takes a different but complementary approach, treating scientific research as a domain where AI systems must develop intuition about what experiments to run, how to scope them to available resources, and how to judge results. Both efforts implicitly measure what researchers call "taste"—the capacity to make nuanced judgments about which problems are worth solving and which approaches are promising—in controlled, evaluable settings.

The significance both teams attach to these tasks stems from their connection to recursive self-improvement. If AI systems learn to discover hidden rules, design novel experiments, and make taste-based judgments about research direction, they may eventually extend those capabilities to improving themselves rather than merely serving human-specified goals. The Import AI newsletter author predicts human-level performance on DiG-bench by mid-2027, framing that milestone as a point at which recursive self-improvement could "seriously kick off." Inherent's team makes a similar argument: the skills Faraday acquires—deciding what to investigate, scoping experiments, judging outcomes—may be the same skills that allow a system to advance the state of the art autonomously. Both teams present these developments as capabilities-centric contributions to understanding how far AI systems have come; neither claims the systems are at human level, but both suggest the gap is narrowing in directions that matter.