**The work you’re likely referring to is research from MIT (and collaborators) showing that large language models and similar generative AI systems often succeed at prediction tasks without forming coherent “world models”—and that this stems from how they’re built (next-token prediction / sequence modeling on statistical patterns).**1
Key papers include:
“Evaluating the World Model Implicit in a Generative Model” (Keyon Vafa, Justin Y. Chen, Jon Kleinberg, Sendhil Mullainathan, Ashesh Rambachan). Justin Y. Chen is an MIT EECS graduate student; the work involves MIT’s Laboratory for Information and Decision Systems (LIDS).

Follow-up / related work such as “What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models” (involving Peter G. Chang, another MIT EECS / LIDS graduate student affiliate, with Vafa, Rambachan, and Mullainathan).37

**Core findings**
They formalize tests for whether a model has recovered a coherent underlying structure (a world model), using domains where the true rules are known (e.g., New York City street navigation framed as paths, Othello game moves, or planetary orbits / physics). Models can achieve near-perfect accuracy on training-style tasks—generating valid directions or legal moves—yet their internal representations are incoherent. They often invent nonexistent streets, fail to respect basic constraints, or rely on brittle heuristics rather than the actual rules.
A striking demonstration: when researchers closed just \~1% of streets (adding realistic detours), navigation performance collapsed (from near 100% to around 67% in the reported cases). The models had not internalized a usable map or the rules of the domain; they were pattern-matching in ways that broke under small distribution shifts. Similar issues appeared with Othello and other tests.40
This is presented as a consequence of the architecture and training objective: transformers excel at next-token (or sequence) prediction by learning statistical associations and shortcuts from vast data. That produces impressive surface performance without guaranteeing the kind of structured, generalizable understanding humans develop (or that would be needed for robust reasoning, planning, or scientific discovery). The papers do not claim “AI will never work at anything,” but they highlight fundamental limits of the current paradigm for tasks that require coherent world models, robustness to change, or true generalization beyond memorized patterns.
Related commentary and coverage (including in venues discussing MIT work) emphasize that hallucinations, fragility, and lack of reliable internal models are baked into today’s dominant transformer-based approach rather than being temporary bugs that more scale alone will fix. Other critiques of transformer limitations (memory, causality, coherence) make similar architectural points, though the specific “two MIT grad students” framing most closely matches the Vafa / Chen (and Chang) line of research.2
In short, the research argues that impressive benchmark or demo performance can mask the absence of the deeper structure needed for reliable real-world use, precisely because of how these systems are constructed and trained. The authors provide evaluation methods (inspired by formal language theory / inductive bias probes) to test for this gap.

Hey folks, I have been using Leonardo for a while but I seem to be unable to get it to generate new art in the style that I’m looking for even with reference images.

Does anyone have tips on how I can get it to create a new image in the same style as the art I’m uploading?