Verification starts before the solver

Jiang and colleagues’ Language Models as Higher-Order Planning Formalizers, revised in July 2026, studies a bottleneck in translating concise natural-language descriptions into planning representations. A short description can imply a much larger formal problem, making direct translation difficult.

The authors propose generating a higher-level program that expands into the planning representation. They report improved performance on their constructed complex planning problems. This is evidence for a particular formalisation approach and evaluation setting, not a demonstration that arbitrary requests can be translated correctly.

Sources: Jiang and colleagues, Language Models as Higher-Order Planning Formalizers, revised 4 July 2026

Our reading is that verification needs to begin with the representation of the task. A solver can produce a valid result for the formal problem it receives while the overall system fails the request that a person intended. Testing the solver and testing the translation are distinct jobs.

Consider an illustrative instruction: every submitted item needs review. A translation that substitutes “some items” changes the obligation before any solver runs. A later proof about that altered formal statement cannot establish that the original instruction was satisfied. This is an explanatory example, not a reported experiment from the paper.

Sources: Jiang and colleagues, Language Models as Higher-Order Planning Formalizers, revised 4 July 2026

Ackren’s earlier evaluation provides a local reason to keep those checks separate. Its replay and structural explanation checks passed across all recorded turns, while the candidate failed the meaning-contrast qualification threshold. A deterministic front end also needs semantic evaluation.

We would therefore ask for separate evidence about the interpreted request, the resulting inference and the explanation shown to the user. This is our editorial recommendation. There is no comparative experiment here showing that Ackren outperforms the planning systems in the paper.

Sources: Recorded Ackren evaluation: counts, scope and source hashes · Jiang and colleagues, Language Models as Higher-Order Planning Formalizers, revised 4 July 2026