Evaluability of latent-space reasoning

Develop mature, standardized supervision and evaluation protocols for latent-space reasoning in large language models that enable process-level verification of latent trajectories and permit fair, comparable assessment across tasks, datasets, and metrics.

Background

The survey argues that latent reasoning trajectories are not directly observable, which hampers process-level verification and makes it difficult to determine whether intermediate computations are correct or relevant. Existing evaluations largely rely on final-answer accuracy or post hoc verbalization, providing only indirect evidence about the latent process.

The authors note that current benchmarking efforts are fragmented and that widely accepted protocols for supervision and evaluation are lacking, creating barriers to fair comparisons and cumulative progress in the field.

References

As a result, improving evaluability remains one of the most pressing open problems for the development of latent reasoning models.

— The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook  (2604.02029 - Yu et al., 2 Apr 2026) in Section 6.2 (Challenge) — Evaluability

Key unresolved questions for PCC itself include the expressiveness of the claim language relative to latent reasoning, scoring rules that reward resolution without incentivizing vagueness or manipulation, the cost of a versioned world model at training scale, and whether retrodiction transfers to genuinely novel discovery.

— Proof-Carrying Cognition: Closing the Verification Gap with Reality-Settled Reward  (2609.09776 - M et al., 9 Sep 2026) in Limitations