Can language models reliably judge their own candidate quality?

The paper defines scientific discovery as optimizing "expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces" such as molecules, protein sequences, and programs. Its diagnosis is that LLMs give expressive priors over such spaces, but "their likelihoods and self-assessments are unreliable proxies for the objectives and calibrated epistemic uncertainty, especially for novel candidates outside the observed data distribution." The conclusion states the same split as complementary limitations. LLMs have "strong structured generative capabilities and domain priors, but cannot reliably estimate the true value of external objectives or their associated uncertainty." Bayesian optimization "delivers uncertainty-aware experimental decisions from sparse observations, yet struggles to autonomously propose valid candidates in complex discrete spaces."

The Large Discovery Model (LDM) is the proposed division of labor. It is a recurrent architecture in which a generative model proposes and refines candidate designs, while a Bayesian non-parametric reward surrogate (a Gaussian process, per the conclusion) predicts performance and quantifies uncertainty. The result is an "uncertainty-aware value" that guides candidate generation, refinement, and selection. The value signal comes from experimental observations rather than from the LLM's own judgment. A discovery memory and the surrogate are updated as each new observation arrives, which makes the loop "empirically grounded."

The introduction places this against inference-time scaling, which has paid off in mathematics, coding, theorem proving, and games, where solutions are "difficult to find but comparatively easy to evaluate" through verifiers, unit tests, or simulators. AlphaZero-like methods and AlphaEvolve are cited as generation-evaluation-search loops with cheap evaluators. Read together with the abstract, discovery is the case where that cheap evaluator is missing. The surrogate takes its place, and it is fitted to a small number of costly experiments rather than assumed.

This sits beside Do foundation models learn world models or task-specific shortcuts?, which also finds that fluent prediction does not mean a reliable internal model of the target. That note is about physical laws and this one about objective values, so the link is thematic. The LDM setting also overlaps with Can autonomous research pipelines discover AI architectures that AutoML cannot?, since neural-network training is one of LDM's three scenarios. That note lets an LLM drive experiments directly, whereas LDM argues the experiment loop needs an explicit uncertainty model. The paper's stress on real observations over internal knowledge is the same instinct as Why do search agents beat memorized retrieval on hard questions?, applied to experiments instead of search.

The excerpt does not establish that LDM works. It names three scenarios (neural-network training, antibody design, and molecular optimization) but gives no results, baselines, evaluation budgets, or effect sizes. It also does not say how candidates are represented for the surrogate, how the uncertainty-aware value is computed, or how much of any gain comes from the surrogate versus the memory or the recurrence. The claim that LLM self-assessments are unreliable is asserted in the abstract and not demonstrated in the passages provided. What the excerpt supports is the design argument, that generation and valuation are separable jobs and the second should not be left to the generator. Whether that argument holds up is a matter for the full paper's experiments.