pith:7ZUQ5KB5
TRIAGE: Evaluating Prospective Metacognitive Control in LLMs under Resource Constraints
Language models lack the ability to prospectively plan task selection and compute allocation under fixed token budgets.
arxiv:2605.13414 v1 · 2026-05-13 · cs.AI
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Claims
current language models exhibit substantial gaps in prospective metacognitive control, revealing a previously unmeasured capability dimension with direct implications for resource-efficient agent deployment.
That an oracle with full knowledge of each problem's solvability and cost for the model provides a valid and unbiased benchmark for measuring prospective control, and that calibrating the token budget to the model's baseline cost does not introduce hindsight or selection effects.
TRIAGE evaluates LLMs on prospective metacognitive control by requiring a single plan for task selection, sequencing, and token allocation under a calibrated budget, revealing substantial gaps in current models across math, science, code, and knowledge tasks.
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| First computed | 2026-05-18T02:44:47.394932Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
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Canonical record JSON
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