Language-Conditioned Token and Reasoning Efficiency in Large Language Models: A Paired Cross-Lingual Study Protocol

Abstract:Large language models incur language-dependent representation and inference costs, but existing comparisons often conflate input language, assigned observable-trace language, and answer realization. We specify a prospective paired study that separates these interfaces while holding the semantic item, checkpoint, and answer oracle fixed. The initial design instantiates 240 exactly scored items rendered from templates in English and seven non-English languages, three distinct-lineage open-weight checkpoints, three trace-token budgets, 22 input- and trace-language conditions, a fixed answer reserve, and a separately counted delimiter: 47,520 initial core runs before prospective sample-size selection. RQ1-RQ3 estimate input and trace effects by intention-to-treat with failure-inclusive terminal accounting and test answer realization by cloning a sealed prefix and runtime-native KV state into eight crossed branches. Pre-freeze independent language review, fixed-form ASCII selectors, and code/surface/solver agreement constrain the realization test. H1-H5 share one Holm family and a global simultaneous component band. A secondary randomized experiment compares one-long-attempt and complete K-short-attempt policies at equal trace allowance under frozen seeds and oracle-blind aggregation; it is a full-policy contrast because answer capacity differs. Outcomes include exact token spans, correctness, latency, runtime-exposed memory, and qualified same-host operating-system-reported energy over prespecified hardware rails. The protocol separates tokenizer expansion, observable-trace cost, and answer-realization cost without treating visible traces as internal cognition or operating-system estimates as physical cross-device energy. No confirmatory model outcome is reported; result fields remain disabled until the frozen evidence ledger passes independent verification.

32 pages, 2 figures, 3 tables. Prospective paired study protocol; no confirmatory model outcomes are reported

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2610.04295 [cs.AI]

(or arXiv:2610.04295v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.04295 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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