A local Llama 3.2 3B model preprocesses multilingual coding prompts via translation and structural rewriting, cutting prompt tokens 34-47% and total tokens up to 18.8% while preserving accuracy on OMH-Polyglot benchmark.
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes agentic framework-based reproduction with a slot-binding interface to turn 16 PHM papers into standardized, assumption-aware benchmark implementations.
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Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing
A local Llama 3.2 3B model preprocesses multilingual coding prompts via translation and structural rewriting, cutting prompt tokens 34-47% and total tokens up to 18.8% while preserving accuracy on OMH-Polyglot benchmark.
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From paper to benchmark: agentic, framework-based reproduction of under-specified methods in machine health intelligence
Proposes agentic framework-based reproduction with a slot-binding interface to turn 16 PHM papers into standardized, assumption-aware benchmark implementations.