Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing
The paper discusses a new approach to optimize coding agents by reducing input-token costs. It introduces a middleware that preprocesses prompts to enhance efficiency, particularly for non-English text. The results show significant reductions in token usage while maintaining or improving task accuracy across various coding benchmarks.
- ▪AI-assisted coding agents face challenges due to input-token costs, especially with non-English text.
- ▪The proposed middleware uses a local model for cross-lingual translation and prompt optimization.
- ▪The method reduces prompt tokens by 34-47 percent and total tokens by up to 18.8 percent without sacrificing accuracy.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03618 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2606.03618 (cs) [Submitted on 2 Jun 2026] Title:Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing Authors:Mehmet Utku Colak View a PDF of the paper titled Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing, by Mehmet Utku Colak View PDF HTML (experimental) Abstract:AI-assisted coding agents are bottlenecked by input-token cost. Two pathologies of raw human input drive much of this overhead: tokenization inefficiency for non-English text and structural entropy in conversational prompts. Existing approaches act reactively by compressing already-bloated contexts or intervening after failures occur.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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