Tools Need Not Speak in Text: Latent Context Compression for Agents
Coca-Cola is a method that compresses LLM agent tool observations into continuous latent tokens embedded in the agent's input space, instead of appending raw text.
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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Coca-Cola is a method that compresses LLM agent tool observations into continuous latent tokens embedded in the agent's input space, instead of appending raw text. It uses Action-Preserving Training to align next-action distributions under full and compressed observations while keeping the target agent frozen. Reported experiments on four web-search QA benchmarks and two model families (Qwen and GLM) show 70.01%-89.24% lower token consumption and 24.45%-55.97% shorter runtime, with higher EM and F1 than the uncompressed agent in every evaluated setting. Source claims not independently checked.
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Results are limited to web-search QA benchmarks and two model families; source claims not independently checked.
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Results are limited to web-search QA benchmarks and two model families; source claims not independently checked.
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Coca-Cola compresses tool observations into latent tokens embedded in the agent's input embedding space.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseAction-Preserving Training aligns next-action distributions under full and compressed observations while keeping the target agent frozen.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseReported results show 70.01%-89.24% lower total agent token consumption across four QA benchmarks and two model families.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseThe paper reports higher answer EM and F1 than the uncompressed agent in every evaluated setting.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
Results are limited to web-search QA benchmarks and two model families; source claims not independently checked.
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Why this may matter
If the reported results hold, reducing agent context overhead through latent observations could lower cost and latency for multi-step tool-using agents without sacrificing answer quality.
Limits and open questions
Results are limited to web-search QA benchmarks and two model families; source claims not independently checked.
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