Yuetong Liu

Each time a web-search agent calls a tool, the long returned text is appended verbatim to its context, so after several steps the context balloons and dilutes attention. Coca-Cola instead compresses those observations into a few continuous latent tokens and embeds them directly in the agent's input embedding space, with action-preserving training aligning the next-action distributions under full and compressed observations. Across four QA benchmarks and two model families, total token consumption falls by 70.01%–89.24% and end-to-end runtime by 24.45%–55.97%, while EM and F1 exceed the uncompressed agent in every setting.

Each time a web-search agent calls a tool, the long returned text is appended verbatim to its context, so after several steps the context balloons and dilutes attention. Coca-Cola instead compresses those observations into a few continuous latent tokens and embeds them directly in the agent's input embedding space, with action-preserving training aligning the next-action distributions under full and compressed observations. Across four QA benchmarks and two model families, total token consumption falls by 70.01%–89.24% and end-to-end runtime by 24.45%–55.97%, while EM and F1 exceed the uncompressed agent in every setting.