ARC-Encoder: learning compressed text representations for large language models
Quick summary
arXiv:2510.20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs. Context compression techniques can reduce these costs, but the most effective approaches require fine-tuning the target model or even modifying its architecture. This can degrade its general abilities when not used for this specific purpose. Here we explore an alternative approach: an encoder that compresses the context into continuous representations which replace token embeddings in decoder
Key takeaways
- arXiv:2510.20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs.
- Context compression techniques can reduce these costs, but the most effective approaches require fine-tuning the target model or even modifying its architecture.
- This can degrade its general abilities when not used for this specific purpose.
Why it matters
“ARC-Encoder: learning compressed text representations for large language models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.