arXiv:2606.12400v1 Announce Type: new Abstract: Long input sequences are central to document understanding and multi-step reasoning in Large Language Models, yet the quadratic cost of attention makes inference both memory-intensive and slow. Context distillation mitigates this by compressing contextual information into model parameters, and recent work such as Doc-to-LoRA amortizes context distillation into a single forward pass that generates one LoRA adapter per document. However, producing a single monolithic adapter for all queries leads to irrelevant-query interference, limited compositio
The paper addresses a critical computational bottleneck in LLMs, specifically the high cost of processing long input sequences, which is becoming increasingly relevant as AI systems handle more complex, multi-document tasks.
Improving efficiency in handling long contexts directly impacts the scalability and capabilities of advanced AI, enabling more sophisticated reasoning and document understanding in real-world applications.
The ability to more efficiently compile and compose 'memory atoms' rather than monolithic adapters could lead to more nuanced and performant long-context LLMs, mitigating issues like irrelevant-query interference.
- · AI developers and researchers
- · Cloud providers
- · Enterprises using LLMs for document processing
- · Startups developing specialized AI models
- · Platforms with inefficient legacy LLM architectures
- · Companies reliant on simple keyword search
Reduced computational cost and improved accuracy for large language models processing extensive documents.
Accelerated development of AI agents capable of complex, multi-step reasoning across vast information datasets.
New enterprise applications leveraging enhanced document understanding may automate sophisticated white-collar tasks, impacting professional services.
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Read at arXiv cs.CL