Inject or Navigate? Token-Efficient Retrieval for LLM Analysis of Transactional Legal Documents
arXiv:2607.05764v1 Announce Type: new Abstract: Answering questions over a set of transactional legal documents is most simply done by injecting the whole corpus into the LLM's context window on every query. That baseline maximises retrieval recall, but its token footprint scales with the corpus rather than the question, and long-context degradation scales with it. We report what it took to replace full-corpus injection in a legal-document analysis system, comparing it against two structured retrieval modes over our proprietary structure-aware chunking: embedding retrieval (NAVEMBED) and LLM n
The growing scale and complexity of applications for large language models, particularly in data-rich sectors like legal, necessitate more efficient and robust retrieval mechanisms to overcome limitations of context windows and degradation.
This research addresses a critical scaling problem for LLMs in enterprise applications, moving beyond naive full-corpus injection to enable more performant, cost-effective, and reliable analyses of extensive document sets.
The focus is shifting from simply injecting entire documents into LLMs to sophisticated, structured retrieval methods, which significantly improves efficiency and accuracy for specific analytical tasks.
- · AI-powered legal tech companies
- · Enterprises with large document corpuses
- · Developers of structured retrieval algorithms
- · Cloud providers offering LLM services
- · LLMs without advanced retrieval integration
- · Traditional manual legal document review processes
- · Companies relying on brute-force LLM context loading
Improved accuracy and efficiency of LLM-based legal document analysis and other data-intensive applications.
Accelerated adoption of LLM solutions in highly regulated and document-heavy industries due to increased reliability and performance.
The development of new LLM architectures and prompting techniques that deeply integrate with structured retrieval, leading to a new paradigm for enterprise AI applications.
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