Abstract
The application of large language models (LLMs) for complex document analysis may be limited by a potential to generate factually incorrect information, which can be unsuitable for fields where high precision and verifiability are beneficial. This disclosure describes a non-deterministic methodology for conducting complex, multi-variable assessments of unstructured data using Large Language Models (LLMs). Conventional systems often fail due to "semantic dilution" and "attention fragmentation" when evaluating high numbers of concurrent rules against large unstructured data or documents. The framework introduces Decoupled Semantic Anchoring and Attention Steering (DSA-AS), which integrates thematic modularization, decoupled knowledge-graph anchoring, and a consensus protocol with rationale validation. This creates a heuristic "expert mindset" for high-fidelity analysis in domains such as regulatory compliance, technical auditing, and complex document review.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Sarkar, Subhankar and Quam, Michael, "A Heuristic Framework for High-Fidelity Domain Assessments via Modularized Attention Steering", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9841