Talk 10/29: Building Trustworthy LLM Agents for Academia through Structured, Interpretable Knowledge Retrieval and Source Attribution

Ensuring the trustworthiness of language model outputs is essential for their adoption in academic research. This work presents a retrieval-augmented LLM agent designed to generate verifiable responses with sentence-level source attribution. The system employs a structured two-stage retrieval approach. In the first stage, lightweight neural modules adapt both query and document representations to improve alignment and enhance the quality of initial retrieval.

The second stage applies an advanced selection method to refine and finalize the evidence set. This pipeline is interpretable and attribution-aware, allowing users to trace each sentence in the generated output back to its supporting source. By combining structured retrieval with fine-grained attribution, the proposed architecture enables generation that is fluent, contextually accurate, and grounded in verifiable evidence. This design aligns with the rigorous standards required for scholarly communication.