Speaker
Description
Across many fields of fundamental physics, researchers must reason over large and growing corpora of specialized publications and increasingly turn to language models for help — yet these models rarely connect their answers to the primary sources they draw on and often lack the information to give a reliable answer. Building on the PathRAG graph-retrieval framework, we adapt it to fundamental-physics literature and demonstrate it on the full corpus of LHCb collaboration papers (about 850), grounding the model in the underlying physics. Documents are turned into an entity–relationship graph, and a query is answered by walking relational paths between the entities it mentions, so the model receives a concise chain of evidence rather than an unstructured collection of similar passages — following, for example, how a measured quantity traces to the decay channel that determines it and on to the particles in its final state, or how two decay channels are linked through the symmetry they share. We anchor the graph in the Particle Data Group database, mapping the many spellings a particle takes across papers to one canonical node, and we specialize entity extraction to high-energy physics so that detectors and datasets do not dominate the graph. The system has ingested the complete corpus using a small 31b Gemma model and retrieves in seconds. Because the model can be run locally, we can process analyses that cannot be submitted to public models. More importantly, the graph retains source provenance that graph-based retrieval pipelines typically lack: each entity and relation records the exact document passages behind it, the context is grouped by source paper, and every model claim carries a citation that resolves to a verbatim source passage, checked by an optional LLM-as-judge pass. The result is a literature-aware assistant whose answers are verifiable and reliable by design.