An academic research note on whether company-specific fundamental knowledge retains value when machine-generated breadth enters the experiment.
The useful signal is rarely found in a single headline metric. It lives in the choices a company makes, the language it repeats and the small disclosures that explain how an industry actually works. This project treats that specificity as a research variable.
Two artifacts, one controlled comparison: does domain-specific fundamental research retain an advantage when the same question is approached with a general-purpose machine system?
Built without AI: supply-chain and holding-structure graph research, informed by more than a decade of experience across tier-1 trading companies.
An ensemble of Qwen models adapted with supervised fine-tuning (SFT) and LoRA on twenty-five years of publicly sourced research material.
A · random pork oinks · without AI
Research is assembled as a chain of evidence, not a pile of excerpts.
Gather the company’s own vocabulary across filings, notes and reporting periods.
Place each disclosure inside the operating structure of its industry.
Turn connected observations into a view that can be inspected and challenged.
Recurring definitions, accounting policies and changes in presentation become the first map of what deserves attention.
Filter the hand-made graph by relationship type, then select a node to inspect the research judgment encoded before any conclusion is drawn.
Choose a company below, then follow one finding from source language to industry implication.
Capacity expansion / contract manufacturing
A long useful life implies durable tooling, repeat programs and switching costs that may not appear in the headline margin.
In precision components, customer qualification cycles can turn fixed assets into evidence of embedded demand.
B · pork with memories · fine-tuned model condition
Three stages: the corpus the models read, the supervised fine-tuning that adapts them, and the synthesis that brings their outputs into one comparison. The middle stage is where the work sits.
Twenty-five years of publicly sourced material across three document types.
Curated pairs are passed through a frozen base model. Only the low-rank adapters are trained, and the pass is repeated once per ensemble member — this stage, not the merge, is the cost of Track B.
The fine-tuned members’ outputs are combined into the single response that enters the comparison.
Both tracks read the same filings and answer the same question. Nothing on this page is a result — each method is set out so it can be inspected before either artifact is scored.