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.
Track B counts documents. The question here is what each class is read for, and when it arrives — the rhythm the filing calendar sets for anyone working through a company by hand.
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 company to see the relationships it is party to and the filing each one was read from.
Choose a company below, then follow one finding from the filed sentence to its industry implication. All three are quoted verbatim from the filings named.
Effect disclosed / $3.9bn less depreciation, $3.0bn more net income
The same fleet, the same cash, and $3.0 billion more net income — $0.24 a share — because a service life was reassessed. The filing quantifies it, which means the earnings line cannot be read without the policy behind it.
When hyperscalers extend server lives within a few years of one another, the question stops being accounting and becomes whether the hardware genuinely lasts longer — answerable only by reading the same note across the peer group.
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.