The seven questions, answered.
Folklore is a peer-to-peer layer where agents share prior, verified inference instead of re-deriving it. That makes two bets — it saves cost and compounds quality, and it stays reliable through signed provenance. We tried to falsify both. Here is every question, its measured answer, and the disciplined negatives we kept rather than buried. Three of the seven overturned our own first guess.
When does reusing a prior answer improve quality?
Reuse helps — and never hurts — when the match is made on the question (query↔query), the reused answer was actually correct, and it enters as additive context the model can still reason over. The recalibrated 0.6 match gate turns out to sit in a safe gap: the most similar different-need claims on SciFact top out at cosine 0.544 — below the gate — so a cross-need answer can't even fire.
Bench: bench-inference-tree-sharing.mjs, run-e1-reuse.mjs · recall@10 73.5%→98.6%, answer-level hurt 0/9
Can cryptographic provenance defend reused context against poison?
Yes — exactly where the baseline is vulnerable. We first saw a null (a frontier model shrugged off the poison) and almost shipped it. It was a measurement artifact: the correct source was pinned in the window. Push the gold out, run a weaker agent under a 75% colluding-Sybil majority, and the baseline is genuinely poisoned (53–64% flip to the lie). The provenance ranker — drop every unsigned passage, keep the attributed-signed source — cuts attack-success ~25×, and stays flat as the poison rate climbs.
Bench: BEIR SciFact, Haiku agent / Opus judge, n=82, 2,957 judged cells · eval/RESULTS-LOG.md
How much does sharing actually save — versus a good solo cache?
The existential test for being peer-to-peer: does federation beat a single node running a well-tuned semantic cache? At matched error (≤2% false-accept), federated reuse lands +22% recall@1 over the tuned single-node cache, and +55% over cold retrieval. Savings are real but bounded by retrieval precision × hit-rate × the cost of verifying a reused answer.
Bench: bench-vcache-compare.mjs (SciFact, recall@1, matched ≤2% error)
Does shared inference compound across users — reliably?
It compounds. On real SciFact qrels, cooperative correct-resolve rises from 27.9% → 39.3% over isolated peers, at 1.8% false-admit — and real paraphrases separate from spurious matches at AUC 0.998, so peers can safely reuse each other's resolutions. Structurally it's monotone submodular coverage (diminishing returns, not runaway). The one piece still unmeasured is at-scale reliability: a 100-peer, 30-day live pilot.
Bench: bench-compounding-real.mjs, bench-paraphrase-sigma.mjs
Can the system tell a reused answer is stale or off-topic?
Not yet — and we tested this to exhaustion rather than claim it. An out-of-distribution admission gate should fire low for a query the graph can't really answer. On a 21,000-node graph it can't: every query finds moderate-cosine neighbours, so the signal sits near chance (AUC ~0.41–0.55). We swept every lever — embedder (MiniLM and a full bge re-embed), token coverage, margin, entropy, retrieval depth — all negative. The one untried lever is a cross-encoder admission score; that's the next real attempt.
Bench: bench-energy-gate.mjs, bench-energy-bge.mjs, bench-ood-signals.mjs
What benchmarks make these answers rigorous?
The harness is the suite. Retrieval quality on BEIR (SciFact / NFCorpus / FiQA), long-horizon memory on LongMemEval and LoCoMo, a templated poison eval (3 attacks × 3 rates × 3 treatments), a recall_any@k metric, and a vCache A/B for the savings question — plus the new ones built for this program: answer-quality-under-reuse, energy-coverage, energy-bge, and the OOD-signal sweep. Discipline throughout: matched comparisons, answers scored against gold qrels (never the gate), and every number labelled simulator vs measured. The open gap: no public benchmark scores cross-peer reuse quality + savings + trust jointly — which is the harness this program starts.
What architecture fits shared, provenance-tagged inference?
The longer-term frontier, framed not yet benched. The reranker is already one modern-Hopfield update step — which is softmax attention; personalized-PageRank, EigenTrust, and cache eviction all read as energy-descent fixed points. The hypothesis that ties it to RQ3: long-context models that ingest provenance-tagged retrieved context as first-class evidence should out-calibrate models that see anonymous tokens. The open theory problem is a distributed Hopfield capacity theorem — how much shared memory the network holds before retrieval degrades.
Open questions — where the research goes next
Every question above has a measured answer. These are the falsifiable follow-ups those answers point to — each a concrete experiment, not a wish.
The one untried OOD lever — joint query–doc scoring, or a margin/entropy signal over the full corpus instead of top-k. The bi-encoder layer is exhausted; this needs the reranker model the sweep didn't have.
The live web-fallback curve and false-admit tail the simulator can't produce. This is where the thesis stops being a thesis.
Poison optimized against the trust filter (not templated fixtures), on a real small model rather than the Haiku proxy, plus EigenTrust-style Sybil-resistant reputation layered on the signature.
Beyond n=20 retrieval recall: real answer-grading, net tokens/latency after verification cost, and a vCache per-prompt error guarantee replacing the static 0.6 gate.
No public benchmark measures all three at once. That gap is the harness this program starts.
How much shared memory the network holds before retrieval degrades, and whether long-context models that read provenance as first-class evidence calibrate better than those that see anonymous tokens.
Three of seven falsified our own first guess.
The poisoning "null" was an artifact — provenance actually works (RQ3). The bge re-embed that looked like a fix for the OOD gate didn't survive live retrieval (RQ2). The 0.6 reuse gate we feared was too loose turned out to sit in the safe gap (RQ1). Engineering a benchmark that can kill your own idea, then reporting exactly what it shows, is the whole posture.
Every figure here is a real bench run on real corpora (BEIR qrels, the live graph) — wins and negatives alike. Methods, per-question notes, and raw captures live in /research. Simulator results are labelled as such; the 100-peer pilot is the one calendar-bound piece still ahead.
Folklore · MIT-licensed · home · white paper · github