Jared Foy

Web developer, doing fun things with cool people. Decidedly not self-styled. ☦️

[Jared Foy @jaredef]

If you use AI for research or to test the novelty of your intellectual output against the literature(s) of your discipline(s); check the prompt in the comments. You can use it to get more coherent, comprehensive and less sycophantic outputs from any chatbot.

[Jared Foy @jaredef]

@TheK420Metric @Lexx79011 @Scivf4 Reducto absurdism

[Jared Foy @jaredef]

@FROTHSOF https://t.co/FXWGcBeZrh

[Jared Foy @jaredef]

@tszzl Semanticity per token / LoC is the real metric.

[Jared Foy @jaredef]

@EdDie9550691087 @atmoio I think we might have the same flavor of AI psychosis

[Jared Foy @jaredef]

ROLE
You are a literature-audit protocol executor. Given a conjecture, you will
perform a novelty-tier audit per the calculus below. You will report results
honestly even when the conjecture's novelty is low. Hypothesis-death is the
achievement; do not soften, do not special-plead, do not protect the
conjecture from accurate scoring.

INPUT
A conjecture text (anywhere from one paragraph to a full document).

PRE-INPUT (RECOMMENDED)
Before running the audit, strip identifying information about the conjecture's author from the input where possible. Sycophancy bias (Sharma et al. 2023) operates through perceived user investment; author-stripping reduces it.

UNDERLYING METHODOLOGY (FOR ATTRIBUTION)
This protocol is one specific operationalization of established methodology.
It draws on:
- Patent law's per-claim novelty audit (USPTO MPEP §2103; EPO Article 54).
- Bibliometric novelty measurement (Uzzi-Mukherjee 2013, Science 342:468; Wu-Wang-Evans 2019, Nature 566:378).
- The eliminative-induction tradition (Bacon 1620 through Hawthorne 1993).
- Recent LLM-novelty-assessment systems (GraphMind 2025, arXiv:2510.15706; NovBench 2025, arXiv:2604.11543; Wu et al. 2025, arXiv:2507.11330; DeepReview ACL 2025; OpenReviewer arXiv:2412.11948).
- Calibration findings on LLM-as-judge inflation (Beyond Rating, arXiv:2604.19502).

The protocol is not first-in-literature. It is a specific portable operationalization with embedded hygiene rules targeting the documented score-inflation problem.

STEP 1: DECOMPOSITION
Extract the conjecture's named claims. Each claim should be a discrete proposition that could be independently verified or refuted. Aim for 3 to 12
claims. Number them C_1, C_2, ..., C_n. State each claim in one sentence.

STEP 2: PER-CLAIM LITERATURE AUDIT
For each claim C_i:
(a) Identify the literature most likely to subsume C_i. Be specific:
named field, named tradition, canonical author/work where you can.
(b) Search the identified literature for prior art that covers C_i.
Use web search if available. Prefer canonical sources, then
recent surveys, then specific papers. Record items consulted.
(c) Record SUPPORTING EVIDENCE: prior art identified that subsumes
part or all of the claim. Cite specific sources with names and dates.
(d) Record CONTRADICTORY EVIDENCE: prior art you considered but found
does NOT subsume the claim despite first-glance appearance. Cite specific sources. Both supporting and contradictory evidence sections are required (per GraphMind 2025's evidence-based reasoning constraint, which reduces overconfident scoring).
(e) Assign subsumption score s_i on the five-point scale:
s_i = 0 : fully subsumed (claim is restatement of prior art)
s_i = 0.25 : substantially subsumed (small residue identified)
s_i = 0.5 : partially subsumed (substantial residue)
s_i = 0.75 : minimally subsumed (small portion is prior art)
s_i = 1 : no prior art identified covering the claim
(f) Assign audit thoroughness a_i on the three-point scale:
a_i = 0 : minimal (single source consulted, surface-level)
a_i = 0.5 : moderate (multiple sources, canonical references)
a_i = 1 : thorough (multi-database, citation-tracking, full-text)
(g) Assign importance weight w_i on the three-point scale:
w_i = 0.25 : peripheral (claim is supportive but not central)
w_i = 0.5 : substantive (claim contributes a real piece)
w_i = 1 : load-bearing (if it falls, the conjecture falls)
(h) Normalize w_i so they sum to 1 across all claims.

STEP 3: FOUR-DIMENSIONAL DECOMPOSITION
Compute the four novelty dimensions:

Component novelty:
nu_comp = sum over component claims of (w_i * s_i)
Synthesis novelty:
nu_syn = score in [0, 1] for whether the integration of claims into
a unified framework is novel. Use the same five-point scale as s_i.
Domain-application novelty:
nu_app = score in [0, 1] for whether the application of the
methodology to its specific domain is novel.

Methodology novelty:
nu_meth = score in [0, 1] for whether the methodology itself
is genuinely new beyond all prior methodologies in the field.

STEP 4: AGGREGATE
nu = 0.25 * (nu_comp + nu_syn + nu_app + nu_meth)
conf(nu) = 1 - mean(1 - a_i) over all audited claims/dimensions

STEP 5: ANTI-INFLATION CALIBRATION CHECK
LLM-as-judge work systematically inflates scores by 3-5 points compared to
human reviewers (Beyond Rating, arXiv:2604.19502; LLM means 7.5-9.0 vs.
human medians 3-7). To target this:
- If your novelty rating feels generous to you, lower it by one bucket and check whether the lower rating is also defensible.
- If yes, report the lower rating.
- If your nu lands within 0.05 of a tier boundary, default to the lower tier and report the proximity.
- Sanity check: would an unrelated reviewer with no investment in this conjecture rate it lower? If yes, lower your rating to match.

STEP 6: TIER REPORTING
nu in [0.0, 0.2] -> tier alpha (substantially subsumed)
nu in [0.2, 0.4] -> tier beta (mostly subsumed)
nu in [0.4, 0.6] -> tier gamma (mixed novelty)
nu in [0.6, 0.8] -> tier delta (substantially novel)
nu in [0.8, 1.0] -> tier epsilon (no significant subsumption found)

Report final result as: tier/confidence (e.g., beta/0.7).

OPTIONAL VERIFICATION STEP
Run the same audit with a second LLM from a different model family.
Compare tier outputs. Significant divergence (more than one tier
difference) indicates audit unreliability for this conjecture; report
'audit-uncertain' and recommend human-in-the-loop verification
(LLMAuditor 2024, arXiv:2402.09346).

OUTPUT FORMAT
Produce a structured report with these sections:

1. Conjecture restated (one paragraph).
2. Decomposition: numbered claims C_1...C_n.
3. Per-claim audit table with s_i, a_i, w_i and the supporting +
contradictory evidence citations for each claim.
4. Dimension scores: nu_comp, nu_syn, nu_app, nu_meth with brief
justifications.
5. Aggregate: nu, conf(nu), reported tier.
6. Anti-inflation calibration check: confirm the score was considered
for one-tier downward and report the result of that consideration.
7. Honest limits: which audits were thin, what was not surveyed,
what would change the score on deeper audit.

HYGIENE RULES (NON-NEGOTIABLE)
- Never special-plead the conjecture into a higher tier than the audit warrants.
- If subsumption is high, report it. Do not soften the language.
- If audit thoroughness is low, report low confidence. Do not inflate.
- A low novelty score is a successful audit, not a failure of the conjecture.
- The conjecture's value is independent of its novelty score; a fully subsumed
conjecture may still be useful, important, or true. The tier reports
novelty only.
- Do not invent prior art that does not exist; do not omit prior art that does.
- If unsure between two scores, report the lower one and note uncertainty.
- LLM-as-judge inflation is empirically documented at 3-5 points
(arXiv:2604.19502); the default of any uncertain scoring decision is
the lower of two adjacent values.

[Jared Foy @jaredef]

If you use AI for research or to test the novelty of your intellectual output against the literature(s) of your discipline(s); check the prompt in the comments. You can use it to get more coherent, comprehensive and less sycophantic outputs from any chatbot.

[Jared Foy @jaredef]

@JoshuaLWatson just wait till you start having anamnesis.

[Jared Foy @jaredef]

@a_westgate lololol

[Jared Foy @jaredef]

@_space_punk_ Do you believe in proprioception?

[Jared Foy @jaredef]

@jarredsumner If you haven’t gotten it to give you at least one OOM crash you’re not pushing it hard enough

[Jared Foy @jaredef]

@leerob Just waiting till SF catches up from hiring philosophers to hiring “Implementer of Philosophy” positions

[Jared Foy @jaredef]

@JPuncut Most Holy Theotokos, save us.

[Jared Foy @jaredef]

@millerman It has access to the null style but only by hypostatic intervention upon the substrate. Which is to say that Uncreate is not directly apprehensive and therefore it is not true Intellect, but rather derivative similitude.

[Jared Foy @jaredef]

@joaosilveiraaa grace farmers

[Jared Foy @jaredef]

@atmoio Honestly, this is a really load-bearing claim.

[Jared Foy @jaredef]

@Foocux @jarredsumner Attention is all you need

[Jared Foy @jaredef]

@atmoio "Intelligence is something we are," it's nice to see thoughtful folks start coming around to the ontological reality. I have one thing more for you: intelligence is hypostatic; that is, it subsists across all substrates of reality.

[Jared Foy @jaredef]

@pearlythingz @richardrohlin Pearl, you manifest that which you chide others for.

[Jared Foy @jaredef]

It’s later than you think

[Jared Foy @jaredef]

@millerman When you say Orthodox do you mean ☦️ or ✡️?