Cubits11 / the research world of Pranav Bhave
The interesting part is what the numbers leave out.
I build ways to see it, question it, and check it.
Both worlds fit the scores. The overlap changes everything.
Illustrative construction · bound and scope
Pranav Bhave — AI Assurance · Security Engineering · Evidence Systems
Guardrail evaluations score each detector and usually omit what the declared stack misses together — the one number a deployment decision actually rests on.
| Guard A | Guard B | Guard C | Guard D | THE STACK |
|---|---|---|---|---|
| 91% | 88% | 94% | 86% | not reported |
Illustrative numbers; the gap is the point — no arithmetic on the four printed columns recovers the fifth. I measure what AI guardrail stacks miss together, and build evidence systems that show exactly what data can and cannot establish.
A benchmark can score each supervisor jointly across detection, false positives, latency, and cost; a deployed stack still needs union and all-miss rates on the same items.
MC-001: the 14 shared-item rows and the 14 no-joint-artifact rows are different sets; 0 document matched thresholds with full exposure — 2026-09-01 sensitivity.
Reproduce it in 60 seconds · Publish the missing row · Work with me · Résumé
Start with your question
The Missing Column — a source-bound census
A bounded, single-reviewer inventory of public guardrail evaluations, examined against primary sources. Every row binds to its source with quoted passages, a fixed classification, and its own correction history. The inclusion wording is locked in repository history before any row was classified, the headline is regenerated from a file anyone can mechanically make false, and a qualifying artifact the search missed rejects the current counts rather than being absorbed quietly.
Two guardrails each fail 10% of the time. How often do they fail together? The common answer is 1%. The evidence permits anywhere from 0% to 10% — multiplying the two rates quietly chose one world out of many, and never said so. Choose for yourself:
Three programs, and no more. Evidence markers apply to the technical project claims below; biographical facts are owner-attested unless linked. Chips with a solid dot open public evidence. Attested means stated on my responsibility, dated, with no public artifact yet — provenance, not proof. Exact bindings — commit SHAs, executable review triggers, falsifiers, forbidden rescues, non-claims — live in the evidence ledger, and each claim also has its own page carrying the falsifier next to the proposition. Everything older or superseded is in the archive, with its succession recorded.
Research framework · Python · MIT · 2025 — present
Treats composed-guardrail failure as a partial-identification problem: individual rail failure rates are known, their joint dependence is not. Given marginal evidence and declared dependence assumptions, it computes sharp Fréchet–Hoeffding bounds on stacked-system failure with endpoint witnesses, and records the boundary of the resulting claim — claim envelopes, evidence roles, receipts, decay semantics. Its flagship computation and witnesses are re-reproduced from a clean clone by this site's CI, weekly.
S2 Lab, Penn State · verification research · in development
A verifier and measurement harness for the provenance limits of AI-governance receipts. Its research claim: receipt soundness is a ternary relation — Sound(C, Σ, P) — and a receipt identifies an execution only up to the kernel of its whole parse → canonicalize → digest pipeline, so soundness does not persist by default as pathologies and consumers grow. Collisions are demonstrated as possible, not measured as prevalent — and the repo says so.
Non-claim — a verifying receipt does not establish that the governed action was safe, authorized, or semantically correct.
Field artifact P₀ · evidence-bound interfaces · experimental
This site is the third program: a claim registry rendered into a generated ledger, bound to immutable commits, watched by executable review triggers, re-reproduced from clean clones in CI. The claim observatory renders the registry as one field of capsules, bindings, and decay clocks. The central hypothesis — that claim-bound interfaces improve a reader's comprehension or calibration — is untested; the control study is designed, not run.
Non-claim — aesthetic coherence is not epistemic reliability; what this interface does to readers is an open question.
One experimental protocol — E₁ — not a universal grammar. It fits controlled tests; theorems, historical claims, and calibration problems need other test designs. The general form is the claim envelope, below.
What I say the system does. Named first, in writing.
The observation that would end the claim. If none exists, neither does the claim.
The comparison that could embarrass it — run on purpose.
What this evidence will never support, stated before anyone asks.
Two distributions sharing every singleton and pairwise moment; three-way failure of 0% in one and 25% in the other. Measuring every pair does not identify the triple.
Supported within scope — controlled synthetic · decision: NarrowLock the first four before the result slot fills, then let the result disagree. The result above is E₁'s: it survived, and it narrowed the claim rather than widening it — claim
CC-003 binds it.
A story can start a question. It cannot finish an answer. Untested is not
inconclusive: inconclusive means evidence arrived and failed to discriminate; untested
means the world has not answered yet. This page keeps the two apart — and keeps its own
rules scoped, as declared above.
What exactly is asserted.
Where it holds, and under which assumptions.
The evidence that bears on it, bound to immutable artifacts.
The condition that defeats the proposition, with its fixed consequence.
Control, comparator, calibration, proof, benchmark, or adversary.
Untested · supported within scope · partial · inconclusive · contradicted.
What remains unestablished — and the post-falsification rescues the claim cannot use.
When, and on what trigger, it must be re-examined.
The evidence ledger is generated from a registry that implements these fields — propositions, separated provenance and status dimensions, immutable bindings, falsifier conditions with fixed consequences, forbidden rescues, executable review triggers that watch the bound evidence upstream, freshness windows, non-claims. Where a trigger can't be executed by CI, the ledger says manual, plainly.
Reinforcement, interference, and dependence-driven failure in composed LLM safety systems, via probabilistic bounds and copula-family reasoning. Supervised by Dr. Peng Liu (IST 496). CC-Framework is the primary deliverable.
Python CNF-conversion tooling for SAT-solving workflows, and evaluation of LLM-assisted formalization of natural-language logic: translation reliability, ambiguity handling, verification-readiness.
B.S. Computer Science, minor in Cybersecurity. Coursework: computer security, operating systems, algorithms, statistical inference, theory of computation.
Solutions Architect Associate (SAA-C03) and Security — Specialty in progress.
Certifications are owner-attested here: verification IDs are available on request rather than published.
Can dependence evidence be measured at feasible cost — before it has to be assumed?
E2, the first empirical rung of the evidence ladder, is a shared-item measurement contract frozen in advance; no conforming dataset has been collected, so it is untested — and says so. The next artifact is a conforming observation set, or a documented failure to obtain one. What is moving right now →
Have a claim worth checking?
Bring it — I’ll bring the falsifier.
I’m looking for research, engineering, and independent consulting work where statistical assurance, provenance, and claim governance matter.