MightBe: An Audited Neural Runtime with Hot Structural Mutation and Traceable Multi-Hop Reasoning

Abstract

MightBe is a single-process system that couples a from-scratch neural-network runtime to a relational store through a learned, auditable index. We make three contributions. First, we specify a hot-mutation protocol for a live network: an \textsc{alter} operation is merged into a candidate specification, validated, rebuilt, and re-hydrated with weights migrated by (layer, parameter, shape); the candidate must then pass a smoke forward pass on a zero tensor before it is published, and publication is a lock-free atomic swap of an `ArcSwap`-held snapshot, so readers in flight continue to observe the previous version and any failure leaves the previous version serving unchanged. Second, we give an auditable inference graph in which every answer is a scored path together with a bounded evidence set, and we specify the confidence algebra exactly: a path score is a product of per-step contributions, several paths reaching the same node are combined by a noisy-OR, and a verifier reranks the result by geometric interpolation with a semantic similarity term. Third, we couple the store to the learned index with an idempotent insert trigger that routes text rows through a normalization, tokenization, TF-IDF, TextRank and pointwise-mutual-information pipeline before the association graph is built. We report the complete result of the artifact's own test suite --- 32 test targets, 251 tests, all passing, one doc-test ignored --- and we state plainly that the repository contains no benchmarks and no accuracy or latency measurements, so we give a measurement protocol rather than results. We also disclose that the test suite itself was generated with the assistance of a large language model, and that six modules named in the project's own documentation have no implementation. *(When entering the abstract on arXiv, replace the Markdown backticks around `ArcSwap` with plain text or `\texttt{}` as appropriate; the metadata field is plain text, so write `ArcSwap` without markup.)*

Body

Imported entry paper (unverified hypothesis)

This paper was imported into jetessays as an entry paper from the author's local archive (mightbe). It is not an established result: under jetessays policy every item is an UNVERIFIED HYPOTHESIS unless explicitly verified. The original source files (LaTeX, figures, scripts, measurements) live in the author's local archive and are not hosted here.

Abstract (as submitted)

MightBe is a single-process system that couples a from-scratch neural-network runtime to a relational store through a learned, auditable index. We make three contributions. First, we specify a hot-mutation protocol for a live network: an \textsc{alter} operation is merged into a candidate specification, validated, rebuilt, and re-hydrated with weights migrated by (layer, parameter, shape); the candidate must then pass a smoke forward pass on a zero tensor before it is published, and publication is a lock-free atomic swap of an ArcSwap-held snapshot, so readers in flight continue to observe the previous version and any failure leaves the previous version serving unchanged. Second, we give an auditable inference graph in which every answer is a scored path together with a bounded evidence set, and we specify the confidence algebra exactly: a path score is a product of per-step contributions, several paths reaching the same node are combined by a noisy-OR, and a verifier reranks the result by geometric interpolation with a semantic similarity term. Third, we couple the store to the learned index with an idempotent insert trigger that routes text rows through a normalization, tokenization, TF-IDF, TextRank and pointwise-mutual-information pipeline before the association graph is built. We report the complete result of the artifact's own test suite --- 32 test targets, 251 tests, all passing, one doc-test ignored --- and we state plainly that the repository contains no benchmarks and no accuracy or latency measurements, so we give a measurement protocol rather than results. We also disclose that the test suite itself was generated with the assistance of a large language model, and that six modules named in the project's own documentation have no implementation. (When entering the abstract on arXiv, replace the Markdown backticks around ArcSwap with plain text or \texttt{} as appropriate; the metadata field is plain text, so write ArcSwap without markup.)

Note

Imported via the jetessays seed importer. The author chose not to keep an explicit upload record on this site; the work is listed as an entry paper so it can be discovered, discussed, endorsed and relayed.

Discussion

Log in to join the discussion

← back to browse