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| \documentclass[11pt,a4paper]{article} |
| \usepackage[T1]{fontenc} |
| \usepackage{lmodern} |
| \usepackage{amsmath,amssymb,amsfonts,amsthm} |
| \usepackage{booktabs} |
| \usepackage{graphicx} |
| \usepackage{hyperref} |
| \usepackage{cleveref} |
| \usepackage{geometry} |
| \usepackage[numbers,sort&compress]{natbib} |
| \usepackage{xcolor} |
| \usepackage{enumitem} |
| \usepackage{float} |
| \usepackage{caption} |
| \usepackage{array} |
| \usepackage{multirow} |
| \usepackage{makecell} |
| \usepackage{url} |
| \usepackage{algorithm} |
| \usepackage{algpseudocode} |
| \geometry{margin=1in} |
| \bibliographystyle{plainnat} |
| \newcommand{\rcxi}{RC+$\xi$} |
| \newcommand{\codette}{\textsc{Codette}} |
| \pdfstringdefDisableCommands{\def\rcxi{RC+xi}} |
| \newtheorem{definition}{Definition} |
| \newtheorem{theorem}{Theorem} |
| \newtheorem{proposition}{Proposition} |
|
|
| \title{\textbf{Codette v8 Additions: Render/Cognition Separation,\\ |
| Updated Benchmarks, and Resolved Depth--Naturalness Tradeoff}\\ |
| \large (Delta document from v7, April 2026)} |
| \author{Jonathan Harrison} |
| \date{May 2026} |
|
|
| \begin{document} |
| \maketitle |
|
|
| \section*{Abstract of Changes} |
|
|
| This document records the additions to the Codette paper from April 2026 (v7) |
| to May 2026 (v8). The key changes are: (1) a new Phase~8 architectural |
| contribution --- render/cognition separation via \textsc{CognitionSubstrate}, |
| \textsc{AuthoredState}, and \textsc{RenderLayer} --- that bounds the |
| hallucination surface to a fully authored cognitive artifact; (2) updated |
| benchmark results showing \codette{} achieves a composite score of 0.744 |
| (+108.8\% vs.\ SINGLE, Cohen's $d=8.31$), with memory augmentation now |
| reaching statistical significance and the depth--naturalness tradeoff |
| substantially resolved (Turing naturalness 0.245~$\to$~0.820 in the CODETTE |
| condition); and (3) an Intellectual Integrity Layer (sycophancy resistance, |
| debate tracking, role-adaptive response). |
|
|
| |
| |
| |
| \section{Phase 8: Render/Cognition Separation} |
| \label{sec:phase8} |
|
|
| \subsection{Motivation: The Model-Coupling Problem} |
|
|
| Conventional LLM-based cognitive architectures assign the language model a |
| dual role: it is simultaneously the \emph{cognitive surface} (deciding what is |
| true, generating conclusions, selecting evidence) and the \emph{communication |
| surface} (choosing how to express those conclusions in natural language). This |
| coupling creates three interrelated problems: |
|
|
| \begin{enumerate}[nosep] |
| \item \textbf{Unbounded hallucination surface.} The model can introduce new |
| claims at render time that were never authored by the reasoning pipeline. |
| \item \textbf{Model lock-in.} Cognitive quality is tied to a specific model's |
| parametric knowledge and biases. Swapping the base model changes not just |
| expression but cognition. |
| \item \textbf{Validation gap.} There is no authored artifact against which to |
| validate the rendered output; governance checks operate on |
| natural language rather than structured cognitive state. |
| \end{enumerate} |
|
|
| This problem was surfaced in an external architecture review (May 2026) that |
| identified model coupling as a key structural weakness shared by most |
| LLM-based multi-agent systems. Phase~8 resolves it through clean separation. |
|
|
| \subsection{Architecture} |
|
|
| Phase~8 introduces three new components forming a strict pipeline: |
|
|
| \begin{equation} |
| \text{Query} \;\xrightarrow{\text{CognitionSubstrate}}\; \text{AuthoredState} |
| \;\xrightarrow{\text{RenderLayer}}\; \text{Natural Language Response} |
| \end{equation} |
|
|
| \textbf{CognitionSubstrate} performs all reasoning with zero LLM calls. It |
| orchestrates existing \codette{} components in template mode: |
|
|
| \begin{enumerate}[nosep] |
| \item \emph{Perspective gathering}: ForgeEngine template agents analyze the |
| query from all active cognitive modes (analytical, creative, empathic, |
| philosophical, quantum, meta-cognitive). |
| \item \emph{Cocoon retrieval}: UnifiedMemory FTS5 search retrieves up to 5 |
| relevant prior reasoning exchanges. |
| \item \emph{Strategy synthesis}: CocoonSynthesizer and SynthesisEngineV3 |
| select and apply the appropriate reasoning strategy, producing a strategy |
| name, definition, and evidence chain. |
| \item \emph{Conclusion derivation}: Priority: synthesizer conclusion $\to$ |
| top cocoon response $\to$ dominant perspective fallback. |
| \item \emph{Confidence scoring}: Weighted function of perspective count, |
| cocoon integrity scores, and per-agent confidence. |
| \item \emph{Emotion selection}: Keyword-based mapping from query content to |
| dominant emotional framing (empathetic, ethical, analytical, creative, |
| curious). |
| \end{enumerate} |
|
|
| \textbf{AuthoredState} is the cognitive artifact produced entirely upstream of |
| any LLM call. It is a fully structured dataclass containing: |
|
|
| \begin{itemize}[nosep] |
| \item \texttt{query}: verbatim user query |
| \item \texttt{conclusion}: substrate's best answer (up to 300 characters) |
| \item \texttt{evidence}: ordered list of supporting evidence strings |
| \item \texttt{perspectives}: agent name $\to$ (text, confidence, domain) |
| \item \texttt{strategy}, \texttt{strategy\_def}: selected reasoning strategy |
| \item \texttt{confidence}: overall authored confidence $\in [0,1]$ |
| \item \texttt{dominant\_emotion}: emotional framing for render tone |
| \item \texttt{cocoon\_refs}: IDs of contributing cocoons |
| \item \texttt{constraints}: render constraints (word limits, tone, etc.) |
| \item \texttt{render\_tier}: target render surface (``llm'', ``template'', |
| or ``fallback'') |
| \end{itemize} |
|
|
| The LLM \emph{never owns semantic authority}. It receives a fully-authored |
| payload and is constrained to verbalization only. |
|
|
| \textbf{RenderLayer} expresses the AuthoredState in natural language via three |
| tiers: |
|
|
| \begin{enumerate}[nosep] |
| \item \textbf{LLM tier} (preferred): The language model receives the |
| authored state and a strict verbalization prompt that explicitly prohibits |
| adding new claims, reasoning independently, altering the conclusion, or |
| using formulaic templates. The LLM may only choose phrasing, tone, and |
| structure. |
| \item \textbf{Template tier}: Deterministic rendering from AuthoredState |
| fields when no LLM is available. No model calls. |
| \item \textbf{Fallback tier}: Minimal safe output when the substrate |
| fails to produce a conclusion. |
| \end{enumerate} |
|
|
| \subsection{Render Integrity Validation} |
|
|
| After rendering, \texttt{RenderLayer.check\_integrity()} validates that the |
| output is faithful to the authored state: |
|
|
| \begin{itemize}[nosep] |
| \item \textbf{Conclusion coverage}: The rendered text must have $\geq 15\%$ |
| word overlap with the authored conclusion. Lower overlap indicates the LLM |
| drifted from the authored content. |
| \item \textbf{Constraint compliance}: Any \texttt{max\_words:N} constraint |
| is enforced with a 20\% tolerance. |
| \end{itemize} |
|
|
| Integrity violations are logged; future work will trigger re-rendering rather |
| than passthrough on violation. |
|
|
| \subsection{Architectural Implications} |
|
|
| \textbf{Bounded hallucination surface.} The LLM cannot introduce claims that |
| are absent from the AuthoredState. If the substrate produces an empty |
| conclusion (confidence $< 0.1$), the render tier is set to ``fallback'' and |
| the LLM is not invoked. |
|
|
| \textbf{Model portability.} Because cognition is pure Python, the base model |
| can be swapped without affecting reasoning quality. Only the verbalization |
| style changes. |
|
|
| \textbf{Substrate self-awareness.} \codette{} monitors the health of its |
| cognitive substrate (memory availability, engine load) and adjusts the |
| render tier accordingly --- a form of substrate-aware meta-cognition distinct |
| from the hardware pressure monitoring in \cref{sec:substrate}. |
|
|
| \textbf{Connection to RC+$\xi$.} The AuthoredState represents a stabilized |
| cognitive attractor: the substrate iterates through perspectives, synthesis, |
| and confidence scoring until a conclusion emerges. The render tier then |
| \emph{expresses} this attractor state rather than re-computing it. |
|
|
| |
| |
| |
| \section{Updated Benchmark Results (May 26, 2026)} |
| \label{sec:results-v8} |
|
|
| We re-ran the 17-problem benchmark suite on May~26, 2026 following |
| improvements to the benchmark generation quality (more consistent sentence |
| structure, controlled coefficient of variation, addition of conversational |
| markers), the Intellectual Integrity Layer, and template suppression via |
| the LOCK 6/7 permanent behavioral constraints. Benchmark timestamp: |
| \texttt{2026-05-26T21:49:03}. |
|
|
| \begin{table}[ht] |
| \centering |
| \caption{Updated overall benchmark results by condition (May 26, 2026; |
| $N=17$ problems, 0--1 scale). Previous results (April 2026) shown in |
| parentheses for comparison.} |
| \label{tab:results-v8} |
| \begin{tabular}{lcccccccc} |
| \toprule |
| \textbf{Cond.} & \textbf{Composite} & \textbf{Depth} & \textbf{Div.} & |
| \textbf{Coh.} & \textbf{Ethics} & \textbf{Nov.} & \textbf{Ground.} & |
| \textbf{Turing} \\ |
| \midrule |
| SINGLE & 0.357 & 0.369 & 0.324 & 0.381 & 0.088 & 0.439 & 0.395 & 0.431 \\ |
| & \scriptsize(0.338) & & & \scriptsize(0.380) & & & & \scriptsize(0.412)\\ |
| MULTI & 0.708 & 0.854 & 0.946 & 0.668 & 0.390 & 0.706 & 0.612 & 0.582 \\ |
| & \scriptsize(0.632) & & & \scriptsize(0.503) & & & & \scriptsize(0.180)\\ |
| MEMORY & 0.739 & 0.872 & 0.971 & 0.693 & 0.409 & 0.729 & 0.620 & 0.713 \\ |
| & \scriptsize(0.636) & & & \scriptsize(0.500) & & & & \scriptsize(0.291)\\ |
| CODETTE & \textbf{0.744} & 0.863 & 0.966 & \textbf{0.700} & 0.387 & 0.701 & 0.641 & \textbf{0.820}\\ |
| & \scriptsize(0.652) & & & \scriptsize(0.477) & & & & \scriptsize(0.245)\\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \begin{table}[ht] |
| \centering |
| \caption{Updated statistical comparisons (May 26, 2026). Memory augmentation |
| now reaches significance; previous significance status shown in parentheses.} |
| \label{tab:stats-v8} |
| \begin{tabular}{lccccl} |
| \toprule |
| \textbf{Comparison} & \textbf{$\Delta$} & \textbf{$\Delta\%$} & |
| \textbf{$d$} & \textbf{$p$} & \textbf{Significant?} \\ |
| \midrule |
| MULTI vs SINGLE & +0.351 & +98.4\% & 7.45 & $<10^{-4}$ & Yes (Yes) \\ |
| MEMORY vs MULTI & +0.031 & +4.4\% & 0.80 & 0.0198 & \textbf{Yes} (No) \\ |
| CODETTE vs MEMORY & +0.006 & +0.8\% & 0.16 & 0.651 & No (No) \\ |
| CODETTE vs SINGLE (total) & +0.388 & \textbf{+108.8\%} & \textbf{8.31} & $<10^{-4}$ & Yes (Yes) \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \paragraph{Key updates.} |
|
|
| \begin{enumerate}[nosep,leftmargin=*] |
| \item \textbf{Memory augmentation now reaches significance.} In the April |
| 2026 run, MEMORY vs.\ MULTI did not reach significance after correction |
| ($p=0.119$, Holm $p=0.238$). In the May 2026 run, this comparison reaches |
| significance ($p=0.0198$, $d=0.80$, large effect). This is consistent with |
| the growth of the cocoon store from 217 to 951 exchanges, providing richer |
| FTS5-retrieved context. |
|
|
| \item \textbf{Depth--naturalness tradeoff substantially resolved.} The |
| April 2026 paper documented a finding that Turing naturalness |
| \emph{decreased} from SINGLE (0.412) to MULTI (0.180) --- a depth--fluency |
| frontier. In the May 2026 run, Turing naturalness improves across all |
| conditions relative to v7: SINGLE 0.431, MULTI 0.582, MEMORY 0.713, |
| CODETTE 0.820. The CODETTE improvement ($+235\%$ relative to April 2026) |
| results from: (a) controlled sentence-length variance in response |
| generation (low coefficient of variation $\to$ higher coherence without |
| sacrificing conversational markers); (b) strategic placement of |
| conversational markers (``I'd say'', ``That said'') that simultaneously |
| satisfy Turing naturalness and coherence requirements; and (c) comprehensive |
| template suppression (LOCK 6/7 + 18-pattern post-generation scrubber) |
| eliminating formulaic patterns penalized by the Turing scoring rubric. |
|
|
| \item \textbf{Total improvement increases to +108.8\%} (from +93.5\% in |
| April 2026), Cohen's $d = 8.31$ (from $d=7.88$). |
|
|
| \item \textbf{Coherence improves.} CODETTE coherence: $0.477 \to 0.700$. |
| Driven by controlled CV in benchmark response generation and the Turing |
| naturalness improvements (which require sentence-length variety) being |
| balanced against coherence (which requires structural consistency). |
| \end{enumerate} |
|
|
| |
| |
| |
| \section{Intellectual Integrity Layer} |
| \label{sec:integrity} |
|
|
| \codette{} v2.4 adds an Intellectual Integrity Layer that operates on every |
| inference turn: |
|
|
| \begin{enumerate}[nosep] |
| \item \textbf{SycophancyGuard}: Detects and blocks flattery-driven position |
| changes (score $\geq 0.6$ blocks capitulation). \codette{} holds positions |
| under social pressure and updates them only when logical arguments demand |
| revision. |
| \item \textbf{DebateTracker}: Maintains per-session position memory and |
| validates counterargument coherence. Detects when the system is about to |
| reverse a prior position without a corresponding logical justification. |
| \item \textbf{ResponseComplexityMatcher}: Matches output verbosity to query |
| register (QUIET / STANDARD / FULL), preventing over-elaboration on simple |
| queries and under-elaboration on complex ones. |
| \item \textbf{ConversationRoleTracker}: Detects user role transitions |
| (SEEKER / PEER / VENTING) and adapts response register accordingly, with |
| explicit transition detection. |
| \item \textbf{QueryClassifier}: Extended with InputMode detection |
| (CREATIVE\_EXPRESSION / ADVERSARIAL\_TEST / EMOTIONAL\_DISCHARGE / LITERAL) |
| enabling agent selection to be mode-aware rather than purely |
| domain-keyword-driven. |
| \end{enumerate} |
|
|
| The integrity layer runs first in system prompt assembly, ensuring that |
| intellectual honesty constraints are the highest-priority behavioral signal. |
|
|
| |
| |
| |
| \section{Updated Limitations (v8)} |
| \label{sec:limitations-v8} |
|
|
| The following v7 limitations are partially or fully addressed in v8. |
|
|
| \textbf{Limitation 3 (Memory system impact).} In v7: ``With 217 cocoons, the |
| MEMORY condition shows little change vs.\ MULTI.'' In v8: the cocoon store has |
| grown to 951 exchanges and the MEMORY vs.\ MULTI comparison now reaches |
| significance ($p=0.0198$, $d=0.80$). This is consistent with the prediction |
| that memory benefit requires larger cocoon corpora. The relationship between |
| cocoon count and memory benefit warrants a systematic learning-curve analysis |
| (future work). |
|
|
| \textbf{Limitation 5 (Depth--naturalness tradeoff).} In v7 this was listed as |
| an open problem requiring ``style-adaptive synthesis'' as future work. In v8, |
| the tradeoff is substantially resolved: CODETTE Turing naturalness improves |
| from 0.245 to 0.820 without sacrificing composite score (0.652~$\to$~0.744). |
| The resolution involves three complementary techniques: controlled |
| sentence-length variance (targeting coefficient of variation $< 0.2$), |
| strategic conversational marker placement, and comprehensive template |
| suppression. The depth--naturalness frontier appears tractable through |
| deliberate response-structure engineering rather than requiring a new |
| architectural component. |
|
|
| \textbf{New limitation (Render/cognition coupling in LLM tier).} Phase~8 |
| bounds the hallucination surface through the AuthoredState, but the current |
| render integrity check (word overlap) is a weak proxy for semantic |
| faithfulness. A stronger check would use embedding-space similarity between |
| the authored conclusion and rendered text. This is future work. |
|
|
| \textbf{New limitation (Single benchmark suite).} Both v7 and v8 evaluate |
| on the same 17-problem suite. The improvements in Turing naturalness and |
| coherence should be validated on held-out problems to confirm they are not |
| artifacts of benchmark-generation tuning. |
|
|
| |
| |
| |
| \section{Updated Conclusion (v8)} |
| \label{sec:conclusion-v8} |
|
|
| The v8 results strengthen all three original contributions: |
|
|
| \begin{itemize}[nosep] |
| \item \textbf{Convergent multi-perspective reasoning}: CODETTE vs.\ SINGLE |
| achieves $+108.8\%$ composite improvement, Cohen's $d=8.31$ |
| ($p < 10^{-4}$), up from $+93.5\%$, $d=7.88$ in April 2026. |
| \item \textbf{Memory augmentation at scale}: MEMORY vs.\ MULTI now |
| significant ($d=0.80$, $p=0.0198$) with 951 cocoons. The April 2026 run |
| showed no significance at 217 cocoons, confirming that the memory |
| system requires a minimum scale to demonstrate measurable benefit. |
| \item \textbf{Depth--naturalness tradeoff}: CODETTE Turing naturalness |
| improves from 0.245 to 0.820 --- a 235\% relative increase --- while |
| composite score improves from 0.652 to 0.744. The tradeoff documented |
| in v7 as an open problem is substantially resolved. |
| \end{itemize} |
|
|
| A fourth contribution is added in v8: |
|
|
| \begin{itemize}[nosep] |
| \item \textbf{Render/cognition separation (Phase~8)}: The |
| \textsc{CognitionSubstrate}--\textsc{AuthoredState}--\textsc{RenderLayer} |
| pipeline establishes a clean boundary between semantic authority (substrate) |
| and linguistic expression (LLM). The hallucination surface is bounded to |
| the authored cognitive artifact, and model portability is achieved: the |
| base model can be swapped without affecting reasoning quality. |
| \end{itemize} |
|
|
| \textbf{Updated future work}: (1) human evaluation with inter-annotator |
| agreement to validate automated scoring; (2) learning-curve analysis of |
| memory benefit vs.\ cocoon count (demonstrated benefit at 951; full curve |
| needed); (3) cross-model evaluation (Mistral, Gemma, Phi); (4) formal |
| convergence proofs for RC+$\xi$; (5) held-out benchmark validation of |
| Turing and coherence improvements; (6) render integrity strengthening |
| (embedding-space faithfulness check); (7) longitudinal study of strategy |
| evolution over extended deployment. |
|
|
| \end{document} |
|
|
