Self-Rehearsal Phase for Vector-Native Reinforcement in Large Vector Models (LVMs)

Self-Rehearsal Phase for Vector-Native Reinforcement in Large Vector Models (LVMs)

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Trent Carter + ChatGPT5

10/24/25

Abstract

_Self-Rehearsal_ is a post-reinforcement training phase for Large Vector Models (LVMs) that strengthens conceptual coherence through autonomous vector replay and refinement.

After reinforcement fine-tuning (e.g., Echo-Loop or RL-based alignment), the model engages in a brief self-supervised rehearsal epoch in which it re-examines its own most recent concept transitions and re-derives their latent relationships.

Instead of relying on new external prompts, the model uses the embeddings and responses generated during reinforcement as its own rehearsal set. During this process, the LVM re-computes internal similarity gradients between its predicted and target concept vectors, consolidating high-coherence patterns and attenuating unstable transitions. The goal is to provide an analog of “sleep-phase consolidation” for concept-based systems—strengthening recently-reinforced pathways without new data or supervision. Empirically, Self-Rehearsal is expected to:

Improve stability of latent vector dynamics across conversational turns.

Reduce catastrophic forgetting of prior conceptual alignments.

Increase coherence in long-range semantic reasoning.

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Functional Requirements ID Category Description SR-1 Triggering Condition Activated automatically after a reinforcement epoch (Echo-Loop, RLHF, or lane-specific reward optimization). SR-2 Input Dataset Uses cached _concept-response pairs_ (C_t, Ĉ_t+1) and their cosine similarity deltas from the latest reinforcement run. SR-3 Objective Function Minimize drift between replayed and original reinforced vectors:  $begin:math:text$\mathcal{L}_{reh} = 1 - \cos(v_{orig}, v_{replay}) + \lambda  SR-4 Gradient Flow Gradients back-propagate only through _vector-mapping parameters_ (not text decoders); weights in semantic-memory layers are updated with low-learning-rate consolidation. SR-5 Sampling Policy Rehearse top-K percent (e.g., K = 10–20%) of high-reward concept pairs per lane, plus a stochastic mix of low-reward outliers for diversity. SR-6 Iteration Count Default 1–3 mini-epochs or until cosine drift < 0.02 for 95% of replayed pairs. SR-7 Integration Point Implemented as an optional _P15.5_ step between P15 (LNSP Training) and P16 (Multi-RAG Query) in the existing pipeline. SR-8 Persistence Stores per-lane rehearsal metrics: mean cosine Δ, vector norm Δ, concept stability index; appended to echo_validation.log. SR-9 Safety Guard Prevents mode collapse by imposing variance floor σ² ≥ baseline_σ²×0.85; aborts rehearsal if collapse detected.

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Implementation Notes

• Location: app/pipeline/self_rehearsal.py

• Dependencies: uses torch.no_grad() replay cache, FAISS or pgvector cosine retrieval, and the existing Echo validation metrics.

• Invocation:

python -m app.pipeline.self_rehearsal --lane science-fact --epochs 2

• Outputs:

• Updated vector weights in latent-space model checkpoint.

• JSON metrics in outputs/self_rehearsal/.

• Optional visualization: drift histograms via matplotlib.

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Evaluation Metrics Metric Description Target Δ Cosine Stability Mean cosine distance between original and replayed vectors ≤ 0.02 Recall@K Retention Fraction of concepts retaining top-K neighborhood consistency ≥ 0.95 Echo Score Drift Change in Echo Score after rehearsal ≤ ±1% Entropy Floor Check Ensure latent diversity not reduced > 15% Pass/Fail

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Rationale Self-Rehearsal acts as vector-space consolidation, analogous to hippocampal replay in biological systems. It provides the model a chance to internalize reinforced behaviors without new input data, leveraging its own activity traces as training stimuli. This not only improves stability and semantic recall but also yields smoother downstream inference by aligning newly-reinforced concept mappings with long-term latent geometry.

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two clean, formal examples:

AI context:

During self-rehearsal, the large vector model replays its recently reinforced concept transitions to consolidate semantic stability in latent space.

Biology context:

In the human brain, rehearsal occurs during sleep when neural circuits spontaneously reactivate recent experiences to strengthen long-term memory traces.

Would you like me to version this as an internal LNSP-RFC (Request for Comments) document — e.g., docs/RFCs/lnsp_rfc_004_self_rehearsal.md — with headers, author/date metadata, and Markdown formatting suitable for your repo?