Selective Memory for Artificial Intelligence: Write-Time Gating with Hierarchical Archiving

Abstract

Retrieval-augmented generation stores all content indiscriminately, degrading accuracy as noise accumulates. Parametric approaches compress knowledge into weights, precluding selective updates. Neither mirrors biological memory, which gates encoding based on salience and archives rather than deletes superseded information. We introduce write-time gating that filters incoming knowledge objects using composite salience scores (source reputation, novelty, reliability) while maintaining version chains that preserve prior states. Using real LLM evaluation without oracle access to quality labels, write gating achieves 100 percent accuracy versus 13 percent for ungated stores. The critical finding emerges under distractor scaling: at 8:1 distractor ratios, read-time filtering (Self-RAG) collapses to 0 percent while write gating maintains 100 percent, revealing a structural advantage of write-time over read-time curation. Validation on Wikipedia (20 entities), procedurally generated pharmacology data, and 2026 arXiv papers confirms these findings. The gating advantage scales inversely with parametric memory support: +25pp for Wikipedia, +48pp for post-cutoff arXiv, +65pp for procedural data with zero training knowledge. Signal ablation confirms the method does not depend on oracle-correlated metadata. Write gating matches Self-RAG accuracy at one-ninth the query-time cost.

Figures (8)

• Figure 1: Complementary Learning Systems in biology and AI. The brain uses fast hippocampal storage for specific episodes and slow neocortical storage for semantic patterns. Our architecture mirrors this with discrete Knowledge Objects (fast, addressable) complementing LLM weights (slow, distributed).
• Figure 2: Write-time salience gating architecture. Incoming knowledge objects are scored on three observable signals (reputation, novelty, source reliability) without oracle access. Objects above threshold $\tau$ enter the active store; objects below are archived in cold storage. On our benchmark: 50 KOs $\rightarrow$ 13 admitted $\rightarrow$ 100% accuracy (vs 13.3% ungated).
• Figure 3: Version chains preserve temporal history through supersession links rather than overwrites. When facts update, prior versions are archived with pointers, enabling temporal queries that overwrite-based systems cannot answer.
• Figure 4: Accuracy versus distractor ratio. Ungated retrieval collapses monotonically. Self-RAG maintains a plateau through 6:1 before catastrophic collapse at 8:1. Write gating remains constant at 100% across all tested ratios. The shaded region indicates noise levels plausible for web-scale retrieval.
• Figure 5: Four-way method comparison at 4:1 distractor ratio. Write gating outperforms Self-RAG by $+6.2$pp. Combining both methods degrades to Self-RAG's accuracy---the critic introduces false negatives in an already-clean store.