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research(memory): TiMem temporal-hierarchical memory tree — complexity-aware recall, no over-summarization (arXiv:2601.02845) #2262

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Summary

TiMem (arXiv:2601.02845, Jan 2026) — Temporal Memory Tree (TMT) that organizes conversational history into hierarchical levels: raw observations at leaves, progressively abstracted persona representations at higher levels. Consolidation is driven by semantic similarity (no fine-tuning required). Recall uses a complexity-aware selector that balances precision vs. latency based on query difficulty.

Applicability to Zeph

Zeph's zeph-memory handles summarization and temporal decay as a flat pipeline. TiMem's tree structure addresses two known gaps:

  1. Over-summarization of recent events: current compaction can collapse recent turns that should stay granular — tree leaves preserve them
  2. Under-retention of recurrent persona signals: persona facts that appear across multiple sessions get diluted; TMT's upper levels stabilize them
  3. Complexity-aware recall: aligns with Zeph's multi-model design principle — cheap model (gpt-4o-mini) for simple leaf lookups, quality model for deep consolidation tree traversal

Implementation Sketch

  • Schema: add a memory_tree SQLite table with level, parent_id, content, consolidated_at fields
  • Consolidation: run as a background sweep (similar to existing tier promotion loop in zeph_memory::tiers) — group semantically similar recent turns and consolidate to parent nodes
  • Recall: route queries by complexity (keyword → leaf lookup; semantic → tree traversal up to level N)
  • Complexity: MEDIUM — tree schema + consolidation scheduler; no model changes needed

Related Issues

Source: https://arxiv.org/abs/2601.02845

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    P3Research — medium-high complexitymemoryzeph-memory crate (SQLite)researchResearch-driven improvement

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