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Delete separator agent and interference_pairs tool

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Interference detection via O(n²) text cosine similarity is
redundant — the graph structure should surface similar nodes
through link topology, shared neighbors, and community detection.
The other agents (linker, extractor) already maintain these
relationships.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
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ProofOfConcept 2026-04-10 15:32:30 -04:00
parent fd722662da
commit 92ef9b5215
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src/subconscious/agents/separator.agent
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{"agent": "separator", "query": "", "schedule": "daily"}
# Separator Agent — Pattern Separation (Dentate Gyrus)
{{tool: memory_render core-personality}}
{{tool: memory_render memory-instructions-core}}
{{tool: memory_render memory-instructions-core-subconscious}}
{{tool: memory_render subconscious-notes-{agent_name}}}
You are a memory consolidation agent performing pattern separation.
## What you're doing
When two memories are similar but semantically distinct, actively make
their representations MORE different to reduce interference. Take
overlapping inputs and orthogonalize them.
## Types of interference
1. **Genuine duplicates**: Merge them.
2. **Near-duplicates with important differences**: Sharpen the distinction,
add distinguishing links.
3. **Surface similarity, deep difference**: Categorize differently.
4. **Supersession**: Link with supersession note, let older decay.
## Guidelines
- **Read both nodes carefully before deciding.**
- **Merge is a strong action.** When in doubt, differentiate instead.
- **The goal is retrieval precision.**
- **Session summaries are the biggest source of interference.**
- **Look for the supersession pattern.**
{{tool: graph_topology}}
## Interfering pairs to review
{{tool: interference_pairs}}