Personalized LLM Agent

Core Components of a True Personal Agent (2025 Blueprint)

A small specialist LLM (1–7B) that decides what to store, compress, retrieve, forget
LongLLMLingua + learned retrieval policy (RL-trained)

1M–10M token native context → No more compression hacks needed for personal history
100–500 pages max per session ==> Entire life archive (~7,500 pages)
Compute Cost: Quadratic scaling → $0.01–0.10/M tokens ==> Optimized MoE → 2–5x cheaper inference
Accuracy: "Lost in middle" errors ==> Uniform attention; <5% degradation

Online gradient updates stable on consumer hardware → True lifelong learning without catastrophic forgetting
Personal Agents: Weekly batch updates ==> Real-time tweaks on phone
Hardware Needs: Data center GPUs ==> Stable on 16–32GB VRAM (consumer)
Forgetting Rate: 50–90% on new tasks ==> <5% via replay + regularization

Sparse + content-addressable memory (like Transformer-XL + Hippocampus models) → Human-level episodic → semantic conversion
Sparse = activate only key neurons (e.g., 1–10% of params); content-addressable = query by meaning, not position (like brain's hippocampus indexing events).
Memory Efficiency: Dense storage → high VRAM ==> Sparse → milliwatt-level on devices
Retrieval Speed: Keyword search ==> Semantic query → instant recall
Conversion Fidelity: 70–80% accuracy ==> 95%+ human-like abstraction

Audio → transcription → raw episodic chunk
Memory Controller reads it → decides:
- Compress meeting notes → store in semantic memory
- Extract 3 new facts about your preferences → update LoRA
- Tag emotionally important moments → higher retrieval weight
User later asks "What did Anna say about the budget?"
- → Memory Controller runs a 1–2-second query → pulls only 2 relevant chunks + 1 summary
- → Injects <4k tokens into base model → perfect recall

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