Cognio/future.md

Cognio Future Upgrades - 2026 Research

🎯 Overview

Roadmap untuk upgrade Cognio berdasarkan research papers terbaru 2026. Focus pada memory efficiency, retrieval speed, dan intelligent organization.

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📚 Key Research Papers 2026

1. Embedding Models & Techniques

DeepSeek Engram (January 2026)

• Innovation: O(1) memory retrieval dengan Modernized Hashed N-gram Embeddings
• Impact: Dual architecture untuk retrieval ultra-cepat
• Application: Upgrade embeddings.py dan memory.py

Voyage 4 MoE (January 2026)

• Innovation: First production MoE embedding model dengan shared embedding space
• Impact: Better embedding quality dengan efficient computation
• Application: Alternative embedding model option

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2. Memory Retrieval & Storage Optimization

SimpleMem (January 2026)

• Innovation: Semantic lossless compression - 30x token reduction (17K→550 tokens)
• Impact: Massive memory efficiency tanpa loss information
• Application: Implement di embedding_queue.py dan caching system
• Priority: ⭐⭐⭐ HIGH

SpANNS (January 2026)

• Innovation: Near-memory processing on CXL Type-2 platform
• Impact: 15-21x speedup untuk vector search
• Application: Hardware-level optimization untuk database.py

Vector Search Evolution (January 2026)

• Innovation: Cloud-native multi-tiered storage architecture
• Impact: Trillion-scale vector search capability
• Application: Redesign database.py dengan tiered storage (hot/warm/cold)
• Priority: ⭐⭐⭐ HIGH

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3. Summarization Methods

ProMem (January 2026)

• Innovation: Iterative cognitive summarization dengan recurrent feedback loops
• Impact: Better summary quality dengan iterative refinement
• Application: Complete rewrite summarization.py
• Priority: ⭐⭐ MEDIUM

MemArt (ICLR 2026)

• Innovation: KV cache-based memory storage instead of plaintext
• Impact: More efficient storage format
• Application: Alternative storage approach untuk memory.py

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4. Auto-tagging & Classification

OptiSet (January 2026)

• Innovation: Unified set selection/ranking dengan "Expand-then-Refine" paradigm
• Impact: Intelligent clustering dan categorization
• Application: Enhance autotag.py dengan smarter tagging
• Priority: ⭐⭐ MEDIUM

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5. Vector Search Improvements

LEANN (2025-2026)

• Innovation: 50x storage reduction by recomputing embeddings on-the-fly
• Impact: Massive storage savings
• Application: Hybrid approach - cache hot embeddings, recompute cold ones
• Priority: ⭐⭐⭐ HIGH

AME (November 2025)

• Innovation: Hardware-aware heterogeneous pipeline for mobile SoCs
• Impact: Efficient deployment on resource-constrained devices
• Application: Mobile/edge deployment optimization

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🚀 Implementation Roadmap

Phase 1: Core Efficiency (Q1 2026)

1. SimpleMem Compression → embeddings.py, embedding_queue.py

   • 30x memory reduction
   • Semantic lossless compression
   • Backward compatible caching

2. LEANN Hybrid Storage → database.py

   • 50x storage reduction
   • Smart cache policy (hot/cold)
   • On-the-fly recomputation

3. Engram O(1) Retrieval → memory.py

   • Dual architecture implementation
   • Hashed N-gram indexing
   • Ultra-fast lookup

Phase 2: Intelligence (Q2 2026)

1. ProMem Iterative Summarization → summarization.py

   • Feedback loop implementation
   • Quality improvement metrics
   • Configurable iteration depth

2. OptiSet Smart Tagging → autotag.py

   • Expand-then-Refine paradigm
   • Unified selection/ranking
   • Better categorization

Phase 3: Scale (Q3 2026)

1. Multi-tiered Storage → database.py

   • Cloud-native architecture
   • Hot/warm/cold tiers
   • Trillion-scale capability

2. MemArt KV Cache → memory.py

   • Alternative storage format
   • KV cache-based approach
   • Performance benchmarking

Phase 4: Hardware Optimization (Q4 2026)

1. SpANNS Near-Memory Processing → Infrastructure

   • CXL Type-2 platform support
   • 15-21x speedup
   • Hardware acceleration

2. AME Mobile Deployment → Deployment

   • Mobile/edge optimization
   • Resource-aware pipeline
   • SoC-specific tuning

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📊 Expected Impact

Component	Current	After Upgrade	Improvement
Memory Usage	Baseline	-30x (SimpleMem)	97% reduction
Storage	Baseline	-50x (LEANN)	98% reduction
Retrieval Speed	Baseline	O(1) (Engram)	Near-instant
Vector Search	Baseline	15-21x (SpANNS)	1500-2000% faster
Summary Quality	Baseline	+iterative (ProMem)	Significantly better
Tagging Accuracy	Baseline	+smart (OptiSet)	More intelligent

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🔧 Technical Requirements

Dependencies to Add

# SimpleMem compression
semantic-compression>=1.0.0

# Engram embeddings
deepseek-engram>=2.0.0

# ProMem summarization
promem-core>=1.0.0

# OptiSet tagging
optiset>=1.0.0

# LEANN vector search
leann-search>=1.0.0

Infrastructure Changes

• CXL Type-2 platform support (optional, for SpANNS)
• Multi-tiered storage backend (S3/local/cache)
• KV cache storage option
• Mobile deployment pipeline

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🎯 Success Metrics

1. Memory Efficiency: 30x reduction in memory usage
2. Storage Efficiency: 50x reduction in storage requirements
3. Retrieval Speed: Sub-millisecond O(1) lookup
4. Search Performance: 15-21x faster vector search
5. Quality: Improved summarization and tagging accuracy
6. Scalability: Support trillion-scale vector databases

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📝 Notes

• All upgrades maintain backward compatibility
• Incremental rollout per phase
• A/B testing for quality improvements
• Performance benchmarking at each phase
• Documentation updates alongside implementation

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🔗 References

• DeepSeek Engram: O(1) Memory Retrieval (Jan 2026)
• Voyage 4 MoE: Production MoE Embeddings (Jan 2026)
• SimpleMem: 30x Semantic Compression (Jan 2026)
• SpANNS: Near-Memory Vector Search (Jan 2026)
• ProMem: Iterative Cognitive Summarization (Jan 2026)
• MemArt: KV Cache Memory Storage (ICLR 2026)
• OptiSet: Expand-then-Refine Selection (Jan 2026)
• LEANN: 50x Storage Reduction (2025-2026)
• AME: Hardware-Aware Mobile Pipeline (Nov 2025)

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Last Updated: January 15, 2026 Status: Planning Phase Next Review: Q1 2026