SuperLocalMemory V3.3: The Living Brain
Summary: arXiv:2604.04514v1 Announce Type: new
Abstract
AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago. Existing memory systems store text in vector databases with single-channel retrieval, require cloud LLMs for core operations, and implement none of the cognitive processes that make human memory effective.
We present SuperLocalMemory V3.3 (“The Living Brain”), a local-first agent memory system implementing the full cognitive memory taxonomy with mathematical lifecycle dynamics. Building on the information-geometric foundations of V3.2 (arXiv:2603.14588), we introduce five contributions:
- Fisher-Rao Quantization-Aware Distance (FRQAD): A new metric on the Gaussian statistical manifold achieving 100% precision at preferring high-fidelity embeddings over quantized ones (vs 85.6% for cosine), with zero prior art.
- Ebbinghaus Adaptive Forgetting: The first mathematical forgetting curve in local agent memory coupled to progressive embedding compression, achieving 6.7x discriminative power.
- 7-channel Cognitive Retrieval: Spanning semantic, keyword, entity graph, temporal, spreading activation, consolidation, and Hopfield associative channels, achieving 70.4% on LoCoMo in zero-LLM Mode A.
- Memory Parameterization: Implementing Long-Term Implicit memory via soft prompts.
- Zero-Friction Auto-Cognitive Pipeline: Automating the complete memory lifecycle.
Performance Insights
On the LoCoMo benchmark, SuperLocalMemory V3.3 achieves a score of 70.4% in Mode A (zero-LLM), with improvements of +23.8pp on multi-hop and +12.7pp on adversarial tasks. In comparison, V3.2 achieved 74.8% in Mode A and 87.7% in Mode C, with the 4.4pp gap reflecting a deliberate architectural trade-off to enhance performance in specific areas.
Open Source and Accessibility
SuperLocalMemory V3.3 is open source under the Elastic License 2.0, ensuring that it remains accessible to developers and researchers alike. It operates entirely on CPU, making it lightweight and easy to implement across various systems. The project has already garnered significant attention, with over 5,000 monthly downloads, indicating a growing interest in biologically-inspired memory systems for AI agents.
Conclusion
The introduction of SuperLocalMemory V3.3 marks a significant advancement in the field of AI memory systems. By incorporating biologically-inspired forgetting, cognitive quantization, and multi-channel retrieval, this new architecture aims to bridge the gap between human cognitive processes and artificial intelligence. As AI continues to evolve, innovations like these will play a crucial role in enhancing the effectiveness and usability of coding agents.