MemCode: Discrete Semantic Representations for Long-Term Agent Memory
We propose MemCode, a long-term agent memory framework that couples retrieval-oriented writing with discrete associative retrieval.
ProofPaper ↗
Key points
- Long-term memory remains a fundamental bottleneck for large language model (LLM) agents across sessions.
- Existing systems typically adopt a write-then-retrieve paradigm, storing context-dependent dialogue fragments and retrieving them via nearest-neighbor search in a continuous embedding space.
- MemCode rewrites dialogue into self-contained atomic memory units (AMUs), maps AMUs into a learnable codebook with isomorphic semantic quantization (ISQ), and uses codebook co-occurrence topology as a retrieval path complementary to vector search.
- For long-term memory stores, MemCode separates online atomic writes from asynchronous offline consolidation.
Sources (1)
- [1]MemCode: Discrete Semantic Representations for Long-Term Agent MemoryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 02:51 AM
We propose MemCode, a long-term agent memory framework that couples retrieval-oriented writing with discrete associative retrieval.
Long-term memory remains a fundamental bottleneck for large language model (LLM) agents across sessions.
Extractive summary: sentences quoted from the sources.
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