Real-Time Introspective Compression for Transformers
10 days ago
- #Introspective Compression
- #Transformer Models
- #Metacognitive Systems
- Transformer-based LLMs lack introspection and have ephemeral cognition, limiting their interpretability and debugging.
- Proposed solution: high-efficiency introspective compression using a learned latent manifold of transformer states.
- System includes a main transformer, sidecar encoder, and decoder to compress and reconstruct internal states.
- Three architectural strategies for state compression: layer-specific, grouped layer, and unified encoder-decoder.
- Specialized KV cache compression using convolutional layers for sequence-aware compression.
- Applications include reasoning backtracking, thought trajectory optimization, and causal debugging.
- Potential for metacognitive operating systems enabling reversible cognitive states and iterative cognition.
- Future work includes attention-based sidecar architectures and integration of reinforcement learning.