Self-Modifying Attention Blocks
Symbolic Override and Live Rerouting of Attention Dynamics
1. Soft Introduction
Transformer attention is normally fixed: weights are computed based on learned matrices and dot-product similarity. Once trained, this logic remains unchanged during inference. Self-modifying attention blocks change that paradigm. They introduce conditional, symbolic, or context-triggered adaptations to the attention computation path—creating an active, reconfigurable attention space.
2. Engineering Definition
A self-modifying attention block is an attention mechanism whose configuration or behavior may change dynamically at inference time. This includes:
- Runtime rerouting of attention heads
- Live alteration of query-key weighting or gating
- Activation of alternate attention subpaths
- Symbol-controlled toggles over multi-head layout or pattern
3. Architectural Layers
Self-modification requires new modularity within attention blocks:
- Gating Subnet: controls symbolic triggers, head routing, and reentry paths
- Modifier Memory: stores alternate configurations or layer deltas
- Control Layer: reads symbolic input (supersymbols, gyrator state, plan injection)
- Rebuild Logic: rewrites parts of the attention logic or path selection
4. Modes of Attention Modification
| Mode | Description | Effect |
|---|
| Path Override | Replace attention logic with alternate subblock | New attention shape or policy |
| Gated Routing | Enable/disable heads based on symbolic input | Selective focus or blind spots |
| Delta Modulation | Apply symbolic perturbations to attention weights | Controlled distortion of focus |
| Dynamic Merge | Merge heads conditionally into fused pathways | Compressed or pooled focus |
| Recurrent Drift | Attention heads evolve slowly across turns | Context-sensitive persistent change |
5. Symbolic Trigger Mechanisms
- Supersymbol Activation: symbolic token triggers head gating or override
- Gyrator Context Injection: injected context alters focus preference
- Cache Feedback: cache eviction or loop triggers cause refocusing
- Turn-based Indexing: head pattern varies per turn index
- Plan Marker Response: symbolic plan triggers conditional head behavior
6. Memory and Reversibility
Each attention mod must support:
- Snapshot and Revert: undo changes if harmful
- Switch Logs: record attention state shifts per token step
- Delta Cache: persistent patches stored in Gyrator RAM
7. Behavior Evaluation Metrics
Modified attention blocks can be evaluated via:
- Δ in attention entropy over recent turns
- Improved thread retention or topic coherence
- Reduced attention drift on symbolic anchors
- Latency vs performance tradeoff tests
- Plan path alignment (does attention match symbolic roadmap?)
8. Integration with Supersymbol Tokenizer
Token-level triggers include:
- Symbolic Gating Tokens: change head activation mask
- Plan Tags: associate attention policy with supersymbol plans
- Context Flags: select attention memory strategy based on symbolic presence
9. Engineering Requirements
- Attention blocks must be built with modular hooks (for runtime insertion)
- Heads must be independently routable or suppressible
- Weights must be mutable post-initialization
- Symbol injection must occur before attention calc (token prepass or gyrator)
- Instrumentation must exist for per-head inspection and reassembly
10. Compatibility Modes
| Compatibility | Condition | Strategy |
|---|
| Legacy Models | No built-in symbolic layers | Wrap with symbolic controller, override head mask externally |
| Transformer w/ Hooks | PyTorch attention blocks modifiable | Inject symbolic gates inline |
| TranSymbolics Native | Built with attention resolver layer | Full symbolic attention runtime active |
11. Future Extensions
- Agentic head agents (mini-programs for each head)
- Head inheritance (patterns that evolve per session)
- Latent symbolic constraints on attention boundaries
- Multi-model shared attention routing (joint symbolic field)