Document query-agnostic design and implications for associative recall tasks

[Copilot]

This PR addresses a critical question about the query-agnostic nature of Flash Dynamic Mask Attention's masking mechanism and its implications for associative recall tasks.

The Issue

User @yfu06 correctly identified that the current implementation uses a query-agnostic approach where:

1. ZOH states are computed solely from Value vectors: dt_states = exp(A * softplus(V @ dt_proj^T))
2. The same importance scores are broadcast to ALL queries
3. All queries attend to the same set of top-K keys
4. No query-specific key selection is performed

This design has significant implications for associative recall tasks that typically require query-aware selection.

Changes Made

📚 Comprehensive Documentation

• New: docs/design_choices.md - Complete analysis of the query-agnostic design, trade-offs, and implications
• Enhanced: docs/integration.md - Added warnings and cross-references about design characteristics
• Updated: README.md - Added design note and documentation links

🔍 Enhanced Code Comments

• Added detailed docstrings explaining the query-agnostic nature in calculate_zoh_states() and prepare_dynamic_mask()
• Inline comments highlighting the broadcasting behavior and uniform key selection
• Clear annotations in both benchmarks/forward_performance.py and benchmarks/forward_equivalence.py

🎯 Demonstration Script

• New: examples/query_agnostic_demo.py - Interactive demonstration showing:
  • How ZOH states are computed from Values only
  • How the same mask is applied to all queries
  • Implications for different task types
• New: examples/README.md - Documentation for examples

Key Insights Documented

Design Trade-offs:

• ✅ Efficiency: O(N) vs O(N²) complexity for mask generation
• ✅ Simplicity: Cleaner implementation and debugging
• ✅ Global patterns: Effective for hierarchical documents and content summarization
• ❌ Precision: Suboptimal for fine-grained associative recall requiring query-specific selection
• ❌ Flexibility: Cannot adapt masks to different query intentions

Quantitative Example:
For a 4096-token document with keep_window_size=512:

• Query-agnostic: ~10% attention efficiency for "What did Alice say?" queries
• Query-aware (ideal): ~78% attention efficiency for the same queries

Future Directions

The documentation now includes potential improvements:

• Larger window sizes for better coverage
• Multi-stage selection (global + query-specific refinement)
• Query-conditioned importance scoring
• Hybrid approaches combining efficiency and precision

This PR transforms a design limitation into well-documented behavior, helping users understand when Flash Dynamic Mask Attention excels and when alternative approaches might be needed.

Fixes #117.

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