📝 docs: update XAttention BSA Policy with benchmarks and memory management

Add new sections to xattn_bsa_policy_design.md:
- Performance benchmarks: 128K context comparison (Full vs XAttn BSA)
- Density trend analysis across chunks
- Memory leak issue and fix (64GB -> 4GB reduction)
- Memory monitoring guide with gpu-monitor agent
- Density statistics API documentation
- Known issues and optimization directions

Update CLAUDE.md description to reflect new content.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

CLAUDE.md

@@ -17,7 +17,7 @@ Nano-vLLM is a lightweight vLLM implementation (~1,200 lines) for fast offline L
 | [`docs/xattention_algorithm_guide.md`](docs/xattention_algorithm_guide.md) | XAttention 算法详解: stride reshape、Triton kernels、BSA 依赖、块选择算法 |
 | [`docs/xattn_kernels_guide.md`](docs/xattn_kernels_guide.md) | XAttention Triton kernels: flat_group_gemm (反对角线求和)、softmax_fuse_block_sum (block 聚合) |
 | [`docs/xattn_chunked_prefill.md`](docs/xattn_chunked_prefill.md) | XAttention chunked prefill: API、使用方式、一致性要求 |
-| [`docs/xattn_bsa_policy_design.md`](docs/xattn_bsa_policy_design.md) | XAttention BSA Policy 设计: select_blocks 算法、majority voting、compute_chunked_prefill |
+| [`docs/xattn_bsa_policy_design.md`](docs/xattn_bsa_policy_design.md) | XAttention BSA Policy: 算法设计、性能基准(128K)、内存管理、density 统计 |
 | [`docs/block_sparse_attn_interface.md`](docs/block_sparse_attn_interface.md) | BSA (Block Sparse Attention) 接口文档: 函数签名、使用示例、约束条件 |
 | [`docs/debugging_guide.md`](docs/debugging_guide.md) | PyTorch hooks for debugging, hook positions, tensor comparison, memory profiling |
 | [`docs/optimization_guide.md`](docs/optimization_guide.md) | Performance optimizations: sgDMA (15x), Triton merge (4.3x), N-way pipeline (2x) |