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Oct 30
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PapersBuildersSelected10:41
FlatCake: Flat KVCache Storage for Faster LLM Serving

The key insight is that KV blocks are write-once and immutable, so caching hierarchies designed for general workloads actively hurt LLM serving — placing blocks on their final medium at creation outperforms migration-based designs. Engineers building or tuning inference infrastructure should evaluate flat layouts and GPUDirect Storage paths against Mooncake-style tiers, especially for agentic sessions with multi-gigabyte prefixes. The benchmark comparisons against Mooncake and Mooncake-GDS provide concrete baselines for such evaluations.

arXiv: DeepSeek@Xin Huang et al. (10)Source
PapersBuildersSelected10:41
Soft-MTP Fixes Multi-Token Prediction Training via KL Gradient Recovery

The KL gradient-recovery trick recovers exact KL divergence gradients from cross-entropy gradients without vocabulary-sized logit matrices, staying compatible with standard fused kernels — a practical detail most MTP implementations overlook. Teams pre-training models with MTP, including DeepSeek-style architectures, can adopt it as a drop-in change with no parameter or throughput cost. The repo offers a direct starting point for replication.

arXiv: DeepSeek@Jayden TeohSource
PapersSelected10:03
ParaTTT Parallelizes Deep Test-Time Training Memory Layers

The core insight is that deep nonlinear test-time memories no longer have to trade expressiveness for parallelism, since PTTT recovers exact online gradient descent as a fixed point while running chunkwise in parallel. Researchers building subquadratic sequence models should examine the algorithm and benchmarks, since it directly compares against Gated DeltaNet and Mamba-2 at matched model sizes. The paper appears on arXiv as 2610.38255v1.

arXiv cs.LG@Noel Loo et al. (6)Source

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