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- claim-1-the-framework-defines-segment-level-execution-semantics-where-training-and-inference-process-sequences-segment-by-segment-with-the-same-cross-segment-interface-definition-3-1
- claim-2-for-the-stated-truncated-consistent-objective-tbptt-computes-the-exact-gradient-rather-than-an-approximation-theorem-3-3
- claim-3-training-inference-alignment-follows-when-the-same-segmented-execution-semantics-and-truncated-objective-are-used-for-training-and-inference-corollary-3-4
- claim-4-the-architecture-uses-head-and-layer-sparse-long-range-retrieval-with-carried-kv-tails-and-forward-only-retrieved-prefixes-figure-3
- claim-5-the-method-achieves-comparable-longbench-e-performance-while-lowering-prefill-memory-and-latency-relative-to-full-context-attention-and-other-efficient-baselines-table-1
- claim-6-at-128k-context-segmented-execution-provides-approximately-6x-lower-peak-prefill-memory-than-full-context-attention-with-flashattention-figure-5
- conclusion
- executive-summary
- 2.22 kB