RECONTEXT is a recursive evidence replay technique that improves long-context reasoning in LLMs by constructing and replaying a query-conditioned evidence pool before final generation.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4representative citing papers
Proves that RoPE attention loses locality bias and token distinction in long contexts, approaching random behavior independent of content.
Key-Value Means replaces full KV-cache attention with block sliding-window attention plus a renormalized, expandable compressed state, yielding sublinear memory growth and subquadratic prefill.
DASH reduces prefill computation in LLMs and vision-language models by halting tokens with low attention-branch residual updates at a chosen layer, achieving 1.7–2× prefill speedups with minimal accuracy loss.
citing papers explorer
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ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
RECONTEXT is a recursive evidence replay technique that improves long-context reasoning in LLMs by constructing and replaying a query-conditioned evidence pool before final generation.
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RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably
Proves that RoPE attention loses locality bias and token distinction in long contexts, approaching random behavior independent of content.
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Key-Value Means: Transformers with Expandable Block-Recurrent Compressed Memory
Key-Value Means replaces full KV-cache attention with block sliding-window attention plus a renormalized, expandable compressed state, yielding sublinear memory growth and subquadratic prefill.
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Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling
DASH reduces prefill computation in LLMs and vision-language models by halting tokens with low attention-branch residual updates at a chosen layer, achieving 1.7–2× prefill speedups with minimal accuracy loss.