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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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
SP-KV trains a utility predictor jointly with the LLM to dynamically prune low-utility KV cache entries, achieving 3-10x memory reduction during generation with negligible performance loss.
Optimizer choice induces distinct connected regions in the loss landscape of two-layer ReLU networks, with AdamW and Muon sometimes separated by provable barriers.
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.
Poetic jailbreaks succeed because they induce distinct attention patterns in LLMs that are independent of harmful-content detection, not because models fail to recognize literary formatting.
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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Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility
SP-KV trains a utility predictor jointly with the LLM to dynamically prune low-utility KV cache entries, achieving 3-10x memory reduction during generation with negligible performance loss.
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Optimizer-Induced Mode Connectivity: From AdamW to Muon
Optimizer choice induces distinct connected regions in the loss landscape of two-layer ReLU networks, with AdamW and Muon sometimes separated by provable barriers.
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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.
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Metaphor Is Not All Attention Needs
Poetic jailbreaks succeed because they induce distinct attention patterns in LLMs that are independent of harmful-content detection, not because models fail to recognize literary formatting.