Fragmentation strictly raises optimal finite-context log-loss on Markov sources while tokenization can make a short token window equivalent to a longer source window under reliability and compression conditions.
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Memory Inception is a training-free method that injects latent KV banks at chosen layers to steer LLMs, achieving superior control-drift balance and up to 118x storage reduction on personality and structured-reasoning tasks.
Hidden layer distillation yields systematic perplexity gains over logit KD in LLM pre-training but does not consistently improve downstream performance.
citing papers explorer
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Effective Context in Transformers: An Analysis of Fragmentation and Tokenization
Fragmentation strictly raises optimal finite-context log-loss on Markov sources while tokenization can make a short token window equivalent to a longer source window under reliability and compression conditions.
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Memory Inception: Latent-Space KV Cache Manipulation for Steering LLMs
Memory Inception is a training-free method that injects latent KV banks at chosen layers to steer LLMs, achieving superior control-drift balance and up to 118x storage reduction on personality and structured-reasoning tasks.
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A Study on Hidden Layer Distillation for Large Language Model Pre-Training
Hidden layer distillation yields systematic perplexity gains over logit KD in LLM pre-training but does not consistently improve downstream performance.