The Residual Stream Is All You Need: On the Redundancy of the KV Cache in Transformer Inference

• URL: http://arxiv.org/abs/2603.19664v1
• Date: Fri, 20 Mar 2026 05:59:50 GMT
• Title: The Residual Stream Is All You Need: On the Redundancy of the KV Cache in Transformer Inference
• Abstract summary: Key-value (KV) cache is widely treated as essential state in transformer inference.<n>We prove that keys and values at every layer are deterministic projections of the residual stream.<n>We build on this result with KV-Direct, a bounded-memory inference scheme.
• Score: 3.378773775514883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The key-value (KV) cache is widely treated as essential state in transformer inference, and a large body of work engineers policies to compress, evict, or approximate its entries. We prove that this state is entirely redundant: keys and values at every layer are deterministic projections of the residual stream, and recomputing them from a single residual vector per token incurs exactly zero reconstruction error, not approximately, but bit-identically. We verify this across six models from four architecture families (135M to 4B parameters). Cross-task residual patching at every layer produces D_KL = 0 between patched and original output distributions, confirming that the residual stream satisfies a Markov property and is the sole information-carrying state. Removing the cache entirely and recomputing from scratch yields token-identical output under greedy decoding on all models tested. We build on this result with KV-Direct, a bounded-memory inference scheme that checkpoints residual vectors (5 KB per token on Gemma 3-4B) instead of full KV pairs (136 KB), recomputing keys and values on demand. Over 20 conversation turns, KV-Direct holds peak memory at 42 MB while the standard cache grows past 103 MB. Against five eviction baselines (H2O, StreamingLLM, SnapKV, TOVA, window-only), KV-Direct maintains 100% token match at every cache budget; all baselines degrade to 5-28%. A per-operation latency analysis shows recomputation runs up to 5x faster than reading cached tensors at moderate batch sizes. Code is available at https://github.com/Kaleemullahqasim/KV-Direct.

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