A central question in the knowledge lifecycle of language models ishow externally injected signals interact with parametric memoryaccumulated during pretraining. We address this through ResidualStream Context Encoding (RSCE), a training-free method that encodesa context document ctx into a single vector C ∈ ℝ^d_Mvia mean-pooling residual stream activations at a calibratedintermediate layer, then injects C as an additive shift at querytime. This replaces O(|T(ctx)|) attention prefill with an O(1)operation and reveals a previously undescribed dual-pathwayinterference effect: vector injection alone suppresses parametricrecall below the question-only baseline across four of fivetested architectures. This finding—absent in behavioral activationsteering—provides mechanistic evidence that LLMs maintain separatecontextual-retrieval and parametric-recall pathways that compete whenexternally injected signals are semantically rich but token-precisiondeficient. A dual-channel design pairing C with a compact explicitfact block F resolves this tension. We evaluate five decoder-onlyarchitectures (7B–70B) on multi-document QA (LongBench, n=108)and six on cross-file code completion (RepoBench-C), comparingagainst LongLLMLingua and EHPC. At extreme compression (∼99%token reduction), RSCE Vec+F is competitive with EHPC on smallerarchitectures (LLaMA-8B F1 0.333 vs. EHPC 0.334; DeepSeek-14Bboth 0.214) while both substantially outperform LongLLMLingua.RSCE is the only method achieving 81% compression at 100%operational reliability on code.