Latent chain-of-thought models move intermediate reasoning into continuous states, obscuring which internal object causally carries a computation. We introduce SCIT, the Suffix Cache Interchange Test, which patches exact source–recipient counterfactuals and crosses cache-segment sufficiency with K/V splits, hidden-state and semantic-source controls, decoded validation, and matched corruption. In GPT-2 arithmetic, counterfactual transfer concentrates in value-cache suffix trajectories rather than hidden states, keys, answer slots, or single-token triggers. Full sufficiency-and-necessity closure holds only for the main CODI-GPT2 checkpoint; the Sim-CoT-style reproduction supports sufficiency and decoded transfer but not necessity. Across broader competent cells, arithmetic-like GPT-2/1B tasks retain latent-tail value/KV transfer, whereas 8B and repaired non-arithmetic tasks shift to prompt-prefix or full-cache K/V; ungated cells receive no call. SCIT thus provides a cache-level diagnostic, a checkpoint-specific arithmetic mechanism, and a competence-gated carrier map—not a universal latent-tail claim. Code: https://github.com/YIDING4869/scit-emnlp-2026

Parametric retrieval-augmented generation (PRAG) injects retrieved evidence into a large language model (LLM) through passage-specific LoRA adapters, reducing reliance on long in-context prompts. When multiple passages are retrieved for the same query, however, evidence-level fusion becomes a bottleneck: equal-weight merging can amplify weak or conflicting evidence, and translating retrieval signals into fusion weights often requires fragile global tuning. We propose FCPRAG, a fusion-controlled parametric RAG framework that adds a lightweight controller for retrieval-conditioned, sample-level adapter fusion. The controller predicts per-passage fusion scores together with sample-level calibration signals, including a mixing gate and an adaptive temperature, enabling fusion that stays selective under informative retrieval signals and conservative under uncertainty. FCPRAG is trained with merge-aware supervision derived from each adapter’s marginal contribution within a multi-adapter merge, using training data only. We further show that a single dataset-level temperature is suboptimal under heteroscedastic retrieval uncertainty, motivating sample-level adaptation. Experiments on HotpotQA, 2WikiMultiHopQA, PopQA, and ComplexWebQuestions (CWQ) across three LLM backbones show that FCPRAG consistently improves F1 over standard RAG and parametric RAG baselines, with gains of up to 4.65% on 2WikiMultiHopQA and 7.55% on CWQ, while also reducing tuning cost and improving robustness under retrieval perturbations. Code is available at https://github.com/ReinerBRO/FCPRAG.