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In-context source attribution, the task of identifying which input passages support an LLM-generated response, is critical for trustworthy grounded generation. A naive solution is to make additional autoregressive LLM calls for citation generation, but the added cost and latency discourage practitioners from incorporating transparency features like attribution. We study efficient no-decode alternatives that instead leverage the model's internal activations. To enable efficient attribution, we introduce Attr-Kit, a modular framework that formalizes the task, unifies several prior approaches, and introduces a new activation signal called Value Flow(VF). To study the theoretical potential of activation-based attribution, we formulate a subset selection problem to estimate the maximum achievable attribution accuracy for any signal. The oracle analysis reveals that all activation signals contain sufficient information for near-perfect attribution, and that VF reaches this ceiling with the fewest layers computed. Across multiple datasets and model families, VF consistently outperforms existing activation signals. VF-based attribution incurs zero decode tokens yet achieves 1.4x higher accuracy on average than autoregressive citation from the same model, while making significantly fewer LLM calls. Notably, our no-decode methods with mid-sized models (4–8B) substantially narrow the accuracy gap to frontier models such as GPT-5.4 and Claude Opus 4.6.