To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining prior knowledge. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. Conversely, discrete structured memory architectures provide robust knowledge retention but often struggle to adapt to fluid narrative evolution. To address this, we propose GAM, a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to effectively resolve the conflict between rapid context perception and stable knowledge retention. By isolating ongoing dialogue in a event progression graph and integrating it into a topic associative network only upon semantic shifts, our approach minimizes interference while preserving long-term consistency. Additionally, we introduce a Graph-guided, Multi-factor Retrieval strategy to enhance context precision. Experiments on LoCoMo and LongDialQA benchmarks indicate that our method consistently outperforms state-of-the-art baselines in both reasoning accuracy and computational efficiency.

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) yet suffers from a mismatch between coarse retrieval granularity and fine-grained generation needs. Specifically, coarse-grained passages inherently conflate valid context with intra-passage noise that semantic retrieval often fails to filter. Existing alignment strategies, typically relying on discrete reranking, struggle to address this granularity mismatch or effectively balance external evidence with internal knowledge. To bridge this gap, we propose **AED-RAG**, a framework that synergizes discrete retrieval with continuous **A**daptive **E**nsemble **D**ecoding. Specifically, we fine-tune a utility predictor using contrastive perplexity to discern the information density differences between unstructured narrative passages and structured knowledge triplets. During inference, this predictor projects passages, triplets, and the model’s parametric memory into a unified probability space, enabling a soft, token-level fusion that dynamically optimizes information gain. Extensive experiments on four open-domain QA benchmarks demonstrate that AED-RAG significantly outperforms competitive baselines, underscoring the effectiveness of integrating multi-granular contexts.