Large Language Model (LLM)-based multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, existing works often rely on manual designs or “one-size-fits-all” automation and lack adaptability after deployment. We study in-context structural adaptation, where structured experience conditions both query-dependent system generation and execution-time reconfiguration without updating LLM parameters. We introduce MASFly, which realizes this adaptation through two complementary mechanisms. First, a retrieval-augmented SOP instantiation mechanism retrieves and adapts successful collaboration patterns to construct a query-specific MAS. Second, an experience-enhanced process supervision mechanism uses a dedicated Watcher agent to monitor execution against prior failure experience and reconfigure the system upon abnormal behavior. Experiments demonstrate that MASFly achieves state-of the-art performance, including a 61.7% success rate on TravelPlanner, with strong task adaptability and robustness.

Long-term assistants must answer questions over conversations beyond the context window while grounding responses in verifiable dialogue evidence. Existing memory systems retrieve compact representations, but these may omit critical details, while similarity search may favor related records over answer-bearing source turns. We introduce GRACE-Mem, a long-term memory architecture that separates candidate discovery, candidate reranking, and final evidence construction. It uses graph records for discovery, compressed records for reranking, and provenance-linked raw dialogue spans as final evidence. After hybrid graph retrieval, multi-hop expansion, and two-stage reranking, the top-K records seed an Evidence Curation Agent. The agent searches the raw corpus, removes irrelevant candidates, recovers missing evidence, and returns provenance identifiers to reconstruct the final context from raw dialogue. GRACE-Mem achieves 85.3% on LoCoMo and 73.2% on LongMemEval, surpassing the previously reported state of the art, AMA, by 7.9 and 3.4 percentage points, respectively.

Knowledge Graph (KG) reasoning aims at finding reasoning paths for relations, in order to solve the problem of incompleteness in KG. Many previous path-based methods like PRA and DeepPath suffer from lacking memory components, or stuck in training. Therefore, their performances always rely on well-pretraining. In this paper, we present a deep reinforcement learning based model named by AttnPath, which incorporates LSTM and Graph Attention Mechanism as the memory components. We define two metrics, Mean Selection Rate (MSR) and Mean Replacement Rate (MRR), to quantitatively measure how difficult it is to learn the query relations, and take advantages of them to fine-tune the model under the framework of reinforcement learning. Meanwhile, a novel mechanism of reinforcement learning is proposed by forcing an agent to walk forward every step to avoid the agent stalling at the same entity node constantly. Based on this operation, the proposed model not only can get rid of the pretraining process, but also achieves state-of-the-art performance comparing with the other models. We test our model on FB15K-237 and NELL-995 datasets with different tasks. Extensive experiments show that our model is effective and competitive with many current state-of-the-art methods, and also performs well in practice.