To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning
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arXiv:2608. 06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning.
arXiv:2607. 00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT).
arXiv:2608. 05124v1 Announce Type: cross Abstract: Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously explore the context, store intermediate state, verify evidence, and produce the final answer.
A new framework called APT‑RAG is introduced for evidence‑intensive question answering. It uses adaptive planning to expand a tree‑based reasoning structure according to question dependencies and evidence needs, and employs topology‑aware evidence gathering that reuses sibling evidence, retrieves directly, and aggregates evidence from child nodes. The approach also includes evidence‑guided batched answer generation to reduce generation overhead, and experimental results show it outperforms existing structured RAG methods on evidence‑intensive QA benchmarks.
AgenticRag‑R1 is a reinforcement‑learning framework that integrates reasoning, retrieval, and memory through a stack and fine‑grained action space. It uses hierarchical action‑aware rewards and an information‑aware trajectory rejection strategy to support long‑horizon learning. Experiments on multi‑hop, open‑domain, and agentic reasoning benchmarks show that AgenticRag‑R1 outperforms strong baselines and produces robust, interpretable, memory‑aware reasoning behaviors.
arXiv:2505. 15062v5 Announce Type: replace-cross Abstract: Knowledge extrapolation is the process of inferring novel information by combining and extending existing knowledge that is explicitly available.