English(EN)The Fellowship of the Query: Learning Retrieval Actions
新研究探讨LLM长上下文检索和RAG策略 · 追踪9个来源
作者PulseAugur 编辑部·[12 个来源]·
近期研究探索了大型语言模型（LLMs）如何处理长上下文，研究调查了性能提升背后的机制。一篇论文检查了思维链（CoT）推理，发现它能够实现定向检索和比广泛检索更紧凑的表示。另一项研究分析了不同的位置编码选择，如RoPE和滑动窗口注意力，如何将模型从位置检索转移到语义检索，从而影响问答等任务的性能。此外，研究比较了各种检索增强生成（RAG）策略，强调了重排序和后期交互对于科学问答的重要性，并引入了学习检索动作和自我评估探索的新框架，以增强知识检索。
AI
arXiv:2609.38958v1 Announce Type: new Abstract: Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mechanisms underlying this improvement remain unclear. We investigate these mechanism…
arXiv:2609.38530v1 Announce Type: new Abstract: Language models increasingly use architectures that vary attention span and positional encoding across layers, such as applying RoPE with sliding-window attention and NoPE with global attention (SWA NoPE). However, how these choices…
arXiv cs.AI
TIER_1English(EN)·Bhagyesh Rathi, Eshan Chawla, William B. Andreopoulos·
arXiv:2609.38473v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is now the standard way to ground Large Language Models (LLMs) in external knowledge, yet the design space of retrieval pipelines is large and the trade-offs between variants are not well under…
arXiv cs.CL
TIER_1English(EN)·Jingyuan Ma, Lynx Aster, He Zhang, Siyao Song, Weijie Yuan, Zhe Zhang, Kai Jia, Zhifang Sui·
arXiv:2609.37082v1 Announce Type: new Abstract: Long-horizon information-seeking agents often accumulate noisy or misleading context, causing early mistakes to persist and making recovery increasingly difficult. We introduce an autonomous search harness in which the agent manages…
arXiv cs.IR (Information Retrieval)
TIER_1English(EN)·William B. Andreopoulos·
Retrieval-Augmented Generation (RAG) is now the standard way to ground Large Language Models (LLMs) in external knowledge, yet the design space of retrieval pipelines is large and the trade-offs between variants are not well understood, especially on domain-specific corpora at re…
arXiv:2609.28653v1 Announce Type: cross Abstract: Retrieval-augmented question answering requires control decisions about when to decompose a question, search, reformulate, extract evidence, synthesize facts, verify progress, and stop. We study whether trajectory fine-tuning can …
LLM-based retrievers and rerankers have advanced passage ranking, yet both paradigms interact with the corpus in a single pass and commit to the resulting candidate set, leaving relevant documents permanently unrecoverable once missed. We introduce Seek, Self-Evaluative Explorati…
Retrieval-augmented question answering requires control decisions about when to decompose a question, search, reformulate, extract evidence, synthesize facts, verify progress, and stop. We study whether trajectory fine-tuning can improve small language models (SLMs) as next-actio…
<p>Anthropic’s own benchmark tells an uncomfortable story about dumping everything into the prompt: even with a generous context window, standard retrieval still missed the right chunk 5.7% of the time, and fixing that took a second retrieval technique, not a bigger window [1]. T…
<h4>What a controlled retrieval experiments revealed about ranking, evidence, latency, and failure modes.</h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*LIOvevb1bkfEFw5HWs6gGg.png" /></figure><p>Most RAG demos are deceptively simple.</p><p>Ingest a docume…
dev.to — LLM tag
TIER_1English(EN)·Ruchita Nimkar·
<h2> What happens when a question looks simple, but answering it correctly requires more than retrieving a few documents? </h2> <p>For my <strong>TigerGraph Hackathon</strong> project, I explored this question by implementing and comparing <strong>RAG, GraphRAG, and Agentic Graph…
