Knowledge Integration Decay in Search-Augmented Reasoning of Large Language Models

• URL: http://arxiv.org/abs/2602.09517v1
• Date: Tue, 10 Feb 2026 08:20:26 GMT
• Title: Knowledge Integration Decay in Search-Augmented Reasoning of Large Language Models
• Authors: Sangwon Yu, Ik-hwan Kim, Donghun Kang, Bongkyu Hwang, Junhwa Choi, Suk-hoon Jung, Seungki Hong, Taehee Lee, Sungroh Yoon,
• Abstract summary: We propose Self-Anchored Knowledge Integration (SAKE), a training-free inference-time strategy designed to stabilize knowledge utilization.<n>SAKE significantly mitigates Knowledge Decay (KID) and improves performance, offering a lightweight yet effective solution for knowledge integration in agentic LLMs.
• Score: 36.1675867877378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks by employing search-augmented reasoning to incorporate external knowledge into long chains of thought. However, we identify a critical yet underexplored bottleneck in this paradigm, termed Knowledge Integration Decay (KID). Specifically, we observe that as the length of reasoning generated before search grows, models increasingly fail to integrate retrieved evidence into subsequent reasoning steps, limiting performance even when relevant information is available. To address this, we propose Self-Anchored Knowledge Encoding (SAKE), a training-free inference-time strategy designed to stabilize knowledge utilization. By anchoring retrieved knowledge at both the beginning and end of the reasoning process, SAKE prevents it from being overshadowed by prior context, thereby preserving its semantic integrity. Extensive experiments on multi-hop QA and complex reasoning benchmarks demonstrate that SAKE significantly mitigates KID and improves performance, offering a lightweight yet effective solution for knowledge integration in agentic LLMs.

Related papers

• Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval [60.25608870901428]
  Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs)<n>We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source robustness.
  arXiv  Detail & Related papers  (2026-03-05T18:42:51Z)
• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• Hindsight Distillation Reasoning with Knowledge Encouragement Preference for Knowledge-based Visual Question Answering [55.368681418311894]
  Existing Knowledge-based Visual Question Answering (KBVQA) methods either utilize implicit knowledge in multimodal large language models (MLLMs) via in-context learning or explicit knowledge via retrieval augmented generation.<n>We provide a Hindsight Distilled Reasoning (HinD) framework with Knowledge Encouragement Preference Optimization (KEPO)<n> Experiments on OK-VQA and A-OKVQA validate the effectiveness of HinD, showing that HinD with elicited reasoning from 7B-size MLLM achieves superior performance without commercial model APIs or outside knowledge.
  arXiv  Detail & Related papers  (2025-11-14T10:03:23Z)
• Improving Context Fidelity via Native Retrieval-Augmented Reasoning [35.50952279309109]
  Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information.<n>We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities.<n>Our method requires limited labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain.
  arXiv  Detail & Related papers  (2025-09-17T04:28:07Z)
• Multimodal Iterative RAG for Knowledge-Intensive Visual Question Answering [8.830228556155673]
  We propose MI-RAG, a framework that leverages reasoning to enhance retrieval and incorporates knowledge synthesis to refine its understanding.<n>Experiments on challenging benchmarks, including Encyclopedic VQA, InfoSeek, and OK-VQA, show that MI-RAG significantly improves both retrieval recall and answer accuracy.
  arXiv  Detail & Related papers  (2025-08-31T11:14:54Z)
• Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation [77.10390725623125]
  retrieval-augmented generation (RAG) is widely employed to expand their knowledge scope.<n>Since RAG has shown promise in knowledge-intensive tasks like open-domain question answering, its broader application to complex tasks and intelligent assistants has further advanced its utility.<n>We present a systematic investigation of the intrinsic mechanisms by which RAGs integrate internal (parametric) and external (retrieved) knowledge.
  arXiv  Detail & Related papers  (2025-05-17T13:13:13Z)
• When to Speak, When to Abstain: Contrastive Decoding with Abstention [12.639085523137998]
  Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging pre-trained (i.e., parametric) and external (i.e., contextual) knowledge.<n>We propose Contrastive Decoding with Abstention, a novel training-free decoding method that allows LLMs to generate responses when relevant knowledge is available and to abstain otherwise.
  arXiv  Detail & Related papers  (2024-12-17T04:38:08Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input.<n>GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever [48.5585921817745]
  Large Language Models (LLMs) are used to automate the knowledge tagging task. We show the strong performance of zero- and few-shot results over math questions knowledge tagging tasks. By proposing a reinforcement learning-based demonstration retriever, we successfully exploit the great potential of different-sized LLMs.
  arXiv  Detail & Related papers  (2024-06-19T23:30:01Z)
• Large Language Models are Limited in Out-of-Context Knowledge Reasoning [65.72847298578071]
  Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. This paper focuses on a significant aspect of out-of-context reasoning: Out-of-Context Knowledge Reasoning (OCKR), which is to combine multiple knowledge to infer new knowledge.
  arXiv  Detail & Related papers  (2024-06-11T15:58:59Z)
• DeepEdit: Knowledge Editing as Decoding with Constraints [118.78008395850888]
  How to edit the knowledge in multi-step reasoning has become the major challenge in the knowledge editing (KE) of large language models (LLMs) We propose a new KE framework: DEEPEDIT, which enhances LLMs's ability to generate coherent reasoning chains with new knowledge through depth-first search. In addition to DEEPEDIT, we propose two new KE benchmarks: MQUAKE-2002 and MQUAKE-HARD, which provide more precise and challenging assessments of KE approaches.
  arXiv  Detail & Related papers  (2024-01-19T03:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.