This report examines the profound impact of chunk retrieval sequence on the multi-step inference performance of Large Language Models (LLMs) within Retrieval-Augmented Generation (RAG) systems. While RAG significantly enhances LLM capabilities by providing external, up-to-date knowledge, the manner in which this information is organized and presented is paramount for complex reasoning tasks. A key finding is that preserving the original document structure, as seen in Document's Original Structure RAG (DOS RAG), often yields superior performance compared to methods that solely prioritize relevance-based sorting. This is attributed to the maintenance of narrative continuity, which facilitates the LLM's sequential processing.

The analysis further reveals that LLMs exhibit a "cognitive linearity", performing optimally when information flows logically. Challenges such as positional bias and the detrimental effects of irrelevant or distracting information can significantly impede multi-step reasoning, even with highly relevant chunks. These issues necessitate robust reranking and filtering mechanisms, alongside a careful balance of context window size to avoid cognitive overload. The report concludes that optimizing chunk retrieval sequence requires a holistic approach, integrating intelligent chunking, strategic reranking, and proactive mitigation of noise to design robust RAG systems capable of advanced, knowledge-intensive tasks.

• 1. Introduction: The Interplay of RAG, LLMs, and Multi-Step Reasoning
• 1.1 Defining Large Language Models (LLMs) and their Reasoning Capabilities
• 1.2 Understanding Retrieval-Augmented Generation (RAG)
• 1.3 The Essence of Multi-Step Inference in LLMs
• 1.4 Purpose and Scope of the Report
• 2. Chunking and Retrieval in RAG Architectures
• 2.1 Principles of Document Chunking for RAG
• 2.2 Overview of Retrieval Mechanisms in RAG
• 3. The Critical Role of Chunk Retrieval Sequence
• 3.1 Impact of Context Order on LLM Performance
• 3.2 Document's Original Structure (DOS RAG) and its Benefits
• 3.3 Reranking Strategies and Context Reordering
• 4. Factors Influencing Multi-Step Inference Performance in RAG
• 4.1 Positional Bias and the "Lost-in-the-Middle" Effect
• 4.2 The Detrimental Impact of Irrelevant and Distracting Information
• 4.3 Cognitive Load and Context Window Management
• 5. Empirical Evidence and Performance Analysis
• 5.1 Case Studies on Chunk Order and Multi-Step Question Answering
• 5.2 Evaluation Benchmarks and Metrics
• 6. Optimizing Chunk Retrieval Sequence for Enhanced Multi-Step Reasoning
• 6.1 Best Practices for Chunking and Reordering
• 6.2 Strategies for Mitigating Positional Bias and Distraction
• 6.3 Advanced Techniques for Multi-Hop Reasoning
• 7. Conclusion and Future Directions
• References

1. Introduction: The Interplay of RAG, LLMs, and Multi-Step Reasoning

The landscape of artificial intelligence has been profoundly reshaped by the emergence of Large Language Models (LLMs), which demonstrate remarkable abilities in understanding and generating human-like text. However, their inherent limitations have spurred the development of advanced frameworks like Retrieval-Augmented Generation (RAG) to unlock even more sophisticated capabilities, particularly in multi-step inference. This report delves into the intricate relationship between the sequence in which information is retrieved and presented to LLMs within RAG systems and its subsequent effect on their ability to perform complex, multi-step reasoning.

1.1 Defining Large Language Models (LLMs) and their Reasoning Capabilities

Large Language Models are sophisticated artificial intelligence systems built upon deep neural networks, trained on vast datasets of text to interpret natural language and generate human-like responses.$^1$ These models comprise numerous layers of neural networks, featuring billions of parameters that are fine-tuned during training. Their architecture is further enhanced by attention mechanisms, which enable them to focus on specific parts of the input data, thereby improving their contextual understanding.$^1$ LLMs demonstrate proficiency across a wide array of natural language processing tasks, including language translation, text summarization, question-answering, and content generation.$^2$ Through extensive training, they acquire a deep understanding of grammar, semantics, and complex conceptual relationships inherent in human language.$^3$

Despite their impressive performance, LLMs face several inherent limitations. A significant challenge is their reliance on static, pre-trained data, which means their knowledge base is frozen at the time of training.$^2$ This characteristic can lead to outdated or potentially inaccurate responses and a phenomenon known as "hallucinations", where the model generates factually incorrect or nonsensical information not present in its training data.$^2$ Furthermore, LLMs often struggle with complex logical reasoning, particularly tasks requiring sophisticated deductive, inductive, or adductive inference, and can sometimes produce self-contradictory outputs.$^5$ These fundamental constraints, especially the static nature of their knowledge and the propensity for factual errors, directly highlighted the necessity for external knowledge augmentation techniques. A mechanism was required to inject dynamic, up-to-date, and verifiable information at inference time, leading to the emergence and widespread adoption of RAG.

1.2 Understanding Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an AI framework designed to optimize the output of LLMs by enabling them to reference an authoritative knowledge base external to their training data before generating a response.$^4$ This framework effectively combines the strengths of traditional information retrieval systems, such as search engines and databases, with the generative capabilities of large language models.$^4$

The core process of RAG typically involves two main stages. First, Retrieval and Pre-processing occurs, where powerful search algorithms query external data sources, including web pages, knowledge bases, and databases. Once retrieved, this relevant information undergoes pre-processing steps such as tokenization, stemming, and the removal of stop words.$^4$ The second stage is Grounded Generation, where the pre-processed, retrieved information is seamlessly incorporated into the pre-trained LLM's context. This integration significantly enhances the LLM's understanding of the topic, allowing it to produce more precise, informative, and engaging responses.$^4$

RAG offers several distinct advantages over conventional text generation methods, particularly for factual or data-driven responses. It provides LLMs with access to fresh, up-to-date information, overcoming the limitations of their pre-trained data.$^4$ This factual grounding is crucial for mitigating "gen AI hallucinations" by supplying verifiable facts as part of the input prompt.$^4$ The framework also leverages advanced search techniques, including vector databases and relevancy re-rankers, to ensure that the most pertinent information is retrieved, thereby improving the overall relevance, accuracy, and quality of the LLM's outputs.$^4$ This capability effectively transforms the LLM from a purely generative model into a knowledge-aware reasoning engine, capable of producing responses grounded in verifiable facts.

1.3 The Essence of Multi-Step Inference in LLMs

Multi-step inference, also referred to as multi-step reasoning or multi-task inference, denotes an LLM's capacity to process multiple pieces of information in a sequential manner, apply logical operations, and execute a series of sub-tasks to arrive at a conclusion.$^5$ This capability extends beyond merely following a single instruction or performing a singular task.$^{10}$

The ability to perform multi-step reasoning is paramount for addressing complex, real-world challenges where each subsequent step builds upon the preceding one, demanding a deeper level of comprehension and structured problem-solving.$^{11}$ It is widely recognized as a key indicator of advanced intelligence in AI systems.$^{10}$ However, LLMs frequently encounter difficulties with intricate logical problems that necessitate sophisticated deductive, inductive, or adductive reasoning. They can also exhibit a tendency to produce self-contradictory responses.$^5$ While existing datasets for multi-hop reasoning, such as HotpotQA and StrategyQA, are designed to test internal reasoning processes, they do not always offer a comprehensive method for assessing the accuracy of intermediate steps or for comparing concurrent versus sequential processing approaches.$^{10}$

To address these assessment gaps, new evaluation benchmarks have been developed. The MTI Bench, for instance, is specifically designed to analyze the multi-task inference capabilities of LLMs, differentiating between tasks with sequential dependencies (Multi-Step subset) and those without (Multi-Part subset).$^{10}$ Similarly, ProcBench focuses on evaluating multi-step reasoning by presenting LLMs with explicit instructions and questions that require strict adherence to provided steps.$^{11}$ The increasing emphasis on these specialized benchmarks indicates a significant evolution in LLM evaluation. It reflects a growing understanding that raw knowledge alone is insufficient; LLMs must also possess robust structured processing capabilities to be truly effective. This shift underscores that future advancements in LLMs and RAG systems must prioritize not just what information is retrieved, but how that information facilitates a structured, step-by-step problem-solving process. This elevates the importance of context organization and coherence within the input.

1.4 Purpose and Scope of the Report

The primary objective of this report is to analyze the intricate relationship between the chunk retrieval sequence within RAG frameworks and the multi-step inference performance of Large Language Models. The scope of this analysis encompasses a detailed examination of various chunking strategies, the mechanisms of information retrieval, and a critical assessment of how the order in which information is presented influences an LLM's capacity to execute complex reasoning tasks. The report will integrate empirical findings from recent studies, discuss pervasive challenges such as positional bias and the impact of distracting information, and propose optimization strategies derived from these observations. This document is intended for AI/ML Researchers, Senior AI Engineers, and Technical Leads seeking to enhance the robustness and efficiency of RAG systems for knowledge-intensive applications.

2. Chunking and Retrieval in RAG Architectures

The efficacy of Retrieval-Augmented Generation (RAG) systems heavily relies on how external knowledge is prepared and accessed. This involves two foundational processes: chunking, which breaks down large documents into manageable pieces, and retrieval, which identifies and fetches the most relevant of these pieces. Understanding these processes is crucial for appreciating how the sequence of retrieved information impacts LLM performance.

2.1 Principles of Document Chunking for RAG

Chunking, in the context of AI, refers to the process of dividing extensive documents into smaller, more manageable segments known as chunks.$^{12}$ These segments can vary in granularity, ranging from entire paragraphs or individual sentences to token-limited blocks.$^{13}$ The primary purpose of chunking is to enhance the efficiency of both retrieval and subsequent processing by the LLM.

The necessity of chunking arises from the vastness of knowledge bases, which can contain millions of words or documents. Without effective chunking, retrieving relevant information efficiently from such large datasets would be computationally prohibitive.$^{13}$ By breaking down documents, chunking enables more precise matching between user queries and relevant text, thereby reducing noise and the inclusion of irrelevant information. Moreover, smaller chunks are processed more rapidly and utilize memory more efficiently, allowing RAG systems to handle large datasets effectively.$^{13}$

Several chunking strategies are employed, each with distinct advantages and use cases:

• Fixed Size Chunking: This straightforward approach divides text into uniform chunks based on a predefined character or token count.$^{13}$ For instance, a document might be split into 500-token chunks, often with an overlap feature to maintain context across boundaries and prevent loss of meaning.$^{13}$ While simple to implement, efficient for large datasets, and consistent in size, this method can lead to context fragmentation, splitting sentences or logical units. Its inflexibility makes it sub-optimal for heterogeneous content.$^{13}$
• Recursive-Based Chunking: A more adaptive strategy, this method breaks text into chunks by applying multiple separators (e.g., paragraphs, sentences, or specific markers) in a specified order of importance. The goal is to identify the most meaningful boundaries within the text, thereby preserving logical flow.$^{13}$
• Sentence-based Chunking: This method ensures that each chunk contains complete thoughts by dividing text into full sentences. It helps maintain the natural logical progression of information.$^{13}$
• Document Structure-based Chunking: This approach chunks documents according to their inherent structural integrity, such as individual sections, headings, or even specific charges within a legal document. This method is crucial for ensuring that key information and its surrounding context remain intact, implicitly supporting narrative continuity.$^{13}$
• Semantic Chunking: This strategy involves segmenting documents into semantically coherent and non-overlapping chunks that are more closely aligned with the specific information needs of a query.$^{14}$

The choice of chunking strategy introduces a critical trade-off between simplicity and efficiency on one hand (e.g., fixed-size chunking) and context preservation and accuracy on the other (e.g., recursive, semantic, or structure-based chunking). For multi-step inference, where the LLM must connect information across multiple segments to build a coherent understanding, chunking strategies that prioritize contextual integrity over simple size uniformity are likely to yield superior results. This is because fixed-size chunking risks breaking logical units, which can impede the LLM's ability to follow a sequential argument. Therefore, the optimal approach to chunking is not universal but depends heavily on the document type and the complexity of the queries, with multi-step reasoning tasks often benefiting significantly from meaningful segmentation that supports logical flow.

2.2 Overview of Retrieval Mechanisms in RAG

A RAG system is fundamentally composed of three key modules that work in concert to enhance LLM performance. First, a Query Encoder transforms the user's input query into a representation suitable for searching the knowledge base.$^8$ Second, a Retriever takes this query representation and fetches a ranked list of relevant documents or chunks from a vast corpus.$^8$Finally, a Generator, typically a pre-trained LLM, conditions its output on both the original input query and the retrieved documents to produce the final response.$^8$

Retrievers can be broadly categorized based on their underlying mechanisms:

• Sparse Retrievers: These methods rely on keyword matching, such as the BM25 algorithm, to identify relevant documents.$^8$
• Dense Retrievers: Utilizing embeddings, these retrievers perform semantic similarity searches within vector databases. This allows for fast and accurate retrieval based on the meaning of the query rather than just keyword overlap.$^4$
• Hybrid Search: Many advanced RAG systems combine both semantic and keyword search techniques to achieve a more comprehensive and relevant set of results.$^4$ The retrieval process in RAG involves powerful search algorithms querying external data sources. Prior to lookup, sophisticated search engines may even transform queries and correct misspellings to optimize relevance.$^4$ After the initial retrieval, an essential step often involves
• re-rankers. These components act as a second-pass filter, reordering the retrieved documents or chunks based on a more refined assessment of their relevance to the query. The top-K most relevant chunks are then passed to the generator as factual context.$^4$ This re-ranking step is critical for ensuring that the LLM receives the most pertinent information, effectively reducing noise and improving the overall quality and accuracy of the generated output.$^4$ The effectiveness of RAG is therefore highly dependent on the retriever's ability to provide relevant information and the re-ranker's capacity to prioritize the most pertinent chunks. If the retriever fetches irrelevant or noisy information, the LLM's performance can degrade, leading to responses that, while "grounded" in the provided context, might be off-topic or factually incorrect.$^4$ The re-ranker serves as a crucial gatekeeper, refining these initial results to ensure that only the highest-quality, most relevant information is presented to the LLM. This highlights that successful retrieval is not merely about finding any relevant information, but about identifying the most relevant and least distracting content, a factor that profoundly influences the subsequent chunk ordering.

3. The Critical Role of Chunk Retrieval Sequence

The order in which retrieved chunks are presented to a Large Language Model is not a trivial detail but a critical determinant of its performance, particularly for tasks requiring multi-step inference. This section explores how context order directly influences an LLM's ability to reason effectively.

3.1 Impact of Context Order on LLM Performance

Observations indicate that the sequence in which text chunks are retrieved and subsequently presented to an LLM significantly influences its overall performance.$^{17}$ This impact extends beyond simple relevance sorting, suggesting a deeper interaction with the LLM's internal processing mechanisms. LLMs demonstrate a distinct preference for premise order in reasoning tasks, achieving optimal performance when the information sequence aligns with the intermediate steps required for logical deduction.$^{18}$ For example, in deductive reasoning problems, presenting premises in the same order as a ground truth proof can drastically increase the model's accuracy.$^{18}$ This suggests that LLMs operate more effectively when processing information in a left-to-right, sequential manner, rather than having to search back and forth across a disordered context.$^{18}$

Conversely, permuting the order of premises can lead to a substantial performance degradation, with drops exceeding 30% observed in some LLMs.$^{18}$ This "ordering effect" is further exacerbated when irrelevant premises are introduced into the prompt.$^{18}$ When the context provided to the LLM is disjointed or randomly shuffled, it negatively impacts the model's ability to synthesize information and produce coherent responses.$^{17}$ This indicates that LLMs, despite their advanced capabilities, exhibit a form of "cognitive linearity" in their processing. They perform optimally when information is presented in a sequential, logically flowing manner. This observation challenges the assumption that LLMs can perfectly synthesize information regardless of its arrangement within the context window. The consistent improvement seen when premises are ordered according to a "ground truth proof" $^{18}$ suggests that the LLM's internal mechanisms, possibly due to their auto-regressive design or biases learned from training data, are more efficient when information is presented sequentially. This parallels human cognitive processes, where understanding is often built step-by-step. If information is jumbled, the LLM must expend additional computational effort to re-establish logical connections, which can lead to reduced performance. For multi-step inference, which inherently relies on sequential processing and building upon previous deductions, maintaining a coherent narrative or logical progression in the input context becomes paramount.

3.2 Document's Original Structure (DOS RAG) and its Benefits

Document's Original Structure RAG (DOS RAG) is a retrieve-then-read strategy that introduces a crucial refinement to the standard RAG pipeline. Instead of solely sorting retrieved chunks by their similarity score to the query, DOS RAG reorders these chunks to match their original sequence within the source document.$^{19}$ This reordering is made possible by tracking the original positions of the chunks during the initial processing phase.$^{19}$

The benefits of DOS RAG are significant and empirically validated. It primarily preserves passage continuity, maintaining the document's structural integrity and narrative flow.$^{13}$ This is particularly crucial for tasks that require understanding underlying narratives or performing complex multi-hop question answering. Studies consistently show that DOS RAG achieves improved accuracy, outperforming traditional Vanilla RAG (which relies on relevance-sorted chunks) across various benchmarks, including ∞Bench, QuALITY, and NarrativeQA.$^{19}$ This performance gain is especially pronounced when the retrieval budget is expanded to tens of thousands of tokens.$^{19}$ For instance, on the ∞Bench, DOS RAG reached 93.1% accuracy at 30K tokens, surpassing Vanilla RAG's 87.8%.$^{19}$ Furthermore, DOS RAG demonstrates notable efficiency, often achieving superior results while utilizing fewer tokens compared to more complex multi-stage methods like ReadAgent.$^{19}$ This suggests that the added complexity of multi-stage approaches does not always translate to better performance when long-context LLMs can effectively incorporate relevant context in a single, well-ordered pass.$^{19}$

The consistent empirical outperformance of DOS RAG over relevance-sorted retrieval fundamentally challenges the prevailing assumption that semantic similarity alone dictates optimal chunk presentation. This observation highlights that for multi-step reasoning, contextual coherence and narrative flow, as preserved by the original document order, are often more critical than isolated high-relevance scores. Traditional RAG pipelines often prioritize retrieving chunks based on their individual semantic similarity to the query, then sorting them by this score, with the expectation that the LLM will best utilize the most relevant information first.$^{17}$ However, DOS RAG's consistent superiority demonstrates that for tasks requiring multi-step reasoning or the understanding of a narrative, the relationship between chunks (specifically, their original sequence) is more valuable than their individual relevance rank.$^{19}$ Complex reasoning frequently requires building a mental model from sequential information, where each piece logically follows the last.$^{11}$ Disrupting this natural flow, even with highly relevant but disjointed chunks, can increase the LLM's processing burden and hinder its ability to perform multi-hop reasoning effectively. This implies that the definition of "relevance" for multi-step tasks should be broadened to include "contextual relevance" or "narrative relevance" in addition to traditional semantic similarity.

3.3 Reranking Strategies and Context Reordering

Reranking serves as a crucial second-pass filter in RAG systems, refining the initial set of retrieved documents or chunks by reordering them based on a more precise assessment of query-document relevance.$^8$ This process is vital for enhancing the quality of the context provided to the LLM, ensuring that the most pertinent information is presented, and ultimately helping to filter out irrelevant documents that could lead to hallucinations.$^{22}$ Various types of rerankers are employed, each with distinct characteristics:

• Cross-Encoders: These models analyze the query and document pair together, enabling a deep and nuanced understanding of their relevance. They offer high precision but are generally computationally intensive.$^{22}$ Examples include Sentence Transformers, Flashrank, and BGE-M3.$^{15}$
• Multi-Vector Rerankers: Models like ColBERT use a "late interaction" approach, encoding query and document representations independently before their interaction and relevance scoring occur. This approach balances performance and efficiency.$^{22}$
• Fine-tuned LLM Rerankers: Pre-trained LLMs are fine-tuned on specific ranking datasets (e.g., MS MARCO) to enhance their ability to measure query-document relevance.$^{22}$ These can be structured as encoder-decoder models (e.g., RankT5) or decoder-only models (e.g., RankZephyr, RankGPT).$^{22}$
• LLM as a Judge: This approach leverages the inherent reasoning capabilities of LLMs to directly assess document relevance through various prompting strategies, including pointwise, listwise, and pairwise methods.$^{22}$ While offering competitive effectiveness, the high computational cost and latency associated with using LLMs directly for reranking can be a practical barrier.$^{22}$ Examples include GPT, Claude, and Gemini.$^{22}$
• Reranking APIs: Commercial services provide convenient solutions for semantic relevance enhancement without requiring significant infrastructure investment.$^{22}$ Examples include Cohere, Jina, and Mixedbread.$^{22}$

Beyond simple relevance scoring, context reordering within the reranking process also plays a role. Inverted Context Ordering is one such strategy, where retrieved or reranked documents are arranged in descending order of relevance, with the highest-ranked document placed immediately before the question.$^{16}$ This method has demonstrated a performance increase in correctness for multi-hop QA tasks.$^{24}$ Other advanced approaches include Fusion-based Reranking, which aggregates evidence from multiple query variants (e.g., RAG-Fusion, R2AG) and is particularly effective for multi-hop and ambiguous tasks $^8$, and Adaptive Reranking, which dynamically adjusts the number of documents reranked based on query complexity (e.g., RLT, ToolRerank).$^8$

While reranking is essential for refining relevance, certain advanced reranking methods (e.g., LLM-as-a-judge, Rank-R1) introduce significant computational overhead. Their benefits might be offset by increased latency, especially for real-time applications or when simpler methods like DOS RAG already leverage long context windows effectively. This creates an optimization paradox where "better" relevance comes at a cost that might negate its practical advantage. The primary goal of reranking is to provide the LLM with the most relevant context.$^8$ However, methods like Rank-R1, despite their explicit reasoning capabilities, can take up to 100 seconds for a single query, making them impractical for time-constrained scenarios.$^{15}$ This illustrates a critical trade-off: a more sophisticated reranker might theoretically provide a more perfectly ordered context, but the practical latency introduced can severely impact the overall system's usability and efficiency. Furthermore, the success of DOS RAG suggests that simply reordering by original document flow can be more effective than complex relevance-based reranking for multi-step tasks, especially with long-context LLMs.$^{19}$ This implies that the "best" reranking strategy is not solely about maximizing relevance scores but about achieving a holistic balance with operational constraints and the specific reasoning demands of the LLM. Table 1: Comparison of Key Chunking and Reordering Strategies in RAG

4. Factors Influencing Multi-Step Inference Performance in RAG

Beyond the direct ordering of retrieved chunks, several other factors interact with the LLM's context window and the presentation of information to significantly affect its ability to perform multi-step inference. These factors highlight the complexities involved in designing truly effective RAG systems.

4.1 Positional Bias and the "Lost-in-the-Middle" Effect

Positional bias refers to the observed tendency of Large Language Models to assign different weights or importance to information based on its location within the input prompt.$^{25}$ A specific manifestation of this is the "lost-in-the-middle" effect, where LLMs tend to focus predominantly on text appearing at the beginning or end of their prompt, often overlooking content situated in the middle.$^{25}$ This bias can affect both the LLM's capacity to leverage relevant passages effectively and its susceptibility to being misled by distracting ones.$^{25}$ Even with the implementation of advanced positional encoding methods, LLMs can still be influenced by this phenomenon.$^{25}$

While earlier analyses frequently reported a prominent positional bias in controlled experimental settings, for instance, by rotating the position of a single relevant passage within an otherwise irrelevant context, its impact has been found to be marginal in real-world RAG scenarios.$^{25}$ This difference arises because practical retrieval pipelines often return both genuinely relevant and highly distracting passages simultaneously. In such complex contexts, the positional bias penalizes both types of passages, effectively balancing out its overall impact.$^{25}$ Consequently, sophisticated strategies that attempt to rearrange passages based on an LLM's presumed positional preferences (e.g., placing the most relevant information at the beginning or end) do not consistently outperform random shuffling in real-world applications.$^{26}$ This is attributed to a "contrastive effect", where the benefit of strategically placing relevant passages is counterbalanced by the unintended placement of highly distracting passages in those same favoured positions.$^{27}$ Furthermore, some LLMs, particularly those with high closed-book accuracy, may exhibit a "parametric bias", relying more on their pre-trained knowledge than on the provided context, especially when relevant passages are not in preferential positions. This can negatively influence their ability to effectively read and utilize external information.$^{27}$

The "lost-in-the-middle" effect and positional bias are not simple, direct inhibitors in RAG but rather complex phenomena whose impact is modulated by the simultaneous presence of both relevant and distracting information. This suggests that merely reordering chunks to "trick" the LLM into overcoming positional bias is often ineffective. A more fundamental solution lies in improving the quality of retrieved content and enhancing the LLM's inherent robustness to distraction. Initial research on positional bias often used simplified setups, leading to conclusions that LLMs heavily ignore middle content.$^{25}$ However, in practical RAG systems, where retrievers often fetch both relevant and highly distracting passages $^{25}$, the impact of positional bias becomes less pronounced. This is because the bias penalizes both beneficial and detrimental information, creating a complex interplay. Therefore, simply trying to place the "best" chunks at the beginning or end is not a guaranteed solution, as highly distracting chunks might also end up in those favored positions, negating the intended benefit.$^{27}$ This shifts the focus from where to place chunks to what chunks are retrieved in the first place, and how resilient the LLM is to imperfect retrieval.

4.2 The Detrimental Impact of Irrelevant and Distracting Information

A well-documented issue in Retrieval-Augmented Generation (RAG) is the negative influence of irrelevant and distracting information. Irrelevant passages are defined as those that do not provide useful information for answering the query. A particularly problematic subset, "distracting passages", contains information that is irrelevant yet semantically related to the query, which can actively mislead the LLM.$^{28}$

The presence of distracting passages can cause LLMs to generate incorrect responses, significantly degrading accuracy even when a truly relevant document is also present in the prompt.$^{28}$ Studies have shown that "hard distracting passages", those with a high quantifiable distracting effect, cause a larger accuracy drop (ranging from 6 to 11 percentage points) compared to "weak" ones, and this detrimental effect persists even in larger LLMs.$^{28}$ Paradoxically, "stronger" retrievers, while designed to maximize the recall of relevant information, can inadvertently deliver more harmful distractors.$^{25}$ This occurs because these retrievers are highly effective at finding semantically similar content, which can include misleading but related information. Reranking, while generally beneficial, can also amplify this problem by increasing the average distracting effect of irrelevant passages that end up in top positions.$^{28}$ Researchers are exploring methods for generating synthetic distracting passages (e.g., related topics, hypothetical scenarios, negations) to improve LLM robustness to such noise.$^{28}$

The very act of retrieval, especially with "stronger" retrievers, presents a "double-edged sword." While it aims to increase the recall of relevant information, it simultaneously increases the likelihood of introducing highly distracting, semantically similar but ultimately unhelpful information. This means that RAG system design must prioritize not just recall, but also robust filtering and LLM resilience to noise. RAG's core purpose is to provide relevant external knowledge.$^4$ However, no retriever is perfect, and they often return irrelevant or "distracting" passages.$^{25}$ The critical observation here is that stronger retrievers, which are designed to find more relevant information, also tend to retrieve more harmful distracting passages.$^{25}$ This creates a paradox: improving the retriever's primary function (recall) can exacerbate the problem of distraction. Therefore, simply optimizing retrieval for "relevance" (as traditionally defined) is insufficient. RAG systems must also incorporate mechanisms, such as robust reranking or LLM fine-tuning with hard negative examples, that specifically address the distracting effect to ensure true performance gains, especially for multi-step tasks where a single misleading piece of information can derail the entire reasoning chain.

4.3 Cognitive Load and Context Window Management

Large Language Models are fundamentally constrained by the knowledge encoded in their parameters and the fixed context window available during inference.$^{30}$ The concept of "cognitive load", analogous to human information processing, is highly relevant here. Cognitive Load Theory (CLT) categorizes load into intrinsic (content complexity), extraneous (poor instruction design), and germane (schema construction).$^{31}$ For LLMs, the inherent "content complexity" of the input is a dominant factor influencing their processing efficiency.$^{31}$

Presenting an LLM with an excessive number of tool descriptions or a large volume of irrelevant information can saturate its context window, thereby increasing its "cognitive load".$^{30}$ This overload can lead to reduced selection accuracy and an increase in hallucinations.$^{30}$ Conversely, supplying only the most relevant context, for instance, through mechanisms like RAG-MCP for tool selection, significantly reduces prompt size and complexity. This mitigation of "prompt bloat" directly lowers the LLM's cognitive load.$^{30}$ By narrowing the choices and freeing up context space for task-specific reasoning, especially in multi-turn dialogues, the LLM's decision-making capabilities are markedly improved.$^{30}$

While long context windows offer the appealing prospect of easy information input, simply pulling in too many chunks can be counterproductive. Beyond a certain point, the inclusion of excessive irrelevant or distracting information can confuse the model, causing performance to decline.$^{17}$ The key lies in identifying the "sweet spot" for context length, where sufficient information is provided to maximize recall without overwhelming the model with unnecessary noise.$^{17}$

The concept of "cognitive load" in LLMs highlights that simply increasing the context window size or the quantity of retrieved information does not guarantee improved multi-step inference. Instead, it introduces a critical trade-off where the quality and conciseness of the retrieved context directly impact the LLM's processing efficiency and reasoning accuracy. This implies a need for highly precise retrieval and filtering mechanisms. While LLMs are capable of handling long contexts $^{19}$, the evidence suggests a point of diminishing returns or even negative impact when too much information, particularly irrelevant or distracting content, is included.$^{17}$ This is framed in terms of "cognitive load".$^{30}$ If the LLM is forced to "sift through hundreds of distractors" $^{30}$, it consumes computational resources and can lead to errors. This directly impacts multi-step reasoning, which requires focused attention on relevant facts. Therefore, effective RAG design is not just about what to retrieve, but how much and how clean that retrieved information is, to ensure the LLM can efficiently process and reason over it without being overwhelmed. This reinforces the importance of advanced reranking and filtering techniques that go beyond simple relevance. Table 2: Factors Affecting LLM Performance in Long Contexts

5. Empirical Evidence and Performance Analysis

Empirical studies provide concrete evidence regarding the impact of chunk retrieval sequence on the multi-step inference capabilities of LLMs within RAG systems. This section synthesizes key findings from various benchmarks and case studies.

5.1 Case Studies on Chunk Order and Multi-Step Question Answering

Comparative studies between DOS RAG and Vanilla RAG consistently demonstrate the superior performance of DOS RAG across a range of benchmarks, including ∞Bench, QuALITY, and NarrativeQA.$^{19}$ This performance advantage is particularly notable when the retrieval budget is expanded to tens of thousands of tokens.$^{19}$ For example, on the ∞Bench dataset, DOS RAG achieved an accuracy of 93.1% at 30K tokens, significantly outperforming Vanilla RAG, which reached 87.8%.$^{19}$ This consistent empirical outperformance of DOS RAG provides strong evidence that LLMs' multi-step reasoning capabilities are profoundly tied to the narrative and structural coherence of the input context, rather than merely the presence of highly relevant, but potentially fragmented, information. This validates the theoretical arguments for sequential processing preference.

Furthermore, these studies reveal that complex multi-stage RAG pipelines, such as ReadAgent and RAPTOR, often underperform simpler methods like DOS RAG, especially at moderate token budgets.$^{19}$ This suggests that the added complexity of multi-stage approaches yields diminishing returns when long-context LLMs can effectively incorporate relevant context in a single, well-ordered pass.$^{20}$ However, there is a "sweet spot" for context length: DOS RAG's performance tends to plateau and even decline beyond a certain retrieval budget (e.g., 30K tokens).$^{17}$ This indicates that simply expanding the context window with more chunks can eventually introduce too much noise or irrelevant information, underscoring the importance of balancing recall with precision and effective filtering.

Research on premise order in reasoning tasks further supports the importance of sequence. Studies demonstrate that permuting the order of premises in deductive reasoning tasks can lead to a performance drop of over 30% in LLMs.$^{18}$ LLMs consistently perform best when premises are aligned with the sequential steps of a ground truth proof.$^{18}$ In the context of multi-hop question answering, reranking also plays a crucial role. Inverted context ordering, where the most relevant chunks are placed first, can lead to improvements in correctness.$^{24}$ When rerankers like BGE-M3 are combined with higher retrieval@k values, more "gold documents" (highly relevant chunks) are retained in the reranked set, enhancing performance for multi-hop questions.$^{24}$ However, increasing rerank@k with a fixed retrieval@k can introduce higher variation in correctness scores, ranging from 1% to 25%.$^{24}$

5.2 Evaluation Benchmarks and Metrics

The field of RAG and LLM evaluation is maturing, evidenced by the proliferation of specialized benchmarks designed to assess complex reasoning capabilities. The MTI Bench, for instance, is specifically tailored to analyze Multi-Task Inference, distinguishing between tasks with sequential dependencies (Multi-Step subset) and those without (Multi-Part subset).$^{10}$ This benchmark has shown that state-of-the-art LLMs, such as Llama-2-Chat-70B and GPT-4, can achieve significantly better performance (up to 12.4%) and speed (1.46 times faster) with Multi-Task Inference compared to Single-Task Inference, particularly for stronger models.$^{10}$

Another important benchmark, ProcBench, is designed to evaluate multi-step reasoning by challenging LLMs with explicit instructions and questions that require strict adherence to provided steps.$^{11}$ Its focus is on assessing the ability to follow step-by-step procedures, a critical skill for applications like automated decision-making and planning.$^{11}$ DataMorgana offers a novel approach for generating customizable synthetic benchmarks with single-hop and multi-hop QA pairs, utilized in challenges such as LiveRAG 2025.$^{15}$ For evaluating multi-modal RAG systems (spanning text, tables, and knowledge graphs), mmRAG provides a modular benchmark that assesses components beyond just generation, including query routing and retrieval accuracy.$^{35}$ Furthermore, RAGChecker is a fine-grained evaluation framework that incorporates diagnostic metrics for both retrieval and generation modules, demonstrating better correlations with human judgments.$^{36}$

These specialized benchmarks employ a variety of evaluation metrics. Accuracy is a common metric, used for instance in the evaluation of ChunkRAG on the PopQA dataset.$^{14}$ For more nuanced assessments, metrics like F1, BLEU-1, BLEU-4, ROUGE-L, and METEOR are employed, particularly for tasks like NarrativeQA.$^{19}$ In challenges like LiveRAG, correctness and faithfulness scores are critical for evaluating the quality of generated answers.$^{16}$ The proliferation of these specialized benchmarks signifies a maturing research field that recognizes the inadequacy of general QA metrics for assessing complex reasoning in RAG. This indicates a growing understanding that multi-step inference requires specific, granular evaluation beyond simple end-to-end accuracy, driving innovation in context organization. The evolution from general QA benchmarks to highly specialized ones, which distinguish sequential tasks $^{10}$, step-by-step procedure following $^{11}$, and multi-hop questions $^{15}$, demonstrates that the research community is moving towards a more nuanced understanding of LLM capabilities within RAG. This shift implies that the design of RAG systems, particularly regarding chunk retrieval sequence, must now be optimized not just for general relevance, but for the specific demands of these complex reasoning tasks. The emphasis on metrics like "correctness" and "faithfulness" further underscores the need for precise and contextually appropriate information delivery, which is directly influenced by chunk order.

Table 3: Overview of Benchmarks for RAG Multi-Step QA Evaluation

6. Optimizing Chunk Retrieval Sequence for Enhanced Multi-Step Reasoning

Translating the empirical findings and observations into actionable strategies is essential for designing RAG systems that excel in multi-step inference tasks. Optimization requires a multi-faceted approach, considering chunking, reordering, and mitigation of detrimental factors.

6.1 Best Practices for Chunking and Reordering

To optimize chunk retrieval sequence, a primary focus must be placed on prioritizing contextual coherence. For multi-step reasoning, chunking strategies should aim to preserve logical units and narrative flow, rather than simply adhering to fixed sizes. Recursive-based chunking, sentence-based chunking, and particularly document structure-based chunking (as exemplified by DOS RAG) are highly beneficial for maintaining this crucial context.$^{13}$ Given its consistent outperformance across various benchmarks, adopting DOS RAG as a baseline is strongly recommended, especially when working with long-context LLMs and tasks that demand narrative understanding.$^{19}$

While initial retrieval provides a set of relevant chunks, a strategic reranking step is indispensable for refining the order and reducing noise. Cross-encoders offer high precision in this regard, while multi-vector rerankers provide a balance between performance and efficiency. For deeper relevance scoring, fine-tuned LLM rerankers and LLM-as-a-judge approaches can be employed, though their associated latency must be carefully considered.$^{22}$ Furthermore, implementing inverted context ordering, where the most relevant (reranked) chunks are placed immediately before the query, has been shown to improve correctness in multi-hop QA tasks.$^{16}$

Optimizing chunk retrieval sequence is not a standalone step but requires a holistic approach, integrating intelligent chunking, robust retrieval, and strategic reranking. The most effective practice involves a dynamic balance between preserving the original document structure for narrative flow and leveraging reranking for query-specific relevance. The various studies present different techniques for chunking and reordering. The key understanding is that these techniques are not mutually exclusive but rather complementary. For instance, while DOS RAG emphasizes maintaining the original structure $^{20}$, effective reranking can still improve the selection of which chunks to include and their final placement within that structure (e.g., inverted context ordering for the most relevant ones).$^{16}$ This suggests that a truly optimized system might involve chunking based on logical units, retrieving a larger initial set, applying a reranker, and then finally reordering the top-K chunks according to their original document sequence or a query-specific optimal order. This integrated view highlights the need for a pipeline approach rather than isolated optimization efforts.

6.2 Strategies for Mitigating Positional Bias and Distraction

To effectively mitigate positional bias and the detrimental impact of distracting information, RAG systems must focus on proactive measures. First, efforts should concentrate on developing retrievers that not only maximize recall but also minimize the retrieval of highly distracting passages.$^{25}$ This is crucial because stronger retrievers can inadvertently bring more harmful distractors into the context. Second, robust LLM fine-tuning with carefully selected "hard distracting passages" can significantly increase the LLM's accuracy and resilience against noise.$^{28}$ Third, implementing LLM-driven chunk filtering (e.g., ChunkRAG) is a powerful strategy to evaluate and filter retrieved information at the chunk level, ensuring that only pertinent chunks are utilized. This directly reduces hallucinations and improves factual accuracy.$^{14}$ Fourth, for complex multi-step queries, query rewriting or decomposition into simpler sub-queries can improve retrieval accuracy and reduce the likelihood of fetching irrelevant information.$^{14}$ Finally, active context window management is vital to avoid overload. Providing only the most relevant context reduces the cognitive load on the LLM, enhancing selection accuracy and reducing hallucinations.$^{30}$ Identifying the "sweet spot" for context length, where recall is maximized without introducing excessive noise, is also paramount.$^{17}$

Mitigating positional bias and the distracting effect shifts the focus from merely reacting to retrieved chunks to proactively ensuring the quality and focus of the context before it reaches the LLM. This implies that pre-processing and intelligent filtering are as crucial as the retrieval itself. The studies indicate that positional bias and distracting information are inherent challenges.$^{25}$ Simply reordering after retrieval is often insufficient to address these issues.$^{26}$ Therefore, the solution must involve proactive measures. This includes improving the initial retrieval to be less prone to fetching distractors, and then employing strong filtering (like ChunkRAG) to eliminate noise before it ever reaches the LLM's context window.$^{14}$ Furthermore, making the LLM itself more robust through fine-tuning with challenging examples $^{28}$ creates a defense-in-depth strategy. This multi-layered approach is essential for reliable multi-step inference.

6.3 Advanced Techniques for Multi-Hop Reasoning

Addressing multi-step reasoning effectively in RAG necessitates moving beyond simple retrieve-and-generate pipelines towards more dynamic, iterative, and potentially graph-aware architectures. For multi-hop reasoning, which intrinsically requires connecting information across multiple sources or steps, iterative retrieval becomes crucial. This involves employing multi-round question refinement processes, decomposing main questions into sub-queries, generating answers for each, and iteratively retrieving additional context as needed.$^{16}$ Adaptive retrieval mechanisms that dynamically determine retrieval necessity and balance performance gains with inference speed also represent a significant advancement.$^{38}$ The integration of structured knowledge, such as graph-based RAG (e.g., knowledge graphs), can enrich the learning context, particularly for complex reasoning over heterogeneous knowledge sources.$^{35,39}$ This approach facilitates multi-hop reasoning by explicitly modeling relationships between entities, which is often difficult to capture through purely semantic similarity. Finally, the use of prompt-based reasoning chains like Chain-of-Thought (CoT), Tree-of-Thought (ToT), or Graph-of-Thought (GoT) can explicitly model logical chains and guide the LLM's reasoning process step-by-step, enhancing its ability to perform complex deductions.$^6$ These architectural advancements demonstrate a recognition that multi-step reasoning demands a more sophisticated and interactive approach to information access and organization.

7. Conclusion and Future Directions

The analysis presented in this report underscores that the chunk retrieval sequence is a critical determinant of a Large Language Model's multi-step inference performance within Retrieval-Augmented Generation systems. The findings consistently highlight the significant benefits of preserving the original document structure, as demonstrated by DOS RAG, which often outperforms relevance-based sorting by maintaining narrative continuity crucial for complex reasoning. The nuanced role of reranking is also evident, as it refines relevance but must be balanced against computational overhead. Furthermore, the pervasive challenges of positional bias and the detrimental impact of distracting information necessitate proactive mitigation strategies.

The implications for RAG system design are clear: a holistic approach is required. This involves considering not only semantic relevance but also contextual coherence, the cognitive load imposed on the LLM, and robustness to noise. Simply increasing the context window size or the quantity of retrieved information does not guarantee improved multi-step inference; instead, the quality and conciseness of the retrieved context directly impact the LLM's processing efficiency and reasoning accuracy.

Future research and development should focus on several promising directions:

• Adaptive Retrieval Architectures: Further development of systems that can dynamically adjust retrieval strategies and context presentation based on the complexity of the query and the current state of the LLM.$^8$
• Real-time Retrieval Integration: Enhancing the seamless and low-latency integration of retrieval within LLM inference loops to support more interactive and dynamic applications.$^9$
• Structured Reasoning over Multi-Hop Evidence: Continued investigation into how RAG systems can better facilitate complex, multi-hop reasoning, potentially through explicit graph-based representations or advanced prompting techniques that guide logical derivations.$^{6,39}$
• Robustness to Adversarial Inputs: Developing RAG systems that are more resilient to noisy or adversarial retrieved content, ensuring reliable performance in challenging environments.$^{8,28}$
• Cross-Modal and Multi-Lingual RAG: Expanding research to encompass multi-modal data (e.g., images, audio, video) and multi-lingual contexts, as current benchmarks are largely single-modal and English-centric.$^{4,35,40}$
• Evaluation Methodologies: Continued refinement of evaluation frameworks and benchmarks to more accurately capture the nuances of multi-step inference and the quality of contextual information.$^9$

These future directions underscore the ongoing evolution of RAG systems, moving towards more intelligent, adaptive, and robust architectures capable of supporting increasingly sophisticated LLM applications.