Multi-chain reasoning is a challenging task not only because the multi-chain evidence is dispersed, but also because different parts of reasoning may require distinct, interacting chains of support. Flat retrieval can flood a model with loosely related text, whereas early selection of a single reasoning path can discard evidence that becomes useful only after other facts are considered. We introduce RESIN (Reasoning with Entity Structured Indexing), which uses structure to locate evidence while keeping the original source as the basis for reasoning. RESIN decomposes a question into focused one-hop needs, retrieves a small set of source-grounded units for each hop, and places only this relevant evidence in a compact reasoning buffer. The buffer preserves alternative supports until final composition, making intermediate decisions explicit without forcing a brittle path. On the full HolisQA benchmark, RESIN achieves 82.92 context recall and 76.70 answer relevancy over all questions, with higher reported CR and AR across five domains and higher F1 in four domains than the published baselines. Ablations show that both typed evidence diversity and hop-wise decomposition are essential, supporting a clear conclusion: Precise evidence organization and delayed commitment make multi-chain reasoning effective and auditable. Our code is available at https://github.com/RamonMeng/RESIN.
Figures & tables
Figure 1: Resin at a glance. Offline, entity-structured records serve as addresses into source-grounded evidence. Online, a dependency-aware plan creates focused one-hop needs; each retrieves a small candidate set, and the resulting evidence buffer remains available until final composition.
Field group
Stored fields
Purpose and example
Identity
kind; stable unit ID
Distinguishes a fact from a rule or directed transition.
Core claim
subject; entity type; predicate; object
Binds “bus fare is $4” to the correct route and property.
Logical structure
conditions and conclusion; source and target states
Preserves conditions and direction, e.g., congestion increasing ride time.
Qualifiers
modality; polarity; scope; confidence
Keeps a weekday travel-time estimate distinct from an unrestricted claim.
Grounding
evidence quote; passage and sentence IDs
Links every normalized record back to the exact source span.
Table 1: Functional schema of a reasoning unit. Optional fields are populated only when supported by the source.
Unit kind
Representation
Why it is useful
entity-fact
entity | property | value
Retrieves an attribute stated for one named entity.
type-property
type | property | value
Stores a generic property that may support type inheritance.
entity-type
entity | is-a | type
Supplies explicit membership evidence before a type property is applied.
relation
entity or type | relation | entity or concept
Preserves a typed link that is neither causal nor conditional.
causal-edge
source state → target state
Keeps causal direction and state change explicit.
rule
conditions → conclusion
Prevents a conclusion from being used when its antecedents are absent.
Table 2: Reasoning-unit kinds and canonical representations. Colors mark entity, type, property, value, relation, state, and condition slots.
Medicine
Computer Science
Business
Biology
Psychology
Method
F1
CR
AR
F1
CR
AR
F1
CR
AR
F1
CR
AR
F1
CR
AR
Naive baselines
NaiveGeneration
12.63
0.00
44.70
18.93
0.00
48.79
18.58
0.00
46.14
11.71
0.00
45.76
22.91
0.00
50.00
BM25
17.72
52.04
50.64
24.00
39.12
52.40
28.11
37.06
55.52
19.61
43.02
52.32
30.46
33.44
56.63
StandardRAG
26.87
61.08
56.24
28.87
49.44
57.10
47.57
46.79
67.42
28.31
42.69
57.58
37.19
52.21
59.85
Graph-based RAG
Table 3: HolisQA results across five domains and all questions. F1 measures answer overlap; CR and AR denote context recall and answer relevancy. All scores are percentages. Baseline scores are transcribed from the published CausalRAG2 table; only the Resin row is measured in our experiment. Highest and second-highest values are bold and underlined ; cross-system evaluation caveats are discussed in the text.
MS MARCO
QASC
Method
F1
CR
AR
F1
CR
AR
Naive baselines
NaiveGeneration
5.28
0.00
15.06
2.69
0.00
13.74
BM25
6.97
45.78
20.33
2.49
6.12
13.17
StandardRAG
14.93
48.55
31.11
2.01
5.50
13.16
Graph-based RAG
Table 4: General-domain results. The Resin row uses 500 MS MARCO queries and 426 QASC queries; baseline F1/CR/AR are transcribed from the published CausalRAG2 full-benchmark table. Highest and second-highest values are bold and underlined .
Figure 2: Ablations on a fixed HolisQA subset. (a) CR improves rapidly with the number of retrieved units and then saturates, while AR peaks near k=10 . (b) Restricting retrieval to any single unit kind underperforms the complete mixed index.
Variant
AR
CR
Full Resin
77.03
89.30
w/o hop decomposition
70.04
42.33
Table 5: Effect of hop-wise query decomposition on a fixed HolisQA subset. The full method uses k candidates per hop; the single-query variant has one set of k .
Benchmark
Domain and source
Question and reasoning focus
Evidence setting
HolisQA ( Wang et al., 2026 )
Scientific articles spanning biology, business, computer science, medicine, and psychology
Open-ended holistic questions whose answers require integrating statements distributed across an article, including entity properties, relations, conditions, and causal processes
Gold answers and multiple supporting sentences are linked to the source articles. We evaluate the complete released dataset and report results separately for its five domains.
MS MARCO ( Bajaj et al., 2016 )
Anonymized queries sampled from a production web-search system, paired with passages from retrieved web documents
Open-ended question answering in which a system must identify useful evidence among noisy, partially relevant search passages and produce a concise response
Candidate web passages accompany each query, and human annotators provide reference answers grounded in passages judged to contain sufficient information.
QASC ( Khot et al., 2020 )
Grade-school science questions paired with a large corpus of elementary scientific facts
Multiple-choice questions are designed so that two complementary facts must be composed, commonly by connecting a concept in the question to the correct answer through an intermediate concept
Gold fact pairs expose the two-step evidence chain supporting each answer, allowing context recall to measure whether both complementary pieces of evidence were retrieved.
Table 6: Benchmarks used in the main evaluation and the distinct evidence-reasoning capabilities they test.
Baseline
Family
Retrieval and reasoning mechanism
NaiveGeneration ( Wang et al., 2026 )
No retrieval
Generates an answer directly from the question, providing a retrieval-free reference for measuring the contribution of external evidence.
BM25 ( Wang et al., 2026 )
Sparse retrieval
Ranks passages by lexical term matching and inverse document frequency, then supplies the highest-ranked passages to the answer generator.
StandardRAG ( Lewis et al., 2020 )
Dense retrieval
Encodes the question and passages in a shared semantic space, retrieves the nearest passages, and conditions generation on their text.
GraphRAG Global ( Edge et al., 2024 )
Graph-based RAG
Aggregates community reports across a corpus-level entity graph to answer broad questions requiring global thematic evidence.
GraphRAG Local ( Edge et al., 2024 )
Graph-based RAG
Starts from query-matched entities and retrieves their local graph neighborhoods, associated relations, claims, and source text.
LightRAG ( Guo et al., 2025 )
Graph-based RAG
Builds a lightweight entity–relation graph and combines local entity retrieval with global concept-level retrieval.
Table 7: Baselines transcribed from the CausalRAG2 evaluation suite. Their reported scores, efficiency values, and costs are taken from that work.
Construction (per 1K corpus tokens)
Online query (per query)
Method
Tokens
Time (s)
Cost ($)
Tokens
Time (s)
Cost ($)
NaiveGeneration
0.00
0.000
0.00000
553.63
0.354
0.000078
BM25
0.00
0.000
0.00000
967.90
0.562
0.000110
StandardRAG
0.00
0.000
0.00000
1016.56
0.638
0.000120
GraphRAG Global
20241.49
19.694
0.00340
6566.85
1.850
0.000680
GraphRAG Local
20241.49
19.694
0.00340
4968.42
1.306
0.000510
Table 8: Cost and latency on HolisQA. Baseline entries are transcribed from the CausalRAG2 efficiency table and were not independently reproduced; only the Resin row is measured in our environment. Construction values are normalized per 1K corpus tokens, online values per query, and evaluation cost is excluded.
Case component
Details
Question
What mechanisms explain low-temperature tolerance in the cold-tolerant Kandelia obovata population, and what molecular insights were obtained?
Required evidence
The answer needs antioxidant/metabolic adaptation, transcription-factor findings, and SNP/alternative-splicing evidence. A physiological explanation alone does not cover the requested molecular insights.
CausalRAG2 evidence
The exported recall context includes SNP and splicing information. The final generation prompt instead retains antioxidant activity, named transcription factors, and metabolic/signaling pathways, without the SNP/splicing findings.
CausalRAG2 response
The answer explains catalase-driven ROS management, WRKY/NAC/MYB/ERF expression, and enriched pathways, but does not report SNP or alternative-splicing findings. Missing molecular evidence.
Why the omission occurs
The recorded evidence assembly removes a distinct molecular evidence category before final generation. The answer then elaborates the retained antioxidant/transcriptional explanation rather than recovering the missing category. The trace locates this loss between broad recall and the final prompt, not at initial extraction.
RESIN organization
Typed units make molecular findings independently addressable. Decomposed needs can distinguish the physiological mechanism from molecular evidence; the latter need not depend on selection of the former’s causal chain.
How does CD1c present lipids to TCRs, and how does this mechanism differ from other antigen-presenting molecules?
Required evidence
The mechanism combines simultaneous two-lipid loading with sideways presentation of bulky headgroups through a side portal. Two-lipid loading is a distinguishing property, not merely another step in the presentation pathway.
CausalRAG2 evidence
Its final generation prompt contains a fragment stating that two lipid antigens are presented from the top and side of the cleft. Its reranking draft also mentions two lipids. The required cardinality fact is therefore available before the final answer.
CausalRAG2 response
The answer describes lipid binding, sideways headgroups, the side portal, and TCR recognition, but omits simultaneous two-lipid loading. Missing distinguishing property.
Why the omission occurs
Available evidence is not fully carried into final synthesis: the answer explains how presentation works without preserving a central property needed to explain how it differs. This is a composition omission, not a failure to retrieve the two-lipid fact.
RESIN organization
An entity-specific property can be represented separately from the mechanistic transition. Need-specific retrieval locates the explanatory and comparative claims; source selection recovers original passages for direct final composition, without passing generated hop answers.
Table 10: CD1c: retaining a comparative structural property during answer composition.
Case component
Details
Question
How does CKL12 contribute to plant growth, and what evidence suggests regulation by brassinosteroid (BR) signaling?
Required evidence
The answer requires a growth phenotype and evidence of regulation. Insertion mutants suggest a contribution to hypocotyl and primary-root growth; promoter binding and reduced transcript after transcription-factor knock-down support the BR connection.
CausalRAG2 evidence
Its final prompt contains a BR-regulation fragment, including promoter sites and knock-down results, but no hypocotyl or primary-root finding. The saved trace does not identify which earlier processing step first excluded the phenotype.
CausalRAG2 response
The answer says CKL12’s exact growth role is unspecified in the provided items, then describes BR regulation. Growth component unanswered.
Why the omission occurs
Regulation and phenotype are different evidence roles. Retaining the regulatory fragment does not supply the missing growth observation, so the final answer cannot connect the two requested parts using its provided evidence.
RESIN organization
Growth effects and regulatory support are directly searchable claims, not interchangeable descriptions of CKL12. Need-specific retrieval allocates candidate capacity to each role; the final buffer can preserve the source passages supporting both roles.
Table 11: CKL12: separating a growth phenotype from evidence of BR regulation.
A central bottleneck in multi-hop Question Answering (QA) is that the granularity at which a question is expressed often differs from the granularity at which corpus evidence is retrievable. Existing methods address this mismatch by imposing fixed graph structures over the corpus, by iteratively reformulating the query, or by executing a generated program over it, but these strategies do not explicitly decide when a query unit is already supported by evidence and when it should be refined. We formulate this bottleneck as retrievable granularity discovery and introduce Hi-Q, an evidence-conditioned framework for hierarchical query refinement. At each query node, a resolution operator tests whether retrieved evidence supports the current query unit; resolved nodes terminate, while unresolved nodes are expanded by a dependency-preserving binary operator and checked by a semantic coverage verifier. Hi-Q therefore grows a query tree whose topology is determined by corpus support signals rather than by a fixed decomposition template or a pre-built graph. We evaluate Hi-Q on three multi-hop QA benchmarks, primarily under full-corpus retrieval, where dependent evidence must be located among open-domain distractors rather than within a small annotated pool. In this setting Hi-Q reaches 52.3 EM and 64.0 F1 averaged over the three benchmarks, ahead of the iterative retrieval baseline IRCoT by 15.1 EM / 18.2 F1 on that same average, and ahead of the graph-based RAG baseline PropRAG by 11.5 EM / 12.0 F1 on MuSiQue-full, without corpus-wide graph construction. In the restricted supporting/distractor setting used by prior work, Hi-Q likewise attains the best accuracy, with 57.9 EM and 69.3 F1 on average, ahead of PropRAG by 5.6 EM / 3.9 F1 and IRCoT by 13.7 EM / 15.8 F1. The project page is available at https://hi-q-project.github.io/.
Jueun Kim, Sungho Park, Wook-Shin Han
Department of Computer Science and Engineering, POSTECH · Graduate School of Artificial Intelligence, POSTECH
Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer a complex question are usually connected through intermediate entities, relations, and constraints. We propose HyperProve, a retrieval-augmented QA framework that addresses this challenge by coupling question decomposition with answer-conditioned expansion over a hypergraph of atomic facts. HyperProve does not use atomic facts, hypergraphs, or iterative retrieval in isolation; instead, it carries intermediate answers and supporting hyperedges as retrieval state, then uses that state to bias the next local hypergraph expansion. This design enables HyperProve to construct coherent evidence chains for final answer generation while making the retrieval process stateful and fact-centered. Across multi-hop QA benchmarks, HyperProve achieves the best overall performance in our evaluation, outperforming the strongest baselines by an average relative improvement of 6.2% in answer accuracy and 4.9% in F1.
An Nguyen Phu, Dung Nguyen Quang, Luu Hieu An +3
Hanoi University of Science and Technology · Monash University · University of Oregon
Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. It presents two key challenges: generating correct reasoning paths in response to the complex user queries, and accurately retrieving essential knowledge in the face of potential limitations in large language models (LLMs). Existing approaches primarily rely on prompt-based methods to generate reasoning paths, which are further combined with traditional sparse or dense retrieval to produce the final answer. However, the generation of reasoning paths commonly lacks effective control over the generative process, thus leading the reasoning astray. Meanwhile, the retrieval methods over-rely on knowledge matching or similarity scores rather than evaluating the practical utility of the information, resulting in retrieving homogeneous or non-useful information. Therefore, we propose a Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator framework named SEARCH-R. Specifically, SEARCH-R trains an end-to-end reasoning path navigator, which is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model. Moreover, a novel dependency tree-based retrieval is designed to evaluate the informational contribution of the document quantitatively. Extensive experiments on three challenging multi-hop datasets validate the effectiveness of the proposed framework. The code and dataset are available at: https://github.com/Applied-Machine-Learning-Lab/ACL2026_SEARCH-R.
Yuqing Fu, Yimin Deng, Wanyu Wang +9
City University of Hong Kong · Xi’an Jiaotong University · Michigan State University +2