cs.CLOct 19, 2026
SaveRESIN: Multi-Chain Reasoning with Entity-Structured Indexing
Organizations: University of Illinois Urbana-Champaign
Abstract
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 . (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 candidates per hop; the single-query variant has one set of .
| 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. |
Table 9: Kandelia: preserving molecular findings alongside the dominant physiological explanation.
| Case component | Details |
|---|---|
| Question | 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.
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