ScholarStack：面向科学智能体的分层研究资产编排与跨任务复用框架

ScholarSeed AI Team ^††thanks: Full author list available in the Contributions section.

Abstract

Scientific agents support a range of literature-based research tasks, such as retrieval, question answering, evidence-grounded generation, and claim assessment. Most existing systems, however, are organized around individual tasks: the same papers are repeatedly retrieved, segmented and interpreted, and the understanding built in one task is difficult to reuse in the next. We present ScholarStack, a layered research asset framework that compiles a paper collection into reusable, versioned, and provenance-preserving assets at three complementary levels: paper-level statements, domain-level organization, and cross-paper syntheses. A common access interface returns task-specific views at the evidence granularity each task requires, preserving study conditions, source traceability, and verification status. We demonstrate the effectiveness of ScholarStack on four scientific task families spanning ten specific scenarios. Quality gains shine on tasks requiring cross-paper evidence, such as multi-paper question answering and literature review generation; query-time token cost also falls as assets are compiled once and reused across tasks. Continuous research chain integration further verifies that task-derived understanding can be efficiently saved, retrieved, and revised. These results suggest that layered research assets can serve as shared infrastructure for scientific agents, shifting literature-based assistance from isolated document processing toward cumulative, evidence-grounded workflows.

1 Introduction

Large language models are increasingly deployed as agents that plan, invoke tools, and execute multi-step workflows. These capabilities are reshaping scientific work: research agents retrieve and synthesize literature, answer technical questions, verify evidence, and assist with proposal and experimental design (Zheng et al., 2025; Ren et al., 2025). Recent systems are also exploring this paradigm toward autonomous experimentation and end-to-end scientific discovery (Lu et al., 2026; Gottweis et al., 2026). However, scientific research is more than a collection of independent requests or solving isolated tasks. It is a cumulative process: the understandings developed while completing one task should remain interpretable, reusable and revisable for the tasks that follow.

This cumulative requirement is particularly evident when several research activities build on the same literature. A researcher may begin with searching for relevant studies and organizing them into a review, then use that understanding to develop ideas, assess their novelty, and design experiments. Terminology and groups of related studies identified during search can guide the review; relationships and limitations established in the review can guide idea development; and comparisons made during novelty assessment can inform baseline selection and later writing. Existing agents support many of these activities (Lála et al., 2023; Wang et al., 2024; Li et al., 2024), but the intermediate judgments developed in these workflows remain embedded in temporary contexts or task-specific reports, and their evidence and scope are difficult to recover independently. Subsequent tasks must then revisit the same papers and reconstruct reasoning that earlier work has already established.

We refer to this limitation as the task-specific research bottleneck: knowledge constructed for one task is difficult to reuse in another without repeated source processing or loss of context. Addressing it requires both content reuse and understanding reuse. Content reuse retains source statements with their conditions and evidence. Understanding reuse retains the alignments, organizational judgments, and scoped interpretations built from those statements. Reliable reuse further requires explicit boundaries: a result must remain attached to its setting, a shared category must not imply comparability across settings, and a system interpretation must remain distinguishable from a source’s assertion. Evidence links and checking status let users inspect a judgment’s basis and determine what needs to be re-checked when that basis changes.

Existing approaches provide promising foundations for these reuse requirements. Retrieval-augmented generation and long-context processing improve access to evidence at inference time (Lewis et al., 2020; Gao et al., 2023); hierarchical and graph-based retrieval maintain persistent structures such as summary trees and entity graphs (Sarthi et al., 2024; Edge et al., 2024); scholarly knowledge bases organize structured research content (Jaradeh et al., 2019; Raimondi et al., 2026; Weiss et al., 2026; Yu and Wang, 2026). Building on these capabilities, reuse across scientific tasks requires each retained object to make explicit what it represents, under which conditions it applies, and what evidence supports it. The requirements differ by the kind of work being reused. Reusing a reported result requires a source statement bound to its study conditions and evidence; reusing the organization of a literature collection requires shared identities, categories, and relations; reusing a cross-paper interpretation requires an account with a declared scope and links to supporting and limiting evidence. These requirements motivate three complementary representations: source-grounded facts, domain organization, and scoped syntheses. Their distinct roles call for separate representations, while their dependencies require explicit links: organizational judgments and syntheses must remain connected to the facts and sources. A common asset model can therefore let tasks reuse each kind of object while retaining the basis needed to interpret and check it.

We introduce ScholarStack, a layered research asset framework for scientific agents. It compiles heterogeneous research materials into three linked layers: L1 records source-grounded scientific facts together with their study context and evidence references; L2 organizes them through a domain taxonomy, canonical entities, category assignments, and typed relations; L3 forms scoped cross-paper syntheses that state their coverage and evidence basis explicitly. A common access interface supports retrieving, selecting, expanding, comparing, and synthesizing knowledge, and returns typed knowledge views over these layers. This allows heterogeneous downstream tasks, from literature retrieval and question answering to evidence-grounded generation and claim assessment, to reuse the same research assets while selecting the evidence granularity they need. The resulting assets can remain revisable as materials change. Selected task-derived objects may optionally be registered after identity and reference checks, with semantic verification recorded separately.

We instantiate and evaluate the framework on four task families spanning a literature-based research workflow: literature retrieval, scientific question answering, evidence-grounded generation, and claim and consistency assessment. These tasks draw on the layers differently. For example, single-paper question answering exercises primarily the source-grounded facts of L1, whereas literature review generation benefits further from L2 organization and L3 syntheses. Assets compiled once from the source papers serve all tasks through the shared interface. We observe general performance and token efficiency benefits of ScholarStack across ten specific research scenarios, while the largest gains appear on tasks that require cross-paper evidence, such as multi-paper question answering and literature review generation. Continuous research chains further demonstrate understanding reuse and revision at lower per-chain token cost. Our contributions are as follows:

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  We characterize the task-specific research bottleneck: repeated source processing and reconstruction of prior judgments across tasks. We distinguish reuse of source-grounded content from derived understanding, and identify the evidence and scope needed for both.

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  We derive a layered research asset model from these requirements: source-grounded facts, domain organization, and scoped syntheses remain distinct but linked, with a common access interface and registration mechanisms for versioned, reusable objects.

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  We evaluate ScholarStack across ten task settings in four families and continuous research chains, demonstrating quality and efficiency benefits alongside traceable reuse and revision of task-derived understanding.

2 Related Work

Scientific agents for literature-based research.

Large language models are increasingly deployed as scientific agents that automate stages of the research lifecycle, and recent surveys chart their rapid progress across literature synthesis, question answering, evidence verification, and experimental design (Zheng et al., 2025; Ren et al., 2025). Beyond literature-based assistance, agents have begun to drive autonomous experimentation and end-to-end scientific discovery (Lu et al., 2026; Gottweis et al., 2026). Closest to our setting are agents built specifically for literature-based research. PaperQA retrieves scientific literature and assesses evidence relevance to answer research questions (Lála et al., 2023), OpenScholar synthesizes the literature through retrieval-augmented generation with citations (Asai et al., 2026), and long-form systems such as STORM organize retrieved material into structured, grounded reports (Shao et al., 2024). These systems are highly capable at individual tasks, but each is organized around a single request. The understanding built while completing one task, including aligned terminology, cross-paper relationships, and verified findings, remains embedded in a transient context or a task-specific output and is rarely registered in a persistent, reusable form. ScholarStack instead compiles this intermediate understanding into versioned, provenance-preserving assets, so that knowledge constructed for one task becomes infrastructure for the next rather than being reconstructed from the sources at each step.

Retrieval augmentation and hierarchical indexing.

Retrieval-augmented generation (RAG) grounds model outputs in external corpora at inference time and has become a standard paradigm for knowledge-intensive tasks (Lewis et al., 2020; Gao et al., 2023). To move beyond flat passage retrieval, hierarchical and graph-based methods build persistent index structures over a corpus. RAPTOR recursively summarizes text into a tree that supports retrieval at multiple levels of abstraction (Sarthi et al., 2024), and GraphRAG links an entity graph with community summaries to answer corpus-level questions (Edge et al., 2024). A related line reasons directly over knowledge-graph structure, as in Think-on-Graph’s traversal of relation paths (Sun et al., 2024) and G-Retriever’s subgraph retrieval for textual-graph question answering (He et al., 2024). These approaches demonstrate the value of access across levels of abstraction, but their primary abstraction is a retrieval structure optimized to fetch relevant context, and it does not separate the distinct evidential roles that reusable research knowledge must carry, namely what an individual source states and under which conditions, how research objects relate within a domain, and what several studies jointly support. ScholarStack keeps these three roles distinct yet linked and exposes them through typed operations, rather than folding them into a single summary tree or entity graph that is queried anew for each request.

Scholarly knowledge bases and structured research representations.

A complementary line represents scholarly knowledge in structured, machine-actionable form. The Open Research Knowledge Graph (ORKG) curates paper contributions as structured, comparable descriptions to support their reuse (Jaradeh et al., 2019). SCALE organizes scientific terminology into reusable concepts within a disciplinary taxonomy (Raimondi et al., 2026), Lacuna links paper-level content to research directions and open problems (Weiss et al., 2026), and Knows exposes agent-native structured representations of research content (Yu and Wang, 2026). More broadly, coupling language models with knowledge graphs has been framed as a spectrum from KG-enhanced models to synergized systems in which the two are mutually reinforcing (Pan et al., 2024). Each of these commits to a single primary representation, whether a contribution graph, a concept hierarchy, a research map, or a typed entry, and while several already record provenance and support reuse, none jointly maintains source-grounded facts, domain organization, and scoped cross-paper syntheses as separate-but-linked assets under shared provenance and revision semantics. ScholarStack targets this integration problem by maintaining a single evolving asset collection that preserves the three evidential roles, tracks their provenance and versions, and serves task-specific views over the same knowledge, so that a sequence of related research activities accumulates rather than repeats.

3 Method: Layered Research Asset Orchestration and Reuse

ScholarStack separates the construction of reusable research assets from their use in downstream tasks. We first derive the asset design from research requirements, then describe its knowledge representations and construction mechanisms. Finally, we explain how agents access these assets for individual tasks and preserve task-derived understanding for subsequent research.

3.1 Research Requirements and Asset Design

Research tasks repeatedly revisit the same literature, but the knowledge worth preserving extends beyond retrieved passages. Answering a question requires recovering what is reported under which conditions; comparing methods requires identifying related research and checking their assumptions, measurements, and evaluation settings; synthesis requires clarifying what multiple studies jointly support and within what scope. When such judgments remain embedded in task-specific outputs, subsequent tasks need to reconstruct their conditions, organization, and evidential basis.

These requirements imply that reusable research assets must preserve three complementary forms of information (Table 1). Source-grounded statements preserve what an individual paper asserts together with its conditions and source locations (L1); domain organization preserves identities, categories, and relations together with the basis and uncertainty of these judgments (L2); scoped syntheses preserve cross-paper interpretations together with their coverage and supporting, opposing, or limiting evidence (L3). The distinction reflects different evidential responsibilities: fidelity to a source, justification of an organizational judgment, and support for a scoped interpretation. Shared identifiers and evidence references connect these objects without treating them as interchangeable forms of knowledge.

ScholarStack realizes this design as three linked knowledge layers (Fig. 1): L1 captures source-grounded scientific facts, L2 represents domain organization, and L3 represents scoped cross-paper syntheses. The layers are complementary rather than a mandatory processing sequence for every task. For example, L2 may identify two methods as belonging to the same category, but whether their empirical results are directly comparable must still be determined from the conditions retained in L1; an L3 synthesis may integrate those results only within a scope justified by its evidence.

During task execution, the agent selects and combines the required objects into a task-specific Knowledge View. Different tasks can therefore select the layers and evidence granularity they require: a single-paper question may rely mainly on L1, a method comparison may combine L2 with L1 conditions, and a literature review may additionally draw on L3 syntheses. The following sections formalize these representations and describe how they are constructed and accessed.

3.2 Knowledge Representation and Construction

The knowledge layer compiles a document collection into a layered representation

where F is a collection of source-grounded Scientific Facts (L1), O is the domain organization that aligns and relates them (L2), and S is a collection of Scoped Syntheses that interpret them jointly (L3). These representations carry different evidential commitments: facts preserve what a source states and under which conditions; domain organization establishes identities, categories, and relations with an explicit basis; syntheses integrate findings within a declared scope. Keeping these roles distinct allows each to be checked and revised according to the kind of knowledge it represents.

Attributes and taxonomy dimensions vary with the discipline and study type, while these three roles remain distinct. Appendix A traces one research topic from source sentences through L1 facts and L2 organization to an L3 synthesis.

3.2.1 L1: Source-Grounded Scientific Facts

Representation.

L1 directly anchors scientific statements to source sentences and provides the evidential basis for higher-layer objects. A Scientific Fact f∈F records one statement from one source together with what is needed to interpret it, f=(statement,context,evidence): the statement identifies a subject and what the source reports about it, the context records the assumptions and conditions under which the report holds, and the evidence field locates the supporting sentence or sentences. Statements carry a type, such as a finding, a method description, a hypothesis, or a limitation, and a single paper typically contributes facts of several kinds. A quantitative fact keeps the reported value bound to the subject and the setting it was measured in, and every fact carries a verification status recording whether it has been checked against its cited sentences. In every case a fact records what the source asserts rather than certifying its truth.

Construction.

Facts are extracted by a language model guided by schemas for the study type, so that quantitative, qualitative, and theoretical content keep their corresponding conditions. Structural checks validate the required fields and source references. Semantic verification assesses whether a statement is attributed to the correct subject and preserves the content and qualifications of its cited sentences. Failed and unresolved semantic checks remain explicit in the fact’s verification status.

Role in access.

Recorded contexts let tasks distinguish relevant facts that concern different populations, assumptions, or evaluation settings. L1 supplies both reusable content and the conditions governing its comparison. Appendix A.1 presents a detailed example for a real paper.

3.2.2 L2: Domain Metadata and Organization

Representation.

Facts from different papers do not line up by themselves: the same object may appear under different names, and F alone does not specify their categories or relationships. The organization O=(T,E,A,R) supplies this structure. A Domain Taxonomy T defines categories and membership criteria along field-specific dimensions, such as method family, problem setting, or guarantee type. Canonical Entities E give a shared identity to mentions identified as the same method, dataset, metric, or other research object. Category Assignments A place an entity in a category of T, cite supporting facts, and record whether membership is author-declared or inferred and with what certainty. Assignments derived from a particular result retain that result’s conditions; labels from different results cannot be combined into a single guarantee. Typed Relations R connect identified objects through named relationships, such as evaluation on a dataset, comparison against a baseline, or improvement over an earlier method, with payloads such as reported results or gains.

Construction.

The three derived kinds are built from the extracted facts rather than from raw text: mentions are merged by deterministic rules with model adjudication for ambiguous cases, assignments must cite supporting facts, and relations are anchored in the extracted records that connect their endpoints. The taxonomy may be supplied by experts or prepared from the collection. Author-declared and inferred membership stay distinguishable, and unresolved cases remain uncertain rather than forced into a category. Construction also runs top-down: the taxonomy guides what extraction attends to and along which dimensions findings are compared, and facts that resist classification prompt taxonomy revision or re-extraction.

Role in access.

O supports selection by category, traversal along relations, and comparison along the dimensions of T. Membership in a common category establishes relevance, while comparability is still decided by the contexts recorded in F: two methods in the same family need not share assumptions, and shared membership alone does not justify aggregating their results. Appendix A.2 shows how the same paper’s facts support domain assignments and relations.

3.2.3 L3: Scoped Cross-Paper Synthesis

Representation.

A Scoped Synthesis s∈S records an interpretation formed across papers, s=(account,scope,basis). It may describe a method family, a qualified disagreement, or an open problem within the covered studies. The account states the interpretation; the scope declares the topic, applicable conditions, and studies considered; the basis cites registered facts and, where relevant, category assignments. Each cited object is marked as supporting, opposing, or limiting, exposing both the grounds for the interpretation and its boundaries.

Construction.

Synthesis uses O to organize and select related material, while its account is grounded in direct citations to the selected facts. Conflicting findings about the same question under comparable conditions are recorded as a disagreement. Differences attributable to study context are described separately, and unresolved uncertainty remains explicit. The generating model may cite only registered facts and assignments; candidates whose citations cannot be resolved are rejected before registration. Reference validation establishes that the basis is identifiable; semantic review separately assesses whether it supports the account and its declared scope. The scope describes the evidence considered, not complete coverage of a field.

Role in access.

Tasks can retrieve an existing synthesis with its basis or request a new account from selected evidence. A new account remains a candidate until registration. Appendix A.3 illustrates a method-family synthesis based on saved records, including its scope, supporting and limiting evidence, and review state.

3.2.4 Cross-Layer Integration and Evidence Traceability

Shared identifiers link assignments to supporting facts, relations to the extracted records that connect their endpoints, and syntheses to facts and relevant assignments. These references connect derived objects to source sentences and make their evidential dependencies inspectable.

Registration turns a knowledge object into a persistent Research Asset by validating required fields, identity, and reference integrity, reusing existing matches, and versioning new or revised objects. Structurally invalid outputs remain outside K. Semantic verification is recorded separately: failed or unresolved objects remain inspectable, and new syntheses do not inherit verification from their evidence. If a source or asset is revised, its dependents require rechecking; earlier versions preserve the basis of prior outputs. Task-derived knowledge objects may also be registered through the same mechanism for reuse in subsequent tasks. The construction cost and its near-linear scaling with corpus size are analyzed in Appendix F.4.

3.3 Task-Oriented Knowledge Access and Application

A task accesses K through a Knowledge View V: a selection of typed objects retaining their conditions, evidence references, and verification states. Table 2 defines five operations for constructing and using such views. The agent chooses operations and layers according to the request’s subject, scope, and constraints; there is no fixed sequence or requirement to use all three layers. A paper-scoped task may also receive the designated paper’s complete fact collection directly.

Task outputs retain the evidential distinctions established in Section 3.2. Comparison exposes differences in conditions without implying a ranking, and newly generated interpretations remain distinct from registered syntheses. Evidence references locate source sentences; when original-text access is allowed, the agent may read surrounding passages to check context. Knowledge-only settings rely on the recorded evidence and disclose its limitations. Missing evidence indicates limited coverage of the available view; it does not establish that no relevant work exists.

3.3.1 Literature Retrieval

For literature search, the view guides query formulation and evidence selection: L1 supplies findings and conditions, while relevant L2 organization and L3 syntheses suggest terminology and related approaches. The agent discovers candidate papers, checks them against source evidence, and refines its search as needed (Fig. 2). Final ranking relies on the retrieved papers.

Fixed-corpus retrieval.

Given a request and a fixed collection, 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞 and 𝐒𝐞𝐥𝐞𝐜𝐭 assemble relevant knowledge. Source links and knowledge-guided queries identify papers within that collection. The agent checks abstracts and relevant passages against the requested conditions and returns ranked candidates, distinguishing supported matches from unresolved details.

Open-world retrieval.

Given a request, the agent uses a view from an existing domain collection to formulate searches over external scholarly sources. It consolidates candidate identities, verifies the requested conditions, and returns papers with their supporting evidence. The compiled collection remains fixed during this search; newly discovered papers need not already belong to it.

Novelty assessment.

Given a proposed contribution and prior-work corpus, the agent decomposes the contribution by problem, mechanism, and setting. Relevant L1 facts supply comparison evidence, and L2 Canonical Entities align naming variants. 𝐂𝐨𝐦𝐩𝐚𝐫𝐞 identifies overlaps and residual differences with cited support. The output is bounded by the corpus and evidence examined; failure to find an overlap does not establish global novelty.

3.3.2 Scientific Question Answering

Given a question and designated papers, the agent identifies the requested information and conditions, assembles evidence, and generates an answer with citations (Fig. 3). Source-reported statements remain distinguishable from interpretations introduced during answering.

Single-paper question answering.

Source-grounded facts and the sentence-level source content indexed with them supply the answer evidence from the designated paper, located at a finer granularity than whole passages. When available, L2 Canonical Entities assist name alignment without expanding the evidence scope to other papers; L3 is generally unnecessary. The answer preserves the facts’ contexts and evidence references. If the selected evidence is insufficient, the agent checks the source when permitted or reports the unresolved information within the available view.

Multi-paper question answering.

Facts retain their paper identities and contexts. L2 entities align subjects, while 𝐂𝐨𝐦𝐩𝐚𝐫𝐞 organizes findings by question-specific dimensions, such as assumptions, settings, and outcomes. The answer combines compatible findings and preserves contextual differences and unresolved conflicts. An existing L3 synthesis may contribute if its scope matches the question; 𝐄𝐱𝐩𝐚𝐧𝐝 exposes its basis. Otherwise, 𝐒𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐳𝐞 can form a candidate account from the selected facts. Answer-time interpretations do not automatically become registered L3 assets.

3.3.3 Evidence-Grounded Generation

Given a writing or research objective, the agent organizes a view into themes, comparisons, or questions (Fig. 4). L2 categories and relations guide organization; L1 facts substantiate claims; L3 syntheses provide scoped interpretations. 𝐂𝐨𝐦𝐩𝐚𝐫𝐞 aligns conditions, and 𝐒𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐳𝐞 supplies an additional account when needed. Outputs distinguish literature-supported statements and interpretations from proposed hypotheses and design choices, which do not inherit verification from their motivating evidence.

Related-work generation.

Given a focal manuscript and reference collection, the agent identifies the target’s research problem, approach, and claimed contribution. L2 Category Assignments group references into research threads. Typed Relations capture support, contrast, and extension among studies. L1 facts provide evidence for comparing methods, assumptions, and findings while preserving their study conditions. L3 Scoped Syntheses provide cross-paper context within an explicit scope. The agent applies 𝐂𝐨𝐦𝐩𝐚𝐫𝐞 to organize the evidence along dimensions relevant to the target. It then writes a coherent account that relates prior work to the target’s approach and claimed contribution. Substantive claims about prior work cite the supplied references.

Literature review generation.

For a survey request, ScholarStack first retrieves a task-specific literature collection and compiles its knowledge assets, then constructs a view for writing. Existing assets can be reused; updates are needed only when the intended coverage requires additional or revised material. The writer uses the L2 Domain Taxonomy and Category Assignments to organize the field, L3 Scoped Syntheses to characterize developments and disagreements, and L1 facts with their source sentences to substantiate technical claims. It links the outline and central assertions to the selected facts and syntheses through their evidence references, then checks attribution, quantities, comparison scope, and citation placement. This top-down organization followed by bottom-up source checking exposes coverage gaps and allows provisional classifications and interpretations to be qualified or rejected.

Experimental-design generation.

Given a contribution’s claims, the agent gathers L1 facts about relevant procedures, baselines, observables, and limitations, using L2 relations and L3 accounts to organize them. For each claim, the plan specifies a change, comparator, observations that would support or weaken it, and applicable conditions. Reported settings provide the rationale; new design choices are marked as proposals.

Idea generation.

Given a topic or preliminary idea, the idea-generation skill identifies the research problem and resource constraints. It develops candidate directions and assesses their scientific value and feasibility. Through internal iteration, it strengthens each candidate’s rationale and outlines how the proposed contribution could be tested. In ScholarStack, the agent applies this skill using evidence accessed through the asset layer: L1 facts and study contexts, L2 relations, and relevant L3 syntheses. These assets support comparisons with prior approaches and help assess whether candidate ideas fit the conditions of the supporting evidence. The final ideas state their proposed contributions, rationale, and supporting references.

3.3.4 Claim and Consistency Assessment

This task family assesses assertions against available evidence. Here we instantiate it as claim verification against a designated cited paper, separately from the extraction verification used during knowledge construction.

Scientific claim verification.

Given a claim and paper, the verifier receives the paper’s complete L1 fact collection, without query-time retrieval or L2/L3 processing. Statements and contexts support a relation label and rationale distinguishing direct support, overstatement, topical similarity without support, and irrelevance (Wadden et al., 2020). Missing qualifications can affect the judgment, and incomplete evidence references limit traceability (Wadden et al., 2022).

3.3.5 Continuous Research

Given a research objective spanning multiple tasks, the agent assembles a Knowledge View for each task from the shared asset collection, including relevant objects retained from earlier tasks. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞 and 𝐄𝐱𝐩𝐚𝐧𝐝 expose prior findings and judgments with their conditions and evidence, supporting reuse in subsequent work. Task order and layer selection depend on the research objective.

Tasks may also contribute new or revised assets through optional registration. For example, literature review generation may identify supplementary L1 facts, L2 entity aliases, or L3 scoped syntheses; counterevidence encountered during novelty assessment may motivate revising an earlier synthesis. These contributions depend on the evidence encountered, with no fixed task-to-layer mapping. Later tasks can access the registered objects and their revision states. Section 4.2.5 evaluates one five-stage instantiation and dependent follow-up chains.

4 Experiments

We evaluate ScholarStack across diverse scientific tasks and continuous research chains, focusing on task quality, token efficiency, and the reuse of prior understanding. We also examine how different knowledge layers contribute to these outcomes.

4.1 Experimental Setup

We evaluate ScholarStack across four task families: literature retrieval, scientific question answering, evidence-grounded generation, and claim and consistency assessment. Unless otherwise specified, we use Qwen3.8-Max as the base model and Codex as the agent framework. Within each matched comparison, conditions share the source collection, base model, task instructions, and execution budget, subject to the task-specific access and prompting variants. Throughout the experiments, Full-text denotes the baseline that accesses the original paper texts, and ScholarStack denotes our method, which accesses research assets compiled from the same sources.

Evaluation uses task-specific quality metrics and, where available, query-time resource usage. Unless noted otherwise, token counts include model input and output. Construction costs, reuse assumptions, and further implementation details are reported in the appendices.

4.1.1 Literature Retrieval

Fixed-corpus retrieval.

We evaluate fixed-corpus literature retrieval on 50 queries sampled from LitSearch (Ajith et al., 2024). ScholarStack uses assets compiled from 900 representative source papers, while Full-text accesses the same sources as raw text. Both follow a four-stage protocol comprising planning, expansion, verification, and ranking under matched search and reading budgets. We additionally compare three deterministic baselines: BM25, Dense embeddings, and Hybrid retrieval. We report macro-averaged gold-paper recall at ranks 1, 5, and 10, MRR@40, and mean time and tokens per query. Corpus construction, baseline implementations, execution limits, and metric definitions are detailed in Appendix B.1.

Open-world retrieval.

In this task, we evaluate whether ScholarStack improves the high-priority results of an agent that can search beyond the layer’s source collection. We use the open-ended portion of SAGE (Hu et al., 2026), which associates each research-style query with two most relevant seed papers and a set of relevant shared references. We select 89 queries covering computer science, natural science, and healthcare domains. Each query has two most relevant seed papers and additional relevant references. A query-independent collection of approximately 1,750 full-text papers per domain supplies the local context; benchmark domain annotations route queries to the corresponding collection. Full-text receives a title-and-abstract catalog followed by selected full-text passages, whereas ScholarStack receives a structured catalog followed by selected L1/L2/L3 records. Both use one catalog call and one read call within a 12,000-character budget, followed by the same multi-round Agentic Search workflow over the public Web and an internal scholarly index. We score the explicit Tier 1 recommendations, averaging 4.95 papers per query, using macro recall, precision, and F1, together with macro and micro weighted recall (weights of 2 and 1 for the two relevance levels). Further details are provided in Appendix B.2.

Novelty assessment.

We evaluate whether a system can distinguish substantive overlap with prior work from topical similarity. For this task, we build a cross-domain benchmark from a citation-linked scientific corpus spanning 12 fields. It contains 40 overlap-present targets and 135 gold overlaps. Ground truth consists of a target’s prior work identified through citation relations; same-domain non-cited papers serve as hard distractors. Both methods share the base model, tool budget, and closed-book setting, differing only in knowledge access: Full-text applies our novelty assessment method while reading the original related-paper texts, and ScholarStack applies the same method over assets compiled offline from those texts. Web search is disabled. We report weighted identification F1, using an LLM matcher and weights of 1.0 for core overlap axes and 0.3 for peripheral axes, together with tokens per query. Appendix B.3 provides matcher, cost-accounting, ablation, and weighting-sensitivity details.

4.1.2 Scientific Question Answering

Single-paper QA.

We evaluate answering from one designated paper on QASPER (Dasigi et al., 2021) (416 papers and 1,428 questions) and PeerQA (Baumgärtner et al., 2025) (208 papers and 579 questions). This setting primarily tests access to local, source-grounded evidence. Full-text receives the entire paper; RAG retrieves fixed passages; ScholarStack retrieves structured facts and sentence-level evidence. Conditions share the answering prompt, and RAG and ScholarStack use the same semantic retriever. For QASPER, we report official Answer-F1, its answer-type breakdown, and Evidence-F1. For PeerQA, we report answerability metrics over 495 questions and generation Rouge-L against free-form answers (FF) and annotated evidence (AE) over 245 questions. Mean input tokens per question measure query-time cost. Appendix C.1 provides benchmark composition and evaluation details and additional analyses.

Multi-paper QA.

We evaluate our method on MDAQA (Huang et al., 2025), which contains 797 questions, each referring to at least two articles. Each question is accompanied by gold answers, from which we identify the corresponding gold papers. Using the same ordinary prompt without additional retrieval, we compare Full-text, which provides the complete parsed texts of the gold papers, with ScholarStack. GPT-6 Astra with high reasoning effort evaluates all candidate answers anonymously against a fixed evidence package, with randomized candidate identifiers and presentation order. Condition denotes the input setting used to generate each answer and is hidden from the evaluator. Correctness (Corr.) assesses evidential support and accurate attribution of subjects, values, and conditions. Completeness (Comp.) measures coverage of the question, while cross-paper synthesis (Synth.) assesses evidence-based integration and comparison across papers. Calibration (Cal.) measures whether the strength of assertions and stated limitations are consistent with the evidence. Directness (Dir.) assesses relevance and the avoidance of redundancy. Tokens denote the mean number of input and output tokens per question, and Sec. denotes the mean generation time in seconds per question. Costs include answer generation only, excluding knowledge construction and judging. Additional details are provided in Appendix C.2.

4.1.3 Evidence-Grounded Generation

Related-work generation.

We evaluate related-work generation on 64 target manuscripts from the OARelatedWork test split (Docekal et al., 2024). Each instance contains a focal manuscript and a fixed reference set of at least five papers. Its original related-work section is withheld from the generation input. The reference set is fixed, so no additional retrieval is performed. We compare the proposed ScholarStack with the Full-text baseline. Both use the same model and write-related-work skill. Full-text applies this skill to the complete reference texts, whereas ScholarStack applies the same skill to frozen paper-level assets and the L1/L2/L3 knowledge layers without access to those full texts. The focal-manuscript context, citation registry, writing procedure, and 300–600-word limit are otherwise identical, so the comparison isolates the evidence representation used by the shared writing skill. An independent LLM judge evaluates each anonymized section separately on Relevance, Coverage, Specificity, Support, and Overall quality. We also audit claims against the original references as True, Wrong Citation, Unverifiable, or False, and compute citation F1 against the benchmark reference set. Appendix D.1 gives selection, access, and evaluation details.

Literature review generation.

We evaluate on the 25-task ReportBench-ML subset used by Lacuna (Li et al., 2025; Weiss et al., 2026). Each task requests an English survey under topic, coverage, and publication-date constraints. For each task, ScholarStack uses an agentic search workflow to retrieve 100 relevant papers, compiles them into an L1–L3 knowledge view, and applies the review skill to produce the survey. Claude Code DeepResearch with Opus 4.8 model serves as the local comparator and cites 13 papers per task on average. Expert-survey content and gold reference membership are withheld from both workflows. We report micro citation precision, recall, and F1 over papers cited in the final body; the count-control condition retains the first 13 papers cited by ScholarStack in retrieval order, without gold-based reranking or regeneration. Content quality is assessed with point-wise RACE (Du et al., 2025) using an independent judge and shared task-specific rubrics. Published Lacuna values provide external context rather than rerun baselines. Appendix D.2 reports resource accounting and trace-backed examples.

Experimental-design generation.

We construct a cross-domain benchmark for experimental-design generation from a citation-linked scientific corpus spanning 12 fields with 38 targets. Each target is a paper whose source text reports an experimental study, and the ground truth is the set of experiments that paper actually performed, extracted from its source text; each target is accompanied by a corpus of related papers (median 14 per case) that a system may draw on when designing experiments. All methods share the base model and tool budget and are closed-book, differing only in knowledge access: Full-text (w/o Skill) reads the raw files with no task method, Full-text (w/ Skill) applies our domain-agnostic design method over the same raw files, and ScholarStack applies that method over assets compiled offline from the related corpus. We report (i) a coverage F1 against the paper’s actual experiments, weighted by experiment kind (main comparison/ablation 1.0; analysis, robustness, human evaluation, theory 0.6; other 0.3), and (ii) a blind pairwise preference in which a neutral LLM judge (GPT-6), shown the contribution and two anonymized designs in randomized order with an anti-verbosity instruction, selects the better design. Per-query cost is reported in tokens. Appendix D.3 details the metric, cost accounting, and honest-comparison notes.

Idea generation.

We evaluate 35 cases across five broad domains drawn from IdeaBench (Guo et al., 2024), MOOSE-Chem (Yang et al., 2024), and AI Idea Bench 2025 (Qiu et al., 2025). Each case provides a research task and reference papers. Full-text (w/o Skill) generates ideas directly from the task and reference full texts; Full-text (w/ Skill) applies the idea-generation skill described in Section 3.3.3 to the same full texts; ScholarStack retains that skill while accessing assets compiled from the same references, without directly reading the full papers. An LLM judge ranks anonymized outputs across five evaluation dimensions adopted from Li et al. (2024): novelty, significance, clarity, feasibility, and effectiveness. First, second, and third place receive 1, 0.5, and 0, respectively; scores are averaged across cases and then equally across dimensions. Appendix D.4 details the benchmark composition and judging protocol.

4.1.4 Claim and Consistency Assessment

Scientific claim verification.

We evaluate 80 claim–paper pairs from the public training/development split of NLPCC 2026 Shared Task 10, Track 2 (NLP2CT, 2026), balanced across Supported, Overstate, Topical Match, and Irrelevant (20 per label). Full-text receives the dataset-provided article text, whereas ScholarStack receives the original L1 objects compiled from the same paper. Neither condition receives task images or external retrieval. We report exact-label accuracy and mean tokens per query, including retries; failures and abstentions remain in the accuracy denominator. Evidence localization is not evaluated. Appendix E.1 provides selection criteria, label definitions, and cost accounting.

4.1.5 Continuous Research

We evaluate research chains on five topics over 500 QA papers from LitSearch. Each topic has an initial question and a distinct follow-up question building on its findings; both follow five stages: fixed-corpus retrieval, literature review generation, idea generation, novelty assessment, and experimental-design generation. Context retains conversation history; Notes uses fresh sessions with self-maintained notes; ScholarStack uses fresh sessions with task outputs and persistent Knowledge Views. All conditions can inspect source sentences. We assess task quality, understanding reuse, and token costs using anonymous LLM judging, rule checks, and access logs. Appendices F.1 and F.3 provide the questions and metric definitions, respectively.

4.2 Main Results

4.2.1 Literature Retrieval

Fixed-corpus retrieval.

Table 3 reports fixed-corpus retrieval results over all 50 LitSearch queries. On the 50 LitSearch queries, ScholarStack exceeds the Full-text baseline at R@1, R@5, and MRR@40, while the Full-text baseline is higher at R@10. ScholarStack also uses slightly less time and fewer tokens per query. The coverage split in Table 4 localizes the difference. For in-source queries, ScholarStack improves all four retrieval metrics. For out-of-source queries, it ties the Full-text baseline at R@1 and R@5 while being slightly lower at R@10 and MRR@40. These results indicate that the aggregate head-ranking gains are concentrated among queries whose gold set overlaps the papers used to compile the layer.

Open-world retrieval.

Table 5 reports pooled Tier 1 results over all 89 queries. Relative to matched raw-text access, ScholarStack raises precision from 0.1376 to 0.1579 (14.7% relative) and recall from 0.1240 to 0.1338 (7.9%), yielding a 10.7% improvement in F1. Macro and Micro Weighted Recall increase by 6.6% and 11.5%, respectively. The source papers and online access budget are matched: Full-text can inspect the same papers in full text, whereas ScholarStack exposes their precompiled cross-layer representation. The difference is therefore associated with representation and access rather than additional source documents. The experiment evaluates the layer as a whole and does not attribute the gain separately to L1, L2, or L3.

Novelty assessment.

Table 6 reports novelty assessment results across 12 fields. ScholarStack uses 21.4k tokens per query, compared with 35.6k for the Full-text method (about 40% fewer). Under our decision-relevance axis weighting its weighted identification F1 is also higher, verifying the benefit of structured knowledge layers.

4.2.2 Scientific Question Answering

Single-paper QA.

Tables 7 and 8 report results on two single-paper QA benchmarks. On QASPER, ScholarStack improves overall Answer-F1 (0.571 versus 0.561) and Evidence-F1 (0.589 versus 0.561) over RAG while using 12% fewer input tokens. ScholarStack reaches about 93% of the full-text Answer-F1 at 34% of its input tokens.

On PeerQA, ScholarStack improves all five aggregate answerability metrics and free-form ROUGE-L over RAG while using 28% fewer input tokens. ScholarStack reaches about 93% of the full-text Macro-F1 with about 11% of its input tokens. Appendix C.1 reports domain-level results and selected qualitative examples.

Multi-paper QA.

Table 9 reports results on 797 MDAQA questions. With the same answering model, ScholarStack achieves an overall score of 18.41, compared with 8.46 for Full-text, an absolute gain of 9.95. Compared with Full-text, ScholarStack improves correctness from 1.11 to 3.89, completeness from 2.01 to 3.35, cross-paper synthesis from 1.41 to 3.48, calibration from 0.92 to 3.90, and directness from 3.01 to 3.80. The improvements in synthesis and calibration suggest that ScholarStack organizes evidence in a form that better supports cross-paper integration and evidence-aligned answers under the same answering model. At the same time, ScholarStack reduces answer-generation costs, using 5.5k total tokens per question compared with 20.8k for Full-text, a reduction of approximately 73.3%. The mean generation time also decreases from 13.13 to 7.54 seconds per question.

ScholarStack achieves higher completeness and synthesis scores while using shorter inputs. The knowledge layers organize information at the levels needed for cross-paper answering. The representations of L1, L2 and L3 establish together an explicit structure for comparing findings and tracing conclusions back to their sources. In the evaluation, this organization is associated with higher coverage and better calibration, while requiring fewer generation tokens and less recorded generation time. This combination is particularly useful for multi-paper questions, where an answer must integrate several findings while preserving the conditions under which each applies. The appendix reports the results for the intermediate-level L1 and L1+L2 configurations, as well as those using the Cross-Paper Evidence Synthesis (CPES) skill.

4.2.3 Evidence-Grounded Generation

Related-work generation.

Table 10 shows that ScholarStack outperforms Full-text on four of the five independently judged quality dimensions. Its gains in Coverage (4.250 vs. 4.219), Specificity (4.453 vs. 4.359), Support (4.438 vs. 4.234), and Overall quality (4.203 vs. 4.156) indicate that the knowledge layer helps the writer cover the relevant literature, make more concrete comparisons, and ground the resulting synthesis in evidence. Table 11 shows the same favorable pattern in evidence use. ScholarStack increases the True rate from 91.82% to 92.43%, while reducing Wrong Citation from 2.14% to 1.10% and False claims from 1.81% to 1.15%. It also improves citation F1 from 0.95 to 0.97, showing closer agreement with the references selected by the target authors. Although its Unverifiable rate is slightly higher, ScholarStack produces fewer citation errors and false claims while selecting a more appropriate reference set.

Taken together, ScholarStack outperforms Full-text on most quality dimensions and on the key citation and factuality indicators. L2 organization and L3 scoped syntheses expose coherent research threads and cross-paper relations, while L1 facts retain the concrete source-grounded details needed to substantiate them. This layered representation lets the writer work from reusable structured assets instead of repeatedly processing full papers, while producing more comprehensive, specific, and well-supported related work with better citation selection.

Literature review generation.

Table 12 shows that ScholarStack achieves the strongest reference coverage, recovering 637 expert-survey references and reaching 0.182 recall and 0.214 micro F1. Claude Code recovers 171 references, with 0.049 recall and 0.090 F1. ScholarStack’s lower full-set precision (0.260 versus 0.553) follows from its substantially broader search: the Agentic Search workflow retrieves approximately 100 papers for each survey, enabling the final report to cover more relevant directions but also increasing the number of citations outside the expert reference set.

To compare the systems at a similar citation budget, we restrict ScholarStack to the first 13 papers it cites in retrieval order, matching Claude Code’s average. Under this control, ScholarStack reaches 0.603 precision, 0.056 recall, and 0.103 F1, exceeding Claude Code on all three metrics. The result shows that the coverage gain is not obtained only by adding a long tail of references: the early ScholarStack citation set is itself more concentrated on expert-survey references, while the remaining citations provide the additional breadth observed in the full condition.

Table 13 further shows that ScholarStack achieves the highest report-quality score overall (9.18) and in every RACE dimension. The largest gains over Claude Code are in comprehensiveness (9.23 versus 7.04) and insight (9.30 versus 7.43), consistent with L2 providing a corpus-level organization and L3 connecting findings across papers. L1 evidence and the review skill’s source-checking workflow help preserve technical conditions and support coherent, well-grounded exposition, contributing to the gains in instruction following and readability as well. These scores evaluate the complete workflows; Appendix D.2.2 provides trace-backed examples of the three mechanisms and discusses the evaluation boundaries in detail.

Experimental-design generation.

Table 14 reports coverage and cost; Table 15 reports the blind preference. On coverage F1, ScholarStack and Full-text (w/ Skill) are essentially tied (0.404 vs. 0.398; we do not read this 0.006 gap as a quality difference), and both clearly exceed Full-text (w/o Skill) (0.339). Both methods are strongly preferred over Full-text (w/o Skill) by the blind judge (32/38 and 34/38 wins); the gain is precision-driven, as Full-text (w/o Skill) over-proposes experiments (mean 22.9 vs. 18.6–18.7) and is penalized for padding. Head-to-head, the judge slightly favors Full-text (w/ Skill) over ScholarStack (22 vs. 16), so we do not claim ScholarStack improves design quality over Full-text (w/ Skill). ScholarStack’s contribution is matching the quality of Full-text (w/ Skill) at the lowest per-query cost of the three methods—about 40% fewer tokens than Full-text (w/ Skill)—while requiring no task-specific assets.

Idea generation.

Table 16 shows that ScholarStack achieves the highest score on all five dimensions, with an overall mean of 0.577, compared with 0.526 for Full-text (w/ Skill) and 0.397 for Full-text (w/o Skill). The generation method improves the mean score by 0.129 under full-text access, while the knowledge layer adds a further 0.051 with the method held fixed.

The largest gains of Full-text (w/ Skill) over Full-text (w/o Skill) occur in clarity and feasibility. This pattern is consistent with the generation method’s explicit organization of candidate ideas and its emphasis on resource constraints and internal refinement. The gains of ScholarStack over Full-text (w/ Skill) suggest that the structured organization of reference evidence supports idea development and refinement. Preserved applicability conditions inform feasibility judgments, while relations among prior approaches support novelty by facilitating the identification of new combinations and distinct contributions. Cross-study syntheses of limitations and conditional findings provide an evidential basis for explaining how proposed directions could improve on existing work, consistent with the gain in effectiveness.

4.2.4 Claim and Consistency Assessment

Scientific claim verification.

Table 17 compares the two paper contexts on all 80 claim–paper pairs. ScholarStack yields 41/80 correct predictions versus 37/80 for the Full-text baseline, an observed accuracy gain of 5.00 percentage points. Mean usage falls from 12,046.29 to 2,817.10 tokens per query, a reduction of 76.61%. The L1 view reduces query-time token use while retaining useful support-relation information. This single-run accuracy comparison on a small, balanced subset is descriptive, with no significance test and nearly half of the labels incorrect. Reliability and total cost, including construction, require further evaluation.

4.2.5 Continuous Research and Understanding Reuse

We further evaluate whether understanding developed during research can support subsequent tasks and follow-up questions in a continuous chain. We compare three ways of retaining research understanding across tasks: Context, which carries forward the conversation history; Notes, which passes self-maintained notes between fresh sessions; and ScholarStack, which reuses persistent research assets through Knowledge Views.

Table 18 shows that ScholarStack achieves strong performance across all five tasks. In literature review generation, ScholarStack achieves citation coverage of 0.720, exceeding Context and Notes by 12.7 and 33.0 percentage points, respectively, and also achieves the highest RACE score. In novelty assessment, ScholarStack achieves overlap support of 0.925, exceeding Context by 12.5 and Notes by 25.0 percentage points. ScholarStack also achieves the highest retrieval recall and matches the best scores for idea and experimental-design generation. Across the whole chain, it passes all checklist items for task requirements, compared with compliance rates of 0.953 for Context and 0.988 for Notes. ScholarStack uses 59% fewer new tokens than Context and 9% fewer than Notes. Chain costs include query, generation, and write-back/checking, but exclude initial knowledge-layer construction.

Table 19 shows that ScholarStack maintains near-complete correct reuse coverage with fewer new tokens than either baseline and the highest literature-review citation coverage. Relative to each method’s own first chain, total input tokens (including cached input) decrease by 13% for ScholarStack’s follow-up chain and increase by 223% for Context’s. Initial knowledge-layer construction requires approximately 23M input and 2M output tokens. Including this one-time cost, ScholarStack uses 68.3M total input and output tokens across the two chains of all five topics, compared with 389.8M for Context. Relative to Context, the construction cost is recovered within the second chain of a topic (Appendix F.4). Queries in each ScholarStack follow-up chain return 21.8 distinct first-chain objects on average; L3 syntheses account for 82% of accesses to first-chain objects, including repeated returns. These objects make individual judgments and their conditions, evidence, and revision state available to later tasks, supporting reuse without reconstructing a judgment’s basis.

Table 20 examines layer contributions using fixed-query retrieval tests and single-topic replays. These settings differ from the five-topic complete-chain evaluations in Tables 18 and 19, so absolute scores are not directly comparable. L2 improves retrieval recall, and L2/L3 broaden literature-review citation coverage. Adding L3 to L1+L2 raises novelty overlap support from 0.875 to 1.000. With upstream outputs fixed, replaying experimental-design generation under all three layer configurations retains full chain-level checklist compliance. Full-layer access also yields the highest observed follow-up reuse coverage. These results support selecting layers according to task needs. Appendix F gives the questions, metric definitions, and an example illustrating how later tasks reuse and revise evidence-linked judgments.

4.3 Analysis of Layer Contributions

The results above show that compiled assets can improve both query-time efficiency and the quality of research outputs. The asset layers contribute to these improvements in different ways across tasks: some tasks rely primarily on source-grounded facts, while others also draw on domain organization or existing syntheses.

L1 preserves source statements together with their study conditions and evidence references. These records provide the basis for answering questions about a paper and assessing whether its findings support a claim. In claim verification, Table 17 shows that using L1 alone reduces token use relative to Full-text, with a small observed improvement in accuracy. Beyond these paper-specific tasks, L1 provides evidence for identifying substantive overlaps in novelty assessment and tracing claims to their sources in scholarly writing. For idea generation and experimental design, reported findings, procedures, and evaluation conditions supply concrete premises for assessing proposed directions and choosing appropriate tests.

L2 organizes research objects through shared identities, categories, and relations. In fixed-corpus retrieval, this structure guides candidate selection; in open-world retrieval, it helps formulate search terms and identify related approaches to investigate. This role in candidate selection is supported by the retrieval results from continuous research reported in Table 20: adding L2 to L1 raises Recall@100 from 0.723 to 0.774 using matched query terms across five topics. Beyond retrieval, categories group studies into research threads for related-work and review generation, while taxonomy dimensions provide explicit axes for comparison.

L3 provides scoped interpretations of findings across studies. These syntheses support multi-paper answers, position contributions in relation to prior work, and explain methodological developments in surveys. Table 33 provides evidence from MDAQA: with the base evidence package and Bare answering procedure held fixed, adding L3 to L1+L2 increases the total score from 17.01 to 18.41 and the cross-paper synthesis score from 2.80 to 3.48. In Table 20, the literature-review results from continuous research show a similar benefit from L3: adding L3 to L1+L2 increases citation coverage from 0.867 to 0.978 and the RACE overall score from 8.60 to 8.80. These observations are consistent with scoped syntheses helping tasks integrate relevant studies into a broader account. The continual-learning review in Appendix D.2.2 illustrates this interpretive role in writing by connecting successive methods through the limitations they address. For idea generation, such accounts can reveal unresolved questions and motivate directions for further investigation. In continuous research, retaining these interpretations allows later tasks to revisit judgments formed earlier. In the follow-up comparison in Table 20, conducted from the same first-chain state, queries with full-layer access return first-chain objects 121 times, compared with 16 under L1+L2. The revision trace in Appendix F.5 shows how counterevidence encountered during novelty assessment narrows an earlier review synthesis, providing a revised premise for experimental design.

Across these tasks, the assets support reuse of both reported content and the understanding derived from it. Tasks draw on source facts, domain organization, and scoped syntheses according to the evidence and reasoning they require.

5 Conclusion

We presented ScholarStack, a layered research asset framework for literature-based scientific agents. It addresses the task-specific research bottleneck by separating source-grounded facts, domain organization, and scoped cross-paper syntheses while linking them through shared identifiers, evidence references, and versioning. Task-adapted knowledge views make these assets reusable across retrieval, question answering, evidence-grounded generation, and claim assessment, while retaining their study conditions and evidential scope.

Our experiments show that structured assets can improve task effectiveness and reduce query-time token use, with benefits varying by task and source coverage. On SAGE, ScholarStack improves Tier 1 retrieval F1 by 10.7% compared to the full-text baseline. Reusing assets built for novelty assessment also yields comparable experimental-design coverage F1 with about 40% fewer query-time tokens. Continuous research chains further demonstrate that earlier findings and scoped judgments can be retrieved and revised in later work, with lower per-chain token use than continuous-context and persistent-note baselines. These query-time savings exclude initial asset construction costs, which can be amortized with repeated reuse. In general, ScholarStack offers a foundation for cumulative scientific assistance in which research knowledge remains inspectable and revisable across tasks. Future work will examine longer task sequences and evolving corpora, strengthen evidence verification, and account systematically for construction, maintenance, and reuse costs.

6 Contributions

The names are listed in alphabetical order by last name.

Leader

Wotao Yin

Core Contributors

Caoqinwei Gong, Xue Jiang, Wei Luo, Xiaoyu Qiu, Jiayi Sheng, Yi Wang, Zheng Yu, Ao Zhang, Haifan Zhang, Hanwei Zhang, Jihai Zhang

Contributors

Yuan Cao, Wei Chen, Liyun Dai, Wenkai Fang, Guanglei Wang, Kai Ying, Tingyu Zhu

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Appendix A Case Study of Layered Knowledge Construction

This appendix illustrates the knowledge representation and construction described in Section 3.2, using variance reduction in stochastic optimization as a single research topic. It follows source sentences through L1 facts and L2 domain organization to an L3 scoped synthesis. SAGA [Defazio et al., 2014] is the focal paper; Prox-SVRG [Xiao and Zhang, 2014] and a study of saddle-point methods [Palaniappan and Bach, 2016] provide the cross-paper evidence. Figure 5 gives the reading path, and Table 21 summarizes the objects used below.

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