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arxiv: 2607.00597 · v1 · pith:BNE6FY6Lnew · submitted 2026-07-01 · 💻 cs.CL · cs.IR

Multi-Turn Agentic Scientific Literature Search via Workflow Induction

Jisen Li (1 , 2) , Bingxuan Li (1) , Nanyi Jiang (3) , Xuying Ning (1) , Xiyao Wang (3) , Yifan Shen (1) , Heng Wang (1)
show 9 more authors
Yuqing Jian (2) Xiaoxia Wu (2) Ben Athiwaratkun (2) Pan Lu (4) Jiaxuan You (1) Bingxin Zhao (3) ((1) University of Illinois Urbana-Champaign (2) Together AI (3) University of Pennsylvania (4) Stanford University)
This is my paper

Pith reviewed 2026-07-02 13:12 UTC · model grok-4.3

classification 💻 cs.CL cs.IR
keywords literature searchworkflow inductionmulti-turn agentssearch agentsDAG workflowspreference optimizationscientific intent
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The pith

Representing literature search as induction of editable operator workflows improves multi-turn agent performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Scientific literature search requires handling underspecified and evolving user intents across multiple turns, which fixed pipelines and implicit language reasoning struggle to manage. The paper frames the task as workflow induction: the agent builds an explicit, executable DAG of operators such as keyword search, citation expansion, filtering, scoring, reranking, and evidence extraction. Training combines supervised imitation of valid workflows with preference optimization on controlled corruptions, allowing the agent to refine both the query and the workflow from feedback. Experiments show higher retrieval metrics and elimination of execution errors relative to the base model. If the approach holds, explicit workflows supply a controllable and inspectable interface for aligning agents with complex scientific search needs.

Core claim

PaperPilot constructs an executable DAG of paper-search operators given an anchor paper and a user query, then uses feedback to refine both the query and the workflow itself; training via supervised workflow imitation and preference optimization over controlled corruptions produces an agent that raises Hit@5 from 58.0 to 77.0, MRR from 47.5 to 59.4, and nDCG@10 from 26.8 to 32.5 while cutting workflow execution errors from 9.5 percent to zero.

What carries the argument

Workflow induction into an executable DAG of operators (keyword search, citation expansion, filtering, scoring, reranking, evidence extraction) that makes the search strategy explicit, inspectable, and refinable through user feedback.

If this is right

  • Explicit workflows make search strategies controllable and inspectable rather than opaque.
  • Multi-turn user feedback can directly edit both the query and the induced workflow.
  • Retrieval metrics improve while workflow execution errors drop to zero.
  • The method supplies a controllable interface for aligning agents with complex scientific intent.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same induction method could be tested on agent tasks outside literature search once suitable operator sets are defined.
  • Human review of the induced DAGs could serve as a debugging and trust mechanism for agent outputs.
  • Failure cases on intents outside the current operator vocabulary would directly bound the method's coverage.

Load-bearing premise

The chosen set of paper-search operators is expressive enough to represent complex, evolving scientific search intents.

What would settle it

A multi-turn literature search task whose required behavior cannot be expressed by any sequence of the listed operators, so that even a correctly induced workflow fails to satisfy the intent.

Figures

Figures reproduced from arXiv: 2607.00597 by 2), (2) Together AI, (3) University of Pennsylvania, (4) Stanford University), Ben Athiwaratkun (2), Bingxin Zhao (3) ((1) University of Illinois Urbana-Champaign, Bingxuan Li (1), Heng Wang (1), Jiaxuan You (1), Jisen Li (1, Nanyi Jiang (3), Pan Lu (4), Xiaoxia Wu (2), Xiyao Wang (3), Xuying Ning (1), Yifan Shen (1), Yuqing Jian (2).

Figure 1
Figure 1. Figure 1: Overview of PAPERPILOT. Compared with single-turn scientific literature search, PAPERPILOT uses multi-turn feedback to clarify user intent and refine the search workflow before producing final results. on adaptive retrieval and interaction rather than static query matching. Recent agentic search systems improve retrieval quality by combining language models with ex￾ternal tools, iterative reasoning, and mu… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of PAPERPILOT. Given an anchor paper and a user search intent, the agent induces a DAG￾structured search workflow from a predefined toolset, executes the workflow over the literature corpus, and refines the workflow through multi-turn user interaction. parison, or a clarification. The user then provides feedback ft , and the state is updated through T . Interaction-driven refinement. Multi-turn se… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of agent actions across turns. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of multi-turn refinement on retrieval [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Workflow-generation similarity between Qwen3.5-9B and PAPERPILOT-9B. 0.30 0.45 0.60 Added nodes Modi f i ed node R s emoved nodes Qwen3.5-9B (base) Kimi K2.6 GPT-5.4 PaperPilot-9B (ours) [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Workflow-editing consistency across different [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Sensitivity analysis over keyword pool scales. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Human Study interface [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Human Study interface 16 [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Workflow refinement example [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: An example PaperPilot search session 17 [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
read the original abstract

Scientific literature search often requires more than retrieving papers from a single query: users' intents are underspecified, preference-dependent, and evolve through interaction. Existing search agents typically rely on fixed pipelines or implicit language-only reasoning, making their search strategies difficult to control, inspect, and refine. We introduce PaperPilot, a multi-turn literature search agent that frames scientific search as workflow induction. Given an anchor paper and a user query, PaperPilot constructs an executable DAG of paper-search operators, including keyword search, citation expansion, filtering, scoring, reranking, and evidence extraction. User feedback is then used to refine both the query and the workflow itself. We train PaperPilot with supervised workflow imitation and preference optimization over controlled workflow corruptions. Experiments show that PaperPilot-9B improves over the base Qwen3.5-9B toolset agent under multi-turn interaction, increasing Hit@5 from 58.0 to 77.0, MRR from 47.5 to 59.4, and nDCG@10 from 26.8 to 32.5, while reducing workflow execution errors from 9.5% to 0%. These results show that explicit, editable search workflows provide an effective and controllable interface for aligning literature search agents with complex scientific intent.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces PaperPilot, a multi-turn literature search agent that frames scientific search as workflow induction: given an anchor paper and user query, it constructs an executable DAG over a fixed set of operators (keyword search, citation expansion, filtering, scoring, reranking, evidence extraction), refines the workflow and query from user feedback, and is trained via supervised workflow imitation plus preference optimization over controlled corruptions. Experiments report that the 9B model improves over a base Qwen3.5-9B toolset agent, raising Hit@5 from 58.0 to 77.0, MRR from 47.5 to 59.4, and nDCG@10 from 26.8 to 32.5 while dropping workflow execution errors from 9.5% to 0%.

Significance. If the empirical gains prove robust, the explicit, editable DAG workflow approach supplies a controllable and inspectable interface for aligning agents with underspecified, preference-dependent, and evolving scientific intents, offering a concrete alternative to purely implicit language-based reasoning.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments section: the reported metric gains (Hit@5 58.0→77.0, MRR 47.5→59.4, nDCG@10 26.8→32.5, error rate 9.5%→0%) are presented without any description of dataset construction, statistical significance tests, baseline implementation details, or potential confounds; these omissions make the central empirical claim unverifiable from the supplied information.
  2. [Abstract] Abstract: the claim that the fixed operator vocabulary (keyword search, citation expansion, filtering, scoring, reranking, evidence extraction) suffices for arbitrary complex, evolving scientific intents is load-bearing for the workflow-induction framing, yet no experiments or analysis address coverage on intents requiring operators outside this set (e.g., temporal filtering across dates or cross-field analogy detection).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important issues of verifiability and scope. We address each major comment below and commit to revisions that improve transparency without altering the core claims.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments section: the reported metric gains (Hit@5 58.0→77.0, MRR 47.5→59.4, nDCG@10 26.8→32.5, error rate 9.5%→0%) are presented without any description of dataset construction, statistical significance tests, baseline implementation details, or potential confounds; these omissions make the central empirical claim unverifiable from the supplied information.

    Authors: We agree that the abstract and Experiments section omit key details required for independent verification. The full manuscript contains the underlying dataset and baseline code, but these were not sufficiently summarized. In the revision we will expand the Experiments section with: (1) a complete description of dataset construction and split statistics, (2) implementation details for all baselines including prompt templates and tool-use configurations, (3) results of paired statistical significance tests (e.g., bootstrap or McNemar), and (4) explicit discussion of potential confounds such as query distribution and interaction length. Corresponding clarifications will be added to the abstract. revision: yes

  2. Referee: [Abstract] Abstract: the claim that the fixed operator vocabulary (keyword search, citation expansion, filtering, scoring, reranking, evidence extraction) suffices for arbitrary complex, evolving scientific intents is load-bearing for the workflow-induction framing, yet no experiments or analysis address coverage on intents requiring operators outside this set (e.g., temporal filtering across dates or cross-field analogy detection).

    Authors: The abstract does not claim the operator set covers every conceivable scientific intent; it states that the set enables controllable alignment with the complex intents arising in our multi-turn literature-search setting. We acknowledge that no dedicated coverage analysis for out-of-vocabulary operators (temporal filtering, cross-field analogy, etc.) is provided. In revision we will (a) rephrase the abstract to avoid any implication of universal coverage and (b) add a dedicated limitations paragraph that enumerates the current operator vocabulary, justifies its selection from observed user intents, and outlines straightforward extensions for missing operators. No new experiments are planned for this revision. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper frames literature search as workflow induction over a fixed operator set and reports empirical gains from supervised imitation plus preference optimization. No equations, derivations, or load-bearing self-citations appear; all central claims reduce to direct comparisons against an external baseline (Qwen3.5-9B toolset agent) on held-out metrics. The operator vocabulary is an explicit modeling choice, not derived from the results themselves, so no reduction by construction occurs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities can be extracted. The operator vocabulary and the assumption that user feedback is sufficient for refinement are implicit modeling choices but cannot be audited without the full text.

pith-pipeline@v0.9.1-grok · 5862 in / 1127 out tokens · 21551 ms · 2026-07-02T13:12:20.853607+00:00 · methodology

discussion (0)

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