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arxiv: 2606.06650 · v1 · pith:TYP2GZYKnew · submitted 2026-06-04 · 💻 cs.HC

LinkNav: Surfacing Interconnected Information in Scientific Articles

Sebastian Joseph , Jennifer Healey , Junyi Jessy Li , Ani Nenkova This is my paper

Pith reviewed 2026-06-27 23:19 UTC · model grok-4.3

classification 💻 cs.HC
keywords academic paper navigationintra-document linkslanguage model questionspassage connectionsscientific readinganswer detectionnon-adjacent segments
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The pith

LinkNav uses a language model to generate questions from passages in academic papers and locate their answers in distant sections, surfacing connections that average ten segments apart.

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

The paper presents a reading tool that turns implicit relationships inside a single scientific article into explicit links. It prompts a language model to create questions that might occur to a reader at one location and then searches the rest of the document for passages that answer those questions. When matches are found, the system marks the pair as connected. Across the papers tested, these linked passages sit on average ten segments away, a distance at which readers would not naturally notice the relationship. The result is a navigation aid that makes the document's internal structure more visible without requiring the reader to hunt manually.

Core claim

LinkNav instructs a language model to generate questions that may arise while reading a passage and then searches for answer passages elsewhere in the document, forming intra-document connections when answers are found. An answer detection pipeline operates with high precision and yields a reasonable number of connections per document. On a dataset of academic papers, connected passages are on average ten segments away from each other.

What carries the argument

Question-generation step that produces potential reader questions from a passage, followed by an answer-detection step that identifies matching passages elsewhere in the same document.

If this is right

  • Readers gain access to connections between sections that are on average ten segments apart and would otherwise remain hidden.
  • The question-and-answer pipeline produces enough valid links to create a usable navigation layer without overwhelming the document.
  • Explicit links turn a linear text into a set of traversable relationships inside one paper.
  • The same building blocks can be applied to any document where non-adjacent passages contain related content.

Where Pith is reading between the lines

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

  • The approach could support new interfaces that let readers jump between related ideas rather than scrolling sequentially.
  • Similar question-generation techniques might help surface connections across multiple papers or across different document types.
  • If the links prove reliable, standard PDF and e-reader tools might eventually embed them by default.

Load-bearing premise

The questions created by the language model and the passages flagged as answers are both accurate and useful to actual human readers.

What would settle it

A study in which readers examine the generated links and judge most of them to be either factually wrong or already obvious from the surrounding text.

Figures

Figures reproduced from arXiv: 2606.06650 by Ani Nenkova, Jennifer Healey, Junyi Jessy Li, Sebastian Joseph.

Figure 1
Figure 1. Figure 1: LinkNav pipeline and experience. (a) A paper is broken down into segments and a language model generates questions for each segment i. (b) Similarity is computed between the questions and all other segments. An LLM decides if one of the top five segments, j is the answer. (c) If so, i and j are considered connected. (d) Connections are surfaced via links and a side panel. reviewers frequently ask questions… view at source ↗
Figure 2
Figure 2. Figure 2: LinkNav surfaces links between different segments in papers. Text portions that likely triggered the [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cumulative percent of PeerQA-Gold answers [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we instruct a language model to generate questions that may arise while reading a passage and then search for answer passages elsewhere in the document, forming intra-document connections when answers are found. We confirm that these building blocks work well to power the experience, with an answer detection pipeline that works with high precision, resulting in a reasonable number of connections being made for a document. On a dataset of academic papers, we find that connected passages are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed.

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 / 2 minor

Summary. The paper presents LinkNav, a reading interface for academic papers that uses an LLM to generate questions from a passage and then detects answer passages elsewhere in the same document to create explicit intra-document links. The central claims are that the answer-detection pipeline achieves high precision, that a reasonable number of connections are surfaced per document, and that on a dataset of papers the connected passages are on average ten segments apart, thereby surfacing links a reader might otherwise miss.

Significance. If the generated connections prove both accurate and useful to readers, the work could contribute a practical HCI technique for improving navigation and comprehension in long scientific documents. The approach of question-generation plus answer retrieval is straightforward and leverages existing LLM capabilities, but the absence of human validation leaves the claimed reader benefit unanchored.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (Evaluation): the claim of 'high precision' for the answer detection pipeline is stated without any quantitative results, dataset size, error bars, baseline comparisons, or inter-annotator agreement; this directly undermines the assertion that the pipeline 'works well to power the experience.'
  2. [Abstract and §5] Abstract and §5 (Results/Discussion): the claim that connected passages are 'on average ten segments away' and 'making explicit connections that a reader may have otherwise missed' rests on automated metrics alone; no human evaluation, usefulness ratings, or user study is reported to support the HCI benefit or the 'may have otherwise missed' assertion.
minor comments (2)
  1. [§4] The manuscript should clarify the exact segmentation method used to compute 'segments' and report the total number of papers and segments in the evaluation dataset.
  2. [Figure 1] Figure captions and the system diagram would benefit from explicit labels indicating which components are LLM-driven versus rule-based.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, acknowledging where the manuscript requires strengthening and outlining specific revisions.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Evaluation): the claim of 'high precision' for the answer detection pipeline is stated without any quantitative results, dataset size, error bars, baseline comparisons, or inter-annotator agreement; this directly undermines the assertion that the pipeline 'works well to power the experience.'

    Authors: We agree the current text states 'high precision' without supporting numbers, dataset details, or comparisons. We will revise §4 to report the exact precision of the answer-detection pipeline, the number of papers and segments evaluated, any variance or error analysis performed, and relevant baselines. The abstract will be updated to reference these results. revision: yes

  2. Referee: [Abstract and §5] Abstract and §5 (Results/Discussion): the claim that connected passages are 'on average ten segments away' and 'making explicit connections that a reader may have otherwise missed' rests on automated metrics alone; no human evaluation, usefulness ratings, or user study is reported to support the HCI benefit or the 'may have otherwise missed' assertion.

    Authors: The reported average distance and connection counts are derived solely from automated segment analysis. We acknowledge that this does not directly demonstrate reader benefit or that connections would otherwise be missed. We will revise the abstract and §5 to qualify these claims as automated observations, add an explicit limitations paragraph, and include future-work discussion of planned human validation studies. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical system description with no derivations or fitted predictions

full rationale

The paper presents LinkNav as a system that uses an LLM to generate questions from passages and detect answers elsewhere in the document to form intra-document links. The central empirical claim (connected passages average ten segments apart) is a direct measurement on a dataset of papers, not a model prediction derived from fitted parameters or prior self-citations. No equations, ansatzes, uniqueness theorems, or self-referential definitions appear. The pipeline is described as a sequence of LLM calls and detection steps whose outputs are evaluated for precision on held-out data; these steps do not reduce to tautologies or rename their own inputs. The absence of human evaluation noted by the skeptic is a question of external validity, not circularity in the reported chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is a system description with no mathematical model, so the ledger contains no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5643 in / 1025 out tokens · 14843 ms · 2026-06-27T23:19:30.484598+00:00 · methodology

discussion (0)

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Reference graph

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