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arxiv: 2606.08122 · v1 · pith:I62I355Unew · submitted 2026-06-06 · 💻 cs.AI

Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction

Qingxiang Liu , Anqi Liang , Zhuoyang Jiang , Yutian Jiang , Sisuo Lyu , Yu Ji , Haomin Wen , Yuxuan Liang This is my paper

Pith reviewed 2026-06-27 19:51 UTC · model grok-4.3

classification 💻 cs.AI
keywords next POI predictionintention-guided reasoningLLM-based location predictiontwo-stage frameworkuser mobility modelingtrajectory analysislocation-based services
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The pith

Next POI prediction improves when models first infer a user's traveling intention before choosing specific locations.

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

The paper claims that current LLM approaches to next POI prediction treat the task as a single direct mapping from past check-ins to the next spot. This leads to reliance on shallow trajectory correlations and how often places appeared in history. The authors argue instead that people first form a traveling intention and only afterward pick a concrete place. Their two-stage method therefore first reasons about that intention from personal history, similar users, and time context, then uses the intention to guide selection from a narrowed candidate set. If the separation works, predictions should better match actual user decision order and show higher accuracy on real check-in records.

Core claim

By explicitly decoupling intention inference from location prediction, IntentPOI transforms the next POI prediction from direct trajectory matching into intention-guided reasoning, where a thinking stage infers intermediate intentions from mobility patterns, peer behaviors, and temporal contexts, and an acting stage constructs a compact candidate pool before performing intention-aligned selection.

What carries the argument

The two-stage intention-guided reasoning framework that separates inference of traveling intentions from final POI selection.

If this is right

  • Predictions become less driven by historical visit frequency and more aligned with inferred goals.
  • Peer mobility patterns and temporal context are used explicitly during intention formation rather than only at final selection.
  • A compact candidate pool is generated before reasoning, reducing the search space for the final choice.
  • The overall method outperforms eleven one-step baselines on three real-world check-in datasets.

Where Pith is reading between the lines

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

  • The same two-stage separation could be tested on other sequential tasks where an intermediate goal precedes the concrete action.
  • If the inferred intentions prove accurate, they could be exposed to users to increase recommendation transparency.
  • Datasets that record explicit user intentions alongside check-ins would allow direct measurement of the intention-inference stage.

Load-bearing premise

Users form a distinct traveling intention before selecting any specific location rather than choosing locations directly from past patterns.

What would settle it

A controlled experiment on the same three datasets that removes the intention inference stage and measures whether accuracy stays the same or increases.

Figures

Figures reproduced from arXiv: 2606.08122 by Anqi Liang, Haomin Wen, Qingxiang Liu, Sisuo Lyu, Yu Ji, Yutian Jiang, Yuxuan Liang, Zhuoyang Jiang.

Figure 1
Figure 1. Figure 1: Both the prompt- and token-based methods per [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The workflow of IntentPOI includes Thinking Stage: Multi-Rationale Intention Inference and Acting Stage: Intention [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The user profile (left) concludes the mobility patterns with specific temporal and spatial habits. The inferred intention [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Coverage efficiency of different candidate filtering [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Inference cost of MI and MR across ablation vari￾ants in terms of input token count and latency. ♠[♥] denotes the inference cost of ♥ in variant ♠. latency of MI and MR on the CA dataset, averaged across all test queries. Note that in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Performance under difference settings of (a) his [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Effect of candidate pool size 𝐵 on MRR and average hit rate of the filtered candidate pool H¯ 𝑛 . ground truth, but also increases MR’s input length and inference cost. The default setting 𝐵 = 30 achieves a good balance between the converged MRR and moderate token consumption. ➌ We vary the number of similar users 𝑘 from 1 to 10, which affects the richness of the peer behaviors for intention inference. As … view at source ↗
Figure 10
Figure 10. Figure 10: A failure case on query trajectory 1052. The his￾torical summary, inferred intention and reasoning details of IntentPOI are provided. evenings. Based on such, the IntentPOI correctly ranks the ground￾truth Beach POI (POI 688) at position 1. The three ablation variants fail with distinct reasons. ➊ A.1 (w/o 𝑃 F 𝑛 in MI ) fails to learn about the user’s long-term mobility patterns, and the generated intenti… view at source ↗
read the original abstract

Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating large language models have shown strong reasoning capabilities and promising results, they typically formulate the prediction task as a one-step trajectory-to-location mapping problem, making predictions prone to shallow trajectory correlations and historical frequency bias. We argue that users rarely choose locations directly and instead, they usually first form a traveling intention and then accordingly select specific POIs. Motivated by this insight, we propose IntentPOI, a two-stage intention-guided reasoning framework. In the thinking stage, we infer users' intermediate intentions by incorporating historical mobility patterns, similar peer behaviors, and the temporal contexts. In the acting stage, we first construct a compact candidate pool, and then perform intention-guided reasoning to identify locations that best align with the inferred intention. By explicitly decoupling intention inference from location prediction, IntentPOI transforms the next POI prediction from direct trajectory matching into intention-guided reasoning. Extensive experiments on three real-world datasets demonstrate that IntentPOI consistently outperforms eleven state-of-the-art baselines.

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

1 major / 0 minor

Summary. The paper proposes IntentPOI, a two-stage LLM-based framework for next POI prediction. In the thinking stage, it infers intermediate user intentions from historical mobility patterns, similar peer behaviors, and temporal contexts. In the acting stage, it constructs a compact candidate pool and performs intention-guided reasoning to select locations aligned with the inferred intention. The central claim is that explicitly decoupling intention inference from location prediction transforms the task from direct trajectory matching into intention-guided reasoning, yielding consistent outperformance over eleven baselines on three real-world datasets.

Significance. If the empirical results hold with adequate controls, the explicit two-stage intention modeling offers a plausible way to mitigate shallow trajectory correlations and frequency bias in LLM-based POI prediction, potentially generalizing to other sequential decision tasks.

major comments (1)
  1. [Experiments] Experiments section: The abstract asserts consistent outperformance on three datasets against eleven baselines, but provides no details on experimental controls, error bars, statistical significance testing, baseline re-implementation choices, or potential post-hoc analysis; this absence makes the central empirical claim difficult to evaluate and is load-bearing for the paper's contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We agree that the Experiments section requires additional details to allow proper evaluation of the empirical claims, and we will revise accordingly.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: The abstract asserts consistent outperformance on three datasets against eleven baselines, but provides no details on experimental controls, error bars, statistical significance testing, baseline re-implementation choices, or potential post-hoc analysis; this absence makes the central empirical claim difficult to evaluate and is load-bearing for the paper's contribution.

    Authors: We agree that these details are essential for rigorous evaluation. In the revised manuscript, we will expand the Experiments section (and associated appendix) to explicitly document: (1) experimental controls including data split ratios, random seeds, hyperparameter settings, and preprocessing steps; (2) error bars computed from at least five independent runs with different seeds; (3) statistical significance testing (e.g., paired t-tests or Wilcoxon signed-rank tests with reported p-values against each baseline); (4) baseline re-implementation details, specifying which baselines used official code versus our re-implementations under identical settings and hardware; and (5) any post-hoc analyses such as ablation studies on the two-stage components and error-case breakdowns. These additions will be presented without changing the reported performance numbers or core claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents an architectural proposal (IntentPOI) consisting of a two-stage LLM-based framework that first infers intentions from mobility patterns, peer behaviors, and temporal context, then performs intention-guided selection from a candidate pool. No equations, parameter-fitting steps, or derivations are described that would reduce any claimed prediction or transformation to its own inputs by construction. The central claim—that explicit decoupling converts trajectory matching into intention-guided reasoning—is a direct consequence of the proposed method design rather than a self-referential loop, and is evaluated via standard empirical comparison on external datasets. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work are invoked in the provided text.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is limited to the explicit motivation stated there.

axioms (1)
  • domain assumption Users form a traveling intention before selecting specific POIs
    Directly stated as the core motivation for the two-stage framework in the abstract.

pith-pipeline@v0.9.1-grok · 5749 in / 1151 out tokens · 20418 ms · 2026-06-27T19:51:40.244663+00:00 · methodology

discussion (0)

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