pith. sign in

arxiv: 2606.11874 · v1 · pith:MLNZGEIDnew · submitted 2026-06-10 · 💻 cs.AI

AutoMine Solution for AV2 2026 Scenario Mining Challenge

Songliang Cao , Jiele Zhao , Yuru Wang , Hao Li , Daqi Liu , Zehan Zhang , Fangzhen Li , Yu Wang
show 5 more authors
Yue Zhang Bing Wang Guang Chen Hao Lu Hangjun Ye
This is my paper

Pith reviewed 2026-06-27 09:49 UTC · model grok-4.3

classification 💻 cs.AI
keywords scenario miningautonomous drivinglarge language modelsvision language modelsprompt augmentationself-refining codedriving logs
0
0 comments X

The pith

A self-refining method using LLMs and VLMs mines high-value scenarios from autonomous driving logs

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

The paper aims to establish that combining large language models and vision-language models, augmented with semantics-preserving prompt changes and execution feedback, produces reliable extraction of safety-critical and planning-relevant scenarios from large driving datasets. A sympathetic reader would care because autonomous driving systems require targeted, high-value test cases drawn from real logs to support data-driven safety evaluation at scale. The approach specifically reduces prompt sensitivity through semantics-preserving augmentation, blends trajectory atomic functions with VLM-based functions to manage noise and visual cues, and iterates on generated code using feedback from actual log execution. If correct, the result is an automated pipeline that turns raw sensor logs into usable scenario descriptions without heavy manual engineering.

Core claim

AutoMine is a robust self-refining scenario mining method based on LLMs and VLMs. It uses semantics-preserving prompt augmentation to reduce LLM prompt sensitivity, combines robust trajectory atomic functions with VLM-based functions to handle perception noise and open-world visual cues, and refines generated code through execution feedback from real logs.

What carries the argument

The self-refining loop that augments prompts to preserve semantics, merges trajectory atomic functions with VLM-based functions, and corrects generated code via execution feedback from real logs.

If this is right

  • Enables scalable extraction of safety-critical scenarios from large-scale driving logs for data-driven autonomous driving evaluation.
  • Improves robustness to perception noise by combining trajectory analysis with open-world visual cues from VLMs.
  • Reduces the need for precise manual prompt engineering through semantics-preserving augmentation.
  • Supports iterative refinement of scenario descriptions directly from real log execution results.

Where Pith is reading between the lines

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

  • The same self-refining structure could be tested on logs from other geographic regions or vehicle platforms to check generalization.
  • Hybrid language-vision feedback loops might transfer to scenario extraction tasks in robotics or traffic monitoring outside driving.
  • Adding further sensor modalities to the atomic functions could extend the method to multi-modal logs without changing the core loop.

Load-bearing premise

The semantics-preserving prompt augmentation and the combination of trajectory atomic functions with VLM-based functions remain robust when applied to new logs that differ in sensor noise or scenario distribution from the competition data.

What would settle it

Running the mined scenario code on an independent collection of driving logs that differ in sensor noise levels or scenario distribution and checking whether the extracted scenarios retain comparable relevance and accuracy.

Figures

Figures reproduced from arXiv: 2606.11874 by Bing Wang, Daqi Liu, Fangzhen Li, Guang Chen, Hangjun Ye, Hao Li, Hao Lu, Jiele Zhao, Songliang Cao, Yue Zhang, Yuru Wang, Yu Wang, Zehan Zhang.

Figure 1
Figure 1. Figure 1: (a): Overview of the AutoMine framework with dual-path design (perception + language). (b): Semantic-preserving prompt [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
read the original abstract

With the development of autonomous driving systems, mining high-value, safety-critical, and planning-relevant scenarios from large-scale driving logs has become essential for data-driven evaluation. In this paper, we propose AutoMine, a robust self-refining scenario mining method based on LLMs and VLMs. AutoMine uses semantics-preserving prompt augmentation to reduce LLM prompt sensitivity, combines robust trajectory atomic functions with VLM-based functions to handle perception noise and open-world visual cues, and refines generated code through execution feedback from real logs. In the Argoverse 2 Scenario Mining Competition at CVPR 2026, AutoMine achieves a HOTA-Temporal score of 36.38 and a Timestamp BA score of 77.21.

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 / 1 minor

Summary. The paper presents AutoMine, a self-refining scenario mining pipeline for large-scale autonomous driving logs that combines LLMs and VLMs. Key components include semantics-preserving prompt augmentation, hybrid trajectory atomic functions with VLM-based functions for perception noise and open-world cues, and iterative refinement via execution feedback on real logs. The method reports a HOTA-Temporal score of 36.38 and Timestamp BA score of 77.21 on the Argoverse 2 Scenario Mining Competition at CVPR 2026.

Significance. If the hybrid symbolic-visual approach generalizes, it offers a practical route to extracting safety-critical and planning-relevant scenarios from fleet logs, potentially improving data-driven AV evaluation. The competition scores demonstrate feasibility of LLM/VLM code generation with feedback, but the absence of component-wise validation limits broader claims.

major comments (2)
  1. [Results/Evaluation] The results section reports only aggregate competition metrics with no ablation studies, baseline comparisons, or error analysis, so the contribution of semantics-preserving prompt augmentation versus the hybrid trajectory/VLM functions cannot be isolated or verified.
  2. [Method and Abstract] No experiments test robustness under distribution shift (different sensor noise, camera intrinsics, or scenario mix), leaving the abstract's claim of a 'robust' method unsupported; this is load-bearing because the skeptic correctly notes that competition logs may match the VLM training distribution.
minor comments (1)
  1. The manuscript should specify the exact LLM and VLM models used, as well as the form of the trajectory atomic functions, to allow reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our competition solution paper. We address each major comment below, clarifying the scope of a challenge-focused submission while agreeing to revisions where claims or evaluation details can be strengthened.

read point-by-point responses

  1. Referee: [Results/Evaluation] The results section reports only aggregate competition metrics with no ablation studies, baseline comparisons, or error analysis, so the contribution of semantics-preserving prompt augmentation versus the hybrid trajectory/VLM functions cannot be isolated or verified.

    Authors: We agree that component-wise ablations would help isolate contributions. As a competition report, the manuscript prioritizes the end-to-end pipeline and leaderboard scores over exhaustive internal analysis. We will add a dedicated discussion subsection on design rationale for each module and include any available error patterns observed during development on the provided logs. revision: partial

  2. Referee: [Method and Abstract] No experiments test robustness under distribution shift (different sensor noise, camera intrinsics, or scenario mix), leaving the abstract's claim of a 'robust' method unsupported; this is load-bearing because the skeptic correctly notes that competition logs may match the VLM training distribution.

    Authors: The abstract's phrasing of 'robust' is not supported by explicit distribution-shift experiments, which we did not conduct. The hybrid design aims to mitigate perception noise and open-world cues, but we accept that this does not constitute tested robustness. We will revise the abstract to remove 'robust' and qualify the method as 'self-refining', and we will add a limitations paragraph noting the competition-specific evaluation setting. revision: yes

Circularity Check

0 steps flagged

No circularity: competition metrics are external benchmarks with no internal derivation chain

full rationale

The paper reports HOTA-Temporal and Timestamp BA scores from the AV2 competition as its primary results. No equations, fitted parameters, self-citations, or derivation steps are described in the provided text. The method is summarized at a high level (prompt augmentation, trajectory/VLM functions, execution feedback) without any reduction of outputs to inputs by construction. The scores are externally evaluated competition results rather than internally generated predictions, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented entities can be extracted. The central claim rests on the unstated assumption that the competition metrics faithfully measure the intended scenario-mining quality.

pith-pipeline@v0.9.1-grok · 5680 in / 996 out tokens · 10572 ms · 2026-06-27T09:49:07.940447+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

6 extracted references · 1 linked inside Pith

  1. [1]

    Refav: Towards planning-centric scenario mining.arXiv preprint arXiv:2505.20981, 2025

    Cainan Davidson, Deva Ramanan, and Neehar Peri. Refav: Towards planning-centric scenario mining.arXiv preprint arXiv:2505.20981, 2025. 1

    arXiv 2025

  2. [2]

    Qwen3.5: Towards native multimodal agents,

    Qwen Team. Qwen3.5: Towards native multimodal agents,

  3. [3]

    Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting

    Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting. InInternational Conference on Learning Representations, 2024. 1, 2

    2024

  4. [4]

    Immortal tracker: Tracklet never dies

    Qitai Wang, Yuntao Chen, Ziqi Pang, Naiyan Wang, and Zhaoxiang Zhang. Immortal tracker: Tracklet never dies. arXiv preprint arXiv:2111.13672, 2021. 1

    arXiv 2021

  5. [5]

    Technical report for argoverse chal- lenges on unified sensor-based detection, tracking, and fore- casting.arXiv preprint arXiv:2311.15615, 2023

    Zhepeng Wang, Feng Chen, Kanokphan Lertniphonphan, Si- wei Chen, Jinyao Bao, Pengfei Zheng, Jinbao Zhang, Kaer Huang, and Tao Zhang. Technical report for argoverse chal- lenges on unified sensor-based detection, tracking, and fore- casting.arXiv preprint arXiv:2311.15615, 2023. 1, 4

    arXiv 2023

  6. [6]

    Argoverse 2: Next gen- eration datasets for self-driving perception and forecasting

    Benjamin Wilson, William Qi, Tanmay Agarwal, John Lam- bert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Rat- nesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, and James Hays. Argoverse 2: Next gen- eration datasets for self-driving perception and forecasting. ArXiv, abs/2301.00493, 2023. 3

    Pith/arXiv arXiv 2023