arxiv: 2604.07362 · v1 · submitted 2026-04-01 · 💻 cs.LG

Recognition: 2 theorem links

· Lean Theorem

LLM-Generated Fault Scenarios for Evaluating Perception-Driven Lane Following in Autonomous Edge Systems

Faezeh Pasandideh , Achim Rettberg

Authors on Pith no claims yet

Pith reviewed 2026-05-13 22:54 UTC · model grok-4.3

classification 💻 cs.LG

keywords fault injectionedge AIautonomous drivinglane followingLLMlatent diffusion modelsrobustness evaluationsensor degradation

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The pith

A decoupled framework uses LLMs and diffusion models to generate fault scenarios that expose up to 99 percent worse performance in lane-following models on edge devices.

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

The paper presents a two-phase fault injection method that splits validation into an offline stage and a lightweight online stage to handle resource limits on edge hardware. Large language models create structured fault scenarios while latent diffusion models produce realistic sensor degradations such as fog or noise; both are compressed into a lookup table. The edge device then performs fast fault-aware predictions without executing heavy models locally. Tests on a ResNet18 lane-following network across 460 scenarios show a clean-data R-squared of 0.85 that falls sharply, with RMSE rising by as much as 99 percent and localization accuracy reaching only 31 percent under fog. This result indicates that clean-data benchmarks miss critical failure modes for real deployment.

Core claim

We introduce a decoupled offline-online fault injection framework. This architecture separates the validation process into two distinct phases: a computationally intensive Offline Phase and a lightweight Online Phase. In the offline phase, we employ Large Language Models (LLMs) to semantically generate structured fault scenarios and Latent Diffusion Models (LDMs) to synthesize high-fidelity sensor degradations. These complex fault dynamics are distilled into a pre-computed lookup table, enabling the edge device to perform real-time fault-aware inference without running heavy AI models locally. We extensively validated this framework on a ResNet18 lane-following model across 460 fault scénarï

What carries the argument

Decoupled offline-online fault injection framework that converts LLM-generated scenarios and LDM-synthesized degradations into a pre-computed lookup table for real-time edge inference.

If this is right

Evaluation on clean data alone overestimates reliability of perception models for autonomous edge deployment.
Edge hardware can perform fault-aware inference at runtime using only a precomputed lookup table.
Systematic generation of diverse sensor degradations can surface robustness gaps absent from static datasets.
Metrics such as RMSE and within-0.10 localization accuracy must be reported under controlled fault conditions to reflect deployment risk.

Where Pith is reading between the lines

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

The same offline generation step could be applied to other perception tasks such as object detection or depth estimation.
Direct comparison of the generated degradations against real sensor recordings from vehicles would test how closely the synthetic faults match physical conditions.
The lookup-table approach might be combined with lightweight online adaptation so the model can adjust predictions once a fault type is detected.

Load-bearing premise

The assumption that LLM-generated fault scenarios and LDM-synthesized degradations are representative of real-world environmental hazards and sensor failures.

What would settle it

Run the identical ResNet18 lane-following model on a physical vehicle in actual fog and measure whether localization accuracy falls to 31 percent and RMSE rises by 99 percent relative to clear conditions.

Figures

Figures reproduced from arXiv: 2604.07362 by Achim Rettberg, Faezeh Pasandideh.

**Figure 2.** Figure 2: The robot platform used for edge inference experiments. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: LDM-generated images using LLM-derived denoising strengths. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: ResNet-18 performance on normal lane-following data (150 epochs): (a) train/val loss converging to [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 7.** Figure 7: ResNet18 performance on LDM-generated faulty images: fog and [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

**Figure 6.** Figure 6: ResNet18 performance on LDM-generated faulty images: rain-related [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 8.** Figure 8: Prediction results for a representative fog and occlusion degradation [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗

read the original abstract

Deploying autonomous vision systems on edge devices faces a critical challenge: resource constraints prevent real-time and predictable execution of comprehensive safety tests. Existing validation methods depend on static datasets or manual fault injection, failing to capture the diverse environmental hazards encountered in real-world deployment. To address this, we introduce a decoupled offline-online fault injection framework. This architecture separates the validation process into two distinct phases: a computationally intensive Offline Phase and a lightweight Online Phase. In the offline phase, we employ Large Language Models (LLMs) to semantically generate structured fault scenarios and Latent Diffusion Models (LDMs) to synthesize high-fidelity sensor degradations. These complex fault dynamics are distilled into a pre-computed lookup table, enabling the edge device to perform real-time fault-aware inference without running heavy AI models locally. We extensively validated this framework on a ResNet18 lane-following model across 460 fault scenarios. Results show that while the model achieves a baseline R^2 of approximately 0.85 on clean data, our generated faults expose significant robustness degradation, with RMSE increasing by up to 99% and within-0.10 localization accuracy dropping to as low as 31.0% under fog conditions, demonstrating the inadequacy of normal-data evaluation for real-world edge AI deployment.

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.

Desk Editor's Note private letter to a colleague

The paper gives a workable offline-online split for fault testing on edge lane followers using LLM scenarios and diffusion degradations distilled to a lookup table, but the big robustness claims rest on unvalidated synthetic faults.

read the letter

The main takeaway is a decoupled framework that generates structured fault scenarios offline with LLMs, synthesizes sensor degradations via latent diffusion models, and compresses the results into a lookup table so the edge device can do quick checks without running heavy models at runtime. They test this on a ResNet18 lane-following model across 460 scenarios and report concrete numbers: clean-data R-squared near 0.85, with RMSE rising up to 99% and localization accuracy falling to 31% under fog conditions. That part is straightforward engineering and shows the method can surface performance gaps that clean-data tests miss. The approach is new in how it combines the generation step with the distillation for resource-constrained inference, and the baseline comparison is reported clearly enough to be useful. The main limitation is that none of the generated faults or degradations are checked against real sensor logs, real fog/rain measurements, or actual failure data. The paper treats the synthetic cases as representative enough to conclude that normal evaluation is inadequate for deployment, but without quantitative similarity metrics or correlation to physical conditions that step stays assumptive. The evaluation itself uses an external model and direct metrics, so there is no circularity in the reported drops. This work is aimed at engineers and researchers who need practical ways to expand safety testing for edge perception systems beyond static datasets. It is worth sending to peer review because the framework is implementable and the results are specific, even if reviewers will likely ask for validation against real data to strengthen the deployment claims.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a decoupled offline-online fault injection framework for evaluating lane-following perception models on edge devices. LLMs generate structured fault scenarios and LDMs synthesize sensor degradations in an offline phase; these are distilled into a lookup table that enables lightweight, real-time fault-aware inference online. The framework is evaluated on a ResNet18 lane-following model across 460 generated scenarios, reporting a clean-data baseline R² of approximately 0.85 that degrades under faults (RMSE increases up to 99%, within-0.10 localization accuracy falls to 31% under fog), arguing that standard clean-data evaluation is inadequate for real-world edge deployment.

Significance. If the synthetic faults are shown to be representative, the work would usefully demonstrate the limitations of clean-data testing for safety-critical autonomous perception and supply a practical architecture that offloads heavy computation while preserving real-time capability on constrained hardware. The distillation step into a lookup table is a concrete engineering contribution that directly addresses edge deployment constraints.

major comments (2)

[Abstract and Experiments section] Abstract and Experiments section: The central claim that normal-data evaluation is inadequate for real-world edge deployment rests on the representativeness of the LLM-generated scenarios and LDM degradations, yet no quantitative validation (FID, perceptual similarity scores, or correlation with real sensor/failure logs) is supplied to link the 460 synthetic cases to physical distributions; this assumption is load-bearing for translating the reported 99% RMSE rise and 31% accuracy drop into deployment-risk conclusions.
[Experiments section] Experiments section: The headline metrics (R² ≈ 0.85 clean, up to 99% RMSE increase, 31% accuracy under fog) are reported across 460 scenarios without error bars, variance estimates, or explicit criteria for scenario selection and fault-parameter ranges, making it impossible to judge whether the observed degradations are statistically robust or sensitive to particular generation choices.

minor comments (1)

[Methods] The construction and lookup-table access protocol for the distilled fault parameters should be given a formal algorithmic description or pseudocode to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback, which helps clarify the need for stronger justification of our synthetic fault scenarios and improved statistical reporting. We address each major comment below.

read point-by-point responses

Referee: [Abstract and Experiments section] Abstract and Experiments section: The central claim that normal-data evaluation is inadequate for real-world edge deployment rests on the representativeness of the LLM-generated scenarios and LDM degradations, yet no quantitative validation (FID, perceptual similarity scores, or correlation with real sensor/failure logs) is supplied to link the 460 synthetic cases to physical distributions; this assumption is load-bearing for translating the reported 99% RMSE rise and 31% accuracy drop into deployment-risk conclusions.

Authors: We agree that the absence of quantitative metrics such as FID scores or explicit correlations with real sensor logs limits the strength of claims about real-world representativeness. The current work focuses on generating diverse, semantically plausible faults via LLMs and LDMs to demonstrate potential robustness gaps rather than claiming exact distributional match. In the revision we will add a dedicated subsection in Experiments detailing the LLM prompt templates, LDM conditioning parameters, and qualitative visual comparisons of synthesized degradations. We will also include a limitations paragraph noting that full correlation with proprietary real-world failure logs is outside the present scope and would require industry collaboration. revision: partial
Referee: [Experiments section] Experiments section: The headline metrics (R² ≈ 0.85 clean, up to 99% RMSE increase, 31% accuracy under fog) are reported across 460 scenarios without error bars, variance estimates, or explicit criteria for scenario selection and fault-parameter ranges, making it impossible to judge whether the observed degradations are statistically robust or sensitive to particular generation choices.

Authors: We accept this criticism. The revised manuscript will expand the Experiments section to report error bars (standard deviation across scenario subsets) and bootstrap-based variance estimates for the key metrics. We will also explicitly state the scenario selection criteria (balanced coverage of weather, lighting, and fault severity categories) and the numerical ranges used for fault parameters in the LLM generation prompts. These additions will be accompanied by a table summarizing the parameter distributions. revision: yes

standing simulated objections not resolved

Direct quantitative validation (FID scores or statistical correlation) against real sensor/failure logs, as no such public or accessible datasets were available to the authors.

Circularity Check

0 steps flagged

No significant circularity; evaluation reports direct metrics on generated scenarios

full rationale

The paper describes an empirical framework that uses LLMs to generate fault scenarios and LDMs to synthesize degradations, distills them into a lookup table, and measures performance on an external ResNet18 lane-following model across 460 cases. Reported quantities (baseline R^2 ≈ 0.85 on clean data, up to 99% RMSE increase, 31% accuracy under fog) are computed directly from model outputs on the synthetic inputs. No equations, fitted parameters, or self-citations reduce these results to the generation process by construction. The central claim rests on the observed degradation numbers rather than any definitional or self-referential loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the premise that LLM and LDM outputs serve as valid proxies for real faults without independent real-world calibration data shown in the abstract. No free parameters are explicitly fitted in the reported results. No new physical entities are postulated.

axioms (1)

domain assumption LLM-generated fault scenarios and LDM-synthesized degradations accurately represent the distribution of real-world environmental hazards and sensor failures.
Invoked in the description of the offline phase and the interpretation of robustness degradation results.

pith-pipeline@v0.9.0 · 5529 in / 1431 out tokens · 22089 ms · 2026-05-13T22:54:31.576480+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost Jcost_pos_of_ne_one unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

ResNet18 lane-following model... R^2 ≈0.85 on clean data... RMSE increasing by up to 99%... within-0.10 localization accuracy dropping to as low as 31.0% under fog

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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