NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing

Kun-Yang Yu; Lan-Zhe Guo; Ming Yang; Shi-Yu Tian; Yu-Feng Li; Zhi Zhou

REVIEW 2 major objections 6 minor 1 cited by

State-of-the-art multimodal models fail constrained remote-sensing route planning: adherence stays low and costs stay high even on optimal gold paths.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.5

2026-07-15 11:59 UTC pith:76CFI2EM

load-bearing objection Solid, useful benchmark: free-form A*-optimal remote-sensing routes at 10 imes prior scale, with hierarchical metrics that cleanly expose the perception-to-planning drop. the 2 major comments →

arxiv 2603.16307 v3 pith:76CFI2EM submitted 2026-03-17 cs.AI

NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing

Ming Yang , Zhi Zhou , Shi-Yu Tian , Kun-Yang Yu , Lan-Zhe Guo , Yu-Feng Li This is my paper

classification cs.AI

keywords remote sensingmultimodal large language modelsconstrained route planningneuro-symbolic benchmarkA-star searchhierarchical evaluationOpenEarthMap

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Existing remote-sensing tests for multimodal large language models mainly check what the models can see and describe. They rarely ask whether a model can turn language constraints and an aerial image into a safe, efficient path. NeSy-Route fills that gap with more than ten thousand automatically generated planning problems whose optimal answers are produced by a symbolic search procedure. The same pipeline also yields two precursor tasks that isolate pure text understanding and text-to-image constraint alignment. When frontier models are run on the full hierarchy they decode rules fairly well, yet region identification collapses and end-to-end path adherence stays below one-third, with many trajectories far longer or more costly than the proven optimum. The authors therefore claim that planning is a distinct bottleneck that current architectures and training data have not solved, and they release the benchmark so that future models can be measured against it.

Core claim

Current multimodal large language models exhibit large, measurable gaps in both perception and planning when forced to generate constrained routes on real remote-sensing imagery; even the strongest systems achieve path-adherence rates of only 18–33 percent and elevated cost ratios relative to the A*-optimal trajectories that the NeSy-Route generation pipeline produces for each of its 10 821 samples.

What carries the argument

An automated neuro-symbolic data-generation loop that (1) samples agent- and task-specific traversability and preference vectors from a fixed knowledge base, (2) grounds them on eroded OpenEarthMap semantic masks, and (3) runs admissible Euclidean-heuristic A* to obtain a mathematically optimal trajectory, together with a three-level hierarchical evaluator that scores text understanding, region alignment, and final path quality separately.

Load-bearing premise

The paper treats its agent-specific traversability tables, preference rankings and pixel cost map as the unique correct definition of the safest or fastest path that a real agent would accept.

What would settle it

Human experts or real-world agents produce routes whose cumulative cost or geometric distance systematically disagrees with the A* trajectories generated under the paper’s cost map on a held-out subset of the same images.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

Solid, useful benchmark: free-form A*-optimal remote-sensing routes at 10 imes prior scale, with hierarchical metrics that cleanly expose the perception-to-planning drop.

read the letter

This is a carefully built evaluation paper that fills a real gap. Prior remote-sensing MLLM benchmarks stop at VQA or multiple-choice route selection (XLRS-Bench has ~1k items). NeSy-Route gives 10,821 free-form planning samples with mathematically optimal trajectories, plus two upstream symbolic tasks that let you diagnose where models fail.

What is new is the closed pipeline: OpenEarthMap masks, dual-LLM query synthesis with self-consistency check against a fixed knowledge base, morphological erosion for clean region probes, and A* under an admissible Euclidean heuristic that guarantees optimality once the cost map W(p) is fixed. The three-level metrics (TM/PR/FM, then RM, then AR/CR/VR/CD) are well-chosen and the tables are consistent: strong text-only scores collapse once vision and actual path generation are required. Code and data are released. That is real engineering value.

The soft spots are external validity, not internal soundness. The agent tables (pedestrian/car/drone/boat traversability and preference rankings) and the cost definition are hand-crafted; there is no human path-agreement study. Complexity-score weights are free parameters. Those choices define the gold standard the models are scored against, so claims about “safest” or “optimal for real agents” should be read as “optimal under these rules.” Internally the math holds and there is no circularity—the oracle is independent of the models.

Who it is for: anyone building or evaluating remote-sensing MLLMs, or anyone who needs a scalable planning-with-vision testbed. It is not a new planning algorithm; it is a diagnostic instrument. I would bring it to reading group, cite the dataset when I need a planning benchmark, and send it to peer review. Minor revision on external-validity caveats would strengthen it, but the core contribution is already solid enough for referee time.

Referee Report

2 major / 6 minor

Summary. The paper introduces NeSy-Route, a large-scale neuro-symbolic benchmark for constrained route planning on remote-sensing imagery. It builds an automated pipeline that (i) samples agent/task configurations from a fixed knowledge base of eight land-cover classes, (ii) synthesizes natural-language queries with dual-LLM logical verification, (iii) grounds them on OpenEarthMap semantic masks via morphological erosion, and (iv) computes mathematically optimal trajectories with admissible Euclidean A* under a cost map W(p) derived from traversability and preference vectors. Three hierarchical tasks (textual constraint understanding, text–image alignment, constrained planning) are evaluated with symbolic metrics (TM/PR/FM, RM, AR/VR/CR/CD) on 3 607 / 12 975 / 10 821 samples respectively—nearly an order of magnitude larger than XLRS-Bench. Zero-shot evaluation of frontier and open-source MLLMs shows a sharp performance drop from text-only constraint parsing to vision-grounded planning (adherence rates ~18–33 %, cost ratios often ≫1), supporting the claim that current MLLMs remain deficient in perception and planning for remote-sensing route tasks.

Significance. If the reported deficiencies hold under the paper’s optimality definition, NeSy-Route supplies the first scalable, symbolically verifiable planning benchmark for remote-sensing MLLMs and a clear diagnostic hierarchy that isolates perception, reasoning and planning failures. Strengths include the closed-loop neuro-symbolic generation pipeline, A*-guaranteed gold trajectories (Sec. 3.3), multi-metric evaluation tables (Tabs. 4–6), and public release of data and code. These assets can drive both architectural improvements and more realistic remote-sensing agents for disaster response and field survey applications.

major comments (2)

Sec. 3.1–3.3 and Eq. (1): the agent-specific TA/TC/TN tables (Tab. 1), preference rankings (Tab. 2) and the resulting cost map W(p) are hand-crafted. While the A* optimality claims are internally consistent under these rules, the paper never validates that the induced “safest/fastest/comfort” paths match human expert or real-agent judgments. A modest human-agreement study (or sensitivity analysis over alternative rankings) is needed to establish external validity of the Cost-Ratio and Chamfer-Distance metrics that underwrite the central deficiency claim.
Sec. 4.1, Eq. (8): the Complexity Score D that stratifies Task 3 into Easy/Medium/Hard depends on free weights λ1–λ4 that are never specified or ablated. Without these values (or a statement that equal weights were used) the difficulty partitions and the reported performance degradation across strata cannot be reproduced or interpreted.

minor comments (6)

Abstract and throughout: “NeSyRoute” / “NeSy-Route” spelling is inconsistent; standardize on one form.
Fig. 1 caption and Task-2 description: the Region Vector is listed as length 8 yet the legend shows IDs 0–7; clarify whether the zero-padding convention is intentional.
Sec. 3.3: the dual-LLM verification (DeepSeek-V3.2 + Gemini-3-Pro) is described only at a high level; a short quantitative report of rejection rates or residual mismatch would strengthen confidence in query fidelity.
Tab. 4: Qwen3-VL-32B reports PR = –0.616; a brief note on how negative Kendall-τ is interpreted (or whether it is an artifact of ranking ties) would help readers.
Sec. 5.1: domain-specific models (GeoChat, SkySenseGPT) are excluded for instruction-following failure; a short qualitative example of their output format would make the exclusion more transparent.
Typographical: “School of Artifical Intelligence” (title page), “whcih” (p. 9), and occasional missing spaces after periods should be corrected.

Circularity Check

0 steps flagged

No significant circularity: GT vectors and A*-optimal trajectories are produced by an independent KB+heuristic pipeline before any evaluated MLLM is queried; model metrics are scored against that external oracle.

full rationale

The paper’s central claim (SOTA MLLMs exhibit large perception-to-planning gaps on 10 821 samples) is measured by comparing model outputs to ground-truth symbolic vectors and trajectories that are generated entirely outside the models under evaluation. Section 3.1 defines agent-specific TA/TC/TN tables and priority rankings from domain knowledge; Eq. (3) deterministically maps a configuration tuple to V_gt_trav and V_gt_pref via the KB; morphological grounding and the admissible/consistent Euclidean A* of Sec. 3.3 then produce mathematically optimal paths under the cost map of Eq. (1). Dual-LLM query synthesis (DeepSeek self-inference + Gemini verification) is used only to filter natural-language text so that it remains consistent with the already-fixed KB vectors; those LLMs never supply the final GT labels or the optimality reference. Consequently no evaluated quantity is fitted from, or defined in terms of, the models being scored, and no uniqueness theorem or ansatz is imported via self-citation. The sole mild self-reference is the dual-LLM filter, which does not enter the reported AR/CR/CD metrics and therefore does not raise the circularity score above 1.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 2 invented entities

As a benchmark paper the load-bearing content is definitional (agent tables, cost map, complexity score) rather than fitted physical constants. The free parameters are the hand-chosen weights and discrete rankings that turn qualitative mission goals into numeric costs; the axioms are standard search theory plus the domain land-cover ontology taken from OpenEarthMap.

free parameters (3)

λ1–λ4 complexity-score weights
Used in Eq. 8 to stratify Task-3 difficulty; values are not derived from data but chosen by the authors to produce Easy/Medium/Hard splits.
agent-specific preference rankings (Tab. 2)
Ordinal priorities for fastest/comfort/safest/shortest are assigned by hand for each agent–land-cover pair and directly determine the numeric cost map W(p).
morphological erosion neighborhood and fill rules
Selective erosion and majority-fill parameters control which pure regions are presented in Task 2; they are engineering choices that affect visual grounding difficulty.

axioms (4)

standard math A* with admissible and consistent Euclidean heuristic returns a globally optimal path under non-negative costs W(p) ≥ 1
Invoked in Sec. 3.3 to guarantee that every generated trajectory τ* is optimal; standard result from Hart et al. 1968.
domain assumption OpenEarthMap eight-class land-cover masks are high-fidelity ground truth for the visual scenes
All region IDs, connectivity graphs and cost maps are projected from these masks (Sec. 3.3).
domain assumption The four agent types and three traversability levels (TA/TC/TN) correctly capture real physical constraints for pedestrian/car/drone/boat
Defined in Tab. 1 and used to derive every V_trav vector; if the tables mis-model real agents the optimality claim loses external validity.
ad hoc to paper Dual-LLM (DeepSeek + Gemini) self-inference and compliance check produces queries whose extracted vectors match the KB
Sec. 3.3 query-synthesis stage; the filter is internal to the generation pipeline and not independently validated against human raters.

invented entities (2)

NeSy-Route three-level hierarchical task suite (textual constraint understanding, text–image alignment, constrained route planning) no independent evidence
purpose: Decouple and separately score perception, reasoning and planning failures of MLLMs
New evaluation construct; no independent existence outside this benchmark.
Complexity Score D (Eq. 8) combining inter/intra-class entropy and topological density no independent evidence
purpose: Stratify Task-3 samples into Easy/Medium/Hard
Author-defined scalar used only for difficulty bucketing inside the paper.

pith-pipeline@v1.1.0-grok45 · 19442 in / 3098 out tokens · 29449 ms · 2026-07-15T11:59:11.207760+00:00 · methodology

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read the original abstract

Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and constraints and make reliable decisions. Current remote-sensing benchmarks mainly focus on evaluating perception and reasoning capabilities of multimodal large language models (MLLMs). They fail to assess planning capability, stemming either from the difficulty of curating and validating planning tasks at scale or from evaluation protocols that are inaccurate and inadequate. To address these limitations, we introduce NeSy-Route, a large-scale neuro-symbolic benchmark for constrained route planning in remote sensing. Within this benchmark, we introduce an automated data-generation framework that integrates high-fidelity semantic masks with heuristic search to produce diverse route-planning tasks with provably optimal solutions. This allows NeSy-Route to comprehensively evaluate planning across 10,821 route-planning samples, nearly 10 times larger than the largest prior benchmark. Furthermore, a three-level hierarchical neuro-symbolic evaluation protocol is developed to enable accurate assessment and support fine-grained analysis on perception, reasoning, and planning simultaneously. Our comprehensive evaluation of various state-of-the-art MLLMs demonstrates that existing MLLMs show significant deficiencies in perception and planning capabilities. We hope NeSy-Route can support further research and development of more powerful MLLMs for remote sensing. The dataset and code are available at https://mingyang1010.github.io/NeSy-Route/.

Figures

Figures reproduced from arXiv: 2603.16307 by Kun-Yang Yu, Lan-Zhe Guo, Ming Yang, Shi-Yu Tian, Yu-Feng Li, Zhi Zhou.

**Figure 1.** Figure 1: A typical example from our NeSy-Route Benchmark. NeSy-Route evaluates MLLMs through a hierarchical reasoning pipeline consisting of three integrated tasks. Task 1 involves extracting symbolic traversability and cost vectors from the provided hiker mission scenario. Task 2 focuses on anchoring these symbolic constraints to specific identified regions within the remote sensing image. Task 3 assesses the cap… view at source ↗

**Figure 2.** Figure 2: An overview of automated data generation framework. The pipeline integrates dual-LLM logical verification for query synthesis, morphological erosion for semantic visual grounding, and constrained A-Star search algorithm for deriving mathematically optimal trajectories under symbolic rules. clude Gemini-3-Pro [7], GPT-5.1 [31], and Qwen3-VL-Plus [2], which demonstrate strong capabilities in perception and … view at source ↗

discussion (0)

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