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HFEPX · Eval paper review

LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains

Ling Xiao, Toshihiko Yamasaki

Published

Mar 3, 2025

Citations

0

Trust level

Low

Usefulness score

15/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Mar 10, 2026

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Automatic Metrics General Web Browsing

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
15/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost. This is especially crucial for real-world applications where robots need to navigate diverse terrains in outdoor environments with limited opportunities for recharging or refueling. Despite its practical importance, cost-efficient path planning across heterogeneous terrains has received relatively limited attention in prior work. In this paper, we propose LLM-Advisor, a prompt-based, planner-agnostic framework that leverages large language models (LLMs) as non-decisive post-processing advisors for cost refinement, without modifying the underlying planner. While we observe that LLMs may occasionally produce implausible suggestions, we introduce two effective hallucination-mitigation strategies. We further introduce two datasets, MultiTerraPath and RUGD_v2, for systematic evaluation of cost-efficient path planning. Extensive experiments reveal that state-of-the-art LLMs, including GPT-4o, GPT-4-turbo, Gemini-2.5-Flash, and Claude-Opus-4, perform poorly in zero-shot terrain-aware path planning, highlighting their limited spatial reasoning capability. In contrast, the proposed LLM-Advisor (with GPT-4o) improves cost efficiency for 72.37% of A*-planned paths, 69.47% of RRT*-planned paths, and 78.70% of LLM-A*-planned paths. On the MultiTerraPath dataset, LLM-Advisor demonstrates stronger performance on the hard subset, further validating its applicability to real-world scenarios.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
Web Browsing
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Cost-efficient path planning across multiple terrains is a crucial task in robot navigation, requiring the identification of a path from the start to the goal that not only avoids obstacles but also minimizes the overall travel cost.
  • This is especially crucial for real-world applications where robots need to navigate diverse terrains in outdoor environments with limited opportunities for recharging or refueling.
  • Despite its practical importance, cost-efficient path planning across heterogeneous terrains has received relatively limited attention in prior work.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • In this paper, we propose LLM-Advisor, a prompt-based, planner-agnostic framework that leverages large language models (LLMs) as non-decisive post-processing advisors for cost refinement, without modifying the underlying planner.
  • While we observe that LLMs may occasionally produce implausible suggestions, we introduce two effective hallucination-mitigation strategies.
  • We further introduce two datasets, MultiTerraPath and RUGD_v2, for systematic evaluation of cost-efficient path planning.

Why it matters for eval

  • We further introduce two datasets, MultiTerraPath and RUGD_v2, for systematic evaluation of cost-efficient path planning.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.