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arxiv: 2604.09604 · v1 · submitted 2026-03-10 · 💻 cs.AI · cs.LG

Recognition: no theorem link

LLMs for Text-Based Exploration and Navigation Under Partial Observability

Stephan Sandfuchs , Maximilian Melchert , J\"org Frochte
Authors on Pith no claims yet

Pith reviewed 2026-05-15 14:00 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords large language modelspartial observabilitygridworld navigationtext-based controlexplorationreasoning-tuned modelsfew-shot promptingpath efficiency
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The pith

Reasoning-tuned LLMs reliably navigate unknown grid layouts from local text observations alone but take longer paths than optimal.

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

The paper tests whether large language models can serve as text-only controllers for exploration and goal-directed navigation in unknown environments under partial observability, without code execution or external tools. It creates a benchmark using fixed ASCII gridworlds where each step supplies only a 5x5 local window around the agent and the model must output one of four directional moves. Reasoning-tuned models succeed at reaching goals across layouts of rising difficulty, while standard instruction-tuned models remain inconsistent. Few-shot examples in the prompt mainly improve outcomes by cutting invalid moves and shortening paths for the stronger models. Training approach and test-time deliberation turn out to matter more than raw size, which leads the authors to recommend lightweight hybrid systems that pair LLMs with classical planners.

Core claim

Large language models, particularly those tuned for reasoning, can function as text-only controllers for exploration and goal-directed navigation in fixed ASCII gridworlds under partial observability, with each step revealing only a local 5x5 window. Reasoning-tuned models reliably complete navigation across all tested layouts of increasing difficulty yet remain less efficient than oracle shortest paths. Few-shot demonstrations chiefly help these models by reducing invalid moves and shortening paths, while classic dense instruction models stay inconsistent. Characteristic action priors such as preferring UP or RIGHT can induce looping under partial observability. Training regimen and test-

What carries the argument

Text-only LLM controller that selects UP/RIGHT/DOWN/LEFT moves from local 5x5 ASCII observations in a reproducible gridworld benchmark.

If this is right

  • Reasoning-tuned models achieve reliable navigation success across layouts of increasing difficulty.
  • Few-shot prompting reduces invalid moves and shortens paths primarily for reasoning-tuned models.
  • Training regimen and test-time deliberation predict control performance better than parameter count.
  • Lightweight hybridisation with classical online planners offers a practical route to deployable partial-map systems.

Where Pith is reading between the lines

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

  • The same controller setup might extend to environments with moving obstacles if the model is given an explicit memory buffer across steps.
  • Action priors that cause looping could be reduced by adding a simple search wrapper around the LLM output without changing the core text interface.
  • Real-world robotics applications would require testing whether vision-to-text conversion preserves the performance seen in clean ASCII grids.

Load-bearing premise

The fixed ASCII gridworlds with oracle localisation and 5x5 local windows sufficiently represent real-world partial observability and navigation challenges.

What would settle it

A new test layout where reasoning-tuned models fail to reach the goal in more than half of trials or produce paths more than twice the oracle length on average would falsify the reliability claim.

Figures

Figures reproduced from arXiv: 2604.09604 by J\"org Frochte, Maximilian Melchert, Stephan Sandfuchs.

Figure 1
Figure 1. Figure 1: Decision loop of an LLM-driven gridworld agent (1) Environment: Ob [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Full view of the used gridworlds with the oracle baseline for shortest [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Task description in a prompt for Exploration Zero-Shot (Excerpt) [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example prompt for Navigation Zero-Shot in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
read the original abstract

Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language models (LLMs) can function as \emph{text-only} controllers under partial observability -- without code execution, tools, or program synthesis. We introduce a reproducible benchmark with oracle localisation in fixed ASCII gridworlds: each step reveals only a local $5\times5$ window around the agent and the model must select one of \texttt{UP/RIGHT/DOWN/LEFT}. Nine contemporary LLMs ranging from open/proprietary, dense / Mixture of Experts and instruction- vs. reasoning-tuned are evaluated on two tasks across three layouts of increasing difficulty: \emph{Exploration} (maximising revealed cells) and \emph{Navigation} (reach the goal on the shortest path). The experimental results are evaluated on quantitative metrics including \emph{success rate}, \emph{efficiency} such as normalised coverage and \emph{path length} vs. oracle as well as qualitative analysis. Reasoning-tuned models reliably complete navigation across all layouts, yet remain less efficient than oracle paths. Few-shot demonstrations in the prompt chiefly help these Reasoning-tuned models by reducing invalid moves and shortening paths, while classic dense instruction models remain inconsistent. We observe characteristic action priors (UP/RIGHT) that can induce looping under partial observability. Overall, training regimen and test-time deliberation predict control ability better than raw parameter count. These findings suggest lightweight hybridisation with classical online planners as a practical route to deployable partial map systems.

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

3 major / 2 minor

Summary. The manuscript introduces a reproducible benchmark for evaluating LLMs as text-only controllers for exploration (maximising revealed cells) and navigation (reaching a goal) tasks in fixed ASCII gridworlds. Each step provides a local 5x5 text window plus oracle localisation; nine LLMs (dense vs. MoE, instruction- vs. reasoning-tuned) are tested on quantitative metrics including success rate, normalised coverage, and path length versus oracle, across three layouts of increasing difficulty. The central claims are that reasoning-tuned models reliably complete navigation but remain less efficient than oracle paths, that few-shot demonstrations primarily help by reducing invalid moves and shortening paths, and that training regimen predicts performance better than parameter count.

Significance. If the results hold under a clarified partial-observability definition, the work supplies a controlled empirical comparison of current LLMs on text-based navigation, identifies characteristic action priors that induce looping, and points to lightweight hybridisation with classical online planners as a practical next step. The reproducible benchmark itself is a concrete contribution for future LLM-control studies.

major comments (3)
  1. [Abstract] Abstract and benchmark description: the setup supplies oracle localisation (global coordinates) together with the 5x5 local window. This reduces the task to action selection from a known position plus local text rather than inferring position from partial observations alone, weakening the claim that the benchmark tests navigation 'under partial observability' in the POMDP sense.
  2. [Results] Results section (quantitative metrics): success rates, normalised coverage, and path lengths are reported for two tasks and three layouts without error bars, number of runs, or statistical tests. This leaves the central claim that 'reasoning-tuned models reliably complete navigation across all layouts' plausible but unverified in detail.
  3. [Benchmark] Benchmark and prompt construction: full prompt templates (including exact few-shot demonstrations and how oracle coordinates are formatted) are not supplied. Without them the reported advantage of few-shot prompting for reasoning-tuned models cannot be reproduced or isolated from prompt-engineering details.
minor comments (2)
  1. [Abstract] Abstract: 'normalised coverage' is used without a one-sentence definition or pointer to its exact formula; a brief parenthetical would improve immediate readability.
  2. [Figures] Figure captions (assumed in results): axis labels and legend entries for path-length ratios should explicitly state whether they are normalised to oracle length or raw step counts.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and revise the manuscript to improve clarity on partial observability, add statistical details, and ensure full reproducibility of prompts.

read point-by-point responses

  1. Referee: [Abstract] Abstract and benchmark description: the setup supplies oracle localisation (global coordinates) together with the 5x5 local window. This reduces the task to action selection from a known position plus local text rather than inferring position from partial observations alone, weakening the claim that the benchmark tests navigation 'under partial observability' in the POMDP sense.

    Authors: We agree that supplying oracle coordinates means the agent's absolute position is known, so the setup does not require inferring position from observations as in a canonical POMDP. The partial observability we target is instead the unknown environment layout (walls, obstacles, goal location), which must be discovered through the local 5x5 text window. We will revise the abstract, introduction, and benchmark section to explicitly distinguish this from full POMDP position inference and to avoid overstating the claim. revision: yes

  2. Referee: [Results] Results section (quantitative metrics): success rates, normalised coverage, and path lengths are reported for two tasks and three layouts without error bars, number of runs, or statistical tests. This leaves the central claim that 'reasoning-tuned models reliably complete navigation across all layouts' plausible but unverified in detail.

    Authors: We accept this point. Each model-layout-task combination was evaluated over 5 independent runs (different random seeds for any stochastic decoding). We will add error bars showing standard deviation to all quantitative plots, explicitly state the number of runs in the experimental setup, and add a short discussion of result consistency. Formal hypothesis tests are omitted because the number of layouts is small, but the qualitative ordering is stable across runs. revision: yes

  3. Referee: [Benchmark] Benchmark and prompt construction: full prompt templates (including exact few-shot demonstrations and how oracle coordinates are formatted) are not supplied. Without them the reported advantage of few-shot prompting for reasoning-tuned models cannot be reproduced or isolated from prompt-engineering details.

    Authors: We agree that exact prompt templates are required for reproducibility. We will append the complete prompt templates (base system prompt, few-shot examples with exact formatting of oracle coordinates, and action output format) to the revised manuscript as a new appendix section. revision: yes

Circularity Check

0 steps flagged

No circularity: pure empirical benchmark evaluation of off-the-shelf LLMs

full rationale

The paper introduces a fixed ASCII gridworld benchmark and directly evaluates nine contemporary LLMs on exploration and navigation tasks using quantitative metrics such as success rate, coverage, and path length. No derivations, equations, fitted parameters, or predictions are present that reduce to inputs by construction. All results follow from running the models on the described environments; the setup contains no self-definitional loops, self-citation load-bearing premises, or renamed known results. The evaluation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The evaluation rests on the assumption that text prompts can faithfully encode local grid observations and that model outputs map directly to valid moves; no free parameters are fitted and no new entities are postulated.

axioms (1)
  • domain assumption LLMs can interpret text descriptions of local grid observations and output valid directional moves
    Invoked throughout the experimental setup for text-only control.

pith-pipeline@v0.9.0 · 5584 in / 1193 out tokens · 90187 ms · 2026-05-15T14:00:12.703235+00:00 · methodology

discussion (0)

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