Cognitive Map for Language Models: Optimal Planning via Verbally Representing the World Model

arXiv 24.6 2024 36.5 method

TLDR

Proposes cognitive maps for path planning in language models, improving extrapolation to unseen environments over conventional methods.

Reasoning

Strengths include a novel human-inspired framework and clear demonstration of failure in standard approaches. Weaknesses are the limited scope to a synthetic Gridworld task and lack of real-world validation.

Read-first score

Read-first score 36.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 13.

Recency 8%

75.1

Uses a gentle age decay so recent papers surface without erasing older foundations. 2024

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=experiment

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 42%

18.6

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Field roles

Candidate

Rank sensitivity

Stability: volatile; rank range: 113.

Keyword Scores

world model

world dynamics prediction

world simulator

model-based reinforcement learning world model

generative world model

interactive world model

video world model

Deep Analysis

Innovations

Proposes cognitive maps for path planning, a novel Chain of Thought (CoT) framework that simulates humanlike mental representations in language models.
Demonstrates that conventional approaches (next token prediction and CoT finetuning) fail to extrapolate to larger, unseen environments in a textualized Gridworld task.
Shows that cognitive maps exhibit humanlike characteristics through structured mental simulation and rapid adaptation.
Reveals that cognitive maps require specialized training schemes and cannot be induced through simple prompting, raising questions about developing general-purpose cognitive maps.
Compares cognitive maps with exploration-based methods, highlighting complementary strengths of offline planning and online exploration.

Methodology

The study introduces a path planning task in a textualized Gridworld to probe language models' extrapolation capabilities. It proposes cognitive maps as a CoT framework that simulates humanlike mental representations, and compares performance against next token prediction, CoT finetuning, and exploration-based methods. Experiments evaluate extrapolation to larger unseen environments and analyze humanlike characteristics.

Key Results

Cognitive maps enhance extrapolation to unseen environments and exhibit humanlike characteristics, while conventional approaches (next token prediction and CoT finetuning) fail to extrapolate. The cognitive maps require specialized training schemes and cannot be induced through simple prompting.

Limitations

Cognitive maps require specialized training schemes and cannot be induced through simple prompting, limiting their generalizability.
The study is limited to a textualized Gridworld path planning task; it is unknown whether the approach generalizes to other domains or more complex environments.
Comparison with exploration-based methods shows complementary strengths, implying cognitive maps may not be optimal for all planning scenarios.
The paper raises open questions about developing general-purpose cognitive maps in language models, indicating current limitations in achieving such generality.

Resources

Paper