MapReason-OSM supplies 6000 graph-verifiable instances across 12 mobility tasks on rendered OSM maps from 10 U.S. downtowns and shows that seven VLMs succeed at simple routing but perform near chance on cost-based facility placement and cross-zoom consistency.
Can large vision language models read maps like a human?
9 Pith papers cite this work. Polarity classification is still indexing.
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A new diagnostic benchmark decomposes LLM spatial navigation into three cognitive scales and shows that cross-scale aggregation, not single-level deficits, causes failure beyond small mazes.
PERIA augments VLMs with vision perception and interaction tools trained via supervised trajectories and OR-GIGPO to deliver 10% and 4.4% gains on in- and out-of-distribution spatial reasoning benchmarks.
TransitLM is a large-scale dataset and benchmark for training LLMs to generate structurally valid map-free transit routes from origin-destination pairs.
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
VLMs fail at exact visual path following primarily at self-intersections, where performance drops sharply after the first crossing on a controlled polyline benchmark.
MapTab introduces a 328-map, 196,800-query benchmark showing that current multimodal LLMs fall far short on multi-criteria route planning from maps-plus-tables.
RLVR on synthetic mazes enables VLMs to solve spatial reasoning tasks unreachable by the base model and generalizes to real-world navigation benchmarks.
A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.
citing papers explorer
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MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ?
MapReason-OSM supplies 6000 graph-verifiable instances across 12 mobility tasks on rendered OSM maps from 10 U.S. downtowns and shows that seven VLMs succeed at simple routing but perform near chance on cost-based facility placement and cross-zoom consistency.
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Lost in Aggregation: A Multi-Scale Diagnostic Benchmark for LLM Spatial Navigation
A new diagnostic benchmark decomposes LLM spatial navigation into three cognitive scales and shows that cross-scale aggregation, not single-level deficits, causes failure beyond small mazes.
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Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning
PERIA augments VLMs with vision perception and interaction tools trained via supervised trajectories and OR-GIGPO to deliver 10% and 4.4% gains on in- and out-of-distribution spatial reasoning benchmarks.
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TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation
TransitLM is a large-scale dataset and benchmark for training LLMs to generate structurally valid map-free transit routes from origin-destination pairs.
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Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
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TraversalBench: Challenging Paths to Follow for Vision Language Models
VLMs fail at exact visual path following primarily at self-intersections, where performance drops sharply after the first crossing on a controlled polyline benchmark.
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MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs
MapTab introduces a 328-map, 196,800-query benchmark showing that current multimodal LLMs fall far short on multi-criteria route planning from maps-plus-tables.
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Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models
RLVR on synthetic mazes enables VLMs to solve spatial reasoning tasks unreachable by the base model and generalizes to real-world navigation benchmarks.
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Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.