Auto57%Harbor: pendingKnowledge & ReasoningInstruction Following

GeoFitBench-LLM

GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks

A benchmark of 48 items assessing LLMs' ability to infer geospatial data requirements from natural language requests across spatio-temporal and semantic dimensions.

How to run

ltb · geofitbench-llm
$ npm i -g lettokenburn-cli
$ ltb bench info geofitbench-llm

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

6 sourced entries
HarnessSourceConfidenceLink
GPT-5.478.2N/AIndependentmediumOct 8, 2026arxiv.org
Gemini 3.1 Pro77.4N/AIndependentmediumOct 8, 2026arxiv.org
Gemini 2.5 Flash73.5N/AIndependentmediumOct 8, 2026arxiv.org
gpt-oss-20b72.7N/AIndependentmediumOct 8, 2026arxiv.org
DeepSeek-R1 14B44.7N/AIndependentmediumOct 8, 2026arxiv.org
Gemma-3n37.1N/AIndependentmediumOct 8, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

All runs
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About

GeoFitBench-LLM contains 48 benchmark items organized by task difficulty (D1-D3) and reasoning dimensions (Spatio-Temporal Relational, Semantic Grounding). It uses a fixed, provider-independent schema aligned with ISO standards for ground truth.

Scoring evaluates resource identification and attribute-level specification. Experiments on seven models show GPT-5.4 Thinking achieved the best result, recovering complete resources in 66.67% of requests, though fitness-for-use specification remains difficult.