Auto43%Harbor: pendingAgentic / Tool UseKnowledge & Reasoning

SearchDecision-Bench

A benchmark unifying six types of search decisions (routing, rewriting, relevance, sufficiency, navigation, verification) for training and evaluating search agents.

How to run

ltb · searchdecision-bench
$ npm i -g lettokenburn-cli
$ ltb bench info searchdecision-bench

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

11 sourced entries
HarnessSourceConfidenceLink
kev 8B90.3N/AIndependentmediumOct 4, 2026arxiv.org
Plumb-4B89.6N/AIndependentmediumOct 4, 2026arxiv.org
Decision 4B v1.288.3N/AIndependentmediumOct 4, 2026arxiv.org
Cygnet87.9N/AIndependentmediumOct 4, 2026arxiv.org
Malkuth-4B86.8N/AIndependentmediumOct 4, 2026arxiv.org
Jev 1.1386.6N/AIndependentmediumOct 4, 2026arxiv.org
Imajev-4B86.2N/AIndependentmediumOct 4, 2026arxiv.org
Winnow-12B85.7N/AIndependentmediumOct 4, 2026arxiv.org
SearchJev -4B80.5N/AIndependentmediumOct 4, 2026arxiv.org
Spark S1 4B V678.7N/AIndependentmediumOct 4, 2026arxiv.org
Qwen3.5 4B78.4N/AIndependentmediumOct 4, 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

SearchDecision-Bench converts heterogeneous Chinese and English resources into a common state-schema format covering six decision types. It evaluates decision quality, calibration, and latency using held-out splits.

The benchmark measures performance via decision quality and calibration error. SearchJev improves decision quality and reduces calibration error by 41-74% compared to same-size autoregressive models.