GELTRE DOCUMENTATION
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EVIDENCE

Benchmarks

Geltre claims are scoped to measured artifacts. Positive results and failed gates are both part of the public technical story.

ServiceOps v0.3

SelectorRequired-evidence recall @ 5JEV accuracy
BM250.62550.8235
Geltre linear0.99611.0000
Geltre neural0.96471.0000

The linear ServiceOps ranker is the current reference because it matched the downstream neural result while being smaller and faster.

CodeOps: keep the failure

Historical negative result: cross-distribution v0.8 failed its predeclared required-evidence gate.
v0.8 metricActualRequired
Required-evidence recall0.808219≥ 0.98
Context reduction0.927891≥ 0.50

The v0.8 cohort is consumed development evidence and is not reused as fresh proof.

CodeOps H256-R4 successors

Geltre then froze new cohorts around the H256-R4 terminal-verification selector instead of repairing v0.8 after the fact.

Frozen cohortCheckpointsRequired-evidence recallExact coverageContext reduction
v0.122151.0000001.0000000.714299
v0.131971.0000001.0000000.680607

v0.13 also froze the fail-closed reliability policy before scoring. It compressed 171/197 records, passed 26/197 through raw state, omitted 0 required evidence overall, and retained 0.716822 total context reduction after fallbacks.

Live v0.13 Geltre → JEV

LaneDecision accuracyJEV input tokensRecorded costMean latency
Raw → JEV0.9695431,685,006$0.07077025259.852 ms
H256-R4 Geltre → JEV0.974619597,196$0.02508223218.378 ms

Across all 197 paired records, Geltre caused 0 damage, rescued 1 Raw → JEV error, reduced JEV input/cost by 64.5582%, and reduced mean remote latency by 15.9606%. These are frozen-protocol results, not a universal cross-domain guarantee.

Release principle

Geltre must reduce context, cost, or distraction while preserving downstream task success better than ordinary retrieval. Context reduction alone is not sufficient. Negative cohorts remain part of the record even after later selectors improve.