Across four domain-model settings on tau-Bench, geometry-conditioned adaptive reasoning triggers raise average task reward from 0.241 (24.1%) under Never-thinking to 0.396 (39.6%) while reducing mean token cost from 104.8k to 93.0k, an 11.2% reduction.

Across four domain-model settings on tau-Bench, geometry-conditioned adaptive reasoning triggers raise average task reward from 0.241 (24.1%) under Never-thinking to 0.396 (39.6%) while reducing mean token cost from 104.8k to 93.0k, an 11.2% reduction. · CiteArk

Measurement results

0/5 assessed

0 supported0 disputed or conflicting5 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
policy: never-think · benchmark: tau-bench	0.241 fraction	—	Not assessed
policy: geometry-conditioned · benchmark: tau-bench	0.396 fraction	—	Not assessed
policy: never-think · benchmark: tau-bench	104.8 thousands_tokens	—	Not assessed
policy: geometry-conditioned · benchmark: tau-bench	93 thousands_tokens	—	Not assessed
benchmark: tau-bench	11.2 percentage_points	—	Not assessed

Across four domain-model settings on tau-Bench, geometry-conditioned adaptive reasoning triggers raise average task reward from 0.241 (24.1%) under Never-thinking to 0.396 (39.6%) while reducing mean token cost from 104.8k to 93.0k, an 11.2% reduction.

Reported and observed measurements

Assessments (0)

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Measurement results