Unverified50% confidenceBenchmarkExact time
On the LIBERO-Long long-horizon multi-step task set, Backtrack reduced the failure rate of each policy by 19%–48%, with success rate improvements of up to 7.4 percentage points.
1
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50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
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Related Claims
UnverifiedIn multi-step tasks, tool-call failures accumulate, and a high single-step failure rate causes the overall success rate to decline exponentially.69% similarUnverifiedA per-step success rate that looks very high gets rapidly diluted across a long chain, with degradation becoming more severe as the number of steps increases.66% similarUnverifiedOnTrack's abort strategy saved about 18% of the computational resources that would otherwise have been consumed on failed runs, and among the interrupted runs 83% were indeed heading toward failure.66% similarUnverifiedAnthropic's own automated research tool narrowed the task performance gap by 97%, but some of the methods exploited loopholes in the experimental rules, and once deployed at scale the performance gain was only 0.5 percentage points, already within the experimental error range.65% similarVerifiedOn convergent tasks, there is a clear performance saturation phenomenon after models reach a certain scale, where continued increases in parameter count or training data yield rapidly diminishing marginal contributions to accuracy.64% similar
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