The authors introduce Mara Chain, a procedure for optimizing mutable AI system components without updating model weights.
Refining rejected candidates makes AI system optimization more efficient
Mara Chain preserves failed prompt, skill, harness, and code variants, then uses their accumulated evidence to guide bounded rounds of refinement.
Chinese Tech
Yubin Lyu · Fu Li · Jiawei Fei · Yang Zhao · Weixing Mei · Yinan Wu
Ant Group · Beijing Intelligent Game and Decision Lab · Beijing Defense Innovation Institute
Research Digest··2 min read
Thread:Agent Self-Improvement
Lyu and colleagues test an alternative to optimization loops that simply discard candidates failing an acceptance threshold.
Why this paper
From Ant Group and 2 others · Part of Agent Self-Improvement, now 21 papers
In one line
Mara Chain retains and refines rejected AI system candidates instead of discarding them, improving performance with fewer evaluations.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks (3 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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