” The method compresses repeated attempts, summarizes successfully completed branches, preserves detail for unresolved branches, and uses evidence of later recovery to reconcile errors identified at different subtask levels.
Trajectory shortcut trees improve agents without outcome labels or annotations
DENSE converts prior agent executions into structured feedback that helps fresh attempts complete terminal tasks more accurately while using fewer recipient-model tokens.
Top University
Siyuan Liu (Fudan University) · Fan Yu (Fudan University) · Dongyu Ru (Meituan Longcat Team) · Yizhu Liu (Meituan Longcat Team) · Yifan Yang (Meituan Longcat Team) · Xuezhi Cao (Meituan Longcat Team) · +2 more
Research Digest··2 min read
Thread:Agent Self-Improvement
Liu et al.
Why this paper
From Fudan University and Meituan Longcat Team · Part of Agent Self-Improvement, now 10 papers
In one line
DENSE organizes agent trajectories into nested shortcut trees to improve task success without external rewards.
What we could check
- ·No code link found
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- ·No stated limitations found
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