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COMPANY · ENTITY #5538

Terminal-Bench 2.1

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RESEARCH · 1 SOURCE · arXiv cs.AI

Self-Direction: agents distill mistakes into persistent low-rank adapters

The authors introduce Self-Direction, a method where agents reflect on failed trajectories to extract lessons and immediately distill them into lightweight low-rank adapters (~0.02% of base model parameters) while keeping base weights frozen; adapters are stored in a persistent library and routed to related problems. Experiments with open-weight Qwen-family models on GAIA, Claw-Eval, and Terminal-Bench 2.1 report 8.9–17.0 percentage-point increases in overall success, reduced repeated mistakes (nearly doubling correction reliability), and transfer of a few adapters to dozens of new tasks.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

DENSE: Distilling agent trajectories into evidence-grounded shortcut trees for self-refinement (arXiv:2609.21423v1)

This paper introduces DENSE (Distilling Evidence from Nested Subtask Executions), a method that compresses and organizes agent execution traces into evidence-grounded nested shortcut trees to link reusable progress with remaining obligations. The authors also propose REFIT, a paired evaluation protocol, and report that DENSE yields the highest strict pass rate among tested non-privileged feedback methods on Terminal-Bench 2.1 (improving strict pass rate by 7.12–15.64 percentage points and reducing observed recipient tokens in reruns by 19.0–43.6%); GPT-5.5 ablations support combining nested subtask analysis with shortcut construction and issue reconciliation.

6.0