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TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery
Emerging long-horizon agentic tasks require repeated model calls, worsening the inference cost of already-costly language models. While narrow agentic tasks suggest potential for aggressive model pruning without performance drop, empirical results show existing methods proposed for question answerin
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