Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-in...
VQV Signal
RESEARCH
SOURCE-BACKED
TECHNICAL
DART-SD: Diamond-topology Aware Retrieval and Tuning for Self-Distillation of Multi-Turn Tool-Calling Agents
Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-in...
Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-independent sub-goals, the optimal solution space forms a...
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Signal Strength 95%
Technical label SOURCE-BACKED
Public Interest 22
Category RESEARCH
Reader Depth TECHNICAL
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Public Interest components
Recognizable Entity Score 0
Practical Impact Score 20
Novelty Interest Score 48
Consequence Score 18
Curiosity Score 16
Shareability Score 41
Topics
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