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Agentic search methods only achieve a maximum Recall@100 of 31.4%, revealing a critical gap in current academic paper retrieval capabilities.
ScholarSum achieves a groundbreaking balance between fluency and factual accuracy in scientific summarization, outperforming existing methods by a significant margin.
TabClaw transforms spreadsheet analysis from a manual, opaque process into an interactive, self-evolving workflow that adapts to user needs and preferences.
Claw-R1 transforms agentic RL by treating interaction data as valuable assets, enabling real-time inspection and curation for optimized training.
LLMs can beat traditional time-series models by orchestrating specialized agents in a dynamic workflow, iteratively refining forecasts with memory and ensemble methods.
StepPO reveals that aligning policy optimization with agent decision-making steps can lead to superior performance in multi-turn interactions, outperforming traditional RL methods.
