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ProfiLLM demonstrates that an agentic LLM pipeline can move beyond structured numerical features in a live, millisecond-latency industrial dispatcher and produce measurable improvements in real-world GMV and completion rates — validated by a 14-day online A/B test on DiDi's production system.
EARS converts sub-agent silence into structured, coordinator-actionable failure signals, directly raising the production response pass rate from 68.5% to 78.9% in a real enterprise deployment.
The evidence-first protocol directly reduces the conversational bias that causes standard LLM assistants to follow misleading user hypotheses, improving diagnostic accuracy over both direct prompting and reasoning-only baselines across multiple LLM backbones.
PhysTool-Bench quantifies a critical and previously underexplored gap between MLLMs' strong digital API performance and their weak physical tool comprehension, pinpointing specific bottlenecks — perception and functional commonsense — that limit the development of practical embodied AI.
TabClaw's combination of transparent, editable execution plans with a self-evolving skill and memory system directly addresses the transparency and adaptability gaps the paper identifies in current LLM-based data-analysis agents.
AI/coding practitioners building or evaluating biological ML pipelines can use AblateCell to automate the otherwise manual, error-prone process of reproducing baselines and identifying which model components actually drive performance gains.