DataPRM is an environment-aware generative process reward model that improves LLM data analysis agents by 7-11% on benchmarks via active verification and reflection-aware ternary rewards.
Agentada: Skill-adaptive data analytics for tailored insight discovery,
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A dual-plane permission framework for LLM agent skills cuts SkillInject attack success by roughly 9 percentage points while nearly preserving benign task success.
DataCOPE uses verifier-guided contrastive distillation from agent trajectories to discover skills, yielding average gains of 9.71% on report-style and 32.30% on reasoning-style data analysis tasks across four model settings.
Describes a multi-agent system for proactive insight discovery in real-time streaming data via LLMs and streaming platforms.
citing papers explorer
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Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
DataPRM is an environment-aware generative process reward model that improves LLM data analysis agents by 7-11% on benchmarks via active verification and reflection-aware ternary rewards.
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SkillGuard: A Permission-Centric Framework for Agent Skill Security
A dual-plane permission framework for LLM agent skills cuts SkillInject attack success by roughly 9 percentage points while nearly preserving benign task success.
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Unsupervised Skill Discovery for Agentic Data Analysis
DataCOPE uses verifier-guided contrastive distillation from agent trajectories to discover skills, yielding average gains of 9.71% on report-style and 32.30% on reasoning-style data analysis tasks across four model settings.
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Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems
Describes a multi-agent system for proactive insight discovery in real-time streaming data via LLMs and streaming platforms.