TraceDance: An Automated System for Building Agent Behavior Benchmarks from Real-World Agent Deployment Traces
Paper • 2609.33295 • Published • 72
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POISE: Position-Aware Undetectable Skill Injection on LLM Agents
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Latent Thoughts Tuning: Bridging Context and Reasoning with Fused Information in Latent Tokens
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We are happy to share our new survey:
Scaling LLM Agent Learning with Data Synthesis: A Comprehensive Survey
LLM agents are moving from passive chatbots to interactive systems that use memory, tools, planning, and external environments. Scaling agent learning requires more than input-output pairs: agents need synthetic tasks, trajectories, feedback signals, and environments that can support long-horizon interaction.
In this survey, we organize data synthesis for LLM agent learning around four core artifacts:
Task-level synthesis
Trajectory-level synthesis
Feedback-level synthesis
Environment-level synthesis
We also review quality control, learning frameworks, synthetic evaluation data, and applications across software engineering, agentic search, AI for science, social good, and AI safety/security.
Links:
ResearchGate: [https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey](https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey)
OpenReview: [https://openreview.net/forum?id=pQYwkpYmLy](https://openreview.net/forum?id=pQYwkpYmLy)
arXiv: under moderation, link coming soon
#LLMAgents #DataSynthesis #AgentLearning #LLM #Survey
Scaling LLM Agent Learning with Data Synthesis: A Comprehensive Survey
LLM agents are moving from passive chatbots to interactive systems that use memory, tools, planning, and external environments. Scaling agent learning requires more than input-output pairs: agents need synthetic tasks, trajectories, feedback signals, and environments that can support long-horizon interaction.
In this survey, we organize data synthesis for LLM agent learning around four core artifacts:
Task-level synthesis
Trajectory-level synthesis
Feedback-level synthesis
Environment-level synthesis
We also review quality control, learning frameworks, synthetic evaluation data, and applications across software engineering, agentic search, AI for science, social good, and AI safety/security.
Links:
ResearchGate: [https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey](https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey)
OpenReview: [https://openreview.net/forum?id=pQYwkpYmLy](https://openreview.net/forum?id=pQYwkpYmLy)
arXiv: under moderation, link coming soon
#LLMAgents #DataSynthesis #AgentLearning #LLM #Survey
Shichengf
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ZhishanQ
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Shichengf
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