TimeBraid: Unifying Time Series and Language for Understanding and Forecasting
Paper • 2609.29792 • Published • 25
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TimeBraid: Unifying Time Series and Language for Understanding and Forecasting
RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments
XinyueWangg
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TMarcus
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dataset 5 days ago
TMarcus
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dataset 5 days ago
WenyiWU0111
submitted a
paper to Daily Papers 10 days ago
XinyueWangg
authored 2
papers 15 days ago
Maxceline
updated a
model 19 days ago
Thrcle
updated a
model 19 days ago
FrancisKunZhou
published a
model 24 days ago
Shichengf
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papers 24 days ago
Shichengf
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paper to Daily Papers 26 days ago
Shichengf
authored a
paper about 1 month ago
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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
authored 4
papers 4 months ago
EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
Paper • 2604.01687 • Published • 8
MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
Paper • 2605.05524 • Published • 1
Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering
Paper • 2605.29648 • Published • 8
TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
Paper • 2601.21135 • Published • 8
XinyueWangg
authored a
paper over 1 year ago
WenyiWU0111
authored a
paper over 1 year ago