SAGE with MHFA improves failure recovery in autonomous research agents, raising metrics-bearing outputs from 42% to 92% on a 12-topic benchmark versus single-reflection baselines.
Data Interpreter: An LLM Agent for Data Science
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Concurrent human-agent interactions occur in 31.8% of turns and follow five action patterns explained by six triggers and four enabling factors, enabled by a context-aware design probe called CLEO.
Presents a new question-based evaluation framework for LLMs on aggregated social media text and reports that performance declines with input scale, task complexity, and numerical operations beyond 500 instances.
citing papers explorer
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One Reflection Is Not Enough: Self-Correcting Autonomous Research via Multi-Hypothesis Failure Attribution
SAGE with MHFA improves failure recovery in autonomous research agents, raising metrics-bearing outputs from 42% to 92% on a 12-topic benchmark versus single-reflection baselines.
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"When to Hand Off, When to Work Together": Expanding Human-Agent Co-Creative Collaboration through Concurrent Interaction
Concurrent human-agent interactions occur in 31.8% of turns and follow five action patterns explained by six triggers and four enabling factors, enabled by a context-aware design probe called CLEO.
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Text Analytics Evaluation Framework: A Case Study on LLMs and Social Media
Presents a new question-based evaluation framework for LLMs on aggregated social media text and reports that performance declines with input scale, task complexity, and numerical operations beyond 500 instances.