arXiv AI
Jun 6
Entropy-Based Evaluation of AI Agents: A Lightweight Framework for Measuring Behavioral Patterns
arXiv:2606. 05872v1 Announce Type: new Abstract: AI agents are commonly evaluated using task success, reward, latency, and cost.
Entropy-Based Evaluation of AI Agents: A Lightweight Framework for Measuring Behavioral Patterns
AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, uses tools effectively, reduces uncertainty over time, or remains robust across repeated runs.
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arXiv AI
Jun 3
Towards a Science of AI Agent Reliability
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Hugging Face Trending Papers
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AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
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arXiv AI
Jun 2
Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation
arXiv:2606. 01725v1 Announce Type: new Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes.
arXiv AI
Jun 30
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
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Hugging Face Trending Papers
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Predicting Task Difficulty Without Rollouts
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arXiv AI
Jun 8
When Does Multi-Agent Collaboration Help? An Entropy Perspective
arXiv:2602. 04234v6 Announce Type: cross Abstract: Multi-agent systems (MAS) have emerged as a prominent paradigm for leveraging large language models (LLMs) to tackle complex tasks.
arXiv AI
Jul 8
An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
arXiv:2607. 06413v1 Announce Type: cross Abstract: Large language model coding agents increasingly perform open-ended data modeling and analysis.
arXiv AI
Jul 29
Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification
arXiv:2607. 25904v1 Announce Type: new Abstract: Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction.
arXiv AI
Jul 7
STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training
arXiv:2607. 04963v1 Announce Type: new Abstract: Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks.
arXiv AI
Aug 10
An End-to-End Agent Auditing Engine
arXiv:2608. 07346v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains.
Hugging Face Trending Papers
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SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration
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