Trust and Task Completion in the World of Consumer AI Agents

arXiv:2609.33017 · cs.AI, cs.CL · Submitted 2026-09-26 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/openclaw/openclaw

Terminology

• Benchmark — 28× in this paper · explained in 2 episodes
• Foundation Model — 9× in this paper · explained in 3 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Benchmarking — 6× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 6× in this paper
• AI Agents — 4× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Agentic System — 1× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 1× in this paper
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Model Context Protocol (MCP) — 1× in this paper · explained in 8 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Concrete Problems in AI Safety
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
• Constitutional AI: Harmlessness from AI Feedback
• $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
• EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents
• ARE: Scaling Up Agent Environments and Evaluations
• Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks
• MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control
• ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
• ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces
• EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings
• Magentic-UI: Towards Human-in-the-loop Agentic Systems
• $\tau$-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domains
• MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations
• Agents of Chaos
• $\tau$-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge
• HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?
• OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety
• ASTRA-bench: Evaluating Tool-Use Agent Reasoning and Action Planning with Personal User Context
• ClawBench: Can AI Agents Complete Everyday Online Tasks?

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