TimeClaw is an exploratory execution learning system that turns multiple valid tool-use paths into hierarchical distilled experience for improved time-series reasoning without test-time adaptation.
SafeClaw-R: Risk analysis and runtime enforcement for OpenClaw skills
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 3roles
background 2polarities
background 2representative citing papers
The paper defines intent-to-execution integrity as the conjunction of Tool Integrity, Instruction Integrity, Judgment Integrity, and Data Flow Integrity, arguing that existing LLM agent defenses provide only partial coverage of these properties.
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.
citing papers explorer
-
TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
TimeClaw is an exploratory execution learning system that turns multiple valid tool-use paths into hierarchical distilled experience for improved time-series reasoning without test-time adaptation.
-
Securing LLM Agents Need Intent-to-Execution Integrity
The paper defines intent-to-execution integrity as the conjunction of Tool Integrity, Instruction Integrity, Judgment Integrity, and Data Flow Integrity, arguing that existing LLM agent defenses provide only partial coverage of these properties.
-
Constraining Host-Level Abuse in Self-Hosted Computer-Use Agents via TEE-Backed Isolation
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.