AUDITA: certified auditing and causal attribution of adverse outcomes in autonomous multi-agent systems
cs.AI
Submitted: 2026-08-23
Updated: 2026-08-29
Code: https://github.com/ZhixuDu/audita
Terminology
Sources
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
- PaLM-E: An Embodied Multimodal Language Model
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
- Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
- Why Do Multi-Agent LLM Systems Fail?
- AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?
- CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures
- Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
- Auditable Agents
- Training Verifiers to Solve Math Word Problems
- Automatic Failure Attribution and Critical Step Prediction Method for Multi-Agent Systems Based on Causal Inference
- Higher-Order Responsibility
- Counterfactual Reasoning for Causal Responsibility Attribution in Probabilistic Multi-Agent Systems
- The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime
- Visibility into AI Agents
- Authenticated Delegation and Authorized AI Agents
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