MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems
cs.AI
Submitted: 2026-09-29
Updated: 2026-09-29
Code: https://github.com/langchain-ai/langgraph
Terminology
Sources
- TRAIL: Trace Reasoning and Agentic Issue Localization
- Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
- Time Travel in LLMs: Tracing Data Contamination in Large Language Models
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems
- Distilling the Knowledge in a Neural Network
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
- Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
- MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
- BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks
- Online Cascade Learning for Efficient Inference over Streams
- Detecting Silent Failures in Multi-Agentic AI Trajectories
- Deep Semi-Supervised Anomaly Detection
- AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems
- EscapeCraft: A 3D Room Escape Environment for Benchmarking Complex Multimodal Reasoning Ability
- LiveBench: A Challenging, Contamination-Limited LLM Benchmark
- TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
- Benchmarking Benchmark Leakage in Large Language Models
- Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
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- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
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