OAT: An Efficient Unsupervised Method for Failure Attribution in LLM Agentic Systems
Failure attribution for LLM-based agentic systems, i.e., identifying which steps in a failure trajectory caused the task to fail, is critical for debugging and improving these systems. Existing…
Read the full articleYou might also wanna read
Parcae: A Stable Looped Language Model Architecture with Predictable Scaling Laws
World models in AI: What they are, how they work, and what remains unresolved
arstechnica.com·3d agoWorld models in AI: What they are, how they work, and what remains unresolved
Ars Technica·3d agoWorld models in AI: What they are, how they work, and what remains unresolved
arstechnica.com·3d agoLLM-as-a-Verifier: A Training-Free Verification Framework for Multi-Modal Agentic Tasks
llm-as-a-verifier.com·6d agoPACE: A Framework for Predicting LLM Agent Performance Using Proxy Evaluations
huggingface.co·8d ago

Comments
Sign in to join the conversation.
No comments yet. Be the first.