arXiv AI
Jul 15
Tracing Agentic Failure from the Flow of Success
arXiv:2607. 12747v1 Announce Type: new Abstract: Failure attribution for LLM-based agentic systems, i.
From Features to Actions: Explainability in Traditional and Agentic AI Systems
arXiv:2602. 06841v4 Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure.
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arXiv AI
Jun 3
StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems
arXiv:2606. 03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks.
arXiv AI
Jul 24
Explainability Framework for Policy-Aware Autonomous Agents
arXiv:2607. 21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal.
arXiv AI
Jun 2
FALAT: Tracing Failures in LLM Agent Trajectories via Dependency-Guided Search
arXiv:2606. 00765v1 Announce Type: new Abstract: LLM-based agents increasingly solve complex tasks through long trajectories involving reasoning steps, tool calls, and inter-agent communication.
arXiv AI
Jun 2
AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
arXiv:2603. 14465v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions.
arXiv AI
Jul 10
Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems
arXiv:2607. 07989v1 Announce Type: cross Abstract: Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed structure also introduces new challenges in diagnosing system-level failures.
arXiv AI
Jun 8
Beyond the Black Box: Interpretability of Agentic AI Tool Use
arXiv:2605. 06890v3 Announce Type: replace Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagnose and control.
arXiv AI
Jul 13
All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models
arXiv:2607. 09502v1 Announce Type: cross Abstract: Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off.
arXiv AI
Jul 17
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
arXiv:2607. 14123v1 Announce Type: cross Abstract: Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows.
arXiv Machine Learning
22h ago
Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents
arXiv:2608. 17524v1 Announce Type: new Abstract: This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods.
arXiv Machine Learning
2d ago
ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning
arXiv:2608. 14352v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents are increasingly used for complex tasks such as software testing and cybersecurity assessment.
arXiv AI
1d ago
Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments
arXiv:2608. 16747v1 Announce Type: cross Abstract: Many areas of AI research, such as language model interpretability and chain of thought faithfulness, seek to explain model behaviors.