CUJBench is the first benchmark for cross-modal LLM-agent failure diagnosis, reporting 19.7% accuracy and identifying evidence attribution as the core bottleneck across six models.
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Trail: Trace reasoning and agentic issue localization
12 Pith papers cite this work. Polarity classification is still indexing.
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A span-decomposed evaluation framework for AI agents achieves state-of-the-art results on GAIA and SWE-Bench with up to 3.5x gains in localization accuracy by breaking traces into independent per-span judgments.
10.7% of passing SWE-agent trajectories are Lucky Passes with chaotic behaviors, and a quality score based on process references changes model rankings across eight backends.
AJ-Bench provides 155 tasks in three domains to evaluate environment-interacting agent judges, showing performance gains over LLM-as-a-Judge but exposing remaining verification challenges.
A large-scale empirical study categorizes bugs in LLM agents and demonstrates that a specialized LLM agent can annotate them accurately at very low cost.
PIVOT refines LLM agent trajectories through plan-inspect-evolve-verify stages using environment feedback, yielding up to 94% relative gains in constraint satisfaction and 3-5x token efficiency over prior refinement methods.
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
Graphectory turns stochastic agent trajectories into analyzable graphs, showing that stronger models and successful fixes follow coherent localization-validation steps while failures are chaotic, and online detection plus rollback improves resolution rates by 6.9-23.5%.
The survey proposes the LIFE framework to unify fragmented research on collaboration, failure attribution, and self-evolution in LLM multi-agent systems into a progression toward self-organizing intelligence.
ErrorProbe introduces a self-improving pipeline for attributing semantic failures in LLM multi-agent systems to specific agents and steps via anomaly detection, backward tracing, and tool-grounded validation with verified episodic memory.
AI agents require distinct regulation as AI systems under the EU AI Act with orchestration-layer oversight and a risk-based traffic light authorization system in contract law to preserve human accountability.
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.
citing papers explorer
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CUJBench: Benchmarking LLM-Agent on Cross-Modal Failure Diagnosis from Browser to Backend
CUJBench is the first benchmark for cross-modal LLM-agent failure diagnosis, reporting 19.7% accuracy and identifying evidence attribution as the core bottleneck across six models.
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Holistic Evaluation and Failure Diagnosis of AI Agents
A span-decomposed evaluation framework for AI agents achieves state-of-the-art results on GAIA and SWE-Bench with up to 3.5x gains in localization accuracy by breaking traces into independent per-span judgments.
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AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation
10.7% of passing SWE-agent trajectories are Lucky Passes with chaotic behaviors, and a quality score based on process references changes model rankings across eight backends.
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AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation
AJ-Bench provides 155 tasks in three domains to evaluate environment-interacting agent judges, showing performance gains over LLM-as-a-Judge but exposing remaining verification challenges.
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When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling
A large-scale empirical study categorizes bugs in LLM agents and demonstrates that a specialized LLM agent can annotate them accurately at very low cost.
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PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement
PIVOT refines LLM agent trajectories through plan-inspect-evolve-verify stages using environment feedback, yielding up to 94% relative gains in constraint satisfaction and 3-5x token efficiency over prior refinement methods.
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SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
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Process-Centric Analysis of Agentic Software Systems
Graphectory turns stochastic agent trajectories into analyzable graphs, showing that stronger models and successful fixes follow coherent localization-validation steps while failures are chaotic, and online detection plus rollback improves resolution rates by 6.9-23.5%.
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Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems
The survey proposes the LIFE framework to unify fragmented research on collaboration, failure attribution, and self-evolution in LLM multi-agent systems into a progression toward self-organizing intelligence.
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Towards Self-Improving Error Diagnosis in Multi-Agent Systems
ErrorProbe introduces a self-improving pipeline for attributing semantic failures in LLM multi-agent systems to specific agents and steps via anomaly detection, backward tracing, and tool-grounded validation with verified episodic memory.
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A pragmatic approach to regulating AI agents
AI agents require distinct regulation as AI systems under the EU AI Act with orchestration-layer oversight and a risk-based traffic light authorization system in contract law to preserve human accountability.
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A Survey of Context Engineering for Large Language Models
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.