The authors introduce a three-part ontology-based verification system for AI agents that generates regulatory and adversarial test scenarios and issues machine-verifiable trust certificates, with pilot results indicating improved coverage over baselines in four industries.
A survey on agent-as-a-judge
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
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SetupX presents an experiential learning framework for LLM agents that reaches 92% pass rate on functionality-correct repository setup by transferring verified fixes across repositories via XPU representations, LIFO Docker snapshots, and Prosecutor-Judge verification.
SWE-Cycle benchmark shows sharp drops in code agent success rates from isolated tasks to full autonomous issue resolution, highlighting cross-phase dependency issues.
A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.
CoARS enables co-evolving recommender and user agents by using interaction-derived rewards and self-distilled credit assignment to internalize multi-turn feedback into model parameters, outperforming prior agentic baselines.
Survey mapping persistent state in LLM agents along six axes and proposing the AOEP-v0 protocol to evaluate governance and recovery obligations.
citing papers explorer
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Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
The authors introduce a three-part ontology-based verification system for AI agents that generates regulatory and adversarial test scenarios and issues machine-verifiable trust certificates, with pilot results indicating improved coverage over baselines in four industries.
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SetupX: Can LLM Agents Learn from Past Failures in Functionality-Correct Code Repository Setup?
SetupX presents an experiential learning framework for LLM agents that reaches 92% pass rate on functionality-correct repository setup by transferring verified fixes across repositories via XPU representations, LIFO Docker snapshots, and Prosecutor-Judge verification.
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SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle
SWE-Cycle benchmark shows sharp drops in code agent success rates from isolated tasks to full autonomous issue resolution, highlighting cross-phase dependency issues.
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Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval
A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.
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Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
CoARS enables co-evolving recommender and user agents by using interaction-derived rewards and self-distilled credit assignment to internalize multi-turn feedback into model parameters, outperforming prior agentic baselines.
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Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents
Survey mapping persistent state in LLM agents along six axes and proposing the AOEP-v0 protocol to evaluate governance and recovery obligations.