NeuroState-Bench: A Human-Calibrated Benchmark for Commitment Integrity in LLM Agent Profiles

📰 ArXiv cs.AI

Learn to evaluate LLM agent profiles with NeuroState-Bench, a human-calibrated benchmark for commitment integrity, and improve multi-turn task coherence

advanced Published 5 May 2026

Action Steps

Define a multi-turn task to evaluate commitment integrity using NeuroState-Bench
Create benchmark-defined side-query probes to operationalize commitment integrity
Run the probes on the LLM agent profile to assess its performance
Analyze the results to identify areas for improvement in commitment preservation
Refine the LLM agent profile based on the evaluation outcomes

Who Needs to Know This

AI researchers and developers can use NeuroState-Bench to assess and refine their LLM agent profiles, ensuring they preserve necessary commitments for coherent task solving

Key Insight

💡 NeuroState-Bench provides a human-calibrated benchmark for assessing commitment integrity in LLM agent profiles, enabling more accurate evaluations

Key Takeaways

Learn to evaluate LLM agent profiles with NeuroState-Bench, a human-calibrated benchmark for commitment integrity, and improve multi-turn task coherence

Full Article

Title: NeuroState-Bench: A Human-Calibrated Benchmark for Commitment Integrity in LLM Agent Profiles

Abstract:
arXiv:2605.01847v1 Announce Type: new Abstract: Outcome-only evaluation under-specifies whether an evaluated agent profile preserves the commitments required to solve a multi-turn task coherently. NeuroState-Bench is a human-calibrated benchmark that operationalizes commitment integrity through benchmark-defined side-query probes rather than inferred hidden activations. The released inventory contains 144 deterministic tasks and 306 benchmark-defined side-query probes spanning eight cognitively

Read full paper → ← Back to Reads