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ARXIV:2604.21480 · AGENT EVALUATION · SUBMITTED 24 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.21480AGENT EVALUATIONSUBMITTED 24 APR · 20:32 UTCFRESHNESS STALEItay Nakash · George Kour · Ateret Anaby-Tavor · arXiv
DIVERT is an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions to evaluate LLM agents.
Opportunity summary
Pain DIVERT is an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions to evaluate LLM agents.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
DIVERT is an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions to evaluate LLM agents. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate…
Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. By focusing evaluation on semantically diverse and underexplored trajectories, DIVERT improves both efficiency and coverage.
Agent Evaluation moved forward this cycle; last verified April 2026. Public score 3.0/10.
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DIVERT is an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions to evaluate LLM agents.
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10.48550/arXiv.2604.21480DIVERT is an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions to evaluate LLM agents.
Abstract
Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate success. However, this approach is computationally inefficient, repeatedly regenerating identical early prefixes, and often fails to uncover deep failure modes that arise from rare user behaviors. We introduce DIVERT (Diversity-Induced Evaluation via Branching of Trajectories), an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions. DIVERT captures the full agent-environment state at critical decision points and resumes execution from these snapshots, enabling reuse of shared conversation prefixes and reducing redundant computation. From each junction, the framework branches using targeted, diversity-inducing user responses, allowing directed exploration of alternative interaction paths. By focusing evaluation on semantically diverse and underexplored trajectories, DIVERT improves both efficiency and coverage. Empirical results show that it discovers more failures per token compared to standard linear rollout protocols, while expanding the set of tasks on which failures are identified.
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PROBLEM
DIVERT is an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions to evaluate LLM agents. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate succe...
METHOD
Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversation...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. By focusing evaluation on semantically diverse and underexplored trajectories, DIVERT improves both efficiency and coverage.
WHY NOW
Agent Evaluation moved forward this cycle; last verified April 2026. Public score 3.0/10.
{"file name": "input.pdf", "number of pages": 22, "author": "Itay Nakash; George Kour; Ateret Anaby-Tavor", "title": "Efficient Agent Evaluation via Diversity-Guided User Simulation", "creation date": null
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DIVERT is an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions to evaluate LLM agents.
Segment
Agent Evaluation
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3.0/10 public viability
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