OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026 · 8 citations)
LLM agents can produce plausible web behavior, but it is hard to check whether they reproduce what a specific person would actually do.
Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026 · 2 citations)
Studies of LLM agents that imitate people have mostly asked whether their behavior looks believable to human judges.
Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026 · 2 citations)
Tool-calling agents used in customer service often meet users whose requests lack key details, shift during the conversation, or break system policy.
LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks (2026)
Real-world domain-specific deployments, such as clinical or legal work, face two problems. Expert labels are scarce, and the incoming data keeps changing over time (data drift). Active Learning (AL) can steadily improve a small model with expert labels, but each drift returns it to a weak cold-start state. LLMs work…

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Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 12 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated 1 November 2026