Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues
Chaogui Gou, Jiarui Liang

TL;DR
Psy-Chronicle introduces a structured framework for generating and evaluating long-horizon psychological counseling dialogues in college settings, addressing the challenge of modeling evolving stress over time.
Contribution
It presents a novel data-generation pipeline, a new long-horizon dialogue dataset CPCD, and a benchmark CPCD-Bench for assessing counseling models' capabilities.
Findings
CPCD dataset contains 90,000 dialogues and 100 student profiles.
Models improved in session response and memory recall using CPCD.
Temporal-causal reasoning remains a significant challenge.
Abstract
In recent years, large language models have shown substantial potential in psychological support tasks. However, existing psychological counseling data mostly rely on single-turn question answering or short multi-turn dialogues, making it difficult to characterize how college students' psychological distress accumulates, interacts, and gradually evolves over long periods within campus life events. To address this issue, this paper proposes Psy-Chronicle, a structured data-generation framework for synthesizing long-horizon campus psychological counseling dialogues. We generate a semester-spanning temporal stress event graph to model the chronological order and evolutionary dependencies among campus stress events. Through interactive simulation between a student agent and a counselor agent, together with a structured memory integration mechanism, Psy-Chronicle generates long-horizon…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
