We present our shared task on predicting variation in emotional valence and arousal over time from ecological essays. The shared task uses a longitudinal dataset collected over 7 data collection phases of 14-day each spanning from 2021 to 2024, consisting of real-time essays and feeling words (e.g., happy, calm, sad, etc.) written in English by U.S. service-industry workers about “how they are feeling”. Each text is associated with self-reported valence (V) (0 - 4, highly negative to highly positive affect) and arousal (A) (0 - 2, low to high energy) scores. The shared task consists of three parts, Subtask (1): Longitudinal Affect Assessment, Subtask (2): Forecasting Variation in Affect as a (2a): \textit{state change}, and (2b): \textit{disposition change}.The task attracted over 200 member registrations on Codabench, receiving official system submissions from 31 teams (total 104 team members), of which 28 teams (with 90 team members) submitted system description papers making it to our leaderboard. We discuss baseline results along with findings from 28 systems, highlighting the best-performing systems, a deeper analysis of performance on essays versus feeling words, and assessments for authors seen versus unseen during training. The datasets for this task are publicly available.

Some psychotherapies, such as written exposure therapy for posttraumatic stress disorder, utilize "scripts" during parts of treatment, but verifying script adherence to ensure engagement of key mechanisms of change is a time-consuming step for therapy supervisors. Here, we formalize therapy script adherence as an NLP task, and evaluate several simple (text similarity) and more complex (few-shot LLM) approaches. Over 351 annotated therapist utterance-script pairs, we find text similarity approaches to be highly competitive with LLMs and produce fewer false positives. ROUGE-L recall achieves F1 = 0.973, and BLEU achieves F1 = 0.972 with full precision and zero false positives. GPT-5.2 achieves F1 = 0.935 and GPT-4o-mini achieves F1 = 0.876. Given that the text similarity techniques are multiple orders of magnitude less complex, our results underscore the ability for simpler NLP techniques to still be effective in the age of LLMs for tasks that are more textual in nature, suggesting that aspects of therapist fidelity to evidence-based treatments can be assessed without using cloud API calls.