United States

The automatic detection of mental health dy-
namics represents a challenging and socially

relevant task, requiring models to capture sub-
tle psychological changes over time. Large

language models have demonstrated remark-
able capabilities in clinical and diagnostic con-
texts, yet their outputs often lack consistency

and may diverge significantly across different
model families and prompting strategies. This
paper presents the USAI team’s submission
to the CLPsych 2026 Shared Task, targeting
Tasks 1.1, 1.2, 2, and 3.1. We propose an
ensemble-based approach combining multiple
open-source large language models, where the

contribution of each model is weighted accord-
ing to its alignment with clinically grounded

human annotations on the training set. Our
system achieves competitive results across the

evaluated subtasks, with particularly strong per-
formance on Tasks 1.2 and 2.

ACL 2026

P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>
<br><br><br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

**Online Registration Form**: https://acl.swoogo.com/acl2026

Registration Required

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

This paper presents our prompt-based approach

for modeling mental health timelines from Red-
dit user posts. We address two tasks: identi-
fying moments of change and generating sum-
maries of clinically meaningful changes across

post sequences. Our framework uses large lan-
guage models with in-context learning to an-
alyze self-states and mental health indicators

without task-specific fine-tuning. We build an
inference pipeline with vLLM and Qwen2.5-
72B-Instruct-GPTQ-Int8, and experiment with
few-shot prompting, and balanced few-shot
sampling. We also examine how the number
of visible posts affects the model’s ability to
capture temporal changes. Our results suggest
that prompt-based methods provide a practical
and competitive baseline in low-resource and
sensitive mental health settings, particularly for
modeling self-state dynamics and generating
summaries of psychological change over time.

Prompt-Based Modeling of Moments of Change and Change Summaries in Mental Health Timelines

We describe our submission to the

CLPsych 2026 Shared Task on captur-
ing and characterizing mental health changes

through social media timeline dynamics. To
infer the dominant self-states in posts (Tasks
1.1 and 1.2), we ensemble in-context learning
of three open-weight large language models
using majority voting. For predicting moments
of change in a timeline (Task 2), we train
supervised classifiers on features derived
from Task 1.1 predictions. To summarize
the patterns of mood dynamics and their
progression over time within a timeline (Task
3.1), we augment in-context example labels
predicted by upstream systems (Tasks 1.1,
1.2, and 2), yielding performance gains over
zero-shot and unaugmented in-context learning
baselines. Our submission ranked first on
Task 1.1, fourth on Task 1.2, fourth on Task 2,
and third on Task 3.1.1

CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Changes

Most existing work on mental health predic-
tion from language focuses on isolated posts,

overlooking temporal dynamics in longitudinal

timelines. We present McMaster NLP’s sys-
tem for the CLPsych 2026 Shared Task, which

centers on modeling mental health dynamics in

social media timelines using the MIND frame-
work (Atzil-Slonim, 2025). The task com-
prises: (1) identifying adaptive and maladap-
tive self-state components within posts, (2) de-
tecting moments of change in wellbeing, and

(3) generating structured summaries. For self-
state prediction, we leverage LLM-generated

archetypal representations of language use as

semantic anchors within a dual-encoder archi-
tecture, enabling interpretable prediction of

subelements and their intensities through align-
ment with prototypical expressions of psycho-
logical states. For temporal dynamics, we

use BiLSTM-based sequence models to de-
tect moments of change. For summarization,

we employ a prompt-based LLM to generate
grounded, structured summaries emphasizing
causal interactions and temporal progression of
self-states. Finally, we analyze model failure
modes with respect to human evaluation and
identify directions for reconciling the MIND
framework with how state-assessment models
encode meaning.

McMasters of Change: Predicting Wellbeing States and Transitions from Longitudinal Language

This paper presents a system for the CLPsych
2026 Shared Task on longitudinal mental
health modeling from social media timelines,

grounded in the MIND framework (Atzil-
Slonim, 2025). MIND conceptualizes mental

health as evolving self-states defined by Affect,

Behavior, Cognition, and Desire (ABCD), pro-
viding a structured lens on mental health trajec-
tories. The system centers on a theory-explicit

prompting framework for structured sequence

summarization (Task 3.1) and recurrent dy-
namic signature extraction (Task 3.2), encoding

the full ABCD taxonomy directly into the LLM

prompt to ensure clinically grounded, inter-
pretable outputs. A three-stage pipeline infers

a direction-of-change label per sequence, pro-
duces structured ABCD summaries with few-
shot exemplar augmentation, and aggregates

these summaries to derive cross-individual re-
current patterns. The system ranks first on

deterioration-related recurrent signatures and

second overall, achieving the top Fit and Speci-
ficity scores in Task 3.2, demonstrating the ben-
efits of explicit clinical grounding for concep-
tual accuracy.

Theory-Explicit Prompting for MIND Self-States: Hierarchical LLMs and Dynamic Signature Extraction in Mental Health Timelines

This paper describes the StateOfMIND team
submission to Task 1 of the CLPsych 2026

shared task on identifying adaptive and mal-
adaptive psychological self-states in social me-
dia posts. For each post, the task requires

systems to predict which fine-grained subele-
ments of a self-state are expressed (Task 1.1)

and to rate how strongly adaptive and maladap-
tive states are present (Task 1.2). We address

both subtasks with a retrieval-augmented in-
context learning ensemble of two open-weight

LLMs, Qwen3.5-27B and Mistral-Small-3.2-

24B-Instruct. Our approach combines label-
aware retrieval of in-context examples, a three-
prompt decomposition targeting adaptive con-
tent and the Affect element, and subtask-
specific ensemble rules that favor precision for

subelement classification and recall for pres-
ence rating. Our best submissions rank 2nd

of 17 teams on Task 1.1 (Macro F1 = 0.441)
and 5th of 17 teams on Task 1.2 (RMSE =

0.994), with ablations showing retrieval and en-
sembling as the largest drivers of performance.

Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs

We describe the Meronym Labs system for
the CLPsych 2026 shared task. Our approach
uses retrieval-augmented in-context learning
with frozen LLMs throughout, requiring no
fine-tuning. Tasks 1 and 2 use a single
Qwen 3.5 27B call per post/timeline with static
and dynamically retrieved examples. Task 3.1
uses a five-agent agentic pipeline including

rule-based agents for change type and direc-
tion, LLM agents for dynamics extraction

and summary writing, and a validator all aug-
mented with NLI-based candidate re-ranking

and iterative contradiction reduction. The sys-
tem ranked 1st on Task 1.2 (RMSE 0.917)

and Task 3.1 (score rank average 4.00), 3rd
on Task 1.1 (F1 0.420), and 8th on Task 2
(F1 0.466).

Downloads

Next from ACL 2026

Prompt-Based Modeling of Moments of Change and Change Summaries in Mental Health Timelines

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2026

Prompt-Based Modeling of Moments of Change and Change Summaries in Mental Health Timelines

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads