United States

Recent advances in Large Language Models
(LLMs) have motivated their adoption across
a wide range of domains, including Artificial
Intelligence (AI) for mental health. Given the
growing prevalence of mental health disorders

worldwide and the limited accessibility of pro-
fessional care, there is an increasing demand

for scalable computational approaches that can

assist in early detection and continuous mon-
itoring of psychological well-being. In this

area, ongoing efforts have focused on curating
domain-specific datasets and leveraging them
to develop LLMs capable of supporting holistic

mental health analysis. In line with this direc-
tion, we propose an LLM-based pipeline for

comprehensive mental health analysis over se-
quentially ordered user posts, as part of the

CLPsych shared task. Our pipeline offers a uni-
fied framework that jointly enables post-level

assessment and user-level temporal modeling.

ACL 2026

Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics

imbalance problem

ai + psychology

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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.

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Targeting the CLPsych 2026 (Ali et al., 2026)

shared task, this study proposes a unified frame-
work based on three complementary strategies.

We implement a pipeline integrating post-level
psychological state recognition, timeline-level

change detection, and evidence-based clini-
cal summarization. For Task 1, we evaluate

three paradigms for extracting ABCD elements:
zero-shot prompting, Qwen-3 fine-tuning, and
TextCNN-based hierarchical classification. For
Task 2, we design a cascaded temporal model
that combines Mental-BERT features, log-time
encoding, and Bi-LSTM to capture mental state

switches and escalations under irregular post-
ing intervals. For Task 3, we adopt a dynamic

retrieval-augmented generation strategy to pro-
duce structured clinical summaries. Experimen-
tal results show that our system ranks second on

the official leaderboard for Task 2, demonstrat-
ing the effectiveness of time-aware modeling

for change detection. Error analysis further

reveals over-prediction bias and valence inver-
sion in Tasks 1 and 3, highlighting pipeline

error propagation as a key challenge for future
work.

A Multi-Strategy Fusion Framework for Dynamic Mental State Modeling

Social media posts are a rich and valuable
source of data for analyzing mental health
states and users’ well-being using automated
analysis tools. In this work, we demonstrate

how we used a range of Natural Language Pro-
cessing (NLP) methods, including Long Short-
Term Memory (LSTM), BERT-based models,

and Large Language Models (LLMs), for self-
state and well-being analysis and summariza-
tion during the CLPsych Shared Task 2026.

Our approach achieved one of the top Consis-
tency and Contradiction scores for the summa-
rization task and also middle-level results for

the other tasks. By testing and developing such

mental health-state estimation systems, we con-
tributed to improving mental health support

systems. We make our code available1
.

psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis

We address the CLPsych 2026 Shared Task on

modeling psychological self-states from lon-
gitudinal social media data. We propose (i) a

hierarchical multi-stage framework that inte-
grates a multi-task transformer encoder and (ii)

a four stage instruction-tuned large language

model finetuning pipeline for subelement clas-
sification, presence estimation, and evidence

extraction. Our approach incorporates element-
conditioned label masking and cross-stage en-
coder transfer, enabling structured prediction

aligned with the ABCD psychological frame-
work. Experiments show improvements over

the baseline on the development setup, with
RoBERTa achieving an 8.3% gain in macro-F1
and improved root mean square error (RMSE),
while a fine-tuned Qwen3 model attains the

best overall performance. These results demon-
strate the effectiveness of combining hierarchi-
cal multi-task learning with structured gener-
ation for interpretable mental health analysis.

All code for this task is publicly available in a
GitHub repository1

Hierarchical Multi-Stage Modeling of Adaptive and Maladaptive Self-States in Social Media Timelines

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.

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

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).

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