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

llms

ensemble learning

mental health

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>
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workshop paper

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).

Agentic Pipelines Meet Retrieval-Augmented ICL: A Zero-Training Approach to Mental Health Modeling

The critique of scalar benchmark rankings as proxies for model quality is now well-established (Raji et al., 2021; Wallach et al., 2025; Bean et al., 2025; Gehrmann et al., 2021). What the field still lacks is a shared structural vocabulary for comparing, combining, and contextualizing metric design choices. This paper provides that vocabulary: a four-primitive typology---representation ($\phi$), comparison ($D$), aggregation ($A$), and context ($C$)---under which existing metrics (BLEU, BERTScore, nDCG, LLM-as-judge, calibration scores, agentic outcome measures) are explicit parameterizations of a common form. This typology is paired with a measurement--decision split: metrics are noisy estimators of latent constructs, and model selection is context-dependent Pareto optimization over construct estimates, not over raw scores. The typology makes implicit metric assumptions comparable and debatable rather than hidden inside a single number.

Toward a Metric Typology for Language Model Evaluation

Recent advances in large language models (LLMs) have demonstrated impressive medical reasoning capabilities. However, current evaluation methods are mostly limited to static case vignettes and multiple-choice questions which fail to reflect the complexity, uncertainty, and iterative nature of real-world clinical decision-making. To bridge this gap, we propose **DiagBench**, a novel benchmark where models interact dynamically with a LLM based Patient Simulator, querying relevant clinical details to formulate accurate diagnoses. To complement this, we introduce **MedConvBench**, a diagnostic conversation benchmark designed to assess the relevance and quality of model-generated clinical reasoning. To further address the interpretability and alignment challenges of AI-assisted diagnosis, we develop a modular and medically grounded framework called **VAIDYA** that mirrors a physician’s stepwise diagnostic reasoning. This structured approach improves transparency and yields substantial performance gains over base LLMs. Our work takes a critical step toward aligning AI systems with real-world clinical practices by combining dynamic interaction, interpretability, and clinical validation.

VAIDYA: Validated Agents for Intelligent Diagnosis and Yielded Analysis

Self-Anchoring Calibration Drift} (SACD), a tendency for large language models (LLMs) to show systematic changes in expressed confidence when building iteratively on their own prior outputs across multi-turn conversations. Through a controlled three-condition study comparing Claude Sonnet 4.6, Gemini 3.1 Pro, and GPT-5.2 across factual, technical, and open-ended domains, we find that SACD is real but multiform: models exhibit distinct self-anchoring signatures ranging from active confidence suppression to calibration improvement suppression, with effects concentrated in open-ended domains. These findings challenge the adequacy of single-turn calibration evaluation for characterizing LLM reliability in realistic multi-turn deployment contexts. Code and data are available at https://github.com/hvardhan878/calibration-drift

Self-Anchoring Calibration Drift in Large Language Models: How Multi-Turn Conversations Reshape Model Confidence

Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level representations or calendar tokens have been proposed. However, the optimal approach remains unclear, especially given the diverse statistical distributions of real-world event data, which range from smooth log-normal to discrete, spiky patterns. This paper presents a systematic empirical study of temporal tokenization for modeling event sequences with LLMs, comparing distinct encoding strategies: naive numeric strings, high-precision byte-level representations, human-semantic calendar tokens, classic uniform binning, and adaptive residual scalar quantization. We evaluate these strategies by fine-tuning LLMs on real-world datasets that exemplify these diverse distributions. Our analysis reveals that no single strategy is universally superior; instead, prediction performance depends heavily on aligning the tokenizer with the data's statistical properties, highlighting temporal tokenization as a critical yet often overlooked design dimension in LLM-based event modeling.

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