@inproceedings{moradian-zehab-etal-2026-perspicere,
title = "Perspicere at {S}em{E}val-2026 Task 2: Modeling Longitudinal Valence and Arousal via Dense Embeddings and Agentic Reasoning",
author = "Moradian Zehab, Kamyar and
Poulaei, Mohammad Sadegh and
Mozayani, Nasser",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.97/",
pages = "671--685",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents our system for SemEval 2026 Task 2 (Subtask 1), modeling affect assessment as a longitudinal trajectory. We evaluate a tripartite affective framework of escalating contextual complexity, spanning zero-context feature extraction, latent temporal modeling via LSTM, and explicit semantic reasoning via the Teacher-Guided Clinical Reasoning Agent utilizing in-context learning. Our results show that robust static extraction outperforms explicit sequence modeling. Specifically, Matryoshka-distilled embeddings (Jasper) paired with XGBoost provided the best balance of speed and accuracy when utilizing the full training corpus (Valence composite r = 0.654, a 17.4{\%} improvement compared with the baseline), mitigating the severe overfitting observed on partitions of the dataset. Additionally, we uncover a distinct agentic advantage: although the reasoning agent trailed mathematical regressors in tracking high-frequency fluctuations, its SOTA psychological profiling yielded the highest Between-User Valence correlation (r = 0.725), demonstrating its efficacy in user-level affective profiling. Finally, a persistent ``arousal bottleneck'' confirms the limitations of text-only modeling for physiological activation."
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<abstract>This paper presents our system for SemEval 2026 Task 2 (Subtask 1), modeling affect assessment as a longitudinal trajectory. We evaluate a tripartite affective framework of escalating contextual complexity, spanning zero-context feature extraction, latent temporal modeling via LSTM, and explicit semantic reasoning via the Teacher-Guided Clinical Reasoning Agent utilizing in-context learning. Our results show that robust static extraction outperforms explicit sequence modeling. Specifically, Matryoshka-distilled embeddings (Jasper) paired with XGBoost provided the best balance of speed and accuracy when utilizing the full training corpus (Valence composite r = 0.654, a 17.4% improvement compared with the baseline), mitigating the severe overfitting observed on partitions of the dataset. Additionally, we uncover a distinct agentic advantage: although the reasoning agent trailed mathematical regressors in tracking high-frequency fluctuations, its SOTA psychological profiling yielded the highest Between-User Valence correlation (r = 0.725), demonstrating its efficacy in user-level affective profiling. Finally, a persistent “arousal bottleneck” confirms the limitations of text-only modeling for physiological activation.</abstract>
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%0 Conference Proceedings
%T Perspicere at SemEval-2026 Task 2: Modeling Longitudinal Valence and Arousal via Dense Embeddings and Agentic Reasoning
%A Moradian Zehab, Kamyar
%A Poulaei, Mohammad Sadegh
%A Mozayani, Nasser
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F moradian-zehab-etal-2026-perspicere
%X This paper presents our system for SemEval 2026 Task 2 (Subtask 1), modeling affect assessment as a longitudinal trajectory. We evaluate a tripartite affective framework of escalating contextual complexity, spanning zero-context feature extraction, latent temporal modeling via LSTM, and explicit semantic reasoning via the Teacher-Guided Clinical Reasoning Agent utilizing in-context learning. Our results show that robust static extraction outperforms explicit sequence modeling. Specifically, Matryoshka-distilled embeddings (Jasper) paired with XGBoost provided the best balance of speed and accuracy when utilizing the full training corpus (Valence composite r = 0.654, a 17.4% improvement compared with the baseline), mitigating the severe overfitting observed on partitions of the dataset. Additionally, we uncover a distinct agentic advantage: although the reasoning agent trailed mathematical regressors in tracking high-frequency fluctuations, its SOTA psychological profiling yielded the highest Between-User Valence correlation (r = 0.725), demonstrating its efficacy in user-level affective profiling. Finally, a persistent “arousal bottleneck” confirms the limitations of text-only modeling for physiological activation.
%U https://aclanthology.org/2026.semeval-1.97/
%P 671-685
Markdown (Informal)
[Perspicere at SemEval-2026 Task 2: Modeling Longitudinal Valence and Arousal via Dense Embeddings and Agentic Reasoning](https://aclanthology.org/2026.semeval-1.97/) (Moradian Zehab et al., SemEval 2026)
ACL