TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health

• URL: http://arxiv.org/abs/2605.21295v1
• Date: Wed, 20 May 2026 15:25:46 GMT
• Title: TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health
• Abstract summary: TimeSRL is a framework that routes predictions through an explicit semantic bottleneck.<n>It achieves state-of-the-art performance on a benchmark designed to stress-test cross-cohort generalization.<n>TimeSRL significantly outperforms prior methods in cross-benchmark transfer across different sensing pipelines.
• Score: 10.41600839007471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Longitudinal passive sensing enables continuous health prediction, yet models often fail under cross-dataset distribution shifts. Traditional ML overfits cohort-specific artifacts, while Large Language Models (LLMs) struggle to reason reliably over long, heterogeneous time-series. We introduce TimeSRL, a two-stage LLM framework that routes predictions through an explicit semantic bottleneck. The model first abstracts raw signals into high-level natural language, then predicts behavioral outcomes from these abstractions alone. This forces the model to reason over semantic concepts that we argue generalize better than raw numbers. We optimize this process end-to-end using Group Relative Policy Optimization (GRPO) with Reinforcement Learning from Verifiable Rewards (RLVR), learning outcome-aligned abstractions without gold intermediate annotations. Instantiated on mental-health prediction, TimeSRL achieves state-of-the-art performance on a benchmark designed to stress-test cross-cohort generalization under a rigorous leave-one-dataset-out (LOSO) protocol, reducing mean absolute error (MAE) over strong non-LLM ML and LLM baselines by 3.1--10.1% and 9.5--44.1% for anxiety, and 3.2--9.6% and 27.4--57.6% for depression (all $p$s<0.05). TimeSRL significantly outperforms prior methods in cross-benchmark transfer across different sensing pipelines, rivaling its own within-domain performance without target-domain fine-tuning. These results demonstrate that semantic abstractions are reusable and point to a new direction for generalizable behavior modeling via RL-tuned LLMs.

Related papers

• Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles [0.0]
  The wisdom of crowds -- the finding that aggregating judgments across individuals often outperforms the best individual -- has been extensively studied with human forecasters.<n>We elicited probability estimates from 15 large language models (LLMs) on 254 binary prediction market questions.<n>LLMs remained substantially less accurate, indicating a genuine gap in collective information aggregation.
  arXiv  Detail & Related papers  (2026-05-26T16:07:01Z)
• Large Language Models for Missing Data Imputation: Understanding Behavior, Hallucination Effects, and Control Mechanisms [2.852894352141041]
  This work investigates the robustness of Large Language Models for missing data imputation in datasets using a zero-shot prompt engineering approach.<n>We compare five widely used LLMs against six state-of-the-art imputation baselines, with missing rates of up to 20%.<n>The results demonstrate that leading LLMs, particularly Gemini 3.0 Flash and Claude 4.5 Sonnet, consistently achieve superior performance on real-world open-source datasets.
  arXiv  Detail & Related papers  (2026-03-20T22:32:45Z)
• REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge [83.2858110368572]
  We propose textbfREAL (underlineREgression-underlineAware Reinforcement underlineLThought), a principled RL framework designed to optimize regression rewards.<n>We show that REAL consistently outperforms both regression-aware SFT baselines and standard RL methods.
  arXiv  Detail & Related papers  (2026-03-17T21:19:08Z)
• Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injection [18.267727687739853]
  Large Language Models (LLMs) have demonstrated effectiveness as zero-shot time series (TS) forecasters.<n>The key challenge lies in tokenizing TS data into textual representations that align with LLMs' pre-trained knowledge.<n>We introduce two novel TS datasets that fall outside all utilized LLMs' pre-training scopes, and consistently observe improved performance.
  arXiv  Detail & Related papers  (2025-12-23T08:02:33Z)
• Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective [85.06838178922791]
  Reinforcement Learning (RL) has proven highly effective for autoregressive language models.<n>But adapting these methods to diffusion large language models (dLLMs) presents fundamental challenges.<n>We propose a principled RL framework that treats entire sequence generation as a single action and uses the ELBO as a tractable sequence-level likelihood proxy.
  arXiv  Detail & Related papers  (2025-12-03T13:05:32Z)
• Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch [63.40752011615843]
  Training tool-augmented language models has emerged as a promising approach to enhancing their capabilities for complex tasks.<n>We propose a dynamic generalization-guided reward design for rule-based reinforcement learning.<n>We show that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models.
  arXiv  Detail & Related papers  (2025-11-02T16:33:45Z)
• MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization [103.74675519953898]
  Long-chain reflective reasoning is a prerequisite for solving complex real-world problems.<n>We build a benchmark consisting 1,260 samples of 42 challenging synthetic tasks.<n>We generate post-training data and explore learning paradigms for exploiting such data.
  arXiv  Detail & Related papers  (2025-10-09T17:53:58Z)
• Principled and Tractable RL for Reasoning with Diffusion Language Models [0.0]
  Diffusion large language models (dLLMs) are trained to predict multiple tokens in parallel and generate text via iterative unmasking.<n>Recent works have successfully pretrained dLLMs to parity with autoregressive LLMs at the 8B scale, but dLLMs have yet to benefit from modern post-training techniques.<n>We present Amortized Group Relative Policy Optimization (AGRPO), a principled on-policy RL algorithm designed specifically for dLLMs.
  arXiv  Detail & Related papers  (2025-10-05T03:53:16Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective [142.36200080384145]
  We propose a single objective which jointly optimize a latent-space model and policy to achieve high returns while remaining self-consistent. We demonstrate that the resulting algorithm matches or improves the sample-efficiency of the best prior model-based and model-free RL methods.
  arXiv  Detail & Related papers  (2022-09-18T03:51:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.