Controlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics
- URL: http://arxiv.org/abs/2601.16087v1
- Date: Thu, 22 Jan 2026 16:34:05 GMT
- Title: Controlling Long-Horizon Behavior in Language Model Agents with Explicit State Dynamics
- Abstract summary: Large language model (LLM) agents exhibit abrupt shifts in tone and persona during extended interaction.<n>The role of explicit affective dynamics in shaping long-horizon agent behavior remains underexplored.<n>We introduce an agent-level affective subsystem that maintains a continuous Valence-Arousal-Dominance (VAD) state external to the language model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language model (LLM) agents often exhibit abrupt shifts in tone and persona during extended interaction, reflecting the absence of explicit temporal structure governing agent-level state. While prior work emphasizes turn-local sentiment or static emotion classification, the role of explicit affective dynamics in shaping long-horizon agent behavior remains underexplored. This work investigates whether imposing dynamical structure on an external affective state can induce temporal coherence and controlled recovery in multi-turn dialogue. We introduce an agent-level affective subsystem that maintains a continuous Valence-Arousal-Dominance (VAD) state external to the language model and governed by first- and second-order update rules. Instantaneous affective signals are extracted using a fixed, memoryless estimator and integrated over time via exponential smoothing or momentum-based dynamics. The resulting affective state is injected back into generation without modifying model parameters. Using a fixed 25-turn dialogue protocol, we compare stateless, first-order, and second-order affective dynamics. Stateless agents fail to exhibit coherent trajectories or recovery, while state persistence enables delayed responses and reliable recovery. Second-order dynamics introduce affective inertia and hysteresis that increase with momentum, revealing a trade-off between stability and responsiveness.
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