ChronoState: Hidden Elapsed-Time Conditioning for Temporal-State Action Selection in Frozen-Backbone Language Models
2026-08-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explore how language models can use hidden elapsed time information, alongside visible task details, to make better decisions involving timing, like deadlines or cache expiration. They create ChronoState, a test where a language model receives both visible symbolic states and hidden time signals, and must choose the correct timed action. Their method works well when the model is trained on similar tasks, showing it can combine time with task state, but struggles to generalize to very different timing tasks or replace visible timestamps. This shows that hidden time signals help but don't solve all timing challenges in language models.
language modelelapsed timesymbolic statefrozen backbonechronometric injectionFiLM modulationLoRAbenchmarkgeneralizationprompt injection
Authors
Sam Siavoshian, Omar Ramadan, Amir K. Saeed, Benjamin A. Johnson, Amin Mohamed El-Amin Diab, Benjamin M. Rodriguez
Abstract
Temporal decisions in language-model systems often depend on both symbolic task state and elapsed wall-clock time, such as cache expiration, job completion, quota resets, deadlines, or stale sessions. We study whether elapsed time can be supplied as a non-token, system-side scalar and composed with visible symbolic state by a frozen-backbone language model. We introduce ChronoState, a compositional temporal-state benchmark in which symbolic state appears in the prompt, elapsed seconds tau are supplied through a hidden chronometric-injection channel, and the model selects a forced-choice temporal action. Here, "hidden" means hidden from the user-visible token sequence, not from model computation. Using Qwen2.5-3B-Instruct as a frozen bf16 backbone with a 31-dimensional sinusoidal-plus-log time encoding, gated FiLM residual modulation, and a rank-8 LoRA action surface, hidden-time CI reaches 0.9305 +/- 0.0134 accuracy and 0.9410 +/- 0.0103 balanced accuracy. No-time and shuffled-time controls fall to 0.5511 +/- 0.0042 and 0.3323 +/- 0.0097, respectively, with high shuffled-time wrong-state consistency supporting causal dependence on the injected scalar within the trained distribution. Generalization remains strong for held-out templates, durations, and multi-constraint compositions, but held-out quota-family transfer is weak at 0.5065 +/- 0.0559, while a fair prompt+LoRA timestamp baseline reaches 0.9893 +/- 0.0052. Thus, ChronoState supports a narrow conclusion: hidden elapsed time can be composed with symbolic task state under direct supervision, but does not establish autonomous time tracking, broad unseen-family abstraction, or superiority over prompt-injected timestamps.