Temporal Preference Concepts and their Functions in a Large Language Model

2026-05-11

AI Summary

How large language models internally represent and balance short-term rewards against long-term consequences remains unclear. This study addresses this gap by applying mechanistic interpretability techniques to identify the causal neural subgraph underlying time preference in Qwen3-4B-Instruct-2507, revealing its geometric encoding structure within the residual stream. Through an integrated approach combining gradient attribution, activation patching, and steering vector interventions, we demonstrate that the model exhibits a significantly lower discount rate for future rewards than humans and displays context-dependent instability in temporal preferences. Furthermore, we show that time preference can be effectively modulated via targeted interventions at mid-to-upper-layer network nodes. Our findings provide both empirical grounding and a novel pathway for controllably shaping the planning capabilities of large language models.

Abstract

Large Language Models (LLMs) are increasingly being deployed to make decisions that require trading off near-term gains against long-term consequences, yet little is known about how they internally represent or resolve these tradeoffs. In this work, we causally localize an underlying subgraph for temporal preference in a distilled LLM (Qwen3-4B-Instruct-2507), identifying mid-to-upper-layer nodes through converging evidence from gradient-based attribution and activation patching. We find that the geometry of time horizon is encoded in the residual stream at the expected localized layers. A behavioral analysis reveals that unintervened LLMs discount the future several times less steeply than humans, yet this preference is unstable across contexts, motivating explicit control rather than implicit reliance on training. Finally, we find suggestive evidence that steering vectors can shift temporal preference. Our work demonstrates how mechanistic interpretability can bring us closer to reliable control over how LLMs plan and reason

Problem

• temporal preference
• large language models
• time horizon
• decision making
• mechanistic interpretability

Innovation

• temporal preference
• mechanistic interpretability
• activation patching
• steering vectors
• time discounting