时间序列预测从业者终于有了一个能主动理解任务意图的LLM方案——InA-Probe在跨域场景误差降低37%，做金融、能源等时序预测的团队值得关注。
现有LLM时间序列预测方法多依赖被动对齐或静态重编程，难以捕捉非平稳模式和细粒度任务意图。本文提出InA-Probe，通过多层级指令注入和自适应查询生成，让模型主动探测时间序列中的关键模式。该方法在7个真实基准上超越现有深度学习和LLM基线，在跨域场景中预测误差降低高达37%，零样本泛化能力也显著提升。消融实验表明，自适应查询与细粒度指令的协同作用是释放LLM推理能力的关键。
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment or static task reprogramming, which often fail to capture fine-grained, non-stationary temporal patterns or to adapt to nuanced task intents. In this paper, we propose Instruction-aware Active Probing (InA-Probe), which shifts the paradigm from passive alignment toward an active, instruction-driven probing mechanism. Specifically, we design a Multi-Level Instruction Injection mechanism that enriches the model with both global task objectives and fine-grained, patch-level semantic priors. Building on this, an Adaptive Query Generation module produces sample-specific probes that are dynamically modulated by the temporal context. These probes are then refined through a dual-stage attention process: they first internalize task-specific intents via Instruction-Aware Self-Attention, and subsequently interrogate the projected temporal representations through Temporal Cross-Attention to extract salient patterns. Comprehensive experiments on seven real-world benchmarks show that InA-Probe consistently outperforms state-of-the-art deep learning and LLM-based baselines, excelling in both one-for-all generalization and zero-shot transfer while reducing forecasting error by up to 37\% in challenging cross-domain scenarios. Ablation studies further confirm that the synergy between adaptive querying and fine-grained instructions is key to unlocking the reasoning power of LLMs for complex time series.