Year: 2026
Type: conference-paper
Abstract: Large Language Models (LLMs) suffer from temporal misalignment, often due to the contradictory nature of their training corpora.While current mitigation strategies rely on computationally expensive fine-tuning or context-heavy retrieval augmented generation (RAG), the internal mechanisms governing time sensitive recall remain under-explored.Unlike prior studies that identify temporal components such as attention heads and MLP layers, we provide the first feature level map of temporal recall by isolating individual MLP features via transcoder circuit tracing.We identify three node categories (common temporal, common to the year and chrono-semantic) which interact to generate a temporal filter during factual recall.By analysing Gemma 2 2B, LLaMA 3.2 1B, and Qwen3-4b, we show that these features do not follow a simple linear pipeline but represent time through a parallel and mixed syntacticsemantic interplay across layers.We additionally discover a class of higher-layer temporal components invisible to existing EAP-IG methods, establishing transcoders as a more complete lens for temporal interpretability for time sensitive factual recall.These findings present MLP components for potential targeted interventions in time-sensitive factual recall.
Authors: Sanjay Govindan, Maurice Pagnucco, Yang Song
Institution: UNSW Sydney
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7214724775