Semantic Drift in Language Models

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8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs
Sep 8, 2026Longfei Ma, Zemin Liu, Fei WuTemporal GNNs Semantic Drift in Language Models
1Zhejiang University
Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks
Jul 23, 2026Amritesh BanerjeeSemantic Drift in Language Models Language Modeling
University of Massachusetts Amherst, USA
Semantic Drift and the Stability of Operator Control in Reasoning-Class Decision Support Systems
Jul 8, 2026M. L. Kaluzhsky, V. A. EfirovLanguage Model Steering Semantic Drift in Language Models
Expert Department, Regional Strategy Development Fund, Omsk, Russia · Omsk State Technical University, Omsk, Russia · Expert Council, Interregional Public Fund “Regional Strategy Development Fund”, Omsk, Russia
Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection
May 28, 2026Geremy Loachamín-Suntaxi, Robert Lazar, Dimitrios G. Giovanis +2Multi-Agent LLM Systems AI Agents for Scientific Discovery
School of Chemical Engineering, National Technical University of Athens, Zographos Campus, 15780, Attiki, Greece · Luxembourg Institute of Science and Technology Esch-sur-Alzette, L-4362, Luxembourg
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
May 12, 2026Zekai Chen, Xun Wu, Xunkai Li +3Multimodal Federated Learning Cross-Modal Representation Learning
Beijing Institute of Technology, Beijing, 100081, China · Minzu University of China, Beijing, 100081, China
A Reproducibility Analysis of PO4ISR: Diagnosing and Mitigating Semantic Drift in LLM-Based Session Recommendation
Apr 29, 2026Aditya Tiwari, Konduri Naga Lakshmi Rekha, Rajesh Kumar MundotiyaSequential Recommendation LLM Prompting
MATRA Lab Department of Computer Science and Engineering Indian Institute of Technology Bhilai Chhattisgarh, India 491002
Semantic Substrate Dynamics Theory: An Operator-Theoretic Framework for Geometric Semantic Drift
Feb 21, 2026Stephen RussellSemantic Drift in Language Models Semantic Change Detection
Intelligent Systems and Robotics Department University of West Florida
Verifiable Semantics for Agent-to-Agent Communication
Feb 18, 2026Philipp Schoenegger, Matt Carlson, Chris Schneider +1Semantic Drift in Language Models Multi-Agent Communication
Microsoft AI · Wabash College

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TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs

Longfei Ma, Zemin Liu, Fei Wu

1Zhejiang University

Temporal GNNs Semantic Drift in Language Models Temporal Link Prediction LLMs for Graph Learning

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Temporal graph learning models the evolution of dynamic systems, where both structural interactions and semantic states change over time. However, existing benchmarks primarily emphasize structural evolution via temporal link prediction (TLP), while support for semantic evolution remains limited. Although temporal node classification (TNC) is sometimes included, it is typically restricted to simplistic binary settings that fail to capture realistic semantic drift. Moreover, commonly used datasets exhibit high link repetition, leading to inflated performance estimates and obscuring true model capability. To address these limitations, we introduce TTGBench, a new benchmark that jointly evaluates structural and semantic evolution. TTGBench comprises six real-world, text-rich datasets characterized by Dual Volatility, enabling rigorous and fair evaluation of existing models. Notably, it is the first benchmark to support both multi-class and multi-label TNC, filling a critical gap in evaluating temporal semantic drift. We conduct a comprehensive evaluation of 17 state-of-the-art methods across Temporal Graph Neural Networks (TGNNs) and Large Language Model (LLM)-based paradigms. The results reveal a clear capability divide between the two paradigms: TGNN-based methods excel at structural prediction but fail at semantic tracking, whereas LLM-based predictors show the opposite trend. Through in-depth analysis, we uncover their fundamental limitations and provide insights for developing more comprehensive temporal graph models.