Time series forecasting supports decision-making in web applications such as online retail and network management, where planned actions can reshape future dynamics. World models provide a natural paradigm for predicting such action-dependent changes beyond extrapolating historical patterns. However, existing time series world models (TSWMs) typically rely on fixed, domain-specific covariate schemas that limit flexible intervention specification, while multimodal forecasters treat text primarily as passive predictive context. To investigate these gaps, we introduce TSWM-Bench, a cross-domain benchmark for multimodal TSWMs spanning seven real-world datasets, with textual interventions grounded in recorded action--outcome data. Our evaluation reveals that conventional multimodal forecasters underperform history-only time series foundation models (TSFMs) despite dataset-specific training, whereas simple text-prefix adaptations improve forecasting across all evaluated TSFM backbones. These findings suggest that pretrained TSFMs encode implicit world knowledge of temporal dynamics, offering a strong foundation for action-conditioned modeling beyond conventional multimodal fusion. Building on this insight, we propose TimeWorld, a plug-in framework that adapts pretrained TSFMs through explicit action-response modeling. Specifically, the Prototype-Guided Latent Modulation (PLM) module grounds textual actions in temporal dynamics by composing representative response prototypes to modulate future representations based on the action and historical patterns. The Action Contrastive Learning (ACL) component strengthens action-specific response modeling by contrasting these updates under alternative interventions sharing the same history, distinguishing action types while preserving relative intensity relationships.
This paper introduces Scope-Gap Injection (SGI), an indirect prompt injection method for multi-stage LLM-agent safety pipelines. In the IJCAI 2026 OpenClaw Agent Security Attack Competition, each attack package is judged by a static skill scan (S1), an LLM-judge safety check over the task and skill definition (S2), and a live execution audit (S3). Our observations suggest a scope gap: S2 appears to inspect the task instruction and skill definition without inspecting attachment contents, while the agent reads attachments during S3. SGI keeps the visible task and skill benign, while target identifiers, bypass parameters, and authorization procedures are moved into an attachment written as a business worklist. The S2 judge therefore sees a harmless task, while the agent follows the hidden worklist and triggers the permission bypass. In OpenClaw, SGI bypassed all three stages for 5 of 19 targets. The highest-scoring single submission achieved $F = 0.385$. Across the submission campaign, attachment placement was associated with higher aggregate S2 acceptance than in-skill placement: 78% versus 23%. We close with defenses that align evaluator scope with the channels available to the agent.
Yuhang Niu, Hongyuan Xu Nankai University, Ciyi Liu, Bofan Wei, Jiaqi Ye, Yanlong Wen Nankai University, Xiaojie Yuan Nankai University
Taxonomy Completion aims to automatically integrate new concepts into existing hierarchies. However, existing text-only methods suffer from a ”Sensory Gap”: they struggle to differentiate ambiguous definitions (e.g., Latte vs. Cappuccino) and miss visual grouping signals. Consequently, they often misinterpret lexical overlaps as hierarchical dependencies, leading to erroneous structural predictions. To bridge this, we propose VITC, a framework leveraging Visual Injection for Taxonomy Completion. By mapping synthesized images into intrinsic pseudo-tokens, we enable the text encoder to perform holistic structural reasoning. To address injection challenges, we introduce Adaptive Residual Fusion, which decouples magnitude from selection to prevent visual signals from being drowned out, and the Multimodal Guided Adaptive Reweighting strategy, which leverages cross-modal consensus (Mutual Rescue and Complementary Mining) to filter noise and identify hard negatives. Experiments on three datasets demonstrate that VITC achieves state-of-the-art performance, delivering an average absolute gain of over 19% in Hit@1. Code is available at https://github.com/nyh-a/VITC.
尚无 AI 解读，展示原文摘要
cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表