Accurate electrical load forecasting supports power-system operation across countries, regions, cities, and buildings, but repeated development of target-specific models is costly. Time series foundation models (TSFMs) provide reusable pretrained backbones, although multi-domain pretraining may not fully capture load-s...
Aliullah, Julian Oelhaf, Edouard Fouch\'e et al.· 0 citations
Object-centric reinforcement learning agents typically receive each object's position in a fixed global frame, even though their decisions usually depend on where objects are relative to one another. These absolute views often fail to capture the local context that is most relevant to the agent's behavior. As a result,...
Cedric Derstroff, Jannis Bl\"uml, Janosch Moos et al.· 0 citations
On-policy distillation (OPD) supervises student-generated responses using sampled-token student-teacher log-ratios. A positive log-ratio penalizes a token that the student assigns higher probability to than the teacher, but its current value does not distinguish sustained high penalties from abrupt increases along a re...
Guang-Yu Liu, Si-Yuan Gan, Shi-Long Zhou et al.· 0 citations
Token merging is an effective approach to reducing KV-cache memory consumption in autoregressive video generation by clustering temporally redundant tokens and retaining only a small set of representatives. Existing methods, however, typically apply a uniform merging ratio across all attention layers and heads. We obse...
Zhen Wang, Ning Lai, Qingtao Yu· 0 citations
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The results separate hazard perception from hazard control in driving VLAs, show that unsafe behavior can arise even when the model has both the evidence and the ability to act on it, and propose a simple fix for bridging this binding gap.
Matteo Cercola, Ka-Tie Z. Luo, Simone Formentin et al.· 0 citations
Temporal Evidence-Parameter Co-Alignment (TEPCA), a framework for LLM reasoning over TKGs that aligns LLM reasoning with temporal knowledge at both the evidence and parameter levels, and enables LLMs to better capture fine-grained entity evolution and time-dependent relation semantics while preserving parameter efficie...
Large Language Models (LLMs) depend fundamentally on the diversity, depth, and factual rigor of their pre-training corpora. However, contemporary foundational pre-training datasets (such as The Pile, S2ORC, and Dolma) exhibit a severe structural omission: African scientific, medical, legal, and agricultural scholarship...
This work presents a plant-aware conversational copilot that extends this paradigm in three directions: plant-specific context ingestion, a chat-first interaction model for explanation and debugging, and a graduated tool-access ladder from chat to deployment.
Joschka Kersting, G. Benndorf· IEEE International Conferenc...· 0 citations
Causal discovery with latent confounding is particularly challenging, but a common setting in real-world data. In this work, we focus on linear causal models with latent confounding, represented by acyclic directed mixed graphs (ADMGs). Existing methods that fully identify ADMGs typically assume non-Gaussianity or grap...
Danru Xu, Aapo Hyv\"arinen, Sara Magliacane· 0 citations
Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pretrained experts. We ask what a single-step predicted future must preserve to support accurate single-step action prediction. In the evaluated WAM, resampling video noise h...
Shao-Yi Zheng, Zhong-Tao Jiang, Yue Peng et al.· 0 citations
AI agents keep long-term memory, much of which comes from web pages and tool results. Agent harnesses, the software that runs the model, do not record who may act on this memory, so an instruction hidden in a fetched page can be treated as the user's own instruction. We propose ClawMMU, a protection layer that labels e...
Jin-Hao Hu, Bardia Mohammadi, Mofasshara Rafique et al.· 0 citations
It is suggested that the evaluated LLMs respond to surface cues rather than to the mechanisms that make a passage manipulative, which would suggest that the evaluated LLMs respond to surface cues rather than to the mechanisms that make a passage manipulative.
