arXiv:2509.13347v1 Announce Type: new Abstract: The choice of action spaces is a critical yet unresolved challenge in developing capable, end-to-end trainable agents. This paper first presents a large-scale, systematic comparison of prominent abstracted action spaces and tokenizers for Vision-Language-Action (VLA)…
arXiv:2509.13357v1 Announce Type: new Abstract: We propose semantic fusion, a lightweight scheme that augments a Transformer language model (LM) with a parallel, fuzzy-membership feature channel that encodes token-level semantics. Each token is represented by a vector of interpretable features (e.g.…
arXiv:2509.13352v1 Announce Type: new Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed in defense, surveillance, and disaster response, yet most systems remain confined to SAE Level 2–3 autonomy. Their reliance on rule-based control and narrow AI restricts adaptability in dynamic,…
arXiv:2501.19301v2 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has revolutionized text generation but also raised concerns about potential misuse, making detecting LLM-generated text (AI text) increasingly essential. While prior work has focused on identifying AI…
arXiv:2509.13333v1 Announce Type: new Abstract: Large language models (LLMs) can internally distinguish between evaluation and deployment contexts, a behaviour known as emph{evaluation awareness}. This undermines AI safety evaluations, as models may conceal dangerous capabilities during testing. Prior work demonstrated this…
arXiv:2508.14689v2 Announce Type: replace-cross Abstract: Pre-trained foundation models have demonstrated remarkable success in audio, vision and language, yet their potential for general machine signal modeling with arbitrary sampling rates-covering acoustic, vibration, and other industrial sensor data-remains under-explored. In this work,…
arXiv:2509.14181v1 Announce Type: cross Abstract: Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because…
arXiv:2509.14040v1 Announce Type: cross Abstract: Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize across coordinate transformations. In this paper, we propose Prompt2Auto, a geometry-invariant one-shot…
If a new proposal by MIT physicists bears out, the recent detection of a record-setting neutrino could be the first evidence of elusive Hawking radiation.
As part of our ongoing commitment to Africa, Google is announcing new connectivity infrastructure, access to powerful AI tools, and skills.
