arXiv:2603.26788v1 Announce Type: new Abstract: Zero-shot object navigation requires agents to locate unseen target objects in unfamiliar environments without prior maps or task-specific training which remains a significant challenge. Although recent advancements in vision-language models(VLMs) provide promising commonsense reasoning capabilities for this task, these models still suffer from spatial hallucinations, local exploration deadlocks, and a disconnect between high-level semantic intent and low-level control. In this regard, we propose a novel hierarchical navigation framework named ReMemNav, which seamlessly integrates […]

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Table of Links Abstract and Introduction Definitions Literature Review Argument Development The AI Model’s Potential for Feeling During Inference Conclusion and References 3 Literature Review Our analysis draws upon a range of interdisciplinary literature that bridges machine learning, artificial intelligence, neuroscience, and philosophy of mind. The following key works inform our discussion: • Relating Transformers to Models and Neural Representations of the Hippocampal Formation (Whittington et al., 2022): Whittington and Behrens explore the parallels between transformer architectures in […]

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We consider the problem of learning the optimal policy for Markov decision processes with safety constraints. We formulate the problem in a reach-avoid setup. Our goal is to design online reinforcement learning algorithms that ensure safety constraints with arbitrarily high probability during the learning phase. To this end, we first propose an algorithm based on the optimism in the face of uncertainty (OFU) principle. Based on the first algorithm, we propose our main algorithm, which utilizes entropy regularization. […]

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arXiv:2605.00020v1 Announce Type: new Abstract: The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical layer design. However, existing models often operate on channel state information (CSI) in the space-time-frequency (STF) domain, where distinct multipath components are inherently superimposed and structurally entangled. This hinders the learning of universal channel representation. Meanwhile, their reliance on global attention mechanisms incurs prohibitive computational overhead. In this paper, we propose AirFM-DDA, […]

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arXiv:2601.18802v1 Announce Type: new Abstract: Augmented Reality (AR) is a powerful perceptual technology that can alter what users see, hear, feel, and experience throughout their daily lives. When combined with the speed and flexibility of context-aware generative AI, the power is greatly expanded, allowing individual users to be targeted with custom-generated AR experiences that are instantly tailored to who they are, where they are, and what they are doing. This can transform the physical world into a magical […]

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hi folks, im a researcher and have a ton of TPU/GPU credits granted for me. Specifically for coding agent RL (preferably front end coding RL). Ive been working on RL rollout stuff (on the scheduling and infrastructure side). Would love to collab with someone who wants to collab and maybe get a paper out for neurips or something ? at the very least do a arxiv release. submitted by /u/vnwarrior [link] [comments]

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How I designed a single Python backend that serves a real estate agency in Dubai, a dental practice in Brazil, and a food retailer — each with fully isolated AI behavior, context memory, and business logic — without any off-the-shelf automation tools. Most WhatsApp AI tutorials show you how to build a bot for one use case. You connect to the API, craft a system prompt, call OpenAI, send a reply. It works. But it doesn’t scale. When I started building AI automation […]

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arXiv:2602.05259v1 Announce Type: cross Abstract: The population $mathrm{KL}_{inf}$ is a fundamental quantity that appears in lower bounds for (asymptotically) optimal regret of pure-exploration stochastic bandit algorithms, and optimal stopping time of sequential tests. Motivated by this, an empirical $mathrm{KL}_{inf}$ statistic is frequently used in the design of (asymptotically) optimal bandit algorithms and sequential tests. While nonasymptotic concentration bounds for the empirical $mathrm{KL}_{inf}$ have been developed, their optimality in terms of constants and rates is questionable, and their generality […]

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