This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.

Recommender systems struggle to provide accurate suggestions to new users with limited interaction history, a challenge known as the cold-user problem. This paper proposes a reinforcement learning approach using Double and Dueling Deep Q-Networks (DQN) to dynamically learn user preferences from sparse feedback, enhancing recommendation accuracy without relying on sensitive demographic data. By integrating these advanced DQN variants with a matrix factorization model, we achieve superior performance on a large e-commerce dataset compared to traditional methods like popularity-based and active learning strategies. Experimental results show that our method, particularly Dueling DQN, reduces Root Mean Square Error (RMSE) for cold users, offering an effective solution for privacy-constrained environments.

We thank all the reviewers for their constructive feedback! Here we provide more experimental results in Figure 1. Hence, gradient descent can get stuck in local optima more easily. R3's observations for collaborative filtering (CF) are valid but they apply

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OpenAI has been doubling its audience for ChatGPT at a rapid rate, and the addition of its latest image generation feature has increased the AI assistant's popularity. Today, CEO Sam Altman posted to X that the service "added one million users in the last hour," calling it a "biblical demand" for the image generation. When the company announced the rollout of image generation in ChatGPT last week, the tool was meant to be available to all user tiers. However, the high degree of interest meant that access for free users was walked back. Now, the company is reporting "issues with new signups," which has been its status for more than a day.

While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences across users, overlooking diverse human values and minority viewpoints. Although personalized preference learning addresses this by tailoring separate preferences for individual users, the field lacks standardized methods to assess its effectiveness. We present a multi-faceted evaluation framework that measures not only performance but also fairness, unintended effects, and adaptability across varying levels of preference divergence. Through extensive experiments comparing eight personalization methods across three preference datasets, we demonstrate that performance differences between methods could reach 36% when users strongly disagree, and personalization can introduce up to 20% safety misalignment. These findings highlight the critical need for holistic evaluation approaches to advance the development of more effective and inclusive preference learning systems.

Wireless sensing is of great benefits to our daily lives. However, wireless signals are sensitive to the surroundings. Various factors, e.g. environments, locations, and individuals, may induce extra impact on wireless propagation. Such a change can be regarded as a domain, in which the data distribution shifts. A vast majority of the sensing schemes are learning-based. They are dependent on the training domains, resulting in performance degradation in unseen domains. Researchers have proposed various solutions to address this issue. But these solutions leverage either semi-supervised or unsupervised domain adaptation techniques. They still require some data in the target domains and do not perform well in unseen domains. In this paper, we propose a domain generalization framework DGSense, to eliminate the domain dependence problem in wireless sensing. The framework is a general solution working across diverse sensing tasks and wireless technologies. Once the sensing model is built, it can generalize to unseen domains without any data from the target domain. To achieve the goal, we first increase the diversity of the training set by a virtual data generator, and then extract the domain independent features via episodic training between the main feature extractor and the domain feature extractors. The feature extractors employ a pre-trained Residual Network (ResNet) with an attention mechanism for spatial features, and a 1D Convolutional Neural Network (1DCNN) for temporal features. To demonstrate the effectiveness and generality of DGSense, we evaluated on WiFi gesture recognition, Millimeter Wave (mmWave) activity recognition, and acoustic fall detection. All the systems exhibited high generalization capability to unseen domains, including new users, locations, and environments, free of new data and retraining.

Like many others, I recently joined Bluesky. On Thanksgiving, I was delighted to see a private message from a fellow AI reporter, Will Knight from Wired. Or at least that's who I thought I was talking to. I became suspicious when the person claiming to be Knight said they were from Miami, when Knight is, in fact, from the UK. The account handle was almost identical to the real Will Knight's handle, and used his profile photo. Then more messages started to appear.

The social media app Bluesky just reached the top of the free download charts for Apple's app store in the United States, making it--for the moment, at least--more popular than Meta's Threads and OpenAI's ChatGPT. The decentralized social media platform has received a fresh influx of users over the last week as more X users sour on Elon Musk's political ambitions and abandon his social media platform for an alternative. Over a million new users have joined Bluesky since the 2024 US presidential election on November 5, which was shaped by Musk's influence. First launched in 2019 as a project within Twitter, Bluesky gained independence from the company before Musk's acquisition and its subsequent name change. Bluesky also captured the attention of some ex-tweeters back in 2023, when new users were only able to sign up through an invite system.

Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however, in terms of cycle time, user cost, and potential user retention. In developing policies for ``onboarding'' new users, these costs can be especially problematic, since on-boarding occurs only once. In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments. We illustrate its deployment for the evaluation of ``preference elicitation'' algorithms used to onboard new users of the YouTube Music platform. By developing counterfactually robust user behavior models, and a simulation service that couples such models with production infrastructure, we are able to test new algorithms in a way that reliably predicts their performance on key metrics when deployed live. We describe our domain, our simulation models and platform, results of experiments and deployment, and suggest future steps needed to further realistic simulation as a powerful complement to live experiments.