Deep learning's success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain's System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors.

Emotional support dialogue systems aim to reduce help-seekers' distress and help them overcome challenges. While human values$\unicode{x2013}$core beliefs that shape an individual's priorities$\unicode{x2013}$are increasingly emphasized in contemporary psychological therapy for their role in fostering internal transformation and long-term emotional well-being, their integration into emotional support systems remains underexplored. To bridge this gap, we present a value-driven method for training emotional support dialogue systems designed to reinforce positive values in seekers. Our model learns to identify which values to reinforce at each turn and how to do so, by leveraging online support conversations from Reddit. The model demonstrated superior performance in emotional support capabilities, outperforming various baselines. Notably, it more effectively explored and elicited values from seekers. Expert assessments by therapists highlighted two key strengths of our model: its ability to validate users' challenges and its effectiveness in emphasizing positive aspects of their situations$\unicode{x2013}$both crucial elements of value reinforcement. Our work validates the effectiveness of value reinforcement for emotional support systems and establishes a foundation for future research.

As Large Language Models (LLMs) advance in their capabilities, researchers have increasingly employed them for social simulation. In this paper, we investigate whether interactions among LLM agents resemble those of humans. Specifically, we focus on the pronoun usage difference between leaders and non-leaders, examining whether the simulation would lead to human-like pronoun usage patterns during the LLMs' interactions. Our evaluation reveals the significant discrepancies between LLM-based simulations and human pronoun usage, with prompt-based or specialized agents failing to demonstrate human-like pronoun usage patterns. In addition, we reveal that even if LLMs understand the human pronoun usage patterns, they fail to demonstrate them in the actual interaction process. Our study highlights the limitations of social simulations based on LLM agents, urging caution in using such social simulation in practitioners' decision-making process.

Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to the need for high-quality data and expert annotators. Synthetic data generation offers an alternative, though pre-trained models may not capture the syntactic diversity of clinical notes. We propose an embedding-driven approach that uses diversity sampling from a small set of real clinical notes to guide large language models in few-shot prompting, generating synthetic text that better reflects clinical syntax. We evaluated this method using the CheXpert dataset on a classification task, comparing it to random few-shot and zero-shot approaches. Using cosine similarity and a Turing test, our approach produced synthetic notes that more closely align with real clinical text. Our pipeline reduced the data needed to reach the 0.85 AUC cutoff by 40% for AUROC and 30% for AUPRC, while augmenting models with synthetic data improved AUROC by 57% and AUPRC by 68%. Additionally, our synthetic data was 0.9 times as effective as real data, a 60% improvement in value. 1 Introduction

Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into large language models, thereby minimizing hallucinations. A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation. However, these components are typically optimized separately through supervised fine-tuning, which can lead to misalignments between the objectives of individual modules and the overarching aim of generating accurate answers in question-answering (QA) tasks. Although recent efforts have explored reinforcement learning (RL) to optimize specific RAG components, these approaches often focus on overly simplistic pipelines with only two components or do not adequately address the complex interdependencies and collaborative interactions among the modules. To overcome these challenges, we propose treating the RAG pipeline as a multi-agent cooperative task, with each component regarded as an RL agent. Specifically, we present MMOA-RAG, a Multi-Module joint Optimization Algorithm for RAG, which employs multi-agent reinforcement learning to harmonize all agents' goals towards a unified reward, such as the F1 score of the final answer. Experiments conducted on various QA datasets demonstrate that MMOA-RAG improves the overall pipeline performance and outperforms existing baselines. Furthermore, comprehensive ablation studies validate the contributions of individual components and the adaptability of MMOA-RAG across different RAG components and datasets. The code of MMOA-RAG is on https://github.com/chenyiqun/MMOA-RAG.

Summary and Contributions: The authors are proposing an improvement on existing approaches for imitation learning of policies for embodied agents. The approach is a hybrid between sim-to-real RL approaches (which require a simulator closely matching the real world) and real world imitation learning approaches such as GAIL. The general idea of the paper is that there is a simulator, which, however is allowed to have a different dynamics than the "real world". In particular, the assumption is that two policies can reach the same goal state from the same starting point within H steps in the real-world. The algorithm is tested on the OpenAI Gym environment, where both the real world and the simulator environment are simulations (with different parametrization).

"This could be a truly equalizing breakthrough that is great for researchers and developers with limited resources, especially those from the Global South," says Hancheng Cao, an assistant professor in information systems at Emory University. DeepSeek's success is even more remarkable given the constraints facing Chinese AI companies in the form of increasing US export controls on cutting-edge chips. But early evidence shows that these measures are not working as intended. Rather than weakening China's AI capabilities, the sanctions appear to be driving startups like DeepSeek to innovate in ways that prioritize efficiency, resource-pooling, and collaboration. To create R1, DeepSeek had to rework its training process to reduce the strain on its GPUs, a variety released by Nvidia for the Chinese market that have their performance capped at half the speed of its top products, according to Zihan Wang, a former DeepSeek employee and current PhD student in computer science at Northwestern University.

It doesn't really matter: Chances are that if you've signed up for Microsoft 365, you're now paying more for Microsoft's integration of Copilot features, which was done without your consent. Microsoft is making available "classic" versions of its Microsoft 365 Personal and Microsoft 365 Family plans, that remove the Copilot surcharge. But, as Microsoft said when it announced the changes, these new Classic plans are available for a limited time. This article will show you how to switch back. Right now, here's what you'll see when you sign up for Microsoft 365, with the option to pay 12.99/mo and 9.99/mo, too. Those reflect the higher rates with the Copilot surcharge included.

What's new: After weeks of buzz, OpenAI has released Operator, its first AI agent. Operator is a web app that can carry out simple online tasks in a browser, such as booking concert tickets or filling an online grocery order. The app is powered by a new model called Computer-Using Agent--CUA for short--built on top of OpenAI's multimodal large language model GPT-4o. Why it matters: OpenAI claims that Operator outperforms similar rival tools, including Anthropic's Computer Use and Google DeepMind's Mariner. The fact that three of the world's top AI firms have converged on the same vision of what agent-based models could be makes one thing clear.

"The Agency" star Katherine Waterston admitted she finds AI generally "terrifying" for Hollywood and beyond. Screenwriter Paul Schrader, known for his critically acclaimed works like "Taxi Driver," "Raging Bull" and "First Reformed," surprised fans when he shared his apparent approval of artificial intelligence. In a series of posts last week, the Oscar-nominee marveled at AI and ChatGPT's capabilities when it came to his profession. "I've just come to realize AI is smarter than I am. Has better ideas, has more efficient ways to execute them," he wrote on Jan 16. "Taxi Driver" screenwriter and director Paul Schrader surprised fans with his interest in artificial intelligence.