In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Kate Candon is a PhD student at Yale University interested in understanding how we can create interactive agents that are more effectively able to help people. We spoke to Kate to find out more about how she is leveraging explicit and implicit feedback in human-robot interactions. Specifically I'm interested in how we can get robots to better learn from humans in the way that they naturally teach. Typically, a lot of work in robot learning is with a human teacher who is only tasked with giving explicit feedback to the robot, but they're not necessarily engaged in the task.

Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating ``believable'' human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPERA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. OPERA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPERA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user's next action and rationale with a given persona and history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.

Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering or supervised fine-tuning to enable models to imitate character behaviors in specific scenarios, but often neglect the underlying \emph{cognitive} mechanisms driving these behaviors. Inspired by cognitive psychology, we introduce \textbf{CogDual}, a novel RPLA adopting a \textit{cognize-then-respond } reasoning paradigm. By jointly modeling external situational awareness and internal self-awareness, CogDual generates responses with improved character consistency and contextual alignment. To further optimize the performance, we employ reinforcement learning with two general-purpose reward schemes designed for open-domain text generation. Extensive experiments on the CoSER benchmark, as well as Cross-MR and LifeChoice, demonstrate that CogDual consistently outperforms existing baselines and generalizes effectively across diverse role-playing tasks.

From Angelina Jolie to Megan Fox, many celebrities are known for their luscious lips. But what exactly does the perfect pout look like? A new study has revealed the answer - and it's bad news for fans of lip fillers. Scientists from the American University of Beirut showed 200 people AI-generated pictures of a woman, whose lips had been adjusted in various ways. An analysis of their preferences revealed that the perfect pout features an upper-to-lower lip ratio (U/L) of between 0.618:1 and 1:1.

U.S. President Donald Trump and Ukrainian President Volodymyr Zelenskyy are considering a deal that involves Washington buying battlefield-tested Ukrainian drones in exchange for Kyiv purchasing weapons from the U.S., Zelenskyy said in an interview with the New York Post. Zelenskyy said his latest talks with Trump focused on a deal that would help each country bolster its aerial technology. Ukrainian drones have been able to strike targets as deep as 1,300 kilometers into Russian territory. "The people of America need this technology, and you need to have it in your arsenal," Zelenskyy told the Post in the interview conducted Wednesday. The Ukrainian leader said drones were the key tool that has allowed his country to fight off Russia's invasion for more than three years.

Equipped with artificial intelligence (AI) and advanced sensing capabilities, social robots are gaining interest among consumers in the United States. These robots seem like a natural evolution of traditional smart home devices. However, their extensive data collection capabilities, anthropomorphic features, and capacity to interact with their environment make social robots a more significant security and privacy threat. Increased risks include data linkage, unauthorized data sharing, and the physical safety of users and their homes. It is critical to investigate U.S. users' security and privacy needs and concerns to guide the design of social robots while these devices are still in the early stages of commercialization in the U.S. market. Through 19 semi-structured interviews, we identified significant security and privacy concerns, highlighting the need for transparency, usability, and robust privacy controls to support adoption. For educational applications, participants worried most about misinformation, and in medical use cases, they worried about the reliability of these devices. Participants were also concerned with the data inference that social robots could enable. We found that participants expect tangible privacy controls, indicators of data collection, and context-appropriate functionality.

While pressing, this narrow focus overlooks critical human-centric considerations that shape the long-term trajectory of a society. In this position paper, we identify the risks of overlooking the impact of AI on the future of work and recommend comprehensive transition support towards the evolution of meaningful labor with human agency. Through the lens of economic theories, we highlight the intertemporal impacts of AI on human livelihood and the structural changes in labor markets that exacerbate income inequality. Additionally, the closed-source approach of major stakeholders in AI development resembles rent-seeking behavior through exploiting resources, breeding mediocrity in creative labor, and monopolizing innovation.

Individuals are increasingly relying on large language model (LLM)-enabled conversational agents for emotional support. While prior research has examined privacy and security issues in chatbots specifically designed for mental health purposes, these chatbots are overwhelmingly "rule-based" offerings that do not leverage generative AI. Little empirical research currently measures users' privacy and security concerns, attitudes, and expectations when using general-purpose LLM-enabled chatbots to manage and improve mental health. Through 21 semi-structured interviews with U.S. participants, we identified critical misconceptions and a general lack of risk awareness. Participants conflated the human-like empathy exhibited by LLMs with human-like accountability and mistakenly believed that their interactions with these chatbots were safeguarded by the same regulations (e.g., HIPAA) as disclosures with a licensed therapist. We introduce the concept of "intangible vulnerability," where emotional or psychological disclosures are undervalued compared to more tangible forms of information (e.g., financial or location-based data). To address this, we propose recommendations to safeguard user mental health disclosures with general-purpose LLM-enabled chatbots more effectively.

A screenshot from the new simulator that will be trialled for a special challenge at RoboCup2025. The annual RoboCup event, where teams gather from across the globe to take part in competitions across a number of leagues, will this year take place in Brazil, from 15-21 July. In advance of kick-off, we spoke to two members of the RoboCup Soccer 3D Simulation League: Executive Committee Member Klaus Dorer, and Stefan Glaser, who is on the Maintenance Committee and who has been recently developing a new simulator for the League. Could start by just giving us a quick introduction to the Simulation League? Klaus Dorer: There are two Simulation Leagues in Soccer: the 2D Simulation League and the 3D Simulation League. The 2D Simulation League, as the name suggests, is a flat league where the players and ball are simulated with simplified physics and the main focus is on team strategy.

When David Fajgenbaum was a twenty-five-year-old medical student, at the University of Pennsylvania, he started to feel so tired that he could barely stand. Fajgenbaum, a former college quarterback, could still bench-press three hundred and seventy-five pounds; he was known for doing pullups on a tree near his workplace. But now he was desperately ill. The lymph nodes in his groin and neck swelled. Small red dots--blood moles--emerged on his chest, and he woke up soaked in sweat. One day, at the hospital where he was doing his rotation, he stumbled down the hall into the emergency room, and doctors told him that his liver, bone marrow, and kidneys were failing. Fluid had leaked out of his blood vessels, into his abdomen and around his heart; bleeding in his retina temporarily blinded him in his left eye.