'Is AI keeping us from getting the social support that we need?' asks Dr Dan Wolfson. 'I created my mom and talk to her': AI ghosts and deathslop are changing the way we mourn'Is AI keeping us from getting the social support that we need?' asks Dr Dan Wolfson. The rise of AI-generated tributes and bots for'talking' to the dead has experts concerned more people will grieve in isolation T ributes rolled in after the death of Dolly Parton last month. Jack White performed Jolene at a London gig. Kesha sang Old Flames (Can't Hold a Candle to You), a single her mother wrote for the country music titan. Even Pitbull stopped his Madison Square Garden show to memorialize Parton and all the other "powerful women" in the crowd.

Why Do Some Soccer Players Cut the Heels Off Their Cleats? An image of Portugal forward Pedro Neto's cleats at the World Cup has reignited a practice among some soccer players: modifying their cleats to relieve heel discomfort. During this year's World Cup, one very peculiar image caught the attention of fans and commentators: Portuguese forward Pedro Neto played a match with the back of his cleats cut off, right above the heel. The modification was intended to relieve pressure on a particularly sensitive area of his foot. Although the image went viral, it's not new.

New research has discovered that a neural circuit may explain procrastination. Scientists were able to disrupt this connection using a drug. The brain avoids unpleasant tasks even if they promise reward, according to a recent study. The reason you decide to postpone household chores and spend your time browsing social media could be explained by the workings of a brain circuit. Recent research has identified a neural connection responsible for delaying the start of activities associated with unpleasant experiences, even when these activities offer a clear reward.

Social robots must adjust to human proxemic norms to ensure user comfort and engagement. While prior research demonstrates that eye-tracking features reliably estimate comfort in human-human interactions, their applicability to interactions with humanoid robots remains unexplored. In this study, we investigate user comfort with the robot "Ameca" across four experimentally controlled distances (0.5 m to 2.0 m) using mobile eye-tracking and subjective reporting (N=19). We evaluate multiple machine learning and deep learning models to estimate comfort based on gaze features. Contrary to previous human-human studies where Transformer models excelled, a Decision Tree classifier achieved the highest performance (F1-score = 0.73), with minimum pupil diameter identified as the most critical predictor. These findings suggest that physiological comfort thresholds in human-robot interaction differ from human-human dynamics and can be effectively modeled using interpretable logic.

Navigating human-populated environments without causing discomfort is a critical capability for socially-aware agents. While rule-based approaches offer interpretability through predefined psychological principles, they often lack gener-alizability and flexibility. Conversely, data-driven methods can learn complex behaviors from large-scale datasets, but are typically inefficient, opaque, and difficult to align with human intuitions. To bridge this gap, we propose RLSLM, a hybrid Reinforcement Learning framework that integrates a rule-based Social Locomotion Model, grounded in empirical behavioral experiments, into the reward function of a reinforcement learning framework. The social locomotion model generates an orientation-sensitive social comfort field that quantifies human comfort across space, enabling socially aligned navigation policies with minimal training. RL-SLM then jointly optimizes mechanical energy and social comfort, allowing agents to avoid intrusions into personal or group space. A human-agent interaction experiment using an immersive VR-based setup demonstrates that RLSLM outperforms state-of-the-art rule-based models in user experience. Ablation and sensitivity analyses further show the model's significantly improved interpretability over conventional data-driven methods. This work presents a scalable, human-centered methodology that effectively integrates cognitive science and machine learning for real-world social navigation.

Robotic assistance in minimally invasive surgery can greatly enhance surgical precision and reduce surgeon fatigue. This paper presents a focused investigation on the kinematic and ergonomic design principles for a laparoscopic surgical robotic arm aimed at high-precision tasks. We propose a 7-degree-of-freedom (7-DOF) robotic arm system that incorporates a remote center of motion (RCM) at the instrument insertion point and ergonomic considerations to improve surgeon interaction. The design is implemented on a general-purpose robotic platform, and a series of simulated surgical tasks were performed to evaluate targeting accuracy, task efficiency, and surgeon comfort compared to conventional manual laparoscopy. Experimental results demonstrate that the optimized robotic design achieves significantly improved targeting accuracy (error reduced by over 50%) and shorter task completion times, while substantially lowering operator muscle strain and discomfort. These findings validate the importance of kinematic optimization (such as added articulations and tremor filtering) and human-centered ergonomic design in enhancing the performance of robot-assisted surgery. The insights from this work can guide the development of next-generation surgical robots that improve surgical outcomes and ergonomics for the operating team.

This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its core, AutoRAN pioneers an execution simulation paradigm that leverages a weaker but less-aligned model to simulate execution reasoning for initial hijacking attempts and iteratively refine attacks by exploiting reasoning patterns leaked through the target LRM's refusals. This approach steers the target model to bypass its own safety guardrails and elaborate on harmful instructions. We evaluate AutoRAN against state-of-the-art LRMs, including GPT-o3/o4-mini and Gemini-2.5-Flash, across multiple benchmarks (AdvBench, HarmBench, and StrongReject). Results show that AutoRAN achieves approaching 100% success rate within one or few turns, effectively neutralizing reasoning-based defenses even when evaluated by robustly aligned external models. This work reveals that the transparency of the reasoning process itself creates a critical and exploitable attack surface, highlighting the urgent need for new defenses that protect models' reasoning traces rather than merely their final outputs.

Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been limited to everyday scenarios. To close this gap, we introduce CLASH (Character perspective-based LLM Assessments in Situations with High-stakes), a meticulously curated dataset consisting of 345 high-impact dilemmas along with 3,795 individual perspectives of diverse values. CLASH enables the study of critical yet underex-plored aspects of value-based decision-making processes, including understanding of decision ambivalence and psychological discomfort as well as capturing the temporal shifts of values in the perspectives of characters. By benchmarking 14 non-thinking and thinking models, we uncover several key findings. Instead, new failure patterns emerge, including early commitment and overcom-mitment. This paper aims to address a core question: Can LLMs make proper judgments in high-stakes dilemmas according to different perspectives?

We consider a setting where goods are allocated to agents by way of an allocation platform (e.g., a matching platform). An "allocation facilitator" aims to increase the overall utility/social-good of the allocation by encouraging (some of the) agents to relax (some of) their restrictions. At the same time, the advice must not hurt agents who would otherwise be better off. Additionally, the facilitator may be constrained by a "bound" (a.k.a. 'budget'), limiting the number and/or type of restrictions it may seek to relax. We consider the facilitator's optimization problem of choosing an optimal set of restrictions to request to relax under the aforementioned constraints. Our contributions are three-fold: (i) We provide a formal definition of the problem, including the participation guarantees to which the facilitator should adhere. We define a hierarchy of participation guarantees and also consider several social-good functions.

How to Do It is Slate's sex advice column. Send it to Jessica and Rich here. I recently started casually online dating after leaving an abusive marriage, and it's been going great! There have been lots of nice guys, and we have had some sexy fun. That said, I've run into a weird situation that I'm almost certainly overthinking but am baffled by.