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.

--Back exoskeletons can reduce musculoskeletal strain, but their effectiveness depends on support modulation and adaptive control. This study addresses two challenges: defining optimal support strategies and developing adaptive control based on payload estimation. We introduce an optimization space based on muscle activity reduction, perceived discomfort, and user preference, constructing functions to identify optimal strategies. Experiments with 12 subjects revealed optimal operating regions, highlighting the need for dynamic modulation. Based on these insights, we developed a vision-based adaptive control pipeline that estimates payloads in real-time by enhancing exoskeleton contextual understanding, minimising latency and enabling support adaptation within the defined optimisation space. V alidation with 12 more subjects showed over 80% accuracy and improvements across all metrics. Compared to static control, adaptive modulation reduced peak back muscle activation by up to 23% while preserving user preference and minimising discomfort. These findings validate the proposed framework and highlight the potential of intelligent, context-aware control in industrial exoskeletons. Musculoskeletal disorders are a growing concern in industrial workplaces, where workers in construction sites, production, logistics, and others who regularly lift heavy loads face significant risks. To address these issues, back support exoskeletons (BEs) have been developed, aiming to reduce muscular activation and spinal loadskey contributors to back impairment [1]-[4]. Over the past few decades, advancements in the design of active BEs have improved their kinematic compatibility, reduced their weight, enhanced their acceptability, and proposed innovative designs [5]-[9].

Tran* sat across from me, phone in hand, scrolling. "I just wanted to make sure I didn't say the wrong thing," he explained, referring to a disagreement with his partner. "So I asked ChatGPT what I should say." He read the chatbot-generated message aloud. It was articulate, logical and composed – too composed.