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Thesegames were chosen soastocovertwonatural application domains for EFCE: conflict resolution via a mediator,andbargainingandnegotiation.

As virtual reality (VR) devices become increasingly integrated into everyday settings, a growing number of users without prior experience will engage with VR systems. Automatically detecting a user's familiarity with VR as an interaction medium enables real-time, adaptive training and interface adjustments, minimizing user frustration and improving task performance. In this study, we explore the automatic detection of VR familiarity by analyzing hand movement patterns during a passcode-based door-opening task, which is a well-known interaction in collaborative virtual environments such as meeting rooms, offices, and healthcare spaces. While novice users may lack prior VR experience, they are likely to be familiar with analogous real-world tasks involving keypad entry. We conducted a pilot study with 26 participants, evenly split between experienced and inexperienced VR users, who performed tasks using both controller-based and hand-tracking interactions. Our approach uses state-of-the-art deep classifiers for automatic VR familiarity detection, achieving the highest accuracies of 92.05% and 83.42% for hand-tracking and controller-based interactions, respectively. In the cross-device evaluation, where classifiers trained on controller data were tested using hand-tracking data, the model achieved an accuracy of 78.89%. The integration of both modalities in the mixed-device evaluation obtained an accuracy of 94.19%. Our results underline the promise of using hand movement biometrics for the real-time detection of user familiarity in critical VR applications, paving the way for personalized and adaptive VR experiences.

Most work interpreting reasoning models studies only a single chain-of-thought (CoT), yet these models define distributions over many possible CoTs. We argue that studying a single sample is inadequate for understanding causal influence and the underlying computation. Though fully specifying this distribution is intractable, it can be understood by sampling. We present case studies using resampling to investigate model decisions. First, when a model states a reason for its action, does that reason actually cause the action? In "agentic misalignment" scenarios, we resample specific sentences to measure their downstream effects. Self-preservation sentences have small causal impact, suggesting they do not meaningfully drive blackmail. Second, are artificial edits to CoT sufficient for steering reasoning? These are common in literature, yet take the model off-policy. Resampling and selecting a completion with the desired property is a principled on-policy alternative. We find off-policy interventions yield small and unstable effects compared to resampling in decision-making tasks. Third, how do we understand the effect of removing a reasoning step when the model may repeat it post-edit? We introduce a resilience metric that repeatedly resamples to prevent similar content from reappearing downstream. Critical planning statements resist removal but have large effects when eliminated. Fourth, since CoT is sometimes "unfaithful", can our methods teach us anything in these settings? Adapting causal mediation analysis, we find that hints that have a causal effect on the output without being explicitly mentioned exert a subtle and cumulative influence on the CoT that persists even if the hint is removed. Overall, studying distributions via resampling enables reliable causal analysis, clearer narratives of model reasoning, and principled CoT interventions.

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Apple today launched a new tool for iPhones to help reduce what a thief with your phone and passcode can access. The feature, called Stolen Device Protection, adds extra layers of protection to your iPhone when someone tries to access or change sensitive settings on your device. If someone tries to access passwords stored in Apple's keychain, for instance, they won't be able to unless they also use a fingerprint or the phone's face recognition to prove they're the legitimate owner. You don't need to look far to find stories of stolen phones. In London, a phone is stolen every six minutes.

A single man from Michigan recounted in a viral video how he nearly gave up on dating entirely and went "mentally insane" after a woman he met on an online dating app committed a heist on the date, earning the nickname "Felony Melanie." After reviewing security footage from the restaurant – he's convinced he may have finally solved the mystery of what really happened and why his eye is slightly red. I may take a sabbatical from going on internet dates," influencer Ryan Michael Annese said. The date nightmare story went viral on TikTok, amassing over 3 million views. "I doubt any of you guys can top it.

Apple is set to add even more protection to the iPhone in the next iOS update, which will stop thieves from accessing smartphones with passcodes. Called'Stolen Device Protection,' the new setting promises to prevent cyber-criminals from locking iPhone users out of their Apple accounts or accessing any of their passwords stored in Apple's Keychain. If the feature detects an unknown location of the iPhone, it will require Apple's FaceID to unlock the device. Stolen Device Protection is set to roll out with Apple's iOS 17.3 but is currently being tested in beta. Apple is rolling out a new feature to protect its customers' passcodes, online banking access, private iCloud photos and videos, and everything else that a stolen, unlocked iPhone leaves vulnerable.