Security researchers found that in less than 60 seconds, they could open a hatch on a plane's exterior, plug in a tiny device, and redirect the aircraft's autopilot or sabotage its flight plan. Even as the digital components of so many life-critical systems have proven susceptible to cybersabotage-- cars, medical devices, even water utilities and power grids --the computer systems of airplanes have, thankfully, remained uniquely inaccessible to hackers. But one group of academic researchers has spent years testing a different, devious approach to aviation cybersecurity. Perhaps, they suggest, a plane could be hacked the same way that spies and saboteurs have targeted other high-value, offline computers: by surreptitiously gaining physical access to one and plugging in a device designed to silently run the attackers' malicious code. Tomorrow at the Usenix Cybersecurity Conference, researchers from the University of California at San Diego and Oberlin College will present a hacking technique capable of commandeering the autopilot of a Boeing 737 to redirect its navigation or silently altering key values in the plane's takeoff and fuel calculations while spoofing the results on the pilot's screen--subtle changes the researchers say could potentially cause anything from runway overruns on takeoff to diversions to a different country's airspace to catastrophic crashes.

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Despite recent advances in Large Video Language Models (LVLMs), they still struggle with fine-grained temporal understanding, hallucinate, and often make simple mistakes on even simple video question-answering tasks, all of which pose significant challenges to their safe and reliable deployment in real-world applications. To address these limitations, we propose a self-alignment framework that enables LVLMs to learn from their own errors. Our proposed framework first obtains a training set of preferred and non-preferred response pairs, where non-preferred responses are generated by incorporating common error patterns that often occur due to inadequate spatio-temporal understanding, spurious correlations between co-occurring concepts, and over-reliance on linguistic cues while neglecting the vision modality, among others. To facilitate self-alignment of LVLMs with the constructed preferred and non-preferred response pairs, we introduce Refined Regularized Preference Optimization (RRPO), a novel preference optimization method that utilizes sub-sequence-level refined rewards and token-wise KL regularization to address the limitations of Direct Preference Optimization (DPO). We demonstrate that RRPO achieves more precise alignment and more stable training compared to DPO.

An example prompt for constructing PDDL models of the action "close a

PlanBench seeks to cover a broad range of tasks related to planning and reasoning.

We provide hyper-parameters of our models in Table A.1. Table A.1: Hyper-parameters used for training our VisualCSE and AudioCSE. Vision, we use Dropout augmentation (the same strategy in SimCSE) for AudioCSE. We compare unsup-SimCSE and unsup-VisualCSE on a small scale retrieval test. As shown in Table C.1, VisualCSE generally retrieves qualitatively different sentences than SimCSE.

Xiaoqian Wu Shanghai Jiao Tong University enlighten@sjtu.edu.cn In Tab. 1, we conclude the notations in this work for clarity.Notation Definition r A rule. The size of the premise symbols set M . S is the symbol set, and R is the rule set. A \ B The set difference of A and B. D A very large-scale activity images database.