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The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits, the fundamental quantum information carriers prepared in high-dimensional superposition states. However, preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks, which require precise and simultaneous maneuver of 2 (N-1) parameters across multiple degrees of freedom. Here, using an integrated approach, we explore the synergy from two degrees of freedom of light, spatial mode and polarization, to generate, encode, and manipulate flying structured photons and their formed qudits in a four-dimensional Hilbert space with high quantum fidelity, intrinsically enabling enhanced noise resilience and higher quantum data rates. The four eigen spin–orbit modes of our qudits possess identical spatial–temporal characteristics in terms of intensity distribution and group velocity, thereby preserving long-haul coherence within the entirety of the quantum data transmission link. Judiciously leveraging the bi-photon entanglement, which is well preserved in the integrated manipulation process, we present versatile spin–orbit cluster states in an extensive dimensional Hilbert space. Such cluster states hold the promise for quantum error correction which can further bolster the channel robustness in long-range quantum communication.

 

Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human's perception of reality isn't always faithful to the physical world. This raises a key question: do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to represent reality? To investigate this question, we build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our findings have shown that although the overall alignment is low, larger models are closer to human perception and more susceptible to visual illusions. Our dataset and initial findings will promote a better understanding of visual illusions in humans and machines and provide a stepping stone for future computational models that can better align humans and machines in perceiving and communicating about the shared visual world. 

Despite tremendous advances in AI, it remains a significant challenge to develop interactive task guidance systems that can offer situated, personalized guidance and assist humans in various tasks. These systems need to have a sophisticated understanding of the user as well as the environment, and make timely accurate decisions on when and what to say. To address this issue, we created a new multimodal benchmark dataset, Watch, Talk and Guide (WTaG) based on natural interaction between a human user and a human instructor. We further proposed two tasks: User and Environment Understanding, and Instructor Decision Making. We leveraged several foundation models to study to what extent these models can be quickly adapted to perceptually enabled task guidance. Our quantitative, qualitative, and human evaluation results show that these models can demonstrate fair performances in some cases with no task-specific training, but a fast and reliable adaptation remains a significant challenge. Our benchmark and baselines will provide a stepping stone for future work on situated task guidance. 

Peer prediction aims to incentivize truthful reports from agents whose reports cannot be assessed with any objective ground truthful information. In the multi-task setting where each agent is asked multiple questions, a sequence of mechanisms have been proposed which are truthful — truth-telling is guaranteed to be an equilibrium, or even better, informed truthful — truth-telling is guaranteed to be one of the best-paid equilibria. However, these guarantees assume agents’ strategies are restricted to be task-independent: an agent’s report on a task is not affected by her information about other tasks. We provide the first discussion on how to design (informed) truthful mechanisms for task-dependent strategies, which allows the agents to report based on all her information on the assigned tasks. We call such stronger mechanisms (informed) omni-truthful. In particular, we propose the joint-disjoint task framework, a new paradigm which builds upon the previous penalty-bonus task framework. First, we show a natural reduction from mechanisms in the penalty-bonus task framework to mechanisms in the joint-disjoint task framework that maps every truthful mechanism to an omni-truthful mechanism. Such a reduction is non-trivial as we show that current penalty-bonus task mechanisms are not, in general, omni-truthful. Second, for a stronger truthful guarantee, we design the matching agreement (MA) mechanism which is informed omni-truthful. Finally, for the MA mechanism in the detail-free setting where no prior knowledge is assumed, we show how many tasks are required to (approximately) retain the truthful guarantees. 

We consider the crowdsourcing setting where, in response to the assigned tasks, agents strategically decide both how much effort to exert (from a continuum) and whether to manipulate their reports. The goal is to design payment mechanisms that (1) satisfy limited liability (all payments are non-negative), (2) reduce the principal’s cost of budget, (3) incentivize effort and (4) incentivize truthful responses. In our framework, the payment mechanism composes a performance measurement, which noisily evaluates agents’ effort based on their reports, and a payment function, which converts the scores output by the performance measurement to payments. Previous literature suggests applying a peer prediction mechanism combined with a linear payment function. This method can achieve either (1), (3) and (4), or (2), (3) and (4) in the binary effort setting. In this paper, we suggest using a rank-order payment function (tournament). Assuming Gaussian noise, we analytically optimize the rank-order payment function, and identify a sufficient statistic, sensitivity, which serves as a metric for optimizing the performance measurements. This helps us obtain (1), (2) and (3) simultaneously. Additionally, we show that adding noise to agents’ scores can preserve the truthfulness of the performance measurements under the non-linear tournament, which gives us all four objectives. Our real-data estimated agent-based model experiments show that our method can greatly reduce the payment of effort elicitation while preserving the truthfulness of the performance measurement. In addition, we empirically evaluate several commonly used performance measurements in terms of their sensitivities and strategic robustness.