Robot teleoperation with extended reality (XR teleoperation) enables intuitive interaction by mapping user motions to remote robots with real-time 3D feedback. However, existing systems suffer from large completion delays and trajectory deviations under prolonged network latency, rooted in their exclusive reliance on network communication and strict synchronous execution architecture. Moreover, network fluctuations destabilize teleoperation accuracy, while dynamic user motions amplify teleoperation errors. We present MATER, an end-to-end XR teleoperation framework that introduces a mutually-aware architecture in which each side reconstructs its counterpart's delayed or missing state to decouple the execution from network dependency. MATER includes latency-adaptive input window and user motion gap interpolation techniques to handle unstable network communication. It also proposes motion-driven robot state rollback and robot trajectory coordination to handle complex motions. The key idea behind these techniques is to adapt on the fly by reshaping reconstruction and filling gaps as network conditions fluctuate, and by realigning states when motions become fast or complex. Together with lightweight local synchronization and bandwidth optimizations, these system-level advances make MATER resilient to both network and motion dynamics. We implement MATER across three hardware settings, including simulated and physical robots, and evaluate it on 9,500 real-world teleoperation trials from the RoboSet dataset, covering single- and multi-step missions. Compared to state-of-the-art XR teleoperation frameworks, MATER reduces teleoperation error by up to 69.8% on WLAN and 73.1% on cellular networks with only 6.7% maximum runtime overhead. It also shortens mission completion time by up to 47.7%, enabling smoother teleoperation. A real-world case study on ten stationary and mobile missions further shows MATER achieves up to 37.7% faster completion while lowering average teleoperation error by up to 57.2%. MATER code is available at: https://github.com/rtenlab/mater
This paper studies an online resource allocation problem, where resources are reusable, allocated in combinatorial bundles, and subject to endogenous performance deterioration. Unlike existing models that assume singleton decisions or stationary or exogenous dynamics, we consider a combinatorial decision structure where decisions involve selecting among resource-bundled options, while capturing a self-induced feedback loop in which system performance, like rewards, activation probabilities, and service times, degrade as a function of cumulative resource usage. We propose a unified framework that subsumes a broad class of existing models as special cases, and develop a ranking-like online policy, termed IPRANK, which employs an indicator-perturbation mechanism to induce a randomized ranking over configurations based on their size-penalized rewards. Our analysis is based on a queue-coupling argument and shows that under adversarial arrivals, the greedy policy inevitably suffers a competitive ratio with linear dependence on reward heterogeneity, whereas IPRANK achieves a competitive ratio with only logarithmic dependence, making it far more robust in heterogeneous environments.
The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS 2026 conference on June 8-12, 2026 in Ann Arbor, Michigan, USA.
We study the idea of information design for inducing prosocial behavior. As an example, we ground our study in the context of electricity consumption. Electricity utility providers would like to reduce the total power consumed by their residential consumer segment. Supply to this segment is often subsidized, and the saved power can be diverted to more profitable segments. Alternatively, the provider may be keen on earning carbon credits by inducing reduced consumption in this segment. The societal network in which the consumers reside may have a prevailing norm, for example, saving power is environment friendly and is considered good. Those that consume less are considered more prosocial and may derive a larger reputational benefit. How can the service provider, who is familiar with the prevailing norm and the consumption of all users, design suitable feedback signals that exploit reputation benefits to reduce net consumption? We call this a problem of information design and address this question in this paper. We consider a continuum of agents. Each agent has a different intrinsic motivation to reduce her power consumption. Each agent models the power consumption of the others via a distribution. Using this distribution, agents will anticipate their reputational benefit and choose a power consumption by trading off their own intrinsic motivation to do a prosocial action, the cost of this prosocial action and their reputation. We assume that the service provider can provide two types of quantized feedbacks of the power consumption. We study their advantages and disadvantages. For each feedback, we characterize the corresponding mean field equilibrium, using a fixed point equation. Besides computing the mean field equilibrium, we highlight that, in some situations, revealing less information can lead to more prosociality. We illustrate the result on London smart-meter data. We also introduce the notion of privacy and provide a new quantized feedback respecting agents' privacy concerns yet improving prosociality. The results of this study are not restricted to the framework of energy efficiency but are also applicable to congestion problems in road traffic and other resource sharing problems.
Continuously updated corpora make freshness a first-class systems objective, yet periodic TTL refresh introduces unavoidable stale windows. We formalize a staleness-latency-cost tradeoff and show that any periodic refresh policy has violation rate at least p dep (τ)/2, where p dep (τ) is the probability that a query depends on an update committed within the TTL window τ. We present EviDex, a provenance-weighted evidence-path index that continuously compacts a commit stream into intent-partitioned path buckets and supports auditable multi-hop retrieval with bounded visibility delay. On clinical and Wikipedia update streams, EviDex reduces evidence-set violation to 1.3% and 1.1% at T=15 minutes, versus 2.4% and 2.1% for the strongest streaming-aware baseline, while reducing clinical cost by 42%. In a physician-rated clinical QA study, EviDex reaches 0.884 correctness with 0.84% macro-SCER.
