Related papers: Operational Memory Architecture for Kubernetes:Pre…

Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access bandwidth.While static pre-allocation preserves memory contiguity,it incurs significant overhead…

Oblivious RAM (ORAM) is a provable secure primitive to prevent access pattern leakage on the memory bus. It serves as the intermediate layer between the trusted on-chip components and the untrusted external memory systems to modulate the…

Long-context language models exhibit impressive performance but remain challenging to deploy due to high GPU memory demands during inference. We propose Memory-efficient Offloaded Mini-sequence Inference (MOM), a method that partitions…

Modern foundation model architectures rely on attention mechanisms to effectively capture context. However, these methods require linear or quadratic memory in terms of the number of inputs/datapoints, limiting their applicability in…

An oblivious data structure is a data structure where the memory access patterns reveals no information about the operations performed on it. Such data structures were introduced by Wang et al. [ACM SIGSAC'14] and are intended for…

Misconfiguration, excessive privilege, and fragmented controls remain major causes of cloud-infrastructure incidents. This paper proposes an open-source framework that contributes a cross-platform identity-resource graph for Kubernetes and…

We provide the first oracle efficient sublinear regret algorithms for adversarial versions of the contextual bandit problem. In this problem, the learner repeatedly makes an action on the basis of a context and receives reward for the…

Long-horizon interactive agents often accumulate large trajectory histories yet still fail to answer questions about earlier events reliably. We argue that the main bottleneck is not context length alone, but the trajectory-to-answer…

Despite widespread interest in multicore computing, concur- rency models in mainstream languages often lead to subtle, error-prone code. Observationally Cooperative Multithreading (OCM) is a new approach to shared-memory parallelism.…

The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline tasks likely leads to the catastrophic forgetting issue under…

We introduce Context Kubernetes, an architecture for orchestrating enterprise knowledge in agentic AI systems, with a prototype implementation and eight experiments. The core observation is that delivering the right knowledge, to the right…

Long-horizon reasoning is a key challenge for autonomous agents, yet existing benchmarks evaluate agents on single tasks in isolation. Real organizational work requires managing many concurrent long-horizon tasks with interleaving,…

Time series anomaly detection is critical for maintaining the reliability of mission-critical systems. While Transformer-based models like PatchTST have shown remarkable performance, their $\mathcal{O}(L^2)$ computational complexity…

The Transformer architecture, underpinned by the self-attention mechanism, has become the de facto standard for sequence modeling tasks. However, its core computational primitive scales quadratically with sequence length (O(N^2)), creating…

The CMSWEB cluster is pivotal to the activities of the Compact Muon Solenoid (CMS) experiment, as it hosts critical services required for the operational needs of the CMS experiment. The security of these services and the corresponding data…

Real-time robotic systems require advanced perception, computation, and action capability. However, the main bottleneck in current autonomous systems is the trade-off between computational capability, energy efficiency and model…

Hardware and OS mechanisms for memory tiering are widely deployed, yet datacenters still overprovision DRAM. The root cause is hotness fragmentation: allocators place objects by size rather than access pattern, so hot and cold objects…

In this paper, we propose a new event memory architecture (MemNet) for recurrent neural networks, which is universal for different types of time series data such as scalar, multivariate or symbolic. Unlike other external neural memory…

Memory consistency model (MCM) issues in out-of-order-issue microprocessor-based shared-memory systems are notoriously non-intuitive and a source of hardware design bugs. Prior hardware verification work is limited to in-order-issue…

Multimodal Large Language Models (MLLMs) have significantly advanced GUI agents, yet long-horizon automation remains constrained by two critical bottlenecks: context overload from raw sequential trajectory dependence and architectural…