99 papers
This collection explores the dynamic frontier of research spanning from carbon nanotubes to organic semiconductors, where chemists and materials scientists are redefining what is possible at the atomic scale. These studies investigate how molecular structures interact to create new technologies, often bridging the gap between theoretical chemistry and real-world applications like flexible electronics or advanced energy storage.
Every new preprint in this category arrives directly from arXiv, and Gist.Science immediately processes each submission to make the findings accessible to everyone. We provide both clear, plain-language overviews for general readers and detailed technical summaries for specialists, ensuring that complex discoveries in this rapidly evolving field are easy to understand and verify. Below are the latest papers exploring these groundbreaking materials and their transformative potential.
ThunderAgent is a novel, program-aware agentic inference system that unifies LLM and tool resource management through an "LLM Program" abstraction, achieving significant throughput and memory efficiency gains by optimizing KV cache utilization and enabling asynchronous environment preparation.
This paper introduces Pichay, a demand paging system that treats LLM context windows as a memory hierarchy rather than a static cache, successfully reducing context consumption by up to 93% in production by evicting stale content and dynamically reloading it only when needed.
This paper proposes a task-based scheduling framework that ensures end-to-end data freshness in safety-critical multi-rate systems by introducing a Consensus Offset Search algorithm to align task releases with data lifespan constraints, thereby eliminating the artificial latency of Logical Execution Time and the inefficiency of redundant oversampling while preserving Global EDF schedulability.
This paper presents CAEC, a system built on Arm Confidential Compute Architecture that introduces Confidential Shared Memory to enable secure, high-performance, and attestable direct data sharing between Confidential Virtual Machines while remaining inaccessible to the hypervisor.
This paper presents Mica, a confidential computing architecture built on Arm CCA that decouples confidentiality from trust by enabling tenants to explicitly define, restrict, and attest communication paths between distrustful TEE components, thereby preventing sensitive data leakage without significantly expanding the trusted computing base.
This paper demonstrates that leveraging eBPF-based system-level monitoring reveals new leakage patterns in Searchable Symmetric Encryption (SSE) that extend beyond traditional threat models, thereby enabling more powerful leakage abuse attacks and highlighting the critical need to address system-level exposures in SSE defenses.
This dissertation addresses the memory bottleneck in modern computing by advocating a shift from data-agnostic to data-informed microarchitectural designs, proposing four machine learning-driven and data-aware mechanisms that significantly enhance performance and energy efficiency.
This paper argues that achieving high-performance end-to-end data movement requires shifting focus from raw network bandwidth to a holistic hardware-software co-design approach, introducing the "Drainage Basin Pattern" to identify and resolve bottlenecks across six critical paradigms ranging from network latency to host-side factors.
This paper presents empirical results from a production-grade C++ implementation of the Compute ICE-AGE, a deterministic semantic state substrate that achieves invariant traversal latency and thermodynamic stability by evolving a persistent addressable memory graph under bounded local operators, thereby decoupling compute costs from token volume and context horizon.
This paper introduces vmcache, an -tier virtual-memory-assisted buffer pool that generalizes traditional two-tier techniques to DRAM-remote memory-disk architectures and utilizes a new `move_pages2` system call to optimize page migration, achieving up to 4 higher query throughput on TPC-C workloads compared to existing solutions.