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.
StepCache is a backend-agnostic system that optimizes LLM serving for workloads with shared structures but localized constraints by implementing step-level reuse, lightweight verification, and selective patching to significantly reduce latency and token usage while guaranteeing output correctness.
This paper presents a systematic literature review and industry survey to analyze Software-Defined Vehicle (SDV) security and privacy, resulting in a comprehensive security framework that addresses mixed-criticality challenges, layered defenses, and the harmonization of in-vehicle and cloud-based protections for Intelligent Transportation Systems.
This paper presents KRONOS, a non-intrusive software-based fault injection framework used to evaluate FreeRTOS dependability under ionizing radiation, revealing that corruption of pointer and scheduler variables frequently causes system crashes while many Task Control Block fields have limited impact on availability.
This paper introduces the Declarative Model Interface (DMI), a novel OS abstraction that separates high-level semantic planning from low-level interaction mechanisms to transform human-oriented GUIs into LLM-friendly primitives, thereby significantly boosting computer-use agent success rates and reducing interaction steps without requiring application source code modifications.
The paper presents Wayfinder, an automated framework that utilizes a neural network-based search algorithm to specialize operating system configurations for specific applications and workloads, achieving significant performance improvements and memory reductions without requiring expert knowledge.
GateANN is an I/O-efficient SSD-based system for filtered vector search that achieves significant performance gains by decoupling graph traversal from vector retrieval, enabling in-memory filtering to bypass unnecessary disk reads for non-matching nodes without requiring index rebuilds.
This paper introduces 2DIO, a portable storage microbenchmark that uses a compact parameter triplet to generate cache-accurate I/O traces with tunable complex behaviors, such as performance cliffs and plateaus, enabling researchers to systematically explore cache dynamics and faithfully replicate real-world workloads.
This paper introduces "branch context," a new OS abstraction realized through the user-space BranchFS filesystem and a proposed Linux syscall, to provide AI agents with isolated, copy-on-write environments featuring atomic commit, rollback, and first-commit-wins resolution for safe parallel exploration.
Drawing inspiration from operating system principles, the paper introduces AgentRM, a middleware resource manager that utilizes a Multi-Level Feedback Queue scheduler and a Context Lifecycle Manager to effectively resolve critical scheduling failures and context degradation issues in LLM agent systems, significantly improving latency, throughput, and information retention.
NCCLbpf is a verified, high-performance framework that embeds a userspace eBPF runtime into NCCL's plugin interfaces to enable safe, composable, and zero-downtime policy updates for GPU collective communication, achieving negligible overhead while significantly improving throughput.