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.
TClone is a low-latency forkable workspace system that enables computer-use agents to safely snapshot, branch, and rollback live GUI environments using copy-on-write memory and asynchronous checkpointing, thereby significantly reducing task latency while ensuring isolation and state recovery.
SemaTune is a host-side framework that leverages large language models to perform semantic-aware online OS tuning, significantly outperforming existing black-box controllers by reasoning about knob meanings and indirect performance signals while maintaining low latency, cost, and safety constraints.
This paper demonstrates that for small language models (2-3B parameters), a structured 4-stage harness pipeline significantly outperforms raw prompting or minimal wrappers in operational stability and task success, while also revealing that insufficient scaffolding can lead to structural format collapse in certain models.
This paper introduces the bounded local generator class (BLGC) for deterministic state evolution on graph-indexed systems, demonstrating that finite-range, bounded local transformations enable incremental update costs that remain constant () regardless of the total system size, thereby structurally decoupling global state capacity from computational work.
AgenTEE is a system built on Arm Confidential Compute Architecture (CCA) that enables secure, confidential execution of Large Language Model agents on edge devices by isolating the runtime, inference engine, and third-party applications within independently attested confidential virtual machines, achieving near-native performance with less than 5.15% overhead.
This paper introduces PI-DLinear, a novel physics-informed time-series model that integrates a multi-node thermal RC network with GPU utilization data to achieve state-of-the-art short-term power forecasting accuracy for AI data centers while ensuring physical consistency during load transients.
The paper introduces CacheTTL, a novel KV cache management system that employs a dynamic time-to-live mechanism to selectively retain cache during tool-call pauses in multi-turn LLM agent workflows, thereby achieving over 8x improvement in job completion time and enhanced throughput compared to existing eviction policies.
SAGA is a distributed scheduler that improves the efficiency of compound AI agent workflows on GPU clusters by shifting from request-level to program-level scheduling, which preserves intermediate KV cache states and reduces task completion time by 1.64x despite a tradeoff in peak throughput.
MARS is an efficient, adaptive co-scheduling system that globally coordinates heterogeneous GPU-CPU resources for autonomous LLM agents by decoupling admission from execution and optimizing state retention, thereby significantly reducing end-to-end latency while maintaining high throughput.
Affinity Tailor is a userspace-guided kernel scheduling system that improves locality and throughput on large multicore systems by treating dynamic, demand-sized CPU sets as soft affinity hints rather than hard partitions, thereby balancing resource utilization with microarchitectural efficiency.