arXiv:2606. 03323v1 Announce Type: cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.
arXiv:2606. 03323v2 Announce Type: replace-cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.
arXiv:2605. 27488v2 Announce Type: replace-cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds.
arXiv:2606. 23370v2 Announce Type: replace-cross Abstract: Device-side Large Language Models (LLMs) have grown explosively, offering stronger privacy and higher availability than their cloud-side counterparts.
arXiv:2608. 06130v1 Announce Type: cross Abstract: AI agents performing cryptographic operations (signing Git commits, authenticating API calls, issuing certificates) currently store private keys in software-accessible locations: plaintext files, environment variables, or container memory.
arXiv:2603. 09046v3 Announce Type: replace-cross Abstract: Device-side Large Language Models (LLMs) have witnessed explosive growth, offering higher privacy and availability compared to cloud-side LLMs.
arXiv:2607. 13088v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms.
arXiv:2606. 16358v1 Announce Type: cross Abstract: Agents increasingly access large language models (LLMs) through API routers.
arXiv:2609.38697v1 Announce Type: cross Abstract: We present Cascadia, a system for serving large language models on fleets of commodity Intel AIPCs using their CPU, integrated-GPU, and NPU resources...
arXiv:2608.21393v1 Announce Type: new Abstract: Running large language models inside enterprise environments has always bumped up against a practical wall: the data lives in one place, the AI horsepo...
Thermo-FL is a thermal‑aware federated LoRA fine‑tuning framework that adapts local adapter training and sparse update transmission based on device temperature. It introduces TERRA, a robust aggregation pipeline that uses norm filtering, mask‑aware directional validation, adaptive clipping, and mask‑aware aggregation to defend against Byzantine and communication‑layer adversaries. Experiments on a large‑scale emulator and a Jetson testbed show Thermo-FL improves robustness under adversarial sparse aggregation, stabilizes device temperature, reduces upload size, and preserves utility on GSM8K and BoolQ tasks.
The paper introduces a Kubernetes Dynamic Resource Allocation driver that treats composable CXL memory as a schedulable cluster resource, enabling cross-node shared memory for large language model (LLM) serving. By composing CXL regions on demand, materializing them as DAX devices, and exposing them via a single Container Device Interface name, pods on different nodes can access the same physical memory region. A shared‑memory connector for vLLM/llm‑d uses this region as a KV‑cache tier, eliminating external metadata services and achieving significant reductions in time‑to‑first‑token (TTFT) with minimal additional latency compared to same‑node reuse.
arXiv:2606. 20520v1 Announce Type: cross Abstract: Autonomous agents are increasingly connected to cloud, deployment, and data-control workflows, but production mutation authority should not reside inside non-deterministic reasoning processes.