arXiv:2606. 01862v1 Announce Type: cross Abstract: Translating user intents into physical radio signals represents the critical yet notoriously tedious final step in wireless prototyping, as it requires intricate knowledge of physical layer details and presents immense implementation challenges.
arXiv:2610.19015v1 Announce Type: new Abstract: Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pre...
RFChipAgent is a novel multi‑agent flow that uses large language model agents to automate end‑to‑end analog/RF circuit design under human supervision. The system comprises four key components: a retrieval‑augmented generation subsystem that pulls design knowledge from documentation, agents that handle topology selection and schematic/testbench assembly, a hybrid sizing engine that blends TPE and CMA‑ES optimization with simulator feedback, and a trust‑scored simulation database that informs future optimization runs. Validation on GF22FDSOI 60 GHz wideband mm‑wave low‑noise amplifiers demonstrates automated topology generation, specification‑driven exploration, and simulator‑guided optimization, yielding significant reductions in design effort while preserving sign‑off quality.
MaxKernel is a multi‑agent system designed to generate high‑performance custom kernels for TPUs. It offers three paradigms: a Human‑in‑the‑Loop agent for collaborative design, an Autonomous agent that runs a fully automated optimization loop, and a Graph‑Based Autonomous Search for global exploration. All paradigms share specialized sub‑agents for planning, implementation, debugging, testing, and profiling, and the system consistently matches expert hand‑tuned baselines on the JaxBench suite and real‑world workloads.
arXiv:2610.26999v1 Announce Type: new Abstract: Deep learning for radio frequency (RF) signal analysis relies on large annotated datasets, yet synthetic datasets trade physical realism for controllab...
arXiv:2607. 14165v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have demonstrated significant capability in software code generation, their application to analog Electronic Design Automation (EDA) is bottlenecked.
AIR-LLM is an edge inference architecture that broadcasts large language model (LLM) weights over radio, allowing edge devices to perform matrix-vector multiplications directly in the RF domain without storing or loading the weights. The system uses MIMO spatial multiplexing and an energy‑efficient precoder‑postcoder pair to reduce airtime and calibrate the wireless channel, enabling a single broadcast to serve unlimited users. Experiments on real urban channel models show that AIR-LLM achieves only a 4.0% perplexity loss on LLaMA‑3.1‑8B while saving energy by up to 157.7× compared to FP16 and reducing airtime by over 100× for 20 users.
arXiv:2607. 09727v1 Announce Type: cross Abstract: WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolated silos where models are tailored to specific hardware dialects, fixed environments, and narrow tasks.
arXiv:2607. 16269v1 Announce Type: cross Abstract: Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks.
arXiv:2607. 13465v1 Announce Type: cross Abstract: LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes.
TrustAgent‑RFF is a framework that enhances the reliability of radio frequency fingerprinting (RFF) models when faced with domain shifts such as changes in receiver, distance, or environment. It operates on a frozen I/Q classifier and gathers diagnostic evidence—counterfactual stability, prototype consistency, acquisition domain similarity, and signal quality—to estimate prediction reliability. A large language model planner selects diagnostic actions based on evidence, trust state, costs, and budget, while a calibrated decision layer classifies predictions as Accept, Reject, or Defer. Experiments on five RFF datasets show a 38.7% drop in source‑to‑target accuracy and highlight that many incorrect target‑domain predictions remain highly confident.
The paper introduces RadioDecomp, a method for radiomap blind prediction that corrects a prior‑guided predictor using deterministic residual refinement. It identifies the conditional‑mean radiomap as the optimal target under squared loss and decomposes domain risk into approximation error and irreducible uncertainty. Experiments show that the RadioLSR variant excels in cross‑configuration generalization and outperforms a monolithic baseline in cross‑environment scenarios.
QiMeng-ChipV-RTL is a multi‑agent framework that tackles the challenges of generating Register‑Transfer Level (RTL) code for industrial IP‑level hardware design. By partitioning long design documents into short, localized tasks and using hierarchical planning, localized code generation, interface‑consistent merging, and AST‑guided debugging, it scales to complex specifications. Experiments on the RealBench benchmark show ChipV-RTL achieves a 45.0% pass rate, outperforming state‑of‑the‑art LLMs and agents which reach only 21.6%.