arXiv AI
Small Language Model enabled Autonomous agent for Language-Conditioned Cognitive Radar
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 2
RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
arXiv:2605. 09907v2 Announce Type: replace Abstract: Compared with individual agents, large language model based multi-agent systems have shown great capabilities consistently across diverse tasks, including code generation, mathematical reasoning, and planning, etc.
arXiv Machine Learning
Oct 2
SCORE-LM: State-Space Radar Representations with Language Models for Fault Diagnosis
arXiv:2609.38980v1 Announce Type: new Abstract: Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE...
arXiv Computer Vision
Sep 22
STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radar
arXiv:2609.24151v1 Announce Type: new Abstract: Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements whi...
arXiv AI
Aug 17
Can Language Models Understand mmWave Data? Benchmarking Large Language Models for mmWave Radar-Based Human Understanding
arXiv:2608. 14179v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable reasoning and generative capabilities, motivating their use as universal reasoning engines for perception.
arXiv Machine Learning
Jun 29
Temporal Convolutional Autoencoder for Interference Mitigation in FMCW Radar Altimeters
arXiv:2505. 22783v2 Announce Type: replace-cross Abstract: Reliable altitude estimation with frequency-modulated continuous wave (FMCW) radar altimeters is increasingly a challenge due to in-band interference from modern communication systems.
arXiv AI
Sep 28
PTC-Decoder: Towards Intelligent SLMs on Offline Resource-Constrained Edge Devices
The paper introduces PTC-Decoder, a training‑free, plug‑and‑play decoder designed to improve the reliability of small language models (SLMs) on offline, resource‑constrained edge devices such as remote sensing satellites. PTC-Decoder combines a Plan‑to‑Act paradigm that forces the model to invoke a planning tool at the first inference step with a deterministic finite automaton (TC‑Decoder) that imposes token‑level hard constraints on tool names while preserving the model’s reasoning capability. Evaluated on 200 real satellite tasks across seven SLMs, the approach achieves a statistically significant mean overall score gain of +1.21, with the TC‑Decoder component identified as the primary contributor to performance gains.