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Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critical bottleneck. Furthermore, applying Low-Rank Adaptation (LoRA) in FL faces a fundamental"aggregation dilemma"between the accurate Sum-of-Products (SoP) and the communic...
The TelecomGPT-R1 family of open source unified telecom reasoning models structured around four complementary axes: protocol, knowledge, modeling, and fault are introduced, and supervised fine-tuning instills telecom knowledge and evidence-grounded reasoning patterns to overcome the cold start barrier for reinforcement...
The PhysAI-Bench is introduced, a benchmark for evaluating the agentic decision-making required for reliable autonomy in Physical AI, which contains 10,178 standardized decision instances automatically extracted from conversational traces of autonomous UAV missions.
Autonomous aerial systems increasingly rely on large language models (LLMs) for mission planning, perception, and decision-making; yet, the lack of standardized, physically grounded benchmarks limits systematic evaluation of their reasoning capabilities. To address this gap, we introduce UAVBench, an open benchmark dat...
MulRobBench provides a reproducible benchmark for trustworthy multimodal UAV decision making under realistic operational constraints and identifies modality-trust selection, constraint extraction, glare, missing data, and operator shorthand as the primary causes of decision instability.
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