M Rajendra Prasad Sourabh Jain

Sandro Papais, Letian Wang, Mudit Jain et al.

Autonomous driving operates in partially observable environments where actors may become fully occluded by other vehicles or infrastructure. Most end-to-end driving systems implicitly couple actor existence to instantaneous observations, causing actor hypotheses to degrade or disappear during prolonged occlusion and removing potentially critical agents from downstream prediction and planning. We introduce BeyondSight, a permanence-aware end-to-end driving framework that decouples actor existence from observability by maintaining persistent actor hypotheses over time. BeyondSight propagates actor queries temporally and updates them with observation-conditioned evidence, enabling joint perception, prediction, and planning to reason about actors even when they are temporarily unobservable. To enable principled training and evaluation of persistence-aware models, we further introduce nuScenes-Permanence, an extension of nuScenes that provides supervision and observability-conditioned evaluation for unobservable actors. Experiments show that BeyondSight substantially improves reasoning under occlusion, increasing detection performance for unobservable actors from 0 to 0.249 mAP while reducing planning error from 0.61 to 0.54 L2avg. These results highlight object permanence as an important modeling principle for robust end-to-end autonomous driving.

Chenhua Shi, Bhavika Jalli, John Zou et al.

Telecom troubleshooting at edge sites requires low-latency model responses and localized model adaptation to satisfy operational and data sovereignty requirements. However, deploying large language models (LLMs) at telecom edge sites is constrained by limited power, cooling, space, and weight budgets for GPU infrastructure. These challenges are further amplified by human-patterned Radio Access Network (RAN) traffic that often results in low GPU utilization and poor return on investment, as well as by architectural mismatches between deterministic ASIC-based telecom processing and GPU-oriented AI workloads. Consequently, single-GPU fine-tuning becomes a practical requirement for scalable edge AI deployment rather than merely a resource limitation. This paper presents a GPU profiling study of LLM fine-tuning using the Unsloth framework on a single edge-class accelerator. We systematically analyze the effects of maximum sequence length, GPU memory utilization, Low-Rank Adaptation (LoRA) rank, and generation count on training stability and resource efficiency. We further investigate trade-offs in KV cache usage, activation memory overhead, and runtime stability under inductor compilation. In addition, we show that reasoning and non-reasoning model architectures exhibit substantially different behaviors during supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) because of differences in chat template structures, reasoning tags, and control flags. Experiments are conducted on a telecom troubleshooting dataset consisting of question-answer pairs augmented with top-3 retrieved contextual documents. The results provide practical configuration guidelines for stable, efficient, and resource-aware LLM fine-tuning in telecom edge environments.