LLM-e Guess: Can LLMs Capabilities Advance Without Hardware Progress?

This paper examines whether large language model (LLM) capabilities can continue to advance without additional compute by analyzing the development and role of algorithms used in state-of-the-art LLMs. Motivated by regulatory efforts that have largely focused on restricting access to high-performance hardware, we ask: Can LLMs progress in a compute-constrained environment, and how do algorithmic innovations perform under such conditions? To address these questions, we introduce a novel classification framework that distinguishes between compute-dependent innovations -- which yield disproportionate benefits at high compute levels (e.g., the Transformer architecture and mixture-of-experts models) and compute-independent innovations, which improve efficiency across all compute scales (e.g., rotary positional encoding, FlashAttention, or layer normalization). We quantify these contributions using a metric called compute-equivalent gain (CEG), which estimates the additional compute that would be required to achieve similar improvements without these algorithmic advancements. To validate this framework, we conduct small-scale training experiments with a scaled-down GPT-2 model. Our results confirm that compute-independent advancements yield meaningful performance gains even in resource-constrained settings, with a CEG of up to $3.5\times$ over a baseline model. By contrast, compute-dependent advancements provided little benefit or even degraded performance at the small scale, reinforcing the importance of compute availability for certain algorithmic gains.

Which2comm: An Efficient Collaborative Perception Framework for 3D Object Detection

Collaborative perception allows real-time inter-agent information exchange and thus offers invaluable opportunities to enhance the perception capabilities of individual agents. However, limited communication bandwidth in practical scenarios restricts the inter-agent data transmission volume, consequently resulting in performance declines in collaborative perception systems. This implies a trade-off between perception performance and communication cost. To address this issue, we propose Which2comm, a novel multi-agent 3D object detection framework leveraging object-level sparse features. By integrating semantic information of objects into 3D object detection boxes, we introduce semantic detection boxes (SemDBs). Innovatively transmitting these information-rich object-level sparse features among agents not only significantly reduces the demanding communication volume, but also improves 3D object detection performance. Specifically, a fully sparse network is constructed to extract SemDBs from individual agents; a temporal fusion approach with a relative temporal encoding mechanism is utilized to obtain the comprehensive spatiotemporal features. Extensive experiments on the V2XSet and OPV2V datasets demonstrate that Which2comm consistently outperforms other state-of-the-art methods on both perception performance and communication cost, exhibiting better robustness to real-world latency. These results present that for multi-agent collaborative 3D object detection, transmitting only object-level sparse features is sufficient to achieve high-precision and robust performance.