Topic
scalability
FM-Agent: New Framework Automates Formal Code Verification for Large-Scale LLM-Generated Software
FM-Agent, a new framework from researchers, automates compositional reasoning for large-scale systems using LLMs. It generates function-level specifications from caller expectations, enabling verification against natural-language intent. In evaluation, it found 522 new bugs in systems up to 143,000 lines of code within 2 days.
TelcoAgent: Foundation Model-Based Framework for Scalable and Explainable 5G KPM Forecasting
TelcoAgent is a foundation model-based framework for 5G key performance measurement forecasting that addresses scalability and explainability issues. It uses a 3GPP knowledge graph and time-series foundation model to deliver zero-shot predictions across multiple cells. Evaluated on a real-world city-scale 5G dataset from a U.S. operator, it achieved high accuracy for seven KPMs per cell across 200 cells.
Pruning Optimisations Boost LUT-Based Neural Network Scalability and Efficiency
Researchers propose a pruning-optimised Look-Up Table (LUT) matrix multiplication unit (LUT-MU) to address scalability limits in LUT-based neural networks. Deployed on FPGAs, it delivers up to 1.6x throughput improvement and 4.2x energy efficiency gains over CUDA-based implementations, with 1.3 to 2.6x resource savings versus original MADDNESS-based networks.
Service-Induced Congestion Threatens LLM Serving Throughput, New Model Shows
A new mathematical model from researchers at MIT and elsewhere shows that in large language model serving, persistent GPU memory consumption from key-value caches creates a 'service-induced congestion' effect. Under high concurrency, this can lead to instability and throughput losses as high as 50%. The paper identifies scheduling design principles to avoid these losses.