8 citations · 16 across the 24 of their papers we have counts for
10 papers · 1 filter
LAM: Efficient Lossy Agent Memory Framework With A Retrieval-Score Error Bound
Baixi Sun, Le Chen, Anjir Ahmed Chowdhury +10
Agent memory grows as agents read inputs, reason, and call tools. Longer histories increase inference cost and eventually exceed the context window. LLM-based summarization reduces…
FABRICA: Agentic CUDA-to-CSL Translation and Optimization for Wafer-Scale Systems
Yuebo Luo, Eliu Huerta, Venkatram Vishwanath +3
Porting GPU kernels across architectures requires architectural remapping, not syntax substitution. CUDA encodes decomposition, locality, and synchronization through threads, block…
LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks
Chih-Hsuan Yang, Jingyan Jiang, Cheng-Hau Yang +4
Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when extra collaboration is worth its cost. We i…
HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation
Yuebo Luo, Ahmad Sedigh Baroughi, Philip Stachura +4
Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring mon…
Precise but Uncoupled: Reviewer Precision Does Not Guarantee Critique Uptake in Multi-Agent Math Reasoning
Chih-Hsuan Yang, Jingyan Jiang, Vikram Vasudevan +7
Many math- and science-oriented agent systems use hierarchical designs with specialized reviewer roles, assuming that a dedicated review stage should help turn wrong candidates int…
Prefill/Decode-Aware Evaluation of LLM Inference on Emerging AI Accelerators
Shun Usami, Venkatram Vishwanath, E. Wes Bethel
As large language models (LLMs) are increasingly deployed in latency- and cost-sensitive settings, inference efficiency has become a central systems challenge. While GPUs dominate…
Probabilistic Attribution For Large Language Models
Shilpika Shilpika, Carlo Graziani, Bethany Lusch +2
The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities they compute to sample each response token given the previous tokens. These prob…
Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System
Thang Duc Pham, Harikrishna Tummalapalli, Fakhrul Hasan Bhuiyan +5
The integration of Artificial Intelligence (AI) with High-Performance Computing (HPC) is transforming scientific workflows from human-directed pipelines into adaptive systems capab…
Swimba: Switch Mamba Model Scales State Space Models
Zhixu Du, Krishna Teja Chitty-Venkata, Murali Emani +3
Mixture-of-experts (MoE) is a common approach for increasing parameter capacity, but applying MoE to state space model (SSM) token mixers can multiply the cost of the recurrent sta…
Extending P: Spectral Conditions for Feature Learning Across Optimizers
Akshita Gupta, Marieme Ngom, Sam Foreman +1
Several variations of adaptive first-order and second-order optimization methods have been proposed to accelerate and scale the training of large language models. The performance o…