SAGA introduces workflow-atomic scheduling for compound AI agents, achieving 1.64x lower task completion time and 1.22x better memory utilization than vLLM on a 64-GPU cluster at the cost of 30% lower peak throughput.
Canonical reference
BurstGPT: A real-world workload dataset to optimize LLM serving systems
Canonical reference. 80% of citing Pith papers cite this work as background.
10
Pith papers citing it
Background
80% of classified citations
citation-role summary
background 4
dataset 1
citation-polarity summary
years
2026 10verdicts
UNVERDICTED 10representative citing papers
InfiniLoRA decouples LoRA execution from base-model inference and reports 3.05x higher request throughput plus 54% more adapters meeting strict latency SLOs.
DiLaServe improves SLO attainment for diffusion language models by up to 56.6 percentage points and reduces latency by up to 46% with less than 1% accuracy drop via deadline-aware scheduling and dynamic reconfiguration.
LiveServe exposes audio playback and barge-in signals to the scheduler and KV manager, lowering P90 audio TTFP by 1.55x on average and raising completed-request throughput by 1.15x on two Omni-LMs.
IPO-Mine releases a toolkit and large multimodal dataset for structured analysis of IPO filings and shows state-of-the-art models diverge from human judgments on chart quality and misleadingness.
LEAPBench shows trajectory scoring changes best-model rankings on 53% of tasks, LLMs do not beat Bayesian optimization, and domain-aware prompting underperforms domain-agnostic on biology tasks aligned with published literature.
Execution-idle accounts for 19.7% of GPU execution time and 10.7% of energy in a large cluster, motivating power management that treats it as a distinct operating state.
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.
Techno-economic framework shows that GPU AI-RAN deployments can offset extra costs via AI revenue for up to 8x ROI across scenarios with varying token depreciation, demand, and GPU densities.
GPT-4o exhibits daily and weekly periodic fluctuations in performance on a fixed physics task, accounting for about 20% of observed variance.
citing papers explorer
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SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters
SAGA introduces workflow-atomic scheduling for compound AI agents, achieving 1.64x lower task completion time and 1.22x better memory utilization than vLLM on a 64-GPU cluster at the cost of 30% lower peak throughput.
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InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models
InfiniLoRA decouples LoRA execution from base-model inference and reports 3.05x higher request throughput plus 54% more adapters meeting strict latency SLOs.
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DiLaServe: High SLO Attainment Serving for Diffusion Language Models
DiLaServe improves SLO attainment for diffusion language models by up to 56.6 percentage points and reduces latency by up to 46% with less than 1% accuracy drop via deadline-aware scheduling and dynamic reconfiguration.
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LiveServe: Interaction-Aware Serving for Real-Time Omni-Modal LLMs
LiveServe exposes audio playback and barge-in signals to the scheduler and KV manager, lowering P90 audio TTFP by 1.55x on average and raising completed-request throughput by 1.15x on two Omni-LMs.
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IPO-Mine: A Toolkit and Dataset for Section-Structured Analysis of Long, Multimodal IPO Documents
IPO-Mine releases a toolkit and large multimodal dataset for structured analysis of IPO filings and shows state-of-the-art models diverge from human judgments on chart quality and misleadingness.
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LEAP: Trajectory-Level Evaluation of LLMs in Iterative Scientific Design
LEAPBench shows trajectory scoring changes best-model rankings on 53% of tasks, LLMs do not beat Bayesian optimization, and domain-aware prompting underperforms domain-agnostic on biology tasks aligned with published literature.
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The Energy Cost of Execution-Idle in GPU Clusters
Execution-idle accounts for 19.7% of GPU execution time and 10.7% of energy in a large cluster, motivating power management that treats it as a distinct operating state.
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Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.
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A Techno-Economic Framework for Cost Modeling and Revenue Opportunities in Open and Programmable AI-RAN
Techno-economic framework shows that GPU AI-RAN deployments can offset extra costs via AI revenue for up to 8x ROI across scenarios with varying token depreciation, demand, and GPU densities.
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Daily and Weekly Periodicity in Large Language Model Performance and Its Implications for Research
GPT-4o exhibits daily and weekly periodic fluctuations in performance on a fixed physics task, accounting for about 20% of observed variance.