·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Deezer launches an AI music detector for other streaming services2h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing6h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning6h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!6h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions6h◆The Impossibility of Eliciting Latent Knowledge6h◆Mapping Scientific Literature with Large Language Models and Topic Modeling6h◆Grounding Computer Use Agents on Human Demonstrations6h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models6h◆LSTM based IoT Device Identification6h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse6h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM6h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models6h◆RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning6h◆A Geometric Profile of Semantic Information in Text: Frame-Conditional Uniqueness and a Trade-Off Triangle for Scalar Summaries6h◆Making Foresight Actionable: Repurposing Representation Alignment in World Action Models6h◆Fixed-Parameter Tractability of Private Synthetic Data Generation6h◆DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?6h◆Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation6h◆CoVEBench: Can Video Editing Models Handle Complex Instructions?6h◆Deezer launches an AI music detector for other streaming services2h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing6h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning6h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!6h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions6h◆The Impossibility of Eliciting Latent Knowledge6h◆Mapping Scientific Literature with Large Language Models and Topic Modeling6h◆Grounding Computer Use Agents on Human Demonstrations6h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models6h◆LSTM based IoT Device Identification6h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse6h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM6h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models6h◆RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning6h◆A Geometric Profile of Semantic Information in Text: Frame-Conditional Uniqueness and a Trade-Off Triangle for Scalar Summaries6h◆Making Foresight Actionable: Repurposing Representation Alignment in World Action Models6h◆Fixed-Parameter Tractability of Private Synthetic Data Generation6h◆DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?6h◆Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation6h◆CoVEBench: Can Video Editing Models Handle Complex Instructions?6h◆
News/Characterizing Software Aging in GPU-Based LLM Serving Systems
arxiv
PublishedJune 11, 2026 at 4:00 AM
—neutral

Characterizing Software Aging in GPU-Based LLM Serving Systems

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.11916v1 Announce Type: cross Abstract: This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric software with relatively regular workloads; LLM serving is different, spanning a Python host and a C

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning6harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!6harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions6harxivThe Impossibility of Eliciting Latent Knowledge6h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews