FlyCatcher infers 300 correct stateful runtime checkers from 400 tests across four systems, yielding 2.6x more correct checkers and 5.2x more error detections than prior work.
Flow-of-action: SOP enhanced LLM-based multi-agent system for root cause analysis
4 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
4
Pith papers citing it
6
external citations · external index
citation-role summary
background 1
baseline 1
citation-polarity summary
verdicts
UNVERDICTED 4representative citing papers
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
PRAXIS combines LLM-driven structured traversal of service dependency graphs and hammock-block program dependence graphs to improve root-cause analysis accuracy by up to 6.3x while cutting token consumption by 5.3x on 30 real-world cloud incidents.
Rec-Distill is an industrial distillation pipeline that transfers substantial performance from large-scale recommendation models to efficient students, reporting over 60% transferability and measurable business gains.
citing papers explorer
-
FlyCatcher: Neural Inference of Runtime Checkers from Tests
FlyCatcher infers 300 correct stateful runtime checkers from 400 tests across four systems, yielding 2.6x more correct checkers and 5.2x more error detections than prior work.
-
Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
-
PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis
PRAXIS combines LLM-driven structured traversal of service dependency graphs and hammock-block program dependence graphs to improve root-cause analysis accuracy by up to 6.3x while cutting token consumption by 5.3x on 30 real-world cloud incidents.
-
Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models
Rec-Distill is an industrial distillation pipeline that transfers substantial performance from large-scale recommendation models to efficient students, reporting over 60% transferability and measurable business gains.