QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market benchmarks.
Mixed citations
Adaptive root cause localization for microservice systems with multi-agent recursion-of-thought.arXiv preprint arXiv:2508.20370, 2025
Mixed citation behavior. Most common role is background (40%).
6
Pith papers citing it
Background
40% of classified citations
citation-role summary
background 3
baseline 2
citation-polarity summary
years
2026 6verdicts
UNVERDICTED 6representative citing papers
SREGym is a modular, open-source live benchmark with 90 high-fidelity SRE failure scenarios built on real cloud stacks for evaluating AI agents on diagnosis and mitigation tasks.
Introduces the first benchmark for fine-grained failures in reinforcement fine-tuning of LLMs and an automatic management framework that detects, diagnoses, and remediates them.
E2E-REME outperforms nine LLMs in accuracy and efficiency for end-to-end microservice remediation by using experience-simulation reinforcement fine-tuning on a new benchmark called MicroRemed.
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that uses multimodal alignment, vector-quantized symptom tokens, and a hypothesis-evidence-test workflow to separate root causes from cascading symptoms.
Systematic review of 145 papers on LLM-based log analysis, providing a unified taxonomy, common design patterns, evaluation practices, and challenges for deployment under drift and limited labels.
citing papers explorer
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From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery
QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market benchmarks.
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SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios
SREGym is a modular, open-source live benchmark with 90 high-fidelity SRE failure scenarios built on real cloud stacks for evaluating AI agents on diagnosis and mitigation tasks.
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Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
Introduces the first benchmark for fine-grained failures in reinforcement fine-tuning of LLMs and an automatic management framework that detects, diagnoses, and remediates them.
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E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning
E2E-REME outperforms nine LLMs in accuracy and efficiency for end-to-end microservice remediation by using experience-simulation reinforcement fine-tuning on a new benchmark called MicroRemed.
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TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that uses multimodal alignment, vector-quantized symptom tokens, and a hypothesis-evidence-test workflow to separate root causes from cascading symptoms.
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LLM4Log: A Systematic Review of Large Language Model-based Log Analysis
Systematic review of 145 papers on LLM-based log analysis, providing a unified taxonomy, common design patterns, evaluation practices, and challenges for deployment under drift and limited labels.