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Pennsylvania State University
- University Park, PA
- https://fairyfali.github.io/
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[ICML 2024] COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
[NeurIPS 2024] Official implementation for "AgentPoison: Red-teaming LLM Agents via Memory or Knowledge Base Backdoor Poisoning"
Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
A curated collection of research papers exploring the utilization of LLMs for graph-related tasks.
A Tree Search Library with Flexible API for LLM Inference-Time Scaling
TPAMI 2026 | This repository collects awesome survey, resource, and paper for lifelong learning LLM agents
[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning
[ICLR 2025] Automated Design of Agentic Systems
This repository serves as a comprehensive knowledge hub, curating cutting-edge research papers and developments across 25+ specialized domains
[Survey] A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
🤖 24/7 AI agent that maximizes Claude Code Pro usage via Slack. Auto-processes tasks, manages isolated workspaces, creates Git commits/PRs, and optimizes day/night usage thresholds.
The official repository of "A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications".
Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.
A simple pip-installable Python tool to generate your own HTML citation world map from your Google Scholar ID.
A collection of LLMs for optimization, including modeling and solving
Official implementation of MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
[ICML'25 Oral] Multi-agent Architecture Search via Agentic Supernet
Implementation for OAgents: An Empirical Study of Building Effective Agents
Code for paper ACL'25 "Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study over Open-ended Question Answering"