https://github.com/ipa-lab/hackingBuddyGPT
공식 코드Getting pwn'd by AI: Penetration Testing with Large Language Models
Papers with Code 아카이브
Task Planning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/joeyy5588/planning-as-inpainting
공식 코드https://github.com/kchu/llm-map
공식 코드https://github.com/kkskkkskr/epd
공식 코드https://github.com/lbaa2022/llmtaskplanning
공식 코드LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
https://github.com/lostwolves/bedi
공식 코드BEDI: A Comprehensive Benchmark for Evaluating Embodied Agents on UAVs
https://github.com/lrj-2000/nngtl
공식 코드NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for Mobile Robots
https://github.com/lt-asset/selp
공식 코드SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models
https://github.com/marc1198/chat-hsr
공식 코드LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics
https://github.com/microsoft/JARVIS
공식 코드HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
https://github.com/microsoft/scene-aware-robot-BT-planner
공식 코드https://github.com/mingchaoqi/skill_transfer
공식 코드https://github.com/mobilellm/personal_llm_agents_survey
공식 코드Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
https://github.com/mrkeee/coherent
공식 코드COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
https://github.com/nichattgh/gideon
공식 코드Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM
https://github.com/nomizy/think_net_prompt
공식 코드https://github.com/nyu-llm-ctf/llm_ctf_automation
공식 코드NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
https://github.com/nyu-llm-ctf/llm_ctf_database
공식 코드NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
https://github.com/nyu-llm-ctf/nyu_ctf_bench
공식 코드NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
https://github.com/nyu-llm-ctf/nyuctf_agents
공식 코드https://github.com/nyu-llm-ctf/nyuctf_agents_craken
공식 코드CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution
https://github.com/omron-sinicx/vilain
공식 코드https://github.com/openbmb/workflowllm
공식 코드WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models
https://github.com/opengvlab/vinci
공식 코드Vinci: A Real-time Embodied Smart Assistant based on Egocentric Vision-Language Model
https://github.com/portal-cornell/robotouille
공식 코드Robotouille: An Asynchronous Planning Benchmark for LLM Agents
https://github.com/qncsn2016/TR2
공식 코드Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs
https://github.com/quchangle1/llm-tool-survey
공식 코드https://github.com/rstrudel/rlbc
공식 코드Learning to combine primitive skills: A step towards versatile robotic manipulation
https://github.com/rvmi/skiros2
공식 코드https://github.com/s-bl/cwyc
공식 코드Control What You Can: Intrinsically Motivated Task-Planning Agent
https://github.com/saSerrano/hpomdp
공식 코드https://github.com/scienceaix/deepresearch
공식 코드A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications
https://github.com/shengyin1224/safeagentbench
공식 코드SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents
https://github.com/simular-ai/agent-s
공식 코드Agent S: An Open Agentic Framework that Uses Computers Like a Human
https://github.com/singularity0104/equilibrium-planner
공식 코드Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modeling
https://github.com/sisl/difs
공식 코드Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.