├── .gitignore ├── README.md ├── config_v5.json ├── download_pdf.py ├── papers_v5.json ├── parsed_v5 ├── 2022.json ├── 2023.json ├── 2024.json └── 2025.json ├── script_v5_step1.py └── script_v5_step2.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | .idea/ 131 | src/ 132 | PDF/ 133 | config_v2.json 134 | script.py 135 | script_v2.py 136 | script_v3.py 137 | readmeBeforeREADME.md 138 | papers/ 139 | parsed/ 140 | parsed_v4/ 141 | papers_v4.json 142 | script_v4_step1.py 143 | script_v4_step2.py 144 | script_v3.py -------------------------------------------------------------------------------- /config_v5.json: -------------------------------------------------------------------------------- 1 | { 2 | "Name": "LLM-Agents-Papers", 3 | "Category_Sort": ["Survey", "Technique For Enhancement", "Interaction", "Application", "Automation", "Training", "Scaling","Stability", "Infrastructure","Others"], 4 | "Description": { 5 | "description": "A repo lists papers related to LLM based agent. Includes", 6 | "items":{ 7 | "Survey": [], 8 | "Technique For Enhancement": ["Planning","Memory Mechanism","Feedback&Reflection","RAG","Search"], 9 | "Interaction": ["Role Playing","Conversation","Game Playing","Human-Agent Interaction","Tool Usage","Simulation"], 10 | "Application": ["Math","Chemistry","Biology","Physics","Geography","Art","Medicine","Finance","Software Engineering","Research"], 11 | "Automation": ["Workflow","Automatic Evaluation"], 12 | "Training": ["Fine tuning","RL","DPO"], 13 | "Scaling": ["Single-Agent Framework","Multi-Agent System"], 14 | "Stability": ["Safety","Bias","Hallucination"], 15 | "Infrastructure": ["Benchmark&Evaluation","Environment&Platform","Dataset"], 16 | "Others": [] 17 | }, 18 | "date": "Last Updated Time: {}" 19 | }, 20 | "Recommendation": { 21 | "description": "For more comprehensive reading, we also recommend other paper lists:", 22 | "items": [ 23 | { 24 | "name": "zjunlp/LLMAgentPapers", 25 | "url": "https://github.com/zjunlp/LLMAgentPapers", 26 | "description": "Must-read Papers on Large Language Model Agents." 27 | }, 28 | { 29 | "name": "teacherpeterpan/self-correction-llm-papers", 30 | "url": "https://github.com/teacherpeterpan/self-correction-llm-papers", 31 | "description": "This is a collection of research papers for Self-Correcting Large Language Models with Automated Feedback." 32 | }, 33 | { 34 | "name": "Paitesanshi/LLM-Agent-Survey", 35 | "url": "https://github.com/Paitesanshi/LLM-Agent-Survey", 36 | "description": "A Survey on LLM-based Autonomous Agents." 37 | }, 38 | { 39 | "name": "woooodyy/llm-agent-paper-list", 40 | "url": "https://github.com/woooodyy/llm-agent-paper-list", 41 | "description": "Must-read papers for LLM-based agents." 42 | }, 43 | { 44 | "name": "git-disl/awesome-LLM-game-agent-papers", 45 | "url": "https://github.com/git-disl/awesome-LLM-game-agent-papers", 46 | "description": "Must-read papers for LLM-based Game agents." 47 | } 48 | ] 49 | }, 50 | "Star_History": "## :star: Star History\n\n[![Star History Chart](https://api.star-history.com/svg?repos=AGI-Edgerunners/LLM-Agents-Papers&type=Date)](https://star-history.com/#AGI-Edgerunners/LLM-Agents-Papers&Date)\n" 51 | } -------------------------------------------------------------------------------- /download_pdf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @org: 辰风科技 3 | # @author: lyh 4 | # @fileName: download_pdf.py 5 | # @date: 2023/6/6 23:40 6 | # 7 | # describe: 8 | # 9 | # @version 1.0 2023/6/6 23:40 10 | import json 11 | import os 12 | import re 13 | import warnings 14 | 15 | import requests 16 | 17 | PROXY = None 18 | 19 | 20 | def read_json(file: str) -> dict: 21 | f = open(file, 'r', encoding='utf-8') 22 | json_data = json.load(f) 23 | f.close() 24 | return json_data 25 | 26 | 27 | def pdf_downloader(url: str, pdf_file: str): 28 | try: 29 | res = requests.get(url, proxies=PROXY if PROXY else None) 30 | except Exception as e: 31 | warnings.warn(f"something wrong when visit the url {url} ERROR:{str(e)}") 32 | res = None 33 | if res is not None and res.status_code == 200: 34 | with open(pdf_file, 'wb+') as f: 35 | f.write(res.content) 36 | f.close() 37 | else: 38 | warnings.warn(f'the file {pdf_file}({url}) download failed') 39 | 40 | 41 | Parsed_Folder = 'parsed_v4' 42 | PDF_Folder = 'PDF' 43 | if not os.path.exists(Parsed_Folder): 44 | warnings.warn(f'the folder ./{Parsed_Folder}/ is not exist') 45 | exit(0) 46 | 47 | if not os.path.exists(PDF_Folder): 48 | os.mkdir(PDF_Folder) 49 | 50 | files = os.listdir(Parsed_Folder) 51 | for file in files: 52 | datas: dict = read_json(os.path.join(Parsed_Folder, file)) 53 | for id_, data in datas.items(): 54 | pdf_link = data.get('pdf') 55 | title = data.get('title') 56 | if not pdf_link or not title: 57 | warnings.warn(f'pdf link or title not found from {data}') 58 | continue 59 | pattern = '.*(?=.json)' 60 | match = re.search(pattern, file) 61 | list_title = match.group() if match else 'Paper List' 62 | pdf_file = f'{PDF_Folder}/{list_title}/{title}.pdf' 63 | if not os.path.exists(f'{PDF_Folder}/{list_title}'): 64 | os.mkdir(f'{PDF_Folder}/{list_title}') 65 | 66 | pdf_file = pdf_file.replace(':', '：').replace('?','？') 67 | if not os.path.exists(pdf_file): 68 | print(f'{pdf_link} downloading...') 69 | pdf_downloader(pdf_link, pdf_file) 70 | print(f'{pdf_link} save to {pdf_file}') 71 | -------------------------------------------------------------------------------- /papers_v5.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "url": "https://arxiv.org/abs/2310.01444v2" 4 | }, 5 | { 6 | "url": "https://arxiv.org/abs/2310.12823" 7 | }, 8 | { 9 | "url": "https://arxiv.org/abs/2310.05915" 10 | }, 11 | { 12 | "url": "https://arxiv.org/abs/2312.12999" 13 | }, 14 | { 15 | "url": "https://arxiv.org/abs/2401.05566" 16 | }, 17 | { 18 | "url": "https://arxiv.org/abs/2401.05033" 19 | }, 20 | { 21 | "url": "https://arxiv.org/abs/2401.02777" 22 | }, 23 | { 24 | "url": "https://arxiv.org/abs/2401.05268" 25 | }, 26 | { 27 | "url": "https://arxiv.org/abs/2307.16883" 28 | }, 29 | { 30 | "url": "https://arxiv.org/abs/2308.03688" 31 | }, 32 | { 33 | "url": "https://arxiv.org/abs/2308.05960" 34 | }, 35 | { 36 | "url": "https://arxiv.org/abs/2306.06070" 37 | }, 38 | { 39 | "url": "https://arxiv.org/abs/2309.07870" 40 | }, 41 | { 42 | "url": "https://arxiv.org/abs/2310.05746" 43 | }, 44 | { 45 | "url": "https://arxiv.org/abs/2310.01557" 46 | }, 47 | { 48 | "url": "https://arxiv.org/abs/2309.17382" 49 | }, 50 | { 51 | "url": "https://arxiv.org/abs/2311.10775" 52 | }, 53 | { 54 | "url": "https://arxiv.org/abs/2311.10751" 55 | }, 56 | { 57 | "url": "https://arxiv.org/abs/2311.10538" 58 | }, 59 | { 60 | "url": "https://arxiv.org/abs/2311.09835" 61 | }, 62 | { 63 | "url": "https://arxiv.org/abs/2308.11339" 64 | }, 65 | { 66 | "url": "https://arxiv.org/abs/2401.01275" 67 | }, 68 | { 69 | "url": "https://arxiv.org/abs/2312.16132" 70 | }, 71 | { 72 | "url": "https://arxiv.org/abs/2312.17115" 73 | }, 74 | { 75 | "url": "https://arxiv.org/abs/2401.02870" 76 | }, 77 | { 78 | "url": "https://arxiv.org/abs/2310.15421" 79 | }, 80 | { 81 | "url": "https://arxiv.org/abs/2305.19118" 82 | }, 83 | { 84 | "url": "https://arxiv.org/abs/2304.10750" 85 | }, 86 | { 87 | "url": "https://arxiv.org/abs/2308.00436" 88 | }, 89 | { 90 | "url": "https://arxiv.org/abs/2307.14936" 91 | }, 92 | { 93 | "url": "https://arxiv.org/abs/2303.17651" 94 | }, 95 | { 96 | "url": "https://arxiv.org/abs/2305.16653" 97 | }, 98 | { 99 | "url": "https://arxiv.org/abs/2305.13068" 100 | }, 101 | { 102 | "url": "https://arxiv.org/abs/2304.05128" 103 | }, 104 | { 105 | "url": "https://arxiv.org/abs/2311.07961" 106 | }, 107 | { 108 | "url": "https://arxiv.org/abs/2310.20689" 109 | }, 110 | { 111 | "url": "https://arxiv.org/abs/2303.08127" 112 | }, 113 | { 114 | "url": "https://arxiv.org/abs/2303.16563" 115 | }, 116 | { 117 | "url": "https://arxiv.org/abs/2305.05091" 118 | }, 119 | { 120 | "url": "https://arxiv.org/abs/2305.17144" 121 | }, 122 | { 123 | "url": "https://arxiv.org/abs/2305.16867" 124 | }, 125 | { 126 | "url": "https://arxiv.org/abs/2305.10142" 127 | }, 128 | { 129 | "url": "https://arxiv.org/abs/2305.19761" 130 | }, 131 | { 132 | "url": "https://arxiv.org/abs/2305.16291" 133 | }, 134 | { 135 | "url": "https://arxiv.org/abs/2309.05076" 136 | }, 137 | { 138 | "url": "https://arxiv.org/abs/2309.04658" 139 | }, 140 | { 141 | "url": "https://arxiv.org/abs/2309.17277" 142 | }, 143 | { 144 | "url": "https://arxiv.org/abs/2308.12466" 145 | }, 146 | { 147 | "url": "https://arxiv.org/abs/2310.20499" 148 | }, 149 | { 150 | "url": "https://arxiv.org/abs/2312.17515" 151 | }, 152 | { 153 | "url": "https://arxiv.org/abs/2304.11158" 154 | }, 155 | { 156 | "url": "https://arxiv.org/abs/2304.14106" 157 | }, 158 | { 159 | "url": "https://arxiv.org/abs/2305.00833" 160 | }, 161 | { 162 | "url": "https://arxiv.org/abs/2305.10250" 163 | }, 164 | { 165 | "url": "https://arxiv.org/abs/2305.14322" 166 | }, 167 | { 168 | "url": "https://arxiv.org/abs/2304.13343" 169 | }, 170 | { 171 | "url": "https://arxiv.org/abs/2305.11554" 172 | }, 173 | { 174 | "url": "https://arxiv.org/abs/2306.03901" 175 | }, 176 | { 177 | "url": "https://arxiv.org/abs/2310.10134" 178 | }, 179 | { 180 | "url": "https://arxiv.org/abs/2312.17259" 181 | }, 182 | { 183 | "url": "https://arxiv.org/abs/2312.17257" 184 | }, 185 | { 186 | "url": "https://arxiv.org/abs/2308.01552" 187 | }, 188 | { 189 | "url": "https://arxiv.org/abs/2308.00352" 190 | }, 191 | { 192 | "url": "https://arxiv.org/abs/2307.07924" 193 | }, 194 | { 195 | "url": "https://arxiv.org/abs/2307.05300" 196 | }, 197 | { 198 | "url": "https://arxiv.org/abs/2307.02485" 199 | }, 200 | { 201 | "url": "https://arxiv.org/abs/2306.03314" 202 | }, 203 | { 204 | "url": "https://arxiv.org/abs/2308.10848" 205 | }, 206 | { 207 | "url": "https://arxiv.org/abs/2310.06500" 208 | }, 209 | { 210 | "url": "https://arxiv.org/abs/2309.12633" 211 | }, 212 | { 213 | "url": "https://arxiv.org/abs/2310.02124" 214 | }, 215 | { 216 | "url": "https://arxiv.org/abs/2312.13010" 217 | }, 218 | { 219 | "url": "https://arxiv.org/abs/2401.04259" 220 | }, 221 | { 222 | "url": "https://arxiv.org/abs/2401.03945" 223 | }, 224 | { 225 | "url": "https://arxiv.org/abs/2401.03630" 226 | }, 227 | { 228 | "url": "https://arxiv.org/abs/2401.05998" 229 | }, 230 | { 231 | "url": "https://arxiv.org/abs/2310.16730" 232 | }, 233 | { 234 | "url": "https://arxiv.org/abs/2312.00746" 235 | }, 236 | { 237 | "url": "https://arxiv.org/abs/2310.20151" 238 | }, 239 | { 240 | "url": "https://arxiv.org/abs/2304.03442" 241 | }, 242 | { 243 | "url": "https://arxiv.org/abs/2304.12998" 244 | }, 245 | { 246 | "url": "https://arxiv.org/abs/2304.13835" 247 | }, 248 | { 249 | "url": "https://arxiv.org/abs/2305.01253" 250 | }, 251 | { 252 | "url": "https://arxiv.org/abs/2305.05658" 253 | }, 254 | { 255 | "url": "https://arxiv.org/abs/2305.10601" 256 | }, 257 | { 258 | "url": "https://arxiv.org/abs/2305.16367" 259 | }, 260 | { 261 | "url": "https://arxiv.org/abs/2305.16960" 262 | }, 263 | { 264 | "url": "https://arxiv.org/abs/2305.17390" 265 | }, 266 | { 267 | "url": "https://arxiv.org/abs/2303.17760", 268 | "code": "https://github.com/camel-ai/camel" 269 | }, 270 | { 271 | "url": "https://arxiv.org/abs/2304.07590" 272 | }, 273 | { 274 | "url": "https://arxiv.org/abs/2306.16207" 275 | }, 276 | { 277 | "url": "https://fablestudio.github.io/showrunner-agents/" 278 | }, 279 | { 280 | "url": "https://arxiv.org/abs/2308.03427" 281 | }, 282 | { 283 | "url": "https://arxiv.org/abs/2308.07201" 284 | }, 285 | { 286 | "url": "https://arxiv.org/abs/2308.05481" 287 | }, 288 | { 289 | "url": "https://arxiv.org/abs/2212.04088" 290 | }, 291 | { 292 | "url": "https://arxiv.org/abs/2309.02427" 293 | }, 294 | { 295 | "url": "https://arxiv.org/abs/2309.04565" 296 | }, 297 | { 298 | "url": "https://arxiv.org/abs/2310.00746" 299 | }, 300 | { 301 | "url": "https://arxiv.org/abs/2311.06330" 302 | }, 303 | { 304 | "url": "https://arxiv.org/abs/2312.13961" 305 | }, 306 | { 307 | "url": "https://arxiv.org/abs/2312.12006" 308 | }, 309 | { 310 | "url": "https://arxiv.org/abs/2305.14992" 311 | }, 312 | { 313 | "url": "https://arxiv.org/abs/2311.17227" 314 | }, 315 | { 316 | "url": "https://arxiv.org/abs/2312.03815" 317 | }, 318 | { 319 | "url": "https://arxiv.org/abs/2312.16378" 320 | }, 321 | { 322 | "url": "https://arxiv.org/abs/2312.17025" 323 | }, 324 | { 325 | "url": "https://arxiv.org/abs/2401.04620" 326 | }, 327 | { 328 | "url": "https://arxiv.org/abs/2308.11807" 329 | }, 330 | { 331 | "url": "https://arxiv.org/abs/2311.13884" 332 | }, 333 | { 334 | "url": "https://arxiv.org/abs/2304.02868" 335 | }, 336 | { 337 | "url": "https://arxiv.org/abs/2306.15774" 338 | }, 339 | { 340 | "url": "https://arxiv.org/abs/2308.11432" 341 | }, 342 | { 343 | "url": "https://arxiv.org/abs/2309.07864" 344 | }, 345 | { 346 | "url": "https://arxiv.org/abs/2312.11970" 347 | }, 348 | { 349 | "url": "https://arxiv.org/abs/2401.00812" 350 | }, 351 | { 352 | "url": "https://arxiv.org/abs/2401.00609" 353 | }, 354 | { 355 | "url": "https://arxiv.org/abs/2310.09233" 356 | }, 357 | { 358 | "url": "https://arxiv.org/abs/2306.02552" 359 | }, 360 | { 361 | "url": "https://arxiv.org/abs/2310.11954" 362 | }, 363 | { 364 | "url": "https://arxiv.org/abs/2308.03427" 365 | }, 366 | { 367 | "url": "https://arxiv.org/abs/2309.00986" 368 | }, 369 | { 370 | "url": "https://arxiv.org/abs/2310.08740" 371 | }, 372 | { 373 | "url": "https://arxiv.org/abs/2311.11315" 374 | }, 375 | { 376 | "url": "https://arxiv.org/abs/2312.08914" 377 | }, 378 | { 379 | "url": "https://arxiv.org/abs/2312.13816" 380 | }, 381 | { 382 | "url": "https://arxiv.org/abs/2312.13771" 383 | }, 384 | { 385 | "url": "https://arxiv.org/abs/2401.01614" 386 | }, 387 | { 388 | "url": "https://arxiv.org/abs/2309.09971", "code": "https://mindagent.github.io/" 389 | }, 390 | { 391 | "url": "https://arxiv.org/abs/2312.10908","code": "https://clova-tool.github.io/" 392 | }, 393 | { 394 | "url": "https://arxiv.org/abs/2311.05997","code": "https://github.com/CraftJarvis/JARVIS-1" 395 | }, 396 | { 397 | "url": "https://arxiv.org/abs/2302.01560" 398 | }, 399 | { 400 | "url": "https://arxiv.org/abs/2305.14909", "code": "https://github.com/GuanSuns/LLMs-World-Models-for-Planning" 401 | }, 402 | { 403 | "url": "https://arxiv.org/abs/2402.19273" 404 | }, 405 | { 406 | "url": "https://arxiv.org/abs/2402.18807" 407 | }, 408 | { 409 | "url": "https://arxiv.org/abs/2402.18659" 410 | }, 411 | { 412 | "url": "https://arxiv.org/abs/2402.17753" 413 | }, 414 | { 415 | "url": "https://arxiv.org/abs/2402.17505" 416 | }, 417 | { 418 | "url": "https://arxiv.org/abs/2402.17168" 419 | }, 420 | { 421 | "url": "https://arxiv.org/abs/2402.16667" 422 | }, 423 | { 424 | "url": "https://arxiv.org/abs/2402.16823", "code": "https://github.com/metauto-ai/gptswarm" 425 | }, 426 | { 427 | "url": "https://arxiv.org/abs/2402.16333" 428 | }, 429 | { 430 | "url": "https://arxiv.org/abs/2402.15506" 431 | }, 432 | { 433 | "url": "https://arxiv.org/abs/2402.01051" 434 | }, 435 | { 436 | "url": "https://arxiv.org/abs/2402.01108" 437 | }, 438 | { 439 | "url": "https://arxiv.org/abs/2402.01586" 440 | }, 441 | { 442 | "url": "https://arxiv.org/abs/2402.01622","code": "https://github.com/OSU-NLP-Group/TravelPlanner" 443 | }, 444 | { 445 | "url": "https://arxiv.org/abs/2402.01737" 446 | }, 447 | { 448 | "url": "https://arxiv.org/abs/2402.01765" 449 | }, 450 | { 451 | "url": "https://arxiv.org/abs/2402.01030" 452 | }, 453 | { 454 | "url": "https://arxiv.org/abs/2402.02559" 455 | }, 456 | { 457 | "url": "https://arxiv.org/abs/2402.02896" 458 | }, 459 | { 460 | "url": "https://arxiv.org/abs/2402.03628" 461 | }, 462 | { 463 | "url": "https://arxiv.org/abs/2402.04253","code": "https://github.com/dyabel/anytool" 464 | }, 465 | { 466 | "url": "https://arxiv.org/abs/2402.05120" 467 | }, 468 | { 469 | "url": "https://arxiv.org/abs/2402.10153" 470 | }, 471 | { 472 | "url": "https://arxiv.org/abs/2402.10178" 473 | }, 474 | { 475 | "url": "https://arxiv.org/abs/2402.10753","code": "https://github.com/junjie-ye/toolsword" 476 | }, 477 | { 478 | "url": "https://arxiv.org/abs/2402.10890","code": "https://github.com/osu-nlp-group/llm-planning-eval" 479 | }, 480 | { 481 | "url": "https://arxiv.org/abs/2402.11163" 482 | }, 483 | { 484 | "url": "https://arxiv.org/abs/2402.11271" 485 | }, 486 | { 487 | "url": "https://arxiv.org/abs/2402.11443","code": "https://github.com/nanshineloong/self-evolving-benchmark" 488 | }, 489 | { 490 | "url": "https://arxiv.org/abs/2402.11451" 491 | }, 492 | { 493 | "url": "https://arxiv.org/abs/2402.11453" 494 | }, 495 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"https://arxiv.org/abs/2402.12738" 539 | }, 540 | { 541 | "url": "https://arxiv.org/abs/2402.12914" 542 | }, 543 | { 544 | "url": "https://arxiv.org/abs/2402.13184" 545 | }, 546 | { 547 | "url": "https://arxiv.org/abs/2402.13212" 548 | }, 549 | { 550 | "url": "https://arxiv.org/abs/2402.13225" 551 | }, 552 | { 553 | "url": "https://arxiv.org/abs/2312.14878" 554 | }, 555 | { 556 | "url": "https://arxiv.org/abs/2402.13717" 557 | }, 558 | { 559 | "url": "https://arxiv.org/abs/2402.14320" 560 | }, 561 | { 562 | "url": "https://arxiv.org/abs/2402.14846" 563 | }, 564 | { 565 | "url": "https://arxiv.org/abs/2402.14850" 566 | }, 567 | { 568 | "url": "https://arxiv.org/abs/2402.14871" 569 | }, 570 | { 571 | "url": "https://arxiv.org/abs/2402.14963" 572 | }, 573 | { 574 | "url": "https://arxiv.org/abs/2402.15057" 575 | }, 576 | { 577 | "url": "https://arxiv.org/abs/2402.16313" 578 | }, 579 | { 580 | "url": "https://arxiv.org/abs/2402.16499" 581 | }, 582 | { 583 | "url": 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3477 | { 3478 | "url": "" 3479 | }, 3480 | { 3481 | "url": "" 3482 | }, 3483 | { 3484 | "url": "" 3485 | }, 3486 | { 3487 | "url": "" 3488 | }, 3489 | { 3490 | "url": "" 3491 | }, 3492 | { 3493 | "url": "" 3494 | }, 3495 | { 3496 | "url": "" 3497 | }, 3498 | { 3499 | "url": "" 3500 | }, 3501 | { 3502 | "url": "" 3503 | }, 3504 | { 3505 | "url": "" 3506 | }, 3507 | { 3508 | "url": "" 3509 | }, 3510 | { 3511 | "url": "" 3512 | }, 3513 | { 3514 | "url": "" 3515 | }, 3516 | { 3517 | "url": "" 3518 | }, 3519 | { 3520 | "url": "" 3521 | }, 3522 | { 3523 | "url": "" 3524 | }, 3525 | { 3526 | "url": "" 3527 | }, 3528 | { 3529 | "url": "" 3530 | }, 3531 | { 3532 | "url": "" 3533 | }, 3534 | { 3535 | "url": "" 3536 | }, 3537 | { 3538 | "url": "" 3539 | }, 3540 | { 3541 | "url": "" 3542 | }, 3543 | { 3544 | "url": "" 3545 | }, 3546 | { 3547 | "url": "" 3548 | }, 3549 | { 3550 | "url": "" 3551 | }, 3552 | { 3553 | "url": "" 3554 | }, 3555 | { 3556 | "url": "" 3557 | }, 3558 | { 3559 | "url": "" 3560 | }, 3561 | { 3562 | "url": "" 3563 | }, 3564 | { 3565 | "url": "" 3566 | }, 3567 | { 3568 | "url": "" 3569 | } 3570 | ] -------------------------------------------------------------------------------- /parsed_v5/2022.json: -------------------------------------------------------------------------------- 1 | { 2 | "9008aec82292bda0c6baa1c8ad5bc9b0": { 3 | "title": "LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models", 4 | "authors": [ 5 | "Chan Hee Song", 6 | "Jiaman Wu", 7 | "Clayton Washington", 8 | "Brian M. Sadler", 9 | "Wei-Lun Chao", 10 | "Yu Su" 11 | ], 12 | "date": "2022/12/08", 13 | "pdf": "http://arxiv.org/pdf/2212.04088", 14 | "abstract": "This study focuses on using large language models (LLMs) as a planner for embodied agents that can follow natural language instructions to complete complex tasks in a visually-perceived environment. The high data cost and poor sample efficiency of existing methods hinders the development of versatile agents that are capable of many tasks and can learn new tasks quickly. In this work, we propose a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents. We further propose a simple but effective way to enhance LLMs with physical grounding to generate and update plans that are grounded in the current environment. Experiments on the ALFRED dataset show that our method can achieve very competitive few-shot performance: Despite using less than 0.5% of paired training data, LLM-Planner achieves competitive performance with recent baselines that are trained using the full training data. Existing methods can barely complete any task successfully under the same few-shot setting. Our work opens the door for developing versatile and sample-efficient embodied agents that can quickly learn many tasks. Website: https://dki-lab.github.io/LLM-Planner", 15 | "code": "", 16 | "category": [ 17 | [ 18 | "Technique For Enhancement", 19 | "Planning" 20 | ] 21 | ], 22 | "url": "https://arxiv.org/abs/2212.04088" 23 | } 24 | } -------------------------------------------------------------------------------- /script_v5_step1.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Project: LLM-Agents-Papers 5 | File: script_v5_step1.py 6 | Date: 2025/2/23 02:00 7 | """ 8 | import json 9 | import os 10 | import warnings 11 | import re 12 | from hashlib import md5 13 | 14 | import requests 15 | 16 | URL_FILE = "papers_v5.json" 17 | PARSED_FOLDER = "parsed_v5" 18 | CSS_FILE = "src/style.css" 19 | CONFIG_FILE = "config_v5.json" 20 | USE_CSS = False 21 | 22 | HEADERS = {} 23 | PROXY = None #{"https": "127.0.0.1:4780", "http": "127.0.0.1:4780"} 24 | 25 | PARSED_DATA = [] 26 | 27 | stored_datas = {} 28 | all_parsed_datas = {} 29 | 30 | def read_json(file: str) -> dict: 31 | f = open(file, 'r', encoding='utf-8') 32 | data = json.load(f) 33 | f.close() 34 | return data 35 | 36 | 37 | def write_json(file: str, data: dict) -> None: 38 | f = open(file, 'w+', encoding='utf-8') 39 | json.dump(data, f, ensure_ascii=False, indent=4) 40 | f.close() 41 | 42 | def parse_by_crawl(url: str): 43 | try: 44 | res = requests.get(url, proxies=PROXY if PROXY else None) 45 | except Exception as e: 46 | warnings.warn(f"something wrong when visit the url {url} ERROR:{str(e)}") 47 | res = None 48 | if res is not None and res.status_code == 200: 49 | html_text = res.content.decode() 50 | 51 | # title 52 | pattern = '(?<=)' 53 | match = re.search(pattern, html_text) 54 | title = match.group() if match else '' 55 | 56 | # authors 57 | pattern = '(?<=)' 58 | authors = re.findall(pattern, html_text) 59 | authors = [f"{author.split(', ')[1]} {author.split(', ')[0]}" if len(author.split(', ')) == 2 else author 60 | for author in authors] 61 | 62 | # date 63 | pattern = '(?<=)' 64 | match = re.search(pattern, html_text) 65 | date = match.group() if match else '' 66 | 67 | # pdf 68 | pattern = '(?<=)' 69 | match = re.search(pattern, html_text) 70 | pdf = match.group() if match else '' 71 | 72 | # abs 73 | pattern = '(?<=)' 74 | match = re.search(pattern, html_text, re.S) 75 | abstract = match.group() if match else '' 76 | else: 77 | title = '' 78 | authors = [] 79 | date = '' 80 | pdf = '' 81 | abstract = '' 82 | return {} 83 | return {'title': title, 'authors': authors, 'date': date, 'pdf': pdf, 'abstract': abstract, 'code': '', 84 | 'category': []} 85 | 86 | def pretty_category(all_categories): 87 | columns = 2 88 | max_len = max(len(cate.split(".")[0]) for cate in all_categories) 89 | rows = (len(all_categories) + 1) // columns 90 | 91 | lines = [] 92 | for i in range(rows): 93 | left = f'{i}.{all_categories[i].split(".")[0]}'.ljust(max_len + 4) 94 | right_idx = i + rows 95 | right = f'{right_idx}.{all_categories[right_idx].split(".")[0]}' if right_idx < len(all_categories) else '' 96 | lines.append(left + right) 97 | 98 | # print('\n'.join(lines)) 99 | return '\n'.join(lines) 100 | 101 | 102 | def parse_paper(): 103 | global stored_datas, all_parsed_datas 104 | if os.path.exists(CONFIG_FILE): 105 | config = read_json(CONFIG_FILE) 106 | else: 107 | config = {} 108 | # all_category = [cate.split('.')[0] for cate in config.get('Category_Sort',[])] 109 | all_categories = [] 110 | category_mapping = {} 111 | cat_idx = 0 112 | for category_1, sub_categories in config.get("Description",{}).get("items", {}).items(): 113 | if not sub_categories: 114 | all_categories.append(category_1) 115 | category_mapping[cat_idx] = [category_1, None] 116 | cat_idx += 1 117 | else: 118 | for sub_category in sub_categories: 119 | all_categories.append(sub_category) 120 | category_mapping[cat_idx] = [category_1, sub_category] 121 | cat_idx += 1 122 | 123 | files = os.listdir(PARSED_FOLDER) 124 | for file_name in files: 125 | year = file_name.split('.')[0] 126 | parsed_datas = read_json(f"{PARSED_FOLDER}/{file_name}") 127 | stored_datas[year] = parsed_datas 128 | all_parsed_datas.update(parsed_datas) 129 | datas = read_json(URL_FILE) 130 | for data_idx,data in enumerate(datas): 131 | print(f"parse data: {json.dumps(data)}") 132 | print("{}/{}".format(data_idx+1, len(datas))) 133 | url = data.get('url', '') 134 | code = data.get('code') 135 | md5_id = md5(url.encode()).hexdigest() 136 | parsed = {} 137 | if url: 138 | parsed = all_parsed_datas.get(md5_id, {}) 139 | 140 | if not parsed and url.startswith('https://arxiv.org/abs/'): 141 | parsed = parse_by_crawl(url) 142 | if parsed and code: 143 | parsed['code'] = code 144 | if parsed: 145 | parsed['url'] = url 146 | category = parsed.get('category') 147 | if not category: 148 | print('-'*20) 149 | print(f"title: {parsed.get('title')} link: {url}") 150 | # print(f"link: {url}") 151 | # category_str = '\n'.join([f'{idx}.{cate.split(".")[0]}' for idx,cate in enumerate(all_categories)]) 152 | # for idx in range(len(all_categories)): 153 | # if idx%2==1: 154 | # category_str = category_str.replace(f"\n{idx}",f" {idx}",1) 155 | category_str = pretty_category(all_categories) 156 | input_str = input(f"Please label the category of this paper: \n{category_str}\nInput the numbers with blank:") 157 | category = [category_mapping[int(idx)] for idx in input_str.split(' ')] 158 | parsed['category'] = category 159 | all_parsed_datas.update({md5_id: parsed}) 160 | 161 | t = parsed.get('date') 162 | year = t.split('/')[0] 163 | 164 | if year in stored_datas: 165 | stored_datas[year].update({md5_id: parsed}) 166 | else: 167 | stored_datas[year] = {md5_id: parsed} 168 | for year, datas in stored_datas.items(): 169 | write_json(f'{PARSED_FOLDER}/{year}.json', datas) 170 | 171 | 172 | if __name__ == '__main__': 173 | try: 174 | parse_paper() 175 | except Exception as e: 176 | for year, datas in stored_datas.items(): 177 | write_json(f'{PARSED_FOLDER}/{year}.json', datas) 178 | -------------------------------------------------------------------------------- /script_v5_step2.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # encoding: utf-8 3 | # @author: yihuai lan 4 | # @fileName: script_v4_step2.py 5 | # @date: 2024/3/3 14:36 6 | # 7 | # describe: 8 | # 9 | # -*- coding: utf-8 -*- 10 | 11 | import json 12 | import os 13 | import re 14 | import time 15 | import warnings 16 | 17 | import requests 18 | from hashlib import md5 19 | 20 | PAPER_FOLDER = "papers" 21 | PARSED_FOLDER = "parsed_v5" 22 | CSS_FILE = "src/style.css" 23 | CONFIG_FILE = "config_v5.json" 24 | USE_CSS = False 25 | 26 | PARSED_DATA = [] 27 | 28 | 29 | def read_json(file: str) -> dict: 30 | f = open(file, 'r', encoding='utf-8') 31 | data = json.load(f) 32 | f.close() 33 | return data 34 | 35 | 36 | def write_json(file: str, data: dict) -> None: 37 | f = open(file, 'w+', encoding='utf-8') 38 | json.dump(data, f, ensure_ascii=False, indent=4) 39 | 40 | 41 | def parse_recommendation_section(data: dict) -> str: 42 | readme_lines = ["## :yellow_heart: Recommendation", data.get('description', '')] 43 | for item in data.get('items', []): 44 | readme_lines.append(f"* [{item.get('name', '')}]({item.get('url', '')}): {item.get('description', '')}") 45 | return '\n'.join(readme_lines) 46 | 47 | 48 | def parse_description_section(data: dict) -> str: 49 | now = time.localtime(time.time()) 50 | readme_lines = ["## :writing_hand: Description", 51 | data.get('date', '').format(f"{now.tm_year}/{now.tm_mon}/{now.tm_mday}\n"), 52 | data.get('description', '')] 53 | # temp_item = [] 54 | # for item,sub_items in data.get('items', {}).items(): 55 | # # temp_item = [f"[{i}](#{i.replace(' ','-').replace('&','')})" for i in item] 56 | # temp_item.append(f"[{item}](#{item.replace(' ','-').replace('&','')})") 57 | # for sub_item in sub_items: 58 | # temp_item.append(f"[{sub_item}](#{sub_item.replace(' ', '-').replace('&', '')})") 59 | # readme_lines.append(f"* {', '.join(temp_item)}") 60 | items = data.get('items', {}) 61 | for item, sub_items in items.items(): 62 | item_link = item.replace(' ', '-').replace('&', '') 63 | readme_lines.append(f"- [{item}](#{item_link})") 64 | for sub_item in sub_items: 65 | sub_item_link = sub_item.replace(' ', '-').replace('&', '') 66 | readme_lines.append(f" - [{sub_item}](#{sub_item_link})") 67 | return '\n'.join(readme_lines) 68 | 69 | 70 | def parse_paper_item(data: dict) -> str: 71 | title = data.get('title') 72 | if not title: 73 | return '' 74 | date = data.get('date', '') 75 | url = data.get('url') if data.get('url') else data.get('url') 76 | code = data.get('code') if data.get('code') else data.get('code') 77 | if url: 78 | url_tag = f'[[paper]]({url})' 79 | else: 80 | url_tag = '[paper]' 81 | if code: 82 | code_tag = f'[[code]]({code})' 83 | else: 84 | code_tag = '[code]' 85 | readme_line = f"""- [{date}] **{title}** | {url_tag} | {code_tag}\n""" 86 | return readme_line 87 | 88 | 89 | def parse_paper_block(block_title, sub_block_titles, block_datas) -> str: 90 | readme_lines = [f"### {block_title}"] 91 | if sub_block_titles: 92 | for sub_block_title in sub_block_titles: 93 | sub_block_datas = block_datas.get(sub_block_title, []) 94 | # sub_block_datas = sorted(sub_block_datas, key=lambda x: x.get('date', ''), reverse=True) 95 | readme_lines.append(parse_sub_block(sub_block_title, sub_block_datas)) 96 | else: 97 | block_datas = sorted(block_datas, key=lambda x: x.get('date', ''), reverse=True) 98 | for data in block_datas: 99 | readme_line = parse_paper_item(data) 100 | if readme_line: 101 | readme_lines.append(readme_line) 102 | readme_lines.append("---") 103 | 104 | if not os.path.exists(PARSED_FOLDER): 105 | os.mkdir(PARSED_FOLDER) 106 | return '\n'.join(readme_lines) 107 | 108 | def parse_sub_block(block_title, sub_block_datas) -> str: 109 | readme_lines = [f"#### {block_title}"] 110 | block_datas = sorted(sub_block_datas, key=lambda x: x.get('date', ''), reverse=True) 111 | for data in block_datas: 112 | readme_line = parse_paper_item(data) 113 | if readme_line: 114 | readme_lines.append(readme_line) 115 | # readme_lines.append("---") 116 | 117 | if not os.path.exists(PARSED_FOLDER): 118 | os.mkdir(PARSED_FOLDER) 119 | return '\n'.join(readme_lines) 120 | 121 | 122 | def read_parsed_by_category(): 123 | files = os.listdir(PARSED_FOLDER) 124 | all_parsed_datas = {} 125 | for file_name in files: 126 | parsed_datas = read_json(f"{PARSED_FOLDER}/{file_name}") 127 | # all_parsed_datas.update(parsed_datas) 128 | for id_, data in parsed_datas.items(): 129 | categories = data.get('category', []) 130 | if not categories: 131 | categories = ['Others',None] 132 | for [category, sub_category] in categories: 133 | if sub_category is None: 134 | if category in all_parsed_datas: 135 | all_parsed_datas[category].append(data) 136 | else: 137 | all_parsed_datas[category] = [data] 138 | else: 139 | if category in all_parsed_datas and sub_category in all_parsed_datas[category]: 140 | all_parsed_datas[category][sub_category].append(data) 141 | elif category not in all_parsed_datas: 142 | all_parsed_datas[category] = {sub_category: [data]} 143 | else: 144 | all_parsed_datas[category][sub_category] = [data] 145 | return all_parsed_datas 146 | 147 | 148 | def parse_paper_section(category_sort: list, sub_category_dict:dict) -> str: 149 | readme_lines = ["## :newspaper: Papers"] 150 | all_parsed = read_parsed_by_category() 151 | for category in category_sort: 152 | sub_category_sort = sub_category_dict[category] 153 | # if sub_category_sort: 154 | # for sub_category in sub_category_sort: 155 | readme_line = parse_paper_block(category, sub_category_sort, all_parsed.get(category, {})) 156 | readme_lines.append(readme_line) 157 | return '\n'.join(readme_lines) 158 | 159 | 160 | def init(): 161 | if os.path.exists(CONFIG_FILE): 162 | config = read_json(CONFIG_FILE) 163 | else: 164 | config = {} 165 | title = config.get('Name', '') 166 | readme_sections = [ 167 | f"# {title}", 168 | parse_description_section(config.get('Description')), 169 | parse_recommendation_section(config.get('Recommendation')), 170 | parse_paper_section(config.get('Category_Sort'), config.get("Description",{}).get("items")), 171 | config.get('Star_History', '') 172 | ] 173 | md_text = '\n'.join(readme_sections) 174 | 175 | with open('README.md', 'w+', encoding='utf-8') as f: 176 | f.write(md_text) 177 | f.close() 178 | 179 | 180 | if __name__ == '__main__': 181 | init() 182 | --------------------------------------------------------------------------------