Step 4: tool results

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{
  "url": "https://arxiv.org/abs/2506.20803",
  "max_chars": 4000
}

{"ok":true,"url":"https://arxiv.org/abs/2506.20803","text":"[2506.20803] The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas\nSkip to main content\nSearch\nSubmit\nDonate\nLog in\nSearch arXiv\nPress Enter to search &middot; Advanced search\n-->\nComputer Science > Computation and Language\narXiv:2506.20803 (cs)\n[Submitted on 25 Jun 2025]\nTitle: The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas\nAuthors: Chenglei Si , Tatsunori Hashimoto , Diyi Yang\nView a PDF of the paper titled The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas, by Chenglei Si and 2 other authors\nView PDF\nHTML (experimental)\nAbstract: Large Language Models (LLMs) have shown promise in accelerating the scientific research pipeline. A key capability for this process is the ability to generate novel research ideas, and prior studies have found settings in which LLM-generated research ideas were judged as more novel than human-expert ideas. However, a good idea should not simply appear to be novel, it should also result in better research after being executed. To test whether AI-generated ideas lead to better research outcomes, we conduct an execution study by recruiting 43 expert researchers to execute randomly-assigned ideas, either written by experts or generated by an LLM. Each expert spent over 100 hours implementing the idea and wrote a 4-page short paper to document the experiments. All the executed projects are then reviewed blindly by expert NLP researchers. Comparing the review scores of the same ideas before and after execution, the scores of the LLM-generated ideas decrease significantly more than expert-written ideas on all evaluation metrics (novelty, excitement, effectiveness, and overall; p < 0.05), closing the gap between LLM and human ideas observed at the ideation stage. When comparing the aggregated review scores from the execution study, we even observe that for many metrics there is a flip in rankings where human ideas score higher than LLM ideas. This ideation-execution gap highlights the limitations of current LLMs in generating truly effective research ideas and the challenge of evaluating research ideas in the absence of execution outcomes.\nComments:\nmain paper is 14 pages\nSubjects:\nComputation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)\nCite as:\narXiv:2506.20803 [cs.CL]\n(or\narXiv:2506.20803v1 [cs.CL] for this version)\nhttps://doi.org/10.48550/arXiv.2506.20803\nFocus to learn more\narXiv-issued DOI via DataCite\nSubmission history\nFrom: Chenglei Si [ view email ] [v1]\nWed, 25 Jun 2025 19:47:23 UTC (5,607 KB)\nFull-text links:\nAccess Paper:\nView a PDF of the paper titled The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas, by Chenglei Si and 2 other authors\nView PDF\nHTML (experimental)\nTeX Source\nview license\nAdditional Features\nAudio Summary\nCurrent browse context:\ncs.CL\n< prev\n|\nnext >\nnew\n|\nrecent\n| 2025-06\nChange to browse by:\ncs\ncs.AI\ncs.CY\ncs.HC\ncs.LG\nReferences & Citations\nSciX\nGoogle Scholar\nSemantic Scholar\nexport BibTeX citation\nLoading...\nBibTeX formatted citation\n&times;\nloading...\nData provided by:\nBookmark\nBibliographic Tools\nBibliographic and Citation Tools\nBibliographic Explorer Toggle\nBibliographic Explorer ( What is the Explorer? )\nConnected Papers Toggle\nConnected Papers ( What is Connected Papers? )\nLitmaps Toggle\nLitmaps ( What is Litmaps? )\nscite.ai Toggle\nscite Smart Citations ( What are Smart Citations? )\nCode, Data, Media\nCode, Data and Media Associated with this Article\nalphaXiv Toggle\nalphaXiv ( What is alphaXiv? )\nLinks to Code Toggle\nCatalyzeX Code Finder for Papers ( What is CatalyzeX? )\nDagsHub Toggle\nDagsHub ( What is DagsHub? )\nGotitPub Toggle\nGotit.pub ( What is GotitPub? )\nHuggingface Toggle\nHugging Face ( What is Huggingface? )\nScienceCast Toggle\nScienceCast ( What is ScienceCast? )\nDemos\nDemos\nReplicate Toggle\nRe","chars":4000,"total_chars":5220,"offset":0,"truncated":true,"next_offset":4000}