Step 18: tool results

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{
  "url": "https://arxiv.org/abs/2505.20063",
  "max_chars": 4000
}

{"ok":true,"url":"https://arxiv.org/abs/2505.20063","text":"[2505.20063] SAEs Are Good for Steering -- If You Select the Right Features\nSkip to main content\nSearch\nSubmit\nDonate\nLog in\nSearch arXiv\nPress Enter to search &middot; Advanced search\n-->\nComputer Science > Machine Learning\narXiv:2505.20063 (cs)\n[Submitted on 26 May 2025 ( v1 ), last revised 22 Dec 2025 (this version, v2)]\nTitle: SAEs Are Good for Steering -- If You Select the Right Features\nAuthors: Dana Arad , Aaron Mueller , Yonatan Belinkov\nView a PDF of the paper titled SAEs Are Good for Steering -- If You Select the Right Features, by Dana Arad and 2 other authors\nView PDF\nHTML (experimental)\nAbstract: Sparse Autoencoders (SAEs) have been proposed as an unsupervised approach to learn a decomposition of a model's latent space. This enables useful applications such as steering - influencing the output of a model towards a desired concept - without requiring labeled data. Current methods identify SAE features to steer by analyzing the input tokens that activate them. However, recent work has highlighted that activations alone do not fully describe the effect of a feature on the model's output. In this work, we draw a distinction between two types of features: input features, which mainly capture patterns in the model's input, and output features, which have a human-understandable effect on the model's output. We propose input and output scores to characterize and locate these types of features, and show that high values for both scores rarely co-occur in the same features. These findings have practical implications: after filtering out features with low output scores, we obtain 2-3x improvements when steering with SAEs, making them competitive with supervised methods.\nSubjects:\nMachine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL)\nCite as:\narXiv:2505.20063 [cs.LG]\n(or\narXiv:2505.20063v2 [cs.LG] for this version)\nhttps://doi.org/10.48550/arXiv.2505.20063\nFocus to learn more\narXiv-issued DOI via DataCite\nRelated DOI :\nhttps://doi.org/10.18653/v1/2025.emnlp-main.519\nFocus to learn more\nDOI(s) linking to related resources\nSubmission history\nFrom: Dana Arad [ view email ] [v1]\nMon, 26 May 2025 14:47:59 UTC (4,746 KB)\n[v2]\nMon, 22 Dec 2025 15:49:59 UTC (5,507 KB)\nFull-text links:\nAccess Paper:\nView a PDF of the paper titled SAEs Are Good for Steering -- If You Select the Right Features, by Dana Arad and 2 other authors\nView PDF\nHTML (experimental)\nTeX Source\nview license\nAdditional Features\nAudio Summary\nCurrent browse context:\ncs.LG\n< prev\n|\nnext >\nnew\n|\nrecent\n| 2025-05\nChange to browse by:\ncs\ncs.AI\ncs.CL\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\nReplicate ( What is Replicate? )\nSpaces Toggle\nHugging Face Spaces ( What is Spaces? )\nSpaces Toggle\nTXYZ.AI ( What is TXYZ.AI? )\nRelated Papers\nRecommenders and Search Tools\nLink to Influence Flower\nInfluence Flower ( What are Influence Flowers? )\nCore recommender toggle\nCORE Recommender ( What is CORE? )\nIArxiv recommender toggle\nIArxiv Recommender\n( What is IArxiv? )\nAuthor\nVenue\nInstitution\nTopic\nAbout arXivLabs\narXivLabs: experimental projects with community collaborators\narXivLabs is a framew","chars":4000,"total_chars":4784,"offset":0,"truncated":true,"next_offset":4000}

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{
  "url": "https://arxiv.org/abs/2605.31183",
  "max_chars": 4000
}

{"ok":true,"url":"https://arxiv.org/abs/2605.31183","text":"[2605.31183] Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines\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:2605.31183 (cs)\n[Submitted on 29 May 2026]\nTitle: Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines\nAuthors: Mikkel Godsk Jørgensen , Lars Kai Hansen\nView a PDF of the paper titled Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines, by Mikkel Godsk J{\\o}rgensen and 1 other authors\nView PDF\nHTML (experimental)\nAbstract: Sparse Autoencoders (SAEs) have been seen as a promising avenue for exploring the internals of Large Language Models (LLMs) and for steering model output generation. When AxBench - a model steering benchmark - was introduced in Wu et al. (2025), SAEs did not seem to live up to their original hype due to poor steering performance relative to a set of simple baselines. This work serves as a partial rebuttal for Sparse Autoencoders and suggests that the results of Wu et al. (2025) did not do them full justice. We find that Sparse Autoencoders can, in fact, perform close to on par with the reference LoRA performance on the AxBench benchmark, when features are selected and labelled with our supervised pipeline. We also find that our pipeline selects features that are surprisingly causal of their identified labels when using only its interpretability-based components. Lastly, we present evidence that high sparsity (low l0) may not be crucial for successful steering based on interpretability, which is in contrast to the earlier findings in Wang et al. (2025).\nSubjects:\nComputation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG)\nCite as:\narXiv:2605.31183 [cs.CL]\n(or\narXiv:2605.31183v1 [cs.CL] for this version)\nhttps://doi.org/10.48550/arXiv.2605.31183\nFocus to learn more\narXiv-issued DOI via DataCite\nSubmission history\nFrom: Mikkel Godsk Jørgensen [ view email ] [v1]\nFri, 29 May 2026 11:53:54 UTC (4,566 KB)\nFull-text links:\nAccess Paper:\nView a PDF of the paper titled Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselines, by Mikkel Godsk J{\\o}rgensen and 1 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| 2026-05\nChange to browse by:\ncs\ncs.AI\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\nReplicate ( What is Replicate? )\nSpaces Toggle\nHugging Face Spaces ( What is Spaces? )\nSpaces Toggle\nTXYZ.AI ( What is TXYZ.AI? )\nRelated Papers\nRecommenders and Search Tools\nLink to Influence Flower\nInfluence Flower ( What are Influence Flowers? )\nCore recommender toggle\nCORE Recommender ( What is CORE? )\nAuthor\nVenue\nInstitution\nTopic\nAbout arXivLabs\narXivLabs: experimental projects with community collaborators\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our value","chars":4000,"total_chars":4677,"offset":0,"truncated":true,"next_offset":4000}