Step 4: tool results

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{"ok":true,"url":"https://arxiv.org/abs/2603.04426","text":"[2603.04426] Delta-Crosscoder: Robust Crosscoder Model Diffing in Narrow Fine-Tuning Regimes\nSkip to main content\nSearch\nSubmit\nDonate\nLog in\nSearch arXiv\nPress Enter to search &middot; Advanced search\n-->\nComputer Science > Machine Learning\narXiv:2603.04426 (cs)\n[Submitted on 16 Feb 2026]\nTitle: Delta-Crosscoder: Robust Crosscoder Model Diffing in Narrow Fine-Tuning Regimes\nAuthors: Aly Kassem , Thomas Jiralerspong , Negar Rostamzadeh , Golnoosh Farnadi\nView a PDF of the paper titled Delta-Crosscoder: Robust Crosscoder Model Diffing in Narrow Fine-Tuning Regimes, by Aly Kassem and 3 other authors\nView PDF\nHTML (experimental)\nAbstract: Model diffing methods aim to identify how fine-tuning changes a model's internal representations. Crosscoders approach this by learning shared dictionaries of interpretable latent directions between base and fine-tuned models. However, existing formulations struggle with narrow fine-tuning, where behavioral changes are localized and asymmetric. We introduce Delta-Crosscoder, which combines BatchTopK sparsity with a delta-based loss prioritizing directions that change between models, plus an implicit contrastive signal from paired activations on matched inputs. Evaluated across 10 model organisms, including synthetic false facts, emergent misalignment, subliminal learning, and taboo word guessing (Gemma, LLaMA, Qwen; 1B-9B parameters), Delta-Crosscoder reliably isolates latent directions causally responsible for fine-tuned behaviors and enables effective mitigation, outperforming SAE-based baselines, while matching the Non-SAE-based. Our results demonstrate that crosscoders remain a powerful tool for model diffing.\nSubjects:\nMachine Learning (cs.LG) ; Artificial Intelligence (cs.AI)\nCite as:\narXiv:2603.04426 [cs.LG]\n(or\narXiv:2603.04426v1 [cs.LG] for this version)\nhttps://doi.org/10.48550/arXiv.2603.04426\nFocus to learn more\narXiv-issued DOI via DataCite\nSubmission history\nFrom: Thomas Jiralerspong [ view email ] [v1]\nMon, 16 Feb 2026 23:35:29 UTC (5,445 KB)\nFull-text links:\nAccess Paper:\nView a PDF of the paper titled Delta-Crosscoder: Robust Crosscoder Model Diffing in Narrow Fine-Tuning Regimes, by Aly Kassem and 3 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| 2026-03\nChange to browse by:\ncs\ncs.AI\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 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 values of opennes","chars":4000,"total_chars":4581,"offset":0,"truncated":true,"next_offset":4000}

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