Findings of EMNLP 2026</p>\n","updatedAt":"2026-09-03T06:03:00.959Z","author":{"_id":"66a246cae22bfd8d72560696","avatarUrl":"/avatars/97cec177a0eb0a8465e3532b888ef1d4.svg","fullname":"zrchen","name":"zrchen03","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8191799521446228},"editors":["zrchen03"],"editorAvatarUrls":["/avatars/97cec177a0eb0a8465e3532b888ef1d4.svg"],"reactions":[],"isReport":false}},{"id":"6a990ebd37f5ca9d9d4ec93d","author":{"_id":"66a246cae22bfd8d72560696","avatarUrl":"/avatars/97cec177a0eb0a8465e3532b888ef1d4.svg","fullname":"zrchen","name":"zrchen03","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false},"createdAt":"2026-09-03T06:07:57.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"Findings of EMNLP 2026","html":"<p>Findings of EMNLP 2026</p>\n","updatedAt":"2026-09-03T06:07:57.085Z","author":{"_id":"66a246cae22bfd8d72560696","avatarUrl":"/avatars/97cec177a0eb0a8465e3532b888ef1d4.svg","fullname":"zrchen","name":"zrchen03","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8191799521446228},"editors":["zrchen03"],"editorAvatarUrls":["/avatars/97cec177a0eb0a8465e3532b888ef1d4.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.29607","authors":[{"_id":"6a96d53243140794820a7789","name":"Zirong Chen","hidden":false},{"_id":"6a96d53243140794820a778a","name":"Fuda Ye","hidden":false},{"_id":"6a96d53243140794820a778b","name":"Kuan Zhang","hidden":false},{"_id":"6a96d53243140794820a778c","name":"Enjun Du","hidden":false},{"_id":"6a96d53243140794820a778d","name":"Junfu Pu","hidden":false},{"_id":"6a96d53243140794820a778e","name":"Xinlei Wang","hidden":false},{"_id":"6a96d53243140794820a778f","name":"Xinyu Zuo","hidden":false},{"_id":"6a96d53243140794820a7790","name":"Lisheng Duan","hidden":false},{"_id":"6a96d53243140794820a7791","name":"Jin Ma","hidden":false},{"_id":"6a96d53243140794820a7792","name":"Yongqi Zhang","hidden":false}],"publishedAt":"2026-08-30T00:00:00.000Z","submittedOnDailyAt":"2026-09-03T00:00:00.000Z","title":"SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions","submittedOnDailyBy":{"_id":"66a246cae22bfd8d72560696","avatarUrl":"/avatars/97cec177a0eb0a8465e3532b888ef1d4.svg","isPro":false,"fullname":"zrchen","user":"zrchen03","type":"user","name":"zrchen03"},"summary":"Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Snap-and-ask retrieval is now one of the most common entry points for mobile AI, yet photos are often blurry, while text questions may be short or mistyped. Existing benchmarks only test on clean inputs or do not isolate paired robustness in snap-and-ask retrieval. Therefore, we introduce SnapBench, the first paired benchmark for robust snap-and-ask multimodal retrieval, spanning 1,145 queries, 9,085 gallery items under 53 controlled corruption conditions with human annotations. We evaluate 16 multimodal retrievers, covering dual-tower encoders and embedding-based VLMs. Results show that image corruptions substantially degrade retrieval, while text corruptions mainly affect text-only retrieval and have limited impact on joint retrieval. Clean image-only retrieval often outperforms joint retrieval, indicating the coarse-text drag and the lack of cross-modal fallback under noisy inputs. SnapBench provides a controlled testbed for evaluating robust retrieval in snap-and-ask scenarios. We further propose MOOR (Modality-anchored, Outlier-aware, Optimal Reweighting), a simple adaptive fusion approach, highlighting the need for reliability-aware modality calibration in snap-and-ask retrieval.","upvotes":3,"discussionId":"6a96d53243140794820a7793","ai_summary":"SnapBench introduces paired corruption benchmarks for mobile snap-and-ask retrieval, revealing that image noise severely degrades multimodal retrieval and proposing an adaptive fusion method to calibrate modality reliability.","ai_keywords":["snap-and-ask retrieval","multimodal retrieval","dual-tower encoders","embedding-based VLMs","cross-modal fallback","MOOR","Modality-anchored Outlier-aware Optimal Reweighting","reliability-aware modality calibration"],"ai_summary_model":"thinkingmachines/Inkling-Small"},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"6a92aebc307ede1af2a6cf67","avatarUrl":"/avatars/2b2ea1eda4e5404dba536a99dcc5305b.svg","isPro":false,"fullname":"Fuda YE","user":"yefd","type":"user"},{"_id":"698f94336a82ac3be8daa3ba","avatarUrl":"/avatars/8778f90d212f382f52ac07e1bc1cfc5f.svg","isPro":false,"fullname":"Hyh426twgxcw","user":"hyh426twgxcw","type":"user"},{"_id":"63ac5701c21e60a3e9b58aa7","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63ac5701c21e60a3e9b58aa7/g6EX7diOpuA94R2ab-rZC.png","isPro":true,"fullname":"Dipankar Sarkar","user":"dipankarsarkar","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2608/2608.29607.md","query":{}}">

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