Encoding neural representations into semantic latent spaces provides a new way to do thought-to-text decoding by reconstructing text from a semantic manifold.</p>\n","updatedAt":"2026-09-10T11:57:24.024Z","author":{"_id":"672b79e99380700b60cd5c71","avatarUrl":"/avatars/46f79c736a49b2d6ab4339871eea698d.svg","fullname":"Dulhan Jayalath","name":"latentdulhan","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8299245834350586},"editors":["latentdulhan"],"editorAvatarUrls":["/avatars/46f79c736a49b2d6ab4339871eea698d.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2609.10296","authors":[{"_id":"6aa29aefa2aeb74440b1e0b7","name":"Gilad D. Landau","hidden":false},{"_id":"6aa29aefa2aeb74440b1e0b8","name":"Dulhan Jayalath","hidden":false},{"_id":"6aa29aefa2aeb74440b1e0b9","name":"Oiwi Parker Jones","hidden":false}],"publishedAt":"2026-09-09T00:00:00.000Z","submittedOnDailyAt":"2026-09-10T00:00:00.000Z","title":"The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding","submittedOnDailyBy":{"_id":"672b79e99380700b60cd5c71","avatarUrl":"/avatars/46f79c736a49b2d6ab4339871eea698d.svg","isPro":false,"fullname":"Dulhan Jayalath","user":"latentdulhan","type":"user","name":"latentdulhan"},"summary":"Non-invasive speech decoding remains constrained by the low signal-to-noise ratio of neural recordings, which makes fine-grained reconstruction of phonemes or individual words difficult. Motivated by neuroscientific evidence that high-level semantic representations are distributed across cortical regions and evolve over slower temporal scales, we hypothesize that semantic content may provide a more suitable target for non-invasive decoding than low-level acoustic or lexical features. We introduce Brain2Semantics2Text, a method that reconstructs text through an intermediate semantic embedding space. Our model maps sentence-level MEG responses into a semantic manifold and then inverts the predicted embeddings into natural language. This semantic bottleneck enables recovery of high-level meaning without word-level alignment. We describe the core principles of the approach, its implementation, and the strategies used to mitigate the challenges of learning a reliable neural-to-semantic mapping. Finally, we compare against prior non-invasive Brain2Text methods and show improved sentence-level results.","upvotes":2,"discussionId":"6aa29aefa2aeb74440b1e0ba","ai_summary":"A non-invasive brain decoding approach maps MEG responses to semantic embeddings to reconstruct sentence-level text without requiring word-level alignment.","ai_keywords":["MEG","semantic embedding space","neural-to-semantic mapping","Brain2Semantics2Text","non-invasive speech decoding"],"ai_summary_model":"thinkingmachines/Inkling-Small","organization":{"_id":"67ab53f09cc9741800da0fba","name":"pnpl","fullname":"Parker Jones Neural Processing Lab (PNPL)","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/67643f6acf6b09fe21937f13/t0VJCh-B2rgcjiXbA2C3Q.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"672b79e99380700b60cd5c71","avatarUrl":"/avatars/46f79c736a49b2d6ab4339871eea698d.svg","isPro":false,"fullname":"Dulhan Jayalath","user":"latentdulhan","type":"user"},{"_id":"6a2da6c8ca070ee12c6e396c","avatarUrl":"/avatars/0355287dcabaa67dbc7f0b10b87451f9.svg","isPro":false,"fullname":"Joe Mama","user":"JoeMama123123123","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"67ab53f09cc9741800da0fba","name":"pnpl","fullname":"Parker Jones Neural Processing Lab (PNPL)","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/67643f6acf6b09fe21937f13/t0VJCh-B2rgcjiXbA2C3Q.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2609/2609.10296.md","query":{}}">
The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding
Abstract
A non-invasive brain decoding approach maps MEG responses to semantic embeddings to reconstruct sentence-level text without requiring word-level alignment.
Non-invasive speech decoding remains constrained by the low signal-to-noise ratio of neural recordings, which makes fine-grained reconstruction of phonemes or individual words difficult. Motivated by neuroscientific evidence that high-level semantic representations are distributed across cortical regions and evolve over slower temporal scales, we hypothesize that semantic content may provide a more suitable target for non-invasive decoding than low-level acoustic or lexical features. We introduce Brain2Semantics2Text, a method that reconstructs text through an intermediate semantic embedding space. Our model maps sentence-level MEG responses into a semantic manifold and then inverts the predicted embeddings into natural language. This semantic bottleneck enables recovery of high-level meaning without word-level alignment. We describe the core principles of the approach, its implementation, and the strategies used to mitigate the challenges of learning a reliable neural-to-semantic mapping. Finally, we compare against prior non-invasive Brain2Text methods and show improved sentence-level results.
Community
Encoding neural representations into semantic latent spaces provides a new way to do thought-to-text decoding by reconstructing text from a semantic manifold.
Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.
Tap or paste here to upload images
Cite arxiv.org/abs/2609.10296 in a model README.md to link it from this page.
Cite arxiv.org/abs/2609.10296 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2609.10296 in a Space README.md to link it from this page.
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.