{"id":25179580,"url":"https://github.com/tegridydev/face-based-attention-circuits","last_synced_at":"2026-02-18T01:05:37.044Z","repository":{"id":270171791,"uuid":"909535229","full_name":"tegridydev/Face-Based-Attention-Circuits","owner":"tegridydev","description":"Face-Based Attention Circuits (FBAC): A Theoretical Framework for Context-Aware Embeddings","archived":false,"fork":false,"pushed_at":"2024-12-29T02:32:52.000Z","size":138,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-10T06:19:05.342Z","etag":null,"topics":["attention-mechanism","embedding-vectors","interpretability","m-f-d-m","machine-learning","neural-networks","python","research-project"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tegridydev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-29T02:03:02.000Z","updated_at":"2025-03-06T01:35:19.000Z","dependencies_parsed_at":"2024-12-29T04:55:47.135Z","dependency_job_id":null,"html_url":"https://github.com/tegridydev/Face-Based-Attention-Circuits","commit_stats":null,"previous_names":["tegridydev/face-based-attention-circuits"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tegridydev/Face-Based-Attention-Circuits","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tegridydev%2FFace-Based-Attention-Circuits","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tegridydev%2FFace-Based-Attention-Circuits/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tegridydev%2FFace-Based-Attention-Circuits/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tegridydev%2FFace-Based-Attention-Circuits/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tegridydev","download_url":"https://codeload.github.com/tegridydev/Face-Based-Attention-Circuits/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tegridydev%2FFace-Based-Attention-Circuits/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29565018,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-18T00:47:08.760Z","status":"ssl_error","status_checked_at":"2026-02-18T00:45:26.718Z","response_time":100,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["attention-mechanism","embedding-vectors","interpretability","m-f-d-m","machine-learning","neural-networks","python","research-project"],"created_at":"2025-02-09T15:37:09.877Z","updated_at":"2026-02-18T01:05:37.025Z","avatar_url":"https://github.com/tegridydev.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Face-Based Attention Circuits (FBAC): A Theoretical Framework for Context-Aware Embeddings - [MI Research] - [M.F.D.M]\n\nA theoretical framework aimed at improving the adaptability, interpretability, and efficiency of token embeddings in machine learning. Motivated by the limitations of high-dimensional static embedding spaces, FBAC conceptualizes each embedding as a multi-faceted “cube,” in which each face specializes in a distinct linguistic or modal property (e.g., semantics, syntax, pragmatics, cross-modal features). An attention mechanism identifies which faces are most relevant for a given context. Upon this identification, a sparse unfolding of the cube is performed so that only the activated geometric path is “flattened” and integrated into a geometric lattice activation circuit. By modularly structuring embeddings and leveraging dynamic activation, FBAC may offer improved interpretability—facilitating clearer analysis of how various linguistic features influence model decisions—and provide context-aware adaptation without the need for task-specific embeddings. \n\nThis repo will contain the theoretical foundations of FBAC, outline its hypothesized benefits, and discusses principal challenges and future research directions.\n\n## Hypothesis and Potential Benefits\nFBAC is based on the hypothesis that a structured, modular approach to embedding representation leads to more efficient, adaptable, and interpretable models. \n\nThe potential benefits that could arise include:\n\n### 1. Richer Token Representations\nBy encoding multi-faceted information within a single embedding cube, FBAC can capture a broader range of linguistic or modal properties with less redundancy.\n\n\n### 2. Dynamic Contextual Adaptation\nThe selective activation and sparse unfolding enable a single embedding to fulfill different roles across contexts and tasks without relying on specialized embeddings for each scenario.\n\n\n### 3. Improved Interpretability\nThe modular design provides a clearer view of how different linguistic features contribute to a model’s decisions. Visualizing activated faces or analyzing the geometric lattice pathways can yield more transparent insights into the embedding process.\n\n\n### 4. Enhanced Efficiency\nPotential exists for computational and memory savings by activating only the relevant faces and focusing attention on core token attributes, thus reducing the overhead associated with uniform, high-dimensional embeddings.\n\n## The Embedding Cube\n\nThe “Embedding Cube” forms the foundation of FBAC. Each token is represented by this multi-faceted cube, where each face corresponds to a distinct attention circuit specialized for a specific linguistic or modal property.\n\n### Semantic Face (S): \nEncodes the core meaning of the token and its relationships with other words, potentially using techniques such as distributional semantics or knowledge graph embeddings.\n\n### Syntactic Face (Y): \nCaptures the grammatical role of the token, encoding information such as part-of-speech tags, dependency relations, and grammatical functions.\n\n### Contextual Face (C): \nRepresents the token’s role within its immediate context, capturing relationships with surrounding words and phrases (e.g., via recurrent or convolutional neural networks).\n\n### Cross-modal Face (M): \nFor multi-modal tasks, encodes information from other modalities, such as visual or auditory features.\n\n### Pragmatic Face (P): \nFocuses on aspects related to intended meaning and communicative function, such as sentiment, irony, or speaker intent.\n\nThe number and type of faces can be tailored to specific applications and domains.\n\n![Visual diagram Face-Based Attention Circuits (FBAC) - The \"Embedding Cube\"](diagrams/FBAC-embedding-cube.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftegridydev%2Fface-based-attention-circuits","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftegridydev%2Fface-based-attention-circuits","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftegridydev%2Fface-based-attention-circuits/lists"}