{"data":{"featured":{"edges":[{"node":{"frontmatter":{"title":"Attention-Free Hybrid SSM Architecture","cover":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"images":{"fallback":{"src":"/static/59dac4d55ccec8cb9459ba61fbb572c3/6f31e/demo.png","srcSet":"/static/59dac4d55ccec8cb9459ba61fbb572c3/40ee4/demo.png 175w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/70051/demo.png 350w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/6f31e/demo.png 700w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/fa19a/demo.png 1400w","sizes":"(min-width: 700px) 700px, 100vw"},"sources":[{"srcSet":"/static/59dac4d55ccec8cb9459ba61fbb572c3/92db5/demo.avif 175w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/327cd/demo.avif 350w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/1fc06/demo.avif 700w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/d59c4/demo.avif 1400w","type":"image/avif","sizes":"(min-width: 700px) 700px, 100vw"},{"srcSet":"/static/59dac4d55ccec8cb9459ba61fbb572c3/94714/demo.webp 175w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/b4a32/demo.webp 350w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/a75d3/demo.webp 700w,\n/static/59dac4d55ccec8cb9459ba61fbb572c3/1ddf9/demo.webp 1400w","type":"image/webp","sizes":"(min-width: 700px) 700px, 100vw"}]},"width":700,"height":322}}},"cover2":null,"cover3":null,"tech":["State Space Models","Transformer","PyTorch","ImageNet-1K","Food-101","AGNews / ACL-IMDb"],"github":null,"external":null,"cta":""},"html":"

Co-first author on a research project addressing how to balance local detail precision and long-range dependency memory in large-scale sequence and vision modeling under a constrained compute budget. We propose an attention-free hybrid architecture unifying the efficiency of SSMs with the fine-grained feature capacity of Transformers, using k learnable concept centers for soft bucketing that reduces O(n²) complexity to near-linear O(nk + k²), achieving SOTA on multiple benchmarks.

\n

I designed and implemented the core routing and interaction module — a dynamic router for token soft-bucketing and a concept graph for global interaction with adaptive routing policy and graph complexity — built the training framework with key regularizers (e.g. load-balancing loss) to prevent mode collapse, and ran end-to-end experiments by restructuring ImageNet-1K, Food-101, AGNews, and ACL-IMDb from semi-structured raw data into the training format.

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In collaboration with Associate Professor Zheng Gang at Zhejiang University, this project builds an LLM + research-database agent that helps entrepreneurs quickly find and match with professors for research collaboration, lowering the information barrier between industry and academia.

\n

I collected and cleaned roughly 7,000 professor records with deduplication and exception handling, applying a data-warehouse layered model (raw → cleaned → topic). I independently shipped the three core backend cloud functions — AI agent orchestration, search and aggregation, and favorites CRUD — and built the chat experience, including the main conversation page with message sending and side-panel history loading.

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Boot","LangChain","PostgreSQL + pgvector","Top-K Retrieval + LLM Rerank"],"github":"https://github.com/Elliottt001/IntelliDeploy","external":"https://github.com/Elliottt001/IntelliDeploy","cta":null},"html":"

Users describe their needs in natural language and IntelliDeploy matches the best GitHub repository, then a multi-agent system auto-resolves heterogeneous environments, missing dependencies, and runtime errors before deploying to the Sealos cloud — delivering a live, accessible product end to end.

\n

As tech lead I designed the full algorithm: a multi-agent decision system (Builder drafts the plan, Reviewer checks deployability, Security screens risks, Consensus finalizes the decision), four fallback deployment strategies (direct deploy, patch fix, scaffold composition, from-scratch authoring), and a self-healing loop with parallel repair, retry, timeout control, circuit breaking, and rollback after failed deploys. I also drove the API design and the full-stack frontend/backend implementation and integration.

","parent":{"relativeDirectory":"featured/IntelliDeploy"}}}]}}}