SWMP Labs is building tools that help researchers explore scientific literature, uncover potential research gaps, connect findings, and develop research ideas more efficiently.
Research Workflows & Scientific Tooling Stack
SWMP Labs is an early-stage AI research startup focused on making scientific exploration more structured, accessible, and efficient. We are developing workflows that help researchers analyze scientific literature, organize evidence, and investigate potential research directions.
SWMP Labs is currently in active development. We are refining the platform, evaluating research workflows, and improving the quality and reliability of generated insights before broader release.
Early-Stage AI Research Startup
From parallel literature ingestion to structured research synthesis and vector PlantUML topologies.
Traditional quorum consensus protocols introduce heavy tail-latency bottlenecks when scaling across disaggregated storage tiers due to serialized disk I/O and synchronous RPC round-trips under high-concurrency write surges.
Lock contention on leader commit indexes causes CPU core pinning. PCIe bus backpressure throttles RDMA write operations at high network saturation, degrading tail latency under write bursts.
Current literature lacks adaptive epoch pipelining that dynamically switches between optimistic speculative commits and two-phase commits based on real-time RDMA hardware telemetry.
Proposed Direction: Implement an asynchronous log-decoupled Raft state machine utilizing NVMe-oF zero-copy memory buffers. Benchmark against baseline consensus implementations under clustered write workloads.
node "Consensus Core" -> node "Storage Fabric" : sync_replicate()
1-Click Export Vector SVG
# NotebookLM Source Pack: SWMP Labs Systems Literature Review
## Paper Dossier: Distributed Consensus Under Hardware Disaggregation
> **Curated Sources**: ArXiv:2403.11982, SemanticScholar:441209, OpenAlex:W9821049
> **Key Arguments for Deep Audio Discussion**: Speculative execution vs serialized Raft logs.
@article{chen2025speculative, title={BFT-Mesh}, author={Chen, S. et al.}, year={2025}}
Designed specifically for systems engineers, graduate researchers, and thesis authors.
Query literature concurrently across arXiv, Semantic Scholar, and OpenAlex by topic, author, or research lab.
Extract core bottlenecks, technical trade-offs, and open research gaps—not just superficial summaries.
Generate system architecture diagrams, cross-paper comparison matrices, and 1-click Google NotebookLM source bundles.
We are actively developing and evaluating AI-assisted research workflows, with a focus on output quality, reliability, and useful scientific insights. Our current priority is to improve the research experience and validate the system through systematic testing.
We are exploring Claude's capabilities for complex reasoning, scientific text analysis, and evidence synthesis. Our goal is to evaluate how advanced language models can support research workflows while maintaining clear distinctions between evidence, interpretation, and unverified hypotheses.
Evaluating reasoning chains across dense systems literature, distributed protocols, and formal algorithms.
Extracting core problem statements, empirical constraints, and architectural assumptions from long-form papers.
Connecting findings across multiple publications to construct structured comparison matrices.
Discovering relationships, methodological tensions, and unaddressed research gaps across disciplines.
Evaluating workflow reliability, citation fidelity, and factual consistency in AI-assisted discovery.
Standard literature searches force researchers to juggle fragmented tabs across Google Scholar, ArXiv, and GitHub while manually parsing 40-page papers for methodology differences. SWMP Labs unifies these pipelines into a single high-throughput execution graph.
Normalized Title Disambiguation: Fuzzy similarity scoring eliminates duplicate preprints across ArXiv and peer-reviewed conference proceedings.
Resilient Model Routing: Multi-provider architecture ensures consistent availability during research sprints.
Deterministic Citations: Auto-generated BibTeX entries conform strictly to standard ACM/IEEE formatting.
SWMP Labs is purpose-built for depth, not mass consumer clickbait. To ensure rigorous qualitative feedback, we are onboarding an initial curated cohort of researchers.
Researchers and architects tackling consensus algorithms, disaggregated storage, low-latency RPCs, and cluster scheduling.
Engineers building GPU kernel optimizations, attention mechanisms, parallelism strategies, and model inference systems.
Master's and PhD candidates actively conducting literature surveys who require deep gap discovery and formal citation taxonomy.
Principal investigators and postdocs steering research groups seeking automated architecture diagrams and cross-paper comparisons.
Applications are evaluated on a rolling basis per evaluation cycle.
Whether you are a researcher interested in early evaluation, an engineer with workflow feedback, or reaching out for general inquiries, we'd love to hear from you.
Fill out the technical screening application below to be considered for Cohort 01 closed beta access.
Submitting this application does not guarantee acceptance into this cohort. We select a limited number of active researchers per review cycle to maintain compute performance. If selected, your machine credentials and private access key will be delivered directly to your personal/delivery email, not your institutional address.
Your application is currently being evaluated by our team. Expect an update delivered to your inbox within 2–3 business days.
Because Cohort 01 admission is strictly limited, space is allocated on a rolling review basis. If you are not selected for this initial wave, your application will automatically receive priority placement for Cohort 02.