01⚡
Foundational Architecture &
Inference Scaling
Investigating transformer variants, State Space Models (SSMs/Mamba), mixture-of-experts (MoE), context window extension mechanisms, and low-latency inference optimization.
VC AI FOUNDATIONAL MODEL RESEARCH LAB
Pushing the Frontiers of
Foundational AI Models.
Vertical Combinator's research initiative dedicated to foundational model architectures, autonomous agentic reasoning, domain fine-tuning, and open-source machine intelligence.
PUBLIC REPOSITORY
AnkushMulkar/ai-research-lab
Open-source codebase, research notebooks, model benchmarks, and experiment logs.
PRIVATE RESEARCH LAB
Enterprise Foundational Models
Proprietary model weights, specialized domain datasets, and advanced agentic pipelines.
Core Pillars
Research Focus
Areas
Our lab explores fundamental problems in artificial intelligence to bridge the gap between theoretical research and production systems.
Foundational Architecture &
02🤖
Agentic Reasoning &
Multi-Agent Systems
Designing autonomous multi-step decision loops, tool-calling frameworks, long-term memory architectures, subagent delegation, and self-correcting execution pipelines.
03🎯
Domain Fine-Tuning &
Alignment (DPO/RLHF)
Curating specialized domain datasets, parameter-efficient adaptation (LoRA/QLoRA), Direct Preference Optimization (DPO), and aligning foundation models for enterprise tasks.
04🧪
Benchmark Infrastructure &
Safety Red-Teaming
Building robust evaluation benchmarks, automated hallucination detection, reliability scoring, and safety protocols for production-grade AI deployments.
Research Collaboration
Request to
Contribute
We welcome researchers, ML engineers, software architects, and ambitious builders to collaborate on foundational AI research. Share your ideas, paper proposals, or codebase contributions with Vertical Combinator.
Submit Contribution Proposal
Fill in your details and research idea to request access or join the lab.