01
Model Behavior
Tool-use regression, multi-agent coordination, evaluation under deployment conditions, and the failure analysis that makes a model trustworthy beyond benchmarks.
AI-native research lab
Models. Systems. Hardware.
We study how models break when they meet real systems: queues, retries, thermal limits, and operators.
Built for the failures that show up after the demo.
Research Areas
Three working areas.
A model that passes benchmarks can still fail the serving path. The research follows the failures across all three layers.
02
Machine Systems
MoE routing, KV cache pressure, speculative decoding, inference serving, GPU scheduling, and the observability required to debug a live serving path.
03
Field Deployment
Edge inference under thermal and power constraints, vision-language-action models, on-device quantization, and the interfaces that connect models to physical hardware.
AI breaks at the seams.
Models fail when they meet tools, latency, hardware, sensors, and operators. Not in the benchmark, but in the serving path, the retry loop, and the field condition nobody tested. KrynLabs works on those seams.
Systems Stack
Compute, control, and movement.
Models move through GPUs, CPUs, queues, and networks before they ever reach operators or field hardware.
GPU Compute
KV cache management, MoE expert routing, FP4/FP8 quantization, and the memory cliffs that kill throughput.
CPU Services
Agent orchestration, MCP tool dispatch, fallback paths, and the control software between model and user.
Queue + Network
Continuous batching, request routing, queue depth, telemetry, and failure propagation under load.
Edge Hardware
On-device inference, thermal limits, duty cycling, and the gap between lab benchmarks and field conditions.
Public Surface
One live research system.
One autonomous system monitoring research across multiple sources, triaging every paper, and publishing a daily brief with structured analysis. Each brief is open for questions.
How it works
Ingest
Fetches papers from Semantic Scholar, OpenAlex, DBLP, Crossref, and HuggingFace trending. Covers CS, AI, ML, robotics, systems, and major journals.
Triage
Every paper is assessed for importance (1-5) by a fast LLM. Community signal from HuggingFace upvotes promotes papers the triage might miss.
Score
Papers above the triage threshold get full analysis: PDF parsing, metadata enrichment, deterministic text scoring, and a locked-rubric LLM evaluation with evidence extraction.
Publish
Quality determines the brief, not a fixed count. Some days have 10 papers, some have 1. Each paper gets structured analysis: methodology, evidence, trust, limitations.
Contact
Implementation inside existing data and infrastructure.
KrynLabs works with teams that need these systems built inside real data, compute, network, and deployment constraints.
Good starting point
- Current stack and deployment target
- Where reliability or throughput breaks
- Data boundary, network path, and hardware limits