Neural Docs turns research papers into grounded summaries and citation-backed conversations.
Explore Neural Docs →AI software from real research.
NeuralMind Labs turns cutting-edge LLM papers into usable tools for teams. We read the latest research, extract what works, and ship it inside approachable SaaS workflows.
LLM infra, evals, UX — handled by the team that reads the papers.
Paper ingestion
Cache-aware decoding.pdf
SyncedResearch note
Key idea → Reuse KV blocks
ReadyModel output
Auto-optimizes decoding paths for long contexts
Shipping target
Ops Copilot v2
How it works
We do the hard ML so you don’t have to.
We study the papers
Context windows, caching, retrieval tricks, agents — we read and distill the work that matters.
We build real products
Auth, monitoring, onboarding, delightful UX. All the production layers research repos skip.
You just use it
Ship AI workflows without spinning up a research org or hiring a dozen ML engineers.
Products
View all products →Tokn
See what your AI coding tools cost.
Track what Claude Code, Copilot, and Cursor cost — directly from your Mac menu bar.
Neural Docs
Upload a research paper, get a grounded summary, and learn through citation-backed conversations.
Eval Studio
Test model responses, automate guardrails, and catch regressions before you ship.
Inference Kit
Opinionated, production-friendly LLM infrastructure that plugs into your stack.
Research
Latest research, rewritten for builders.
Practical breakdowns of new AI papers, focused on what builders can use.
Chain-of-Thought Is a Press Release, Not the Reasoning
A new position paper argues that LLM reasoning lives in hidden-state trajectories — and that the chain-of-thought you can read is often an imperfect, after-the-fact translation.
LLM Reasoning Is Latent, Not the Chain of Thought
arXiv
Apr 21, 2026
Your LLM's Reasoning Chain Is Only as Strong as Its Weakest Step — Here's a Framework That Enforces That
A symbolic scaffold built on 150-year-old logic from Charles Sanders Peirce enforces five algebraic invariants that prevent LLMs from smuggling weak premises into confident conclusions.
Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
arXiv
Apr 21, 2026
Your Prediction Agent Is Learning From Its Own Past Guesses — Before the Answer Even Arrives
Milkyway improves future-prediction accuracy by mining temporal contrasts between repeated guesses on the same unresolved question — no model retraining required.
The World Leaks the Future: Harness Evolution for Future Prediction Agents
arXiv
Apr 21, 2026
Don’t speak ML? That’s fine.
We translate the research into plain SaaS.
Tell us your workflow. We’ll map the right LLM research, ship a demo, and keep it running with you.
For founders
Ship AI-powered workflows without recruiting a research lab. We partner on roadmap, delivery, and iteration.
For product teams
Drop-in capabilities, docs, and support. Your PMs stay focused on users while we handle the models.