ASI Advanced Research Community
A global research institution — and the most advanced research community in the world. Medicine, agriculture, and the systems that think. Open to everyone building the frontier.
Our strength
Deep, specific capability across general AI, cryptography, and applied science — research on a ten-year horizon, not a quarterly one.
A multi-LLM orchestration architecture routing cognitive tasks across Claude, DeepSeek R1, Grok 3, GPT-4o, Gemini, Mistral, and offline local models — one command interface, zero context-switching.
Post-quantum secure messaging using a hybrid Kyber-1024 + X25519 key encapsulation (NIST ML-KEM), with an on-chain token economy on Base L2.
AlphaFold structural predictions applied to dengue NS5 polymerase, malaria PfCRT, and thalassemia beta-globin variants — screening approved compounds for repurposing.
Multi-model consensus mechanisms and cryptographically-verified communication between agents making high-stakes, real-world decisions.
We welcome the world. Researchers, partners, and investors who think in decades — there is a place for you in the ASI Advanced Research Community.
Work with us →The Three Research Monoliths
Three laboratories, each with a defined mandate, concrete focus areas, and a dated first output. Not categories — specific research, on a timeline.
Monolith 01
Autonomous multi-agent systems and post-quantum cryptographic infrastructure for the next internet.
Phase 01 · active
Enter Lab →Monolith 02
AI diagnostic tools for diseases endemic to South Asia — the diseases Western AI labs do not build for.
First research output · Dec 2026
Enter Lab →Monolith 03
Precision farming intelligence and climate modeling for the systems that feed two billion people.
First pilot · within 6 months
Enter Lab →Live research feed
The latest research across medicine, agriculture, and general AI — cached from arXiv and PubMed, every paper validated against a strict schema before it appears here.
Feed updated: 7 Aug, 04:08 UTC
Work with ASI Research Lab
Researchers, partners, and investors who think in decades. Choose your path — every inquiry reaches the team.
We partner with hospitals, universities, agricultural agencies, and technology organizations on specific research questions with defined outputs. Active: Sri Lanka Department of Agriculture; Sri Lankan hospital network (in negotiation).
AI labs, cryptographic research groups, precision agriculture technology companies, and South Asian climate modeling organizations.
ASI Research Lab is accepting institutional investment inquiries. We do not work with speculative capital. We work with investors who understand the 10-year horizon.
What happens next
You write
Tell us who you are and what you have in mind — a research question, a partnership, an investment thesis.
We review
Every inquiry is read by the team. Concrete proposals get faster, sharper responses than introductions.
We reply
Expect a response within five business days — to the right people, on the right thread.
Prefer email? info@asiresearch.io
Generative research, in public
A public demonstration of where generative AI now stands — a feature-scale film authored, performed, scored, and rendered by machine intelligence under human direction.
ASI Insights
Long-form analysis on medical AI, precision agriculture, post-quantum cryptography, and the frontier of artificial intelligence.
In an outbreak the hard question is not who has dengue, but who will get worse. The data shows machine learning on routine clinical markers outperforms warning signs alone — by a margin that matters at the bedside.
Read the analysis →Sentinel-2 gives smallholder agriculture something it never had: free, frequent, field-scale imagery. But turning pixels into a reliable yield forecast is harder than the marketing suggests, and the honest accuracy numbers explain why.
The worst dengue year on record reset the scale of the problem, and the data still defines it in 2026. The disease is a good test of where medical AI earns its place: it helps most with severity triage and outbreak forecasting, and least where data and validation are missing.
Questions
ASI Research Labs Pvt. Ltd. is a global artificial superintelligence research organization. It conducts research in medical AI for South Asian diseases, precision agriculture, and autonomous multi-agent systems.
The Advanced Medicine laboratory researches AI diagnostic tools for diseases disproportionately affecting South Asia: dengue fever, malaria, typhoid, and thalassemia. These diseases affect hundreds of millions of people in South Asia and are largely underrepresented in Western AI medical research. First published research output is targeted for December 2026.
PHANTOM Protocol is a post-quantum secure messaging protocol built by ASI Research Lab. It uses a hybrid key encapsulation mechanism combining Kyber-1024 (NIST ML-KEM standard) and X25519, providing security against both classical and quantum adversaries simultaneously. It includes an on-chain token economy (PHTM) built on Base L2.
The ASI Global Summit is a premier global AI research summit, scheduled for March–June 2027. It will feature 500+ attendees, global livestreaming, internal lab research presentations, and the premiere of a feature-scale AI-generated film produced by orchestrating frontier generative models under human direction.
ASI Research Lab accepts research partnership applications from universities, hospitals, agricultural agencies, and independent researchers globally. Applications are reviewed by the founding team. Priority is given to institutions with data access in South Asian medical, agricultural, or cryptographic domains. Contact info@asiresearch.io.
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Soorya Ram Shimgekar +12
Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' workload.
arXiv→Boning Li +2
Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time.
arXiv→Fanzhe Meng +8
Training terminal agents requires executable and verifiable tasks that are not merely solvable, but appropriately challenging for learning.
arXiv→Yunjia Qi +12
LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging.
arXiv→Daniel Paulin +1
Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series.
arXiv→Donna Hooshmand +6
From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.
arXiv→Noam Koren +2
Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.
arXiv→Varun Ursekar +6
As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding them.
arXiv→Arya Labroo +2
Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their scores depend on speaking proficiency rather than irrelevant speaker attributes such as first language (L1) or age.
arXiv→Yijiang Li +5
On-policy (Self-)Distillation (OPD / OPSD) has shown strong potential for post-training large language models (LLMs).
arXiv→Mutasim Fuad Sarker +5
Cardiac arrest remains one of the most lethal conditions encountered in intensive care units.
arXiv→Farzana Nasrin
Persistence diagrams (PDs) provide stable and interpretable summaries of multiscale topological structure.
arXiv→Zhiheng Wang +3
The "thinking-with-images" paradigm equips multimodal LLMs with active visual operations such as crop-and-zoom.
arXiv→Omid Bazgir +8
Synthetic clinical benchmarks for enterprise AI agents can pass existing utility checks and still remain structurally unrealistic, especially in privacy-sensitive healthcare settings where operational data are hard to access.
arXiv→Robin Trombetta +1
The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors from large volumes to characterize pathologies.
arXiv→Zonghuan Xu
Model evaluations may fix all tests before observing any responses or select later tests using earlier responses.
arXiv→Christo Kurisummoottil Thomas +2
Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws.
arXiv→Daira Velandia +6
Understanding dengue dynamics from a spatiotemporal perspective has become increasingly relevant in recent years, as it allows the identification of factors influencing disease transmission, including climatic conditions, human behavior, and the distribution of Aedes aegypti, the primary vector responsible for dengue virus transmission in tropical and subtropical regions.
PubMed→