Medicine. Agriculture. General AI. Each lab operates with a specific research mandate, concrete timelines, and measurable outputs.
Monolith 01
Autonomous multi-agent systems and post-quantum cryptographic infrastructure for the next internet.
Monolith 02
AI diagnostic tools for diseases that kill disproportionately in South Asia — the diseases Western AI labs do not build for.
Monolith 03
Precision farming intelligence and climate modeling for the agricultural systems that feed 2 billion people.
What the frontier published
Pulled live from arXiv and PubMed across medicine, agriculture, and general AI, refreshed hourly. Every paper is validated against a strict schema before it appears.
Questions
ASI Research Lab operates three laboratories: General AI (autonomous multi-agent systems and post-quantum cryptography), Advanced Medicine (AI diagnostics for dengue, malaria, typhoid, and thalassemia), and Advanced Agriculture (precision farming AI for rice, tea, and coconut). Each lab has a defined mandate and concrete timeline.
The first published research output from the Advanced Medicine laboratory is targeted for December 2026 — a computational analysis of binding-site predictions for dengue NS5 polymerase. The first agriculture pilot, a rice-yield prediction model for the North Central Province, is targeted within six months of launch.
The live feed is fetched directly from arXiv and PubMed (NCBI) and validated against a strict schema before it is displayed — any paper that fails validation never appears. The feed refreshes hourly, and every paper links directly to its arXiv or PubMed source.
Research partnership applications are open to universities, hospitals, agricultural agencies, and independent researchers. Priority goes to institutions with data access in South Asian medical, agricultural, or cryptographic domains. Contact info@asiresearch.io.
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Mohammad Mehdi Hosseini +2
Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories.
arXiv→Tianyi Men +5
Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions.
arXiv→Nicklas Hansen +1
Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics.
arXiv→Nathanaël Jacquier +2
Sparse autoencoders (SAEs) have become a leading tool for interpreting the representations of vision foundation models, decomposing their polysemantic activations into a larger set of sparse, more monosemantic features.
arXiv→Brian W. Lee +3
Gradient equilibrium (GEQ) is a recently introduced online optimization framework that generalizes first-order stationarity from offline optimization and abstracts problems like online conformal prediction.
arXiv→Ping Liu +13
Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles.
arXiv→Josef Chen
Multi-model LLM systems such as routing, voting, cascades, fusion, and mixture-of-agents are used to beat single-model accuracy.
arXiv→Preet Baxi +3
Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate algorithmic hiring systems.
arXiv→Junwei Luo +5
Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions.
arXiv→Junhao Shi +9
Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from physical failures that inevitably arise over extended operation.
arXiv→Andrew Bond +1
We study a solvable high-dimensional model of generative adversarial network (GAN) training in which a linear generator learns a low-dimensional subspace from data with structured latent covariance.
arXiv→Zhengyuan Liu +3
We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaboration.
arXiv→Sayak Dutta
Recurrent models must forget in order to remember, yet the state of the art decides what to erase without consulting what is stored -- the gate sees only the arriving token, not the memory it is about to modify.
arXiv→Sangwoo Cho +6
Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug.
arXiv→Fabiana Fournier +1
We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacing the underlying workflow engine.
arXiv→Henrik Müller +1
Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly attribute the sparse success rewards to relevant parts of the trajectory.
arXiv→Ali Semih Atalay +1
Medical device recalls are a critical regulatory mechanism for protecting patient safety.
arXiv→Aoyang Fang +9
Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use.
arXiv→Qingkui Ma +2
We develop the fTNN, a deterministic tensor neural network subspace method for problems involving the fractional Laplacian on bounded domains, taking the fractional Poisson equation and time-dependent fractional advection-diffusion equation as typical representatives.
arXiv→Shicheng Ye +1
For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.
arXiv→Ge Yan +6
Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficient decoding at large code distances.
arXiv→Gerhard Backfried +3
We present a novel approach for applying Large Language Models (LLMs) to threat assessment in the context of foreign peacekeeping missions.
arXiv→Augustinas Jučas +1
Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.
arXiv→Harsh Choudhary +4
Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency.
arXiv→Sahil Shrivastava
Multi-agent large language model (LLM) loops, for example a Writer that drafts and a Critic that revises, are almost always terminated by a fixed iteration cap (max_iterations).
arXiv→Yan Xiao +5
Dengue fever, a mosquito-borne disease caused by dengue virus (DENV), has become a global health problem, and no FDA-approved drug is currently available.
PubMed→Hawa Sophia Bouzidi +2
Dengue virus (DENV) continues to pose a major global health challenge, with widespread distribution, frequent outbreaks, and four antigenically distinct serotypes complicating efforts for effective prevention and treatment.
PubMed→Dang Anh Tuan +1
Dengue transmission in inland Southeast Asia shows strong seasonality and short-term surges that challenge timely public-health response.
PubMed→Natanael Duarte +2
Dengue is a globally prevalent arboviral infection with an expanding spectrum of recognized neurological complications.
PubMed→Shravya Chitrapady +10
Acute febrile illnesses are significant clinical challenge in tropical regions due to overlapping symptomatology among infections such as dengue, scrub typhus, and leptospirosis.
PubMed→