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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Ananya Sahu +2
While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.g., story generation) as well as those that require it implicitly, e.g., reinforcement learning (RL).
arXiv→Mingxuan Zheng +8
LLM agents increasingly adapt to recurring tasks by accumulating procedural knowledge in skills.
arXiv→Afreen Alam +6
Rapid adoption of large language models (LLMs) in enterprise settings has introduced operational, security, and governance risks.
arXiv→Md Tarek Hassan +2
Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) based sixth-generation (6G) networks, especially if the direct base-station-UE links are unavailable.
arXiv→Mohammad Amanlou +5
Human-like cognition does not select past experience by topical similarity alone: affective significance and unresolved conflict also shape what becomes accessible.
arXiv→Jiacheng Miao +3
Reliable hypothesis testing is the foundation of many empirical scientific claims.
arXiv→Ali Janati +3
Under the standard split, Muon gets hidden matrices and AdamW embeddings/output head.
arXiv→Elena Dumitrescu +3
Diffusion Large Language Models (DLLMs) replace autoregressive next-token prediction with iterative parallel denoising, yet their internal safety mechanisms remain poorly understood.
arXiv→Yan Zhou +3
Long-term memory enables language agents to reuse past facts, preferences, and task experience.
arXiv→Bhavika Jalli +2
LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens.
arXiv→Ruochen Jin +4
Preference alignment often makes large language models (LLMs) overconfident and poorly calibrated.
arXiv→Valentin Liévin +34
In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.
arXiv→Rodrigo Ferreira Rodrigues +3
In the context of geodata, existing Large Language Models have often been studied in a homogeneous setting, which has considerably limited insights into their generalization capabilities.
arXiv→Zhuoliang Xie +4
Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs.
arXiv→Ioannis Ziogas +6
Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing.
arXiv→Xin Wang +4
Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes.
arXiv→Haoyu Zheng +3
Recent agentic reinforcement learning methods use hindsight to complement sparse outcome rewards.
arXiv→Karim Radouane +2
Understanding concepts is fundamental to generalization.
arXiv→Yao Wu
Learning from heterogeneous representations is usually reduced to feature concatenation, which erases which representation produced an error.
arXiv→Taha Shieenavaz +2
Recent advancements in deep reinforcement learning have increasingly favored simplified, highly parallelized paradigms.
arXiv→Guilherme S. Imai Aldeia +7
Headache is the most common neurological disorder in children and substantially affects quality of life.
arXiv→Kuangyu Ding +1
We prove that mirror descent converges to a KKT point for the nonconvex problem without excluding boundary limits.
arXiv→Vasanth Iyer
Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class accelerators remains limited.
arXiv→Beatrice Alessandra Motetti +9
Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems.
arXiv→Alireza Moayedikia
Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
arXiv→Adam Štafa +3
Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample efficiency and asymptotic performance without altering the underlying algorithms.
arXiv→Qi Zhao +11
Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many tightly coupled factors.
arXiv→Zhu Wang +6
Early-stage molecular design is an iterative process, not just a task of generating molecules.
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→Pikkanet Suttirat +8
Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental and socioeconomic factors.
PubMed→