AGENTIC-SYSTEMSMay 16, 20261 min read6 views

Accelerating discovery of liver disease mechanisms

At the University of Edinburgh, bioengineer Filippo Menolascina is using the AI tool Co-Scientist to comb through biomedical literature, successfully identifying a key molecular mechanism in liver disease that could pave the way for targeted dual-therapies.

Biomedical research produces a flood of information that no scientist can realistically absorb. At the University of Edinburgh, bioengineer Filippo Menolascina is using Co-Scientist to comb the literature for overlooked links and generate new hypotheses.

His team focused on a common liver disease called metabolic dysfunctionâassociated steatohepatitis (MASH). Developing treatments is challenging because MASH involves intertwined biological processes, including liver inflammation and metabolism, meaning singleâtarget drugs fall short. That pushes researchers toward combination treatments, but the number of potential drug pairings is overwhelming.

Faced with that combinatorial explosion, Menolascina used CoâScientist to narrow the search. In his hands, CoâScientist synthesised evidence across liver biology and pharmacology, highlighted mechanisms worth focusing on, and flagged candidate combination therapies that his team could test.

In one emblematic case, CoâScientist tackled a live, practical question: Why does the drug resmetirom â a recently approved treatment prescribed for a specific stage of MASH â only help a narrow slice of those eligible patients? The system produced a hypothesis pinpointing the NLRP3 inflammasome as the specific molecular bridge coupling inflammation and metabolism in the disease â a connection never previously pulled together into a single, actionable explanation. The hypothesis, later experimentally verified, could pave the way for targeted dual-therapies.

Source: Google DeepMind Blog

More in this category

agentic-systems

Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models

Researchers introduce Generative Ontology Induction (GOI), a domain-agnostic framework that automatically extracts structured ontologies from document corpora using LLMs. Achieving 95-100% structural coverage, GOI addresses a major bottleneck in knowledge-intensive AI systems.

agentic-systems

JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

Researchers have proposed JUMP, a novel single-pass membership inference attack designed for fine-tuned discrete diffusion language models (dLLMs). By leveraging the unique properties of dLLMs, JUMP significantly improves detection accuracy while drastically reducing the number of required queries.

agentic-systems

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

Researchers demonstrate that Masked Diffusion Language Models (MDLMs) serve as highly effective, steerable text-based world models for agentic reinforcement learning. By leveraging bidirectional denoising, MDLMs outperform autoregressive models four times their size in coherence, groundedness, and rollout diversity.

NOW LET US Related – Democratizing AI with Small Language Models: Structured Benchmarking and Parameter-Efficient Fine-Tuning for Local Deployment

agentic-systems

Democratizing AI with Small Language Models: Structured Benchmarking and Parameter-Efficient Fine-Tuning for Local Deployment

A new study demonstrates that small language models (SLMs) under 3 billion parameters can serve as highly capable local experts for specialized tasks. By combining structured benchmarking with low-cost parameter-efficient fine-tuning (PEFT), institutions can achieve AI autonomy without relying on expensive hardware.

agentic-systems

PlanFlip: Attacking Multi-Agent LLM Systems via Planning-Phase Prompt Injection

Researchers have introduced PlanFlip, a novel prompt injection attack framework targeting the planning phase of multi-agent LLM systems. The study reveals critical security blind spots in homogeneous agent pipelines and demonstrates that reasoning-augmented models like DeepSeek-R1 exhibit strong resistance.

agentic-systems

Rater State Bias in RLHF Preference Data: An Audit Framework

A new study identifies a structured bias in Reinforcement Learning from Human Feedback (RLHF) caused by the psychological state of human raters. Under stress, raters' shifting preferences can propagate through reward modeling, potentially compromising AI policy optimization.