Browse Articles
Discover research articles across all indexed journals
An agentic system for rare disease diagnosis with traceable reasoning
‘Virtual cell’ captures the most-basic process of life: bacterial division
AI succeeds in diagnosing rare diseases
In vivo base editing of Chd3 rescues behavioural abnormalities in mice
How fast does a protein fold? Real-time technique captures the moment
A Cambrian soft-bodied biota after the first Phanerozoic mass extinction
Psychedelics elicit their effects by 5-HT2A receptor-mediated Gi signalling
Mathematicians put AI model AlphaProof to the test
Climate change is speeding up — the pace nearly doubled in ten years
GlycoRNA complexed with heparan sulfate regulates VEGF-A signalling
A cross-population compendium of gene–environment interactions
Advancing operational global aerosol forecasting with machine learning
Bulk hexagonal diamond
Olympiad-level formal mathematical reasoning with reinforcement learning
Abstract A long-standing goal of artificial intelligence (AI) is to build systems capable of complex reasoning in vast domains, a task epitomized by mathematics with its boundless concepts and demand for rigorous proof. Recent AI systems, often reliant on human data, typically lack the formal verification necessary to guarantee correctness. By contrast, formal languages such as Lean 1 offer an interactive environment that grounds reasoning, and reinforcement learning (RL) provides a mechanism for learning in such environments. Here we present AlphaProof, an AlphaZero-inspired 2 agent that learns to find formal proofs through RL by training on millions of auto-formalized problems. For the most difficult problems, it uses test-time RL, a method of generating and learning from millions of related problem variants at inference time to enable deep, problem-specific adaptation. AlphaProof substantially improves state-of-the-art results on historical mathematics competition problems. At the 2024 International Mathematical Olympiad competition, our AI system, with AlphaProof as its core reasoning engine, solved three out of the five non-geometry problems, including the competition’s most difficult problem. Combined with AlphaGeometry 2 3 , this performance, achieved with multi-day computation, resulted in reaching a score equivalent to that of a silver medallist, marking the first time an AI system achieved any medal-level performance, to our knowledge. Our work demonstrates that learning at scale from grounded experience produces agents with complex mathematical reasoning strategies, paving the way for a reliable AI tool in complex mathematical problem solving.
Insight on the impact of Acacia nilotica leaves extract on the characteristics of natural and synthetic rubber composites
Abstract The purpose of this study is to assess the extract of Acacia nilotica leaves as a sustainable natural antioxidant for natural rubber (NR) and styrene-butadiene rubber (SBR) composites. The Acacia extract was incorporated to NR and SBR at concentrations from 0.5 to 2 phr, and its effectiveness was compared to that of commercial antioxidant polymerized 2,2,4-trimethyl-1,2-dihydroquinoline (TMQ). Rubber compositions were evaluated for chemical interactions and dispersion using FTIR and FESEM, and curing behavior was realized by rheometric studies. Physico-mechanical characteristics were assessed prior to and following thermo-oxidative aging at 90°Cfor different periods (2, 4, 6, and 7 days). The findings show that in both rubber matrices, theAcacia extract significantly improves aging resistance. While greater loadings (≥ 1 phr) negatively impacted initial strength without further improving aging resistance in comparison to TMQ. NR composites demonstrated an optimal antioxidant performance at 0.5 phr, offering superior retention of tensile strength during aging. At ideal concentrations of 0.5–1 phr, the extract dramatically increased tensile strength and elongation at break in SBR composites, exceeding TMQ in preventing excessive modulus expansion during aging. These results validate the potential of Acacia extractas an environmentally friendly antioxidant for rubber applications, especially in automotive, sealing, and vibration-damping components where improved durability and sustainable material solutions are needed.