Major Discoveries A central innovation presented in the report is the Mixture-of-Rewards (MoR) framework for reinforcement learning. Unlike traditional methods that often struggle to balance diverse training objectives, MoR enables the model to simultaneously optimize for over 1,000 different tasks. By synergizing rewards from verified rules, model-based feedback, and environmental interactions, this approach significantly enhances the model’s scalability and adaptability, allowing it to learn professional scientific skills with far greater data efficiency than previous approaches.

Bridging the Gap with “InternBootCamp” The researchers developed InternBootCamp, a comprehensive post-training environment that facilitates the model’s evolution from a generalist to a scientific expert.

• Verifiable Task Scaling: The platform hosts over 1,000 domain-diverse environments, enabling the automated generation and verification of training cases to ensure rigorous learning.

• Agent-Driven Curation: By employing agent workflows for data mining, the team successfully increased the purity of scientific data in their pre-training corpus from approximately 2% to over 50%.

• Scientific Reasoning: This infrastructure allowed Intern-S1 to achieve state-of-the-art performance in reasoning tasks, proving that open-source models can rival closed-source systems when supported by high-quality, verifiable task environments.

Key Scientific Capabilities The study identifies specific domains where Intern-S1 demonstrates exceptional proficiency, often surpassing leading closed-source models.

• Molecular Synthesis: Intern-S1 excels in professional tasks such as planning synthesis routes for complex molecules and predicting chemical reaction outcomes, showcasing deep domain expertise.

• Multimodal Mastery: The model integrates a dynamic tokenizer and specialized encoders (e.g., for time-series and non-natural visual data), enabling it to natively understand and process diverse scientific modalities like protein sequences and seismic signals.

• Competitive General Reasoning: Despite its intense scientific specialization, the model maintains top-tier performance on general benchmarks (e.g., MMLU-Pro, GPQA), demonstrating that deep domain focus need not compromise general intelligence.

Overall, the Intern-S1 Technical Report outlines a path toward Artificial General Intelligence (AGI) in the scientific domain. By combining massive-scale scientific pre-training with innovative reinforcement learning strategies, Intern-S1 not only democratizes access to high-level scientific reasoning but also establishes a new benchmark for open-source foundation models.

This paper presents a comprehensive proteomic atlas of human aging, analyzing 516 samples from 13 different tissues across a 50-year lifespan to uncover the molecular signatures and dynamics of how our organs age.

Major Discoveries

A central finding is that as we age, the link between our genes (transcriptome) and the proteins they code for (proteome) weakens, a phenomenon described as transcriptome-proteome decoupling. This decoupling is a hallmark of aging tissues and is accompanied by a decline in proteostasis, which is the cell’s ability to maintain a healthy balance of proteins. A key feature of this decline is the accumulation of amyloid proteins across various organs.

Organ-Specific Aging and “Senohubs”

The researchers developed tissue-specific “proteomic clocks” to measure the biological age of different organs. This revealed several key insights:

• Asynchronous Aging: Organs do not age at the same rate.

• Aging Inflection Point: Many tissues show a significant acceleration in proteomic changes around the age of 50.

• Vascular Senescence: Blood vessels, particularly the aorta, are among the earliest and most dramatically aging tissues, acting as a potential “senohub” that drives systemic aging.

Key Proteins Driving Aging

The study identified specific circulating proteins, termed “senoproteins,” that appear to drive the aging process.

• GAS6: This protein was identified as a key “senokine” (a cytokine released by senescent cells) that originates from aging vascular tissue. When administered to middle-aged mice, GAS6 was shown to promote vascular and systemic aging, impairing physical function and causing senescence-like changes in multiple organs.

• SAP (Serum Amyloid P-component): Identified as a “ubiquitous upregulated protein with aging” (UUPA), SAP was found to be elevated in multiple aging tissues. Experiments showed that SAP promotes vascular aging by inducing senescence in human aortic endothelial cells.

• Circulating Biomarkers: The study identified numerous plasma proteins (like GPNMB, COMP, and HTRA1) whose levels correspond to aging in specific tissues, offering a path toward non-invasive biomarkers for organ aging. Several of these proteins were shown to induce features of vascular senescence in lab experiments.

Overall, the research provides a foundational atlas for understanding human aging at the protein level, highlighting that vascular aging is a critical initiator of systemic decline and identifying specific circulating factors that could be targeted for future anti-aging therapies.