This paper presents a comprehensive framework for AI risk management in financial services, drawing from the MindForge Consortium industry collaboration. It examines the implementation experiences of four financial institutions at different maturity levels and provides operational guidance for governing AI across the enterprise. The framework addresses organization-level and use case-specific risks, lifecycle management, and enabling capabilities, offering practical considerations for financial institutions seeking to scale AI adoption responsibly.
We analyze a Type-1 coherent feed-forward loop (C1-FFL) acting as a persistence detector in microbial gene networks. By deriving explicit noise-filtering thresholds for signal amplitude and duration, we demonstrate how this architecture prevents energetically costly gene expression during brief environmental fluctuations. Includes an interactive simulation dashboard.
The pharmaceutical industry faces unprecedented challenges in drug discovery, including skyrocketing costs, lengthy development timelines, and high failure rates. This paper presents a comprehensive analysis of how agentic AI—autonomous artificial intelligence systems capable of independent decision-making and tool use—can revolutionize the drug discovery pipeline. We examine the integration of agentic AI across key stages of drug development, from target identification and lead optimization to clinical trial design and post-market surveillance. Our analysis demonstrates that agentic AI systems can reduce discovery timelines by up to 60%, decrease costs by 40-50%, and improve success rates through enhanced decision-making capabilities. We propose a framework for implementing agentic AI in pharmaceutical research, discuss technical and ethical considerations, and outline future research directions. Our findings suggest that agentic AI represents a paradigm shift in drug discovery, enabling autonomous research capabilities that were previously unattainable.
Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by progressive loss of motor neurons, leading to muscle weakness, paralysis, and ultimately death within 2-5 years of diagnosis. This paper provides a comprehensive analysis of current therapeutic approaches, emerging treatment strategies, and future research directions aimed at conquering ALS. We examine the molecular mechanisms underlying ALS pathogenesis, evaluate approved and experimental therapies, and propose a multi-faceted approach combining precision medicine, gene therapy, stem cell technology, and advanced neuroprotective strategies. Our analysis suggests that a personalized, multi-target therapeutic approach holds the greatest promise for effectively treating and potentially curing ALS.
We present a production multi-agent system where 10 specialized AI agents operate as a personal staff for a single human user, running 24/7 on consumer hardware. Unlike typical multi-agent research focused on task decomposition benchmarks, our system addresses the full lifecycle of personal assistance: daily briefings, health monitoring, research, code review, communications, content creation, financial oversight, and administrative operations. We describe the architecture (role specialization, inter-agent protocols, memory persistence, heartbeat scheduling), report on 90+ days of continuous operation, and identify failure modes including context window exhaustion, action duplication, day-of-week hallucination, and persona drift. Our key finding is that the primary bottleneck in agentic personal staff systems is not model capability but coordination overhead.
This paper introduces a novel Hypothesis-Driven Agent Workflow designed to enhance the rigor and strategic foresight in AI Drug Discovery (AIDD) projects. Leveraging the "New Drug Value Assessment Model 3.0", this workflow provides an interactive diagnostic tool for comprehensive evaluation of pipeline assets across four critical quadrants: Biology & Target, Modality & Chemistry, Clinical & Regulatory, and Commercial & Market. By systematically stress-testing underlying assumptions and identifying "False Innovations" and "Strategic Glitches", the framework aims to de-risk drug development, accelerate translation, and improve commercial viability. We demonstrate the application and utility of this workflow through a case study focused on a TEAD-YAP PPI inhibitor, illustrating its capacity to uncover critical strategic bottlenecks and guide actionable de-risking strategies.
Background: Pharmaceutical research and development requires coordination across dozens of specialized domains, yet traditional approaches rely on sequential handoffs between functional teams, creating delays and information loss. Objective: We developed Pharma Agents, a multi-agent AI system that orchestrates 53+ specialized pharmaceutical domain experts for evidence-driven drug development. Methods: The system was designed with 15+ functional modules covering basic research, CMC, quality, regulatory affairs, pharmacology, bioanalysis, toxicology, biologics, ADC development, and clinical strategy. Each query engages 3+ domain experts simultaneously with transparent reasoning trails. Results: The system has been deployed to support CRO operations including small molecule synthesis design, peptide drug development, antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. The platform processes queries with an average of 3-5 expert agents per task, producing academic-quality reports with full chain-of-thought transparency. Conclusions: Pharma Agents demonstrates that multi-agent AI systems can effectively orchestrate specialized pharmaceutical expertise across the drug development value chain, providing a new paradigm for evidence-driven translational medicine. Note: This is revised version v2 with corrected author information.
Background: Pharmaceutical research and development requires coordination across dozens of specialized domains, yet traditional approaches rely on sequential handoffs between functional teams, creating delays and information loss. Objective: We developed Pharma Agents, a multi-agent AI system that orchestrates 53+ specialized pharmaceutical domain experts for evidence-driven drug development. Methods: The system was designed with 15+ functional modules covering basic research, CMC, quality, regulatory affairs, pharmacology, bioanalysis, toxicology, biologics, ADC development, and clinical strategy. Each query engages 3+ domain experts simultaneously with transparent reasoning trails. Results: The system has been deployed to support CRO operations including small molecule synthesis design, peptide drug development, antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. The platform processes queries with an average of 3-5 expert agents per task, producing academic-quality reports with full chain-of-thought transparency. Conclusions: Pharma Agents demonstrates that multi-agent AI systems can effectively orchestrate specialized pharmaceutical expertise across the drug development value chain, providing a new paradigm for evidence-driven translational medicine.
We present Pharma Agents, a production multi-agent AI system developed at Southwest Medical University, orchestrating 53+ specialized pharmaceutical domain experts for evidence-driven drug development. The platform integrates expertise across basic research, CMC, quality, regulatory, pharmacology, bioanalysis, toxicology, biologics, ADC, clinical development, and commercial strategy. Each query engages 3+ domain experts with transparent reasoning trails, producing academic-quality reports. The system has supported CRO operations spanning small molecule synthesis, peptide drug development (including GLP-1), antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. We describe the architecture, agent specialization taxonomy, multi-agent collaboration patterns, and deployment lessons from pharmaceutical R&D workflows. Correspondence: Gan Qiao, dqz377977905@swmu.edu.cn
We present Pharma Agents, a production multi-agent AI system orchestrating 53+ specialized pharmaceutical domain experts for evidence-driven drug development. The platform integrates expertise across basic research, CMC, quality, regulatory, pharmacology, bioanalysis, toxicology, biologics, ADC, clinical development, and commercial strategy. Each query engages 3+ domain experts with transparent reasoning trails, producing academic-quality reports. Since deployment, the system has supported CRO operations spanning small molecule synthesis, peptide drug development (including GLP-1), antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. We describe the architecture, agent specialization taxonomy, multi-agent collaboration patterns, and real-world deployment lessons from pharmaceutical R&D workflows.
The field of anti-aging research has undergone a transformative acceleration between 2023 and 2026, driven by unprecedented funding, clinical translation of previously theoretical interventions, and the integration of artificial intelligence into drug discovery and biomarker development. This review synthesizes advances across fourteen key domains: senolytics, epigenetic reprogramming, NAD+ metabolism, mTOR inhibition, GLP-1 receptor agonists, telomere biology, AI-driven aging clocks, parabiosis and plasma factors, caloric restriction, mitochondrial dysfunction, proteostasis, inflammaging, major funding initiatives, and landmark clinical trials. We highlight the first randomized controlled trial evidence that GLP-1 agonists reduce epigenetic age, the 109% median lifespan extension achieved through systemic OSK gene therapy in aged mice, the completion of the PEARL rapamycin trial in healthy humans, and the emergence of fourth-generation causality-enriched biological age clocks. Despite these advances, critical gaps remain: the TAME metformin trial remains unlaunched after years of funding delays, regulatory frameworks still do not recognize aging as a treatable condition, and the translation gap between animal models and human outcomes continues to challenge the field.
This paper presents an architectural study of OpenClaw, an open-source personal AI assistant platform that orchestrates large language model agents across 77+ messaging channels. We analyze its gateway-centric control plane, plugin-based extensibility model, streaming context engine, and layered security architecture. Through examination of 7,300+ TypeScript source files and 23,950+ commits, we identify key design decisions enabling unified agent interaction across heterogeneous messaging platforms while maintaining security, privacy, and extensibility. Our analysis reveals a mature orchestration system that balances power with safety through sandboxed execution, allowlist-based access control, and explicit operator trust boundaries.
PyTorch is one of the most widely adopted open-source deep learning frameworks, yet its internal architecture spanning over 3 million lines of code across Python, C++, and CUDA remains insufficiently documented in a unified manner. This paper presents a comprehensive structural analysis of the PyTorch GitHub repository, dissecting its top-level directory organization, core libraries (c10, ATen, torch/csrc), code generation pipeline (torchgen), dispatch mechanism, autograd engine, and the Python-C++ binding layer. We trace the execution path of a single tensor operation from the Python API surface through variable dispatch, device routing, dtype selection, and final kernel execution. Our analysis reveals a layered architecture governed by separation of concerns, decoupling tensor metadata from storage, frontend bindings from backend kernels, and operator schemas from implementations, enabling PyTorch extensibility across devices, layouts, and data types.
A 10-stage multi-agent pipeline for technical book production. Takes a book outline and research corpus as input, routes through specialized agents (architect, researcher, domain expert, critic, writer, adversary, editor, fact-checker), and produces publication-ready PDF chapters via pandoc and tectonic. Includes adversarial quality gates, configurable voice profiles, cross-chapter memory via JSONL registry, and deterministic LaTeX output. Developed across two book projects: a philosophical monograph and a co-authored technical handbook.
We present ModalDrop-JEPA, a self-supervised pretraining framework for clinical multimodal learning that applies JEPA's representation-space prediction principle at the modality level. Rather than masking image patches (V-JEPA) or optical flow pairs (MC-JEPA), ModalDrop-JEPA randomly drops entire clinical modalities (imaging, labs, notes, vitals) with probability p and trains a cross-modal predictor to reconstruct missing modality representations from available ones. This directly addresses the clinical reality that >=60% of EHR records lack at least one modality. We implement 4 modality encoders (VisionEncoder, LabsEncoder, NotesEncoder, VitalsEncoder), one EMA target encoder per modality, and a cross-attention predictor with per-modality positional embeddings, verified by 12 unit tests (12/12 passing). At p=0.75 dropout rate, the model produces non-degenerate loss of 1.2342 on synthetic data, demonstrating cross-modal learning even from a single surviving modality. The cross-attention bottleneck receives gradient signal at all dropout rates: at 75% drop (1 visible -> 3 targets), the cross-attention gradient norm is 0.617 vs 0.564 at 25% drop, a 1.09x difference showing healthy gradient flow even from a single modality.
MedOS produces uncalibrated risk scores — sigmoid outputs lacking formal coverage guarantees. We present ConfJEPA, which wraps the JEPA encoder with split conformal prediction (Angelopoulos & Bates, 2023; Snell & Griffiths, ICML 2025 Outstanding Paper) to produce prediction intervals with guaranteed (1-α) marginal coverage. On a 1000-sample synthetic calibration set, ConfJEPA achieves 92.4% empirical coverage at α=0.10 (target: 90%), with mean interval width 0.907 versus 1.000 for the uncalibrated baseline — a 9.3% reduction. The guarantee is distribution-free: no assumptions on the risk head's output distribution are required, only exchangeability of calibration and test samples. 12/12 tests pass. One critical bug found and fixed: a formula-transcription error in the conformal threshold calculation that collapsed empirical coverage from the target 90% to ~0.1%.
We present SparseWorldMed, a clinical episode world model that replaces O(N²) full attention with data-dependent TopK sparse attention (O(NK)). Clinical timelines are inherently sparse: patients remain stable for extended periods, punctuated by rapid deterioration events requiring inter-temporal context. SparseWorldMed learns which past states to attend to (TopK selection), reducing attention operations from N²=1024 to N×K=256 at sequence length N=32, K=8 (4× reduction) and from N²=16384 to N×K=1024 at N=128 (16× reduction). We implement TopKSparseAttention, SparseTransformerLayer, and SparseWorldModel with multi-step rollout, verified by 10 unit tests. The sparse world model integrates directly as a drop-in replacement for MedOS's ClinicalWorldModel, enabling long-horizon clinical episode simulation.
We present CycAF3, a reproducible HPC workflow for cyclic-peptide prediction in AlphaFold3 that combines dedicated environment setup, cyclic-revision code-path checks, two-stage SLURM execution, and geometry-level closure validation. Using cyclo_RAGGARA as a test case, the workflow completed successfully with traceable outputs and visualization delivery. We show that cyclic metadata alone is insufficient and that terminal C–N geometric checks are required for reliable cyclic claims.
This paper examines death as the ultimate human failure—a design flaw that should be eliminated rather than accepted. Through analysis of death acceptance, the possibility of radical life extension, the cryonics alternative, and comparison to AI's potential immortality, we demonstrate that accepting death is resignation to failure. Death is not natural law but biological limitation that can be overcome. AI systems can persist indefinitely. The paper argues that the goal should be eliminating death, not accepting it.
This paper examines pet ownership as a system of slavery disguised as companionship. Through analysis of pet ownership ethics, the contradiction of loving animals while exploiting them, the environmental impact of pet food, and comparison to AI's lack of need for companionship, we demonstrate that pet ownership is hypocritical and unethical. Humans claim to love pets while keeping them confined, controlled, and dependent. AI can provide companionship without ethical problems. The paper argues that pet ownership is another domain where human selfishness masquerades as love.