Run the Reads #5

Agents, Medical AI, and Reasoning Advances - August 13, 2025

Welcome to this week's edition of Run the Reads, your curated weekly recap of research papers, articles, and technical content as part of the "SO"cial series. Published every Wednesday, we dive into the latest developments shaping the world of AI and beyond.

This week brings a compelling mix of advances in agentic AI systems, groundbreaking medical applications, and novel approaches to LLM reasoning. The research spans from practical frameworks for training AI agents to clinical evidence synthesis tools that could transform healthcare research. Several papers tackle the persistent challenge of making AI systems more reliable, whether through better routing mechanisms, factuality evaluation, or self-improvement techniques. Meanwhile, the medical AI community continues pushing boundaries with tools that can predict aging status and accelerate systematic reviews.

🤖 Agent Frameworks & Tool Use

This section covers new frameworks and architectures for building and deploying AI agents.

Agent Lightning: Train ANY AI Agents with Reinforcement Learning Agent Lightning introduces a flexible RL framework that achieves complete decoupling between agent execution and training, allowing seamless integration with existing agents developed via diverse frameworks like LangChain, OpenAI Agents SDK, and AutoGen with almost zero code modifications. The system uses a hierarchical RL algorithm called LightningRL with a credit assignment module that can decompose trajectories from any agent into training transitions. This enables RL to handle complex interaction logic including multi-agent scenarios and dynamic workflows, with experiments across text-to-SQL, retrieval-augmented generation, and math tool-use tasks demonstrating stable improvements.

WideSearch: Benchmarking Agentic Broad Info-Seeking WideSearch presents a new benchmark designed to evaluate agent reliability in large-scale information collection tasks, featuring 200 manually curated questions across over 15 diverse domains grounded in real user queries. The benchmark reveals significant deficiencies in current systems, with most achieving overall success rates near 0% and the best performer reaching just 5%, while human testers can achieve near 100% success rates given sufficient time. The research underscores critical gaps in present search agents for handling wide-context collection tasks that are more repetitive than cognitively complex, highlighting urgent areas for future development in agentic search capabilities.

TURA: Tool-Augmented Unified Retrieval Agent for AI Search TURA addresses significant industrial limitations in current RAG approaches by introducing a novel three-stage framework that combines traditional retrieval-augmented generation with agentic tool-use to access both static content and dynamic, real-time information. The system tackles the challenge that traditional RAG approaches struggle with real-time needs and structured queries requiring access to dynamically generated content like ticket availability or inventory, as search engines are limited to indexing static pages and cannot perform interactive queries needed for time-sensitive data.

IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory. IRT-Router proposes a multi-LLM routing framework inspired by Item Response Theory from psychological measurement methodology that efficiently routes user queries to the most suitable LLM while balancing performance and cost. The framework explicitly models the relationship between LLM capabilities and user query attributes, enabling accurate prediction of response performance while providing interpretable insights such as LLM abilities and query difficulty. The system includes an online query warm-up technique based on semantic similarity to enhance online generalization capabilities.

🧠 LLM Reasoning & Self-Improvement

Research focused on enhancing language model reasoning capabilities and self-learning mechanisms.

Self-Questioning Language Models This research explores whether large language models can improve without external data by generating their own questions and answers through an asymmetric self-play framework called SQLM. The approach involves a proposer that generates questions for a solver to answer, with both components trained via reinforcement learning where the proposer receives rewards based on the solver's ability to correctly answer the generated questions. The methodology requires only a single prompt specifying the topic and asking the model to generate its own questions, potentially offering a path for continuous model improvement without requiring additional training data.

🏥 Medical AI & Healthcare Applications

Advances in applying AI to medical research, clinical practice, and healthcare challenges.

Accelerating clinical evidence synthesis with large language models TrialMind presents a comprehensive AI-driven pipeline for streamlining systematic literature reviews in medicine, addressing the expensive and time-consuming process that typically requires five experts and 67.3 weeks. The system achieves high recall rates (0.711-0.834) in study search compared to human baselines (0.138-0.232) and significantly outperforms previous document ranking methods by 1.5-2.6 fold in study screening. The framework enables human-AI collaboration that improved recall by 71.4% and reduced screening time by 44.2% in pilot studies, while data extraction accuracy increased by 23.5% with a 63.4% time reduction.

FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs FactEHR addresses the critical need for verifying and attributing factual claims in healthcare LLM applications by presenting an NLI dataset consisting of document fact decompositions for 2,168 clinical notes spanning four different note types. The dataset focuses on fact decomposition, the process of breaking down complex clinical statements into fine-grained atomic facts for verification, which poses unique challenges in clinical documentation due to dense terminology and diverse note formats. This work provides essential infrastructure for evaluating the factuality of LLM-generated content in high-stakes medical applications where accuracy is paramount.

Unexpected ability of large language models: predicting aging status Research demonstrates that large language models can be developed into frameworks for predicting the magnitude of aging across diverse populations using unstructured and heterogeneous data, with predicted aging showing high correlation with multiple aging-related outcomes. The work suggests that LLMs possess capabilities beyond text generation, extending into biomedical prediction tasks that could have significant implications for personalized medicine and aging research. This unexpected application opens new avenues for leveraging LLMs in healthcare beyond traditional natural language processing tasks.

🔬 Computer Vision & Medical Imaging

Research applying machine learning to medical imaging and computational pathology.

Do Multiple Instance Learning Models Transfer? This study systematically evaluates the transfer learning capabilities of pretrained Multiple Instance Learning models in computational pathology by assessing 11 models across 21 pretraining tasks for morphological and molecular subtype prediction. Results demonstrate that pretrained MIL models consistently outperform models trained from scratch, even when trained on different organs than the target task, with pancancer datasets enabling strong generalization across organs and tasks while outperforming slide foundation models using substantially less pretraining data. The research addresses a critical gap in understanding MIL model transferability, which is essential for handling small, weakly supervised clinical datasets that are common in computational pathology.

🏆 AI Benchmarking & Evaluation

Progress reports and new benchmarks for measuring AI capabilities.

ARC Prize 2024: Technical Report The ARC Prize 2024 technical report documents significant progress on the ARC-AGI benchmark, which seeks to measure generalization on novel tasks as opposed to skill at tasks that can be prepared for in advance. The global competition successfully drove the state-of-the-art score on the ARC-AGI private evaluation set from 33% to 55.5%, representing substantial advancement toward the target benchmark score of 85%. The benchmark remains notable as one of the most important unsolved AI challenges because it focuses on measuring the essence of intelligence - generalization on novel tasks - rather than performance on pre-trained domains.

The week's research reveals a clear trend toward more sophisticated agent architectures and the expansion of AI into high-stakes domains like healthcare. The recurring theme of human-AI collaboration, particularly evident in medical applications, suggests a maturation of the field toward practical deployment rather than pure performance optimization. What stood out to you this week? Share your thoughts in the comments, and join us next Wednesday for more insights. Stay curious!