Elicit and Enhance: Advancing Multimodal Reasoning in Medical Scenarios

January Ventures is focusing on funding underrepresented AI founders who possess deep expertise in traditional industries like healthcare, manufacturing, and supply chain. The firm aims to address the funding gap that exists in the AI startup ecosystem, particularly in San Francisco, where many promising companies are overlooked. By providing pre-seed checks, January Ventures seeks to empower these founders to innovate and transform their respective sectors.

The article discusses fairness in multi-agent reinforcement learning (MARL) within healthcare, emphasizing the need for equitable task allocation that considers both workload balance and agent expertise. It introduces FairSkillMARL, a framework that aims to align skill and task distribution to prevent burnout among healthcare workers. Additionally, MARLHospital is presented as a customizable environment for modeling team dynamics and scheduling impacts on fairness, addressing gaps in existing simulators.

The article discusses Fair-GNE, a framework designed to ensure fair workload allocation among multiple agents in healthcare settings. It addresses the limitations of existing multi-agent reinforcement learning (MARL) approaches that do not guarantee self-enforceable fairness during runtime. By employing a generalized Nash equilibrium (GNE) framework, Fair-GNE enables agents to optimize their decisions while ensuring that no single agent can unilaterally improve its utility, thus promoting equitable resource sharing among healthcare workers.

The Virtual Human Generative Model (VHGM) is a generative model designed to approximate the joint probability of over 2000 healthcare-related human attributes. The core algorithm, VHGM-MAE, is a masked autoencoder specifically developed to manage high-dimensional, sparse healthcare data. It addresses challenges such as data heterogeneity, probability distribution modeling, systematic missingness, and the small-$n$-large-$p$ problem by employing a likelihood-based approach and a transformer-based architecture to capture complex dependencies.

Large language models (LLMs) are known for their impressive text generation capabilities; however, they frequently produce factually incorrect content, a phenomenon referred to as hallucination. This issue is particularly concerning in critical fields such as healthcare and finance. Traditional methods for detecting these inaccuracies often require multiple API calls, leading to increased latency and costs. The introduction of CONFACTCHECK offers a new approach that checks for consistency in responses to factual queries, enhancing the reliability of LLM outputs without needing external knowled…

Vision-Language Models (VLMs) are emerging models that integrate visual content with natural language. Current methods typically fuse data either early in the encoding process or late through pooled embeddings. This paper introduces a lightweight fusion module utilizing cross-only, bidirectional attention layers to align hidden states from both modalities, enhancing understanding while keeping encoders non-causal. The proposed method aims to improve the performance of VLMs by leveraging the inherent structure of visual and textual data.

The article discusses a novel bi-level contextual bandit framework aimed at individualized resource allocation in high-stakes domains such as education, employment, and healthcare. This framework addresses the challenges of delayed feedback, hidden heterogeneity, and ethical constraints, which are often overlooked in traditional learning-based allocation methods. The proposed model optimizes budget allocations at the subgroup level while identifying responsive individuals using a neural network trained on observational data.

The paper titled 'Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoning' introduces a new method called Bias-REstrained Prefix Representation FineTuning (BREP ReFT). This approach aims to enhance the mathematical reasoning capabilities of models by addressing the limitations of existing Representation finetuning (ReFT) methods, which struggle with mathematical tasks. The study demonstrates that BREP ReFT outperforms both standard ReFT and weight-based Parameter-Efficient finetuning (PEFT) methods through extensive experiments.