Towards Robust and Fair Next Visit Diagnosis Prediction under Noisy Clinical Notes with Large Language Models

The legal sector is witnessing a significant shift as law firms increasingly adopt generative AI tools, marking a pivotal moment in the integration of artificial intelligence within enterprise environments. This trend follows a historical pattern where legal services have been early adopters of technology for document management and classification.

Anthropic has entered the competitive landscape of artificial intelligence with the launch of its latest model, Claude Opus 4.5, which is touted as a significant advancement in AI capabilities, promising improved performance and efficiency across various tasks.

Insurers are reducing coverage for artificial intelligence (AI) systems due to concerns over potential billion-dollar claims arising from AI errors. This shift reflects a growing unease among insurers about the financial implications of AI's integration into business operations.

A new framework named Reading Comprehension Exercise Generation (RCEG) has been proposed to leverage large language models (LLMs) for automatically generating personalized English reading comprehension exercises. This framework utilizes fine-tuned LLMs to create content candidates, which are then evaluated by a discriminator to select the highest quality output, significantly enhancing the educational content generation process.

A new study introduces an innovative pipeline for transforming public Zoom recordings into speaker-attributed transcripts, enhancing the realism of civic simulations using large language models (LLMs). This method incorporates persona profiles and action tags, significantly improving the modeling of multi-party deliberation in local government settings such as Appellate Court hearings and School Board meetings.

PocketLLM has been introduced as a novel method for compressing large language models (LLMs) using meta-networks, enabling significant reductions in model size without compromising accuracy. This approach utilizes a simple encoder to project LLM weights into discrete latent vectors, which are then represented by a compact codebook and decoded back to the original weight space. Extensive experiments demonstrate its effectiveness, particularly with models like Llama 2-7B.

The recent introduction of SPINE, a token-selective test-time reinforcement learning framework, addresses challenges faced by large language models (LLMs) and multimodal LLMs (MLLMs) during test-time distribution shifts and lack of verifiable supervision. SPINE enhances performance by selectively updating high-entropy tokens and applying an entropy-band regularizer to maintain exploration and suppress noisy supervision.

A recent position paper emphasizes the need for literary scholars to engage with research on large language model (LLM) interpretability, suggesting that the red team could serve as a platform for this ideological struggle. The paper argues that current interpretability standards are insufficient for evaluating LLMs.