New AI system could accelerate clinical research
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
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Study shows mucus contains molecules that block Salmonella infection
MIT researchers now hope to develop synthetic versions of these molecules, which could be used to treat or prevent foodborne illnesses.
Diffusion Curriculum: Synthetic-to-Real Generative Curriculum Learning via Image-Guided Diffusion
arXiv:2410.13674v3 Announce Type: replace-cross Abstract: Low-quality or scarce data has posed significant challenges for training deep neural networks in practice. While classical data augmentation cannot contribute very different new data, diffusion models opens up a new door to build self-evolving…
A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
arXiv:2509.20240v1 Announce Type: cross Abstract: Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction…
Reinforcement Learning and Machine ethics:a systematic review
arXiv:2407.02425v2 Announce Type: replace Abstract: Machine ethics is the field that studies how ethical behaviour can be accomplished by autonomous systems. While there exist some systematic reviews aiming to consolidate the state of the art in machine ethics prior to…
Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models
arXiv:2509.11686v3 Announce Type: replace-cross Abstract: Code Large Language Models (Code LLMs) have opened a new era in programming with their impressive capabilities. However, recent research has revealed critical limitations in their ability to reason about runtime behavior and understand the…
EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models
arXiv:2509.20146v1 Announce Type: cross Abstract: Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety. We study sycophancy — models’ tendency to uncritically echo user-provided information — in high-stakes clinical settings. We introduce EchoBench, a…
SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation
arXiv:2509.19623v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle with complex Text-to-SQL queries that demand both sophisticated mathematical reasoning and intricate schema navigation. Existing methods often tackle these challenges in isolation, creating a fractured reasoning process that compromises logical…
Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving
arXiv:2509.19681v1 Announce Type: new Abstract: Advanced test-time computing strategies are essential for scaling reasoning models, but their effectiveness is capped by the models’ poor self-evaluation. We propose a pairwise Explanatory Verifier, trained via reinforcement learning (GRPO), that produces calibrated confidence…
Nano Bio-Agents (NBA): Small Language Model Agents for Genomics
arXiv:2509.19566v1 Announce Type: new Abstract: We investigate the application of Small Language Models (<10 billion parameters) for genomics question answering via agentic framework to address hallucination issues and computational cost challenges. The Nano Bio-Agent (NBA) framework we implemented incorporates task…