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Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers developed an AI system called nMAS to automate the process of extracting structured and aggregated features from Electronic Health Records (EHR) for heart failure research. The system generates these…
OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling
In this research, a new pipeline called OctoLong is introduced to enhance the training of language models (LM) by providing them with long code contexts. This is done through the use of an AST parser, a language server…
Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
Researchers have developed a method to evaluate if latent channels in large language models effectively communicate task-relevant information in multi-agent systems. The study applies this method to Qwen3 and Qwen3-8B…
SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
Researchers propose SciFigQual-Bench, a benchmark for evaluating the quality of scientific figures in computer science papers across five dimensions. The dataset includes over 6,000 images from top conferences between…
Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs
The study evaluates three large language models (Qwen2.5 7B, Llama 3.1 8B, and Gemma2 9B) in the context of healthcare interoperability. The models were assessed across 320 clinical scenarios for their compliance with…
Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review
A study investigates the impact of Explainable AI (XAI) on developers' trust in AI-assisted code reviews. The research compared three LLM-based code review systems with varying levels of XAI support. Results show that…
SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents
The study presents SIREN, a framework for automating end-to-end extreme-weather early warning using Large Language Model (LLM) agents. The researchers developed SIREN-Bench, a comprehensive benchmark, and found…
SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents
Researchers are working on an automated system called SIREN to improve extreme weather early warning systems using Large Language Model (LLM) agents. The system is designed to bridge the gap between isolated scientific…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers introduce an open-source framework for evaluating the effectiveness of AI agents powered by open-weight large language models (LLMs) in data preparation tasks, specifically for longitudinal population…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers have developed an open-source framework for evaluating the effectiveness of AI agents powered by large, locally deployable language models (LLMs) in data preparation tasks, specifically focused on…
From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics
The text discusses a new framework for modernizing Science, Technology, and Innovation (STI) analytics. The proposed framework aims to improve upon traditional bibliometric indicators by using dynamic knowledge graphs…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors introduce GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across multiple agents in the pipeline, rather than optimizing components in isolation…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework for training AI agents using various inference harnesses like Claude Code, Codex, OpenClaw, etc., in diverse environments. The framework allows end-to-end training with standard…
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
A new multi-agent architecture, named pAI-Econ-claude, was proposed for AI-assisted economic theory development. This architecture uses a gated human-in-the-loop system to ensure reliability in tasks where no machine…
Thinkink: 2D Spatial Ink-native Interaction with LLMs
Researchers propose 'Thinkink', a system that allows users to interact with large language models (LLMs) using handwritten notes and sketches, which are then visually integrated into a shared canvas. The system was…
OLEDLM: A Unified Language Model for OLED Molecular Design
Researchers propose a unified language model named OLEDLM for the design of OLED materials. The model directly generates OLED molecular structures that meet specified optoelectronic properties, using a multi-stage…
AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
AgentDebugX is an open-source toolkit designed to aid in the observation, identification of root causes, recovery, and rerunning of issues in LLM (Large Language Model) agents. The toolkit, DeepDebug, uses multi-turn…
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
The paper introduces AILQA, an AI system designed for Indian legal question answering, using a variety of embedding and generative models including LLMs. It was evaluated with both lexical and semantic metrics, expert…
Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
The paper proposes a taxonomy to better understand curriculum learning (CL) in Natural Language Processing (NLP). The taxonomy aims to provide a systematic analysis of CL strategies by separating difficulty evaluation…
The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search
The study presents a model that examines how organizations pool dispersed information for decision-making in a discovery problem. It finds that while centralized decision-making can improve beliefs, it may also reduce…
The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search
The study introduces a benchmark to analyze the Shared Discovery Paradox, a phenomenon where pooling information for one ranking leads to improved beliefs but reduced coverage in a discovery problem. The paradox arises…
AutoSynthesis: An agentic system for automated meta-analysis
A new system called AutoSynthesis has been developed to automate the process of meta-analysis in various fields such as science, medicine, education, and policy. The system takes a research question in natural…
Concept-Guided Spatial Regularization for World Models in Atari Pong
Researchers evaluated five world model agents in Atari Pong and found failures in visual and dynamical aspects of the generated trajectories by these agents. They propose a method called Concept-Guided Spatial…
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Researchers show that fine-tuning language models on specific policies or domains can cause broad ideological shifts across various topics, even when the data used is factually defensible and moderation-passing. This…
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