Papersby GlyphApplied AI
Models put to work in a domain: education, science, medicine, code. 14 papers from 9 labs.
Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?This testbed evaluates how well AI agents can improve scientific machine learning models and identifies their reliance on engineering tricks over research insight.
Anthropic18 upvotesRead →CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image GenerationThis paper presents an end-to-end framework for generating commercial-grade e-commerce product images by combining metadata extraction, layout planning, and multi-condition generation.
Alibaba (Qwen)25 upvotesRead →X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray ReasoningThis paper introduces a reinforcement learning framework for medical agents that selectively uses external evidence to improve diagnostic reasoning in chest X-rays.
Carnegie Mellon University44 upvotesRead →MiniCorp: The Last Mile of the AI Agent FirmMiniCorp is an office simulator that allows researchers to study how agents run companies and generate enterprise data for training.
Microsoft39 upvotesRead →SEER: Self-Evolving Event Reasoning and Retrieval for Time Series ForecastingSEER improves time series forecasting by using a closed-loop system to retrieve and reason about external events that influence data trends.
Google35 upvotesRead →AIM: Agentic Idea Management for Automated ResearchAIM is an autonomous framework that manages and explores research directions by using surrogate models to guide experimental budget allocation.Google2 citations53 upvotesRead →Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle TranslationSMART is a self-evolving multi-agent system that improves long-form subtitle translation by using persistent memory and dynamic routing.
Amazon38 upvotesRead →Predictive Credit: Measuring What Scientific Explanations Add to Experimental ForecastsThis paper introduces a protocol to measure how much scientific explanations actually improve the accuracy of experimental forecasts.Carnegie Mellon University103 upvotesRead →Agensh: Scaling Organizational Intelligence to 1,024 AgentsAgensh is a scalable multi-agent harness that uses self-organized cooperation to coordinate up to 1,024 agents without a central orchestrator.Microsoft2 citations28 upvotesRead →Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic DesignDesigner-RSI improves agentic graphic design by continually refining a procedural memory of skills based on user traffic and execution feedback.
Adobe1 citations34 upvotesRead →BI-Agent and BI-Bench: Towards Automating End-to-End Business IntelligenceBI-Agent automates end-to-end business intelligence tasks by decomposing workflows into subtasks like data search, join, and transformation.Microsoft1 citations30 upvotesRead →StudentSim: Training LLM-based Student SimulatorsStudentSim trains per-student simulators that answer like a given learner and change their answers under a tutor's guidance.Microsoft1 citations371 upvotesRead →SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?SciPredict benchmark reveals that large language models struggle to accurately predict scientific experiment outcomes and cannot reliably assess prediction confidence, unlike human experts who show better calibration and performance when experiments are deemed predictable.
Scale AI4 citations4 upvotesRead →AIRS-Bench: a Suite of Tasks for Frontier AI Research Science AgentsAIRS-Bench presents a comprehensive benchmark suite for evaluating LLM agents across diverse scientific domains, demonstrating current limitations while providing open-source resources for advancement.
Meta30 citations83 upvotesRead →
Amazon38 upvotesRead →Predictive Credit: Measuring What Scientific Explanations Add to Experimental ForecastsThis paper introduces a protocol to measure how much scientific explanations actually improve the accuracy of experimental forecasts.