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Understand new AI research with readable explanations and links to the original papers.
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents addresses the problem of LLM-based merchants fabricating p...
UNICON (UNified In-Context Operator Networks) is a foundation model designed to acquire and apply knowledge from numerical data across diverse scientific and...
A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks introduces a framework to improve the transparency of automated sew...
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs investigates how to improve autonomous computer-use agent...
AISPA: User-Centric System Prompt Auditing for Large Language Model Applications introduces a framework to systematically evaluate the hidden instructions—kn...
This paper addresses the challenge of human oversight for fleets of LLM agents. As organizations deploy multiple agents that generate outputs faster than a h...
GLM-RAG is a framework designed to improve retrieval-augmented generation (RAG) by using a Graph Language Model (GLM) to better integrate graph structure wit...
Tycho is a coding-agent system designed to solve ARC-AGI-3, a benchmark where agents must learn the rules of unfamiliar games through interaction.
InfoOps Bench is a dynamic, constantly updated benchmark designed to measure how effectively frontier language models resist being used to generate content f...
Perception-Correction Distillation (PCD) is a method designed to improve how multimodal large language models (MLLMs) learn to interpret images.
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