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Understand new AI research with readable explanations and links to the original papers.
Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory Long-horizon language agents often struggle with memory because they...
Audio-visual large language models (AVLLMs) are designed to process audio, video, and text simultaneously, allowing them to reason about the world in a way t...
Shepherd is a new runtime system designed to help "meta-agents"—AI agents that manage or supervise other agents—operate more effectively.
Scientific discovery is a process defined by constraints. Scientists must constantly balance the quality of their measurements against the costs of time, ene...
From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World This paper addresses a critical gap in the field of offensive AI: current ben...
VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection Scaling inference-time reasoning is a...
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners This research investigates whether modern Large Reasoning Models...
Abductive Reasoning with Probabilistic Commonsense Recent advancements in AI have sought to improve the reasoning capabilities of Large Language Models (LLMs...
Rubric-Grounded RL: Structured Judge Rewards for Generalizable Reasoning This paper introduces "rubric-grounded reinforcement learning," a new framework for...
The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty In many humanitarian and social policy settings, decision-makers must distr...
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