MEMPROBE: Probing Long-Term Agent Memory via Hidden User-State Recovery
Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving unders...
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Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving unders...
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to...
Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answ...
Generic text-to-video models can be used as rich open-world scene priors. Despite the high quality of today's generat...
Quantum computers could outperform classical machines on important problems, but only if the errors that pervade quan...
Modeling chaotic systems is crucial yet challenging. Inverse problems in chaotic dynamics, namely inferring initial c...
Over a series of seven papers, Andreas & Günther have introduced seven definitions of actual causation and have class...
LLM-based dialogue assistants have become mainstream tools for software developers, yet current evaluation benchmarks...
Agentic data analysis systems produce rich outputs, including code, numerical results, and verbal diagnostics. This m...
Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impa...
In the big-world regime, agents cannot be universally capable and their ability is inevitably specialized across a wo...
Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still st...