4 accepted papers
Deep research becomes increasingly important as people seek to solve complex problems that require gathering and synthesizing information from diverse sources. A key capability in this process is agentic search, where an LLM-agent iteratively retrieves relevant information across multiple sources wh…
Multimodal generative models can process instructions in various modalities and demonstrate outstanding performance across a wide range of image generation tasks. However, their robustness in complex real-world scenarios remains limited due to insufficient generalized instruction alignment. We intro
Recent advances in large vision-language models (VLMs) and large language models (LLMs) have enabled zero-shot approaches to Visual Language Navigation (VLN), where an agent follows natural language instructions using only ego perception and reasoning. However, existing zero‑shot methods typically c…
Finance is a particularly challenging playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bi…