14 accepted papers
In highly interactive urban driving, decision making is often naturally multi-stage, and decisions at different stages can lead to different reactions from surrounding vehicles. This calls for stage-wise evaluation and selection. Tree-based planning naturally supports multi-stage search and evaluati
As real-world knowledge continues to evolve, the parametric knowledge acquired by multimodal models during pretraining becomes increasingly difficult to remain consistent with real-world knowledge. Existing research on multimodal knowledge updating focuses only on learning previously unknown knowled…
Training web agents to navigate complex, real-world websites requires them to master subtasks—short-horizon interactions on multiple UI components (e.g., choosing the correct date in a date picker, or scrolling in a container to extract information). We introduce WARC-Bench (Web Archive Benchmark),…
Spatially Resolved Transcriptomics (SRT) has become an indispensable tool in various fields, including tumor microenvironment identification, neurobiology, and the study of complex tissue architecture. However, the accuracy of these insights is often compromised by noise in spatial transcriptomics…
Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilitating their transition from controlled simulations to complex, real-world applications across various platforms. However, the effectiveness of these agents hinges on the robust…
Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. Ho…
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the pr…
Data-driven trajectory prediction is critical in autonomous vehicles, which requires high-quality data. However, discussions about the compatibility of data collected from different countries remain limited, with a typical issue being the different driving rules in various countries. Therefore, we p
Recent works leverage LLMs to roleplay realistic social scenarios, aiding novices in practicing their social skills. However, simulating sensitive interactions, such as in the domain of mental health, is challenging. Privacy concerns restrict data access, and collecting expert feedback, although vit…
Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. Despite this progress, their reasoning is often constrained by their intrinsic understanding, lacking external insights. To address this, we propose Exchange-of-Thou…
We propose a novel graph-based approach for image retrieval. Given a nearest neighbor graph produced by the global descriptor model, we traverse it by alternating between exploit and explore steps. The exploit step maximally utilizes the immediate neighborhood of each vertex, while the explore step…
Despite recent progress in computer vision, image retrieval remains a challenging open problem. Numerous variations such as view angle, lighting and occlusion make it difficult to design models that are both robust and efficient. Many leading methods traverse the nearest neighbor graph to exploit hi…
Video dubbing is a new type of language learning practice. Because of the fun it brings into learning, video dubbing mobile applications have become quite popular. During video dubbing, learners not only mimic characters' pronunciations but also other voicing characteristics, e.g., emotions. In this…
Policy optimization is the core part of statistical dialogue management. Deep reinforcement learning has been successfully used for dialogue policy optimization for a static pre-defined domain. However, when the domain changes dynamically, e.g. a new previously unseen concept (or slot) which can be…