13 accepted papers
The dominant paradigm for training large reasoning models—combining Supervised Fine-Tuning (SFT) with Reinforcement Learning with Verifiable Rewards (RLVR)—is fundamentally constrained by its reliance on high-quality, human-annotated reasoning data and external verifiers. This dependency incurs sign…
Vision-Language Models (VLMs) have been increasingly integrated into object navigation tasks for their rich prior knowledge and strong reasoning abilities. However, applying VLMs to navigation presents two key challenges: effectively parsing and structuring complex environment information and determ…
Reasoning Video Object Segmentation (ReasonVOS) is a challenging task that requires stable object segmentation across video sequences using implicit and complex textual inputs. Previous methods fine-tune Multimodal Large Language Models (MLLMs) to produce segmentation outputs, which demand substanti
In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: $\textbf{their hidden representations encode future outputs beyond the next token}$. Through simple probing, we demonstrate that LLM prompt representations en…
We introduce a diffusion-based approach for generating privacy-preserving digital twins of multi-room indoor environments from depth images only. Central to our approach is a novel Multi-view Overlapped Scene Alignment with Implicit Consistency (MOSAIC) model that explicitly considers cross-view dep…
Large language models are typically fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex. In this work, we introduce **Integrated Value Guidance (IVG)**, a method that uses implicit and explicit value functions to guide language model decoding…
Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are known to produce images with harmful stereotypes and misrepresentations of cultures. We improve inclusive representation i…
The utilization of single-view depth cameras for image capture is prevalent in the context of robotic grasping. However, the incomplete point cloud structure generated from a single-view depth image poses challenges in accurately predicting grasping configurations, due to the lack of some important
Large language models are usually fine-tuned to align with human preferences. However, fine-tuning a large language model can be challenging. In this work, we introduce $\textit{weak-to-strong search}$, framing the alignment of a large language model as a test-time greedy search to maximize the log-…
Robotic grasping is a fundamental skill for robots, but it is quite challenging in cluttered scenes. In cluttered scenes, the precise prediction of high-quality grasp configurations such as rotation and grasping width while avoiding collisions is essential. To accomplish this, the grasp detection mo…
Deep neural networks, empowered by pre-trained language models, have achieved remarkable results in natural language understanding (NLU) tasks. However, their performances can drastically deteriorate when logical reasoning is needed. This is because NLU in principle depends on not only analogical re…
Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We present an approach for generating styl…
Robotic grasping pose detection that predicts the configuration of the robotic gripper for object grasping is fundamental in robot manipulation. Based on point clouds, most of the existing methods predict grasp pose with the hierarchical PointNet++ backbone, while the non-local geometric information…