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ReInsVLN: Revisiting Historical Evidence for Training-Free Vision-and-Language Navigation
Siwei Cheng, Hao Wu, Zitong Qi +7
Vision-and-language navigation (VLN) relies on retaining and using relevant observations. Yet growing histories introduce redundancy, and retained information may remain underused.…
CeDR-Nav: Cross-Embodiment Mapless Navigation via Offline Demonstration Recasting
Xianghui Wang, Xinming Zhang, Yiwei Qian +4
Learning-based methods have demonstrated strong performance in mapless robot navigation. However, a learned policy remains restricted to the embodiment on which it was trained. Man…
TAME-Nav: Temporal Asymmetric Mixture-of-Experts Distillation for Agile and Versatile Robot Navigation
Lingjie Zhang, Jun Xue, Shanze Wang +4
Agile and reliable navigation is a core capability for embodied robots navigating complex spatial constraints and dynamic interactions. However, a single policy often struggles to…
Local-Minimum Escaper: Programmatic Subgoal Generation for Robust Navigation in Unknown Environments
Yin Gu, Xinming Zhang, Shanze Wang +2
Mapless navigation in unknown and partially observable environments remains challenging for mobile robots, particularly when local minima prevent the robot from making progress tow…
Action Shaping: Policies Absorb What They Can Express
Yanjun Chen, Jinghan Wang, Xiaoyu Shen +2
Reward shaping has a theorem: a potential-based term can be removed without changing the optimal policy. The same practice on the action channel, an offset added in training and dr…
GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces
Tianhao Zang, Zihan Liu, Shanze Wang +4
In this paper, we present a Goal-Aware Uncertainty-Guided Exploration (GAUGE) framework for planner-conditioned active calibration of opaque quadruped velocity interfaces. Commerci…
FlashNav: Training Deployable Robot Navigation Policies in Seconds
Shanze Wang, Yiwei Qian, Xinming Zhang +8
Training Deep Reinforcement Learning (DRL) navigation policies for different robot configurations remains time-consuming. We present FlashNav, a GPU-based framework that trains rob…
AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation
Tianhao Zang, Siwei Cheng, Haidong Huang +2
Monocular local navigation is attractive for lightweight robots, but existing vision-based policies often couple perception to a specific body, camera height, and footprint, making…
From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models
Junlong Tong, Zilong Wang, YuJie Ren +4
Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To…
Evaluation Before Generation: A Paradigm for Robust Multimodal Sentiment Analysis with Missing Modalities
Rongfei Chen, Tingting Zhang, Xiaoyu Shen +1
The missing modality problem poses a fundamental challenge in multimodal sentiment analysis, significantly degrading model accuracy and generalization in real world scenarios. Exis…
MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models
Haohang Huang, Xuan Lu, Mingyi Su +9
Multimodal embedding models aim to map heterogeneous inputs, such as text, images, videos, and audio, into a shared semantic space. However, existing methods and benchmarks remain…
CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation
Haoyu Xi, Mingao Tan, Xinming Zhang +5
Visual navigation for cross-embodiment robots is challenging due to variations in robot and camera configurations, which can lead to the failure of navigation tasks. Previous appro…
Rethinking the Role of LLMs in Time Series Forecasting
Xin Qiu, Junlong Tong, Yirong Sun +3
Large language models (LLMs) have been introduced to time series forecasting (TSF) to incorporate contextual knowledge beyond numerical signals. However, existing studies question…
The Trace Is the State: Exact Credit Assignment for LLM Agent Teams
Yanjun Chen, Yirong Sun, Hanlin Wang +5
Credit assignment for a team of LLM agents, what each message was worth, has mostly been treated as prediction: a learned critic, a trajectory-level score, or an agent ablation sta…
SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models
Yirong Sun, Yanjun Chen, Xin Qiu +8
Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness,…