arXiv:2609.38028v1 Announce Type: cross Abstract: Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple st...
The paper introduces DriveMind, a large-scale driving Visual Question Answering corpus that aligns chain-of-thought reasoning with trajectory planning. Experiments with Vision‑Language Model agents show a causal disconnect: removing navigation priors significantly harms planning, while removing the chain-of-thought has little effect. The authors propose the Reasoning‑Planning Decoupling Hypothesis and present a training‑free probe to assess an agent’s reliance on priors.
arXiv:2609.05834v1 Announce Type: new Abstract: World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the...
arXiv:2610.17310v1 Announce Type: new Abstract: Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral pattern...
arXiv:2610.07954v1 Announce Type: new Abstract: Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This f...
arXiv:2607. 04681v1 Announce Type: cross Abstract: Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models.
MotiveMob is a motivation-driven autoregressive framework for generating human mobility trajectories. It models the "why-where-when" decision process by first predicting a plausible motivation for each movement step and then jointly generating the next location and arrival time. The approach is evaluated under user-level and temporal distribution shifts, including seasonal changes and COVID-19 disruptions, and consistently outperforms pretraining- and prompting-based baselines in distributional fidelity.
The paper analyzes state‑of‑the‑art vehicle trajectory predictors and finds that many surrounding agents actually worsen prediction accuracy. Using Shapley attribution, it shows models learn unstable, non‑causal decision schemes that vary across training runs. The authors introduce a Conditional Information Bottleneck that compresses agent features and ignores unhelpful ones, improving accuracy and robustness across datasets and architectures.
arXiv:2606. 31106v1 Announce Type: cross Abstract: Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal scenarios can still mask flawed reasoning and unsafe heuristics.
arXiv:2606. 05130v1 Announce Type: cross Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis.
The paper argues that current embodied vision‑language planning benchmarks favor linguistic next‑token prediction over physically grounded next‑state reasoning, leading models to rely on language priors rather than true causal dependencies. To address this, the authors introduce Causal‑Plan‑Bench, a diagnostic suite covering four causal dimensions, and Causal‑Plan‑1M, a million‑scale corpus of explicit causal reasoning traces extracted from egocentric videos. Extensive experiments show that existing models perform poorly on these tasks, while a new model trained with a tailored recipe—Causal Planner based on Qwen3‑VL‑8B—achieves significant gains, demonstrating the feasibility of physically grounded causal reasoning.
arXiv:2609.17064v1 Announce Type: new Abstract: Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inferenc...