arXiv:2602. 13319v2 Announce Type: replace Abstract: Perspective-aware AI requires modeling evolving internal states---goals, emotions, contexts---not merely preferences.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
Relationally Grounded Latent World Models for Autonomous Driving proposes using traffic scene graphs as privileged semantic supervision for latent world representations. The approach builds actor‑centric scene graphs from nuScenes 3D annotations, encodes their relational structure with a frozen text embedding model, and aligns visual latent representations to this semantic target during training. At inference the supervision branch is removed, requiring no scene graphs or 3D annotations and adding no extra computation, while achieving a 5.9% reduction in average trajectory L2 error and a 52.4% drop in collision rate compared to the LAW baseline.
arXiv:2509. 21489v4 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into question the very feasibility of creating such models.
arXiv:2606. 01810v1 Announce Type: new Abstract: Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning.
arXiv:2603. 22281v2 Announce Type: replace-cross Abstract: Recent progress in latent world models (e.
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.
SIMLIFE is a scalable platform that simulates long-term household life with rich visual observations, ground-truth action logs, and synthetic dialogues. It introduces the SimLife-BP benchmark, which tests long-context pattern understanding by requiring agents to infer latent behavioral rules from weeks or months of everyday observations across 106 episodes. The benchmark includes 1,439 question-answer pairs that probe direct, counterfactual, noisy, and inverse reasoning under varying rule hints.