arXiv:2607. 19548v1 Announce Type: new Abstract: Understanding animal behavior at an algorithmic level -- what animals attend to, how they form internal models and plans, and how this maps to action -- remains a central challenge in neuroscience and ethology.
arXiv:2503.10212v3 Announce Type: replace Abstract: Analyzing animal behavior is crucial in neuroscience, yet deciphering its intricate dynamics remains challenging. Traditional machine vision approa...
arXiv:2609.36217v1 Announce Type: new Abstract: A deeper understanding of brain function requires a precise, structured characterization of behavior.Yet, extracting behavioral representations from vi...
arXiv:2610.27140v1 Announce Type: cross Abstract: How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science. However, a major chal...
MC-DeTra is a reimplementation of the DeTra model that jointly performs object detection and socially-aware trajectory forecasting in bird's-eye-view images. It introduces motion-consistency mechanisms that add supervision from each actor’s past motion, surrounding traffic occupancy, and a consistency constraint aligning predicted heading with motion direction. The added losses are train‑only and inference‑safe, improving dynamic trajectory forecasting on the Waymo Open Dataset while maintaining or enhancing detection accuracy.
arXiv:2511. 20532v3 Announce Type: replace-cross Abstract: The primary output of the nervous system is movement and behavior.
The paper investigates how scaling ego‑centric human video data affects world model performance. Using 30,000 hours of video across diverse scenes, the authors find that while agent‑level fidelity improves markedly with more data, object‑level fidelity remains low and improves slowly. They demonstrate that careful visual conditioning and a new supervision scheme can shift model capacity toward object dynamics, yet a substantial gap persists, indicating that data quantity alone is insufficient for full world modeling.
The paper introduces BRAID, a hierarchical latent-variable model that generates multi-person human motion by explicitly modeling both group-level interaction dynamics and individual behavior conditioned on evolving group context. It treats social motion generation as a meta-transfer learning problem, learning shared interaction priors across datasets and adapting them to arbitrary context sets of observed people and joints. BRAID supports coherent generation under full, sparse, or partial observations and produces compact social-state vectors useful for downstream embodied-agent systems, with evaluations on social forecasting, tracking, in-filling, and response generation.
arXiv:2609.13778v1 Announce Type: cross Abstract: Human trajectory prediction requires modeling both individual motion patterns and social interactions among agents. Existing methods have made substa...
arXiv:2603. 28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems.
The paper introduces Multi-agent Egocentric World Model (ME-World), a framework that generates synchronized first-person video streams for multiple agents interacting in a shared environment. It addresses fine-grained embodied actions by ensuring cross-view action consistency, shared-environment consistency, and consistent state updates across agents. Experiments on real and synthetic data demonstrate that ME-World outperforms existing methods in shared-world consistency, action control, identity preservation, and video quality.
The paper investigates how encouraging action predictability in latent world models can preserve intrinsic dynamics, which are crucial for planning. By deriving a theoretical lower bound and conducting empirical tests, the authors show that action predictability prevents the encoder from merging distinct action outcomes, even when visual appearance varies. Using a method called Concat IDM, they achieve a substantial increase in planning success on the Push‑T benchmark, from 9.1% to 77.2%, demonstrating the practical benefit of this inductive bias.
arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.