2 citations · 2 across the 10 of their papers we have counts for
7 papers · 1 filter
ActionSplice: In-Flight Action Editing for Interactive World Models
Pardis Taghavi, Tingyu Guo, Jonas Lossner +2
Chunk-autoregressive video world models typically generate each chunk under one action. When an action changes during sampling, waiting until the next chunk delays the response, wh…
Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models
Pardis Taghavi, Reza Langari, Gaurav Pandey
Block-sparse attention accelerates video transformers by grouping queries and keys and computing attention only between selected groups. However, it faces two challenges: (1) queri…
From Numbers to Judgment: Specialist LLM Agents and Reinforcement Learning for European Listed Real Estate
Pardis Taghavi, Santosh Bhavani
We study whether the localized numerical operations and integrative judgments of financial analysis benefit from the same form of LLM specialization. Larix maps a 16-lens European…
Spec Kit Agents: Context-Grounded Agentic Workflows
Pardis Taghavi, Santosh Bhavani
Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain "context blind" in large, evolving repositories, leading to hallucinated…
Training a Student Expert via Semi-Supervised Foundation Model Distillation
Pardis Taghavi, Tian Liu, Renjie Li +2
Foundation models deliver strong perception but are often too computationally heavy to deploy, and adapting them typically requires costly annotations. We introduce a semi-supervis…
The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
Xiangbo Gao, Mingyang Wu, Siyuan Yang +4
While recent generative video models have achieved remarkable visual realism and are being explored as world models, true physical simulation requires mastering both space and time…
NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
Ximeng Tao, Pardis Taghavi, Dimitar Filev +2
Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-ba…