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cs.CV2026

If, Then, Otherwise: Diagnosing Conditional Branching in Vision-Language Navigation

Seoyoung Lee, Neel P. Bhatt, Pranay Samineni +8

Vision-language navigation agents are often evaluated on their ability to follow route-like instructions toward a fixed goal. Yet, real navigation instructions often depend on obse…

cs.CV2026

CrossView: Can Vision-Language Models Reason Across Cameras?

Sahil Shah, S P Sharan, Harsh Goel +4

Video understanding benchmarks have long centered on single-camera settings, where modern multi-modal language models achieve strong performance across image and video tasks. Yet,…

cs.CV2026

Incentivizing Vision Language Models to Search for Long Video Question Answering

Harsh Goel, S P Sharan, Sahil Shah +4

We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process. VSeek…

cs.CV2025

NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoning

Sahil Shah, S P Sharan, Harsh Goel +5

While vision-language models (VLMs) excel at tasks involving single images or short videos, they still struggle with Long Video Question Answering (LVQA) due to its demand for comp…

cs.CV2025

We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback

Minkyu Choi, S P Sharan, Harsh Goel +2

Current text-to-video (T2V) generation models are increasingly popular due to their ability to produce coherent videos from textual prompts. However, these models often struggle to…

cs.CV2024

Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification

S P Sharan, Minkyu Choi, Sahil Shah +3

Recent advancements in text-to-video models such as Sora, Gen-3, MovieGen, and CogVideoX are pushing the boundaries of synthetic video generation, with adoption seen in fields like…