3 citations · 8 across the 15 of their papers we have counts for
17 papers
SphMind: Towards Robust, Training-Free VLM-based Spatial Reasoning with a 360 Camera
Shriram Damodaran, Soumyaratna Debnath, Cheston Tan +1
Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use of Multi-modal Large Language Mod…
StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation
Zijun Lin, Zeqing Wang, Cheston Tan +2
Recent game world models can generate visually realistic and interactive environments conditioned on player actions. However, games are not defined by pixels alone; they are govern…
Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol
Flint Xiaofeng Fan, Cheston Tan, Roger Wattenhofer +1
As AI agents powered by large language models (LLMs) increasingly use external tools for high-stakes decisions, a critical reliability question arises: how do errors propagate acro…
TangramSR: Can Vision-Language Models Reason in Continuous Geometric Space?
Yikun Zong, Cheston Tan
Humans excel at spatial reasoning tasks like Tangram puzzle assembly through cognitive processes involving mental rotation, iterative refinement, and visual feedback. Inspired by h…
FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models
Zijun Lin, Jiafei Duan, Haoquan Fang +4
Recent advances in robotic manipulation have integrated low-level robotic control into Vision-Language Models (VLMs), extending them into Vision-Language-Action (VLA) models. Altho…
Stencil: Subject-Driven Generation with Context Guidance
Gordon Chen, Ziqi Huang, Cheston Tan +1
Recent text-to-image diffusion models can generate striking visuals from text prompts, but they often fail to maintain subject consistency across generations and contexts. One majo…
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio, Tegan Maharaj, Luke Ong +84
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy,…
GroundFlow: A Plug-in Module for Temporal Reasoning on 3D Point Cloud Sequential Grounding
Zijun Lin, Shuting He, Cheston Tan +1
Sequential grounding in 3D point clouds (SG3D) refers to locating sequences of objects by following text instructions for a daily activity with detailed steps. Current 3D visual gr…
From Grunts to Lexicons: Emergent Language from Cooperative Foraging
Maytus Piriyajitakonkij, Rujikorn Charakorn, Weicheng Tao +4
Language is a powerful communicative and cognitive tool. It enables humans to express thoughts, share intentions, and reason about complex phenomena. Despite our fluency in using a…
Human-like compositional learning of visually-grounded concepts using synthetic environments
Zijun Lin, M Ganesh Kumar, Cheston Tan
The compositional structure of language enables humans to decompose complex phrases and map them to novel visual concepts, showcasing flexible intelligence. While several algorithm…
Evaluating the Generation of Spatial Relations in Text and Image Generative Models
Shang Hong Sim, Clarence Lee, Alvin Tan +1
Understanding spatial relations is a crucial cognitive ability for both humans and AI. While current research has predominantly focused on the benchmarking of text-to-image (T2I) m…
Super Tiny Language Models
Dylan Hillier, Leon Guertler, Cheston Tan +3
The rapid advancement of large language models (LLMs) has led to significant improvements in natural language processing but also poses challenges due to their high computational a…
Can LLMs perform structured graph reasoning?
Palaash Agrawal, Shavak Vasania, Cheston Tan
Pretrained Large Language Models (LLMs) have demonstrated various reasoning capabilities through language-based prompts alone, particularly in unstructured task settings (tasks pur…
Advancing Perception in Artificial Intelligence through Principles of Cognitive Science
Palaash Agrawal, Cheston Tan, Heena Rathore
Although artificial intelligence (AI) has achieved many feats at a rapid pace, there still exist open problems and fundamental shortcomings related to performance and resource effi…
DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using Determiners
Clarence Lee, M Ganesh Kumar, Cheston Tan
State-of-the-art visual grounding models can achieve high detection accuracy, but they are not designed to distinguish between all objects versus only certain objects of interest.…
STUPD: A Synthetic Dataset for Spatial and Temporal Relation Reasoning
Palaash Agrawal, Haidi Azaman, Cheston Tan
Understanding relations between objects is crucial for understanding the semantics of a visual scene. It is also an essential step in order to bridge visual and language models. Ho…
Compositional Learning of Visually-Grounded Concepts Using Reinforcement
Zijun Lin, Haidi Azaman, M Ganesh Kumar +1
Children can rapidly generalize compositionally-constructed rules to unseen test sets. On the other hand, deep reinforcement learning (RL) agents need to be trained over millions o…