Deep Cognition and Language Research Lab
Research across Six Themes in AI
DeCLaRe is a research group at NTU working across Safety, Trustworthiness, Multimodality, AI for Science, Efficiency, and Embodied AI. Recent notable work includes online memory for LLMs, dynamic data selection for efficient ML training, vision-language-action models, text-to-audio generation, trustworthy RAG, and operational AI safety.
About DeCLaRe
DeCLaRe, short for Deep Cognition and Language Research, was founded by Soujanya Poria at the Singapore University of Technology and Design in 2019 with Navonil Majumder, Devamanyu Hazarika, and Deepanway Ghosal. The lab moved to Nanyang Technological University in 2025.
The robot recalls early computing while forming 宣 (xuān), “to declare”: an intelligent social agent connecting computing’s history with collaborative AI.
Research Themes
Safety
Operational safety, red-teaming, refusal behavior, and alignment interventions.
Trustworthiness
Grounded attribution, reliable retrieval, uncertainty, and citation-aware responses.
Multimodality
Language, vision, audio, and video models for reasoning, generation, and interaction.
AI for Science
Scientific hypothesis discovery, chemistry, and literature-grounded reasoning.
Efficiency
Data selection, adapters, model merging, memory, and compact model training.
Embodied AI
Vision-language-action systems, action grounding, and interactive evaluation.
Hot Papers 🔥
δ-mem: Efficient Online Memory for Large Language Models
A compact online state gives language models working memory without extending the visible context.
OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!
An operational-safety benchmark for whether task-specific agents respect their intended boundaries.
Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
A training-aware policy selects useful data as the target model evolves.
Measuring and Enhancing Trustworthiness of LLMs in RAG
Trust-Score and Trust-Align evaluate and improve grounded attribution, citations, and refusal.
Funded Research Directions
Active and completed research support.