7 citations · 8 across the 12 of their papers we have counts for
12 papers
GraphMemix: Query-Aware Evidence Forests for Long-Term Multimodal Agent Memory
Geng Li, Yuhao Wang, Dong Li +2
Organizing long-term memory for multimodal agents remains challenging because existing methods either suffer from expensive question-agnostic offline summaries or naive embedding s…
OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Yuecheng Liu, Dafeng Chi, Shiguang Wu +10
Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction,…
Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs
Mohammad Ali Alomrani, Yingxue Zhang, Derek Li +14
Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoni…
Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
Derek Li, Jiaming Zhou, Leo Maxime Brunswic +8
The pursuit of general-purpose artificial intelligence depends on large language models (LLMs) that can handle both structured reasoning and open-ended generation. We present Omni-…
Scalable In-Context Q-Learning
Jinmei Liu, Fuhong Liu, Zhenhong Sun +6
Recent advancements in language models have demonstrated remarkable in-context learning abilities, prompting the exploration of in-context reinforcement learning (ICRL) to extend t…
One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration
Jinbang Huang, Yixin Xiao, Zhanguang Zhang +3
Pre-trained large language models (LLMs) show promise for robotic task planning but often struggle to guarantee correctness in long-horizon problems. Task and motion planning (TAMP…
Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Yihong Wu, Liheng Ma, Muzhi Li +7
Large Language Models (LLMs) equipped with modern Retrieval-Augmented Generation (RAG) systems often employ multi-turn interaction pipelines to interface with search engines for co…
Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Ge Zhang, Mohammad Ali Alomrani, Hongjian Gu +7
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational reasoning problems such as kinship or spati…
The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation
Reza Moravej, Saurabh Bodhe, Zhanguang Zhang +6
Logic synthesis is a crucial phase in the circuit design process, responsible for transforming hardware description language (HDL) designs into optimized netlists. However, traditi…
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Jiaming Zhou, Abbas Ghaddar, Ge Zhang +7
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that in…
EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems
Mohammad Dehghan, Mohammad Ali Alomrani, Sunyam Bagga +12
The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems i…
Neuro-Symbolic Hierarchical Rule Induction
Claire Glanois, Xuening Feng, Zhaohui Jiang +4
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in…