Accepted Papers

Morning Session

• SQA3D: Situated Question Answering in 3D Scenes
  Xiaojian Ma, Silong Yong, Zilong Zheng, Qing Li, Yitao Liang, Song-Chun Zhu, Siyuan Huang
• Retrieval-Augmented Multimodal Language Modeling
  Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih
• On the Aggregation of Rules for Knowledge Graph Completion
  Patrick Betz, Stefan Lüdtke, Christian Meilicke, Heiner Stuckenschmidt
• Large Language Model Programs
  Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, Xian Li
• LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
  Kaiyu Yang, Aidan Swope, Alex Gu, Rahul R Chalamala, Shixing Yu, Saad Godil, Ryan Prenger, Animashree Anandkumar
• Semantically Adversarial Scene Generation with Explicit Knowledge Guidance for Autonomous Driving
  Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
• Towards true discovery of the differential equations
  Alexander Hvatov, Roman Titov
• Neural Priority Queues for GNNs
  Rishabh Jain, Petar Veličković, Pietro Lió
• VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
  Eleonora Misino, Giuseppe Marra, Emanuele Sansone
• Explanatory Learning: Towards Artificial Scientific Discovery
  Antonio Norelli, Giorgio Mariani, Luca Moschella, Andrea Santilli, Giambattista Parascandolo, Simone Melzi, Emanuele Rodola
• ANet: A Scalable Path-based Reasoning Approach for Knowledge Graphs
  Zhaocheng Zhu, XINYU YUAN, Mikhail Galkin, Louis-Pascal Xhonneux, Ming Zhang, Maxime Gazeau, Jian Tang
• DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
  Shima Imani, Ali Beyram, Harsh Shrivastava
• Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
  Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, SIMONE CALDERARA, Andrea Passerini, Stefano Teso
• OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning
  Rim Assouel, Pau Rodriguez, Perouz Taslakian, David Vazquez, Yoshua Bengio
• Look, Remember and Reason: Visual Reasoning with Grounded Rationales
  Apratim Bhattacharyya, Sunny P Panchal, Mingu Lee, Reza Pourreza, Pulkit Madan, Roland Memisevic
• Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents
  Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian Ma, Yitao Liang
• Recursive Algorithmic Reasoning
  Dulhan Jayalath, Jonas Jürß, Petar Veličković
• EXPLAIN, AGREE and LEARN: A Recipe for Scalable Neural-Symbolic Learning
  Victor Verreet, Lennert De Smet, Emanuele Sansone
• Semantic Conditioning at Inference : Improving Neural-based Systems with Logical Background Knowledge
  Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia
• Continuous-Discrete Message Passing for Graph Logic Reasoning
  Cristóbal Corvalán Morbiducci, Francesco Alesiani, Markus Zopf
• Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
  Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, He He
• Evidence of Meaning in Language Models Trained on Programs
  Charles Jin, Martin Rinard
• Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs
  Lauren Nicole DeLong, Ramon Fernández Mir, Zonglin Ji, Fiona Niamh Coulter Smith, Jacques D. Fleuriot
• Bayesian Neural Networks with Domain Knowledge
  Dylan Sam, Rattana Pukdee, Daniel P Jeong, Yewon Byun, Zico Kolter
• Exposing Attention Glitches with Flip-Flop Language Modeling
  Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
• Does End-to-End Visual Pretraining Help Reasoning?
  Chen Sun, Calvin Luo, Xingyi Zhou, Anurag Arnab, Cordelia Schmid
• On the Planning Abilities of Large Language Models - A Critical Investigation
  Karthik Valmeekam, Matthew D Marquez, Sarath Sreedharan, Subbarao Kambhampati
• Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
  Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati
• On The Ability of Transformers To Learn Recursive Patterns
  Dylan Zhang, Curt Tigges, Talia Ringer, Stella Biderman, Maxim Raginsky
• Reasoning Ability Emerges in Large Language Models as Aggregation of Reasoning Paths
  Xinyi Wang, William Yang Wang
• Exploring the Impact of Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning
  Roshanak Mirzaee, Parisa Kordjamshidi
• Plan, Eliminate, and Track --- Language Models are Good Teachers for Embodied Agents.
  Yue Wu, So Yeon Min, Yonatan Bisk, Ruslan Salakhutdinov, Amos Azaria, Yuanzhi Li, Tom Mitchell, Shrimai Prabhumoye
• SPRING: Studying Papers and Reasoning to play Games
  Yue Wu, Shrimai Prabhumoye, So Yeon Min, Yonatan Bisk, Ruslan Salakhutdinov, Amos Azaria, Tom Mitchell, Yuanzhi Li

Afternoon Session

• Parallel Algorithms Align with Neural Execution
  Valerie Engelmayer, Dobrik G. Georgiev, Petar Veličković
• Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language Models
  Daman Arora, Subbarao Kambhampati
• Latent Space Representations of Neural Algorithmic Reasoners
  Viktor Mirjanic, Razvan Pascanu, Petar Veličković
• Towards More Likely Models for AI Planning
  Turgay Caglar, sirine belhaj, Tathagata Chakraborti, Michael Katz, Sarath Sreedharan
• A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints
  Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck
• Asynchronous Algorithmic Alignment with Cocycles
  Andrew J Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Veličković
• Learning with Explanation Constraints
  Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, Pradeep Ravikumar
• BoardgameQA: Natural Language Reasoning with Contradictory Information
  Mehran Kazemi, Quan Yuan, DEEPTI BHATIA, Najoung Kim, Xin Xu, Vaiva Imbrasaite, Deepak Ramachandran
• Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting
  Rylan Schaeffer, Kateryna Pistunova, Samar Khanna, Sarthak Consul, Sanmi Koyejo
• interpretability of Transformers: a case study with Dyck grammars
  Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski
• dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning
  Renato L Geh, Jonas L Goncalves, Igor Silveira, Denis D Maua, Fabio Cozman
• Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
  Yunhao Ge, Jie Ren, Jiaping Zhao, Kaifeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan
• Disaster Occurrence Detection through GNN Models using Disaster Knowledge Graphs
  Seonhyeong Kim, Irshad Khan, Young-Woo Kwon
• Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
  Zhiyuan Li, Hong Liu, Denny Zhou, Tengyu Ma
• Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
  Hejie Cui, Xinyu Fang, Zihan Zhang, Ran Xu, Xuan Kan, Xin Liu, Manling Li, Yangqiu Song, Carl Yang
• Towards A Unified Neural Architecture for Visual Recognition and Reasoning
  Calvin Luo, Boqing Gong, Ting Chen, Chen Sun
• How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
  Yuchen Li, Yuanzhi Li, Andrej Risteski
• Large Language Models are Zero-Shot Multi-Tool Users
  Luca Beurer-Kellner, Marc Fischer, Martin Vechev
• Training LLMs with Noisy Algorithmic Chain of Thought
  Alexander Havrilla
• The Role of Semantic Parsing in Understanding Procedural Text
  Hossein Rajaby Faghihi, Parisa Kordjamshidi, Choh Man Teng, James Allen
• Partial Label Learning meets Active Learning: Enhancing Annotation Efficiency through Binary Questioning
  Shivangana Rawat, Chaitanya Devaguptapu, Vineeth Balasubramanian
• Learning to Initiate and Reason in Event-Driven Cascading Processes
  Yuval Atzmon, eli meirom, Shie Mannor, Gal Chechik
• LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
  Long Lian, Boyi Li, Adam Yala, Trevor Darrell
• Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
  Xiaoming Shi, Siqiao Xue, Kangrui Wang, Fan Zhou, James Y Zhang, Jun Zhou, Chenhao Tan, Hongyuan Mei
• What’s left can’t be right - The remaining positional incompetence of contrastive vision-language models
  Nils Höhing, Ellen Rushe, Anthony Ventresque
• Deep Neuro-Symbolic Weight Learning in Neural Probabilistic Soft Logic
  Connor Pryor, Charles Dickens, Lise Getoor
• Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks
  Savannah Thais, Daniel Murnane
• Revealing the Intrinsic Ability of Generative Language Models in Relation Prediction
  Qi Li, Lyuwen Wu, Luoyi Fu, Xinbing Wang, Lei Zhou, Chenghu Zhou, Shiyu Liang
• Augmenting the Knowledge to Large Model from Federated Small Models
  Miru Kim, Minhae Kwon
• Explicit Planning Helps Language Models in Logical Reasoning
  Hongyu Zhao, Kangrui Wang, Mo Yu, Hongyuan Mei
• Evaluating the Casual Reasoning Abilities of Large Language Models
  Isha Puri, Himabindu Lakkaraju