6 citations · 8 across the 12 of their papers we have counts for
13 papers
Aligning Faithfulness for Theory-Scale Autoformalization
Yuming Feng, Tadd Mao, Dhruva Arekar +4
Autoformalization (AF) requires more than compilation: formal statements must preserve the meaning of their informal sources, a property known as faithfulness. An emerging frontier…
Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs
Tadd Mao, Tianjun Zhong, Dhruva Arekar +5
In formal verification, both the autoformalization of statements and automated proof search have been studied extensively. While automated proof search can produce a formal proof t…
Theory-Scale Auto-Formalization of Logics for Computer Science
Yuming Feng, Frederick Pu, One An +5
Auto-formalization is critical for scalable formal verification, but existing progress largely focuses on isolated statements, while theory-scale auto-formalization, which coherent…
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning
Jiani Huang, Shijie Wang, Liangbo Ning +2
With the rise of LLMs, there is an increasing need for intelligent recommendation assistants that can handle complex queries and provide personalized, reasoning-driven recommendati…
ESCA: Contextualizing Embodied Agents via Scene-Graph Generation
Jiani Huang, Amish Sethi, Matthew Kuo +6
Multi-modal large language models (MLLMs) are making rapid progress toward general-purpose embodied agents. However, existing MLLMs do not reliably capture fine-grained links betwe…
MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs
Jiani Huang, Xingchen Zou, Lianghao Xia +1
The application of Large Language Models (LLMs) in recommender systems faces key challenges in delivering deep personalization and intelligent reasoning, especially for interactive…
Vision Language Models Cannot Plan, but Can They Formalize?
Muyu He, Yuxi Zheng, Yuchen Liu +7
The advancement of vision language models (VLMs) has empowered embodied agents to accomplish simple multimodal planning tasks, but not long-horizon ones requiring long sequences of…
TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games
Yuan Yuan, Muyu He, Muhammad Adil Shahid +3
This paper introduces TurnaboutLLM, a novel framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gam…
Relational Programming with Foundation Models
Ziyang Li, Jiani Huang, Jason Liu +6
Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them w…
Space-aware Socioeconomic Indicator Inference with Heterogeneous Graphs
Xingchen Zou, Jiani Huang, Xixuan Hao +6
Regional socioeconomic indicators are critical across various domains, yet their acquisition can be costly. Inferring global socioeconomic indicators from a limited number of regio…
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
Hanlin Zhang, Jiani Huang, Ziyang Li +2
Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge throug…
Scallop: A Language for Neurosymbolic Programming
Ziyang Li, Jiani Huang, Mayur Naik
We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and tra…
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision
Jiani Huang, Ziyang Li, Mayur Naik +1
Supervised approaches for learning spatio-temporal scene graphs (STSG) from video are greatly hindered due to their reliance on STSG-annotated videos, which are labor-intensive to…