Search Results for author:
Found 20 papers, 16 papers with code
Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning
Large language models (LLMs) can prove mathematical theorems formally by generating proof steps (\textit{a. k. a.}
Spectral Journey: How Transformers Predict the Shortest Path
In this setting, the representations and the dynamics learned by the model are interpretable.
Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
On the miniF2F benchmark, it achieves a 57. 6% success rate (Pass@32), exceeding the previous best open-source model by 7. 6%.
Formal Mathematical Reasoning: A New Frontier in AI
AI for Mathematics (AI4Math) is not only intriguing intellectually but also crucial for AI-driven discovery in science, engineering, and beyond.
Autoformalizing Euclidean Geometry
One challenge in Euclidean geometry is that informal proofs rely on diagrams, leaving gaps in texts that are hard to formalize.
Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean
Neural theorem proving combines large language models (LLMs) with proof assistants such as Lean, where the correctness of formal proofs can be rigorously verified, leaving no room for hallucination.
A Survey on Deep Learning for Theorem Proving
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in natural language to rigorous derivations in formal systems.
SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models
To bridge these gaps, we introduce SciInstruct, a suite of scientific instructions for training scientific language models capable of college-level scientific reasoning.
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Using this data, we develop ReProver (Retrieval-Augmented Prover): an LLM-based prover augmented with retrieval for selecting premises from a vast math library.
Infinite Photorealistic Worlds using Procedural Generation
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world.
Generating Natural Language Proofs with Verifier-Guided Search
In this paper, we present a novel stepwise method, NLProofS (Natural Language Proof Search), which learns to generate relevant steps conditioning on the hypothesis.
Bidding Agent Design in the LinkedIn Ad Marketplace
We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces.
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
In this work, we ask how we can build a rule-based system that can reason with natural language input but without the manual construction of rules.
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
The 3D scenes in our dataset come in minimally contrastive pairs: two scenes in a pair are almost identical, but a spatial relation holds in one and fails in the other.
Ranked #1 on
Spatial Relation Recognition
on Rel3D
Strongly Incremental Constituency Parsing with Graph Neural Networks
Based on our transition system, we develop a strongly incremental parser.
Ranked #1 on
Constituency Parsing
on CTB5
Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy
Computer vision technology is being used by many but remains representative of only a few.
SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition
Understanding the spatial relations between objects in images is a surprisingly challenging task.
Learning to Prove Theorems via Interacting with Proof Assistants
Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics.
Ranked #1 on
Automated Theorem Proving
on CoqGym
Stacked Hourglass Networks for Human Pose Estimation
This work introduces a novel convolutional network architecture for the task of human pose estimation.
Ranked #1 on
Pose Estimation
on FLIC Wrists
