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Atharva Pandey

3 accepted papers

2025

RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation

ICML 2025poster

Recent Large Language Models (LLMs) have reported high accuracy on reasoning benchmarks. However, it is still unclear whether the observed results arise from true “reasoning” or from statistical recall of the training set. Inspired by the ladder of causation (Pearl, 2009) and its three levels (assoc…

Cited by 0SourcePDFScholar
2025

Teaching Transformers Causal Reasoning through Axiomatic Training

ICML 2025poster

For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since interventional data is costly to generate, we study to what extent an agent can learn causal reasoning from passive data. Specifically, we consider an axiomatic training setup where an agent learn…

Cited by 4SourcePDFScholar
2024

LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions

AAAI 2024technical

Implicit 3D surface reconstruction of an object from its partial and noisy 3D point cloud scan is the classical geometry processing and 3D computer vision problem. In the literature, various 3D shape representations have been developed, differing in memory efficiency and shape retrieval effectivenes…

Cited by 5SourcePDFScholar