HalluWorld is a controlled benchmark using explicit reference world models to automatically label and disentangle hallucinations in LLMs across synthetic environments with varying complexity and observability.
The babyview dataset: High-resolution egocentric videos of infants’ and young children’s everyday experiences
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
Introduces a baby-centric touch coding system and 264k-clip dataset for contrastive pretraining to study touch's role in infant visual learning.
citing papers explorer
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HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models
HalluWorld is a controlled benchmark using explicit reference world models to automatically label and disentangle hallucinations in LLMs across synthetic environments with varying complexity and observability.
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Zero-shot World Models Are Developmentally Efficient Learners
A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
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Making Sense of Touch from the Child's View for Contrastive Learning
Introduces a baby-centric touch coding system and 264k-clip dataset for contrastive pretraining to study touch's role in infant visual learning.