From the 1 of 7 linked papers with an AI index.
4 papers · 1 filter
What We Talk About When We Talk About LLM Planning: Evidence for Two Distinct Planning Abilities
Sukai Huang, Chenyuan Zhang, Fucai Ke +4
The paper investigates whether large language models (LLMs) have distinct planning abilities by applying multidimensional item response theory to benchmark data, uncovering two sep…
Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents
Sukai Huang, Chenyuan Zhang, Fucai Ke +4
Instruction granularity is an important yet poorly controlled variable in language-guided embodied AI. Existing benchmarks typically pair each task with a single static instruction…
MATA: A Trainable Hierarchical Automaton System for Multi-Agent Visual Reasoning
Zhixi Cai, Fucai Ke, Kevin Leo +4
Recent vision-language models have strong perceptual ability but their implicit reasoning is hard to explain and easily generates hallucinations on complex queries. Compositional m…
Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts
Sukai Huang, Nir Lipovetzky, Trevor Cohn
Large Language Models (LLMs) have shown promise in solving natural language-described planning tasks, but their direct use often leads to inconsistent reasoning and hallucination.…