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OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling
Adam Bawatneh, Sagar Sapkota, Amrit Singh Bedi +2
Theory of Mind (ToM), the ability to infer others' knowledge, intentions, and emotions, is commonly evaluated in large language models (LLMs) using end-point question answering, wh…
LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
Mahyar Alinejad, Yue Wang, Amrit Singh Bedi +1
Transfer learning in reinforcement learning (RL) seeks to accelerate learning in new tasks by leveraging knowledge from related sources. Existing neurosymbolic transfer methods, ho…
Code Comprehension then Auditing for Unsupervised LLM Evaluation
Bhrij Patel, Souradip Chakraborty, Mengdi Wang +2
Large Language Models (LLMs) for unsupervised code correctness evaluation have recently gained attention because they can judge if code runs as intended without requiring reference…
Does Thinking More always Help? Mirage of Test-Time Scaling in Reasoning Models
Soumya Suvra Ghosal, Souradip Chakraborty, Avinash Reddy +6
Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek R1) have led to a popular belief that extending thinking traces using prompts like "Wait" or "Let…
VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferences
Anukriti Singh, Amisha Bhaskar, Peihong Yu +4
Designing reward functions for continuous-control robotics often leads to subtle misalignments or reward hacking, especially in complex tasks. Preference-based RL mitigates some of…
Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks
Xingpeng Sun, Haoming Meng, Souradip Chakraborty +2
While LLMs excel in processing text in these human conversations, they struggle with the nuances of verbal instructions in scenarios like social navigation, where ambiguity and unc…