8 papers
Aligning What LLMs Do and Say: Towards Self-Consistent Explanations
Sahar Admoni, Ofra Amir, Assaf Hallak +1
Large language models (LLMs) seem to offer an easy path to interpretability: just ask them to explain their answers. Yet the features driving an answer often differ from those emph…
More Test-Time Compute Can Hurt: Overestimation Bias in LLM Beam Search
Gal Dalal, Assaf Hallak, Gal Chechik +1
Wider beam search should improve LLM reasoning, but when should you stop widening? Prior work on beam width selection has focused on inference efficiency \citep{qin2025dsbd, freita…
Who Said Neural Networks Aren't Linear?
Nimrod Berman, Assaf Hallak, Assaf Shocher
Neural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, . Leveraging the algebraic concept of transport of structure,…
From Actions to Words: Towards Abstractive-Textual Policy Summarization in RL
Sahar Admoni, Assaf Hallak, Yftah Ziser +2
Explaining reinforcement learning agents is challenging because policies emerge from complex reward structures and neural representations that are difficult for humans to interpret…
Policy Gradient with Tree Expansion
Gal Dalal, Assaf Hallak, Gugan Thoppe +2
Policy gradient methods are notorious for having a large variance and high sample complexity. To mitigate this, we introduce SoftTreeMax -- a generalization of softmax that employs…
"Trust me on this" Explaining Agent Behavior to a Human Terminator
Uri Menkes, Assaf Hallak, Ofra Amir
Consider a setting where a pre-trained agent is operating in an environment and a human operator can decide to temporarily terminate its operation and take-over for some duration o…