Re-derivation of activation patching NIE reveals it captures interaction effects in addition to direct causal effects, demonstrated via GPT-2 IOI circuit where INT explains component ranking issues and faithfulness instability.
VisualToolAgent (VisTA): A reinforcement learning framework for visual tool selection
4 Pith papers cite this work. Polarity classification is still indexing.
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A prompt-based uncertainty decomposition separates action confidence from request uncertainty to enable clarification seeking in LLM agents, yielding F1 gains of 73% and 36% over baselines on two new underspecified benchmarks across five models.
Formalizes concept learning in sparse autoencoders as set alignment between human-defined and model-induced concepts, distinguishing detection, separation, and approximation with geometric conditions for neuron representation.
Chain-of-Focus enables VLMs to adaptively search and zoom on important image areas via a two-stage SFT and RL pipeline on a custom 3K-sample dataset, yielding 5% gains on the V* benchmark across resolutions from 224 to 4K.
citing papers explorer
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The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching
Re-derivation of activation patching NIE reveals it captures interaction effects in addition to direct causal effects, demonstrated via GPT-2 IOI circuit where INT explains component ranking issues and faithfulness instability.
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Uncertainty Decomposition for Clarification Seeking in LLM Agents
A prompt-based uncertainty decomposition separates action confidence from request uncertainty to enable clarification seeking in LLM agents, yielding F1 gains of 73% and 36% over baselines on two new underspecified benchmarks across five models.
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A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
Formalizes concept learning in sparse autoencoders as set alignment between human-defined and model-induced concepts, distinguishing detection, separation, and approximation with geometric conditions for neuron representation.
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Adaptive Chain-of-Focus Reasoning via Dynamic Visual Search and Zooming for Efficient VLMs
Chain-of-Focus enables VLMs to adaptively search and zoom on important image areas via a two-stage SFT and RL pipeline on a custom 3K-sample dataset, yielding 5% gains on the V* benchmark across resolutions from 224 to 4K.