2 citations · 5 across the 23 of their papers we have counts for
10 papers · 1 filter
Capturing In-Context Learning Dynamics with Task Operators
Guangzhi Xiong, Zhenghao He, Bohan Liu +3
In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requires processing the full set of…
Rethinking Reasoning Paths as Phase-Structured Trajectories
Zhenghao He, Guangzhi Xiong, Sanchit Sinha +3
Large language models often improve problem-solving performance by generating multi-step reasoning paths, yet how to analyze the hidden states along these paths remains unclear. Ex…
Monitorable Chart Reasoning Agents via Verifiable Process Rewards
Sanchit Sinha, Oana Frunza, Kashif Rasul +1
Chart reasoning agents are increasingly used to extract actionable insights in critical domains, achieving state-of-the-art performance on multiple benchmarks. Yet, high benchmark…
Towards Robustness against Typographic Attack with Training-free Concept Localization
Bohan Liu, Wenqian Ye, Guangzhi Xiong +3
Models trained via Contrastive Language-Image Pretraining (CLIP) serve as the foundational vision encoders for most modern Large Vision Language Models (LVLMs). Despite their wides…
Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
Sanchit Sinha, Guangzhi Xiong, Bohan Liu +2
The effectiveness of Chain-of-Thought (CoT) prompting in Multimodal Large Language Models (MLLMs) remains uncertain: across several visual reasoning benchmarks, CoT prompting often…
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
Guangzhi Xiong, Qiao Jin, Sanchit Sinha +2
Large Vision Language Models (LVLMs) show promise in medical applications, but their inability to faithfully ground responses in visual evidence raises serious concerns about clini…
Retrieving Counterfactuals Improves Visual In-Context Learning
Guangzhi Xiong, Sanchit Sinha, Zhenghao He +1
Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine-grained visual att…
Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity
Guangzhi Xiong, Sanchit Sinha, Aidong Zhang
The trade-off between interpretability and accuracy remains a core challenge in machine learning. Standard Generalized Additive Models (GAMs) offer clear feature attributions but a…
CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models
Zhenghao He, Guangzhi Xiong, Boyang Wang +2
Internal activations of diffusion models encode rich semantic information, but interpreting such representations remains challenging. While Sparse Autoencoders (SAEs) have shown pr…
Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models
Zhenghao He, Guangzhi Xiong, Bohan Liu +2
Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that triggers this behavior remains poorly unders…