1 citations · 1 across the 12 of their papers we have counts for
13 papers
Topology optimization of shipboard components for Navy shock certification via the Dynamic Design Analysis Method
Miguel A. Aguilo
Shipboard equipment must survive the transient loads of underwater explosions. The U.S. Navy certifies such equipment with the Dynamic Design Analysis Method (DDAM), a modal respon…
Structured Thoughts For Improved Reasoning And Context Pruning
Zain Sarwar, Supriyo Chakraborty, Berkcan Kapusuzoglu +5
Large language models (LLMs) excel at generating long chains of thought, but long reasoning traces are often verbose and memory-inefficient. In this work, we introduce Structured T…
On the Policy Gradient Foundations of Group Relative Policy Optimization: Credit Assignment, Gradient Sparsity, and Rank Collapse
Amritansh Mishra, Supriyo Chakraborty, Berkcan Kapusuzoglu
Group Relative Policy Optimization (GRPO) eliminates the learned critic in PPO by using the mean reward of grouped rollouts as a baseline. We provide a rigorous derivation of GRPO…
A History-Aware Visually Grounded Critic for Computer Use Agents
Jaewoo Lee, Zaid Khan, Archiki Prasad +7
Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in compl…
CoT-Guard: Small Models for Strong Monitoring
Nirav Diwan, Han Wang, Berkcan Kapusuzoglu +6
Monitoring the chain-of-thought (CoT) of reasoning models is a promising approach for detecting covert misbehavior (i.e., hidden objectives) in code generation tasks. While large m…
Know Thy Reasoner: Not All Language Models Explore Alike
Moulik Choraria, Argyrios Gerogiannis, Anirban Das +4
Compute scaling for LLM reasoning trades off exploring solution approaches (\emph{breadth}) against refining promising ones (\emph{depth}), yet why a given trade-off works, and why…
Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection
Tianyi Niu, Justin Chih-Yao Chen, Genta Indra Winata +6
Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground-truth labeled data, which is often unav…
Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs
Stefan Horoi, Sangwoo Cho, Supriyo Chakraborty +4
Task arithmetic is a powerful technique for transferring skills between Large Language Models (LLMs), but it often suffers from negative interference when models have diverged duri…
SPEAR-MM: Selective Parameter Evaluation and Restoration via Model Merging for Efficient Financial LLM Adaptation
Berkcan Kapusuzoglu, Supriyo Chakraborty, Renkun Ni +2
Large language models (LLMs) adapted to financial domains often suffer from catastrophic forgetting of general reasoning capabilities essential for customer interactions and comple…
Optimizing Reasoning Efficiency through Prompt Difficulty Prediction
Bo Zhao, Berkcan Kapusuzoglu, Kartik Balasubramaniam +3
Reasoning language models perform well on complex tasks but are costly to deploy due to their size and long reasoning traces. We propose a routing approach that assigns each proble…
Critique-Guided Distillation for Robust Reasoning via Refinement
Berkcan Kapusuzoglu, Supriyo Chakraborty, Zain Sarwar +2
Supervised fine-tuning with expert demonstrations often produces models that imitate outputs without internalizing the reasoning processes needed for robust generalization. While c…
LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
Akshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das +4
Large language models (LLMs) have revolutionized various domains, yet their utility comes with significant challenges related to outdated or problematic knowledge embedded during p…
Knowledge from Uncertainty in Evidential Deep Learning
Cai Davies, Marc Roig Vilamala, Alun D. Preece +3
This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning a…