11 citations · 20 across the 10 of their papers we have counts for
11 papers
Slaying the Hydra: Interaction-Aware Circuit Discovery in Language Models
Sankaran Vaidyanathan, Rafal Urbaniak, Emily Bunnapradist +2
Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability. However, scoring components one at a time misses context-depende…
A Computationally Feasible Framework for Causal Probabilistic Explanation
Rafal Urbaniak, Sam Witty, Daniel Waxman +7
Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis. Existing tools split into tw…
Hierarchical Experimentalist Agents
Abhranil Chandra, Sankaran Vaidyanathan, Utsav Dhanuka +2
Large language models (LLMs) are increasingly used to act in the real world and augment human decision-making, yet most systems rely on parametric knowledge acquired through imitat…
The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching
Sankaran Vaidyanathan, David Arbour, Aaron Mueller +2
Activation patching is the primary tool in mechanistic interpretability. It attributes causal responsibility for a model behavior to each of its individual components by estimating…
Evaluation Awareness Is Not One Capability: Evidence from Open Language Models
Nilesh Nayan, Aishwarya Sampath Kumar, Rishiraj Girmal +5
Safety benchmarks assume that test-condition behavior predicts deployment behavior, an assumption that fails if models detect evaluation cues and adapt. This opens a gap between be…
Detecting and Characterizing Planning in Language Models
Jatin Nainani, Sankaran Vaidyanathan, Connor Watts +2
Modern large language models (LLMs) have demonstrated impressive performance across a wide range of multi-step reasoning tasks. Recent work suggests that LLMs may perform planning…
Quantitative LLM Judges
Aishwarya Sahoo, Jeevana Kruthi Karnuthala, Tushar Parmanand Budhwani +9
LLM-as-a-judge is a framework where a large language model (LLM) evaluates the output of another LLM. While LLMs excel at producing qualitative textual evaluations, they often stru…
Adaptive Circuit Behavior and Generalization in Mechanistic Interpretability
Jatin Nainani, Sankaran Vaidyanathan, AJ Yeung +2
Mechanistic interpretability aims to understand the inner workings of large neural networks by identifying circuits, or minimal subgraphs within the model that implement algorithms…
Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges
Aman Singh Thakur, Kartik Choudhary, Venkat Srinik Ramayapally +2
Offering a promising solution to the scalability challenges associated with human evaluation, the LLM-as-a-judge paradigm is rapidly gaining traction as an approach to evaluating l…
Automated Discovery of Functional Actual Causes in Complex Environments
Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere +3
Reinforcement learning (RL) algorithms often struggle to learn policies that generalize to novel situations due to issues such as causal confusion, overfitting to irrelevant factor…
Hypergraph Clustering: A Modularity Maximization Approach
Tarun Kumar, Sankaran Vaidyanathan, Harini Ananthapadmanabhan +2
Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we ge…