1 citations · 2 across the 8 of their papers we have counts for
9 papers
Predicting Privileged Information Effectiveness for On-Policy Self-Distillation
Kaijian Zou, Emiliano Penaloza, MohammadHossein Rezaei +5
On-policy self-distillation (OPSD) enables large language models (LLMs) to learn from privileged information (PI) without a separate teacher model. However, prior work largely trea…
SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal +1
We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing frontier SWE benchmarks typically…
SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution
Mohit Raghavendra, Soham Dan, Miguel Romero Calvo +12
We introduce SWE Atlas, a benchmark suite for coding agents spanning three professional software engineering workflows: Codebase Q&A (124 tasks), Test Writing (90 tasks), and Refac…
LHAW: Controllable Underspecification for Long-Horizon Tasks
George Pu, Michael S. Lee, Udari Madhushani Sehwag +6
Long-horizon workflow agents that operate effectively over extended periods are essential for truly autonomous systems. Their reliable execution critically depends on the ability t…
Agentic Rubrics as Contextual Verifiers for SWE Agents
Mohit Raghavendra, Anisha Gunjal, Bing Liu +1
Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scaling (TTS). Despite it…
Balancing the Budget: Understanding Trade-offs Between Supervised and Preference-Based Finetuning
Mohit Raghavendra, Junmo Kang, Alan Ritter
Post-training of Large Language Models often involves a pipeline of Supervised Finetuning (SFT) followed by Preference Finetuning (PFT) using methods like Direct Preference Optimiz…
Revisiting the Superficial Alignment Hypothesis
Mohit Raghavendra, Vaskar Nath, Sean Hendryx
The Superficial Alignment Hypothesis posits that almost all of a language model's abilities and knowledge are learned during pre-training, while post-training is about giving a mod…
Representation Learning in Continuous-Time Dynamic Signed Networks
Kartik Sharma, Mohit Raghavendra, Yeon Chang Lee +2
Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such…
AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements
Mohit Raghavendra, Pravan Omprakash, B R Mukesh +1
Biometric systems based on Machine learning and Deep learning are being extensively used as authentication mechanisms in resource-constrained environments like smartphones and othe…