24 papers
CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability
Pratinav Seth, Hem Gosalia, Aditya Kasliwal +1
Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning. However, conducting such an…
-: Circuit-Restricted Weight Arithmetic for Selective Refusal
Aditya Kasliwal, Pratinav Seth, Vinay Kumar Sankarapu
Modern deployments require LLMs to enforce safety policies at scale, yet many controls rely on inference-time interventions that add recurring compute cost and serving complexity.…
Faithfulness to Refusal: A Causal Audit of Neuron Selectors
Ananth Eswar, Pratinav Seth, Utsav Avaiya +1
Attribution scores increasingly identify which neuron rows of a language model matter for applications such as pruning, interpretability, and editing for safety, yet whether they i…
CuratorKIT : Data Curation and Synthetic Data Generation for LLM Post-Training
Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu +1
Data curation is a critical part of post-training pipelines for large language models, yet existing tools often treat ingestion, deduplication, synthetic generation, and quality fi…
ALIGNBEAM : Inference-Time Alignment Transfer via Cross-Vocabulary Logit Mixing
Chirag Chawla, Pratinav Seth, Vinay Kumar Sankarapu
Domain fine-tuning degrades the safety of large language models: fine-tuned specialists readily comply with harmful prompts framed in domain language. Existing inference-time defen…
Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation
Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu +1
Synthetic post-training pipelines commonly filter generated samples with reward models or holistic LLM judges, yet two practices remain rarely examined together: whether the filter…