5 papers
Attention Mechanism and Heuristic Approach: Context-Aware File Ranking Using Multi-Head Self-Attention
Pradeep Kumar Sharma, Shantanu Godbole, Sarada Prasad Jena +1
The identification and ranking of impacted files within software reposi-tories is a key challenge in change impact analysis. Existing deterministic approaches that combine heuristi…
Keeping Code-Aware LLMs Fresh: Full Refresh, In-Context Deltas, and Incremental Fine-Tuning
Pradeep Kumar Sharma, Ishaan Puri, Mantinder Jit Singh +2
Modern codebases evolve continuously: files are renamed or deleted; public APIs drift; behavior shifts within otherwise familiar modules. A model trained yesterday to map a develop…
Scalable and Explainable Enterprise Knowledge Discovery Using Graph-Centric Hybrid Retrieval
Nilima Rao, Jagriti Srivastava, Pradeep Kumar Sharma +1
Modern enterprises manage vast knowledge distributed across heterogeneous systems such as Jira, Git repositories, Confluence, and wikis. Conventional retrieval methods based on key…
Production-Grade Local LLM Inference on Apple Silicon: A Comparative Study of MLX, MLC-LLM, Ollama, llama.cpp, and PyTorch MPS
Varun Rajesh, Om Jodhpurkar, Pooja Anbuselvan +5
We present a systematic, empirical evaluation of five local large language model (LLM) runtimes on Apple Silicon: MLX, MLC-LLM, llama.cpp, Ollama, and PyTorch MPS. Experiments were…
Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Vasudha Yanuganti, Ishaan Puri, Swapnil Chhatre +4
Modern codebases make it hard for developers and AI coding assistants to find the right source files when answering questions like "How does this feature work?" or "Where was the b…