5 papers · 1 filter
Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer
Suparna Bhattacharya, Tarun Kumar, Cong Xu +7
AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as protocols (e.g., MCP, A2A) ea…
Neuro-symbolic PRM: Enhancing Scientific Reasoning via Structured Traces and Symbolic Verification
Yuxin Zi, Cong Xu, Suparna Bhattacharya +2
While tool-augmented Large Language Models have significantly improved multi-step reasoning in quantitative STEM tasks, a critical residual failure mode remains: intermediate reaso…
MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs
Shubhadip Nag, Srinjoy Das, Agniva Saha +5
LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful conc…
Latent Performance Profiling of Large Language Models
Tanmoy Chakraborty, Ayan Sengupta, Suparna Bhattacharya +7
Large language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of their capabilities. Evaluating open-source…
Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
Renjith Prasad, Rishabh Sharma, Andrew E. Shao +8
Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, making them both difficult to a…