11 papers
Searching for What Matters: Open-Ended Discovery of Multiple Hypotheses with Agentic Data Exploration
Sanchit Kabra, Nurendra Choudhary, Jiangshu Du +3
Discovering what drives an outcome from data is central to science and often requires deciding what is worth investigating before a specific hypothesis is known. Yet most existing…
LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search
Nikhil Abhyankar, Sha Li, Sanchit Kabra +3
Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains. Existing approaches cast disco…
LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs
Sanchit Kabra, Nikhil Abhyankar, Saaketh Desai +2
Scientific discovery is a closed-loop process in which hypotheses guide data acquisition and observations refine the hypothesis space. Yet most approaches reduce discovery to super…
Beyond Single Bugs: Benchmarking Large Language Models for Multi-Vulnerability Detection
Chinmay Pushkar, Sanchit Kabra, Dhruv Kumar +1
Large Language Models (LLMs) have demonstrated significant potential in automated software security, particularly in vulnerability detection. However, existing benchmarks primarily…
RUST-BENCH: Benchmarking LLM Reasoning on Unstructured Text within Structured Tables
Nikhil Abhyankar, Purvi Chaurasia, Sanchit Kabra +3
Existing tabular reasoning benchmarks mostly test models on small, uniform tables, underrepresenting the complexity of real-world data and giving an incomplete view of Large Langua…
SURFACEBENCH: A Geometry-Aware Benchmark for Symbolic Surface Discovery
Sanchit Kabra, Shobhnik Kriplani, Parshin Shojaee +1
Equation discovery from data is a central challenge in machine learning for science, which requires the recovery of concise symbolic expressions that govern complex physical and ge…
LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery
Nikhil Abhyankar, Sanchit Kabra, Saaketh Desai +1
Materials discovery requires navigating vast chemical and structural spaces while satisfying multiple, often conflicting, objectives. We present LLM-guided Evolution for MAterials…
Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning
Sanchit Kabra, Akshita Jha, Chandan K. Reddy
Recent advances in large-scale generative language models have shown that reasoning capabilities can significantly improve model performance across a variety of tasks. However, the…
Biased or Flawed? Mitigating Stereotypes in Generative Language Models by Addressing Task-Specific Flaws
Akshita Jha, Sanchit Kabra, Chandan K. Reddy
Recent studies have shown that generative language models often reflect and amplify societal biases in their outputs. However, these studies frequently conflate observed biases wit…
Aligned at the Start: Conceptual Groupings in LLM Embeddings
Mehrdad Khatir, Sanchit Kabra, Chandan K. Reddy
This paper shifts focus to the often-overlooked input embeddings - the initial representations fed into transformer blocks. Using fuzzy graph, k-nearest neighbor (k-NN), and commun…
AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection
Anish Mall, Sanchit Kabra, Ankur Lhila +1
This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using mult…