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Vipul Gupta

Pennsylvania State University

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#natural language process... Preprint Sep 2026

Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion

This extension to GROBID, a modular font-stream parser running on CPU, is the de-facto standard for structuring scientific articles and underpins several of the largest open scholarly corpora and has the best paragraph precision on both corpora, the best section detection on Materials Science, and a character error rat...

Luca Foppiano, S. Khamassi, Vipul Gupta · 0 citations
#artificial intelligence Review Sep 2026

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms outcome-level signals into actionable interventions. The sheer scale of the data renders human review impractical, driving the need for automa...

Harsh Raj, David Lee, Anas Mahmoud et al. · 0 citations
Jul 2026

The INRIA DataLake: A Generic and Scalable Ecosystem of Pipelines for HAL Applied to Software Mentions Tracking

The INRIA DataLake project is presented, which provides an ecosystem of scalable and interconnected pipelines for preparing scientific literature, extracting structured information, and applying specialised treatments, and contributes to open science through improved visibility and tracking of research software.

Luca Foppiano, Vipul Gupta, Samuel Scalbert et al. · 0 citations
Preprint Jul 2026

CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data

CRAFT is introduced, a method that converts any rubric based evaluation dataset into a model specific diagnosis of weak capabilities, and yields both a sharper picture of what a model cannot do and measurably better models after finetuning on that diagnosis.

Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru et al. · 0 citations
Preprint Jul 2026

Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

This work introduces an interaction-centric taxonomy that localizes failures to the interactions in which they originate and identifies the responsible component, and organizes 41 failure modes by assigning each to an edge between two components and a fault side indicating where the repair belongs.

Harsh Raj, Vipul Gupta, Anas Mahmoud et al. · 0 citations

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