13 citations · 42 across the 24 of their papers we have counts for
9 papers · 1 filter
Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion
Luca Foppiano, Sana Khamassi, Vipul Gupta
Transforming scholarly PDFs into machine-readable fulltext remains a bottleneck for large-scale information systems. Recent vision-based parsers improve accuracy, but need GPUs and…
Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
Harsh Raj, David Lee, Anas Mahmoud +7
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 ou…
Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures
Harsh Raj, Vipul Gupta, Anas Mahmoud +4
Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system. This creates…
CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data
Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru +3
Evaluations should do more than measure a models current performance. They should tell us what to fix for the next model iteration and provide a way to generate targeted post train…
The INRIA DataLake: A Generic and Scalable Ecosystem of Pipelines for HAL Applied to Software Mentions Tracking
Luca Foppiano, Vipul Gupta, Samuel Scalbert +5
Research repositories contain a large amount of scientific knowledge, but access to structured articles and specialised information, such as datasets or software metadata, remains…
Model Unlearning Objectives Vary for Distinct Language Functions
Berk Atil, Vipul Gupta, Rebecca J. Passonneau
Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-training uses different objectiv…
HARNESS-LM: A Three-Phase Training Recipe for Harnessing SLMs in Sponsored Search Retrieval
Vipul Gupta, Shikhar Mohan, Lakshya Kumar +4
In the competitive landscape of sponsored search, balancing retrieval quality with production latency is a critical challenge. While large retrieval models based on Small Language…
BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks
Nishant Balepur, Bhavya Rajasekaran, Jane Oh +7
Multiple-choice question answering (MCQA) is standard in NLP, but benchmarks lack rigorous quality control. We present BenchMarker, an education-inspired toolkit using LLM judges t…
MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers
Chaithanya Bandi, Razvan-Gabriel Dumitru, Ben Hertzberg +20
The Model Context Protocol (MCP) is emerging as a standard interface through which large language model (LLM) agents discover and invoke external tools. However, existing MCP evalu…