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From the 1 of 7 linked papers with an AI index.

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7 papers

cs.SE2026

What We Observe as LLM Behavior Can Be a Side-effect of Inference Backend

Shahed Masoudian, Passant Shafaei, Monorama Swain +1

Benchmark scores are reported as properties of a model, yet the inference framework used to produce them, such as HuggingFace, vLLM, or Ollama, are considered non-influential and t…

cs.CV2026

Learning Speaker Identity Beyond Language and Modality Constraints: Insights from the POLY-SIM 2026 Challenge

Marta Moscati, Muhammad Saad Saeed, Marina Zanoni +9

The paper describes the POLY-SIM 2026 challenge, which focuses on developing multimodal speaker identification systems that remain robust when audio or visual data are missing and…

cs.CL2026

Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG

Passant Elchafei, Monorama Swain, Shahed Masoudian +1

Retrieval-Augmented Generation (RAG) aims to reduce hallucination by grounding answers in retrieved evidence, yet hallucinated answers remain common even when relevant documents ar…

cs.CL2026

H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations

Passant Elchafei, Hossam Emam, Mohamed Alansary +2

We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages). Task A evaluates standalone…

cs.CV2026

POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan

Marta Moscati, Muhammad Saad Saeed, Marina Zanoni +8

Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-w…

cs.CL2026

Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuning

Monorama Swain, Bubai Maji, Jagabandhu Mishra +3

In this work, we address the challenge of building fair English ASR systems for second-language speakers. Our analysis of widely used ASR models, Whisper and Seamless-M4T, reveals…