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cs.CL2026

Who is the Agent to Blame? Localizing Faithfulness and Citation Mistakes in Agentic Deep Research

Eran Hirsch, David Wan, Han Wang +3

Deep research (DR) systems produce long-form cited reports by orchestrating multiple agents that search and synthesize information from the web. Citations are the primary mechanism…

cs.CL2026

Multimodal Fact-Level Attribution for Verifiable Reasoning

David Wan, Han Wang, Ziyang Wang +3

Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation, where reliability requires grounding mo…

cs.CL2026

MERRIN: A Benchmark for Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments

Han Wang, David Wan, Hyunji Lee +6

Motivated by the underspecified, multi-hop nature of search queries and the multimodal, heterogeneous, and often conflicting nature of real-world web results, we introduce MERRIN (…

cs.CL2025

PrefixNLI: Detecting Factual Inconsistencies as Soon as They Arise

Sapir Harary, Eran Hirsch, Aviv Slobodkin +3

Natural Language Inference (NLI) models have been used in various ways to improve the factuality of LLM outputs. This is typically done by applying an NLI model to judge whether th…

cs.CL2025

QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization

Shiyue Zhang, David Wan, Arie Cattan +3

How to properly conduct human evaluations for text summarization is a longstanding challenge. The Pyramid human evaluation protocol, which assesses content selection by breaking th…

cs.CL2025

Localizing Factual Inconsistencies in Attributable Text Generation

Arie Cattan, Paul Roit, Shiyue Zhang +5

There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existin…