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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

User-Centric Evidence Ranking for Attribution and Fact Verification

Guy Alt, Eran Hirsch, Serwar Basch +2

Attribution and fact verification are critical challenges in natural language processing for assessing information reliability. While automated systems and Large Language Models (L…

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

CRISP: Complex Reasoning with Interpretable Step-based Plans

Matan Vetzler, Koren Lazar, Guy Uziel +3

Recent advancements in large language models (LLMs) underscore the need for stronger reasoning capabilities to solve complex problems effectively. While Chain-of-Thought (CoT) reas…

cs.CL2025

GenerationPrograms: Fine-grained Attribution with Executable Programs

David Wan, Eran Hirsch, Elias Stengel-Eskin +2

Recent large language models (LLMs) achieve impressive performance in source-conditioned text generation but often fail to correctly provide fine-grained attributions for their out…

cs.CL2025

CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection

Ron Eliav, Arie Cattan, Eran Hirsch +4

A common approach to hallucination detection casts it as a natural language inference (NLI) task, often using LLMs to classify whether the generated text is entailed by correspondi…