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Explicit Evidence Grounding via Structured Inline Citation Generation

new Abstract: As AI systems become more widely adopted, the demand for factual and faithful generation grows. Properly attributing information through citations becomes, therefore, crucial. This work introduces FullCite, a framework that, in contrast to most previous works, generates structured inline citations linking each claim to both its source document and supporting evidence.

arXiv CS 2d ago

TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination

arXiv:2606.01737v1 Announce Type: new Abstract: Traffic accident liability analysis is a critical yet challenging task in intelligent transportation and legal assistance. Existing methods often suffer from low efficiency, subjective judgment, and inconsistent analysis results. Meanwhile, large language models are constrained by noisy video inputs and insufficient legal domain knowledge.

arXiv CS 8d ago

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv:2605.28910v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce \itermodelfull (\itermodel), an inference-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections. Building on this, we propose \itermodel for...

arXiv CS 8d ago

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv:2605.28910v3 Announce Type: replace Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce Hallucination Detection Guided Self-Refinement (HDSR), an inference-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections. Building on this, we...

arXiv CS 7d ago

Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time

arXiv:2606.01923v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently exhibit "contextual disregard" when faced with input evidence that conflicts with their internal parametric memory, leading to persistent factual hallucinations. Existing mitigation strategies primarily rely on suppressing specific neuron activations or employing computationally expensive contrastive decoding mechanisms, which often result in increased perplexity or significantly elevated inference...

arXiv CS 8d ago

Summarization is Not Dead Yet

arXiv:2606.08000v1 Announce Type: new Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem. We re-examine this narrative through a multi-track evaluation covering five diverse datasets and five state-of-the-art LLMs, combining controlled human assessment, bias-mitigated LLM-as-Judge protocols, factuality verification...

arXiv CS 1d ago

Building Reliable Long-Form Generation via Hallucination Rejection Sampling

arXiv:2606.03628v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in open-ended text generation, yet they remain prone to hallucinating incorrect or unsupported content, which undermines their reliability. This issue is exacerbated in long-form generation due to hallucination snowballing, a phenomenon where early errors propagate and compound into subsequent outputs. To address this challenge, we propose a novel inference-time hallucination...

arXiv CS 7d ago

Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy

arXiv:2606.03142v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) show strong visualization interpretation, yet it is unclear whether their responses reflect genuine reasoning over visual evidence or factual priors learned during training. Current evaluations mix these two sources, obscuring when correct visual interpretation is overridden by memorized facts. We present a framework that isolates visual correctness from factual correctness, revealing validity limitations in...

arXiv CS 7d ago

No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand

Announce Type: replace Abstract: The Plain Writing Act in the United States requires government documents to be accessible in clear and simple language that the general public can easily understand, yet existing summarization systems struggle to address diverse linguistic and cognitive barriers among general readers. We present NRLB (No Reader Left Behind), a multi-agent framework for plain language summarization that simulates three representative reader groups: elementary school student...

arXiv CS 9d ago

Karmelo Anthony’s self-defense claim could define murder trial in high school track meet stabbing: analyst

More than a year after 17-year-old Austin Metcalf was fatally stabbed during a confrontation at a Frisco high school track meet, the Texas suspect accused of killing him is expected back in court as jury selection begins for his murder trial on Monday. Karmelo Anthony, 18, faces a first-degree murder charge in connection with Metcalf's death. The start of jury selection is expected to provide the latest indication of how prosecutors and defense attorneys plan to navigate a case that has...

Fox News 9d ago