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LLMs struggle with dispersed facts and safety prompts in long-context tasks

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper investigates how Large Language Models (LLMs) with large context windows handle information distribution and anti-hallucination prompts. The study, which tested Gemini 2.5-Flash, ChatGPT-5-mini, Claude 4.5 Haiku, and Deepseek-v3.2-chat, found that models struggle with dispersed facts and that safety prompts can lead to over-conservative refusals. These issues suggest that models often fail due to ineffective context utilization, highlighting a need for improved robustness in long-horizon agentic workflows. AI

IMPACT Highlights challenges in LLM context utilization and safety prompting, suggesting areas for future model development.

RANK_REASON Academic paper published on arXiv detailing LLM performance on specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with dispersed facts and safety prompts in long-context tasks

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Academic paper published on arXiv detailing LLM performance on specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Amirali Ebrahimzadeh, Seyyed M. Salili ·

    Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs

    arXiv:2601.02023v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) increasingly utilize massive context windows as working memory for autonomous tasks, their reliability fluctuates significantly depending on how information is distributed in real-world corp…

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