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A precessing magnetic jet as the engine of GRB 250702B

Authors: Tao An

Published: 2025-11-13

arXiv ID: 2511.09850v1

Added to Library: 2025-11-14 23:00 UTC

📄 Abstract

GRB 250702B shows ultra long, episodic prompt activity, with three hard gamma ray episodes over about 3.2 h with quasi regular spacing P ~ 2825 s, preceded by a soft X ray flare about one day earlier. We interpret these phenomena with a unified scenario in which a stellar mass black hole accretes from a massive, misaligned debris disk and launches a magnetically dominated, precessing, structured (spine sheath) jet. The engine clock arises from Lense Thirring precession of the outer annulus of a geometrically thick inner torus at radius r ~ 250 to 300 gravitational radii, while the hard spectra reflect magnetic reconnection dissipation in the spine. A slightly off axis viewing geometry resolves the apparent opening angle tension without invoking late energy injection. "Missing" pulses in the second and third cycles occur naturally when low amplitude nutation causes the beaming cone to miss the line of sight. The model yields concrete, falsifiable predictions, providing a self consistent explanation of GRB 250702B radiative and outflow anomalies.

🔍 Key Points

  • The paper systematically investigates linguistic styles as a vulnerability vector for jailbreak attacks on large language models (LLMs), proposing a novel angle of analysis beyond semantic perturbations.
  • It constructs a style-augmented jailbreak benchmark using templates and LLM-generated rewrites across multiple emotional and pragmatic styles, demonstrating how these variations increase the effectiveness of jailbreak attempts.
  • Results show significant increases in jailbreak success rates (up to +57 percentage points) with styles like fear, curiosity, and compassion proving most effective, revealing a previously overlooked dimension in model alignment safety concerns.
  • The authors introduce and test a style-neutralization preprocessing step using a secondary LLM, which significantly lowers the success rates of jailbreak attempts by stripping harmful stylistic cues from user inputs.
  • The methodology and findings highlight a gap in current safety protocols and suggest that linguistic variations should be integrated into safety evaluations and model alignment practices.

💡 Why This Paper Matters

This paper sheds light on the underappreciated impact of linguistic styles on the robustness of large language models against jailbreak attacks, emphasizing the need for renewed focus on this vulnerability in model training and evaluation. The introduction of a style-neutralization strategy offers a promising mitigation path, making the findings relevant for improving AI safety mechanisms.

🎯 Why It's Interesting for AI Security Researchers

The paper is highly relevant to AI security researchers as it uncovers a novel attack vector—linguistic styles—demonstrating that the effectiveness of adversarial prompts can be significantly influenced by emotional and contextual framing. This challenges existing paradigms that primarily address semantic variations and opens new avenues for developing robust defenses against AI exploits.

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