Neural Uncertainty Principle: A Unified View of Adversarial Fragility and LLM Hallucination
arXiv:2603.19562v1 Announce Type: new Abstract: Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with modality-specific patches. This study first reveals that they share a common geometric origin: the input and its loss gradient are conjugate observables subject to an irreducible uncertainty bound. Formalizing a Neural Uncertainty Principle (NUP) under a loss-induced state, we find that in near-bound regimes, further compression must be accompanied by increased sensitivity dispersion (adversarial fragility), while weak prompt-gradient coupling leaves generation under-constrained (hallucination). Crucially, this bound is modulated by an input-gradient correlation channel, captured by a specifically designed single-backward probe. In vision, masking highly coupled components improves robustness without costly adversarial training; in language, the same prefill-stage probe detects hallucination r
arXiv:2603.19562v1 Announce Type: new Abstract: Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with modality-specific patches. This study first reveals that they share a common geometric origin: the input and its loss gradient are conjugate observables subject to an irreducible uncertainty bound. Formalizing a Neural Uncertainty Principle (NUP) under a loss-induced state, we find that in near-bound regimes, further compression must be accompanied by increased sensitivity dispersion (adversarial fragility), while weak prompt-gradient coupling leaves generation under-constrained (hallucination). Crucially, this bound is modulated by an input-gradient correlation channel, captured by a specifically designed single-backward probe. In vision, masking highly coupled components improves robustness without costly adversarial training; in language, the same prefill-stage probe detects hallucination risk before generating any answer tokens. NUP thus turns two seemingly separate failure taxonomies into a shared uncertainty-budget view and provides a principled lens for reliability analysis. Guided by this NUP theory, we propose ConjMask (masking high-contribution input components) and LogitReg (logit-side regularization) to improve robustness without adversarial training, and use the probe as a decoding-free risk signal for LLMs, enabling hallucination detection and prompt selection. NUP thus provides a unified, practical framework for diagnosing and mitigating boundary anomalies across perception and generation tasks.
Executive Summary
This study introduces the Neural Uncertainty Principle (NUP), a unified framework for understanding adversarial fragility in vision and hallucination in large language models. By mathematically formalizing the relationship between input and loss gradient, the authors reveal a common geometric origin for these phenomena. They propose two novel methods, ConjMask and LogitReg, to improve robustness without adversarial training, and a decoding-free risk signal for LLMs to detect hallucination risk. The NUP provides a principled lens for reliability analysis and a unified view of two seemingly separate failure taxonomies, offering practical implications for diagnosing and mitigating boundary anomalies across perception and generation tasks.
Key Points
- ▸ The Neural Uncertainty Principle (NUP) formalizes a unified view of adversarial fragility and LLM hallucination.
- ▸ ConjMask and LogitReg are proposed as novel methods to improve robustness without adversarial training.
- ▸ A decoding-free risk signal is introduced for LLMs to detect hallucination risk.
Merits
Strength in Mathematical Formalism
The study's mathematical formalism provides a rigorous and principled foundation for understanding the relationship between input and loss gradient, offering a unified view of two seemingly separate phenomena.
Practical Implications for Reliability Analysis
The NUP provides a principled lens for reliability analysis, enabling the diagnosis and mitigation of boundary anomalies across perception and generation tasks.
Demerits
Limitation in Generalizability
The study's findings and proposed methods may not be directly applicable to other domains or tasks beyond vision and LLMs, limiting their generalizability.
Technical Complexity
The mathematical formalism and proposed methods may be technically complex and challenging to implement, potentially limiting their adoption and practical impact.
Expert Commentary
The Neural Uncertainty Principle (NUP) offers a groundbreaking framework for understanding adversarial fragility and LLM hallucination. By formalizing the relationship between input and loss gradient, the authors provide a principled lens for reliability analysis and a unified view of two seemingly separate phenomena. The proposed methods, ConjMask and LogitReg, offer practical implications for improving robustness without adversarial training, and the decoding-free risk signal for LLMs is a significant contribution to the field of LLM evaluation. However, the study's technical complexity and potential limitations in generalizability may limit its adoption and practical impact.
Recommendations
- ✓ Further research is needed to explore the generalizability of the NUP and proposed methods to other domains and tasks beyond vision and LLMs.
- ✓ The study's findings and proposed methods should be implemented and evaluated in real-world applications to assess their practical impact and limitations.
Sources
Original: arXiv - cs.LG
