This paper identifies that object hallucinations in large vision-language models (LVLMs) originate from visual encoders, uncovering three core issues: statistical bias, inherent bias, and vulnerability. To address these, SHIELD is introduced as a training-free framework that applies three strategies: re-weighting visual tokens to reduce statistical bias, injecting noise-derived tokens to counteract inherent bias, and employing adversarial attacks with contrastive decoding to mitigate vulnerability. Experiments across multiple benchmarks and LVLM families demonstrate SHIELD effectively reduces object hallucinations while maintaining strong general performance, and the code is publicly available.
The paper introduces Self-Aligned Reward (SAR), a fine-grained RL signal that complements verifiable rewards to improve both accuracy and efficiency of LLM reasoning. SAR is defined as the relative perplexity difference between a query-conditioned answer and the standalone answer, thereby favoring concise, query-specific responses and penalizing redundancy. Quantitative analysis confirms that SAR reliably ranks answer quality, assigning higher scores to concise correct answers than to verbose ones. Integrating SAR with PPO or GRPO reduces average answer length by 30% while boosting accuracy by 4% across four model families and seven benchmarks, with strong out-of-domain generalization. The approach achieves a Pareto-optimal frontier between correctness and efficiency, shortening unnecessary elaboration without hurting advanced reasoning behaviors. Code and data are publicly released.