Vision-language models (VLMs) aim to reason by jointly leveraging visual and textual modalities. While allocating additional inference-time computation has proven effective for large language models (LLMs), achieving similar scaling in VLMs remains challenging. A key obstacle is that visual inputs are typically provided only once at the start of generation, while textual reasoning (e.g., early visual summaries) is generated autoregressively, causing reasoning to become increasingly text-dominated and allowing early visual grounding errors to accumulate. Moreover, vanilla guidance for visual grounding during inference is often coarse and noisy, making it difficult to steer reasoning over long texts. To address these challenges, we propose Saliency-Aware Principle (SAP) selection. SAP operates on high-level reasoning principles rather than token-level trajectories, which enable stable control over discrete generation under noisy feedback while allowing later reasoning steps to re-consult visual evidence when renewed grounding is required. In addition, SAP supports multi-route inference, enabling parallel exploration of diverse reasoning behaviors. SAP is model-agnostic and data-free, requiring no additional training. Empirical results show that SAP achieves competitive performance, especially in reducing object hallucination, under comparable token-generation budgets while yielding more stable reasoning and lower response latency than CoT-style long sequential reasoning.
Figures & tables
Figure 1: Text reliance in vision-language reasoning. Our SAP, a multi-route approach, employs visual grounding as guidance, alleviating object hallucination in text-dominated LongCoT.
Figure 2: Better perception (Right) is obtained by SAP (Middle) evaluated by perception-intensive benchmarks, OCRVQA and POPE-recall, about object hallucination. Left: Text reliance in reasoning from Qwen3-VL-8B-Thinking on MS-COCO. Right: Saliency-Aware Principle (SAP) selection pipeline and empirical comparison between SAP implementation with Qwen3-VL-8B serie on benchmarks.
LLM, utility and fitness metric on ( r , s ) [those given]
Table 1: Notation: S aliency- A ware P rinciple Selection (SAP).
Reproduced Results
Reported Results (References)
benchmarks ( ↑ )
instruct
thinking
ins.+SAP (ours)
Grok-1.5V
GPT-4V
Claude-S
Claude-O
Gemini-1.5
avg. except MME
75.4
75.9
76.6
Set I. Vision and Thinking: Perception / Reasoning / General Knowledge
POPE-recall ( Li et al., 2023b )
83.9
79.6 ↓
89.9 ↑
–
–
–
–
–
MME-p ( Fu et al., 2025 )
1744.7
1663.2 ↓
1755.5 ↑
–
–
–
–
–
MME-r ( Fu et al., 2025 )
656.7
722.5 ↑
689.9 ↑
–
–
–
–
–
Table 2: Comparison between longer and wider reasoning strategies across 16 vision-language benchmarks. SAP yields better perception and higher average performance with stable visual grounding. Benchmarks are grouped into two sets according to their evaluation focus. All methods use the same backbone and inference-time decoding budget unless otherwise specified. Baselines: Qwen3-VL-8B-Instruct; Qwen3-VL-8B-Thinking: LongCoT, sequential inference-time scaling; SAP (ours): parallel inference-time scaling on routes.
settings
#gen. evo.
#routes τ
decision
avg. perf.
instruct
0
1
–
75.4
w/o evolution
0
τ
elite
73.4
w/o multi-route
T
1
elite
73.0
full SAP
T
τ
elite
76.6
w/ summarizer
T
τ
agg.
74.8
Table 3: Ablation of SAP modules. Harmful principles should not be aggregated. Evolution and filtering are verified important.
λ #new born
2
2
4
4
6
μ #elites
2
4
2
4
6
avg. perf.
76.6
74.8
76.8
76.5
76.8
Table 6
Figure 3: Ablation on weights of rewarding terms in fitness (loss design): aggregation of routes for final answer reduces the reliance on consensus in multimodal reasoning. Diversity, consensus, evidence refer to weights ratio of wd,wc,we where ∑w=1 and uncertainty penalty is always 1, wu=1 .
Figure 4: SAP supports inference-time speedup via parallelism.