---
title: Balanced Position Assignment in LVLMs
url: https://www.emergentmind.com/topics/balanced-position-assignment-bapa
type: topic
---

# Balanced Position Assignment in LVLMs

Balanced Position Assignment (BaPA) is a position-encoding intervention for large vision-language models (LVLMs) in which all image tokens are assigned the same position ID before entering the language model. It was introduced to mitigate a specific form of spatial bias: semantically identical visual evidence can elicit different answers solely because it appears in a different part of the image, even when the visual content itself is unchanged [2509.21984]. In this usage, “BaPA” denotes a multimodal position-assignment mechanism inside an LVLM; it is distinct from the operations-research term “Balanced Assignment Problem,” where “balanced” refers to equal cardinality in a square one-to-one matching problem rather than to cross-modal token positioning [2306.16287].

## 1. Spatial bias as the motivating failure mode

The motivating observation behind BaPA is that current LVLMs can be position-sensitive in a way that is misaligned with visual semantics. The paper studies this through a controlled probe built from 10,000 image-caption pairs \((I_m, C_m)\) from LAION. For each pair, it creates a \(3\times 3\) composite image with one “key” image and eight distractors; the caption always describes the key image, and the model is asked a binary image-text matching question: whether any sub-image in the composite matches the given caption. The same key image is placed in each of the nine grid locations while everything else remains fixed, yielding nine composites \(\{\boldsymbol{I}_{m,0},\dots,\boldsymbol{I}_{m,8}\}\) per pair and 90,000 probe samples in total. Because the semantic evidence is unchanged and only its spatial location varies, any output variation directly reveals spatial bias [2509.21984].

Across Gemma3, LLaVA-v1.6, LLaVA-NeXT, and Qwen2.5-VL, the study reports substantial position-dependent accuracy differences, sometimes even contradictory predictions. The substantive claim is not merely that LVLMs are imperfectly robust to translation, but that multimodal reasoning can become partially driven by artifacts of how image tokens are serialized into the language model. This matters because relation understanding, grounding, and hallucination resistance should depend on what is present, not on where the relevant image patch happened to be placed in a rasterized token stream.

## 2. Localization of the source of bias

The paper’s diagnostic program separates perception from cross-modal reasoning. It first tests whether the vision encoder is the source of the failure. In a perception-level analysis on 180 sampled composites, each image is divided into 400 regions, one region is masked at a time, and the change in output logits is used as an importance score. The resulting heatmaps show that the models consistently identify the key visual region regardless of where it is placed. This suggests that the visual front-end can perceptually locate relevant content robustly across positions [2509.21984].

A second analysis tests semantic stability more directly. For 1,000 image-caption pairs, the image is pasted into different positions on a white background, and the cosine similarity between the projected visual features fed into the LLM and the caption embedding is computed as
\[
\text{Similarity} = \cos(g(f_v(\boldsymbol{I'}_{m,n})), E(C'_m)).
\]
These similarities are reported to be high and stable across positions. The combined implication is that the vision encoder-projector stack already extracts semantically aligned features in a spatially robust way, and that the instability emerges later, during language-model-side cross-modal processing.

## 3. Positional encoding as the diagnosed mechanism

The paper attributes the principal source of the bias to positional encoding inside the LLM, especially Rotary Position Embedding (RoPE). In a standard LVLM pipeline, image patch features are projected and inserted into the language model as a contiguous sequence of tokens, usually in raster-scan order, and these tokens receive distinct sequential position IDs just like text tokens. Under RoPE, attention depends on relative position, so query-key interaction strength is modulated by \(q-p\), the relative position. In text-only autoregressive modeling this is beneficial because order carries meaning; in cross-modal interaction, however, assigning a set of image tokens distinct one-dimensional sequence positions creates an artificial hierarchy among tokens that belong to the same image representation [2509.21984].

The paper explicitly characterizes the phenomenon as an imbalance problem rather than a long-context decay problem. The claim is that some image tokens become “closer” or otherwise differently rotated relative to text tokens and therefore exert unequal influence even when they are equally semantically relevant. Qwen2.5-VL’s MRoPE reduces but does not eliminate the issue, which the authors use to argue that the underlying problem is broader than any single positional scheme: any encoding that induces nonuniform cross-modal weighting over image tokens can distort visual grounding.

## 4. BaPA mechanism and implementation

BaPA removes this artificial positional asymmetry by assigning identical position embeddings to all image tokens. The multimodal input sequence is defined as
\[
Z=\{s_1,\dots,s_i,\tilde{v}_1,\dots,\tilde{v}_j,x_1,\dots,x_k\},
\]
where \(\{s_1,\dots,s_i\}\) are system prompt tokens, \(\{\tilde{v}_1,\dots,\tilde{v}_j\}\) are image tokens from the vision encoder and projector, and \(\{x_1,\dots,x_k\}\) are user prompt tokens. In a standard LVLM, the image tokens would receive distinct sequential positions \(i, i+1, \dots, i+j-1\). Under BaPA, they instead satisfy
\[
p_{\tilde{v}_1} = p_{\tilde{v}_2} = \dots = p_{\tilde{v}_j} = p_{img} = i,
\]
so all image tokens share one identical position ID [2509.21984].

BaPA is applied at the LLM input stage, after visual features have been extracted and projected into image tokens but before the transformer layers compute RoPE-conditioned attention. It does not alter the vision encoder, projector architecture, or LLM weights. The token ordering is unchanged—system prompt first, then the contiguous block of image patch tokens, then the user prompt—and the method does not reorder, pool, or merge visual tokens. The intervention is therefore low-friction and can be used as an inference-time modification alone, without retraining. The paper also studies lightweight downstream adaptation by fine-tuning with LoRA on 10K LLaVA instruction-tuning samples. For Gemma3, LLaVA-v1.6, and Qwen2.5-VL, it uses LoRA rank 8, alpha 16, batch size 32, learning rate \(1.0\times10^{-4}\), with 2 epochs for Gemma3 and 1 epoch for the others; for LLaVA-v1.5, it uses LoRA rank 128, alpha 256, batch size 128, learning rate \(2.0\times10^{-4}\), 1 epoch.

## 5. Empirical behavior on probes and downstream benchmarks

The clearest empirical effect appears on the spatial-bias probe. Without retraining, BaPA improves both mean accuracy and positional consistency for models with severe baseline imbalance.

| Model | Without BaPA | With BaPA |
|---|---|---|
| Gemma3 | 82.82, \(\Delta=8.75\) | 92.08, \(\Delta=1.49\) |
| LLaVA-NeXT | 64.95, \(\Delta=54.90\) | 96.41, \(\Delta=0.02\) |
| LLaVA-v1.6 | 70.82, \(\Delta=16.79\) | 93.78, \(\Delta=9.97\) |
| Qwen2.5-VL-32B | 81.96, \(\Delta=0.28\) | 82.48, \(\Delta=0.80\) |
| Qwen2.5-VL-7B | 82.49, \(\Delta=1.74\) | 81.28, \(\Delta=2.06\) |

These results support a selective interpretation: BaPA is most effective when the baseline model already exhibits strong spatial bias. The gains are largest for Gemma3, LLaVA-v1.6, and especially LLaVA-NeXT, whereas Qwen2.5-VL shows smaller or mixed changes, which the paper attributes to MRoPE already mitigating much of the imbalance.

On downstream tasks, the paper reports mostly neutral-to-positive transfer when BaPA is combined with lightweight LoRA fine-tuning, but not uniform gains. On MMMU-Pro, Gemma3 drops from 0.4357 to 0.4162 on 4-option questions but rises from 0.2536 to 0.2944 on 10-option questions; Qwen2.5-VL improves from 0.4763 to 0.4915 and from 0.3427 to 0.3735; LLaVA-v1.6 changes from 0.3390 to 0.3365 and from 0.2015 to 0.1927. On ScienceQA, Gemma3 moves from 0.8156 to 0.8180, Qwen2.5-VL from 0.7898 to 0.8909, and LLaVA-v1.6 remains at 0.7288. On CRPE relation reasoning, Gemma3 changes from 0.7002 to 0.6819, Qwen2.5-VL from 0.7658 to 0.7690, and LLaVA-v1.6 from 0.6884 to 0.6905. On HallusionBench, Gemma3 changes from 0.6393 to 0.6015, Qwen2.5-VL from 0.7066 to 0.6909, and LLaVA-v1.6 from 0.5363 to 0.5489. On MME perception subtasks, Gemma3 rises from 611.66 to 616.67, Qwen2.5-VL from 710.00 to 733.33, and LLaVA-v1.6 remains at 673.33 while improving position accuracy from 133.33 to 145.00; for LLaVA-v1.5, BaPA reaches 648.33 total, higher than both UAC at 638.33 and CCA at 641.66 [2509.21984].

## 6. Information flow, interpretation, and limitations

The mechanistic interpretation offered by the paper is that BaPA changes information flow from “few-token domination” to more distributed visual utilization. Average attention from text tokens to image tokens is analyzed across layers and heads on ScienceQA, CRPE, and HallusionBench, comparing LLaVA-v1.6 to a BaPA-finetuned version. In the baseline, attention tends to collapse onto a few image tokens while many others receive little weight; in the BaPA model, attention is more evenly spread across the image-token set. A HallusionBench case study illustrates this visually: standard LLaVA-v1.6 focuses on a few isolated regions, whereas BaPA attends more broadly, especially to regions corresponding to the decisive curve and words in the image. This suggests that equalizing image-token positions reduces arbitrary serialization effects and enables more holistic visual understanding [2509.21984].

Several boundaries of applicability are explicit. The strongest evidence is for RoPE-based or similarly position-sensitive LLM backbones used inside LVLMs. The method is most useful when the baseline model exhibits noticeable spatial bias; models with improved positional handling, such as Qwen2.5-VL with MRoPE, show smaller or mixed gains. The downstream results are not universally positive, so BaPA should not be treated as a guaranteed accuracy improvement on every benchmark-model pair. The probe dataset uses a \(3\times 3\) grid and therefore does not fully establish robustness under finer-grained or irregular spatial transformations. The experiments are also limited to medium-scale models; very large LVLMs such as Qwen2.5-VL-72B are not tested. Within those limits, BaPA functions as a minimal intervention that preserves normal text positioning while neutralizing position-induced imbalance among image tokens, thereby shifting multimodal reasoning toward semantic content rather than arbitrary token order.

Source: https://www.emergentmind.com/topics/balanced-position-assignment-bapa