Video-LevelGauge: Diagnosing LVLM Positional Bias

• Video-LevelGauge is a benchmark that identifies contextual positional bias in LVLMs, clarifying how performance varies with the probe’s position within long or multimodal sequences.
• It employs a standardized probe combined with customized contexts and statistical metrics such as Relative Score and Morphological Recognition to isolate positional effects.
• Empirical results indicate that while commercial models show minimal bias, many open-source LVLMs exhibit clear failure modes like head preference, U-shaped degradation, or volatile performance.

Searching arXiv for the primary paper and closely related benchmark/evaluation work on Video-LevelGauge and LVLM positional/temporal evaluation. Video-LevelGauge is a benchmark for diagnosing contextual positional bias in large video LLMs (LVLMs): the tendency of a model to perform unevenly on the same visual content when that content is placed at different positions in a long or multimodal input sequence. It was introduced to address a limitation of prevailing video benchmarks, which typically report aggregate accuracy over entire videos and therefore do not expose whether recognition or reasoning quality depends on input position. The benchmark uses a “standardized probe + customized context” paradigm, combines controlled context construction with statistical and morphological bias analysis, and evaluates both commercial and open-source LVLMs under long-context video and video-text conditions (Xia et al., 27 Aug 2025).

1. Definition and research motivation

Video-LevelGauge was created in response to a methodological gap in LVLM evaluation. Benchmarks such as MVBench, TempCompass, VideoMME, and MLVU emphasize end-to-end performance on video understanding tasks, but they do not reveal whether a model’s answer changes because relevant content appears near the beginning, middle, or end of the context window. Video-LevelGauge formalizes this hidden failure mode as contextual positional bias, drawing motivation from the serial position effect in human memory and the “lost in the middle” phenomenon observed in text LLMs (Xia et al., 27 Aug 2025).

The benchmark’s central premise is that positional bias is not reducible to overall accuracy. A model may achieve high task-level scores while still relying on head preference, neighbor-content preference, or other position-dependent heuristics. Video-LevelGauge therefore separates two questions that are often conflated in conventional benchmarking: whether a model can solve a probe in isolation, and whether it can do so consistently when the same probe is embedded in longer or mixed-modal contexts (Xia et al., 27 Aug 2025).

In this sense, Video-LevelGauge belongs to a broader line of work that treats evaluation itself as a video-level gauge of latent model behavior. In video-language understanding, the atemporal probe (ATP) was proposed as a video-level gauge of how much temporal reasoning a benchmark genuinely requires, by constraining the model to select exactly one frame and thereby establishing an atemporal lower bound (Buch et al., 2022). In Video-LMM evaluation, CVRR-ES similarly reframes benchmarking around failure modes in reasoning and robustness rather than aggregate comprehension alone (Khattak et al., 2024). Video-LevelGauge extends this evaluative perspective to long-context positional robustness in LVLMs (Xia et al., 27 Aug 2025).

2. Benchmark construction: standardized probes and customized contexts

The benchmark adopts a standardized probe + customized context design to isolate positional effects from confounds. A probe is a short video segment, using 6 sampled frames by default, annotated with high-quality questions. The benchmark defines six structured Multiple-Choice QA (MCQA) tasks—Optical Character Recognition (OCR), Attribute Perception (AP), Object Reasoning (OR), Counting (CP), Relationship Recognition (RR), and Action Reasoning (AR)—together with one open-ended descriptive task, “Describe the scene mentioned …” (Xia et al., 27 Aug 2025).

Probe construction proceeds through a multi-stage pipeline. Frames are captioned via GPT-4o at 1 FPS. A prompted LLM then generates Q–A candidates, reported as approximately 7,319 items. These candidates undergo blind filtering to remove text-only solvable or commonsense questions, hallucination filtering, and human refinement, producing 1,177 MCQAs. Distractors are then generated, vetted, and randomly shuffled. The validation procedure shows that the resulting questions yield near-random accuracy under text-only input and improve with more frames or larger models, which is used as evidence that they require genuine visual understanding rather than superficial textual inference (Xia et al., 27 Aug 2025).

The customized context side of the benchmark simulates long-context deployment conditions. Four context types are defined. A template video sets all frames to ImageNet mean pixel values to blank out context; multi-video concatenates nine unrelated short videos with average context length ≈ 7.2 minutes; long natural videos use nine long clips from LVBench; and interleaved video-text mixes video clips and text passages from OBELICS with 50% replacement by short video. These contexts support controlled variation in context length, probe insertion position, and context type, allowing systematic stress testing across video-only, text-only, and mixed sequences (Xia et al., 27 Aug 2025).

This design is explicitly diagnostic rather than purely task-oriented. The probe remains semantically constant while only its surrounding context and insertion slot change. A plausible implication is that performance variation can be more confidently attributed to positional sensitivity than in conventional end-to-end datasets, where content, difficulty, and context length are typically entangled.

3. Quantification framework

Video-LevelGauge introduces a metric suite intended to normalize for baseline task ability and then characterize the geometry of positional degradation. The primary normalized measure is the Relative Score (RS) at insertion position $i$: $RS_i=\frac{S_i}{S_{\rm meta}},$ where $S_i$ is the model’s accuracy when the probe is inserted at position $i$, and $S_{\rm meta}$ is the accuracy on the probe alone, without context (Xia et al., 27 Aug 2025).

Three statistical metrics are then defined on the sequence $\{RS_i\}_{i=1}^N$: $P_{\rm mean}=\frac1N\sum_{i=1}^N RS_i,$

$P_{\rm ran}=\max_i RS_i-\min_i RS_i,$

$P_{\rm var}=\mathrm{Var}\bigl\{RS_i\bigr\}.$

These quantify, respectively, mean normalized retention under context, range of positional fluctuation, and variability of the positional response curve (Xia et al., 27 Aug 2025).

A distinctive feature of Video-LevelGauge is its Morphological Recognition (MR) procedure. The benchmark fits the RS curve with a linear model $\hat y^{(1)}(x)=kx+h$ and a quadratic model $RS_i=\frac{S_i}{S_{\rm meta}},$0, using their mean-squared errors to classify the positional profile into five patterns: Stable (---), Neighbor pref. ($RS_i=\frac{S_i}{S_{\rm meta}},$1), Head pref. ($RS_i=\frac{S_i}{S_{\rm meta}},$2), Lost in the middle (U), and Volatile (W). The thresholds are specified concretely: Stable requires $RS_i=\frac{S_i}{S_{\rm meta}},$3 and $RS_i=\frac{S_i}{S_{\rm meta}},$4; Neighbor pref. requires $RS_i=\frac{S_i}{S_{\rm meta}},$5 and $RS_i=\frac{S_i}{S_{\rm meta}},$6; Head pref. requires $RS_i=\frac{S_i}{S_{\rm meta}},$7 and $RS_i=\frac{S_i}{S_{\rm meta}},$8; Lost in the middle requires $RS_i=\frac{S_i}{S_{\rm meta}},$9 and $S_i$0; all other cases are Volatile (Xia et al., 27 Aug 2025).

For ranking, the benchmark defines a Composite Metric (CM): $S_i$1 Higher CM indicates more pronounced positional bias (Xia et al., 27 Aug 2025). The explicit use of RS, statistical dispersion, and morphological classification distinguishes Video-LevelGauge from evaluation suites that report only overall accuracy or retrieval score. It also makes the benchmark directly comparable, in spirit, to work such as ATP, where the key contribution is a controlled lower bound on non-temporal performance rather than an unconstrained task score (Buch et al., 2022).

4. Dataset composition and evaluation protocol

The benchmark comprises 438 manually curated videos with no overlap with training data. The sources include 42 aerial (VisDrone), 49 surveillance (UCF-Crime), 50 egocentric (Ego-4D), 152 media, 58 life-record, and 87 synthetic (MLVU, VideoMME) videos. The dataset contains 1,177 high-quality MCQAs across the six structured tasks, with average question length 30.4 words, and 120 open-ended descriptive items. Videos are segmented via PySceneDetect and filtered to remove blurry/static/duplicate content (Xia et al., 27 Aug 2025).

The evaluation protocol uses ten probe positions per context and six frames per probe. Reported metrics are $S_i$2, with CM used for ranking. The model pool comprises 27 total systems: 6 commercial models, two-stage baselines such as Caption + Qwen3 and Caption + GPT-4, and 21 open-source LVLMs. The named models include Gemini 2.5 Pro, GPT-4o-latest, QVQ-Max, Doubao-Seed-1.6, Qwen-VL-Max, Claude-Sonnet-4, as well as open-source systems such as MiniGPT4-Video, MA-LMM, LongVA, LLaMA-VID, Kangaroo, LLaVA-OV, LLaVA-Video (7B/72B), Qwen2.5-VL (7B/72B), InternVL3 (8B/9B/78B), VideoLLaMA2/3, NVILA, LongVILA-1M, Video-XL, Video-XL2, VideoRefer, T-Star, MiMo-VL-RL, and GLM-4.5V (Xia et al., 27 Aug 2025).

The resulting benchmark is narrower in sample count than some broad-spectrum reasoning suites, but it is more controlled in what it measures. By contrast, CVRR-ES evaluates 217 videos and 2,400 human-validated, open-ended QA pairs across 11 real-world video dimensions, focusing on complex reasoning and robustness under user prompts rather than positional effects (Khattak et al., 2024). The two resources are therefore complementary: CVRR-ES asks whether a Video-LMM is robust to misleading or context-heavy questions, whereas Video-LevelGauge asks whether identical evidence remains equally accessible across a long context (Khattak et al., 2024, Xia et al., 27 Aug 2025).

5. Empirical findings

The principal empirical result is that commercial models exhibit the mildest bias, while many open-source LVLMs show marked positional asymmetries. The benchmark reports that Gemini 2.5 Pro attains $S_i$3, $S_i$4, $S_i$5, with MR = Stable. GPT-4o and Doubao-Seed-1.6 are also reported as low-bias systems (Xia et al., 27 Aug 2025).

Many open-source LVLMs instead exhibit interpretable failure morphologies. The benchmark identifies head preference for systems such as MiniGPT4-Video, neighbor preference for MA-LMM, a U-shaped “lost in the middle” pattern for Caption+LLM and Qwen2.5-VL-7B, and volatile fluctuations for LLaMA-VID. At the same time, the paper notes that GLM-4.5V (108 B) and MiMo-VL-RL$S_i$6 with reasoning achieve MR = Stable, indicating that low-bias behavior is not exclusive to commercial systems (Xia et al., 27 Aug 2025).

Three systematic effects are emphasized. First, context length effect: positional bias intensifies as context grows, and some models transition from head-pref to U-shape to neighbor-pref as the context becomes longer. Second, context type effect: template contexts induce minimal bias; multi-video and long video increase bias; interleaved video-text yields the worst performance and highest bias. Third, model scale effect: larger variants within the same family show flatter RS curves, higher $S_i$7, and lower $S_i$8 and $S_i$9 (Xia et al., 27 Aug 2025).

The benchmark also reports that open-ended tasks show slightly worse bias than MCQA, attributing this to the richer perceptual demands of description (Xia et al., 27 Aug 2025). This parallels a broader trend in Video-LMM evaluation: as task format moves from constrained multiple-choice toward open-ended reasoning or description, brittleness becomes more visible. In CVRR-ES, for example, open-source models underperform sharply on robustness dimensions such as Non-existent Actions and Non-existent Actions & Non-existent Scene, while even closed-source models remain substantially below the reported human upper bound ≈ 96.7% on socially and emotionally grounded reasoning (Khattak et al., 2024). The common implication is that aggregate success on narrow benchmarks may obscure systematic context-management failures.

6. Interpretation, misconceptions, and relation to adjacent “video-level gauge” concepts

A common misconception is that positional bias is simply another name for poor long-video accuracy. Video-LevelGauge treats them as distinct. Long-context degradation can reflect limited capacity, weak retrieval, poor compression, or multimodal interference; contextual positional bias specifically refers to uneven performance on identical probe content as its position changes (Xia et al., 27 Aug 2025). The use of RS and MR is intended to isolate this dependence on position rather than conflate it with absolute competence.

A second misconception is that benchmarking positional bias is equivalent to testing temporal reasoning. The two are related but not identical. ATP shows that many video-language tasks can be solved by selecting a single frame, and therefore do not require genuine temporal aggregation even when they appear to do so (Buch et al., 2022). Video-LevelGauge instead assumes the probe is already valid and visually grounded, and asks whether the model’s access to that evidence varies with where it appears in the context window (Xia et al., 27 Aug 2025). One benchmark is a gauge of temporal necessity; the other is a gauge of contextual positional robustness.

The phrase video-level gauge also has independent meanings outside LVLM evaluation. In LiqD, it denotes a video-level measurement of liquid-level states—rising, falling, or stable—derived from frame differencing and a lightweight classifier after container segmentation (Ma et al., 2024). In XLR, it denotes a video-level quality-of-experience indicator, piXel Loss Rate, for packet-loss impairments in IP video delivery (Díaz et al., 2024). These uses are conceptually different from Video-LevelGauge the benchmark, but they share a family resemblance: all treat a video not merely as a source of per-frame predictions, but as an object requiring aggregate or sequence-level diagnosis.

This suggests a broader taxonomy of “video-level gauges.” Some gauges quantify whether a dataset truly demands video reasoning, as in ATP (Buch et al., 2022). Some quantify robustness to prompt or world-centric failure modes, as in CVRR-ES (Khattak et al., 2024). Some quantify positional invariance in long multimodal contexts, as in Video-LevelGauge (Xia et al., 27 Aug 2025). Others measure physical state transitions or transmission quality in applied vision and networking settings (Ma et al., 2024, Díaz et al., 2024). The shared methodological theme is the use of controlled probes and sequence-level metrics to reveal failure modes hidden by coarse aggregate scores.

7. Recommendations and significance for LVLM development

The authors of Video-LevelGauge recommend incorporating explicit bias evaluation during model development, rather than relying solely on aggregate benchmark accuracy. They further suggest training on interleaved video-text sequences to improve mixed-modal long-context handling, enhancing long-video post-training and cross-modal context search algorithms to reduce U-shaped failures and position sensitivity, and exploring improved positional encodings and video token compression to maintain stable performance over very long inputs. They also report that reasoning-mode prompts (“thinking”) can partially alleviate bias (Xia et al., 27 Aug 2025).

These recommendations align with findings from adjacent evaluation work. CVRR-ES proposes Dual-Step Contextual Prompting (DSCP) as a training-free prompting scheme that improves both reasoning and robustness, with reported gains across open- and closed-source Video-LMMs (Khattak et al., 2024). Although DSCP was not designed specifically for positional bias, it supports the broader claim that inference-time context structuring can materially affect model robustness under realistic video inputs.

The significance of Video-LevelGauge lies in making positional uniformity an explicit benchmark target. By providing 438 manually curated videos, 1,177 high-quality multiple-choice questions, 120 open-ended questions, ten controlled insertion positions, and a metric suite that includes both statistical and morphological analysis, it turns positional bias from an anecdotal observation into a measurable property of LVLM behavior (Xia et al., 27 Aug 2025). For long-context video systems, especially those expected to process concatenated clips, surveillance streams, egocentric recordings, or interleaved video-text documents, this property is not peripheral. It determines whether relevant evidence remains usable when embedded in realistic context, rather than only when presented in isolation.