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ARXIV:2603.07786 · VISION-LANGUAGE MODELS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.07786VISION-LANGUAGE MODELSSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding.
Opportunity summary
Pain OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding. We present OrdinalBench, a diagnostic…
Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large indices. We present OrdinalBench, a diagnostic…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions…
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10.
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OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding.
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10.48550/arXiv.2603.07786OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding.
Abstract
Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large indices. We present OrdinalBench, a diagnostic benchmark that standardizes ordinal number understanding as an evaluation task for VLMs. The core task is N-th object identification, defined by a starting reference and traversal rule. Task difficulty is controlled along three axes: (i) ordinal magnitude, from small numbers to extreme cases up to 300; (ii) arrangement complexity, from single loops to maze-like paths; and (iii) object count. The benchmark provides 39,000 question-answer pairs, each annotated with a ground-truth reasoning trajectory and balanced across difficulty levels for controlled large-scale testing. Beyond answer-only evaluation, our framework requires models to generate structured stepwise traces of the counting process and provides an open evaluation toolkit that measures both final accuracy and step-level path consistency. Zero-shot evaluations of GPT-5, Gemini 2.5 Flash Lite, Qwen2.5-VL, InternVL3.5, and Molmo reveal sharp degradation under large-ordinal and complex-path conditions, highlighting weak generalization despite strong scores on standard multimodal tasks. By framing ordinal number understanding as a core target, OrdinalBench provides a reproducible benchmark and diagnostic framework for developing VLMs with stronger sequential reasoning. All data and code are available at https://ordinalbench.github.io/
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Dimensions overall score 7.0
PROBLEM
OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding. We present OrdinalBench, a diagnostic benchmar...
METHOD
Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large indices. We present OrdinalBench, a diagnostic benchmark that standardizes or...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large in...
WHY NOW
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding. We present OrdinalBench, a diagnostic benchmark that standardizes ordinal number understanding as an evaluation task for VLMs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large indices. We present OrdinalBench, a diagnostic benchmark that standardizes ordinal number understanding as an evaluation task for VLMs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large indices.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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OrdinalBench is a diagnostic benchmark and toolkit for evaluating and improving the ordinal reasoning capabilities of Vision-Language Models, enabling more robust and reliable performance in tasks requiring sequential understanding.
Segment
Vision-Language Models
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