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ARXIV:2603.11410 · VISION-LANGUAGE BENCHMARKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11410VISION-LANGUAGE BENCHMARKINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding.
Opportunity summary
Pain DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding. Current vision-language benchmarks largely conflate orientation with position and general scene understanding.
Humans learn object orientation progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about orientations between objects. Current vision-language benchmarks largely conflate orientation with position and general scene understanding.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Evaluating 24 state-of-the-art vision-language models shows a clear pattern: models that perform well on general spatial benchmarks are near-random on object-centric orientation tasks.
Vision-Language Benchmarking moved forward this cycle; last verified April 2026. Public score 4.0/10.
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DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding.
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Paper Pack
10.48550/arXiv.2603.11410DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding.
Abstract
Humans learn object orientation progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about orientations between objects. Current vision-language benchmarks largely conflate orientation with position and general scene understanding. We introduce Discriminative Orientation Reasoning Intelligence (DORI), a cognitively grounded hierarchical benchmark that makes object orientation the primary target. Inspired by stages of human orientation cognition, DORI decomposes orientation into four dimensions, each evaluated at coarse (categorical) and granular (metric) levels. Composed from 13,652 images across 14 sources, DORI provides 33,656 multiple-choice questions covering 67 object categories in real-world and synthetic settings. Its coarse-to-granular design isolates orientation from confounds such as object recognition difficulty, scene clutter, and linguistic ambiguity via bounding-box isolation, standardized spatial reference frames, and structured prompts. Evaluating 24 state-of-the-art vision-language models shows a clear pattern: models that perform well on general spatial benchmarks are near-random on object-centric orientation tasks. The best models reach only 54.2% on coarse and 45.0% on granular judgments, with largest failures on compound rotations and shifts in inter-object reference frames. Large coarse-to-granular gaps reveal reliance on categorical heuristics rather than geometric reasoning, a limitation hidden by existing benchmarks. These results identify orientation understanding as an unsolved challenge for multimodal systems, with implications for robotic manipulation, 3D scene reconstruction, and human-AI interaction.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Dimensions overall score 4.0
PROBLEM
DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding. Current vision-language benchmarks largely conflate orientation with position and general scene understanding.
METHOD
Humans learn object orientation progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about orientations between objects. Current vision-language benchmarks largely conflate orientation with position and general scene understanding.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Evaluating 24 state-of-the-art vision-language models shows a clear pattern: models that perform well on general spatial benchmarks are near-random on object-centric orientation tasks.
WHY NOW
Vision-Language Benchmarking moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding. Current vision-language benchmarks largely conflate orientation with position and general scene understanding.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Humans learn object orientation progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about orientations between objects. Current vision-language benchmarks largely conflate orientation with position and general scene understanding.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Evaluating 24 state-of-the-art vision-language models shows a clear pattern: models that perform well on general spatial benchmarks are near-random on object-centric orientation tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Benchmarking moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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DORI is a benchmark that isolates object orientation reasoning to improve multimodal AI understanding.
Segment
Vision-Language Benchmarking
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Commercial read
4.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
next verification path
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passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Market urgency
missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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BUZZ
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