Can Vision-Language Models Solve the Shell Game? | Signal Canvas | ScienceToStartup

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Can Vision-Language Models Solve the Shell Game?

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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Can Vision-Language Models Solve the Shell Game?

Canonical ID can-vision-language-models-solve-the-shell-game | Route /signal-canvas/can-vision-language-models-solve-the-shell-game

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/can-vision-language-models-solve-the-shell-game

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source_context

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Our experiments reveal that current state-of-the-art VLMs perform at or near chance level on VET-Bench
Implicationpartial
Directly stated in abstract with clear performance metric
Verificationpartial
partial
Evidencepartial
proving that fixed-depth transformer-based VLMs are fundamentally limited in tracking indistinguishable objects without intermediate supervision due to expressivity constraints
Implicationpartial
Directly stated in abstract as proven theoretical analysis
Verificationpartial
partial
Evidencepartial
Our method achieves state-of-the-art accuracy exceeding 90% on VET-Bench
Implicationpartial
Directly stated in abstract with specific numeric performance
Verificationpartial
partial
Evidencepartial
demonstrating that VLMs can reliably solve the video shell-game task end-to-end without external tools
Implicationpartial
Directly stated in abstract as conclusion of the research
Verificationpartial
partial
Evidencepartial
Visual entity tracking is an innate cognitive ability in humans, yet it remains a critical bottleneck for Vision-Language Models (VLMs)
Implicationpartial
Directly stated in abstract as foundational problem statement
Verificationpartial
partial
Evidencepartial
This deficit is often obscured in existing video benchmarks by visual shortcuts
Implicationpartial
Directly stated in abstract but requires some inference about what 'obscured' means
Verificationpartial
partial
Evidencepartial
a synthetic diagnostic testbed featuring visually identical objects that necessitate tracking exclusively through spatiotemporal continuity
Implicationpartial
Directly stated in abstract with clear description of the benchmark
Verificationpartial
partial
Evidencepartial
we propose Spatiotemporal Grounded Chain-of-Thought (SGCoT): generating object trajectories as explicit intermediate states
Implicationpartial
Directly stated in abstract as method description
Verificationpartial
partial