Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning

Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/hidden-meanings-in-plain-sight-rebusbench-for-evaluating-cognitive-visual-reasoning

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

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Canonical ID hidden-meanings-in-plain-sight-rebusbench-for-evaluating-cognitive-visual-reasoning | Route /signal-canvas/hidden-meanings-in-plain-sight-rebusbench-for-evaluating-cognitive-visual-reasoning

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/hidden-meanings-in-plain-sight-rebusbench-for-evaluating-cognitive-visual-reasoning

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning

PDF: https://arxiv.org/pdf/2604.01764v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.820Z

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Watch and verify: Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning

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Subject: Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning

Verdict

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Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
However, a critical cognitive gap emerges when the visual input serves only as a clue rather than the answer. We identify that current models struggle with the complex, multi-step reasoning required to solve problems where information is not explicitly depicted.
Implicationpartial
Directly stated in abstract with clear description of the cognitive gap
Verificationpartial
partial
Evidencepartial
Successfully solving a rebus puzzle requires a distinct cognitive workflow: the model must extract visual and textual attributes, retrieve linguistic prior knowledge (such as idioms), and perform abstract mapping to synthesize these elements into a meaning that exists outside the pixel space.
Implicationpartial
Directly stated in abstract as the required cognitive workflow
Verificationpartial
partial
Evidencepartial
To evaluate this neurosymbolic capability, we introduce RebusBench, a benchmark of 1,164 puzzles designed to test this specific integration of perception and knowledge.
Implicationpartial
Explicit numeric count provided in abstract
Verificationpartial
partial
Evidencepartial
Our evaluation of state-of-the-art models (including Qwen, InternVL, and LLaVA) shows a severe deficiency: performance saturates below 10% Exact Match and 20% semantic accuracy
Implicationpartial
Clear numeric performance metrics stated in abstract
Verificationpartial
partial
Evidencepartial
with no significant improvement observed from model scaling or In-Context Learning (ICL).
Implicationpartial
Directly stated in abstract as a finding
Verificationpartial
partial
Evidencepartial
These findings suggest that while models possess the necessary visual and linguistic components, they lack the cognitive reasoning glue to connect them.
Implicationpartial
Directly stated conclusion in abstract, though slightly interpretive
Verificationpartial
partial
Evidencepartial
Large Vision-Language Models (LVLMs) have achieved remarkable proficiency in explicit visual recognition, effectively describing what is directly visible in an image.
Implicationpartial
Directly stated in abstract as background context
Verificationpartial
partial

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Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning

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