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  3. Mastering Negation: Boosting Grounding Models via Grouped Op
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Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning

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Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning

PDF: https://arxiv.org/pdf/2603.12606v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning

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Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

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References: 0

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Coverage: 17%

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Interpreting Negation in GPT-2: Layer- and Head-Level Causal Analysis
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When Prohibitions Become Permissions: Auditing Negation Sensitivity in Language Models
Score 3.0down
Builds On This
Via Negativa for AI Alignment: Why Negative Constraints Are Structurally Superior to Positive Preferences
Score 4.0down
Prior Work
NEGATE: Constrained Semantic Guidance for Linguistic Negation in Text-to-Video Diffusion
Score 7.0stable
Prior Work
No Hard Negatives Required: Concept Centric Learning Leads to Compositionality without Degrading Zero-shot Capabilities of Contrastive Models
Score 7.0stable
Prior Work
DEO: Training-Free Direct Embedding Optimization for Negation-Aware Retrieval
Score 7.0stable
Prior Work
Beyond Language: Grounding Referring Expressions with Hand Pointing in Egocentric Vision
Score 7.0stable
Higher Viability
GeM-VG: Towards Generalized Multi-image Visual Grounding with Multimodal Large Language Models
Score 8.0up

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