{"paper":{"title":"G$^2$TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Generation-guided selection from the VAE latent cuts visual tokens by 1.94x in separate-encoder unified multimodal models while preserving both reasoning accuracy and editing quality.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junxian Li, Kai Liu, Renjing Pei, Yulun Zhang, Zhikai Chen, Zhixin Wang, Zizhong Ding","submitted_at":"2026-05-12T15:56:22Z","abstract_excerpt":"The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. In this paper, we focus on understanding-side visual token reduction for improving the efficiency of separate-encoder UMMs. While this topic has been widely studied for MLLMs, existing methods typically rely on attention scores, text-image similarity and so on, implicitly assuming that the final objective is discriminative reasoning. This assumption does not hold for UMMs, where understanding-side visual tokens must also preserve the model's cap"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on image understanding and editing benchmarks show that G²TR substantially reduces visual tokens and prefill computation by 1.94x while maintaining both reasoning accuracy and editing quality, outperforming baselines on almost all benchmarks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That token importance estimated from consistency with VAE latent provides a task-agnostic signal that preserves the model's editing and generation capabilities without degradation, even though the selection is performed only on the understanding-side tokens.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"G²TR reduces visual tokens and prefill compute by 1.94x in separate-encoder UMMs via generation-guided importance from VAE latent consistency, balanced selection, and merging, while preserving reasoning accuracy and editing quality.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Generation-guided selection from the VAE latent cuts visual tokens by 1.94x in separate-encoder unified multimodal models while preserving both reasoning accuracy and editing quality.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"815f6c2fbd94908d792d62590e99c63d628330ec8cf270d6c8c8d22fcabbd8ad"},"source":{"id":"2605.12309","kind":"arxiv","version":2},"verdict":{"id":"0da71e8b-4374-4707-a2d9-6e37374a51d1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T16:49:05.384592Z","strongest_claim":"Experiments on image understanding and editing benchmarks show that G²TR substantially reduces visual tokens and prefill computation by 1.94x while maintaining both reasoning accuracy and editing quality, outperforming baselines on almost all benchmarks.","one_line_summary":"G²TR reduces visual tokens and prefill compute by 1.94x in separate-encoder UMMs via generation-guided importance from VAE latent consistency, balanced selection, and merging, while preserving reasoning accuracy and editing quality.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That token importance estimated from consistency with VAE latent provides a task-agnostic signal that preserves the model's editing and generation capabilities without degradation, even though the selection is performed only on the understanding-side tokens.","pith_extraction_headline":"Generation-guided selection from the VAE latent cuts visual tokens by 1.94x in separate-encoder unified multimodal models while preserving both reasoning accuracy and editing 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