ARXIV:2604.03231 · VISION-LANGUAGE MODELS · SUBMITTED 06 APR · 20:12 UTC · FRESHNESS UNKNOWN

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

Ankan Deria · Komal Kumar · Xilin He · Imran Razzak · Hisham Cholakkal · Fahad Shahbaz Khan · +1 at arXiv

A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Evidence 0 refs | 0 sources | 0% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results. While contrastive encoders are effective for cross-modal alignment and retrieval, self-supervised visual encoders often capture richer…

METHOD

Full abstract

Recent vision-language models (VLMs) typically rely on a single vision encoder trained with contrastive image-text objectives, such as CLIP-style pretraining. While contrastive encoders are effective for cross-modal alignment and retrieval, self-supervised visual encoders often capture richer dense semantics and exhibit stronger robustness on recognition and understanding tasks. In this work, we investigate how to scale the fusion of these complementary visual representations for vision-language modeling. We propose CoME-VL: Complementary Multi-Encoder Vision-Language, a modular fusion framework that integrates a contrastively trained vision encoder with a self-supervised DINO encoder. Our approach performs representation-level fusion by (i) entropy-guided multi-layer aggregation with orthogonality-constrained projections to reduce redundancy, and (ii) RoPE-enhanced cross-attention to align heterogeneous token grids and produce compact fused visual tokens. The fused tokens can be injected into a decoder-only LLM with minimal changes to standard VLM pipelines. Extensive experiments across diverse vision-language benchmarks demonstrate that CoME-VL consistently outperforms single-encoder baselines. In particular, we observe an average improvement of 4.9% on visual understanding tasks and 5.4% on grounding tasks. Our method achieves state-of-the-art performance on RefCOCO for detection while improving over the baseline by a large margin. Finally, we conduct ablation studies on layer merging, non-redundant feature mixing, and fusion capacity to evaluate how complementary contrastive and self-supervised signals affect VLM performance.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments across diverse vision-language benchmarks demonstrate that CoME-VL consistently outperforms single-encoder baselines. Code availability is flagged in the production record; the public repository…

WHY NOW

Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Evidence0 refs | 0 sources | 0% coverage

Blockerno shell-level blocker reported

Analysis summary

A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2604.03231 · VISION-LANGUAGE MODELS · SUBMITTED 06 APR · 20:12 UTC · FRESHNESS UNKNOWN

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

Ankan Deria · Komal Kumar · Xilin He · Imran Razzak · Hisham Cholakkal · Fahad Shahbaz Khan · +1 at arXiv

A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Evidence 0 refs | 0 sources | 0% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Evidence0 refs | 0 sources | 0% coverage

Blockerno shell-level blocker reported

Analysis summary

A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2604.03231

CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 0% coverage.

What was readable

linkedon filenot materializedderived fallback40 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

code

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results. While contrastive encoders are effective for cross-modal alignment and retrieval, self-supervised visual encoders often capture richer dense semantics and exhibit stronger robustness on recognition and understanding tasks.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Recent vision-language models (VLMs) typically rely on a single vision encoder trained with contrastive image-text objectives, such as CLIP-style pretraining. While contrastive encoders are effective for cross-modal alignment and retrieval, self-supervised visual encoders often capture richer dense semantics and exhibit stronger robustness on recognition and understanding tasks.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments across diverse vision-language benchmarks demonstrate that CoME-VL consistently outperforms single-encoder baselines. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Vision-Language Models

Competitors

not indexed

Competitive landscape

A modular framework that fuses complementary vision encoders to significantly improve performance on vision-language tasks, achieving state-of-the-art results.

Segment

Vision-Language Models

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2604.03231 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(40)

Data or Language Supervision: What Makes CLIP Better than DINO?

2025Yiming Liu, Yuhui Zhang et al.

MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

2025Tianyu Yu, Zefan Wang et al.

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

2025Weiyun Wang, Zhangwei Gao et al.

Phi-4-reasoning Technical Report

2025Marah Abdin, Sahaj Agarwal et al.

Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision

2025Xiaofeng Han, Shunpeng Chen et al.

GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

2025Wenyi Hong, Wenmeng Yu et al.

CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image Collections

2024Mohamed Fazli Mohamed Imam, Rufael Marew et al.

LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

2024Guowei Xu, Peng Jin et al.

Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

2024Min Shi, Fuxiao Liu et al.

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

2024Shengbang Tong, Ellis Brown et al.

EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees

2024Yuhui Li, Fangyun Wei et al.

DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning

2024Mengfei Du, Binhao Wu et al.

Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

2024Siddharth Karamcheti, Suraj Nair et al.

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

2024Quan Sun, Jinsheng Wang et al.

SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities

2024Boyuan Chen, Zhuo Xu et al.

CLIP-DINOiser: Teaching CLIP a few DINO tricks

2023Monika Wysocza'nska, Oriane Siméoni et al.

Llama 2: Open Foundation and Fine-Tuned Chat Models

2023Hugo Touvron, Louis Martin et al.

GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest

2023Shilong Zhang, Pei Sun et al.

Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

2023Ke Chen, Zhao Zhang et al.

Kosmos-2: Grounding Multimodal Large Language Models to the World

2023Zhiliang Peng, Wenhui Wang et al.

Showing 20 of 40 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkVLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation

7.0

Prior WorkFineViT: Progressively Unlocking Fine-Grained Perception with Dense Recaptions

7.0

Prior WorkHow to Utilize Complementary Vision-Text Information for 2D Structure Understanding

7.0

Extension

Builds On ThisVEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding

0.0

Builds On ThisSecuring the Floor and Raising the Ceiling: A Merging-based Paradigm for Multi-modal Search Agents

5.0

Commercially relevant

Higher ViabilityVision Verification Enhanced Fusion of VLMs for Efficient Visual Reasoning

8.0

Higher ViabilityPenguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders

8.0

Higher ViabilityMMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings

8.0

Conflicting

Competing ApproachHierarchical Pre-Training of Vision Encoders with Large Language Models

7.0

Competing ApproachBeyond Encoder Accumulation: Measuring Encoder Roles in Multi-Encoder VLMs

7.0

Related Resources

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2604.03231
Route: /paper/come-vl-scaling-complementary-multi-encoder-vision-language-learning
Active tab: read
Artifact: come-vl-scaling-complementary-multi-encoder-vision-language-learning

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/come-vl-scaling-complementary-multi-encoder-vision-language-learning/paper-pack
REST build passport API/api/v1/paper/come-vl-scaling-complementary-multi-encoder-vision-language-learning/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "active_tab": "synced from current hash by the drawer client",
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  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/come-vl-scaling-complementary-multi-encoder-vision-language-learning

stale

Proof freshness: unknown
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-06
Score updated: 2026-04-06
Score fresh until: 2026-05-06
References: 0
Source count: 0
Coverage: 0%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

Canonical ID come-vl-scaling-complementary-multi-encoder-vision-language-learning | Route /paper/come-vl-scaling-complementary-multi-encoder-vision-language-learning

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/come-vl-scaling-complementary-multi-encoder-vision-language-learning

MCP example

{
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  "arguments": {
    "arxiv_id": "2604.03231"
  }
}

source_context

{
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  "mode": "paper",
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  "paper_ref": "come-vl-scaling-complementary-multi-encoder-vision-language-learning",
  "topic_slug": null,
  "benchmark_ref": null,
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}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

/buildability/come-vl-scaling-complementary-multi-encoder-vision-language-learning

Watchwatch

Subject: CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/come-vl-scaling-complementary-multi-encoder-vision-language-learning

Paper ref

come-vl-scaling-complementary-multi-encoder-vision-language-learning

arXiv id

2604.03231

Freshness

Generated at

2026-04-06T20:12:49.631Z

Evidence freshness

unknown

Last verification

2026-04-06T20:12:49.631Z

Sources

References

Coverage

Hash state

Lineage hash

ce85597f59273eb844bac587a78335a03b1718c963efd62829b2b1dfa1b7781b

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: paper_evidence_receipts.references_count
Missing: paper_evidence_receipts.coverage
Unknown: Canonical evidence receipt has not been materialized yet.

Verification pending / evidence receipt incomplete

paper_evidence_receipts.references_count

paper_evidence_receipts.coverage

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

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