ARXIV:2602.12892 · MLLM EVALUATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

arXiv

RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning. However, the lack of an efficient evaluation framework impedes the diagnosis of their performance bottlenecks.

METHOD

Full abstract

Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training by leveraging their inherent perception and reasoning capabilities to solve complex tasks. However, the lack of an efficient evaluation framework impedes the diagnosis of their performance bottlenecks. Current evaluation primarily relies on testing after supervised fine-tuning, which introduces laborious additional training and autoregressive decoding costs. Meanwhile, common pre-training metrics cannot quantify a model's perception and reasoning abilities in a disentangled manner. Furthermore, existing evaluation benchmarks are typically limited in scale or misaligned with pre-training objectives. Thus, we propose RADAR, an efficient ability-centric evaluation framework for Revealing Asymmetric Development of Abilities in MLLM pRe-training. RADAR involves two key components: (1) Soft Discrimination Score, a novel metric for robustly tracking ability development without fine-tuning, based on quantifying nuanced gradations of the model preference for the correct answer over distractors; and (2) Multi-Modal Mixture Benchmark, a new 15K+ sample benchmark for comprehensively evaluating pre-trained MLLMs' perception and reasoning abilities in a 0-shot manner, where we unify authoritative benchmark datasets and carefully collect new datasets, extending the evaluation scope and addressing the critical gaps in current benchmarks. With RADAR, we comprehensively reveal the asymmetric development of perceptual and reasoning capabilities in pretrained MLLMs across diverse factors, including data volume, model size, and pretraining strategy. Our RADAR underscores the need for a decomposed perspective on pre-training ability bottlenecks, informing targeted interventions to advance MLLMs efficiently. Our code is publicly available at https://github.com/Nieysh/RADAR.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our code is publicly available at https://github.com/Nieysh/RADAR.

WHY NOW

MLLM Evaluation moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainRADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.12892 · MLLM EVALUATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

arXiv

RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our code is publicly available at https://github.com/Nieysh/RADAR.

WHY NOW

MLLM Evaluation moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainRADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.12892

RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

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; 33% coverage.

What was readable

linkedon filenot materializedderived fallback58 indexednot indexed

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

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our code is publicly available at https://github.com/Nieysh/RADAR.

WHY NOW

MLLM Evaluation moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning. However, the lack of an efficient evaluation framework impedes the diagnosis of their performance bottlenecks.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training by leveraging their inherent perception and reasoning capabilities to solve complex tasks. However, the lack of an efficient evaluation framework impedes the diagnosis of their performance bottlenecks.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our code is publicly available at https://github.com/Nieysh/RADAR.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
MLLM Evaluation moved forward this cycle; last verified April 2026. Public score 5.0/10.
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

MLLM Evaluation

Competitors

not indexed

Competitive landscape

RADAR provides an efficient framework to evaluate the asymmetric development of skills in Multi-modal Large Language Models without fine-tuning.

Segment

MLLM Evaluation

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.12892 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(58)

Qwen3-VL Technical Report

2025Shuai Bai, Yuxuan Cai et al.

Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training

2025Junlin Han, Shengbang Tong et al.

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

2025Weiyun Wang, Zhangwei Gao et al.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models

2025Mengdi Jia, Zekun Qi et al.

SeePhys: Does Seeing Help Thinking? - Benchmarking Vision-Based Physics Reasoning

2025Kun Xiang, Heng Li et al.

PhyX: Does Your Model Have the "Wits" for Physical Reasoning?

2025Hui Shen, Taiqiang Wu et al.

Qwen3 Technical Report

2025An Yang, Anfeng Li et al.

Seed1.5-VL Technical Report

2025Dong Guo, Faming Wu et al.

π0.5: a Vision-Language-Action Model with Open-World Generalization

2025Physical Intelligence, Kevin Black et al.

InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

2025Jinguo Zhu, Weiyun Wang et al.

Gemma 3 Technical Report

2025Gemma Team Aishwarya Kamath, Johan Ferret et al.

R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

2025Yi Yang, Xiaoxuan He et al.

SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models

2025Xianfu Cheng, Wei Zhang et al.

PhysUniBench: An Undergraduate-Level Physics Reasoning Benchmark for Multimodal Models

2025Lintao Wang, Encheng Su et al.

Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces

2024Jihan Yang, Shusheng Yang et al.

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

2024Zhe Chen, Weiyun Wang et al.

GPT-4o System Card

2024OpenAI Aaron Hurst, Adam Lerer et al.

Baichuan-Omni Technical Report

2024Yadong Li, Haoze Sun et al.

Pixtral 12B

2024Pravesh Agrawal, Szymon Antoniak et al.

Showing 20 of 58 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 WorkVision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

5.0

Extension

Builds On ThisMMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning

4.0

Builds On ThisMHPR: Multidimensional Human Perception and Reasoning Benchmark for Large Vision-Languate Models

4.0

Builds On ThisSPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility

3.0

Commercially relevant

Higher ViabilityUnlocking Cognitive Capabilities and Analyzing the Perception-Logic Trade-off

7.0

Higher ViabilityLinking Perception, Confidence and Accuracy in MLLMs

7.0

Higher ViabilityCAMD: Coverage-Aware Multimodal Decoding for Efficient Reasoning of Multimodal Large Language Models

6.0

Higher ViabilityReflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification

7.0

Higher ViabilityThinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models

8.0

Higher ViabilityGrounding Robot Generalization in Training Data via Retrieval-Augmented VLMs

7.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2602.12892
Route: /paper/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training
Active tab: read
Artifact: radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training/paper-pack
REST build passport API/api/v1/paper/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training/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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  "canonical_route": "/paper/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training",
  "endpoints": {
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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/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

Canonical ID radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training | Route /paper/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.12892"
  }
}

source_context

{
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  "mode": "paper",
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  "paper_ref": "radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

/buildability/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training

Watchwatch

Subject: RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

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/radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training

Paper ref

radar-revealing-asymmetric-development-of-abilities-in-mllm-pre-training

arXiv id

2602.12892

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

33%

Hash state

Lineage hash

4c8c51639d730828f516cffd1c9de3dc0c58747f9e67cd3aa5bb5a13d5652083

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: repo_url
Missing: references
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet

Verification pending / evidence receipt incomplete

repo_url

references

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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RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training

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