ARXIV:2602.05496 · EMOTION RECOGNITION · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

Q: What is the startup potential of "XEmoGPT: An Explainable Multimodal Emotion Recognition Frame"?

XEmoGPT is an explainable AI framework enhancing emotion recognition through detailed cue-level analysis across audio and video.

Q: What products could be built from this research?

To productize XEmoGPT, an API could be created that integrates with video conferencing tools, allowing real-time emotion analytics based on explainable cues.

Q: What are the practical use cases?

Develop an AI plugin for telehealth applications that analyzes patient emotions during virtual consultations, providing doctors with insights based on visual and audio cues.

Q: What industries could this research disrupt?

This framework could replace existing emotion recognition solutions that do not offer transparent, explainable insights into the emotional state of the user.

arXiv

XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

Blocked on Code›Score8.0Evidence failed

Opportunity summary

Pain XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence failed

Open Build Read PDF Signal Canvas Track

PROBLEM

XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks. However, current approaches struggle with cue-level perception and reasoning due to two main challenges: 1) general-purpose…

METHOD

Full abstract

Explainable Multimodal Emotion Recognition plays a crucial role in applications such as human-computer interaction and social media analytics. However, current approaches struggle with cue-level perception and reasoning due to two main challenges: 1) general-purpose modality encoders are pretrained to capture global structures and general semantics rather than fine-grained emotional cues, resulting in limited sensitivity to emotional signals; and 2) available datasets usually involve a trade-off between annotation quality and scale, which leads to insufficient supervision for emotional cues and ultimately limits cue-level reasoning. Moreover, existing evaluation metrics are inadequate for assessing cue-level reasoning performance. To address these challenges, we propose eXplainable Emotion GPT (XEmoGPT), a novel EMER framework capable of both perceiving and reasoning over emotional cues. It incorporates two specialized modules: the Video Emotional Cue Bridge (VECB) and the Audio Emotional Cue Bridge (AECB), which enhance the video and audio encoders through carefully designed tasks for fine-grained emotional cue perception. To further support cue-level reasoning, we construct a large-scale dataset, EmoCue, designed to teach XEmoGPT how to reason over multimodal emotional cues. In addition, we introduce EmoCue-360, an automated metric that extracts and matches emotional cues using semantic similarity, and release EmoCue-Eval, a benchmark of 400 expert-annotated samples covering diverse emotional scenarios. Experimental results show that XEmoGPT achieves strong performance in both emotional cue perception and reasoning.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. To further support cue-level reasoning, we construct a large-scale dataset, EmoCue, designed to teach XEmoGPT how to reason over multimodal emotional cues.

WHY NOW

Emotion Recognition moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainXEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

ARXIV:2602.05496 · EMOTION RECOGNITION · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

arXiv

XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

Blocked on Code›Score8.0Evidence failed

Opportunity summary

Pain XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence failed

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Emotion Recognition moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainXEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsErrorProof: failed

Paper Pack

10.48550/arXiv.2602.05496

XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

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

failed

0 refs; 0 sources; 33% coverage.

What was readable

linkedon filenot materializedderived fallback55 indexednot indexed

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

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Emotion Recognition moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks. However, current approaches struggle with cue-level perception and reasoning due to two main challenges: 1) general-purpose modality encoders are pretrained to capture global structures and general semantics rather than fine-grained emotional cues, resulting in limited sensitivity to emotional signals; and 2) available datasets usually involve a trade-off between annotation quality and scale, which leads to insufficient supervision for emotional cues and ultimately limits cue-level reasoning.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Explainable Multimodal Emotion Recognition plays a crucial role in applications such as human-computer interaction and social media analytics. However, current approaches struggle with cue-level perception and reasoning due to two main challenges: 1) general-purpose modality encoders are pretrained to capture global structures and general semantics rather than fine-grained emotional cues, resulting in limited sensitivity to emotional signals; and 2) available datasets usually involve a trade-off between annotation quality and scale, which leads to insufficient supervision for emotional cues and ultimately limits cue-level reasoning.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To further support cue-level reasoning, we construct a large-scale dataset, EmoCue, designed to teach XEmoGPT how to reason over multimodal emotional cues.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Emotion Recognition moved forward this cycle; last verified April 2026. Public score 8.0/10.
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

Emotion Recognition

Competitors

not indexed

Competitive landscape

XEmoGPT revolutionizes emotion recognition in multimedia by perceiving and reasoning fine-grained emotional cues with advanced datasets and benchmarks.

Segment

Emotion Recognition

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.05496 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(55)

Reference metadata pending (1dc2281d4df5bd34f66aeb10e5b4741a27e23a9a)

Reference metadata pending (b5f50369c4418476887ef8206f0bfd894262a873)

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Reference metadata pending (5806ac4ea722f0c7d01eea436bfa26028bec41b3)

Reference metadata pending (f1cd31132081f2fa89a54c3e5605ecba27cb6bdf)

Reference metadata pending (88aa6b1f37d1fd8e0a40499ce9bb87873f03aaa8)

Reference metadata pending (feab5206295c4f7203ea4b83c6f3e106f6dc1b19)

Reference metadata pending (b43d4a3077555752de4ca0ffcf4daab60227968d)

Reference metadata pending (d754ff9879a7715a5a4d4f93e3e9017f9b57d02f)

Reference metadata pending (8ca1fdca69d780881f905ef9a4612e62f49a1014)

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Reference metadata pending (5c16e5224890dcb1aeeabe0cd4a373de72561c13)

Reference metadata pending (8a42e384c414bdd041d65e9044c8fa49626dc407)

Reference metadata pending (4956e8f57227ec8f642dc54e2c0c3742ebb388e7)

Reference metadata pending (2ffc9e530f4efff9e9f07997fa13535c3fef3e3c)

Reference metadata pending (351a2d50b4aff8e9754dc7074dd589b10a7465d4)

Showing 20 of 55 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

none indexed

Extension

Builds On ThisMMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control

7.0

Builds On ThisNano-EmoX: Unifying Multimodal Emotional Intelligence from Perception to Empathy

7.0

Builds On ThisEmotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understanding

5.0

Builds On ThisFoundation Model Embeddings Meet Blended Emotions: A Multimodal Fusion Approach for the BLEMORE Challenge

4.0

Builds On ThisFollow the Clues, Frame the Truth: Hybrid-evidential Deductive Reasoning in Open-Vocabulary Multimodal Emotion Recognition

3.0

Builds On ThisDisentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction

7.0

Builds On ThisEgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding

7.0

Commercially relevant

none indexed

Conflicting

Competing ApproachEMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models

4.0

Competing ApproachMultimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling

6.0

Competing ApproachSELF-EMO: Emotional Self-Evolution from Recognition to Consistent Expression

8.0

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: 2602.05496
Route: /paper/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning
Active tab: read
Artifact: xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning/paper-pack
REST build passport API/api/v1/paper/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning/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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    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
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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/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning

degraded

Proof freshness: stale
Proof status: failed
Display score: 8/10
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 has proof data, but the latest verification did not complete cleanly.

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

Agent Handoff

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

Agent Handoff

XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

Canonical ID xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning | Route /paper/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.05496"
  }
}

source_context

{
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  "topic_slug": null,
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  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

/buildability/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning

Watchwatch

Subject: XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

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/xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning

Paper ref

xemogpt-an-explainable-multimodal-emotion-recognition-framework-with-cue-level-perception-and-reasoning

arXiv id

2602.05496

Freshness

Generated at

2026-03-19T21:31:49.672Z

Evidence freshness

stale

Last verification

2026-03-19T21:31:49.672Z

Sources

References

Coverage

33%

Hash state

Lineage hash

0a904f3b37495e36f715af090ab5dc2f6715086916c0e6ee83fd7c399a98ba6e

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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XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning

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