Opportunity summary
Score0.0Public score shown from the verified overall while the stale axis breakdown refreshesPaincustomer pain not on file
Evidence0 refs | 3 sources | 50% coverage
Blockerno shell-level blocker reported
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ARXIV:2605.25748 · UNCATEGORIZED · SUBMITTED 27 MAY · 00:05 UTC · FRESHNESS STALE
ARXIV:2605.25748UNCATEGORIZEDSUBMITTED 27 MAY · 00:05 UTCFRESHNESS STALEYanping Wu · Ji Zhang · Hao Chen · Edmond S. L. Ho · Chongfeng Wei · arXiv
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Extensive experiments on five public benchmarks demonstrate that FEP-Diff consistently outperforms state-of-the-art methods under restricted observability. Code availability is flagged…
Opportunity summary
Pain customer pain not on file
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns.
Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns. However, existing methods rely on global state assumptions, suffer from insufficient belief inference under partial observability, and lack cognitive behavioral constraints in…
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Extensive experiments on five public benchmarks demonstrate that FEP-Diff consistently outperforms state-of-the-art methods under restricted observability. Code availability is flagged in the production record;…
Uncategorized moved forward this cycle; last verified May 2026. Public score 0.0/10. Production flags indicate code availability.
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Score0.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Extensive experiments on five public benchmarks demonstrate that FEP-Diff consistently outperforms state-of-the-art methods under restricted observability. Code availability is flagged…
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Paper Pack
10.48550/arXiv.2605.25748Abstract
Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns. However, existing methods rely on global state assumptions, suffer from insufficient belief inference under partial observability, and lack cognitive behavioral constraints in prediction. These limitations severely compromise both deployment feasibility and physical plausibility in real-world settings. In this work, we propose FEP-Diff, an agent-centric trajectory prediction framework grounded in the Free Energy Principle, aimed at achieving cognitively plausible predictions under realistic constraints. Specifically, a dual-branch spatiotemporal encoder extracts ego-motion dynamics and social interaction cues from local observations. Building upon this, a goal-conditioned belief learner infers multimodal latent belief distributions optimized via a free-energy objective, with a social consistency constraint on the local neighborhood graph to promote cognitive alignment among neighboring agents. Finally, a residual diffusion trajectory generator is conditioned on the learned belief representations with token-level proxy conditioning, producing precise and diverse future predictions. Extensive experiments on five public benchmarks demonstrate that FEP-Diff consistently outperforms state-of-the-art methods under restricted observability. Code: https://anonymous.4open.science/r/FEP-Diff-8876.
Source availability
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Preparing verified analysis
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PROBLEM
Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns.
METHOD
Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns. However, existing methods rely on global state assumptions, suffer from insufficient belief inference under partial observability, and lack cognitive behavioral constrai...
RESULT
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Extensive experiments on five public benchmarks demonstrate that FEP-Diff consistently outperforms state-of-the-art methods under restricted observability. Code availability is flagged in the production r...
WHY NOW
Uncategorized moved forward this cycle; last verified May 2026. Public score 0.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 10, "author": "Yanping Wu; Ji Zhang; Hao Chen; Edmond S. L. Ho; Chongfeng Wei", "title": "Agent-Centric Social Trajectory Prediction: A Free Energy Principle Perspective"
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partial
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Methods
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Segment
Uncategorized
Adoption evidence
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Commercial read
0.0/10 public viability
Direct
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Substitute
Unknown
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Market urgency
missing
Current read
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Evidence
0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
missing
Current read
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Evidence
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Defensibility
missing
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Regulatory load
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Gaps
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Classify regulatory flags before commercialization planning.
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People
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Prototype owner missing.
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People
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Operator workflow not sourced.
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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