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Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model

Stale1d agoPending verification refs / 3 sources / Verification pending
Viability
0.0/10

Compared to this week’s papers

Verification pending

Use This Via API or MCP

Use Signal Canvas as the narrative proof surface

Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp

ready
Proof freshness
fresh
Proof status
unverified
Display score
2/10
Last proof check
2026-04-23
Score updated
2026-04-23
Score fresh until
2026-05-23
References
0
Source count
3
Coverage
50%

Page-specific freshness sourced from this paper's evidence receipt and score bundle.

Agent Handoff

Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model

Canonical ID resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp | Route /signal-canvas/resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp",
    "query_text": "Summarize Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model",
  "normalized_query": "2604.19838",
  "route": "/signal-canvas/resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp",
  "paper_ref": "resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 1

References: Pending verification

Proof: Verification pending

Freshness state: computing

Signal Canvas receipt window

Not build-ready: Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model

/buildability/resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp

Ignoreblocked

Subject: Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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/resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp

Paper ref

resolving-space-sharing-conflicts-in-road-user-interactions-through-uncertainty-reduction-an-active-inference-based-comp

arXiv id

2604.19838

Freshness

Generated at

2026-04-23T05:11:32.366Z

Evidence freshness

fresh

Last verification

2026-04-23T05:11:32.366Z

Sources

3

References

0

Coverage

50%

Hash state

Lineage hash

af6da365e2c97c871f9723f13ff22988a0669be0258378cc4d8418325e93a4d0

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: proof_status
  • Unknown: proof verification has not been recorded yet

Pending verification refs / 3 sources / Verification pending

repo_url

references

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

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model

Overall score: 2/10
Lineage: af6da365e2c9

Canonical Paper Receipt

Last verification: 2026-04-23T05:11:32.366Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 3

Coverage: 50%

Missingness
  • - repo_url
  • - references
  • - proof_status
Unknowns
  • - proof verification has not been recorded yet

Preparing verified analysis

Dimensions overall score 2.0

GitHub Code Pulse

No public code linked for this paper yet.

Key claims

Strong 1Mixed 0Weak 0
Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Startup potential card

Startup potential card preview

Related Resources

Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.

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