proof blockPage 664%
This equation defines the loss the model is optimizing during training.
Page and bbox are available; crop image is pending.
Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models explores Build AI-powered game agents leveraging large language models for creating interactive learning environments.. Commercial viability score: 5/10 in AI for Gaming.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/nemobot-games-crafting-strategic-ai-gaming-agents-for-interactive-learning-with-large-language-models
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID nemobot-games-crafting-strategic-ai-gaming-agents-for-interactive-learning-with-large-language-models | Route /paper/nemobot-games-crafting-strategic-ai-gaming-agents-for-interactive-learning-with-large-language-models
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/nemobot-games-crafting-strategic-ai-gaming-agents-for-interactive-learning-with-large-language-modelsMCP example
{
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"arguments": {
"arxiv_id": "2604.21896"
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"query": "Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models",
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}Paper proof page receipt window
/buildability/nemobot-games-crafting-strategic-ai-gaming-agents-for-interactive-learning-with-large-language-models
Subject: Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models
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.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/nemobot-games-crafting-strategic-ai-gaming-agents-for-interactive-learning-with-large-language-models
Paper ref
nemobot-games-crafting-strategic-ai-gaming-agents-for-interactive-learning-with-large-language-models
arXiv id
2604.21896
Generated at
2026-04-24T20:25:20.979Z
Evidence freshness
fresh
Last verification
2026-04-24T20:25:20.979Z
Sources
3
References
0
Coverage
50%
Lineage hash
769ab6d3c913c962c2674cdec97ee98c3d836f60b7f673a85a8998348932b8ff
Canonical opportunity-kernel lineage hash.
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.
Pending verification refs / 3 sources / Verification pending
repo_url
references
Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
Research neighborhood
Interactive graph renders after load.
Preparing verified analysis
Dimensions overall score 5.0
Visual citation anchors from the paper document graph.
This equation defines the loss the model is optimizing during training.
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. the AI’s move as nAI = max(r, 3 −nuser), where r is the
Page and bbox are available; crop image is pending.
if loss(R) ≤τ then
Page and bbox are available; crop image is pending.
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