TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs explores TriEx is a game-based framework for explaining multi-agent LLM reasoning through structured self-reasoning, belief states, and oracle audits.. Commercial viability score: 6/10 in Agents.
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Canonical ID triex-a-game-based-tri-view-framework-for-explaining-internal-reasoning-in-multi-agent-llms | Route /paper/triex-a-game-based-tri-view-framework-for-explaining-internal-reasoning-in-multi-agent-llms
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/triex-a-game-based-tri-view-framework-for-explaining-internal-reasoning-in-multi-agent-llmsMCP example
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}Paper proof page receipt window
/buildability/triex-a-game-based-tri-view-framework-for-explaining-internal-reasoning-in-multi-agent-llms
Subject: TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs
Verdict
Watch
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Receipt path
/buildability/triex-a-game-based-tri-view-framework-for-explaining-internal-reasoning-in-multi-agent-llms
Paper ref
triex-a-game-based-tri-view-framework-for-explaining-internal-reasoning-in-multi-agent-llms
arXiv id
2604.20043
Generated at
2026-04-23T05:09:42.457Z
Evidence freshness
fresh
Last verification
2026-04-23T05:09:42.457Z
Sources
4
References
0
Coverage
67%
Lineage hash
a27700e2a2c6a5681ec1e06469a8e85b40bd59b961b7ea2523295a6ec700c22a
Canonical opportunity-kernel lineage hash.
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unsigned_external
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not_verified
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Pending verification refs / 4 sources / Verification pending
references
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Dimensions overall score 6.0
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This equation captures one of the core mathematical components of the system. e the explanations they evaluate. Formally, given a trace slice τ i t = ⟨oi t, Ai t, ai t, ri t, {P i j,t}j̸=i, ϕt⟩at ti
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This equation captures one of the core mathematical components of the system. action ai t, first-person artifact ri t, opponent profiles {P i j,t}j̸=i, and objective reference features ϕt Output: st
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This equation captures one of the core mathematical components of the system. p′ k = σ(logit(pk) + δ) where δ controls the intervention magnitude (δ = 2.5); see Table 4) and σ(·) denotes the sigmoid
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