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ARXIV:2605.30632 · HUMAN-AI ALIGNMENT · SUBMITTED 01 JUN · 20:33 UTC · FRESHNESS STALE
ARXIV:2605.30632HUMAN-AI ALIGNMENTSUBMITTED 01 JUN · 20:33 UTCFRESHNESS STALEAritra Dasgupta · Naga Datha Saikiran Battula · Avina Nakarmi · Sohom Sen · Subhodeep Ghosh · Xun Song · arXiv
Rationalize: A framework for shared semantic reasoning between humans and AI, conceptualizing interaction as complementary role pairs for improved alignment.
Opportunity summary
Pain Rationalize: A framework for shared semantic reasoning between humans and AI, conceptualizing interaction as complementary role pairs for improved alignment.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Rationalize: A framework for shared semantic reasoning between humans and AI, conceptualizing interaction as complementary role pairs for improved alignment. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as…
We introduce Rationalize, a role-pair framework for shared semantic reasoning between humans and AI models in data-driven sensemaking. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as a series…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We relate these role pairs to the bidirectional human-AI alignment framework, illustrating how "aligning AI to humans" and "aligning humans to AI" differ by…
Human-AI Alignment moved forward this cycle; last verified June 2026. Public score 3.0/10.
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Rationalize: A framework for shared semantic reasoning between humans and AI, conceptualizing interaction as complementary role pairs for improved alignment.
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10.48550/arXiv.2605.30632Rationalize: A framework for shared semantic reasoning between humans and AI, conceptualizing interaction as complementary role pairs for improved alignment.
Abstract
We introduce Rationalize, a role-pair framework for shared semantic reasoning between humans and AI models in data-driven sensemaking. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as a series of complementary role pairs (Explorer-Guide, Investigator-Informant, Teacher-Student, Judge-Advocate) operating in a shared reasoning space. In this space, human analysts and AI models (such as LLMs) make purposes, questions, assumptions, evidence, inferences, and implications explicit, facilitating alignment not only at the output level but at the level of rationalization of intent and action by each side. We relate these role pairs to the bidirectional human-AI alignment framework, illustrating how "aligning AI to humans" and "aligning humans to AI" differ by role, and sketch a collaborative research agenda for alignment design and assessment using element-level and role-specific approaches.
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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.
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Dimensions overall score 3.0
PROBLEM
Rationalize: A framework for shared semantic reasoning between humans and AI, conceptualizing interaction as complementary role pairs for improved alignment. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as a series of co...
METHOD
We introduce Rationalize, a role-pair framework for shared semantic reasoning between humans and AI models in data-driven sensemaking. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as a series of complementary role pairs...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We relate these role pairs to the bidirectional human-AI alignment framework, illustrating how "aligning AI to humans" and "aligning humans to AI" differ by role, and sketch a collaborative research agend...
WHY NOW
Human-AI Alignment moved forward this cycle; last verified June 2026. Public score 3.0/10.
{"file name": "input.pdf", "number of pages": 6, "author": "Aritra Dasgupta; Naga Datha Saikiran Battula; Avina Nakarmi; Sohom Sen; Subhodeep Ghosh; Xun Song"
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Rationalize: A framework for shared semantic reasoning between humans and AI, conceptualizing interaction as complementary role pairs for improved alignment.
Segment
Human-AI Alignment
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Commercial read
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proof status
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Buyer clarity
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Integration burden
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Current read
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DEFENSIBILITY
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