aiqbench analysis
This study investigates the utility of rationales in role-specialized QA pipelines by intervening specifically on the message passed from a reasoner to a verifier. The findings reveal that while faithful rationales offer negligible accuracy gains over none at all, corrupted rationales significantly distort support assessments and can be exploited to manipulate verification outcomes without changing final answers. Consequently, rationale sharing should be treated as a critical verification-message mechanism rather than a simple proxy for answer correctness.
What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA
arXiv:2610.00018v1 Announce Type: new Abstract: Role-specialized QA pipelines increasingly pass rationales from a reasoner to a verifier, but it is unclear what this message actually buys: better answers, stronger support assessment, or a new failure surface. We introduce a message-intervention diagnostic that fixes the evidence and candidate answer while varying only the rationale passed across the reasoner-to-verifier boundary. On 400 MuSiQue, HotpotQA, and 2WikiMultiHopQA examples with DeepSeek as generator and verifier, faithful rationales add almost no answer accuracy over no rationale, while corrupted rationales strongly alter support judgments. Under a blind verifier prompt, harmless paraphrases shift support by only 0--2.5%, whereas corrupted rationales shift support by 10--22%; an explicit rationale-checking prompt amplifies the same pattern to 34--55%. Final answers move less (2--30%), and only 2.9--35.3% of corrupted support flips co-occur with answer changes. Human audits show why this matters: 16/42 valid corruptions are corruption-overtrust cases, and blind humans reject or mark unclear 9/10 audited corrupted rationales that the model accepts. Cross-model and task-boundary checks show when the channel is active, amplified, inert, or folded into the task label. Rationale sharing should be evaluated as a verification-message mechanism, not merely as a route to higher answer accuracy.
Title:What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA
Abstract:Role-specialized QA pipelines increasingly pass rationales from a reasoner to a verifier, but it is unclear what this message actually buys: better answers, stronger support assessment, or a new failure surface. We introduce a message-intervention diagnostic that fixes the evidence and candidate answer while varying only the rationale passed across the reasoner-to-verifier boundary. On 400 MuSiQue, HotpotQA, and 2WikiMultiHopQA examples with DeepSeek as generator and verifier, faithful rationales add almost no answer accuracy over no rationale, while corrupted rationales strongly alter support judgments. Under a blind verifier prompt, harmless paraphrases shift support by only 0--2.5%, whereas corrupted rationales shift support by 10--22%; an explicit rationale-checking prompt amplifies the same pattern to 34--55%. Final answers move less (2--30%), and only 2.9--35.3% of corrupted support flips co-occur with answer changes. Human audits show why this matters: 16/42 valid corruptions are corruption-overtrust cases, and blind humans reject or mark unclear 9/10 audited corrupted rationales that the model accepts. Cross-model and task-boundary checks show when the channel is active, amplified, inert, or folded into the task label. Rationale sharing should be evaluated as a verification-message mechanism, not merely as a route to higher answer accuracy.
14 pages, 6 figures, 9 tables. Preprint
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2610.00018 [cs.AI]
(or arXiv:2610.00018v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.00018 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 304 words
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