On-Device Deep Research at 4B: Exposure Bounds Faithfulness, Retrieval Bounds Coverage
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Computer Science > Artificial Intelligence
Title:On-Device Deep Research at 4B: Exposure Bounds Faithfulness, Retrieval Bounds Coverage
Abstract:On-device research agents search a corpus, read sources, and write a cited brief on a personal laptop. Whether their citations are faithful, and at what cost, is unmeasured for a deployable small model. This study fixes one 4B generator on a 24 GB laptop and asks what makes its citations faithful. It separates two quantities usually reported as one number. Cited claim faithfulness asks whether the cited source supports the claim. Trustworthy coverage asks whether the agent also cites the right sources. The study crosses how much of each source the generator sees, 400 against 1500 characters, with the quality of the sources supplied, gold papers against retrieved papers. Two levers fall out, and they act on different outcomes. Exposure sets faithfulness. More of each source lifts faithfulness from 0.45 to 0.58 on retrieved sources and from 0.37 to 0.58 on gold sources, and the two settings converge, so faithfulness is bound by exposure, not by whether the source is correct. The exposure lift is robust to a second, independent judge; the exact convergence is tight under the primary judge and only approximate under the second. Retrieval sets coverage. Trustworthy coverage stays near 0.22 on retrieved sources at any exposure, because recall is held near 0.40, so exposure cannot fix which sources are cited. The extra exposure costs about 235 output tokens. The practical recipe is to raise per source exposure first, cheaply, and then treat retrieval recall as the only remaining lever.
| Comments: | 13 pages, 2 figures, appendix |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.12257 [cs.AI] |
| (or arXiv:2607.12257v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.12257
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Vinay Kumar Chaganti [view email][v1] Tue, 14 Jul 2026 01:57:39 UTC (63 KB)
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