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ARXIV:2604.03159 · LLM AGENTS · SUBMITTED 06 APR · 20:12 UTC · FRESHNESS UNKNOWN
ARXIV:2604.03159LLM AGENTSSUBMITTED 06 APR · 20:12 UTCFRESHNESS UNKNOWNDelip Rao · Chris Callison-Burch · arXiv
A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records.
Opportunity summary
Pain A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records. Prior evaluations tested base models without search, which does not reflect current practice.
Large language models with web search are increasingly used in scientific publishing agents, yet they still produce BibTeX entries with pervasive field-level errors. Prior evaluations tested base models without search, which does not reflect…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. An ablation comparing single-stage and two-stage integration shows that separating search from revision yields larger gains and lower regression (0.8% vs. Code availability is…
LLM Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records.
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10.48550/arXiv.2604.03159A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records.
Abstract
Large language models with web search are increasingly used in scientific publishing agents, yet they still produce BibTeX entries with pervasive field-level errors. Prior evaluations tested base models without search, which does not reflect current practice. We construct a benchmark of 931 papers across four scientific domains and three citation tiers -- popular, low-citation, and recent post-cutoff -- designed to disentangle parametric memory from search dependence, with version-aware ground truth accounting for multiple citable versions of the same paper. Three search-enabled frontier models (GPT-5, Claude Sonnet-4.6, Gemini-3 Flash) generate BibTeX entries scored on nine fields and a six-way error taxonomy, producing ~23,000 field-level observations. Overall accuracy is 83.6%, but only 50.9% of entries are fully correct; accuracy drops 27.7pp from popular to recent papers, revealing heavy reliance on parametric memory even when search is available. Field-error co-occurrence analysis identifies two failure modes: wholesale entry substitution (identity fields fail together) and isolated field error. We evaluate clibib, an open-source tool for deterministic BibTeX retrieval from the Zotero Translation Server with CrossRef fallback, as a mitigation mechanism. In a two-stage integration where baseline entries are revised against authoritative records, accuracy rises +8.0pp to 91.5%, fully correct entries rise from 50.9% to 78.3%, and regression rate is only 0.8%. An ablation comparing single-stage and two-stage integration shows that separating search from revision yields larger gains and lower regression (0.8% vs. 4.8%), demonstrating that integration architecture matters independently of model capability. We release the benchmark, error taxonomy, and clibib tool to support evaluation and mitigation of citation hallucinations in LLM-based scientific writing.
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Proof status
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What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records. Prior evaluations tested base models without search, which does not reflect current practice.
METHOD
Large language models with web search are increasingly used in scientific publishing agents, yet they still produce BibTeX entries with pervasive field-level errors. Prior evaluations tested base models without search, which does not reflect current practice.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. An ablation comparing single-stage and two-stage integration shows that separating search from revision yields larger gains and lower regression (0.8% vs. Code availability is flagged in the production re...
WHY NOW
LLM Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records. Prior evaluations tested base models without search, which does not reflect current practice.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models with web search are increasingly used in scientific publishing agents, yet they still produce BibTeX entries with pervasive field-level errors. Prior evaluations tested base models without search, which does not reflect current practice.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. An ablation comparing single-stage and two-stage integration shows that separating search from revision yields larger gains and lower regression (0.8% vs. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A tool to significantly improve the accuracy of BibTeX entries generated by LLM-powered scientific publishing agents by integrating authoritative citation records.
Segment
LLM Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
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CITED BY
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Build readiness
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passport absent
unknown
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Artifact maturity
GitHub and Hugging Face maturity payloads
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unknown
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Defensibility
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Operator workflow not sourced.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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