Kierston & KierstonBench: Simulation of Allentown, PA and Evaluation of Language Models in Crime Investigation and Surveillance Tasks
The paper presents Kierston, a city-scale simulation of about 125,000 Allentown, Pennsylvania residents generating telecommunications, financial, administrative and license-plate records, and KierstonBench, a benchmark of 32 task families…
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper presents Kierston, a city-scale simulation of about 125,000 Allentown, Pennsylvania residents generating telecommunications, financial, administrative and license-plate records, and KierstonBench, a benchmark of 32 task families testing language models on record linkage, routine reconstruction, incident investigation and detecting deceptive accounts. Models query a restricted records interface without access to the underlying simulation. Among sixteen evaluated models, most could not perform records investigation and submitted unsupported identifications. A small number of frontier models solved most tasks and abstained when evidence was insufficient, though they still fell for some deceptions. The authors conclude none replaced an investigator.
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Attribution, source and conditionsSource-stated points from the stored brief. Claim-level assessments have not been recorded.
Results come from a synthetic simulation of one city; real surveillance records and investigative conditions may differ. Source claims not independently checked.
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Questions worth asking
Open questions for further reportingWhat remains unconfirmed?
Results come from a synthetic simulation of one city; real surveillance records and investigative conditions may differ. Source claims not independently checked.
What material was available for this brief?
The recorded brief basis is fetched source page. These points are AINewsIQ paraphrases, not quotations or independent confirmations.
Most of the sixteen evaluated language models could not perform records investigation and submitted identifications the records did not support.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseA small number of frontier models solved most tasks and abstained when evidence was insufficient.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseEven the strongest models fell for some common deceptions and assumptions.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseThe benchmark covers 32 task families including record linkage, routine reconstruction and incident investigation.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
Results come from a synthetic simulation of one city; real surveillance records and investigative conditions may differ. Source claims not independently checked.
This is a source-based abstract, not independent validation of the source's claims.
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The paper page is linked. Peer review and findings have not been independently checked by AINewsIQ.
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Why this may matter
The work proposes a way to measure whether language models can handle surveillance-style record analysis when ground truth is normally unavailable, which bears directly on the safety and reliability of such deployments.
Limits and open questions
Results come from a synthetic simulation of one city; real surveillance records and investigative conditions may differ. Source claims not independently checked.
Organization-specific financial, security, and operational effects require the organization's own context and review.
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