Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility

• URL: http://arxiv.org/abs/2609.04269v1
• Date: Wed, 02 Sep 2026 19:16:47 GMT
• Title: Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility
• Abstract summary: CorpFam is a public benchmark of 54,864 candidate pairs over 10,307 corporate families.<n>It is based on 6,638,350 US federal award records in which every supplier self-reports its ultimate parent to a government registry.
• Score: 1.3367903535457364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deciding whether two supplier records belong to the same corporate family is a prerequisite for spend consolidation, credit exposure aggregation and sanctions screening. It is usually treated as entity matching, but the tasks differ: a family link connects records that are deliberately different entities, and the evidence often appears in neither record. We introduce CorpFam, a public benchmark of 54,864 candidate pairs over 10,307 corporate families, derived from 6,638,350 US federal award records in which every supplier self-reports its ultimate parent to a government registry. Pairs are stratified by name visibility: whether the names are identical after normalisation, share a distinctive token, or share none. Because strata have positive rates from 10.2% to 97.3%, we report per-stratum recall, base-rate invariant, rather than F1, which is not. The strongest of 5 matchers recovers 100.0% of identical pairs and 4.2% of invisible ones; no method exceeds 4.7% on the latter. The failure begins before matching. Blocking decides which pairs a matcher sees, and we evaluate 7 schemes spanning phonetic keys, attribute keys that ignore the name, and semantic nearest neighbours. None reaches three percent on invisible pairs, and their union recovers 6.8%. 93.2% of these links never enter the candidate set, so no matching-stage improvement can reach them. The links are real: against SEC Exhibit 21 subsidiary schedules, which share no provenance with procurement registration, 64.2% of invisible links are corroborated, against 0.16% under permuted parents and 0.41% against the same parent's wrong exhibit: two unrelated nulls agreeing to within 0.25 points. Corporate-family resolution is a retrieval problem misfiled as a matching problem; the intervention point is candidate generation, not ranking. The benchmark, adjudication log, and code reproducing every number are released.

Related papers

• Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure [51.736723807086385]
  Population-marginal agreement is evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation.<n>On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons.
  arXiv  Detail & Related papers  (2026-09-07T10:22:22Z)
• Privacy Washing: Detecting Internal Contradictions in Privacy Policies [0.0]
  Privacy policies may contain internal contradictions in which commitments are undermined by practices documented elsewhere in the same policy.<n>We operationalize this phenomenon through a four-stage pipeline: statement extraction, compatibility filtering and natural language inference screening, multi-model judge verification, and thematic analysis.
  arXiv  Detail & Related papers  (2026-09-02T03:33:58Z)
• Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay [0.6768558752130311]
  We audit against policy-conditional ground truth from executed replay in a single-agent tool environment.<n>No step-level credit signals we audit show reliable incremental fidelity beyond marginal-matched shuffled control.<n>A confidence-only router recovers pivotal steps at chance level, but cuts judge cost by 13.1% per turn.
  arXiv  Detail & Related papers  (2026-08-20T08:04:00Z)
• TrustRoboReward: Preference-Ordered Isotonic Score Editing for Multi-Paradigm Robot Reward Models [40.42873860258522]
  We propose TrustRoboReward, a multi-paradigm reward modeling framework equipped with Preference-Ordered Isotonic Score Editing (POISE)<n>POISE rectifies pointwise scores and eliminates cross-paradigm reversal conflicts unresolved by TrustJudge.<n>Qwen3-VL-4B trained with POISE achieves an overall reward score of 77.96%, nearly matching GPT-5-mini (78.09%, gap 0.13%) and outperforming the strongest RoboReward-4B baseline by 10.13%.
  arXiv  Detail & Related papers  (2026-08-09T05:25:22Z)
• Best Friends, Not Forever: Evaluating Long-Horizon Persona Collapse and Behavioral Drift in AI Companions [34.277389276831435]
  We study two observable long-horizon failures: 'persona collapse', the loss of a deployed role, boundaries, values, or style, and 'behavioral drift'<n>We introduce ANCHOR, a controlled synthetic audit that separately measures persona enactment and trajectory recall.<n>The study contains 2,008 conversations spanning 27 personas, nine interaction schedules, three generated memory settings, and four evaluated models.
  arXiv  Detail & Related papers  (2026-07-30T20:18:39Z)
• Visual Credit Audit for Multimodal Spatial Reasoning [70.16915309526443]
  Visual Credit Audit separates two Estimands: whether the benchmark image gives the model's declared decision more support than text-only and blank controls, and whether the model responds to relation-specific visual evidence.<n>Applying labels yields dependence-credited correctness (D-CC)<n>Across four open MLLMs and two spatial benchmarks, 12.73-26.25% of decisions are correct yet uncredited.
  arXiv  Detail & Related papers  (2026-07-29T15:55:31Z)
• Who Gets Named: Citation Type Predicts Individual Naming by Grounded Language Models, and a Roster Instrument Captures 0.5% of It [0.0]
  It issued 2,400 grounded API calls in one two-hour window on 24 July 2026.<n>Every response was coded for whether it named an individual professional.<n>Models named an individual in 25.8% of responses.
  arXiv  Detail & Related papers  (2026-07-26T23:37:34Z)
• Narration-of-Thought: Inference-Time Scaffolding for Defeasible Ethical Reasoning in Large Language Models [6.628401122676601]
  We introduce narration-of-thought (NoT), a system prompt that structures chain-of-thought into five sections.<n>NoT adds no training, parameters, or fine-tuning.<n>On 100 DailyDilemmas scenarios across four generators from three vendors, NoT cuts stakeholder collapse from up to 31% to under 1%.
  arXiv  Detail & Related papers  (2026-06-24T20:23:16Z)
• Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents [45.148328075418156]
  We study a deployed multi-turn food-and-beverage ordering agent and measure how many genuine quality problems it catches.<n>Our blind-spot taxonomy shows the failure is structured, not random.<n>For production multi-turn agents, automated judging is a regression floor, not a substitute for human review.
  arXiv  Detail & Related papers  (2026-06-09T02:11:01Z)
• CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks [47.027290803102666]
  We present CoEval, an open framework that supplies a trustworthy, task-specific signal through ensemble self-evaluation.<n>A pool of models rotates through all three roles, teacher, student, and judge, to generate a fresh, contamination-free benchmark.
  arXiv  Detail & Related papers  (2026-06-02T13:41:43Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• When LLM Judge Scores Look Good but Best-of-N Decisions Fail [0.29465623430708904]
  Large language models are often used as judges to score candidate responses, then validated with a single global metric.<n>This can be misleading when the real deployment task is best-of-n selection within a prompt.<n>For judge-based selection, the relevant audit should report within-prompt signal, tie rates, and recovery/top-1 accuracy, not global agreement alone.
  arXiv  Detail & Related papers  (2026-03-12T23:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.