arXiv:2606. 13436v1 Announce Type: new Abstract: Evaluation in machine learning is typically treated as a neutral measurement process.
arXiv:2606. 09809v1 Announce Type: new Abstract: AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs.
The study investigates how prior scores influence large language model (LLM) judgments in the LLM-as-a-Judge paradigm. By testing three prompt conditions—no metadata, revision framing, and anchored metadata containing prior scores—the authors find that prior scores systematically bias evaluations, shifting ratings toward those scores across 192,000 attempts. The bias also affects categorical decisions, blocking 48% of error corrections and flipping 10.18% of correct judgments, and is not mitigated by Chain-of-Thought or a warning, underscoring the need for careful context engineering.
arXiv:2610.33262v1 Announce Type: cross Abstract: We study efficient, adaptive auditing of categorical labels in a fixed dataset, motivated by biodiversity-credit programs in which expert verificatio...
arXiv:2610.07132v1 Announce Type: cross Abstract: Croissant has emerged as a standard for machine-readable dataset metadata, yet populating its fields remains labor-intensive and requires careful rea...
arXiv:2605. 26068v3 Announce Type: replace-cross Abstract: Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision.
arXiv:2607. 25891v1 Announce Type: new Abstract: Evaluating AI agents in interactive environments is hindered by fragmented tasks, scaffolds, verifiers, and scoring rules.
The paper investigates how reliable large language models (LLMs) are when used as judges to evaluate entity alignment (EA) systems, which match equivalent entities across knowledge graphs. Through systematic benchmarking across three frontier LLMs, three datasets, and four EA systems, the study identifies a failure mode called anchor bias, where judges invert discrimination when the system’s decision label is visible. The authors show that a label‑free evaluation protocol restores high discrimination accuracy, while visible labels cause significant score shifts, especially in stronger models—a phenomenon they term the frontier model paradox. They also provide a new biomedical EA benchmark (MeSH–SNOMED CT) and an open‑source framework for auditing LLM judge reliability.
Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways. Detecting a conflict is only the first step: review workflows may also need to determine its type, since numerical, temporal, referential, factual, and normative inconsistencies require different evidence and downstream checks.
arXiv:2607. 26368v1 Announce Type: cross Abstract: Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.
The paper audits the reproducibility of knowledge‑graph extraction from threat reports by re‑implementing matching rules for only five of twelve systems and re‑scoring ten system outputs under eight protocols. The audit shows that different matching protocols can reverse most pairwise system rankings and that a fixed prediction set can vary from 0.16 to 0.70 F1. The authors also build CTIForge to isolate validation effects, finding that validation changes precision across backbones and increases entity‑type disputes, and they release the full pipeline, protocol suite, and audit records.
The paper introduces BenchGuard, a framework that uses advanced language models to audit execution-based agent benchmarks. BenchGuard systematically cross-verifies benchmark artifacts through structured LLM protocols, optionally incorporating agent solutions or execution traces for deeper diagnostics. In tests on ScienceAgentBench and BIXBench Verified-50, BenchGuard uncovered numerous author-confirmed and expert-identified issues, including fatal errors that made tasks unsolvable, and demonstrated cost-effective auditing for complex bioinformatics tasks.
arXiv:2609.33357v2 Announce Type: replace Abstract: Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially red...