AuthTrace: Diagnosing Evidence Construction in Thematically Dense Single-Author Corpora

• URL: http://arxiv.org/abs/2605.25382v2
• Date: Tue, 26 May 2026 10:32:44 GMT
• Title: AuthTrace: Diagnosing Evidence Construction in Thematically Dense Single-Author Corpora
• Abstract summary: We introduce AuthTrace, a diagnostic benchmark built on thematically dense single-author corpora.<n>AuthTrace provides explicit quoted evidence, exact fan-in annotation, and a unified pack-level protocol measuring evidence recall, evidence precision, and answer correctness.
• Score: 6.956097396264084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evidence construction--the stage that determines which passages reach the language model before generation begins--is evaluated paradigm by paradigm, leaving practitioners with no principled way to diagnose which organization strategy fails, where, or why. We introduce AuthTrace, a diagnostic benchmark built on thematically dense single-author corpora where near-miss distractors share style, topic, and vocabulary with the required evidence. AuthTrace provides explicit quoted evidence, exact fan-in annotation, and a unified pack-level protocol measuring evidence recall, evidence precision, and answer correctness. A fan-in gradient--the number of source documents required to support the answer--serves as the primary diagnostic axis, enabling controlled comparison across retrieval, memory, graph, and structured-evidence paradigms. Evaluating eight systems across two QA models, we find that evidence recall is the strongest observed predictor of answer correctness under the primary reader-judge pair (r = 0.96); most failures stem from missing evidence rather than answer synthesis. Fan-in further exposes paradigm-specific collapse patterns: flat retrieval degrades 2-3x faster than thematically organized evidence construction. These results show fan-in decomposition to be a reusable diagnostic lens for identifying where evidence-construction systems fail and which paradigm best serves a given workload.

Related papers

• Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation [50.92223196290564]
  We propose a unified agent loop for explicit evidence selection and consolidation.<n>During exploration, SCoRE retains only query-relevant observations and their source pointers in a maintained textual ledger.<n>At termination, it reloads the referenced original images and consolidates the visual evidence for answering, arranging it into a logical sequence.
  arXiv  Detail & Related papers  (2026-09-14T16:11:35Z)
• Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning [72.86819994769634]
  Whole-slide image (WSI) reasoning requires an agent to sequentially acquire visual evidence before answering a diagnostic question.<n>We propose BEACON, a plug-and-play agentic framework that reformulates WSI reasoning as a Bayesian evidence acquisition problem.
  arXiv  Detail & Related papers  (2026-08-06T08:40:57Z)
• HiEviDR-Bench: A Benchmark for Hierarchical Evidence Aggregation in Deep Research [52.87128503345243]
  We introduce HiEviDR-Bench, a benchmark for evaluating Hierarchical Evidence Aggregation in Deep Research.<n>HiEviDR-Bench represents each instance with an explicit evidence graph that captures evidence selection, cross-source linking, and aggregation.<n>We develop a traceability-oriented evaluation framework with five dimensions: report quality, evidence traceability, citation accuracy, claim verification, and answer correctness.
  arXiv  Detail & Related papers  (2026-07-27T23:50:46Z)
• SAAG: Structured Agent Assessment and Grounding [13.62148061055744]
  We propose a cascaded diagnostic framework that decomposes agent-calling evaluation into three sequential stages.<n>We evaluate this framework on a benchmark derived from Glaive's function-calling dataset across registry sizes of 5, 10, and 15 agents.
  arXiv  Detail & Related papers  (2026-04-30T19:33:58Z)
• DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding [63.257540233507626]
  We propose a paradigm that requires the model to execute a structured Analysis, Localization and Reasoning workflow.<n>We show it robustly generalizes from short-page training to ultra-long documents and is naturally synergistic with visual Retrieval-Augmented Generation systems.
  arXiv  Detail & Related papers  (2026-04-14T14:39:26Z)
• Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG [8.18791900871137]
  Retrieval-Augmented Generation aims to reduce hallucinations by grounding answers in retrieved evidence.<n>Existing evaluations focus on answer-level or passage-level accuracy, offering limited insight into how evidence is used during generation.<n>We introduce a facet-level diagnostics framework for QA that decomposes each input question into atomic reasoning facets.
  arXiv  Detail & Related papers  (2026-04-10T09:59:43Z)
• AI-Supervisor: Autonomous AI Research Supervision via a Persistent Research World Model [1.14219428942199]
  Existing automated research systems operate as stateless, linear pipelines.<n>We present textbfAI-Supervisor, a multi-agent orchestration framework.<n>Agents provide end-to-end AI research supervision driven by human interests.
  arXiv  Detail & Related papers  (2026-03-25T15:16:51Z)
• Logics-Parsing-Omni Technical Report [18.897248420641386]
  This paper proposes the Omni Parsing framework to address the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing.<n>A pivotal advantage of this framework is its evidence anchoring mechanism, which enforces a strict alignment between high-level semantic descriptions and low-level facts.<n>This enables evidence-based'' logical induction, transforming unstructured signals into standardized knowledge that is locatable, enumerable, and traceable.
  arXiv  Detail & Related papers  (2026-03-10T13:46:32Z)
• Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities [32.76303717104482]
  We introduce DeR2, a controlled deep-research sandbox that isolates document-grounded reasoning.<n>DeR2 decouples evidence access from reasoning via four regimes--Instruction-only, Concepts, Related-only, and Full-set.<n> Experiments across a diverse set of state-of-the-art foundation models reveal substantial variation and significant headroom.
  arXiv  Detail & Related papers  (2026-01-29T16:26:19Z)
• Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics [89.1999907891494]
  We present WebDetective, a benchmark of hint-free multi-hop questions paired with a controlled Wikipedia sandbox.<n>Our evaluation of 25 state-of-the-art models reveals systematic weaknesses across all architectures.<n>We develop an agentic workflow, EvidenceLoop, that explicitly targets the challenges our benchmark identifies.
  arXiv  Detail & Related papers  (2025-10-01T07:59:03Z)
• RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark [71.3555284685426]
  We introduce RealUnify, a benchmark designed to evaluate bidirectional capability synergy.<n>RealUnify comprises 1,000 meticulously human-annotated instances spanning 10 categories and 32 subtasks.<n>We find that current unified models still struggle to achieve effective synergy, indicating that architectural unification alone is insufficient.
  arXiv  Detail & Related papers  (2025-09-29T15:07:28Z)
• Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning [4.230202411425062]
  We analyze the relationship between the retrieval and interpretation of within-document evidence for large language model.<n>We perform two ablation studies to investigate when both label prediction and evidence retrieval errors can be attributed to qualities of the relevant evidence.
  arXiv  Detail & Related papers  (2025-02-19T20:48:09Z)
• Localizing Factual Inconsistencies in Attributable Text Generation [74.11403803488643]
  We introduce QASemConsistency, a new formalism for localizing factual inconsistencies in attributable text generation.<n>We show that QASemConsistency yields factual consistency scores that correlate well with human judgments.
  arXiv  Detail & Related papers  (2024-10-09T22:53:48Z)
• DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection [55.70982767084996]
  A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. We present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions. DeepfakeBench contains 15 state-of-the-art detection methods, 9CL datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations.
  arXiv  Detail & Related papers  (2023-07-04T01:34:41Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)

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.