@inproceedings{vinayaka-2026-groundedness,
title = "Groundedness Is Not Truth: Evidence-Chain Forensics for Retrieval-Augmented Generation",
author = "Vinayaka, Samarth",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.54/",
pages = "732--742",
ISBN = "979-8-89176-478-1",
abstract = "A single faithfulness score can hide failures that require different repairs: a RAG answer may be grounded in text whose source is stale, incomplete, contradicted, low-authority, or insufficient. We introduce ContextTrace, a deterministic, local-first SDK and CLI for evidence-chain forensics that separates claim support, citation state, source condition, abstention need, root cause, repair, and regression prevention. We also introduce Groundedness-Truth-Gap, a suite for controlled conformance, while frozen RAGTruth and stress tests serve as failure-safety controls. ContextTrace{'}s central contribution is an inspectable evidence-chain contract. On a fresh 84-case public-document evaluation, it catches 76 of 77 failure-bearing cases and preserves all seven clean cases (balanced accuracy 0.994; MCC 0.929), the same binary confusion counts as a frozen GPT-4.1-mini judge, while producing typed diagnostic records without additional model calls. The clean-case sample is small, and fine-grained label, root, and unverifiable attribution remains limited. Post-review counterfactuals further show strong dependence on source metadata and temporal cues. We release a blinded 50-case annotation packet and scorer; independent adjudication remains pending. Our claim is reproducible fail-closed diagnosis, not truth verification or broad evaluator superiority."
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<abstract>A single faithfulness score can hide failures that require different repairs: a RAG answer may be grounded in text whose source is stale, incomplete, contradicted, low-authority, or insufficient. We introduce ContextTrace, a deterministic, local-first SDK and CLI for evidence-chain forensics that separates claim support, citation state, source condition, abstention need, root cause, repair, and regression prevention. We also introduce Groundedness-Truth-Gap, a suite for controlled conformance, while frozen RAGTruth and stress tests serve as failure-safety controls. ContextTrace’s central contribution is an inspectable evidence-chain contract. On a fresh 84-case public-document evaluation, it catches 76 of 77 failure-bearing cases and preserves all seven clean cases (balanced accuracy 0.994; MCC 0.929), the same binary confusion counts as a frozen GPT-4.1-mini judge, while producing typed diagnostic records without additional model calls. The clean-case sample is small, and fine-grained label, root, and unverifiable attribution remains limited. Post-review counterfactuals further show strong dependence on source metadata and temporal cues. We release a blinded 50-case annotation packet and scorer; independent adjudication remains pending. Our claim is reproducible fail-closed diagnosis, not truth verification or broad evaluator superiority.</abstract>
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%0 Conference Proceedings
%T Groundedness Is Not Truth: Evidence-Chain Forensics for Retrieval-Augmented Generation
%A Vinayaka, Samarth
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F vinayaka-2026-groundedness
%X A single faithfulness score can hide failures that require different repairs: a RAG answer may be grounded in text whose source is stale, incomplete, contradicted, low-authority, or insufficient. We introduce ContextTrace, a deterministic, local-first SDK and CLI for evidence-chain forensics that separates claim support, citation state, source condition, abstention need, root cause, repair, and regression prevention. We also introduce Groundedness-Truth-Gap, a suite for controlled conformance, while frozen RAGTruth and stress tests serve as failure-safety controls. ContextTrace’s central contribution is an inspectable evidence-chain contract. On a fresh 84-case public-document evaluation, it catches 76 of 77 failure-bearing cases and preserves all seven clean cases (balanced accuracy 0.994; MCC 0.929), the same binary confusion counts as a frozen GPT-4.1-mini judge, while producing typed diagnostic records without additional model calls. The clean-case sample is small, and fine-grained label, root, and unverifiable attribution remains limited. Post-review counterfactuals further show strong dependence on source metadata and temporal cues. We release a blinded 50-case annotation packet and scorer; independent adjudication remains pending. Our claim is reproducible fail-closed diagnosis, not truth verification or broad evaluator superiority.
%U https://aclanthology.org/2026.groundlm-1.54/
%P 732-742
Markdown (Informal)
[Groundedness Is Not Truth: Evidence-Chain Forensics for Retrieval-Augmented Generation](https://aclanthology.org/2026.groundlm-1.54/) (Vinayaka, GroundLM 2026)
ACL