arXiv AI
Jul 3
GroundEval: A Deterministic Replacement for LLM-as-Judge in Stateful Agent Evaluation
arXiv:2606. 22737v2 Announce Type: replace Abstract: Before letting an agent operate over real context, can you prove it used the right evidence?
L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Audit
arXiv:2509. 00761v4 Announce Type: replace Abstract: Large language models are increasingly deployed for legal question answering, where evaluations typically focus on multiple-choice accuracy.
Related stories
arXiv AI
Jul 24
Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis
arXiv:2607. 20527v1 Announce Type: new Abstract: Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers, and both they and their benchmarks already check whether those citations hold, with a fixed attribution model or human graders.
arXiv AI
1d ago
Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following
arXiv:2505. 02763v2 Announce Type: replace-cross Abstract: One of the central promises of legal AI is to automate drudgery -- the formal, repetitive tasks of lawyers' work that consume time without calling for much discretion.
arXiv AI
Aug 11
Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law
arXiv:2608. 09393v1 Announce Type: cross Abstract: We identify and quantify temporal misgrounding: the systematic retrieval and citation of the currently in-force version of a legal article when the applicable version is an earlier or future one.
arXiv AI
Jul 22
Calibrated Selective Fact-Checking via Evidence Chain Evaluation
arXiv:2607. 18240v1 Announce Type: new Abstract: Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent.
arXiv AI
Jun 26
ProvenAI: Provenance-Native Traces of Evidence in Generated Answers
arXiv:2606. 26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source meaningfully shaped the output.
arXiv AI
Aug 7
HERALD: Counterfactual Audits and Minimal Repairs for Proof-of-Retrieval Rewards
arXiv:2608. 06012v1 Announce Type: new Abstract: Search-agent rewards mix answer quality, citation grounding, tool cost, and anti-hacking terms; a high score therefore need not imply that cited evidence was retrieved, and added penalties can cancel.
arXiv AI
Jun 30
How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation
arXiv:2606. 28358v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external documents, often using inline citations for verifiability.
arXiv AI
Aug 6
HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents
arXiv:2608. 02009v2 Announce Type: replace Abstract: Retrieval-augmented search agents answer multi-hop questions by repeatedly issuing search queries and accumulating evidence.
arXiv AI
2d ago
CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA
arXiv:2608. 13706v1 Announce Type: cross Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the final text.
arXiv AI
Aug 5
Knowing the Form, Not the Function: Automatically Auditing Answer--Authority Decoupling in Legal Benchmarks
arXiv:2608. 02621v1 Announce Type: cross Abstract: Legal benchmarks typically score final answers even when models also state legal authority.
arXiv AI
Jul 2
Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences
arXiv:2607. 00738v1 Announce Type: cross Abstract: Large language models can generate polished scientific text that includes unsupported claims, allowing hallucinations to enter the archival record.