arxiv: 2604.18362 · v1 · submitted 2026-04-20 · 💻 cs.CL · cs.IR

Recognition: unknown

ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation

Qingying Niu , Yuhao Wang , Ruiyang Ren , Bohui Fang , Wayne Xin Zhao

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:48 UTC · model grok-4.3

classification 💻 cs.CL cs.IR

keywords retrieval-augmented generationconflict resolutionevidence graphlong-form generationfactual consistencyhallucination reductionatomic claims

0 comments

The pith

ArbGraph improves factual consistency in long-form RAG by resolving conflicts among atomic claims in a graph before generation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces ArbGraph to handle the problem of noisy and contradictory evidence in retrieval-augmented generation for long-form outputs. The approach breaks down documents into atomic claims, builds a graph showing support and contradiction links, and uses iterative arbitration to weigh credibility before the model generates text. This separation of concerns aims to give the generator a cleaner set of facts. Readers would care because it targets a key failure mode in RAG systems where conflicting sources lead to hallucinations or incomplete answers.

Core claim

ArbGraph decomposes retrieved documents into atomic claims and organizes them into a conflict-aware evidence graph with explicit support and contradiction relations. It then applies an intensity-driven iterative arbitration mechanism that propagates credibility signals through evidence interactions to suppress unreliable and inconsistent claims before final generation, providing a coherent evidence foundation for downstream long-form RAG.

What carries the argument

The conflict-aware evidence graph with support and contradiction relations, processed via an intensity-driven iterative arbitration mechanism that propagates credibility signals to suppress inconsistent claims.

If this is right

ArbGraph improves factual recall and information density on LongFact and RAGChecker benchmarks across multiple LLM backbones.
It reduces hallucinations and sensitivity to retrieval noise.
Performance gains are most evident under conflicting or ambiguous evidence conditions.
Evidence validation is separated from text generation to supply a coherent foundation for long-form outputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same atomic-claim graph approach could be tested on multi-document summarization to check whether conflict resolution improves consistency beyond RAG.
Adding source metadata such as dates or reliability scores to the graph edges might address temporal or authority-based inconsistencies the current model does not cover.
If the arbitration scales efficiently, it could serve as a standard preprocessing filter in production RAG systems handling large evidence sets.

Load-bearing premise

Decomposing documents into atomic claims and modeling their support and contradiction relations in a graph resolves factual conflicts without significant information loss or introduction of new errors.

What would settle it

A controlled test on benchmarks with deliberately injected contradictions where ArbGraph shows no gain or a drop in factual accuracy compared to standard RAG would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.18362 by Bohui Fang, Qingying Niu, Ruiyang Ren, Wayne Xin Zhao, Yuhao Wang.

**Figure 2.** Figure 2: Sensitivity analysis of key parameters. Moderate values of 𝜏𝑞, 𝑀, and 𝑘 achieve a balance between evidence coverage, structural connectivity, and arbitration stability. 4.4 Analysis of Graph Construction Factors We conduct the following sensitivity analyses on the LongFact benchmark, where claim-level annotations enable fine-grained evaluation of how graph construction affects factual coverage and evidence… view at source ↗

**Figure 3.** Figure 3: Comparison of Scheduling Policies. ArbGraph’s conflict-aware scheduling improves factuality and evidence density. 4.4.6 Effectiveness of Conflict-Aware Scheduling. We analyze the impact of different scheduling strategies on the arbitration process. While heuristic-based scheduling policies such as HardFirst and Easy-First rely on static ordering rules, ArbGraph employs a dynamic, conflict-aware scheduling… view at source ↗

**Figure 4.** Figure 4: Comparison of standard RAG vs. ArbGraph. Standard RAG fails due to noise and unresolved conflicts, while ArbGraph resolves conflicts via evidence-level arbitration. 4.5.2 Case Study. We analyze a challenging query: “How is the United States related to the East Asia Summit (EAS)?” ( [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗

read the original abstract

Retrieval-augmented generation (RAG) remains unreliable in long-form settings, where retrieved evidence is noisy or contradictory, making it difficult for RAG pipelines to maintain factual consistency. Existing approaches focus on retrieval expansion or verification during generation, leaving conflict resolution entangled with generation. To address this limitation, we propose ArbGraph, a framework for pre-generation evidence arbitration in long-form RAG that explicitly resolves factual conflicts. ArbGraph decomposes retrieved documents into atomic claims and organizes them into a conflict-aware evidence graph with explicit support and contradiction relations. On top of this graph, we introduce an intensity-driven iterative arbitration mechanism that propagates credibility signals through evidence interactions, enabling the system to suppress unreliable and inconsistent claims before final generation. In this way, ArbGraph separates evidence validation from text generation and provides a coherent evidence foundation for downstream long-form generation. We evaluate ArbGraph on two widely used long-form RAG benchmarks, LongFact and RAGChecker, using multiple large language model backbones. Experimental results show that ArbGraph consistently improves factual recall and information density while reducing hallucinations and sensitivity to retrieval noise. Additional analyses show that these gains are evident under conflicting or ambiguous evidence, highlighting the effectiveness of evidence-level conflict resolution for improving the reliability of long-form RAG. The implementation is publicly available at https://github.com/1212Judy/ArbGraph.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ArbGraph adds a pre-generation graph of atomic claims with support/contradiction edges and intensity-driven propagation to clean evidence, but the gains on LongFact and RAGChecker could easily come from basic filtering rather than true conflict resolution.

read the letter

The paper's core move is to pull evidence validation out of the generator entirely. It breaks retrieved documents into atomic claims, wires them into a graph with explicit support and contradiction links, then runs an iterative credibility propagation that down-weights inconsistent claims before any text is produced. That separation is cleaner than most current RAG pipelines, which either stuff more context in or try to fix contradictions while generating. The experiments report better factual recall, higher information density, and fewer hallucinations across several backbones on the two standard long-form benchmarks, and the code is public, which helps.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces ArbGraph, a pre-generation framework for long-form RAG that decomposes retrieved documents into atomic claims, organizes them into a conflict-aware evidence graph with explicit support and contradiction relations, and applies an intensity-driven iterative arbitration mechanism to propagate credibility signals and suppress unreliable claims before generation. Experiments on LongFact and RAGChecker benchmarks with multiple LLM backbones report consistent gains in factual recall and information density alongside reductions in hallucinations and retrieval-noise sensitivity, with additional analysis under conflicting evidence.

Significance. If the central claims hold after addressing verification gaps, ArbGraph would represent a useful contribution to reliable long-form RAG by explicitly separating evidence-level conflict resolution from generation. The public code release supports reproducibility, and the focus on pre-generation arbitration addresses a practical pain point in noisy retrieval settings.

major comments (2)

[§3 and §4] §3 (ArbGraph Framework) and §4 (Experiments): The central claim that the evidence graph plus iterative arbitration produces a cleaner claim set than raw retrieval hinges on accurate atomic claim decomposition and support/contradiction edge labeling, yet no quantitative audit (e.g., precision/recall of decomposition fidelity or edge-label accuracy on the evaluation sets) is provided. Without this, observed gains on LongFact/RAGChecker could be explained by simple filtering rather than conflict resolution.
[§4.3] §4.3 (Analysis under conflicting evidence): The intensity-driven propagation is asserted to suppress noise without amplifying decomposition or labeling errors, but no error-propagation analysis or ablation isolating the arbitration step from baseline filtering is reported. This leaves the load-bearing assumption that the mechanism reliably corrects rather than compounds upstream errors unverified.

minor comments (2)

[Figure 1 and §3.2] Figure 1 and §3.2: The graph construction notation (nodes as claims, edges as support/contradiction) would benefit from a concrete worked example showing how a multi-fact sentence is split and labeled.
[§4.1] §4.1: Clarify the exact prompting templates used for claim decomposition and relation labeling, as these are implementation-critical for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for your detailed review and constructive feedback on our manuscript. We appreciate the opportunity to clarify and strengthen our work on ArbGraph. Below, we provide point-by-point responses to the major comments.

read point-by-point responses

Referee: [§3 and §4] §3 (ArbGraph Framework) and §4 (Experiments): The central claim that the evidence graph plus iterative arbitration produces a cleaner claim set than raw retrieval hinges on accurate atomic claim decomposition and support/contradiction edge labeling, yet no quantitative audit (e.g., precision/recall of decomposition fidelity or edge-label accuracy on the evaluation sets) is provided. Without this, observed gains on LongFact/RAGChecker could be explained by simple filtering rather than conflict resolution.

Authors: We agree that a quantitative audit of atomic claim decomposition fidelity and support/contradiction edge labeling accuracy would strengthen the validation of the framework components. The current manuscript emphasizes end-to-end gains in factual recall, information density, and hallucination reduction on LongFact and RAGChecker. To address the concern that gains may stem from simple filtering, we will add in revision an ablation comparing full ArbGraph against a baseline using claim decomposition followed by non-graph credibility filtering. We will also include precision/recall metrics on a sampled subset of claims via human or LLM-based judgment to quantify decomposition and labeling quality. revision: yes
Referee: [§4.3] §4.3 (Analysis under conflicting evidence): The intensity-driven propagation is asserted to suppress noise without amplifying decomposition or labeling errors, but no error-propagation analysis or ablation isolating the arbitration step from baseline filtering is reported. This leaves the load-bearing assumption that the mechanism reliably corrects rather than compounds upstream errors unverified.

Authors: We acknowledge that an explicit error-propagation analysis and ablation isolating iterative arbitration from baseline filtering would better verify the mechanism. Section 4.3 shows gains under conflicting evidence, but lacks a dedicated comparison. In revision, we will add an ablation with a single-pass intensity threshold variant (no iterative propagation) to demonstrate the iterative step's contribution. We will also include a sensitivity analysis discussing how the intensity-driven thresholds mitigate potential upstream errors rather than amplify them. revision: yes

Circularity Check

0 steps flagged

No circularity: ArbGraph is an empirical algorithmic framework

full rationale

The paper proposes a new pre-generation arbitration pipeline (claim decomposition into atomic units, construction of a support/contradiction graph, and intensity-driven iterative propagation) and validates it via experiments on LongFact and RAGChecker. No equations, parameter-fitting steps, or predictions appear in the provided text; the central claims rest on benchmark results rather than reducing to inputs by definition or self-citation. The method is self-contained and externally falsifiable.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim relies on the effectiveness of these new structures and mechanisms, which are introduced without prior independent validation in the abstract.

axioms (1)

domain assumption Documents can be decomposed into independent atomic claims without loss of critical context.
This is invoked in the initial decomposition step described in the abstract.

invented entities (2)

Conflict-aware evidence graph no independent evidence
purpose: Organizes atomic claims with explicit support and contradiction relations for arbitration.
New data structure proposed in the framework.
Intensity-driven iterative arbitration mechanism no independent evidence
purpose: Propagates credibility signals through evidence interactions to suppress unreliable claims.
Core algorithmic contribution for conflict resolution.

pith-pipeline@v0.9.0 · 5552 in / 1394 out tokens · 66893 ms · 2026-05-10T04:48:06.258476+00:00 · methodology

discussion (0)

Reference graph

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