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arxiv: 2502.19559 · v3 · submitted 2025-02-26 · 💻 cs.CL

Stay Focused: Problem Drift in Multi-Agent Debate

Jonas Becker , Lars Benedikt Kaesberg , Andreas Stephan , Jan Philip Wahle , Terry Ruas , Bela Gipp This is my paper

Pith reviewed 2026-05-23 01:45 UTC · model grok-4.3

classification 💻 cs.CL
keywords problem driftmulti-agent debatelarge language modelsdebate systemsdrift mitigationDRIFTPolicyDRIFTJudge
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The pith

Multi-agent debates often drift from the original problem over turns, especially in generative tasks where rates reach 76-89 percent.

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

The paper defines problem drift as the tendency of multi-agent LLM discussions to move away from the initial question across multiple turns, which reduces accuracy on complex tasks. It measures this across ten tasks and finds much higher rates in generative work than in knowledge or reasoning tasks. Human review of failed debates points to lack of progress, weak feedback, and unclear statements as the main drivers. The authors introduce DRIFTJudge to spot drift and DRIFTPolicy to cut it by 31 percent.

Core claim

Problem drift is a measurable limitation in multi-agent debate: it occurs in 76-89 percent of generative tasks but only 7-21 percent of high-complexity ones. The dominant causes are lack of progress (35 percent of cases), low-quality feedback (26 percent), and lack of clarity (25 percent). DRIFTPolicy, a mitigation approach, reduces drift incidence by 31 percent.

What carries the argument

Problem drift, the deviation of the debate trajectory from the starting problem across turns, with DRIFTPolicy as the method that intervenes to keep exchanges on track.

If this is right

  • Generative tasks suffer more drift because their answer spaces are subjective.
  • Lack of progress is the single most frequent cause of drift.
  • DRIFTJudge provides a workable first method to detect when a debate has lost focus.
  • Reducing drift improves outcomes specifically when debates run for many turns.

Where Pith is reading between the lines

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

  • The results imply that simply adding more debate rounds can lower performance unless drift controls are added.
  • The same drift mechanisms may appear in other multi-agent setups that rely on free-form discussion.
  • Task instructions could be rewritten to emphasize progress checkpoints and explicit restatement of the original question.

Load-bearing premise

The measured drift rates and the 31 percent mitigation figure depend on the exact definition of drift and the ten tasks selected for testing.

What would settle it

Run the same ten tasks with and without DRIFTPolicy and measure whether final answer accuracy rises in proportion to the reported 31 percent drop in drift cases.

Figures

Figures reproduced from arXiv: 2502.19559 by Andreas Stephan, Bela Gipp, Jan Philip Wahle, Jonas Becker, Lars Benedikt Kaesberg, Terry Ruas.

Figure 1
Figure 1. Figure 1: Problem drift in MAD. DRIFTJudge detects problem drift at test-time. DRIFTPolicy provides on￾demand feedback about the conversation. debate (MAD) in reasoning and creativity (Zhao et al., 2023; Xu et al., 2023; Suzgun and Kalai, 2024). MAD also scales test-time compute to solve challenging tasks similar to reasoning models, such as OpenAI o4 (OpenAI) and DeepSeek R1 (Guo et al., 2025), which can be more ef… view at source ↗
Figure 2
Figure 2. Figure 2: Example of problem drift in MAD. The English instructor induces a logical error in the discussion. The other agents agree without skepticism, leading to the wrong solution and problem drift. propose a first baseline to detect problem drift in￾spired by LLM-as-a-judge (Zheng et al., 2023), which receives a focal turn’s and a consecutive turn’s solution to assess whether problem drift oc￾curs. To ensure that… view at source ↗
Figure 3
Figure 3. Figure 3: Prompt to an agent that contributes to the dis [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Prompt to extract the solution from an agent’s [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Prompt to process the voting at the end of [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt for the policy feedback agent [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt for the LLM-as-a-judge. The current discussion is going badly. Based on the others’ contributions, give construc￾tive feedback about how to improve the discussion habits. Be concise so that the other discussion participants can find a better solution. The following problematic error categories exist. If you identify them in the current discussion, they could help you to provide better feedback: Task… view at source ↗
Figure 9
Figure 9. Figure 9: Instructions for the human annotation [PITH_FULL_IMAGE:figures/full_fig_p032_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example of consensus-based MAD. The draft of the solution is continuously improved without any [PITH_FULL_IMAGE:figures/full_fig_p033_10.png] view at source ↗
read the original abstract

Multi-agent debate - multiple instances of large language models discussing problems in turn-based interaction - has shown promise for solving knowledge and reasoning tasks. However, these methods show limitations when solving complex problems that require longer reasoning chains. We analyze how multi-agent debate drifts away from the initial problem over multiple turns, thus harming task performance. We define this phenomenon as problem drift and quantify its presence across ten tasks (i.e., three generative, three knowledge, three reasoning, and one instruction-following task). We find that generative tasks drift often due to the subjectivity of the answer space (76-89%), compared to high-complexity tasks (7-21%). To identify the reasons, eight human experts analyze 170 multi-agent debates suffering from problem drift. We find the most common issues related to this drift are the lack of progress (35% of cases), low-quality feedback (26% of cases), and a lack of clarity (25% of cases). We propose DRIFTJudge, an LLM-as-a-judge method, as a first baseline to detect problem drift. We also propose DRIFTPolicy, which mitigates 31% of problem drift cases. Our study is a step toward understanding a key limitation of multi-agent debate, highlighting why longer debates can harm task performance and how problem drift could be addressed.

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.

Referee Report

2 major / 0 minor

Summary. The paper claims that multi-agent debate exhibits 'problem drift' away from the initial problem, occurring in 76-89% of generative tasks versus 7-21% in high-complexity tasks. Expert analysis of 170 drifted debates identifies lack of progress (35%), low-quality feedback (26%), and lack of clarity (25%) as primary causes. It introduces DRIFTJudge as an LLM-as-a-judge baseline for detection and DRIFTPolicy as a mitigation method that resolves 31% of drift cases across ten tasks.

Significance. If the empirical rates and mitigation effectiveness hold under explicit, reproducible definitions of drift and controlled experimental conditions, the work would identify a concrete limitation in extended multi-agent reasoning and supply the first baselines for addressing it, which could improve reliability of debate-based LLM systems on complex tasks.

major comments (2)
  1. [Abstract] Abstract: The central quantitative claims (76-89% drift rates in generative tasks; 31% mitigation by DRIFTPolicy) are presented without any operational definition of 'problem drift', description of the ten tasks, criteria for the eight-expert labeling of 170 debates, or implementation details of DRIFTPolicy. These omissions make the reported frequencies and effectiveness non-reproducible and prevent verification of the claims.
  2. [Abstract] Abstract: The identification of causes (lack of progress 35%, low-quality feedback 26%, lack of clarity 25%) and the proposals for DRIFTJudge and DRIFTPolicy lack any account of the annotation protocol, model choices, or evaluation setup, rendering the expert analysis and baseline results impossible to assess for reliability or sensitivity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing reproducibility. The abstract is a concise summary, and the full operational definitions, task descriptions, annotation protocols, and implementation details appear in the main body. We agree that incorporating key elements into the abstract will improve verifiability and will revise accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central quantitative claims (76-89% drift rates in generative tasks; 31% mitigation by DRIFTPolicy) are presented without any operational definition of 'problem drift', description of the ten tasks, criteria for the eight-expert labeling of 170 debates, or implementation details of DRIFTPolicy. These omissions make the reported frequencies and effectiveness non-reproducible and prevent verification of the claims.

    Authors: We acknowledge that the abstract omits these details. The operational definition of problem drift, the ten tasks (three generative, three knowledge, three reasoning, one instruction-following), the expert labeling criteria for the 170 debates, and DRIFTPolicy implementation are specified in Sections 2–4 of the manuscript. To address the concern directly, we will revise the abstract to include a brief operational definition of problem drift, the task categories, and a high-level description of DRIFTPolicy. revision: yes

  2. Referee: [Abstract] Abstract: The identification of causes (lack of progress 35%, low-quality feedback 26%, and lack of clarity 25%) and the proposals for DRIFTJudge and DRIFTPolicy lack any account of the annotation protocol, model choices, or evaluation setup, rendering the expert analysis and baseline results impossible to assess for reliability or sensitivity.

    Authors: The annotation protocol (eight experts), model choices for DRIFTJudge, and evaluation setup for DRIFTPolicy are described in the main text. We agree this information should be referenced in the abstract for completeness. We will add a concise clause summarizing the expert analysis protocol and baseline evaluation approach. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical quantification and baseline proposals rest on external human analysis

full rationale

The abstract defines problem drift, reports frequencies from human expert labeling of 170 debates, and presents DRIFTJudge/DRIFTPolicy as baselines. No equations, fitted parameters renamed as predictions, self-citations, or uniqueness theorems appear. The 76-89% and 31% figures derive from the stated human annotation process rather than reducing to the definition or prior author work by construction. The study is self-contained against external benchmarks (human labels on fixed tasks).

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

Abstract provides limited technical details; no free parameters or axioms explicitly stated beyond the basic setup of multi-agent debate.

axioms (1)
  • domain assumption Multi-agent debate involves turn-based interactions between LLM instances.
    Stated in abstract as the setup for the method.
invented entities (2)
  • DRIFTJudge no independent evidence
    purpose: LLM-as-a-judge baseline to detect problem drift
    New method proposed in abstract without external validation details.
  • DRIFTPolicy no independent evidence
    purpose: Baseline to mitigate problem drift
    New method proposed in abstract without external validation details.

pith-pipeline@v0.9.0 · 5747 in / 1238 out tokens · 72984 ms · 2026-05-23T01:45:27.747216+00:00 · methodology

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Forward citations

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