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arxiv: 2604.15705 · v1 · submitted 2026-04-17 · 💻 cs.LG

Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning

Xiaoyu Yang , En Yu , Wei Duan , Jie Lu This is my paper

Pith reviewed 2026-05-10 08:25 UTC · model grok-4.3

classification 💻 cs.LG
keywords reasoningdriftendogenousmulti-modalacrossmllmsautonomousconcept
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The pith

CPO++ adapts reinforcement fine-tuning of MLLMs to endogenous multi-modal concept drift through counterfactual reasoning and preference optimization, yielding better coherence and cross-domain robustness in safety-critical settings.

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

Multi-modal large language models process images, text, and other inputs together. When they are fine-tuned using reinforcement methods, their step-by-step reasoning can shift unpredictably on its own, even if the outside world stays the same. The authors call this endogenous reasoning drift and treat it as a form of concept drift across modalities. Their CPO++ method creates controlled changes in both thinking and perception steps, then uses preference optimization to break unwanted links between inputs and outputs. Tests in medical and driving scenarios show improved consistency and the ability to handle new situations without retraining.

Core claim

MLLMs are highly susceptible to endogenous reasoning drift... CPO++ achieves superior performance in reasoning coherence, decision-making precision, and inherent robustness against extreme interference with exceptional zero-shot cross-domain generalization.

Load-bearing premise

That controlled counterfactual perturbations combined with preference optimization can reliably disentangle spurious correlations caused by endogenous drift without introducing new instabilities or domain-specific biases.

Figures

Figures reproduced from arXiv: 2604.15705 by En Yu, Jie Lu, Wei Duan, Xiaoyu Yang.

Figure 1
Figure 1. Figure 1: Endogenous Reasoning Drift in RFT. probability and semantic differentiation during the thinking process, the resulting predictions for distinct pathologies can become diametrically opposed. This instability highlights a critical vulnerability where the reasoning trajectory of the model undergoes a systemic divergence, unmooring the final decision from its original logical premises. We further extend the an… view at source ↗
Figure 2
Figure 2. Figure 2: The proposed Counterfactual Preference Optimization ++ (CPO++) framework. To mitigate endogenous reasoning drift, the methodology theoretically characterizes it as multi-modal concept drift, and incorporates counterfactual inference to disentangle spurious correlations from genuine causal logic within the original outputs. By leveraging hierarchical domain knowledge and perception-thinking consistency prot… view at source ↗
Figure 3
Figure 3. Figure 3: Structural Causal Graph. X: Inputs, Z: Prediction Results, T: Chain-of-Thought, and D: Latent Concept Drift within Non-Stationary CoT. Fortunately, counterfactual causes provide an explicit man￾ner to decouple these two competing goals. We construct a structural causal graph [61], [62] to formalize the causal [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example Case of Hierarchical Domain Knowledge Graph in Medical Domain. To disentangle detrimental drift, we introduce the graph that generates plausible counterfactual CoTs through controlled attribute perturbations. Green lines represent attributes that are positively associated with the disease, while the red denotes that they are exclusive. • Entities (E): The core objects of interest (e.g., Pneumo￾nia … view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative evaluation of the attention scores over visual tokens during CoT decoding. When the term ’lung opacity’ is subtly altered to ’opacity’, the model still produces high responses in key areas, such as the visual tokens at the right side of ① and the pneumonia ②. To qualitatively evaluate the effectiveness of the proposed framework in mitigating endogenous reasoning drift, a vi￾sualization of cross… view at source ↗
Figure 6
Figure 6. Figure 6: Quantitative evaluation of diagnostic robustness against counterfactual interference on the medical MS￾CXR-T [84] dataset. It reports the Top-1 accuracy across five pulmonary pathologies, including consolidation (Con.), pleural effusion (PE), pneumonia (Pna.), pneumothorax (Pnx.), and Edema (Ede.) and their overall average (Avg.).To simulate non-stationary and complex reasoning, varying ratios of coun￾terf… view at source ↗
Figure 1
Figure 1. Figure 1: Specifically, for pathologies with highly distinct visual [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation study of the counterfactual decoupling mechanism. The evaluation systematically compares the DPO baseline against isolated interventions, specifically integrating only reasoning counterfactuals or only visual counterfactuals, alongside the complete CPO++ framework featuring dual alignment. Performance is rigorously measured across four critical dimensions: 1) reasoning capability, evaluated via th… view at source ↗
read the original abstract

Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with complex human values and domain-specific requirements. Nevertheless, current research primarily focuses on mitigating exogenous distribution shifts arising from data-centric factors, the non-stationarity inherent in the endogenous reasoning remains largely unexplored. In this work, a critical vulnerability is revealed within MLLMs: they are highly susceptible to endogenous reasoning drift, across both thinking and perception perspectives. It manifests as unpredictable distribution changes that emerge spontaneously during the autoregressive generation process, independent of external environmental perturbations. To adapt it, we first theoretically define endogenous reasoning drift within the RFT of MLLMs as the multi-modal concept drift. In this context, this paper proposes Counterfactual Preference Optimization ++ (CPO++), a comprehensive and autonomous framework adapted to the multi-modal concept drift. It integrates counterfactual reasoning with domain knowledge to execute controlled perturbations across thinking and perception, employing preference optimization to disentangle spurious correlations. Extensive empirical evaluations across two highly dynamic and safety-critical domains: medical diagnosis and autonomous driving. They demonstrate that the proposed framework achieves superior performance in reasoning coherence, decision-making precision, and inherent robustness against extreme interference. The methodology also exhibits exceptional zero-shot cross-domain generalization, providing a principled foundation for reliable multi-modal reasoning in safety-critical applications.

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.

Circularity Check

0 steps flagged

No significant circularity; derivation chain is conceptual with no equations or reductions shown

full rationale

The abstract and available text present a high-level framework proposal (CPO++) and a conceptual definition of endogenous reasoning drift as multi-modal concept drift, but contain no mathematical derivations, equations, parameter fits, or self-citations. No load-bearing steps reduce to inputs by construction, and the description does not invoke uniqueness theorems or rename known results. This is the expected honest non-finding when the paper's chain is not mathematically specified.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract introduces endogenous reasoning drift as a new theoretical construct and relies on the assumption that preference optimization can separate spurious correlations; no explicit free parameters or external benchmarks are stated.

axioms (1)
  • domain assumption Endogenous reasoning drift manifests as unpredictable distribution changes during autoregressive generation independent of external perturbations
    Directly stated in the abstract as the core vulnerability to be addressed.
invented entities (1)
  • endogenous reasoning drift no independent evidence
    purpose: To capture spontaneous multi-modal distribution shifts inside MLLM generation
    Newly defined in the paper as distinct from exogenous shifts

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Autonomous Drift Learning in Data Streams: A Unified Perspective

    cs.LG 2026-05 unverdicted novelty 7.0

    A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.

Reference graph

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