arxiv: 2604.10506 · v1 · submitted 2026-04-12 · 💻 cs.AI

Recognition: unknown

A Progressive Training Strategy for Vision-Language Models to Counteract Spatio-Temporal Hallucinations in Embodied Reasoning

Xiaoda Yang , Shuai Yang , Can Wang , Jingyang Xue , Menglan Tang , Checheng Yu , Xunzhe Zhou , Sashuai Zhou

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Tao Jin Lixin Yang Xiangyu Yue Zhou Zhao

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Pith reviewed 2026-05-10 15:51 UTC · model grok-4.3

classification 💻 cs.AI

keywords vision-language modelsspatiotemporal reasoningchain-of-thoughtprogressive trainingembodied reasoningtemporal hallucinationsdynamic reasoningweakly-labeled data

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The pith

A progressive training strategy using spatiotemporal Chain-of-Thought data followed by weak-label fine-tuning reduces the forward-backward performance gap in vision-language models from over 70 percent to 6.53 percent.

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

Vision-language models show large accuracy drops on reversed temporal queries, indicating reliance on superficial shortcuts rather than causal understanding in spatiotemporal tasks. The paper creates a new Chain-of-Thought dataset that breaks reasoning into explicit steps and judgments, then applies progressive training that first uses supervised learning on this dataset to build logical structures before fine-tuning on scalable weakly-labeled examples for broader use. Experiments show the method raises overall accuracy while shrinking the forward-backward gap dramatically. A reader would care because this points to a practical route for moving models toward genuine dynamic reasoning instead of pattern matching in embodied settings.

Core claim

The progressive training framework begins with supervised pre-training on a new Chain-of-Thought dataset that decomposes intricate spatiotemporal reasoning into detailed steps and definitive judgments, then proceeds to fine-tuning with scalable weakly-labeled data; this not only improves backbone accuracy but reduces the forward-backward performance gap from over 70 percent to 6.53 percent, confirming development of authentic dynamic reasoning and reduction of inherent temporal biases in current VLMs.

What carries the argument

Progressive training framework that starts with supervised pre-training on a spatiotemporal Chain-of-Thought dataset to instill logical structures, followed by fine-tuning on weakly-labeled data to achieve generalization.

If this is right

Backbone accuracy on embodied reasoning tasks increases.
The forward-backward performance gap falls to 6.53 percent.
Dependence on superficial shortcuts decreases in temporal queries.
Models develop capabilities for authentic dynamic reasoning.
Inherent temporal biases of current VLMs are reduced.

Where Pith is reading between the lines

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

The same staged training could be tested on other hallucination types such as spatial or object-relation errors in VLMs.
Extending the CoT dataset to video sequences might further strengthen performance on longer-term temporal tasks.
Applying the framework to robotics control loops would test whether the reduced bias translates to better real-world action planning.
Comparing against reinforcement learning fine-tuning could reveal whether supervised-then-weakly-labeled progression is uniquely effective.

Load-bearing premise

The reduction in the forward-backward performance gap on the tested queries directly demonstrates genuine causal understanding rather than the model adapting to patterns in the new dataset or evaluation format.

What would settle it

A persistent large forward-backward gap or failure on novel causal scenarios outside the CoT dataset and chosen evaluation format would show that the improvement does not reflect authentic dynamic reasoning.

Figures

Figures reproduced from arXiv: 2604.10506 by Can Wang, Checheng Yu, Jingyang Xue, Lixin Yang, Menglan Tang, Sashuai Zhou, Shuai Yang, Tao Jin, Xiangyu Yue, Xiaoda Yang, Xunzhe Zhou, Zhou Zhao.

**Figure 1.** Figure 1: Our progressive training paradigm to mitigate spatio-temporal hallucinations. Stage 1 (Left): A CoT-Supervised Pre-training stage instills foundational causal reasoning by supervising the entire reasoning chain. Stage 2 (Right): A Weakly-Supervised Fine-tuning stage scales this ability using a massive, tag-only dataset, demonstrating a positive scaling law. judgment. This “study-then-practice” progression … view at source ↗

**Figure 2.** Figure 2: Statistical analysis of the STCR-CoT dataset. (a) Sample Scale and Sequence Balance: The dataset reaches a total scale of 34.7 million samples and features a globally balanced design, with forward and reverse sequences each constituting 50% to eliminate temporal bias. (b) Distribution of Task Categories: The distribution of task categories is diverse, covering 7 major categories including “Pick & Place” an… view at source ↗

**Figure 3.** Figure 3: Main results of our training paradigm. (a) Scaling Law of Tag-based Fine-tuning: Model accuracy exhibits a clear positive correlation with the amount of weakly-supervised data. This validates that our paradigm effectively benefits from scaling up, confirming the existence of a scaling law for this task. (b) Robustness against Temporal Bias: Our model’s trajectory (purple/blue line) demonstrates significant… view at source ↗

**Figure 4.** Figure 4: Validation of Temporal Consistency in Embodied Multi-Image Reasoning. The figure compares the reward signals from our model (top) against the baseline VLAC (bottom) (lab, 2025). Finally, our model’s performance as a Reward Model (Figure 4) offers a diagnostic view into its world-modeling capabilities. By implementing a 0.1 penalty for counterproductive actions, we demonstrate that the model has acquired… view at source ↗

**Figure 5.** Figure 5: Comparison with VLAC. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗

read the original abstract

Vision-Language Models (VLMs) have made significant strides in static image understanding but continue to face critical hurdles in spatiotemporal reasoning. A major bottleneck is "multi-image reasoning hallucination", where a massive performance drop between forward and reverse temporal queries reveals a dependence on superficial shortcuts instead of genuine causal understanding. To mitigate this, we first develop a new Chain-of-Thought (CoT) dataset that decomposes intricate reasoning into detailed spatiotemporal steps and definitive judgments. Building on this, we present a progressive training framework: it initiates with supervised pre-training on our CoT dataset to instill logical structures, followed by fine-tuning with scalable weakly-labeled data for broader generalization. Our experiments demonstrate that this approach not only improves backbone accuracy but also slashes the forward-backward performance gap from over 70\% to only 6.53\%. This confirms the method's ability to develop authentic dynamic reasoning and reduce the inherent temporal biases of current VLMs.

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

The paper cuts the forward-reverse gap in VLM temporal reasoning from over 70% to 6.53% via a new decomposed CoT dataset and staged supervised-then-weakly-supervised training, but the abstract gives no controls to show this is genuine causal understanding instead of adaptation to the new data format.

read the letter

The core contribution is a targeted fix for multi-image hallucination in VLMs. They build a Chain-of-Thought dataset that decomposes spatiotemporal reasoning into explicit steps and judgments, then apply progressive training: first supervised fine-tuning on that dataset to install the structure, followed by weakly-supervised data for scale. The headline result is the large reduction in the forward-backward performance gap, which they tie to reduced temporal bias and better dynamic reasoning for embodied tasks.

Referee Report

2 major / 0 minor

Summary. The paper claims that a new Chain-of-Thought (CoT) dataset decomposing spatiotemporal reasoning into detailed steps, combined with a progressive training framework (supervised pre-training on the CoT data followed by fine-tuning on scalable weakly-labeled data), enables vision-language models to reduce multi-image reasoning hallucinations. The central empirical result is a reduction of the forward-backward performance gap from over 70% to 6.53%, which the authors interpret as evidence of authentic dynamic reasoning and reduced temporal bias.

Significance. If the quantitative improvements and their interpretation as genuine causal understanding hold under rigorous controls, the work would offer a practical training recipe for mitigating a known failure mode in VLMs on embodied tasks. The progressive training idea and the emphasis on forward/reverse query consistency are potentially useful for the community, but the current presentation provides insufficient methodological detail to evaluate reproducibility or the strength of the causal claim.

major comments (2)

[Abstract] Abstract: The claim that the drop from >70% to 6.53% 'confirms the method's ability to develop authentic dynamic reasoning' is not supported by any reported controls for query overlap, held-out construction, or format-specific pattern matching between the new CoT dataset and the evaluation queries. Without such evidence the reduction could reflect adaptation to the decomposition style or judgment phrasing rather than reduced temporal bias.
[Abstract] Abstract: No information is given on dataset construction details, baseline models and their exact configurations, statistical significance testing, error bars, or the precise forward/reverse query protocols. These omissions make it impossible to assess whether the reported gap reduction is robust or load-bearing for the central claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We agree that the original abstract made a strong interpretive claim without sufficient supporting controls or methodological details, which limits evaluation of the central result. We have revised the manuscript to moderate the abstract language, add explicit controls for query overlap and format matching, expand all dataset and protocol descriptions, and include statistical reporting. These changes directly address the concerns while preserving the core contribution. We respond point-by-point below.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that the drop from >70% to 6.53% 'confirms the method's ability to develop authentic dynamic reasoning' is not supported by any reported controls for query overlap, held-out construction, or format-specific pattern matching between the new CoT dataset and the evaluation queries. Without such evidence the reduction could reflect adaptation to the decomposition style or judgment phrasing rather than reduced temporal bias.

Authors: The referee correctly identifies that the original abstract's phrasing was not backed by explicit controls. In the revised manuscript we have (1) softened the abstract claim to 'substantially reduces temporal bias' rather than 'confirms authentic dynamic reasoning,' (2) added a new 'Robustness Controls' subsection reporting held-out query sets constructed with deliberately different decomposition styles and judgment phrasing from the training CoT data, and (3) included an ablation showing that performance gains persist (gap reduced to 7.1%) even when format overlap is minimized. These experiments indicate the improvement is not explained by superficial pattern matching. revision: yes
Referee: [Abstract] Abstract: No information is given on dataset construction details, baseline models and their exact configurations, statistical significance testing, error bars, or the precise forward/reverse query protocols. These omissions make it impossible to assess whether the reported gap reduction is robust or load-bearing for the central claim.

Authors: We acknowledge the original submission omitted these reproducibility details. The revised version now contains: a full 'Dataset Construction' section describing data sources, annotation guidelines, and statistics for the spatiotemporal CoT dataset; exact baseline configurations (including model variants, LoRA ranks, and learning rates); forward/reverse query templates with examples; and statistical analysis using paired t-tests with error bars (standard deviation over five random seeds). All numbers in the main results table are now accompanied by these statistics. revision: yes

Circularity Check

0 steps flagged

No significant circularity in training strategy or empirical claims

full rationale

The paper introduces a CoT dataset and progressive training (supervised pre-training followed by weakly-labeled fine-tuning) as an empirical method to reduce forward-backward performance gaps in VLMs. Reported metrics (e.g., gap reduction from >70% to 6.53%) are direct accuracy measurements on test queries, not parameters fitted to the target quantity or quantities defined in terms of the training objective. No equations, self-citations, or uniqueness theorems are invoked as load-bearing steps in the provided text; the derivation chain consists of dataset construction and standard training stages whose outputs are independently evaluated rather than reducing to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work relies on standard supervised learning assumptions and the creation of a new dataset; no new mathematical axioms or physical entities are introduced.

axioms (1)

domain assumption Minimizing cross-entropy loss on chain-of-thought annotations produces models with improved causal reasoning on held-out temporal queries
Invoked implicitly when claiming that CoT pre-training instills logical structures that generalize.

pith-pipeline@v0.9.0 · 5501 in / 1511 out tokens · 54712 ms · 2026-05-10T15:51:41.767381+00:00 · methodology

discussion (0)

Reference graph

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