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arxiv: 2604.15294 · v1 · submitted 2026-04-16 · 💻 cs.AI

Recognition: unknown

How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study

Zhen Yang , Ping Jian , Zhongbin Guo , Zuming Zhang , Chengzhi Li , Yonghong Deng , Xinyue Zhang , Wenpeng Lu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 10:42 UTC · model grok-4.3

classification 💻 cs.AI
keywords viewpoint rotation understandingspatial intelligenceLLM interpretabilityattention head interventionbinding failuretext-only spatial reasoninghallucination mechanisms
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The pith

LLMs and VLMs encode viewpoint information in hidden states but fail to bind it to corresponding observations, producing hallucinations in final layers.

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

The paper examines whether language models can develop spatial intelligence using only text, by testing their ability to track viewpoint rotations and predict resulting observations across multiple steps. Models perform far below human accuracy on this task despite receiving complete textual descriptions of each rotation and observation. Layer-wise analysis shows that viewpoint positions are represented in intermediate hidden states, yet the models do not correctly associate each position with its matching observation. This binding failure persists into the output layers and produces incorrect answers. Targeted intervention on specific attention heads and subsequent fine-tuning of those heads raises task performance while preserving other model capabilities.

Core claim

Although models encode viewpoint information in the hidden states, they appear to struggle to bind the viewpoint position with corresponding observation, resulting in a hallucination in final layers.

What carries the argument

Head-wise causal intervention applied to attention heads, which isolates the heads responsible for linking viewpoint positions to observations.

If this is right

  • Models encode viewpoint positions internally yet still output incorrect observations because the binding step fails.
  • Selective fine-tuning of the attention heads identified by causal intervention raises VRU accuracy.
  • The same fine-tuning leaves generic language abilities intact rather than causing forgetting.
  • Human-level performance on the same textual task demonstrates that the required spatial binding is achievable in principle.

Where Pith is reading between the lines

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

  • The identified binding failure may limit performance on any multi-step text reasoning task that requires maintaining distinct states and their associated content.
  • Architectures that explicitly separate and re-associate positional and content representations could mitigate the same limitation on other spatial or sequential problems.

Load-bearing premise

The constructed textual dataset accurately captures the requirements of viewpoint rotation understanding without introducing ambiguities or task-specific artifacts that humans handle differently from models.

What would settle it

If a model variant is found that maintains accurate position-to-observation bindings through all layers and achieves near-human accuracy on the same textual rotation sequences, the claim of an inherent binding failure would be falsified.

Figures

Figures reproduced from arXiv: 2604.15294 by Chengzhi Li, Ping Jian, Wenpeng Lu, Xinyue Zhang, Yonghong Deng, Zhen Yang, Zhongbin Guo, Zuming Zhang.

Figure 1
Figure 1. Figure 1: For textual viewpoint rotation understand [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a)-(c): Illustration of layer-wise probing and the probing results (direction/angle/orientation) on LLaMA2- [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The VRU performance after knocking out randomly-selected K heads (“random ablation”), and ablating the top-K heads sorted by their causal effects. Recalling that the information encoding viewpoint orientation also declines after layer 20 (Section 5.1.3), it suggests that these key heads must play a pivotal role in this process. 5.2.3 Validation To further validate the faithfulness of the identified key hea… view at source ↗
Figure 4
Figure 4. Figure 4: The attention patterns of key heads within Qwen2.5-VL-7B, where the head index 26.14 represents the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The probing results of other LLMs and VLMs. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: An illustration of the practical application of [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Perturbing the observation (a) or rotation direction at early step (b) could introduce logical inconsistencies [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The results of path patching on other models. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Attention pattern cases of unknown head when the words “unknown” is replaced with other alternative [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The results of path patching before and after full fine-tuning. [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: 1st reasoning example [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: 2nd reasoning example. [task] If the observation after the final action cannot be determined based on previous observations , output unknown . Initial Observation : black berry Action : Turn to the right by 9 0 degrees Observ ation : console Action : Turn to the right by 2 7 0 degrees Observ ation : black berry Action : Turn to the left by 2 7 0 degrees Observ ation : <|im_end|> <|im_start|> assistant Obs… view at source ↗
Figure 14
Figure 14. Figure 14: Example 1 (attention pattern). Model output: console [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Example 2 (attention pattern). Model output: dehumidifier. [task] If the observation after the final action cannot be determined based on previous observations , output unknown . Initial Observation : pan Action : Turn to the right by 2 7 0 degrees Observ ation : bathtub Action : Turn to the left by 2 7 0 degrees Observ ation : pan Action : Turn to the left by 1 8 0 degrees Observ ation : watch Action : T… view at source ↗
Figure 16
Figure 16. Figure 16: Example 3 (attention pattern). Model output: bathtub [PITH_FULL_IMAGE:figures/full_fig_p023_16.png] view at source ↗
read the original abstract

Over the past year, spatial intelligence has drawn increasing attention. Many prior works study it from the perspective of visual-spatial intelligence, where models have access to visuospatial information from visual inputs. However, in the absence of visual information, whether linguistic intelligence alone is sufficient to endow models with spatial intelligence, and how models perform relevant tasks with text-only inputs still remain unexplored. Therefore, in this paper, we focus on a fundamental and critical capability in spatial intelligence from a linguistic perspective: viewpoint rotation understanding (VRU). Specifically, LLMs and VLMs are asked to infer their final viewpoint and predict the corresponding observation in an environment given textual description of viewpoint rotation and observation over multiple steps. We find that both LLMs and VLMs perform poorly on our proposed dataset while human can easily achieve 100% accuracy, indicating a substantial gap between current model capabilities and the requirements of spatial intelligence. To uncover the underlying mechanisms, we conduct a layer-wise probing analysis and head-wise causal intervention. Our findings reveal that although models encode viewpoint information in the hidden states, they appear to struggle to bind the viewpoint position with corresponding observation, resulting in a hallucination in final layers. Finally, we selectively fine-tune the key attention heads identified by causal intervention to improve VRU performance. Experimental results demonstrate that such selective fine-tuning achieves improved VRU performance while avoiding catastrophic forgetting of generic abilities. Our dataset and code will be released at https://github.com/Young-Zhen/VRU_Interpret .

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

3 major / 2 minor

Summary. The paper introduces a text-only Viewpoint Rotation Understanding (VRU) task and dataset in which LLMs and VLMs must infer a final viewpoint and corresponding observation after multi-step textual descriptions of rotations and observations. Models achieve low accuracy while humans reach 100%, and layer-wise probing plus head-wise causal interventions are used to argue that viewpoint information is encoded in hidden states but fails to bind to observations, producing hallucinations in final layers. Selective fine-tuning of the implicated attention heads is shown to improve VRU performance without catastrophic forgetting of general capabilities.

Significance. If the binding-failure mechanism is robustly demonstrated, the work identifies a concrete limitation in how current models perform spatial reasoning from language alone and supplies a targeted intervention (selective head fine-tuning) that could be broadly useful. The public release of the dataset and code would support follow-up studies on linguistic spatial intelligence.

major comments (3)
  1. Dataset construction and validation: the central claim that poor performance reflects a binding deficit rather than dataset artifacts (lexical shortcuts, ambiguous rotation directions, or multiple consistent interpretations) requires explicit controls. The manuscript reports human 100% accuracy but does not appear to include analysis of inter-annotator agreement on the textual descriptions, adversarial variants, or checks for pattern-matching solutions that models could exploit without true spatial binding.
  2. Experimental results section: quantitative details on model accuracies, probing R² or accuracy curves, intervention effect sizes (e.g., accuracy drop when heads are ablated), baselines (random, majority-class, or simpler text models), and error analysis are not provided in the described experiments. Without these, it is difficult to assess whether the observed final-layer hallucination is load-bearing for the binding claim or could arise from other factors.
  3. Causal intervention and fine-tuning: the identification of 'key attention heads' via causal intervention and the subsequent selective fine-tuning lack reported metrics on how many heads were selected, the precise performance delta versus full fine-tuning or LoRA, and controls for whether the improvement is specific to VRU or generalizes to other spatial tasks.
minor comments (2)
  1. Clarify the precise definition of 'hallucination in final layers'—whether it refers to incorrect token generation, internal representation mismatch, or output inconsistency—and provide layer-wise accuracy or logit visualizations to support the claim.
  2. The abstract and introduction would benefit from a short related-work paragraph distinguishing VRU from prior text-based spatial reasoning benchmarks (e.g., those involving navigation or mental rotation in language models).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive suggestions. We have carefully considered each major comment and will make revisions to address them, as detailed below.

read point-by-point responses

  1. Referee: [—] Dataset construction and validation: the central claim that poor performance reflects a binding deficit rather than dataset artifacts (lexical shortcuts, ambiguous rotation directions, or multiple consistent interpretations) requires explicit controls. The manuscript reports human 100% accuracy but does not appear to include analysis of inter-annotator agreement on the textual descriptions, adversarial variants, or checks for pattern-matching solutions that models could exploit without true spatial binding.

    Authors: We agree with the referee that rigorous validation of the dataset is essential to substantiate our claim regarding the binding deficit. While the manuscript highlights the 100% human accuracy to indicate the task's solvability, we acknowledge the absence of inter-annotator agreement metrics and adversarial testing. In the revised version, we will add: (1) inter-annotator agreement scores for the textual descriptions, (2) adversarial variants designed to probe for lexical shortcuts and ambiguous rotations, and (3) analysis showing that models cannot solve the task via pattern matching alone. These additions will strengthen the evidence that the performance gap arises from a failure in binding viewpoint information to observations rather than dataset artifacts. revision: yes

  2. Referee: [—] Experimental results section: quantitative details on model accuracies, probing R² or accuracy curves, intervention effect sizes (e.g., accuracy drop when heads are ablated), baselines (random, majority-class, or simpler text models), and error analysis are not provided in the described experiments. Without these, it is difficult to assess whether the observed final-layer hallucination is load-bearing for the binding claim or could arise from other factors.

    Authors: We appreciate the need for more comprehensive quantitative reporting. The current manuscript describes the overall findings but omits detailed metrics. We will revise the experimental results section to include: specific accuracy numbers for each model with error bars, layer-wise probing results with R² values and accuracy curves, effect sizes from causal interventions (including accuracy drops from head ablations), comparisons against random, majority-class, and simpler baselines, and a thorough error analysis of model failures. This will provide clearer support for the final-layer hallucination phenomenon and its relation to the binding claim. revision: yes

  3. Referee: [—] Causal intervention and fine-tuning: the identification of 'key attention heads' via causal intervention and the subsequent selective fine-tuning lack reported metrics on how many heads were selected, the precise performance delta versus full fine-tuning or LoRA, and controls for whether the improvement is specific to VRU or generalizes to other spatial tasks.

    Authors: We thank the referee for highlighting the need for more precise reporting on the intervention and fine-tuning experiments. In the revision, we will specify the number of key attention heads identified through causal intervention, report the exact performance improvements on the VRU task, provide comparisons of selective fine-tuning against full fine-tuning and LoRA in terms of VRU accuracy gains and retention of general capabilities, and include controls demonstrating that the improvements are specific to VRU by evaluating on additional spatial reasoning tasks. These details will better illustrate the efficacy and targeted nature of our approach. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical dataset, probing, and interventions are independent of inputs

full rationale

The paper constructs a new textual VRU dataset, evaluates LLMs/VLMs on it, performs layer-wise probing to detect encoded viewpoint information, applies head-wise causal interventions to test binding, and reports selective fine-tuning outcomes. No equation, definition, or central claim reduces by construction to a fitted parameter, self-citation chain, or renamed input; the binding-failure and hallucination observations are measured outcomes rather than tautological restatements of the task setup. Human 100% accuracy serves as an external benchmark, and the work remains self-contained without load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard assumptions from transformer interpretability research; no new entities or many free parameters are introduced beyond typical training choices.

axioms (1)
  • domain assumption Causal interventions on attention heads can isolate their functional role in binding information across layers
    Invoked in the head-wise causal intervention analysis to identify key heads for viewpoint binding.

pith-pipeline@v0.9.0 · 5589 in / 1190 out tokens · 42803 ms · 2026-05-10T10:42:15.154211+00:00 · methodology

discussion (0)

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Reference graph

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