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REVIEW 5 major objections 5 minor 61 references

Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A learned gating module, trained on Monte Carlo reasoning trees, can raise the spatial-reasoning accuracy of frozen multimodal LLMs by selecting high-value reasoning steps and trajectories.

desk verdict A useful training-free gating framework with real benchmark gains, but the missing train/eval scene separation leaves the central generalization claim unverified. read the letter →

arxiv 2608.07987 v1 pith:SVDFSKLU submitted 2026-08-08 cs.CV

Ling Lin , Yang Bai , Congcong Zhu , Jiangming Shi , Meng Wang , Yang Long , Jingrun Chen , Ling Shao
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Huazhu Fu
This is my paper · ORCID
classification cs.CV
keywords advantage-guidedgatestep-advantagetrajectory-advantagereasoningtreeMonteCarlovalueestimationspatialintelligencemultimodallargelanguagemodelsfinite-horizondecisionprocess
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper is trying to establish that the main bottleneck in open-ended, vision-based spatial reasoning is not generating potentially correct reasoning paths but continuously identifying and retaining high-value branches as the chain of thought grows. To prove this, it models step-by-step reasoning as a finite-horizon decision process and builds Monte Carlo reasoning trees whose terminal feedback is propagated backward into prefix-level and trajectory-level supervision. A lightweight step-advantage gate (SAG) and trajectory-advantage gate (TAG) are trained on these labels and then used at inference with a frozen multimodal LLM to greedily keep promising prefixes and to rerank completed trajectories. The empirical claim is that this visibly improves accuracy across four 3D scene benchmarks, with the headline example being GPT-5.4 on ScanNet rising from 34.1 to 44.6 average score, while leaving the base model's parameters untouched. If true, this matters because it offers a plug-in route to more reliable spatial reasoning from existing models, without retraining the base model.

What carries the argument

The load-bearing mechanism is a finite-horizon Markov decision process over reasoning steps, where each state $h_t=(x,q,\tau_{1:t-1})$ is the visual input, question, and accepted prefix, and each action is the next reasoning step $r_t$. The paper defines the oracle value-gated policy $\pi^*_T$ that keeps only steps with action value above threshold $T$, then approximates it with two learned heads: SAG, a weighted binary classifier on prefixes labeled by Top-$\rho$ relative ranking of exploration-aware value scores, and TAG, a reweighted multi-class classifier on complete trajectories labeled by answer error ranking. Monte Carlo reasoning-tree rollouts supply the value estimates, and a two-stage training procedure—joint step-trajectory learning followed by stochastic-weight-averaging initialization of shared parameters and multiple task-specialized TAG heads—provides the supervised signal. At inference, the frozen base model produces $M$ next-step candidates, SAG greedily keeps the most promising prefix, and the best TAG head reranks the terminal trajectories.

What would settle it

For a fixed set of held-out questions, compare SAG's accept/reject decisions on prefixes produced by the gated decoding loop itself against the same prefixes' Monte Carlo values computed with a much larger rollout budget; if the agreement collapses once the prefix contains two or more self-selected steps, the closed-loop condition that Eq. (A.22) assumes (a fixed small $\epsilon_{\mathrm{gate}}$) fails and the policy-improvement guarantee would not transfer to new scenes.

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Extended reading notes

Core claim

Step-wise value gating is a policy-improvement operation: an oracle that accepts only steps whose true action value $Q^{\pi_0}_t(h_t, r_t)$ exceeds a threshold $T$ (normalized over retained probability mass) has expected trajectory return at least as high as the original policy's, provided the acceptance set is nonempty. Since the true action values are latent, the paper estimates them by Monte Carlo rollouts over multi-branch reasoning trees, turns terminal answer quality into per-prefix and per-trajectory labels through relative ranking, and distills those labels into a learned gate $g_\phi$. The resulting performance gap relative to the oracle policy is bounded by the sum of a Monte Carlo estimation error decaying as $O(m^{-1/2})$ and a classification approximation error $\epsilon_{\mathrm{gate}}$, so in the limit of large rollout budgets and accurate gates the learned policy inherits the oracle's improvement. Empirically, SAG and TAG are complementary: SAG raises the quality of the entire candidate solution space (19 of 20 rank positions improve), while TAG turns that denser high-quality candidate set into a better final answer, and their combination exceeds either alone.

Load-bearing premise

The learned gate is trained on offline reasoning trees sampled from the base policy, yet at inference it is used inside a closed loop where each selected prefix shifts the state distribution; the theory treats the gate error as a fixed constant and gives no bound for this drift.

Editorial extensions

If this is right

  • A frozen multimodal LLM can be made substantially more accurate on spatial question-answering without weight updates; the gate is a plug-in inference-time controller.
  • Step-level selection and trajectory-level selection are complementary: filtering low-value prefixes first raises the ceiling of what the final answer can be, and reranking then converts that ceiling into accuracy.
  • Expanding the candidate budget only helps if the extra candidates are filtered and selected well: with 5 candidates the gated model already beats Self-Consistency and Tree-of-Thoughts with 20 candidates on ScanNet.
  • The benefit concentrates on tasks dominated by error accumulation (room size, relative direction, relative distance) rather than tasks limited by perception (object counting, absolute distance), indicating the gate is about reasoning-path selection, not perception.
  • Improving the gate—more rollouts per tree, lower classification error, better trajectories—moves the deployed policy toward the oracle improvement bound $\Delta^*$.
  • The relative-ranking supervision is agnostic to domain; the same offline-tree construction could be applied to other open-ended multimodal reasoning tasks where intermediate steps have no gold labels, though the paper only demonstrates spatial question-answering.
  • Because the gate is trained offline on the base policy's trees, a natural stress test is to measure SAG accuracy on prefixes the gate itself selects during decoding, where the input distribution has shifted; the paper does not report this closed-loop accuracy.
  • If the learned value signal is reusable across base models, then a single gate trained on one model's reasoning trees could be applied to future stronger models without retraining; the paper's experiments with GPT-5.4 and Gemini-3.1 are consistent with transferability but do not directly test it.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes a plug-and-play gating framework (Step-Advantage Gate, SAG, and Trajectory-Advantage Gate, TAG) to improve the open-ended spatial reasoning of frozen multimodal large language models. Stepwise reasoning is modeled as a finite-horizon decision process; Monte Carlo reasoning trees are built offline, terminal feedback is propagated bottom-up, and hierarchical supervision is derived from relative rankings. A lightweight gating model is trained on these trees, then used at inference time to greedily select high-value prefixes (SAG) and rerank complete trajectories (TAG). The paper reports large and consistent accuracy gains over CoT, Self-Consistency, and ToT across ScanNet, ScanNet++, Matterport3D, and HM3D for GPT-5.4 and Gemini-3.1-pro-preview, together with a theoretical policy-improvement argument for an ideal value gate and an approximate bound for the learned gate.

Significance. If the empirical results hold, the framework offers a practical way to improve spatial reasoning without modifying base-model parameters, and the released dataset and code would be useful resources. The central idea of converting terminal reward into hierarchical advantage supervision for gating is interesting and the reported gains are substantial. However, the significance is conditional on resolving two load-bearing issues: (i) the training data and evaluation benchmarks appear to share scenes/questions, so the gains may reflect in-distribution memorization rather than generalizable gating; and (ii) the theoretical analysis does not directly establish the claimed benefit of the learned gate as deployed, and the stated lower-confidence-bound verification is not present in the manuscript.

major comments (5)
  1. [Section V.A] The manuscript does not state that the scenes or questions used to construct Reasoning-Tree-160K are disjoint from the four evaluation benchmarks. Since the QA tasks are built from the same ground-truth 3D annotations and the same six question types as the benchmarks (Section V.A), the natural reading is that the training trees and the evaluation tasks share scenes or near-duplicate questions. Under that reading, the headline gains (e.g., ScanNet 34.1 to 44.6 in Table I; ScanNet++ 28.7 to 43.8 in Table II) could reflect in-distribution memorization rather than generalizable advantage gating. The 9:1 split mentioned in Section V.A splits only the Reasoning-Tree-160K data itself; it does not separate scenes from the evaluation sets. This must be settled before the central empirical claim is accepted, for example by providing an explicit disjoint-scene guarantee or by reporting results on held-out scenes.
  2. [Eq. (A.22) (also Eq. (11))] The claimed theoretical verification of the learned gate is not established. The lower bound J(π_ϕ)−J(π_0) ≥ Δ* − δ1·K·√(log(2/η)/(2m)) − δ2·K·ε_gate contains unestimated constants Δ*, δ1, δ2, and ε_gate, and the right-hand side can be negative. The paper never estimates these quantities, nor does it provide a confidence bound on held-out rewards. Moreover, Propositions 1–3 in Appendix A.2 prove improvement only for the ideal threshold gate π*_T, not for the learned gate π_ϕ used in Eqs. (16)–(17). The introduction's statement that the paper provides 'a lower confidence bound (LCB) on held-out task rewards' is not supported by any LCB calculation in the main text or appendix. This gap should be filled or the claim should be softened to an asymptotic motivation.
  3. [Section IV.A, Eq. (13)] The training labels for SAG are derived from per-tree relative ranking (Top-ρ), while the theoretical gating operator in Eq. (A.5) is a fixed threshold T on Q. The paper does not justify that Top-ρ ranking approximates threshold gating; indeed, Top-ρ labels depend on the composition of each tree and on the uncertainty term ασ(τ_{1:t}) in Eq. (12), neither of which appears in the theory. Without an explicit argument connecting relative-ranking supervision to threshold-based policy improvement, the theory does not directly apply to the trained gate.
  4. [Section IV.A and Section V.A] The paper does not state which policy π_0 generated the reasoning trees in Reasoning-Tree-160K. The value estimates in Eq. (7) are defined with respect to the baseline policy π_0, and the theoretical result (Prop. 1) concerns gating that same policy. However, the main results apply the gate to GPT-5.4 and Gemini-3.1-pro-preview (Tables I–IV). If the trees were generated by a different model (e.g., Qwen3-VL-4B, the stated gating backbone), the Monte Carlo estimates do not estimate Q under the target π_0, and the theoretical justification mismatches the deployment. The authors should specify the tree-generating policy and, if it differs from the evaluated models, provide evidence that the gate transfers across model policies.
  5. [Section V.B, Tables I–IV] The 'Stepwise' baseline used in the main tables is not defined. It appears to be the same candidate-generation procedure as AG but without gating, yet the manuscript does not state the number of candidates, the selection rule, or the inference cost for this baseline. Without a precise definition, the matched-comparison claim cannot be verified. Please define the Stepwise baseline explicitly and, ideally, report its performance with the same candidate budget used by AG (M=20, K=6).
minor comments (5)
  1. [Throughout Section IV.B and Figure 3] The text repeatedly writes 'SW A' where 'SWA' (stochastic weight averaging) is meant; please fix this typo.
  2. [Section V.A] The hyperparameters λ (quality score scale) and α (uncertainty weight) in Eqs. (11)–(12) are introduced but their default values and sensitivity are never reported; please provide them.
  3. [Section V.D and Figure 4] The Stepwise baseline is not plotted in the candidate-budget comparison; adding it would clarify how AG compares with its direct baseline at the same budget.
  4. [Tables I–IV] The main results are reported without variance or significance measures; given the size of the gains, at least a small number of seeds or a confidence interval for the overall average would strengthen the claims.
  5. [Section I and Section VI] The introduction claims a 'lower confidence bound (LCB) on held-out task rewards' as a contribution, but no LCB appears anywhere in the manuscript; this claim should be removed or substantiated.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the advantage-gating derivation is self-contained, and the only self-citations are non-load-bearing related-work references.

full rationale

The central claim is a learned gate trained on Monte Carlo reasoning-tree labels; this is ordinary supervised learning, not a hidden fitting loop. Section IV.A constructs labels from terminal scores s(τ)=1/(1+λ e(τ)) and prefix values V(τ)=mean over reachable leaves, and Section IV.B fits SAG/TAG with weighted binary cross-entropy and reweighted cross-entropy objectives. At inference (Eqs. 16-17), the gates are applied to fresh candidate prefixes; nothing in the paper defines the evaluation outcome as the training objective or fits a parameter to the reported benchmark number. The theoretical bound in Eq. A.22 is J(π_φ)−J(π_0) ≥ Δ* − δ1K sqrt(log(2/η)/2m) − δ2K ε_gate; Δ* is the ideal oracle gain and is never estimated, so the bound is uninformative unless Δ* dominates the error terms, but uninformative is not circular: Δ* is not defined as the learned gain. Proposition 1/A.2 is a standard conditional-expectation thresholding argument and does not import its conclusion via a self-citation. The paper cites its authors' own earlier work ([23], [25]) only as related-work examples of MLLM adaptation and retrieval, not as load-bearing evidence for the gating derivation. The open question of whether Reasoning-Tree-160k scenes are disjoint from the four evaluation benchmarks is a data-leakage/correctness risk, but the text does not assert that evaluation scenes appear in training, so it is not an exhibited definitional reduction. Overall, no step in the claimed derivation reduces to its own input; the empirical and theoretical content are independent of the self-citations.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new physical or ontological entities are postulated. The 'gates' are learned classifier heads; the Reasoning-Tree-160k dataset is a new artifact, not an invented entity in the sense of an unobserved cause.

free parameters (6)
  • Top-rho accept ratio (rho) = 0.25
    The fraction of prefixes labeled 'accept' within each tree layer; selected based on the ablation in Fig. 6, which evaluates rho=0.15, 0.25, 0.35 on the benchmark and picks the best.
  • Candidate count M = 20
    Number of next-step candidates generated at inference; chosen after the budget ablation in Fig. 4 shows best performance at 20.
  • Maximum reasoning depth K = 6
    Maximum tree depth and inference depth; reported in Section V.A but no ablation justifies it.
  • Uncertainty weight alpha
    Weight in exploration-aware value score Y = V + alpha*sigma (Section IV.A.1); its value is never stated.
  • Quality score scale lambda
    Appears in s(tau) = 1/(1 + lambda*e(tau)) (Fig. 2 caption); numerical value not given.
  • Tree rollout budget per prefix
    The number of terminal rollouts per prefix is determined by tree construction (branching factor and depth), but these are not specified; the theory's MC error depends on this budget.
assumptions (5)
  • domain assumption Reasoning can be modeled as a finite-horizon MDP with zero intermediate rewards and a bounded terminal return R in [0,1].
    Section III.A; this is the formulation that makes policy improvement theorems applicable.
  • domain assumption Terminal return R(tau) can be computed deterministically from ground truth via the quality score s(tau) = 1/(1 + lambda*e(tau)).
    Section IV.A.1 and Fig. 2; all Monte Carlo labels derive from this score.
  • ad hoc to paper The learned gate g_phi trained on pi_0-generated trees remains accurate on online gated rollouts.
    Section IV.B trains on offline trees while Section IV.C deploys greedy online selection; no distribution-shift correction is provided.
  • ad hoc to paper Relative ranking within a tree is a sufficient proxy for the ideal value threshold gating.
    Section IV.A.2 defines labels via Top-rho rather than an absolute threshold T used in the theory; the theory in Section III.A assumes an absolute threshold.
  • domain assumption Monte Carlo rollouts from a tree node are approximately i.i.d. draws from pi_0 continuations.
    Appendix A.3 uses the strong law of large numbers and Hoeffding bounds; Section IV.A constructs a shared-prefix tree with limited depth and an unreported branching factor, so leaves under a prefix are not independent rollouts.

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Pith. "Pith review of Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence." pith.science (2026). https://pith.science/paper/SVDFSKLU

@misc{pith2026260807987,
  author       = {Pith},
  title        = {Pith review of: Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SVDFSKLU}},
  note         = {Machine review of arXiv:2608.07987}
}
read the original abstract

Multimodal large language models (MLLMs) have demonstrated significant potential in complex spatial scene understanding and reasoning tasks. However, their open-ended reasoning process is prone to decision errors and error accumulation, leading to instability in answer quality. To address this, we propose an advantage-guided gating framework that dynamically intervenes in and corrects deviations during the reasoning process. Specifically, we model step-by-step reasoning as a finite-horizon decision process and introduce Monte Carlo value evaluation on the reasoning tree to provide intermediate supervision signals. The framework includes Step-Advantage Gate and Trajectory-Advantage Gate, which dynamically select high-value reasoning steps and high-quality complete reasoning trajectories, respectively. During training, we perform supervised learning for the gates using reasoning trees generated via multi-branch sampling, and combine shared-parameter initialization with task-specific heads to achieve cross-task robustness and diversity. During inference, the model greedily selects high-value prefix reasoning steps while choosing the optimal reasoning head based on the problem type, thereby significantly improving the accuracy of the final answer. Furthermore, we constructed the Reasoning-Tree-160k dataset and performed two-stage learning on it. Extensive experiments demonstrate that this advantage-guided gating framework effectively enhances the performance of benchmark MLLMs in visual-based spatial understanding and reasoning tasks. The code is open to the public for research: https://github.com/LingLin-ll/Advantage-Guided-Gate.

Figures

Figures reproduced from arXiv: 2608.07987 by the authors.

Figure 1
Figure 1. (a) Spatial instruction-tuned models generate a single autoregressive [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Construction and annotation of the Monte Carlo reasoning tree. The reasoning tree is first expanded top-down by recursively sampling candidate [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Overview of the proposed Advantage-Guided Gate framework. The training phase consists of two stages. Stage I jointly optimizes the Step-Advantage [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Effect of candidate budget on Self-Consistency, Tree-of-Thought (ToT), and the proposed Advantage-Guided Gate (AG) on ScanNet. Overall and [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Comparison of candidate solution-space quality between Stepwise reasoning and Stepwise reasoning with SAG on ScanNet. For each question, 20 [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: analyzes the impact of the Top-ρ labeling threshold on the gated training data. Specifically, samples within the Top-ρ percentile of scores among candidates in the same layer are labeled as “accept,” while the remaining samples are labeled as “reject.” Overall performa…
Figure 7
Figure 7. Figure 7: Qualitative illustration of hierarchical advantage-guided gating on a object-counting example. SAG accepts the visually grounded prefix that correctly [PITH_FULL_IMAGE:figures/full_fig_p024_7.png]

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Reference graph

Works this paper leans on

61 extracted references · 42 canonical work pages

  1. [1]

    Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models

    J. Zhang, M. Zhang, Y . Peng, H. Liu, C. Wang, Y . Long, H. Huang, D. Li, N. Duan, H. Shenet al., “Embodied3dbench: Benchmarking low- level embodied spatial intelligence of vision language models,”arXiv preprint arXiv:2605.29074, 2026

    work page Pith review arXiv 2026

  2. [2]

    Mmsi-bench: A benchmark for multi-image spatial intelligence,

    S. Yang, R. Xu, Y . Xie, S. Yang, M. Li, J. Lin, C. Zhu, X. Chen, H. Duan, X. Yueet al., “Mmsi-bench: A benchmark for multi-image spatial intelligence,”ICLR, 2026

    work page 2026

  3. [3]

    Reltr: Relation transformer for scene graph generation,

    Y . Cong, M. Y . Yang, and B. Rosenhahn, “Reltr: Relation transformer for scene graph generation,”IEEE TPAMI, vol. 45, no. 9, pp. 11 169– 11 183, 2023

    work page 2023

  4. [4]

    Scenehgn: Hierarchical graph networks for 3d indoor scene generation with fine-grained geometry,

    L. Gao, J.-M. Sun, K. Mo, Y .-K. Lai, L. J. Guibas, and J. Yang, “Scenehgn: Hierarchical graph networks for 3d indoor scene generation with fine-grained geometry,”IEEE TPAMI, vol. 45, no. 7, pp. 8902– 8919, 2023

    work page 2023

  5. [5]

    Planner3d: Llm-enhanced graph prior meets 3d indoor scene explicit regularization,

    Y . Wei, M. R. Min, G. V osselman, L. E. Li, and M. Y . Yang, “Planner3d: Llm-enhanced graph prior meets 3d indoor scene explicit regularization,” IEEE TPAMI, 2025

    work page 2025

  6. [6]

    Knowledge-based embodied question answering,

    S. Tan, M. Ge, D. Guo, H. Liu, and F. Sun, “Knowledge-based embodied question answering,”IEEE TPAMI, vol. 45, no. 10, pp. 11 948–11 960, 2023

    work page 2023

  7. [7]

    3d- llava: Towards generalist 3d lmms with omni superpoint transformer,

    J. Deng, T. He, L. Jiang, T. Wang, F. Dayoub, and I. Reid, “3d- llava: Towards generalist 3d lmms with omni superpoint transformer,” inCVPR, 2025, pp. 3772–3782

    work page 2025

  8. [8]

    Chat-scene: Bridging 3d scene and large language models with object identifiers,

    H. Huang, Y . Chen, Z. Wang, R. Huang, R. Xu, T. Wang, L. Liu, X. Cheng, Y . Zhao, J. Panget al., “Chat-scene: Bridging 3d scene and large language models with object identifiers,”NeurIPS, vol. 37, pp. 113 991–114 017, 2024. 16

    work page 2024

Show all 61 references
  1. [9]

    Ll3da: Visual interactive instruction tuning for omni-3d understanding reasoning and planning,

    S. Chen, X. Chen, C. Zhang, M. Li, G. Yu, H. Fei, H. Zhu, J. Fan, and T. Chen, “Ll3da: Visual interactive instruction tuning for omni-3d understanding reasoning and planning,” inCVPR, 2024, pp. 26 428– 26 438

    2024

  2. [10]

    Matrix3d: Large photogrammetry model all-in-one,

    Y . Lu, J. Zhang, T. Fang, J.-D. Nahmias, Y . Tsin, L. Quan, X. Cao, Y . Yao, and S. Li, “Matrix3d: Large photogrammetry model all-in-one,” inCVPR, 2025, pp. 11 250–11 263

    2025

  3. [11]

    Worldmirror: Universal 3d world reconstruction with any-prior prompting,

    Y . Liu, Z. Min, Z. Wang, J. Wu, T. Wang, Y . Yuan, Y . Luo, and C. Guo, “Worldmirror: Universal 3d world reconstruction with any-prior prompting,”arXiv preprint arXiv:2510.10726, 2025

    2025 arXiv

  4. [12]

    Llava-3d: A simple yet effective pathway to empowering lmms with 3d capabilities,

    C. Zhu, T. Wang, W. Zhang, J. Pang, and X. Liu, “Llava-3d: A simple yet effective pathway to empowering lmms with 3d capabilities,” inICCV, 2025, pp. 4295–4305

    2025

  5. [13]

    Video-3d llm: Learning position- aware video representation for 3d scene understanding,

    D. Zheng, S. Huang, and L. Wang, “Video-3d llm: Learning position- aware video representation for 3d scene understanding,” inCVPR, 2025, pp. 8995–9006

    2025

  6. [14]

    Sti-bench: Are mllms ready for precise spatial-temporal world understanding?

    Y . Li, Y . Zhang, T. Lin, X. Liu, W. Cai, Z. Liu, and B. Zhao, “Sti-bench: Are mllms ready for precise spatial-temporal world understanding?” in ICCV, 2025, pp. 5622–5632

    2025

  7. [15]

    Thinking in space: How multimodal large language models see, remember, and recall spaces,

    J. Yang, S. Yang, A. W. Gupta, R. Han, L. Fei-Fei, and S. Xie, “Thinking in space: How multimodal large language models see, remember, and recall spaces,” inCVPR, 2025, pp. 10 632–10 643

    2025

  8. [16]

    Spacemind: Camera-guided modality fusion for spatial reasoning in vision-language models,

    R. Zhao, Z. Zhang, J. Xu, J. Chang, D. Chen, L. Li, W. Sun, and Z. Wei, “Spacemind: Camera-guided modality fusion for spatial reasoning in vision-language models,”arXiv preprint arXiv:2511.23075, 2025

    2025

  9. [17]

    Learning from videos for 3d world: Enhancing mllms with 3d vision geometry priors,

    D. Zheng, Y . Li, L. Wanget al., “Learning from videos for 3d world: Enhancing mllms with 3d vision geometry priors,”NeurIPS, vol. 38, pp. 20 560–20 586, 2026

    2026

  10. [18]

    Spatial-mllm: Boosting mllm capabilities in visual-based spatial intelligence,

    D. Wu, F. Liu, Y .-H. Hung, and Y . Duan, “Spatial-mllm: Boosting mllm capabilities in visual-based spatial intelligence,”NeurIPS, vol. 38, pp. 13 569–13 597, 2026

    2026

  11. [19]

    S 3-TIR: Coupled spatial-spectral splatting for thermal infrared novel view synthesis,

    Y . Wang, Y . Li, Y . Di, and S. Gao, “S 3-TIR: Coupled spatial-spectral splatting for thermal infrared novel view synthesis,” inIJCAI, 2026

    2026

  12. [20]

    Uncertainty-aware medical diagnostic phrase identification and grounding,

    K. Zou, Y . Bai, B. Liu, Y . Chen, Z. Chen, Y . Zhou, X. Yuan, M. Wang, X. Shen, X. Caoet al., “Uncertainty-aware medical diagnostic phrase identification and grounding,”IEEE TPAMI, 2025

    2025

  13. [21]

    Inter-task weaving in image enhancement: From a new unified architecture to a better meta-representation learning,

    N. An, S. Xu, L. Ma, Z. Liu, G. Han, T. Ma, and R. Liu, “Inter-task weaving in image enhancement: From a new unified architecture to a better meta-representation learning,” inACM MM, 2025, pp. 7336–7345

    2025

  14. [22]

    World2vlm: Distilling world model imagination into vlms for dynamic spatial reasoning,

    W. Zhang, W. Wu, W. Xu, J. Luo, H. Zhi, Y . Huang, S. Ren, Z. Liu, and J. Zhang, “World2vlm: Distilling world model imagination into vlms for dynamic spatial reasoning,”arXiv preprint arXiv:2604.26934, 2026

    2026 arXiv

  15. [23]

    From generalist to specialist: Adapting vision language models via task- specific visual instruction tuning,

    Y . Bai, Y . Zhou, J. Zhou, R. S. M. Goh, D. S. W. Ting, and Y . Liu, “From generalist to specialist: Adapting vision language models via task- specific visual instruction tuning,”arXiv preprint arXiv:2410.06456, 2024

    2024 arXiv

  16. [24]

    Striving for faster and better: A one-layer architecture with auto re-parameterization for low- light image enhancement,

    N. An, L. Ma, G. Han, X. Fan, and R. Liu, “Striving for faster and better: A one-layer architecture with auto re-parameterization for low- light image enhancement,”IEEE TCSVT, 2025

    2025

  17. [25]

    Sentence-level prompts benefit composed image retrieval,

    Y . Bai, X. Xu, Y . Liu, S. Khan, F. Khan, W. Zuo, R. S. M. Mong, and C.-M. Feng, “Sentence-level prompts benefit composed image retrieval,” inICLR, vol. 2024, 2024, pp. 30 531–30 548

    2024

  18. [26]

    Biprolora: Bilevel prompt lora for real scene recovery,

    N. An, L. Ma, T. Ma, Z. Liu, Y . Liu, and R. Liu, “Biprolora: Bilevel prompt lora for real scene recovery,” inCVPR, 2026, pp. 15 465–15 475

    2026

  19. [27]

    Openspatial: A principled data engine for em- powering spatial intelligence,

    J. Liu, H. Sun, W. Li, Y . Zhang, R. Yang, Z. Zhu, Y . Yang, S. Zheng, N. Jiang, J. Jianget al., “Openspatial: A principled data engine for em- powering spatial intelligence,”arXiv preprint arXiv:2604.07296, 2026

    2026 arXiv

  20. [28]

    Causalspatial: A benchmark for object-centric causal spatial reasoning,

    W. Ma, C. Wang, R. Yuan, H. Chen, N. Dai, S. K. Zhou, Y . Yang, A. Yuille, and J. Chen, “Causalspatial: A benchmark for object-centric causal spatial reasoning,”arXiv preprint arXiv:2601.13304, 2026

    2026

  21. [29]

    Person re-identification method based on color attack and joint defence,

    Y . Gong, L. Huang, and L. Chen, “Person re-identification method based on color attack and joint defence,” inCVPR, 2022, pp. 4313–4322

    2022

  22. [30]

    A theory-inspired framework for few-shot cross-modal sketch person re-identification,

    Y . Gong, Y . Hou, J. Shi, K. DIEP, and M. Jiang, “A theory-inspired framework for few-shot cross-modal sketch person re-identification,” in AAAI, 2026

    2026

  23. [31]

    3d-llm: Injecting the 3d world into large language models,

    Y . Hong, H. Zhen, P. Chen, S. Zheng, Y . Du, Z. Chen, and C. Gan, “3d-llm: Injecting the 3d world into large language models,”NeurIPS, vol. 36, pp. 20 482–20 494, 2023

    2023

  24. [32]

    Pointllm: Empowering large language models to understand point clouds,

    R. Xu, X. Wang, T. Wang, Y . Chen, J. Pang, and D. Lin, “Pointllm: Empowering large language models to understand point clouds,” in ECCV. Springer-Verlag, 2024, p. 131–147

    2024

  25. [33]

    Jm3d & jm3d-llm: Elevating 3d representation with joint multi-modal cues,

    J. Ji, H. Wang, C. Wu, Y . Ma, X. Sun, and R. Ji, “Jm3d & jm3d-llm: Elevating 3d representation with joint multi-modal cues,”IEEE TPAMI, vol. 47, no. 4, pp. 2475–2492, 2024

    2024

  26. [34]

    Chat-scene++: Exploiting context-rich object identification for 3d llm,

    H. Huang, Y . Chen, Z. Wang, J. Pang, and Z. Zhao, “Chat-scene++: Exploiting context-rich object identification for 3d llm,”IEEE TPAMI, 2026

    2026

  27. [35]

    Think with 3d: Geometric imagination grounded spatial reasoning from limited views,

    Z. Chen, M. Zhang, X. Yu, X. Luo, M. Sun, Z. Pan, X. An, Y . Feng, P. Pei, X. Caiet al., “Think with 3d: Geometric imagination grounded spatial reasoning from limited views,” inCVPR, 2026, pp. 2613–2624

    2026

  28. [36]

    Chain-of-thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V . Le, D. Zhouet al., “Chain-of-thought prompting elicits reasoning in large language models,”NeurIPS, vol. 35, pp. 24 824–24 837, 2022

    2022

  29. [37]

    Tree of thoughts: Deliberate problem solving with large language models,

    S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y . Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,”NeurIPS, vol. 36, pp. 11 809–11 822, 2023

    2023

  30. [38]

    Graph of thoughts: Solving elaborate problems with large language models,

    M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyket al., “Graph of thoughts: Solving elaborate problems with large language models,” inAAAI, vol. 38, no. 16, 2024, pp. 17 682–17 690

    2024

  31. [39]

    Let’s verify step by step,

    H. Lightman, V . Kosaraju, Y . Burda, H. Edwards, B. Baker, T. Lee, J. Leike, J. Schulman, I. Sutskever, and K. Cobbe, “Let’s verify step by step,” inICLR, vol. 2024, 2024, pp. 39 578–39 601

    2024

  32. [40]

    Math-shepherd: Verify and reinforce llms step-by-step without human annotations,

    P. Wang, L. Li, Z. Shao, R. Xu, D. Dai, Y . Li, D. Chen, Y . Wu, and Z. Sui, “Math-shepherd: Verify and reinforce llms step-by-step without human annotations,” inACL, 2024, pp. 9426–9439

    2024

  33. [41]

    Towards hierarchical multi-step reward models for enhanced reasoning in large language models,

    T. Wang, Z. Jiang, Z. He, S. Tong, W. Yang, Y . Zheng, Z. Li, Z. He, H. Gong, Z. Yeet al., “Towards hierarchical multi-step reward models for enhanced reasoning in large language models,”arXiv preprint arXiv:2503.13551, 2025

    2025 arXiv

  34. [42]

    Scannet: Richly-annotated 3d reconstructions of indoor scenes,

    A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” inCVPR, 2017, pp. 5828–5839

    2017

  35. [43]

    Scannet++: A high- fidelity dataset of 3d indoor scenes,

    C. Yeshwanth, Y .-C. Liu, M. Nießner, and A. Dai, “Scannet++: A high- fidelity dataset of 3d indoor scenes,” inICCV, 2023, pp. 12–22

    2023

  36. [44]

    Matterport3d: Learning from rgb-d data in indoor environments,

    A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niebner, M. Savva, S. Song, A. Zeng, and Y . Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” in3DV. IEEE, 2017, pp. 667–676

    2017

  37. [45]

    Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai,

    S. K. Ramakrishnan, A. Gokaslan, E. Wijmans, O. Maksymets, A. Clegg, J. M. Turner, E. Undersander, W. Galuba, A. Westbury, A. X. Changet al., “Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai,” inNeurIPS, 2021

    2021

  38. [46]

    Qwen3-vl technical report,

    S. Bai, Y . Cai, R. Chen, K. Chen, X. Chen, Z. Cheng, L. Deng, W. Ding, C. Gao, C. Geet al., “Qwen3-vl technical report,”arXiv preprint arXiv:2511.21631, 2025

    2025 arXiv

  39. [47]

    Xiaomi mimo-vl-miloco technical report,

    J. Li, J. Chen, Y . Qu, S. Xu, Z. Lin, J. Zhu, B. Xu, W. Tan, P. Fu, J. Juet al., “Xiaomi mimo-vl-miloco technical report,”arXiv preprint arXiv:2512.17436, 2025

    2025

  40. [48]

    The llama 3 herd of models,

    A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughanet al., “The llama 3 herd of models,”arXiv preprint arXiv:2407.21783, 2024

    2024 arXiv

  41. [49]

    Llava-cot: Let vision language models reason step-by-step,

    G. Xu, P. Jin, Z. Wu, H. Li, Y . Song, L. Sun, and L. Yuan, “Llava-cot: Let vision language models reason step-by-step,” inICCV, 2025, pp. 2087–2098

    2025

  42. [50]

    Kimi-vl technical report,

    K. Team, A. Du, B. Yin, B. Xing, B. Qu, B. Wang, C. Chen, C. Zhang, C. Du, C. Weiet al., “Kimi-vl technical report,”arXiv preprint arXiv:2504.07491, 2025

    2025 arXiv

  43. [51]

    Deepseek-vl2: Mixture-of-experts vision-language models for advanced multimodal understanding,

    Z. Wu, X. Chen, Z. Pan, X. Liu, W. Liu, D. Dai, H. Gao, Y . Ma, C. Wu, B. Wanget al., “Deepseek-vl2: Mixture-of-experts vision-language models for advanced multimodal understanding,”arXiv preprint arXiv:2412.10302, 2024

    2024 arXiv

  44. [52]

    Video instruction tuning with synthetic data, 2024,

    Y . Zhang, J. Wu, W. Li, B. Li, Z. Ma, Z. Liu, and C. Li, “Video instruction tuning with synthetic data, 2024,”URL https://arxiv. org/abs/2410.02713, vol. 17, 2024

    2024 arXiv

  45. [53]

    Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models,

    J. Zhu, W. Wang, Z. Chen, Z. Liu, S. Ye, L. Gu, H. Tian, Y . Duan, W. Su, J. Shaoet al., “Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models,”arXiv preprint arXiv:2504.10479, 2025

    2025 arXiv

  46. [54]

    Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,

    B. Chen, Z. Xu, S. Kirmani, B. Ichter, D. Sadigh, L. Guibas, and F. Xia, “Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,” inCVPR, 2024, pp. 14 455–14 465

    2024

  47. [55]

    Spacer: Reinforcing mllms in video spatial reasoning,

    K. Ouyang, Y . Liu, H. Wu, Y . Liu, H. Zhou, J. Zhou, F. Meng, and X. Sun, “Spacer: Reinforcing mllms in video spatial reasoning,”arXiv preprint arXiv:2504.01805, 2025

    2025 arXiv

  48. [56]

    Scaling spatial intelligence with multimodal foundation models,

    Z. Cai, R. Wang, C. Gu, F. Pu, J. Xu, Y . Wang, W. Yin, Z. Yang, C. Wei, T. Zhouet al., “Scaling spatial intelligence with multimodal foundation models,” inCVPR, 2026, pp. 7879–7890

    2026

  49. [57]

    Visual spatial tuning,

    R. Yang, Z. Zhu, Y . Li, J. Huang, S. Yan, S. Zhou, Z. Liu, X. Li, S. Li, W. Wanget al., “Visual spatial tuning,”arXiv preprint arXiv:2511.05491, 2025

    2025

  50. [58]

    Openai gpt-5 system card,

    A. Singh, A. Fry, A. Perelman, A. Tart, A. Ganesh, A. El-Kishky, A. McLaughlin, A. Low, A. Ostrow, A. Ananthramet al., “Openai gpt-5 system card,”arXiv preprint arXiv:2601.03267, 2025. 17

    2025 arXiv

  51. [59]

    Gemini 3.1 pro: Model card,

    Google DeepMind, “Gemini 3.1 pro: Model card,” https://storage.googleapis.com/deepmind-media/Model-Cards/ Gemini-3-1-Pro-Model-Card.pdf, Feb. 2026, accessed: 2026-07- 23

    2026

  52. [60]

    Self-consistency improves chain of thought reasoning in language models,

    X. Wang, J. Wei, D. Schuurmans, Q. V . Le, E. H. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” inICLR, 2023. Ling Linis currently pursuing the Ph.D. degree with the School of Artificial Intelligence and Da...

    2023

  53. [2010]

    He is currently a Professor at the Suzhou Institute for Advanced Research/School of Mathematical Sciences at the University of Science and Technology of China

    From 2010 to 2015, he served as a Visiting Assistant Professor at the University of California, Santa Barbara, and from 2015 to 2021, he was a Distinguished Professor at Soochow University. He is currently a Professor at the Suzhou Institute for Advanced Research/School of Mat...

    2010

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.