pith. machine review for the scientific record. sign in

arxiv: 2604.10219 · v1 · submitted 2026-04-11 · 💻 cs.AI

Recognition: unknown

Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models

Fei Luo, Jungong Han, Xinyu Liu, Yanbiao Ma, Yike Guo, Zhe Qian, Zhonghua Wang, Zhongxing Xu, Zhuohan Ouyang, Zongyuan Ge

Pith reviewed 2026-05-10 15:59 UTC · model grok-4.3

classification 💻 cs.AI
keywords hallucinationsmultimodal reasoningvisual anchoringhigh entropy statescognitive bifurcationattention reinforcementreasoning models
0
0 comments X

The pith

Multimodal reasoning models hallucinate when they stop querying visual evidence at high-entropy decision points.

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

The paper establishes that hallucinations in multimodal large reasoning models align closely with cognitive bifurcation points marked by high entropy. At these transitions the models cease to consult the visual input and instead default to language-based priors, producing a disconnect the authors term Reasoning Vision Truth Disconnect. The proposed fix shifts supervision from final answers alone to internal guidance that reinforces visual attention precisely during those uncertain moments. This is achieved through a training process that detects high-entropy states and rewards anchoring back to the image while also forcing reflection on subsequent steps. If the approach holds, long chains of visual reasoning become more reliable by building the corrective behavior into the model rather than applying it only at test time.

Core claim

Multimodal Large Reasoning Models remain vulnerable to hallucinations during extended reasoning chains. These errors correlate strongly with cognitive bifurcation points that exhibit high entropy states. The root cause is a localized breakdown in visual semantic anchoring within intermediate network layers; at these high-uncertainty transitions the model fails to query visual evidence and reverts to language priors. The authors therefore introduce V-STAR, a training paradigm that augments outcome supervision with fine-grained internal attention guidance. Its central components are the Hierarchical Visual Attention Reward, which dynamically incentivizes visual attention across critical layers

What carries the argument

Hierarchical Visual Attention Reward (HVAR) within the GRPO framework, which detects high-entropy states and rewards visual attention in intermediate layers to restore anchoring to the visual input.

If this is right

  • Outcome-level supervision alone is insufficient; fine-grained internal attention guidance at uncertain steps measurably reduces hallucinations.
  • Detecting high-entropy states allows targeted reinforcement of visual queries that overrides language priors.
  • Forced reflection around bifurcation points converts external debiasing into an internalized habit of visual verification.
  • The resulting capability operates without added test-time compute or performance loss on standard reasoning metrics.

Where Pith is reading between the lines

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

  • The same entropy-triggered anchoring technique could be tested on other chain-of-thought tasks where models drift from input evidence.
  • Because the failure is localized to intermediate layers, lighter interventions focused on those layers may suffice for broader multimodal models.
  • If entropy detection proves reliable across architectures, it offers a general signal for inserting verification steps in any long reasoning sequence.

Load-bearing premise

That dynamically rewarding visual attention at high-entropy points during training will cause the model to maintain visual anchoring automatically in later use without reducing overall reasoning performance.

What would settle it

Train a model with the proposed method, then measure whether hallucinations and visual attention metrics at previously identified high-entropy bifurcation points differ from those of an identical baseline model on the same long-chain visual reasoning tasks.

Figures

Figures reproduced from arXiv: 2604.10219 by Fei Luo, Jungong Han, Xinyu Liu, Yanbiao Ma, Yike Guo, Zhe Qian, Zhonghua Wang, Zhongxing Xu, Zhuohan Ouyang, Zongyuan Ge.

Figure 1
Figure 1. Figure 1: Reasoning relies less on visual evidence. Across layers, reasoning generations show reduced visual feature signals compared to non-reasoning. of visual attention throughout the network. This suggests a mode level shift toward internal linguistic inference and away from visual grounding. [26] To identify the root cause of this phenomenon, we conducted a deep analysis of the internal mechanisms of MLRMs. As … view at source ↗
Figure 2
Figure 2. Figure 2: A multi view analysis of hallucination triggers in multimodal reasoning. The statistics show that hallucinations tend to occur near high entropy transition words such as “However”, and the visual attention ratio is generally lower in these cases. In the example trajectory, “However” coincides with a spike in token entropy and is immediately followed by content that contradicts the image. The token level at… view at source ↗
Figure 3
Figure 3. Figure 3: Intermediate layer divergence between grounded and hallucinated tokens. (a) Text Image Mutual Information: The gap between grounded and hallucinated tokens peaks in the intermediate layers from 11 to 20, where grounded tokens preserve higher Text Image Mutual Information. (b) Visual attention total and concentration: In the same intermediate layer window, grounded tokens exhibit higher total visual attenti… view at source ↗
Figure 4
Figure 4. Figure 4: Head wise visual attention differs for grounded and halluci￾nated tokens. Visual attention heatmaps compare the hallucinated token “Sea” and the grounded token “Town”. In the highlighted intermediate layer region, the grounded token shows stronger and more coherent attention across heads, while the hallucinated token exhibits weaker and more sporadic activation, indicating reduced visual grounding. role of… view at source ↗
Figure 5
Figure 5. Figure 5: High Uncertainty Triggers Hallucination. (a) As the model reasons, its uncertainty (entropy) spikes significantly at logical turning points (e.g., ”However”), creating a ”spiky” pattern. (b) Our statistical analysis reveals that hallucination events (orange dots) are exclusively concentrated within these high entropy pivotal clusters, verifying a strong temporal coupling between semantic uncertainty and vi… view at source ↗
Figure 6
Figure 6. Figure 6: Pinpointing the breakdown of visual semantic anchoring in intermediate layers. (a) The layer wise Mahalanobis distance between the grounded answer token Town and the hallucinated token Sea shows a pronounced separation that becomes evident after Layer 11 and persists throughout the intermediate layer window from 11 to 20, consistent with reduced alignment to grounded states. (b) In an object recognition ex… view at source ↗
Figure 7
Figure 7. Figure 7: The Pseudo Reflection Paradox. Top The per token visual attention score shows that after a pivot token the model enters a visual decoupling zone and then drifts into snowballing hallucination. Even when it produces an explicit reflection cue such as “Let me check”, the visual attention does not rebound, indicating that Pseudo Reflection is not accompanied by renewed visual grounding. Bottom (a) The sample … view at source ↗
Figure 8
Figure 8. Figure 8: The Overall Framework of V-STAR. Our paradigm unifies microscopic attention guidance and macroscopic trajectory editing within the GRPO framework. [32] (Left) Forced Reflection Mechanism (FRM): A trajectory editing strategy that activates a reflection loop around detected high entropy cognitive bifurcation points by inserting trigger tokens. This focuses reflection on the critical transition region, encour… view at source ↗
Figure 9
Figure 9. Figure 9: The Dynamic Dual Stream Data Synthesis Framework. Adopting a Divide and Conquer philosophy, we stratify data processing into two streams to ensure high fidelity. (Top) Logic Intensive Stream: For structured inputs, we employ a Caption then Reason cascade to ensure precise abstract deduction. (Bottom) Semantic Rich Stream: For natural scenes, we use a Generate then Refine pipeline to preserve perceptual nua… view at source ↗
Figure 10
Figure 10. Figure 10: Selection ratio η in data curation. Visual attention scores are reported for the anchoring layers (11–20) and the full network average, measured both in a pivot local window (10 tokens after high entropy pivots) and over the full chain. With increasing η, the visual attention scores steadily rise under both measurement windows, including the pivot local window and the full reasoning chain. and factual fai… view at source ↗
Figure 11
Figure 11. Figure 11: Linguistic quality of reflection outputs. We report automatic text quality scores (Naturalness, Fluency, Grammar; ↑) and perplexities (PPL1, PPL2; ↓) on Bingo [89] and MMHalu [88]. PPL1 and PPL2 are calculated using GPT-2, while the ratings for Grammar, Fluency, and Naturalness are provided by GPT-5. While enabling explicit reflection, V-STAR shows no degradation in language quality and achieves better re… view at source ↗
Figure 12
Figure 12. Figure 12: Attention heatmaps under identical prompting. Compared with representative baselines, V-STAR allocates more token to image attention to visual evidence during generation and concentrates this attention on semantically relevant regions, while suppressing background dominated activation. This pattern is consistent with stronger visual anchoring. 33.8 33.0 32.2 31.4 30.6 29.8 Accuracy 410 430 450 470 490 510… view at source ↗
Figure 13
Figure 13. Figure 13: Accuracy length trade-off on MathVision. We plot accuracy versus average generated token length under the same evaluation protocol. V-STAR achieves higher accuracy with shorter generations, supporting improved reasoning efficiency. wandering, allowing the model to arrive at correct solutions with shorter and more targeted reasoning traces. This is an important observation because it shows that better grou… view at source ↗
Figure 14
Figure 14. Figure 14: Visual attention recovery during reflection. (a) Mean visual attention trajectory of V-STAR around the reflection trigger. After entering the reflection phase, the visual attention score shows a clear rebound. (b) Recovery metrics, including attention drop, recovery gain, and U score, comparing Qwen2.5-VL-7B with V-STAR. V-STAR achieves higher recovery gain and a higher U score, consistent with more groun… view at source ↗
read the original abstract

Multimodal Large Reasoning Models (MLRMs) have achieved remarkable strides in visual reasoning through test time compute scaling, yet long chain reasoning remains prone to hallucinations. We identify a concerning phenomenon termed the Reasoning Vision Truth Disconnect (RVTD): hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states. We attribute this vulnerability to a breakdown in visual semantic anchoring, localized within the network's intermediate layers; specifically, during these high uncertainty transitions, the model fails to query visual evidence, reverting instead to language priors. Consequently, we advocate a shift from solely outcome level supervision to augmenting it with fine grained internal attention guidance. To this end, we propose V-STAR (Visual Structural Training with Attention Reinforcement), a lightweight, holistic training paradigm designed to internalize visually aware reasoning capabilities. Central to our approach is the Hierarchical Visual Attention Reward (HVAR), integrated within the GRPO framework. Upon detecting high entropy states, this mechanism dynamically incentivizes visual attention across critical intermediate layers, thereby anchoring the reasoning process back to the visual input. Furthermore, we introduce the Forced Reflection Mechanism (FRM), a trajectory editing strategy that disrupts cognitive inertia by triggering reflection around high entropy cognitive bifurcation points and encouraging verification of subsequent steps against the visual input, thereby translating external debiasing interventions into an intrinsic capability for hallucination mitigation.

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

1 major / 2 minor

Summary. The paper identifies a phenomenon called Reasoning Vision Truth Disconnect (RVTD) in Multimodal Large Reasoning Models (MLRMs), claiming that hallucinations correlate strongly with high-entropy cognitive bifurcation points in intermediate layers where visual semantic anchoring breaks down and models revert to language priors. It proposes V-STAR, a training paradigm using Hierarchical Visual Attention Reward (HVAR) within the GRPO framework to dynamically incentivize visual attention at high-entropy states, plus Forced Reflection Mechanism (FRM) for trajectory editing to encourage verification against visual input, aiming to internalize hallucination mitigation.

Significance. If the correlation measurements, layer-localized attention breakdowns, and mitigation results hold under the reported experimental controls, this work offers a concrete internal mechanism for addressing hallucinations beyond outcome-level supervision, with potential to improve reliability in long-chain visual reasoning without external debiasing at inference time. The provision of attention visualizations, trajectory analyses, and integration with existing GRPO strengthens the case for practical adoption.

major comments (1)
  1. [Experimental Evaluation] The central RVTD correlation and layer-localization claims are supported by the experimental sections, attention maps, and trajectory analyses, but the assumption that HVAR+FRM translates to intrinsic capability without performance degradation requires explicit reporting of overall reasoning accuracy metrics (e.g., on standard VQA or reasoning benchmarks) alongside hallucination rates to confirm no trade-off.
minor comments (2)
  1. [Introduction] The abstract and introduction introduce multiple new terms (RVTD, HVAR, FRM, V-STAR) without a consolidated notation table; adding one would improve readability.
  2. [Method] Clarify the precise entropy threshold and detection method used to trigger HVAR in the GRPO integration, as the high-level description leaves implementation details ambiguous for reproduction.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive evaluation, recognition of the RVTD phenomenon and V-STAR contributions, and recommendation for minor revision. We address the single major comment below.

read point-by-point responses

  1. Referee: The central RVTD correlation and layer-localization claims are supported by the experimental sections, attention maps, and trajectory analyses, but the assumption that HVAR+FRM translates to intrinsic capability without performance degradation requires explicit reporting of overall reasoning accuracy metrics (e.g., on standard VQA or reasoning benchmarks) alongside hallucination rates to confirm no trade-off.

    Authors: We agree that confirming the absence of performance trade-offs is essential for validating that HVAR and FRM internalize hallucination mitigation as an intrinsic capability. The current manuscript emphasizes hallucination reduction in long-chain visual reasoning; to strengthen the claim, the revised version will include explicit accuracy results on standard benchmarks (e.g., VQA-v2 and visual reasoning tasks) reported alongside hallucination rates under identical experimental controls. This addition will directly demonstrate that V-STAR improves reliability without degrading overall reasoning performance. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper identifies RVTD as an empirical correlation between hallucinations and high-entropy bifurcation points, attributes it to visual anchoring failure in intermediate layers, and proposes V-STAR incorporating HVAR within the pre-existing GRPO framework plus FRM as a trajectory intervention. No equations, parameter fits, or first-principles derivations are present that reduce any claimed result to quantities defined by the paper's own outputs or self-citations. The central claims rest on experimental observations, attention visualizations, and trajectory analyses treated as independent evidence rather than self-referential constructions, rendering the argument self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 3 invented entities

Review is based solely on the abstract; full paper may contain additional parameters, assumptions, or evidence. The central claim rests on the existence of RVTD and the premise that attention reinforcement at high-entropy points can be internalized.

axioms (2)
  • domain assumption High-entropy states during reasoning correspond to cognitive bifurcation points at which visual semantic anchoring fails and language priors dominate.
    Invoked to explain the source of hallucinations and to justify the timing of HVAR intervention.
  • domain assumption Fine-grained internal attention guidance can be translated into an intrinsic model capability via reward shaping and trajectory editing.
    Underpins the claim that V-STAR and FRM produce lasting hallucination mitigation.
invented entities (3)
  • Reasoning Vision Truth Disconnect (RVTD) no independent evidence
    purpose: To name and localize the correlation between hallucinations and high-entropy cognitive points.
    Newly coined term with no independent evidence supplied in the abstract.
  • Hierarchical Visual Attention Reward (HVAR) no independent evidence
    purpose: To provide dynamic incentives for visual attention in intermediate layers when entropy is high.
    New reward mechanism introduced as part of V-STAR.
  • Forced Reflection Mechanism (FRM) no independent evidence
    purpose: To disrupt cognitive inertia by forcing reflection and visual verification at bifurcation points.
    New trajectory-editing strategy presented as complementary to HVAR.

pith-pipeline@v0.9.0 · 5574 in / 1925 out tokens · 44457 ms · 2026-05-10T15:59:59.661589+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

100 extracted references · 57 canonical work pages · 29 internal anchors

  1. [1]

    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

    D. Guo, D. Yang, H. Zhang, J. Song, P . Wang, Q. Zhu, R. Xu, R. Zhang, S. Ma, X. Biet al., “Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,”arXiv preprint arXiv:2501.12948, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    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,”Advances in neural information processing systems, vol. 36, pp. 11 809–11 822, 2023

    2023

  3. [3]

    Sparks of Artificial General Intelligence: Early experiments with GPT-4

    S. Bubeck, V . Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P . Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, and Y. Zhang, “Sparks of artificial general intelligence: Early experiments with gpt-4,”arXiv preprint arXiv:2303.12712, 2023

    work page internal anchor Pith review arXiv 2023

  4. [4]

    Training language models to follow instructions with human feedback,

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P . Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Rayet al., “Training language models to follow instructions with human feedback,”Advances in neural information processing systems, vol. 35, pp. 27 730–27 744, 2022

    2022

  5. [5]

    OpenAI o1 System Card

    A. Jaech, A. Kalai, A. Lerer, A. Richardson, A. El-Kishky, A. Low, A. Helyar, A. Madry, A. Beutel, A. Carneyet al., “Openai o1 system card,”arXiv preprint arXiv:2412.16720, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  6. [6]

    Self-Consistency Improves Chain of Thought Reasoning in Language Models

    X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowd- hery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,”arXiv preprint arXiv:2203.11171, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  7. [7]

    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,”Advances in neural information processing systems, vol. 35, pp. 24 824–24 837, 2022

    2022

  8. [8]

    Large language models are zero-shot reasoners,

    T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,”Advances in neural information processing systems, vol. 35, pp. 22 199–22 213, 2022

    2022

  9. [9]

    Learn to explain: Multimodal reasoning via thought chains for science question answering,

    P . Lu, S. Mishra, T. Xia, L. Qiu, K.-W. Chang, S.-C. Zhu, O. Tafjord, P . Clark, and A. Kalyan, “Learn to explain: Multimodal reasoning via thought chains for science question answering,”Advances in neural information processing systems, vol. 35, pp. 2507–2521, 2022

    2022

  10. [10]

    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,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 2087–2098

    2025

  11. [11]

    Multimodal Chain-of-Thought Reasoning in Language Models

    Z. Zhang, A. Zhang, M. Li, H. Zhao, G. Karypis, and A. Smola, “Multimodal chain-of-thought reasoning in language models,”arXiv preprint arXiv:2302.00923, 2023

    work page internal anchor Pith review arXiv 2023

  12. [12]

    Visual instruction tuning,

    H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems, vol. 36, pp. 34 892– 34 916, 2023

    2023

  13. [13]

    Flamingo: a visual language model for few-shot learning,

    J.-B. Alayrac, J. Donahue, P . Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynoldset al., “Flamingo: a visual language model for few-shot learning,”Advances in neural information processing systems, vol. 35, pp. 23 716–23 736, 2022

    2022

  14. [14]

    Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

    J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,” inInternational conference on machine learning. PMLR, 2023, pp. 19 730–19 742

    2023

  15. [15]

    Instructblip: Towards general-purpose vision-language models with instruction tuning,

    W. Dai, J. Li, D. Li, A. Tiong, J. Zhao, W. Wang, B. Li, P . N. Fung, and S. Hoi, “Instructblip: Towards general-purpose vision-language models with instruction tuning,”Advances in neural information processing systems, vol. 36, pp. 49 250–49 267, 2023

    2023

  16. [16]

    MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

    D. Zhu, J. Chen, X. Shen, X. Li, and M. Elhoseiny, “Minigpt-4: Enhancing vision-language understanding with advanced large language models,”arXiv preprint arXiv:2304.10592, 2023

    work page internal anchor Pith review arXiv 2023

  17. [17]

    Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models,

    P . Xu, W. Shao, K. Zhang, P . Gao, S. Liu, M. Lei, F. Meng, S. Huang, Y. Qiao, and P . Luo, “Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 3, pp. 1877– 1893, 2025

    2025

  18. [19]

    Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback,

    T. Yu, Y. Yao, H. Zhang, T. He, Y. Han, G. Cui, J. Hu, Z. Liu, H.-T. Zheng, M. Sunet al., “Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 13 807–13 816

    2024

  19. [20]

    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,” inProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024, pp. 9426–9439

    2024

  20. [21]

    Mitigating object hallucinations in large vision-language models through visual contrastive decoding,

    S. Leng, H. Zhang, G. Chen, X. Li, S. Lu, C. Miao, and L. Bing, “Mitigating object hallucinations in large vision-language models through visual contrastive decoding,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 13 872–13 882

    2024

  21. [22]

    Evaluating object hallucination in large vision-language models,

    Y. Li, Y. Du, K. Zhou, J. Wang, W. X. Zhao, and J.-R. Wen, “Evaluating object hallucination in large vision-language models,” inProceedings of the 2023 conference on empirical methods in natural language processing, 2023, pp. 292–305

    2023

  22. [23]

    Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

    F. Liu, K. Lin, L. Li, J. Wang, Y. Yacoob, and L. Wang, “Mitigating hallucination in large multi-modal models via robust instruction tuning,”arXiv preprint arXiv:2306.14565, 2023

    work page internal anchor Pith review arXiv 2023

  23. [24]

    Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models,

    T. Guan, F. Liu, X. Wu, R. Xian, Z. Li, X. Liu, X. Wang, L. Chen, F. Huang, Y. Yacoobet al., “Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 14 375– 14 385. JOURNAL OF LATEX...

    2024

  24. [25]

    IEEE Transactions on Pattern Analysis and Machine Intelligence40(6), 1452–1464 (2018).https://doi.org/10.1109/TPAMI

    J. Ma, P . Wang, D. Kong, Z. Wang, J. Liu, H. Pei, and J. Zhao, “ Robust Visual Question Answering: Datasets, Methods, and Future Challenges ,”IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 46, no. 08, pp. 5575–5594, Aug. 2024. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/TPAMI. 2024.3366154

    work page doi:10.1109/tpami 2024

  25. [26]

    Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering ,

    Y. Liu, G. Li, and L. Lin, “ Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering ,”IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 45, no. 10, pp. 11 624–11 641, Oct. 2023. [Online]. Available: https: //doi.ieeecomputersociety.org/10.1109/TPAMI.2023.3284038

    work page doi:10.1109/tpami.2023.3284038 2023

  26. [27]

    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,” inThe twelfth international conference on learning representations, 2023

    2023

  27. [28]

    Star: Bootstrapping reasoning with reasoning,

    E. Zelikman, Y. Wu, J. Mu, and N. Goodman, “Star: Bootstrapping reasoning with reasoning,”Advances in Neural Information Processing Systems, vol. 35, pp. 15 476–15 488, 2022

    2022

  28. [29]

    Reflexion: Language agents with verbal reinforcement learning,

    N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” Advances in neural information processing systems, vol. 36, pp. 8634– 8652, 2023

    2023

  29. [30]

    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

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  30. [31]

    Proximal Policy Optimization Algorithms

    J. Schulman, F. Wolski, P . Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,”arXiv preprint arXiv:1707.06347, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  31. [32]

    DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

    Z. Shao, P . Wang, Q. Zhu, R. Xu, J. Song, X. Bi, H. Zhang, M. Zhang, Y. Li, Y. Wuet al., “Deepseekmath: Pushing the limits of mathematical reasoning in open language models,”arXiv preprint arXiv:2402.03300, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  32. [33]

    Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

    A. Yang, B. Zhang, B. Hui, B. Gao, B. Yu, C. Li, D. Liu, J. Tu, J. Zhou, J. Linet al., “Qwen2. 5-math technical report: Toward mathematical expert model via self-improvement,”arXiv preprint arXiv:2409.12122, 2024

    work page internal anchor Pith review arXiv 2024

  33. [34]

    Self-refine: Itera- tive refinement with self-feedback,

    A. Madaan, N. Tandon, P . Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yanget al., “Self-refine: Itera- tive refinement with self-feedback,”Advances in neural information processing systems, vol. 36, pp. 46 534–46 594, 2023

    2023

  34. [35]

    SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

    W. Zeng, Y. Huang, Q. Liu, W. Liu, K. He, Z. Ma, and J. He, “Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild,”arXiv preprint arXiv:2503.18892, 2025

    work page internal anchor Pith review arXiv 2025

  35. [36]

    Scaling up rl: Unlocking diverse reasoning in llms via prolonged training,

    M. Liu, S. Diao, J. Hu, X. Lu, X. Dong, H. Zhang, A. Bukharin, S. Zhang, J. Zeng, M. N. Sreedharet al., “Scaling up rl: Unlocking diverse reasoning in llms via prolonged training,”arXiv preprint arXiv:2507.12507, 2025

    work page arXiv 2025

  36. [37]

    DAPO: An Open-Source LLM Reinforcement Learning System at Scale

    Q. Yu, Z. Zhang, R. Zhu, Y. Yuan, X. Zuo, Y. Yue, W. Dai, T. Fan, G. Liu, L. Liuet al., “Dapo: An open-source llm reinforcement learning system at scale,”arXiv preprint arXiv:2503.14476, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  37. [38]

    Understanding R1-Zero-Like Training: A Critical Perspective

    Z. Liu, C. Chen, W. Li, P . Qi, T. Pang, C. Du, W. S. Lee, and M. Lin, “Understanding r1-zero-like training: A critical perspective,”arXiv preprint arXiv:2503.20783, 2025

    work page internal anchor Pith review arXiv 2025

  38. [39]

    Deepscaler: Surpassing o1-preview with a 1.5 b model by scaling rl,

    M. Luo, S. Tan, J. Wong, X. Shi, W. Y. Tang, M. Roongta, C. Cai, J. Luo, T. Zhang, L. E. Liet al., “Deepscaler: Surpassing o1-preview with a 1.5 b model by scaling rl,”Notion Blog, vol. 3, no. 5, 2025

    2025

  39. [40]

    Concrete Problems in AI Safety

    D. Amodei, C. Olah, J. Steinhardt, P . Christiano, J. Schulman, and D. Man ´e, “Concrete problems in ai safety,”arXiv preprint arXiv:1606.06565, 2016

    work page internal anchor Pith review arXiv 2016

  40. [41]

    High-Dimensional Continuous Control Using Generalized Advantage Estimation

    J. Schulman, P . Moritz, S. Levine, M. Jordan, and P . Abbeel, “High-dimensional continuous control using generalized advantage estimation,”arXiv preprint arXiv:1506.02438, 2015

    work page internal anchor Pith review arXiv 2015

  41. [42]

    Deep reinforcement learning from human preferences,

    P . F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” Advances in neural information processing systems, vol. 30, 2017

    2017

  42. [43]

    Aligning large multimodal models with factually augmented rlhf,

    Z. Sun, S. Shen, S. Cao, H. Liu, C. Li, Y. Shen, C. Gan, L. Gui, Y.-X. Wang, Y. Yanget al., “Aligning large multimodal models with factually augmented rlhf,” inFindings of the Association for Computational Linguistics: ACL 2024, 2024, pp. 13 088–13 110

    2024

  43. [44]

    Rlaif-v: Open-source ai feedback leads to super gpt-4v trustworthiness,

    T. Yu, H. Zhang, Q. Li, Q. Xu, Y. Yao, D. Chen, X. Lu, G. Cui, Y. Dang, T. Heet al., “Rlaif-v: Open-source ai feedback leads to super gpt-4v trustworthiness,” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 19 985–19 995

    2025

  44. [45]

    Toward Visual Grounding: A Survey ,

    L. Xiao, X. Yang, X. Lan, Y. Wang, and C. Xu, “ Toward Visual Grounding: A Survey ,”IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 48, no. 03, pp. 2749–2771, Mar. 2026. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/ TPAMI.2025.3630635

    work page arXiv 2026

  45. [46]

    From Show to Tell: A Survey on Deep Learning-Based Image Captioning ,

    M. Stefanini, M. Cornia, L. Baraldi, S. Cascianelli, G. Fiameni, and R. Cucchiara, “ From Show to Tell: A Survey on Deep Learning-Based Image Captioning ,”IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 45, no. 01, pp. 539–559, Jan

  46. [47]

    Available: https://doi.ieeecomputersociety.org/10

    [Online]. Available: https://doi.ieeecomputersociety.org/10. 1109/TPAMI.2022.3148210

    work page arXiv 2022

  47. [48]

    Sharegpt4v: Improving large multi-modal models with better captions,

    L. Chen, J. Li, X. Dong, P . Zhang, C. He, J. Wang, F. Zhao, and D. Lin, “Sharegpt4v: Improving large multi-modal models with better captions,” inEuropean Conference on Computer Vision. Springer, 2024, pp. 370–387

    2024

  48. [49]

    GPT-4o System Card

    A. Hurst, A. Lerer, A. P . Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radfordet al., “Gpt-4o system card,”arXiv preprint arXiv:2410.21276, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  49. [50]

    Sphinx: The joint mixing of weights, tasks, and visual embeddings for multi-modal large language models

    Z. Lin, C. Liu, R. Zhang, P . Gao, L. Qiu, H. Xiao, H. Qiu, C. Lin, W. Shao, K. Chenet al., “Sphinx: The joint mixing of weights, tasks, and visual embeddings for multi-modal large language models,” arXiv preprint arXiv:2311.07575, 2023

    work page arXiv 2023

  50. [51]

    Learning to compose and reason with language tree structures for visual grounding,

    R. Hong, D. Liu, X. Mo, X. He, and H. Zhang, “Learning to compose and reason with language tree structures for visual grounding,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 2, pp. 684–696, 2022

    2022

  51. [52]

    Eyes wide shut? exploring the visual shortcomings of multimodal llms,

    S. Tong, Z. Liu, Y. Zhai, Y. Ma, Y. LeCun, and S. Xie, “Eyes wide shut? exploring the visual shortcomings of multimodal llms,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 9568–9578

    2024

  52. [53]

    Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

    K. Chen, Z. Zhang, W. Zeng, R. Zhang, F. Zhu, and R. Zhao, “Shikra: Unleashing multimodal llm’s referential dialogue magic,”arXiv preprint arXiv:2306.15195, 2023

    work page internal anchor Pith review arXiv 2023

  53. [54]

    Cogvlm: Visual expert for pretrained language models,

    W. Wang, Q. Lv, W. Yu, W. Hong, J. Qi, Y. Wang, J. Ji, Z. Yang, L. Zhao, X. Songet al., “Cogvlm: Visual expert for pretrained language models,”Advances in Neural Information Processing Systems, vol. 37, pp. 121 475–121 499, 2024

    2024

  54. [55]

    Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

    P . Wang, S. Bai, S. Tan, S. Wang, Z. Fan, J. Bai, K. Chen, X. Liu, J. Wang, W. Geet al., “Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution,”arXiv preprint arXiv:2409.12191, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  55. [56]

    Mmbench: Is your multi-modal model an all-around player?

    Y. Liu, H. Duan, Y. Zhang, B. Li, S. Zhang, W. Zhao, Y. Yuan, J. Wang, C. He, Z. Liuet al., “Mmbench: Is your multi-modal model an all-around player?” inEuropean conference on computer vision. Springer, 2024, pp. 216–233

    2024

  56. [58]

    Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,

    X. Yue, Y. Ni, K. Zhang, T. Zheng, R. Liu, G. Zhang, S. Stevens, D. Jiang, W. Ren, Y. Sunet al., “Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 9556–9567

    2024

  57. [60]

    Hallucination of Multimodal Large Language Models: A Survey

    Z. Bai, P . Wang, T. Xiao, T. He, Z. Han, Z. Zhang, and M. Z. Shou, “Hallucination of multimodal large language models: A survey,” arXiv preprint arXiv:2404.18930, 2024

    work page internal anchor Pith review arXiv 2024

  58. [61]

    Debiasing multimodal large language models via penalization of language priors,

    Y. Zhang, Y. Shi, W. Yu, Q. Wen, X. Wang, W. Yang, Z. Zhang, L. Wang, and R. Jin, “Debiasing multimodal large language models via penalization of language priors,” inProceedings of the 33rd ACM International Conference on Multimedia, 2025, pp. 4232–4241

    2025

  59. [62]

    Vision-language models for vision tasks: A survey,

    J. Zhang, J. Huang, S. Jin, and S. Lu, “Vision-language models for vision tasks: A survey,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 8, pp. 5625–5644, 2024

    2024

  60. [63]

    Woodpecker: Hallucination correction for multimodal large language models,

    S. Yin, C. Fu, S. Zhao, T. Xu, H. Wang, D. Sui, Y. Shen, K. Li, X. Sun, and E. Chen, “Woodpecker: Hallucination correction for multimodal large language models,”Science China Information Sciences, vol. 67, no. 12, p. 220105, 2024

    2024

  61. [64]

    Trusting your evidence: Hallucinate less with context- aware decoding,

    W. Shi, X. Han, M. Lewis, Y. Tsvetkov, L. Zettlemoyer, and W.- t. Yih, “Trusting your evidence: Hallucinate less with context- aware decoding,” inProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), 2024, pp. 783–791

    2024

  62. [65]

    Dola: Decoding by contrasting layers improves factuality in large language models

    Y.-S. Chuang, Y. Xie, H. Luo, Y. Kim, J. Glass, and P . He, “Dola: De- coding by contrasting layers improves factuality in large language models,”arXiv preprint arXiv:2309.03883, 2023. JOURNAL OF LATEX CLASS FILES, SEPTEMBER 2026 17

    work page arXiv 2023

  63. [66]

    Inference- time intervention: Eliciting truthful answers from a language model,

    K. Li, O. Patel, F. Vi´egas, H. Pfister, and M. Wattenberg, “Inference- time intervention: Eliciting truthful answers from a language model,”Advances in Neural Information Processing Systems, vol. 36, pp. 41 451–41 530, 2023

    2023

  64. [67]

    Locating and editing factual associations in gpt,

    K. Meng, D. Bau, A. Andonian, and Y. Belinkov, “Locating and editing factual associations in gpt,”Advances in neural information processing systems, vol. 35, pp. 17 359–17 372, 2022

    2022

  65. [68]

    In-context Learning and Induction Heads

    C. Olsson, N. Elhage, N. Nanda, N. Joseph, N. DasSarma, T. Henighan, B. Mann, A. Askell, Y. Bai, A. Chenet al., “In-context learning and induction heads,”arXiv preprint arXiv:2209.11895, 2022

    work page internal anchor Pith review arXiv 2022

  66. [69]

    Transformer feed- forward layers are key-value memories,

    M. Geva, R. Schuster, J. Berant, and O. Levy, “Transformer feed- forward layers are key-value memories,” inProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021, pp. 5484–5495

    2021

  67. [70]

    A mathematical framework for transformer circuits,

    N. Elhage, N. Nanda, C. Olsson, T. Henighan, N. Joseph, B. Mann, A. Askell, Y. Bai, A. Chen, T. Conerlyet al., “A mathematical framework for transformer circuits,”Transformer Circuits Thread, vol. 1, no. 1, p. 12, 2021

    2021

  68. [71]

    Toy Models of Superposition

    N. Elhage, T. Hume, C. Olsson, N. Schiefer, T. Henighan, S. Kravec, Z. Hatfield-Dodds, R. Lasenby, D. Drain, C. Chenet al., “Toy models of superposition,”arXiv preprint arXiv:2209.10652, 2022

    work page internal anchor Pith review arXiv 2022

  69. [72]

    The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

    S. Marks and M. Tegmark, “The geometry of truth: Emergent linear structure in large language model representations of true/false datasets,”arXiv preprint arXiv:2310.06824, 2023

    work page internal anchor Pith review arXiv 2023

  70. [73]

    React: Synergizing reasoning and acting in language mod- els,

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. R. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language mod- els,” inThe eleventh international conference on learning representations, 2022

    2022

  71. [74]

    MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

    P . Lu, H. Bansal, T. Xia, J. Liu, C. Li, H. Hajishirzi, H. Cheng, K.-W. Chang, M. Galley, and J. Gao, “Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts,” 2024. [Online]. Available: https://arxiv.org/abs/2310.02255

    work page internal anchor Pith review arXiv 2024

  72. [75]

    Qwen2.5-VL Technical Report

    S. Bai, K. Chen, X. Liu, J. Wang, W. Ge, S. Song, K. Dang, P . Wang, S. Wang, J. Tang, H. Zhong, Y. Zhu, M. Yang, Z. Li, J. Wan, P . Wang, W. Ding, Z. Fu, Y. Xu, J. Ye, X. Zhang, T. Xie, Z. Cheng, H. Zhang, Z. Yang, H. Xu, and J. Lin, “Qwen2.5-vl technical report,” 2025. [Online]. Available: https://arxiv.org/abs/2502.13923

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  73. [76]

    R1-onevision: Advancing gen- eralized multimodal reasoning through cross-modal formal- ization.arXiv preprint arXiv:2503.10615, 2025

    Y. Yang, X. He, H. Pan, X. Jiang, Y. Deng, X. Yang, H. Lu, D. Yin, F. Rao, M. Zhu, B. Zhang, and W. Chen, “R1-onevision: Advancing generalized multimodal reasoning through cross-modal formalization,” 2025. [Online]. Available: https://arxiv.org/abs/2503.10615

    work page arXiv 2025

  74. [77]

    Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

    W. Huang, B. Jia, Z. Zhai, S. Cao, Z. Ye, F. Zhao, Z. Xu, X. Tang, Y. Hu, and S. Lin, “Vision-r1: Incentivizing reasoning capability in multimodal large language models,” 2026. [Online]. Available: https://arxiv.org/abs/2503.06749

    work page internal anchor Pith review arXiv 2026

  75. [78]

    Vl- rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning

    H. Wang, C. Qu, Z. Huang, W. Chu, F. Lin, and W. Chen, “Vl-rethinker: Incentivizing self-reflection of vision-language models with reinforcement learning,” 2025. [Online]. Available: https://arxiv.org/abs/2504.08837

    work page arXiv 2025

  76. [79]

    Vl-cogito: Progressive curriculum reinforcement learning for advanced multimodal reasoning

    R. Yuan, C. Xiao, S. Leng, J. Wang, L. Li, W. Xu, H. P . Chan, D. Zhao, T. Xu, Z. Wei, H. Zhang, and Y. Rong, “Vl-cogito: Progressive curriculum reinforcement learning for advanced multimodal reasoning,” 2025. [Online]. Available: https://arxiv.org/abs/2507.22607

    work page arXiv 2025

  77. [80]

    Open- vlthinker: Complex vision-language reasoning via iterative sft-rl cycles.arXiv preprint arXiv:2503.17352, 2025

    Y. Deng, H. Bansal, F. Yin, N. Peng, W. Wang, and K.-W. Chang, “Openvlthinker: Complex vision-language reasoning via iterative sft-rl cycles,” 2025. [Online]. Available: https: //arxiv.org/abs/2503.17352

    work page arXiv 2025

  78. [81]

    Sota with less: Mcts-guided sample selection for data-efficient visual reasoning self-improvement.arXiv preprint arXiv:2504.07934, 2025

    X. Wang, Z. Yang, C. Feng, H. Lu, L. Li, C.-C. Lin, K. Lin, F. Huang, and L. Wang, “Sota with less: Mcts-guided sample selection for data-efficient visual reasoning self-improvement,” 2025. [Online]. Available: https://arxiv.org/abs/2504.07934

    work page arXiv 2025

  79. [82]

    V*: Guided visual search as a core mechanism in multimodal llms.arXiv preprint arXiv:2312.14135, 2023

    P . Wu and S. Xie, “V*: Guided visual search as a core mechanism in multimodal llms,” 2023. [Online]. Available: https://arxiv.org/abs/2312.14135

    work page arXiv 2023

  80. [83]

    Eyes wide shut? exploring the visual shortcomings of multimodal llms

    S. Tong, Z. Liu, Y. Zhai, Y. Ma, Y. LeCun, and S. Xie, “Eyes wide shut? exploring the visual shortcomings of multimodal llms,” 2024. [Online]. Available: https://arxiv.org/abs/2401.06209

    work page arXiv 2024

Showing first 80 references.