TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks

Gen Li; Jin Dong; Kejian Wu; Wenjun Wu; Xiaotie Deng; Xinyu Wang; Ye Qiu; Yifan Sun; Yuanze Hu; Zhaoxin Fan

REVIEW 3 major objections 4 minor 53 references

TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read In lightweight vision-language models, the frozen language model's limited capacity caps how much multimodal information can be exploited; TinyAlign retrieves memory-bank context to lift that ceiling.

desk verdict TinyAlign has a sensible RAG-based idea for lightweight VLMs, but the headline loss reductions are likely inflated by retrieving the training label, and the theory is a definition presented as a result. read the letter →

arxiv 2505.12884 v2 pith:V53RYMLO submitted 2025-05-19 cs.LG cs.AIcs.CV

Yuanze Hu , Zhaoxin Fan , Xinyu Wang , Gen Li , Ye Qiu , Zhichao Yang , Wenjun Wu , Kejian Wu

show 3 more authors

Yifan Sun Xiaotie Deng Jin Dong

This is my paper · ORCID

classification cs.LGcs.AIcs.CV

keywords vision-languagemodelslightweightVLMsmultimodalalignmenteffectivemutualinformationretrieval-augmentedgenerationmemorybankdataefficiencyfrozenlanguagemodel

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

Lightweight vision-language models usually freeze the vision encoder and language model and train only a small connector, but small language models struggle to absorb visual information. The paper argues this is an information-theoretic bottleneck: the frozen model's limited capacity imposes an irreducible error that lowers the Effective Mutual Information between inputs and output, so the achievable cross-entropy loss stays high no matter how well the connector is trained. TinyAlign attacks the bottleneck by retrieving relevant, pre-compressed image-text embeddings from a memory bank and feeding them to the frozen model as extra context. Empirically this cuts pre-training loss by 13.9–28.2% across 0.5B–2.7B models, speeds convergence, and reaches baseline-level downstream accuracy with only 40% of the instruction-tuning data. The interest is that it offers a mechanism, not just a recipe: retrieval raises the information ceiling of a fixed-capacity model.

What carries the argument

The load-bearing object is the memory bank of 100K image-text samples, each stored as a compact key and a Perceiver-compressed value; a query derived from the current image and instruction retrieves top-k values, and a trainable RAG connector turns them into $H_R$ that the frozen LLM can ingest. The analysis is carried by the Effective Mutual Information identity $\min_{\theta_C}L_{\text{CE}}=H(L)-I_{\text{eff}}$ and the decomposition of $\Delta I_{\text{eff}}$ into newly supplied information plus a reduction in the LLM's irreducible error.

What would settle it

Train a TinyAlign variant whose memory bank has the current batch's images and captions masked out, and compare pre-training loss and downstream accuracy with the reported numbers; if the losses and 40%-data gains persist, the effective-mutual-information story survives, and if they vanish, the headline results are leakage artifacts.

Watch

Extended reading notes

Core claim

Within a pipeline that freezes the vision encoder and LLM while training a connector, the minimum achievable cross-entropy loss decomposes as $H(L|X_V,X_I)+\bar{\epsilon}_{\theta_{\text{LLM}}}$, where the second term is an irreducible error caused by the frozen language model's inability to perfectly interpret optimally transformed visual embeddings. Rewriting through mutual information gives $\min_{\theta_C}L_{\text{CE}}=H(L)-I_{\text{eff}}$, so the model's capacity constrains how much of the true $I(X_V,X_I;L)$ can be exploited. TinyAlign adds retrieved context $E_R$ from a memory bank of multimodal training instances, producing composite input $H'_{\text{in}}=[H_V,H_R,H_I]$. The improvement decomposes into $\Delta I_{\text{true}}=I(E_R;L|X_V,X_I)>0$ plus a reduction in the LLM's irreducible error, $\Delta\bar{\epsilon}_{\theta_{\text{LLM}}}>0$, because the retrieved cues arrive in a form the frozen model can use. The paper therefore claims the RAG-enhanced system has higher $I_{\text{eff}}$ and a lower achievable loss, and supports this with loss curves, UMAP clustering, and downstream gains including baseline-level performance at 40% of fine-tuning data.

Load-bearing premise

The memory bank is built from the same 100K image-text pairs used for the pre-training loss experiments, and nothing in the retrieval design removes the current training image or its own caption from the candidate set, so part or all of the reported loss drop could be the model reading the answer from a retrieved copy of the label.

Editorial extensions

If this is right

If the central claim holds, connector-only training of lightweight VLMs has a hard information ceiling set by the frozen LLM, not just an empirical convenience.
Memory-bank retrieval raises that ceiling, so lightweight VLMs can match full-data baselines at 40% of instruction-tuning data across GQA, VQAv2, TextVQA, POPE, and MME.
The reported loss reductions (16.8% for Phi-2, 28.2% for TinyLLaMA, 13.9% for Qwen2-0.5B) should transfer to other small frozen decoders because they follow from the capacity argument, not from a specific architecture.
Because retrieved cues are compressed by an LLM-independent Perceiver before storage, the added inference cost is small, making the method compatible with edge deployment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper: if the memory bank excluded the current training sample's own caption, the loss-reduction numbers would separate genuine alignment gains from label leakage; the paper does not report this control.
Beyond the paper: the same retrieval scaffold could be tested with external knowledge sources instead of training-set pairs, turning TinyAlign into a way to inject new knowledge into a frozen small LLM.
Beyond the paper: the $\Delta I_{\text{true}}>0$ term suggests a measurable prediction—retrieval quality, such as recall of the true caption, should correlate with loss reduction; plotting that curve would test the mechanism directly.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

For anyone building lightweight VLMs, the architecture here is worth a look: a Perceiver-compressed memory bank of training pairs, retrieved through a RAG connector to supplement the visual tokens before a frozen LLM. That specific combination is not in the cited prior work, and the ablations are informative. The encoder-matching experiment in Appendix F is a nice diagnostic, and the downstream gains across several models and benchmarks are consistent, even if modest. The claim of baseline-level performance at 40% of fine-tuning data is interesting.

But the central empirical evidence does not stand as written. The loss-reduction curves in Fig. 2(a) are computed on the LLaVA pretraining set, and the memory bank in Sec. 4.2 is built from the same 100K image-text pairs sampled from that set. Nothing in the paper excludes the current training sample, its image, or its caption from the retrieved candidates. Since the query is derived from the input image and instruction, and the bank contains the same pair with a Perceiver-compressed embedding of the caption, the top-k retrieval will almost certainly return the ground-truth caption for that very sample. That would make the reported 16.8–28.2% loss drops a measure of label leakage, not improved alignment. The downstream benchmark numbers are less directly contaminated, but they are single-run, mixed, and not enough to independently support the theory.

The theory section is also weaker than it appears. Effective Mutual Information is defined as I minus the irreducible error, so Eq. (5) holds by construction. Eq. (6) then asserts that both terms in the decomposition are positive; the second is essentially the desired conclusion. This is not a derivation, and presenting it as a theoretical demonstration oversells the paper. It could be reframed as an intuition or a heuristic motivation without much loss.

If the authors exclude self-retrieval, re-run the loss curves, report variance, and release code, this could become a useful incremental contribution. As is, I would not rely on the headline numbers. Still, the idea is sensible and the experiments are broad enough that a serious referee could extract the useful core. Send it to peer review with a request for major revision, specifically the self-retrieval exclusion and a rewrite of the theory as a motivation rather than a theorem.

Referee Report

3 major / 4 minor

Summary. The paper studies the alignment of lightweight vision-language models in the standard frozen-ViT/frozen-LLM paradigm, arguing that the LLM's limited capacity reduces the 'Effective Mutual Information' (EMI) between multimodal inputs and outputs. It introduces a theoretical decomposition of the minimum cross-entropy loss into label entropy minus EMI, and then proposes TinyAlign, a retrieval-augmented connector that retrieves compressed image-text embeddings from a memory bank and appends them to the LLM input. Experiments report lower pretraining loss (16.8% for Phi-2, 28.2% for TinyLLaMA, 13.9% for Qwen2), faster convergence, better UMAP separation, improved downstream benchmarks after instruction tuning, and the ability to match baseline accuracy with 40% of the instruction-tuning data.

Significance. If the claims were established, the work would offer a practically valuable recipe for improving small VLMs through memory-bank retrieval, and the data-efficiency result would be of broad interest. The paper also makes a serious attempt to ground the method in an information-theoretic story, and it includes useful ablations on memory-bank size, key-encoder alignment, and top-K retrieval, as well as a FLOPs comparison. However, the theoretical core is a definitional identity with unproven positivity assumptions, and the main empirical evidence is contaminated by a likely label-leakage artifact. As it stands, the central claims are not supported.

major comments (3)

[§3.2, Eqs. (4)-(5)] The definition of Effective Mutual Information as Ieff = I(XV,XI;L) − ¯ϵθLLM makes Eq. (5) an algebraic identity: substituting the definition into Eq. (3) yields min LCE = H(L) − Ieff by construction. No capacity-limited behavior is derived; the only substantive content is the 'Hypothesis on LLM Scale' (¯ϵsmall ≥ ¯ϵlarge), which is asserted and then 'confirmed' by the same loss curves that the theory is supposed to explain. The reasoning is therefore circular.
[§4.1, Eq. (6)] The decomposition of ΔIeff treats ¯ϵθLLM as a function of the input (¯ϵθLLM(X′) vs. ¯ϵθLLM(XV,XI)), whereas §3.2 defines ¯ϵθLLM as an expectation over the data distribution, i.e., a single global constant. This equivocation invalidates the termwise comparison. Moreover, both ΔItrue > 0 and Δ¯ϵθLLM > 0 are asserted without proof, so the central inequality minθ∗C LCE(RAG-enhanced) < minθ∗C LCE(standard) is an assumption, not a derived result.
[§4.2 and §5.2 (Fig. 2(a))] The memory bank is built from 100K image-text pairs sampled from the same LLaVA pretraining set whose loss is plotted in Fig. 2(a), and no passage states that the current training sample, its image, or its caption is excluded from the candidate list at retrieval time. Since each memory value is a Perceiver-compressed embedding of the image and caption, a training sample's own entry is likely to be retrieved among the top-k, injecting the target caption into the LLM input. The reported 16.8%, 28.2%, and 13.9% loss reductions—and the faster convergence in the same figure—are therefore at least partly artifacts of label leakage rather than evidence of improved EMI or reduced irreducible error. These curves are the only empirical validation offered for Eqs. (5)-(6), so this issue is load-bearing.

minor comments (4)

[§5.1] The section contains typos such as 'Instrcution tuning' and 'the LLaV A dataset'; the text should be proofread throughout.
[§1 and Figure captions] The Introduction refers to 'Fig. 1(a)' for loss reductions, but the loss curves appear in Fig. 2(a); Figure 1 is the architecture figure.
[§5.4 and Appendix E] The data-efficiency claims, while not directly affected by the pretraining leakage, are reported as single runs without standard deviations or multiple seeds, so their robustness is unclear.
[§4.1] The sentence ending 'corroborated by faster convergence (Fig. 2(a)),For more theoretical details...' contains a comma splice and is missing a space; this should be corrected.

Circularity Check

3 steps flagged · score 7.0 of 10

Theoretical 'derivation' is definitional plus assumed signs, and the memory-bank loss curves risk label leakage from the same pretraining pairs.

self definitional [Sec. 3.2, Eqs. (3)-(5)]
"To capture the system’s actual capability to utilize information, we define an 'Effective Mutual Information' (Ieff), which represents the mutual information the system can effectively leverage, accounting for the irreducible error ¯ϵθLLM: Ieff(XV,XI;L|θLLM,θViT)=I(XV,XI;L)−¯ϵθLLM (4). Substituting Ieff into Eq. (3), the minimum achievable loss becomes: minθ∗C LCE(θ∗C)≈H(L)−Ieff(XV,XI;L|θLLM,θViT) (5)."

Eq. (5) is obtained by substituting a definition: Ieff is defined as I(X;L) minus the irreducible error, so H(L) − Ieff is exactly the already-derived Eq. (3), H(L) − I(XV,XI;L) + ¯ϵθLLM. The paper presents this as 'demonstrating' that the frozen LLM's capacity limits EMI, but that conclusion is true by construction of Ieff rather than by independent derivation; the 'bottleneck' is a rename of ¯ϵθLLM, the minimum KL gap defined in the preceding paragraph.
other [Sec. 4.1, Eq. (6) and following paragraph]
"∆Ieff = [I(X′;L)− ¯ϵθLLM(X′)]−[I(XV,XI;L)− ¯ϵθLLM(XV,XI)] = I(ER;L|XV,XI) + (¯ϵθLLM(XV,XI)−¯ϵθLLM(X′)) (6). ∆Itrue > 0 as ER (from pertinent captions) provides novel information about target L. ∆¯ϵθLLM > 0 signifies reduced LLM irreducible error. ... Consequently, when ∆Ieff > 0 ... the minimum achievable CE loss is reduced: minθ∗CLCE(RAG-enhanced)<minθ∗CLCE(standard)."

The equality in Eq. (6) is just the chain rule for mutual information combined with the definition of Ieff; it carries no independent content. The desired theorem, that RAG lowers the minimum CE loss, is then made to follow from the assumed signs of the two terms. In particular, ∆¯ϵθLLM > 0 is exactly the claim that retrieved context reduces the frozen LLM's irreducible error; the paper asserts this ('This enables the fixed-capacity LLM to better approximate Ptrue...') rather than proving it. The derivation therefore reduces to assuming the conclusion: if ∆¯ϵθLLM were not positive, the inequality would not follow, and no independent bound or measurement of ∆¯ϵθLLM is supplied.

1 more flagged steps

fitted input called prediction [Sec. 4.2 and Sec. 5.2 (Fig. 2(a))]
"we sample 100K image-text pairs from pre-training dataset declared in Sec. 5.1. ... Each value Vm∈Rdv is the corresponding compressed latent multimodal embedding ERm. An LLM-independent Perceiver model (θP, Table 3) pre-processes original multimodal instances {(XVj, Capj)} into these values."

The memory bank is built from 100K pairs sampled from the same LLaVA pretraining set whose loss trajectories are reported in Fig. 2(a). Each key is derived from an image-text pair and each value is a Perceiver compression of that same image and caption, but no passage states that the current training sample, its image, or its caption is excluded from the candidate list at retrieval time. If the query's own pair is retrieved under top-k embedding similarity, HR is computed from the target caption, so the reported 16.8–28.2% pretraining loss reductions and the faster-convergence claim in Fig. 2(a) can be forced by label leakage rather than by improved alignment.

full rationale

Three issues drive the score. First, the 'Effective Mutual Information' framework is definitional: Ieff is introduced as I minus the irreducible error, so Eq. (5) is the same identity as Eq. (3) with a renamed term; the central theoretical demonstration that capacity limits EMI does not add independent content. Second, the RAG improvement result is an exact identity plus assumed signs: the decomposition in Eq. (6) is the chain rule for mutual information, and the inequality min LCE(RAG-enhanced) < min LCE(standard) is made to depend on ∆¯ϵ > 0, which is asserted as the effect the paper claims to prove. Third, and most damaging empirically, the memory bank is sampled from the same LLaVA pretraining set whose loss curves are presented as validation, with no described exclusion of the current sample or caption; since both keys and values are derived from the image-text pairs, retrieving the example's own caption would force lower cross-entropy loss. The downstream data-efficiency and benchmark results use the separately sourced SFT set and are less directly contaminated, which keeps the score below 8-10; however the paper's central loss-reduction evidence and its claimed confirmation of Eq. (6) are not independent. I found no load-bearing self-citation chain; the problems are definitional identity, assumed positivity of the key term, and training-data leakage in the memory-bank validation.

Assumptions & free parameters 2 free parameters · 5 assumptions · 1 invented entities

The theory rests on one standard identity and two unproven inequalities about irreducible error; the empirical method adds two hand-selected hyperparameters (100K bank size and k=5). The invented quantity Ieff is defined so that the loss formula is tautological, which is the main circularity burden.

free parameters (2)

Top-K retrieved items = k = 5
Chosen after ablating Top-1, Top-5, and Top-10 on the same benchmarks (Appendix F, Table 7); the main results use k=5.
Memory bank size = 100K entries
Chosen after ablating 100k, 300k, and 500k on the same benchmarks (Appendix F, Table 5); the main configuration uses 100k.

assumptions (5)

standard math Standard information-theoretic identities I(X;Y)=H(Y)-H(Y|X) and the chain rule for mutual information.
Used in Sec. 3.2 and Eq. (6) to rewrite cross-entropy loss and Delta Ieff.
domain assumption A frozen LLM has an irreducible alignment error bar_epsilon that cannot be removed by any connector.
Stated in Sec. 3.2 around Eq. (2); it is asserted to justify the bottleneck.
ad hoc to paper Smaller LLMs have bar_epsilon_small greater than or equal to bar_epsilon_large.
Labeled a hypothesis in Sec. 3.2 and supported only by the loss curve in Fig. 2(a), not by a formal argument.
ad hoc to paper Retrieved caption embeddings ER add information about L beyond XV and XI, so Delta I_true > 0.
Assumed in Eq. (6) and Appendix A; no proof is given that retrieved captions are informative after conditioning on the image and instruction.
ad hoc to paper RAG context reduces the LLM's irreducible error, so bar_epsilon(X') < bar_epsilon(XV,XI).
Assumed in Eq. (6) and Appendix A; this is the conclusion the framework needs, and it is incompatible with bar_epsilon being a global constant as defined in Sec. 3.2.

invented entities (1)

Effective Mutual Information (Ieff)
purpose: Quantify how much mutual information a capacity-limited frozen LLM can practically leverage.
Defined as I(XV,XI;L) minus bar_epsilon in Eq. (4). It has no independent falsifiable prediction; the apparent theorem min LCE = H(L) - Ieff is a restatement of this definition.

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Pith. "Pith review of TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks." pith.science (2026). https://pith.science/paper/V53RYMLO

@misc{pith2026250512884,
  author       = {Pith},
  title        = {Pith review of: TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V53RYMLO}},
  note         = {Machine review of arXiv:2505.12884}
}

read the original abstract

Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. The prevailing approach to aligning vision and language models involves freezing both the vision encoder and the language model while training small connector modules. However, this strategy heavily depends on the intrinsic capabilities of the language model, which can be suboptimal for lightweight models with limited representational capacity. In this work, we investigate this alignment bottleneck through the lens of mutual information, demonstrating that the constrained capacity of the language model inherently limits the Effective Mutual Information (EMI) between multimodal inputs and outputs, thereby compromising alignment quality. To address this challenge, we propose TinyAlign, a novel framework inspired by Retrieval-Augmented Generation, which strategically retrieves relevant context from a memory bank to enrich multimodal inputs and enhance their alignment. Extensive empirical evaluations reveal that TinyAlign significantly reduces training loss, accelerates convergence, and enhances task performance. Remarkably, it allows models to achieve baseline-level performance with only 40\% of the fine-tuning data, highlighting exceptional data efficiency. Our work thus offers a practical pathway for developing more capable lightweight VLMs while introducing a fresh theoretical lens to better understand and address alignment bottlenecks in constrained multimodal systems.

Figures

Figures reproduced from arXiv: 2505.12884 by the authors.

**Figure 2.** (a) Comparison of multimodal pre-training loss on the LLaVA dataset. TinyAlign-enhanced models (dashed lines, "+ RAG") exhibit accelerated convergence and lower final training loss compared to baselines (solid lines) across various model scales (e.g., 16.8% loss reduction for Phi2.7B, 28.2% for TinyLLaMA-1.1B, 13.9% for Qwen2-0.5B at specified sample points). All models use SigLIP vision encoders, with only connect… view at source ↗

**Figure 3.** Model weighted accuracy, computed per Eq. (8), is evaluated against the percentage of instruction tuning data utilized. TinyAlign models consistently demonstrate superior performance, achieving higher accuracy with substantially less data relative to baseline models. To systematically assess our model architecture, we conduct a series of targeted ablation studies focusing on three key design choices: (1) the size a… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Detailed data efficiency analysis across individual benchmarks. Performance of TinyAlign [PITH_FULL_IMAGE:figures/full_fig_p019_4.png]

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Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. Microsoft coco: Common objects in context, 2015. URL https://arxiv.org/abs/1405.0312

2015 arXiv
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Visual instruction tuning, 2023

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023. URL https://arxiv.org/abs/2304.08485

2023 arXiv
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Tianyu Liu, Jirui Qi, Paul He, Arianna Bisazza, Mrinmaya Sachan, and Ryan Cotterell. Point- wise mutual information as a performance gauge for retrieval-augmented generation, 2025. URL https://arxiv.org/abs/2411.07773

2025
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Deepseek-vl: Towards real-world vision-language understanding, 2024

Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, and Chong Ruan. Deepseek-vl: Towards real-world vision-language understanding, 2024. URL https://arxiv.org/abs/2403.05525

2024 arXiv
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Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering, 2022. URL https://arxiv.org/abs/2209.09513. 12

2022 arXiv
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Smolvlm: Redefining small and efficient multimodal models, 2025

Andrés Marafioti, Orr Zohar, Miquel Farré, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Zakka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, Vaibhav Srivastav, Joshua Lochner, Hugo Larcher, Mathieu Morlon, Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Smolvlm: Redefi...

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Anand Mishra, Shashank Shekhar, Ajeet Kumar Singh, and Anirban Chakraborty. Ocr-vqa: Visual question answering by reading text in images. In ICDAR, 2019

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Gpt-4v(ision)

OpenAI. Gpt-4v(ision). https://openai.com/research/gpt-4v-system-card , 2023. OpenAI

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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision, 2021. URL https://...

2021 arXiv
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Raven: Multitask retrieval augmented vision-language learning, 2024

Varun Nagaraj Rao, Siddharth Choudhary, Aditya Deshpande, Ravi Kumar Satzoda, and Srikar Appalaraju. Raven: Multitask retrieval augmented vision-language learning, 2024. URL https://arxiv.org/abs/2406.19150

2024 arXiv
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Towards vqa models that can read, 2019

Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read, 2019. URLhttps://arxiv. org/abs/1904.08920

2019 arXiv
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Paligemma 2: A family of versatile vlms for transfer, 2024

Andreas Steiner, André Susano Pinto, Michael Tschannen, Daniel Keysers, Xiao Wang, Yonatan Bitton, Alexey Gritsenko, Matthias Minderer, Anthony Sherbondy, Shangbang Long, Siyang Qin, Reeve Ingle, Emanuele Bugliarello, Sahar Kazemzadeh, Thomas Mesnard, Ibrahim Al- abdulmohsin, ...

2024 arXiv
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Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan...

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Efficientvlm: Fast and accurate vision-language models via knowledge distillation and modal-adaptive pruning

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Qwen2 technical report, 2024

An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze...

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Mm-react: Prompting chatgpt for multimodal reasoning and action, 2023

Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. Mm-react: Prompting chatgpt for multimodal reasoning and action, 2023. URL https://arxiv.org/abs/2303.11381

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Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Ming-Yu Liu, Yuke Zhu, Mohammad Shoeybi, Bryan Catanzaro, Chaowei Xiao, and Anima Anandkumar. Re-vilm: Retrieval-augmented visual language model for zero and ...

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Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, Qianyu Chen, Huarong Zhou, Zhensheng Zou, Haoye Zhang, Shengding Hu, Zhi Zheng, Jie Zhou, Jie Cai, Xu Han, Guoyang Zeng, Dahai Li, Zhiyuan Liu, and Maosong Sun. M...

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Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. Mmmu: A m...

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Sigmoid loss for language image pre-training, 2023

Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training, 2023. URL https://arxiv.org/abs/2303.15343

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Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, and Wei Lu. Tinyllama: An open-source small language model, 2024. URL https://arxiv.org/abs/2401.02385

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2024 arXiv
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Minigpt-4: En- hancing vision-language understanding with advanced large language models, 2023

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2023 arXiv
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An information bottleneck perspective for effective noise filtering on retrieval-augmented generation, 2024

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These represent visual information conditioned for the language model

Input Data Preparation: The UMAP process utilized two sets of high-dimensional feature vectors: • Connector Features: Vectors derived from images post-processing by the vision tower and the model’s connector module. These represent visual information conditioned for the langua...
[52]

This 2D representation facilitates direct scatter plot visualization

UMAP Dimensionality Reduction: The UMAP algorithm was applied to the concatenated high-dimensional dataset, configured to reduce feature dimensionality to two. This 2D representation facilitates direct scatter plot visualization. The algorithm learns a mapping that optimally p...
[53]

collapse

Output and Visualization: The UMAP process yielded: • 2D Coordinates: A two-dimensional coordinate (x, y) for each input feature vector (both connector and LLM embedding). • Scatter Plot Visualization: These 2D coordinates were used to generate a scatter plot. Points correspon...
[2016]

URL https://arxiv.org/abs/1602.07332

arXiv
[2024]

URL https://arxiv.org/abs/2308.02490

arXiv
[2025]

URL https://arxiv.org/abs/2502.13923

arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

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Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, and Jifeng Dai. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks, 2024. URL https://arxiv.org/abs/2312.14238

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Gonzalez, Ion Stoica, and Eric P

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2023