UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings
Authors: Zhibin Lan
Liqiang Niu
Fandong Meng
Jie Zhou
Jinsong Su
Venue: ICLR 2026 Poster (2026)
Keywords: multimodal embedding
representation learning
multimodal large language model
reasoning model
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting their ability to benefit from reasoning-driven generation paradigm. In this work, we pioneer the exploration of reasoning-driven generative embeddings, unifying embedding tasks within a generative paradigm. We propose UME-R1, a universal multimodal embedding framework consisting of a two-stage training strategy: a cold-start supervised fine-tuning equips the model with reasoning capabilities and enables it to generate both discriminative and reasoning-driven generative embeddings; a subsequent reinforcement learning enhances reasoning and further optimizes generative embedding quality. This pioneering work reveals four key insights: 1) reasoning-driven generative embeddings unlock substantial performance gains over conventional discriminative embeddings by leveraging the powerful generative reasoning capabilities of MLLMs; 2) discriminative and reasoning-driven generative embeddings are complementary, whose combined oracle performance far exceeding that of either alone; 3) RL can effectively enhance reasoning-driven generative embeddings, establishing a scalable optimization paradigm; 4) repeated sampling at inference boosts downstream task coverage (pass@k), highlighting the inference-time scalability potential of reasoning-driven generative embeddings. Evaluated on the MMEB-V2 benchmark across 78 tasks spanning video, image, and visual documents, UME-R1 significantly outperforms conventional discriminative embedding models and offers a foundation for more interpretable, reasoning-driven generative multimodal embeddings. Our datasets, models, and code are available at https://github.com/XMUDeepLIT/UME-R1.

Published as a conference paper at ICLR 2026
UME-RI: EXPLORING REASONING-DRIVEN GENER- ATIVE MULTIMODAL EMBEDDINGS
Zhibin Lan' Liqiang Niu² Fandong Mengè Jie Zhou? Jinsong Su School of Informatics, Xiamen University, China,
2 WeChat AI, Tencent Inc, China,
3 Keyy Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China, SSanghaai Artificial Intelligence Laboratory, China
lanzhibin@ste stu. xmu, edu, en, js xmu edu. en {poetniu, fandongmeng, witht withtoomzouu wtw.ehhttttteeeetttccom com
ABSTRACT
The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inher- ently discriminative, limiting their ability to benefit from reasoning-driven gen- eration paradigm. In this work, we pioneer the exploration of reasoning- driven generative embeddings, unifying embedding tasks within a generative paradigm. We propose UME-RI, a universal multimodal embedding framework consist- ing of a two-stage training strategy: cold-start supervised fine-tuning equips the model with reasoning capabilities and enables to generate both discrimi- native and reasoning-driven generative embeddings; subsequent reinforcement learning enhances reasoning and further optimizes generative embedding qual- ity. This pioneering work reveals four key insights: 1) reasoning driven genera- tive embeddings unlock substantial performance gains over conventional discrim- inative embeddings by leveraging the powerful generative reasoning capabilities MILMs; 2) discriminative and reasoning-driven generative embeddings are complementary, whose combined oracle performance far exceeding that of either alone; 3) RL can effectively enhance reasoning-diis -driven generative embeddings, establishing a scalable optimization paradigm; repeated sampling at inference boosts downstream task coverage (pass @ k), highlighting the inference-ting scal- ability potential of reasoning-driven generative embeddings. Evaluated on the M2 benchmark across 78 tasks spanning video, image, and visual doc- uments, UME-RI significantly outperforms conventional discriminative embed- ding models and offers foundation for more interpretable, reasoning-driven gen- erative multimodal embeddings. Our datasets, models, and code are available at com httpteuwwwcccccceeeeeeeeee////ee
INTRODUCTION
Recently, the field of multimodal embeddings has been significantly advanced by the remarkable success of multimodal large language models (MLLMs). For instance, VLM2Vec (Jiang et al. 2025)
and MM-Embed (Lin al. 2025a) construct multimodal embedding models based on MLLMs.
These models demonstrate superior performance across a range of multimodal embedding tasks compared to traditional dual-encoder laniunn models like CLIP (Radford et al. 2021).
In parallel, large reasoning models (LRMs) represented by GPT-40 (Hurst et al. 2024 and DeepSeek-R1 (Guo al., 2025) have made breakthroughs in complex reasoning. distinctive feature of these models is the incorporation of the chain of thought (CoT) (Wei et al. 2022a), which elicits step-by-step reasoning paths and typically produces more accurate and interpretable outputs. Building this success, recent works (Shen al. 2025b: Hong et al. 2025a) have extended these
This work was done when Zhibin Lan was interning at WeChat AI, Tencent Inc, China. Corresponding author.

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Published as a conference paper at ICLR 2026
advances to MLLMs, substantially enhancing their performance on various multimodal tasks. How- ever, multimodal embedding models have derived limited benefit from these advances. The key reason is that existing MLLM-base based multimodal embedding models are discriminative: they di- rectly encode the multimodal input and extract the last token's 's final hidden state as the embedding, without generating any new tokens. Naturally, this raises the question: How make a multimodal embedding model act as a generative one?
Several prior studies (Ouali et al., 2025 Yu al. 2025a) have incorporated next-token prediction loss in training multimodal embedding models, demonstrating that it preserves generative capabili- ties while enhancing discriminative performance. Nevertheless, these approaches merely introduce additional data and losses during training. Ultimately. at inference, they remain discriminative, as their embeddings are obtained by directly encoding the input without generating any intermediate content; we refer to these as discriminative embeddings.
In this paper, we propose UME-RI, a universal multimodal embedding framework that enables multimodal embedding models to produce either discriminative reasoning-driven generative em- beddings on demand. First, we construct cold-start supervised fine-tuning (SFT) dataset by aug- menting the original query-target pairs used for embedding training with intermediate reasoning and summaries. During training, the contrastive loss is applied to embedding tokens that follow the summary, while autoregressive next-token prediction loss is imposed on the reasoning and sum- mary tokens. As a result, the model learns to first generate intermediate reasoning and a summary. and then produce embedding token to obtain representation; generative embeddings. Meanwhile, discriminative embeddings are preserved throughout training, allowing the model to flexibly output either type of embedding as needed. Interestingly, experiments reveal a substantial gap between the oracle upper bound and current discriminative embeddings, in- dicating that there remains considerable room for improvement.
term these as reasoning -driven
We further ask: Can reinforcement learning with verifiable reward (RLVR) also be effective for generative embedding models? A natural approach would assign a positive reward the similarity of a given positive pair exceeds a preset threshold, and no reward otherwise. However, since the degree of similarity varies among different pairs, this approach may render some pairs excessively difficult or easy, resulting in the problem of zero policy gradients (Yu et al. (0025b). To overcome this, we propose a reward policy that considers ranking and similarity gaps simultaneously. and demonstrate that generative embedding models can also benefit from RLVR. Additionally, find that repeated sampling can improve the coverage (i.e.. pass@ k) of generative embedding models, suggesting that embeddings also have the potential for inference-time scaling.
Overall, we make the following four contributions: Based on MMEB-V2 (Meng et al. 2025) training data, build multimodal embedding cold-start SFT dataset with CoT annotations, and construct a small-scale dataset for efficient RL training. designed to endow multimodal embedding models with the flexibility to switch between discrimina- tive and reasoning-die -drivin generative embeddings. To the best of our knowledge, we are the first to explore reasoning-drive driven generative embeddings, demonstrating the significant potential of unifying embeddings within a generative paradigm. 3 We pioneer the successful application of rule-based RI to the multimodal embeddings task, which lacks standard best answers like math, by designing a novel reward policy tailored to embeddings. embedding models on MMEB-V -22 a benchmark comprising 78 tasks across three visual modalities: video, image, and visual documents. Analysis of an oracle upper bound and pass @ &s indicates that UME-RI retains significant potential for further improvement.
We propose UME-R1, a framework
UME-R1 outperforms conventional discriminative
DATASET CONSTRUCTION
To construct the training corpus for generative multimodal embeddings, as illustrated in Figure 1 we sample 50,000 instances from each of the 20 in-distribution datasets within MMEB (Jiang et al,
2025). Following VLM2Vec-V2 V2 (Meng et al.] 2025), we also incorporate the training instances from V.aVVAHHuun (Zhang et al. 2025a). ViDoRe (Faysse et al. 2025b), and VisRAG (Yu et al. 2025ch datasets cover video and disual-document modalities, yielding total of 1.76 million pairs. Subsequently, we employ the pure- thinkininiigg model GLM-4. BLLM-..V-Thinkingg (Hong et al. 2025b) to generate CoT rationales for both the query and the target of each pair.
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CoT Arnotation
Data Filter
(- 1.766)
SFT Data 1,466)
analyze the input. The query... Wait, but the main topic
<thinke
1) Excessive token
Discard
image provide... is... The key elements are.... So core //thinkkannweer Overall, the summary is
2) Overlong reasoning Invalid format
repetition
Balanced
sampling
Three visual modalities: image, video, document
GLM-V
11K)
Figure 1: Illustration of the pipeline for data construction. Specific prompts used for CoT annotation and the resulting data samples are presented in Appendix C
We filter the data by excluding pairs that meet any of the following criteria: (1) contain extensive contiguous token repetition; (2) include reasoning that are excessively long (e.g., exceeding 8,192 tokens); or (3) produce responses that do not conform the <think> <//hink>>>>> <answrr> format. This filtering process results in a final set of 1.46 million cold-start SFT pairs. For RL training. set of 11,136 36 pairs is balanced sampled from various datasets spanning the image, video, and visual- document modalities, prioritizing instances not included in the SFT data to avoid overly simple samples.
UME-RI
3.1 PRELIMINARIES
We adopt the formulation from VLM2Vec (Jiang al. 2025 for discriminative multimodal em- beddings task as follows: given a query q and its corresponding positive target t+, as well as a set of negative targets T (tī t.....k kx) ). the objective is to maximize similarity between q and t+ over all and t- E T- pairs. Here, both queries and targets can be text, image, or interleaved text-image.
In practice we sample a mini-batch of N query-target pairs (41, 4)....,,. (,,, (N), where (4s, 4) forms the positive pair and all targets (t, j き i} serve as negatives for q. Formally, we optimize the model by minimizing the following InfoNCE loss function:
1 N
exp((To(()) mo(ta))/T)
Lactr
log
(I)
exp( (ta))//T) + Ej exp((To ((To(g)) mo(tt))))))
1=1
where (-(-) denotes the normalized representation of the last input token, derived from the MLLM's final-layer hidden state, and r represents the temperature
3.2 ARCHITECTURE
In this work, we introduce a multimodal embedding model capable of producing both discrimina- tive and reavoning-driven generative embeddings. To obtain the reasoning-driver generative embed- dings, the model first generates distinct reasoning and summaries for each query and target. These outputs are then concatenated with the original input produce the final generative representa- tion. Note that the model can simultancously yield discriminative embeddings without incurring additional computation. Specifically, employ the following template realize this process:
Template for Discriminative and Reasoning- Driven Generative Embeddings
USER:
<image> <vi rvideo> (query/target) <dise emb>
Represent the above input text, images, videos, or any combination of the three as embed- dings. First output the thinking process in <think> </think> tags and then summarize the entire input in word sentence. Finally, the <gen_emb> tag to represent the entire input.
ASSISTANT: <think> (reasoning) </think>
<answer> (summary) <gen_emb>
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Published as a conference paper at ICLR 2026
(a) Stage Supervised Fine-tuning
Discriminative Controstive Learning
Auteregressive Modeling
Generative
Controstive Learning
Multimodal Large Language Model
Discriminative Embaddingg
Get representation directly from input
Generative Embedding:
Image coption describing the given image. <Disc_emp>
Represent imput... First output the thinking... then summarize Finally, use the sgan_emb>... Generation Prompt
St let' anclyze the stngg..cthinooorsweerr 'gen_emb> Reasoning Summary & Generative Embedding
First generate reasoning summory, representation input generated content.
Query & Discriminative Embedding
Image/
(b) Stage Reinforcement Learning with Verifiable Reward
KL Divergence
Reference Model
Reward Evaluation
Positive in Top-G
Query
Pos. Cand.
Policy Model
Format Reward
Neg Cand
+dr
Pos.
Positive
Condidate Responses
Mean similonity gop
Embedding Reward
GRPO
Negative
Policy Optimization
Figure 2: Overview of UME-RI. UME-RI introduces two-stage training framework for gen- erative multimodal embedding. (a) Supervised fine-tuning uses query-target pairs with reasoning annotations train the MILM, enabling it generate both discriminative and reasoning-driven generative embeddings well as to possess basic reasoning abilities.
(b) RLVR continues to fine- tune the model using regular query-target pairs, encouraging it to generate reasoning trajectories that lead to more beneficial embeddings.
where <image> and <video> denote placeholders for the input image and video. As illustrated in Figure a()), the last-layer hidden states corresponding the prompt's edisc_embs token and the final model-generated agen_embb token serve as the discriminative and reasoning- driven gen- erative embeddings, respectively.
3.3 MODEL TRAINING
We train the model in stages, enabling it only generate discriminative embeddings but also to develop reasoning capabilities for producing stronger reasoning-driven generative embeddings. Figure 2 illustrates the overall training process.
Stage 1: Supervised Fine-tuning. In this initial stage, we perform SFT on the model using the multimodal embedding dataset constructed in Section which incorporates the step-by-step reason- ing processes. As shown in Figure a)a), alongside the discriminative embedding training objective outlined in Section 3.]
we also include the following generative embedding training objectives:
N log
exp((To((),, of) no(ti, o)))/) exp((To(g; of) To(t, //)//r) + E exp exp((To((s,))) of) To(t;, 0))//)
C getr
N
(2)
where of and of denote the i-th reasoning trajectory and summary of the query and target, respec- tively. Compared to the original input, reasoning process and summarization provide more detailed and useful information, which often enhances the performance of the resulting embeddings.

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Published as a conference paper at ICLR 2026
Furthermore, to endow the model with reasoning capabilities during inference, apply a next- token prediction loss over both the reasoning trajectories and summaries, formalized as
1 N
N
Lg
log To (0% de o,K,)) + Σ log To (of 1, o dizs))
Lee
(3)
where Lg and L, denote the lengths of the reasoning trajectories for the query and the target, respec- tively. Overall, the loss for the SFT stage is defined follows:
Last Ld + Lgetr + Lox-
This stage of training not only equips the model generate both discriminative and reasoning-driven generative embeddings, but also lays the foundation for its reasoning abilities.
Stage 2: Reinforcement Learning with Veritiable Reward. As illustrated in Figure 2b. in this
stage, we further refine the model Te using Group Relative Policy Optimization (GRPO) (Shao et al. 2024). Unlike methods that rely on a learned value function, GRPO utilizes the mean reward across multiple sampled outputs as its baseline. Specifically, for each input query q. it samples group of G candidate responses {o from the old policy Toois and then optimizes the policy model my by maximizing the following objective:
(o 99) As: -Toad (os 4)
Cgpo
E ED (o.) Toladd
min
(5)
clip
Toloi (o: 1 9)"
G, 1, -1 A.)
5ID - (To (
where D denotes the training dataset, and are prper-parameters, and Tref represents the reference model before optimization. A; indicates the advantage of the i-th response, computed based on group of rewards (Fi.....aa) corresponding to the outputs within each group:
A ri (ueam((ri,0) (ra))
(6)
std([ri, ... ,ra))
Accordingly, we design the reward function to include two components: format rewards and em- bedding rewards, which we will now describe in detail.
Format Reward. The use of this reward encourages the model to adhere to a predefined template, en- suring that responses are well-structured and interpretable. Specifically, the model is required per- form reasoning within the <think> and < </think> tags, provide summary after the <answer> tag, and finally generate the <gen_emb> for obtaining the generative embedding. reward of is granted for strict adherence to the template, while any deviation results in reward of 0.
Embedding Reward. This component used to evaluate the quality of the embeddings generated by the model. Since embeddings cannot be directly evaluated against standard answers as in math- ematics, we evaluate them from two aspects: the ranking of positives among negatives, and the similarity gap between positives and negatives. Concretely, for each query with a positive tar- get 1 and negative target 1-, we sample a group of responses {of } corresponding to positive target, another group {0 corresponding to the negative targe["] For the i-th sam- pled response o; of the query, calculate its similarity scores with the positive targets as S g (mo(g, (g. 0;) ( of }} 1=1- and with the negative targets as S = [To 0;) - vp (t_, ,0,
.,7 )} The embedding reward for the i-th response 0; sampled from the query is defined follows:
IS+ (S US
Remb(a;)
(avg(S - avg(S
Similarity Gap
Ranking
where (-p denotes the operation of selecting the top-G' largest elements from input set. By optimizing this reward, the model learns to produce reasoning trajectories that are more conducive to generating high-quality generative embedding.
Forr simplicity, only negative target is illustrated; however, this method can extends any number of negative targets in practice.
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Published as a conference paper at ICLR 2026
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Training Details. Following VLM2Vec-V2 (Meng et al.] 2025). we adopt Qwen2-VL-2B and Owen2-VLL-7BB as backbone models. During the SFT stage, we train using the cold-start dataset con- structed in Section [ which approximately two-thirds the size of the dataset used by VLM2 V2. Consistent with the settings of VLM2Vec-V2, the temperature T is set to 0.02, the batch size to 1,024 (achieved through gradient accumulation), and the number of training steps to 5K. Besides, the maximum sequence length is 12,288 tokens, and the learning rate is 5e-5. During the RL stage, the model is trained on approximately 11K pairs and uses the default GRPO hyperparameter set- tings: group size G = 8, clipping parameter 0.2, and KL-divergence coefficient / 0.04. In this stage, we set the batch size to 256, the learning rate to le-6, and train for one epoch.
Evaluation. We evaluate UME-RI on MMEB-V V2 (enng al. 2025). a benchmark that extends MMEB-VI (Jiang et al. 2025 by introducing 5 meta-tasks focused on video and visual docu- ment, covering a total meta-tasks and 78 tasks. During inference, we use greedy search and set the maximum number of newly generated tokens to 8.192. Unless otherwise specified, we use reasoning-drivee driivinnnddi generative embeddings for evaluation. Hit@1 is used as the evaluation metric for all video and image tasks, while NDCG @ 5 (Järvelin & Kekäläänen 2002) is reported for visual doc- ument tasks. In addition, we compare several strong models on MMEB- VI, with the corresponding results presented in Appendix
Baselines. We compare against several MLLM- based multimodal embedding models, including GME (Zhang et 2025b), ColPali (Faysse et al., 2025a), VLM2Vec (Jiang et al. 2025). LamRA
(Liu et I 2025a). CAFe (Yu et al. 2025a). and VLM2 V2 (Meng et al. 2025 To ensure fair comparison and to clearly assess the role of reasoning- driven generative embeddings, we evaluate model that performs contrastive learning exclusively on discriminative embeddings, using the same dataset and settings as ours. We refer to this model as DUME (discriminative UME).
4.2
MAIN RESULTS
Table presents performance comparison between UME-RI and the Baseline 78 tasks span- ning three visual modalities: images, videos, and visual documents. The results show that UME-RI consistently achieves the best performance in images and videos with the same backbone. Although ColPali and GME perform well on visual document retrieval, the former is specifically optimized for visual document tasks, while the latter uses a large amount of closed-source data. In particular, compared to VLM2 V2, UME-R1 achieves an overall improvement of 2.1 while using only two- thirds of its training data. Compared to the discriminative embedding model DUME trained with the same amount of data, UME-RI increases the total scores for images, videos, and visual docu- ments by 4. 9.0, and 11.1, respectively, fully demonstrating the effectiveness reasoning-driver generative embeddings. Comparative examples of reasoning-driver generative and discriminative embeddings are provided in Appendix [F
Since UME-RI can flexibly choose discriminative reasoning-drive driven generative embeddings as needed, we report an oracle upper bound. For each test instance, the oracle selects the embed- ding mode that yields the best retrieval performance. Under the oracle setting, UME-R1-2B and UME-R1-75 achieve overall score improvements of 4.3 and 3.6, respectively. The results demon- strate that the oracle substantially outperforms using only reasoning-driven generative embeddings, which means that in practical applications users can freely switch modes to obtain more satisfactory retrieval results.
4.3 ABLATION STUDY
Impact of RL Stage and Reward Design on Model Effectiveness. As shown in Table 2 we study the effectiveness different components in the RL stage across 78 tasks of MMEB-V V2. From the second row, we observe that although the RL stage uses only small dataset for training with GRPO and does not incorporate contrastive learning, it still substantially improves model perfor- mance. This finding suggests that effective reasoning paths and summarization contribute to better

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Published as a conference paper at ICLR 2026
Table 1: Comparison of performance between baselines and UME-RI on MMEB- -2. CLS: classi- fication, QA: question answering, RET: retrieval, GD: grounding, MRET: moment retrieval, VDR: ViDoRe, VR: VisRAG, OOD: out-of-domain. Oracle denotes the case where the best result between Geasoning-driven generative and discriminative embeddings is picked. Detailed results can be found in Appendix
Model
Imsage
Video
ViaDue
All
QA
RET
GD
Overall
QA
RET
MRET
Overall
VDRvI
VDRv2
VR
0OD
Overall
10
Barehie Models
CalPal V1. PallGeemmo 3B)
40.3
11.5
48.1
40.3
34,9
26.7
37.8
21.5
25.5
25.2
83.6
52.0
51.1
43.3
71.0
44.4
GME (Qwes2-VE-2B)
29.9
55.3
32.4
52.5
$4.1
GMR (Qum2-V1-78)
54.7
59.3
50.4
38.3
38.6
55.6
$8.0
LamiA (Qwen2 VL. 700)
59.2
26.5
7000
62.7
54.1
19.3
42.6
24.3
34.6
35.2
22.0
11.5
37.4
21.9
23.9
40.4
(0we225-VL-77
56.7
42.6
37.6
VLMEMec (QwenZ VL 28)
58.7
19.3
65.0
72.9
59.7
33.4
30.5
20.6
35.0
29.0
49.8
13.5
51.8
33.5
41.8
41.0
VLMEXz (Qwen2-VL-7B)
62.7
56.9
69.4
82.2
65.5
39.1
30,0
20,0
40.6
34,0
56.9
944
59.1
38.3
46.4
52.3
(Qeen2-V1L-28)
71.9
79.4
CAfe (LLaVA- OV-71))
63.6
61.7
69.1
87.6
67.8
35.5
58.7
54.4
39.5
42.8
70.7
49.6
79.5
38.
63.9
60.6
DUME (Qwez2 VL-2B)
59.3
53.0
66.3
78.0
62.5
37.7
46.5
17.1
30.0
33.2
67.6
43.3
47.1
13.5
52.5
52.7
(Qwen2-VL-78)
12.6
UME-RI (Qum2-N1-228)
64.8
62.8
67.6
77.2
66.5
44.3
$1.2
32.9
19.7
42.2
72.4
46.2
79.2
57.2
63.9
60.1
UMB (Qwen XL-28)
69.2
349
71.5
60.7
39.3
47.5
50.5
83.7
37,6
64.5
UMB (Qwen2 XL-28)
67.6
67.5
71.2
80.1
70.2
47.0
58.7
31.2
48.8
47.9
76.8
51.5
$2.6
41.5
68.2
64.4
A Oues
+2.8
64.7
63.6
+2.9
43.6
42.7
47.5
44.3
+9.1
15.7
+4.4
+5.3
+3.4
64,3
44.3
44.3
UME-RI (Qwan2-V-288)
A- Ouri
42.0
+4.0
429
+2.5
42.9
43.0
46.5
+1.4
+10.3
44.7
44.0
65.3
42.3
43.1
43.7
43.6
Table 2: Ablation study of the RL stage images, videos, and visual documents.
Model
Image
Video
VisDoe
ALL
UME-RI (Qwen2VL-2B)
66.6
42.2
63.9
60.1
2
w/o RL (UME)
65.2 11.4
41.2 11.0
63.5 10.4
59.1 11.0
3
w/o similarity gap reward
65.2 11.4
41.2 11.0
63.6 J0.3
59.2 10.9
4
w/o ranking reward
66.0 J0.6
41.8 J0.4
63.3
59.6 J0.5
w/ threshold reward
65.6 11.0
41.7 10.5
63.5 10.4
59.4 10.7
embeddings. The results the Rows 3 and show that jointly considering ranking and similar- ity differences in the reward is essential. Ranking offers supervision that aligns more closely with downstream tasks, but for relatively easy samples, the ranking reward often saturates. In such cases, similarity differences help guide the model toward learning tion, we explore using fixed threshold (set to 0.5) as the evaluation criterion for assigning rewards, where positive pairs exceeding the threshold receive a reward of and others receive The re- sults in Row 5 show that this approach is mainly beneficial for video tasks but provides limited improvement for other modalities. We attribute this the varying similarity distributions across task categories, which make it difficult to define single fixed threshold. Developing an adaptive threshold for reward assignment may promising solution.
effective reasoning paths. In addi-
Impact of Reasoning- Embedding Training on Discriminative Embeddings. While UME-RI is primar- ily designed for reasoning-driven generative embeddings, it also supports discriminative embeddings. In this study, we investigate how the SFT stage and the RL stage affect the performance of discriminative embed- dings. Table 3 reports the performance of 2B parameter models DUME, UME (with- out RL training), and UME-RI. Under the same training settings, introducing reasoning- generative embeddings and the next-token prediction objective during the SFT stage improves the
Generative
Table 3: Comparison UME and UME-RI using only discriminative embeddings against DUME un- der the same training settings.
Model
Image
Video
VisDoc
ALL
DUME
62.5
33.2
52.8
52.7
UME
63.2 10.7
34.4 11.2
60,3 17.5
55.7 13.0
UME-RI
64.0 +1.5
34,4 +1.2
60.3 17.5
56.0 13.3
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CIRR
Fashionlo
K700
MSVD
35.0
52
32.5
48
50
48-
27.5
44
Coverage
25.0
42-
44
22.5
UME-R1-20
40
42
26.0
38
8 16
64
128
64
Number Samples
Number Samples
Samples
Number of Samplas
Figure 3: pass@ k curves of UME-2B and UME-7B across multiple datasets.
overall score of discriminative embeddings across 78 tasks by points. Notably, for visual docu- ment tasks, the improvement reaches 7.5 points, likely due to the limited amount of such data in the training set, suggesting that incorporating the generative embedding and the next-token predic- tion objective provides richer supervisory signals. Furthermore, UME-RI achieves an additional 0.4-point improvement over UME in the overall score. Although the RL stage only optimizes the reasoning driven generative embeddings, does not compromise the performance of the discrimi- native embeddings, indicating that the two types of embeddings do not conflict during training.
4.4 DEEP ANALYSIS
Potential of Maasoning-Driveer Generative Embeddings for Inference-Time Scaling, One of the key characteristics of generative reasoning models is their ability to scale at inference time, meaning that performance can be improved by allocating more computing resources. Motivated by this, we explore whether reasoning-driven generative embeddings possess similar potential for inference-time scaling. To this end, we evaluate model coverage (pass@k) on four randomly se- lected test sets from the image and video modalities, each containing 128 randomly sampled exam- ples. Pass@k & considers a problem solved if any of the sampled outputs is correct, thereby indicat- ing the model's 's ability to retrieve the correct result through multiple attempts. To reduce variance in coverage estimation, we apply the unbiased estimation formula proposed by |Bronn et al.) (2024). As illustrated in Figure both UME-R1-21 and UME-R1-7B yield improved embedding represen- tations through repeated sampling, underscoring that reasoning-drivee generative embeddings also hold strong promise for inference-tine scaling. Appendix G presents visual illustrations of how repeated sampling affects retrieval results.
CIRR
FashioniQ
K700
MSVD
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35-
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DUME
DUME++en UME-R]
DUME DUME-+Gen UME-RI
DUME DeME=+Ge UME-R1
DUME DUME++Gen UMÉ-RI
Figure 4: Comparison between DUME, DUME+Gen, and UME-RI. DUME+Gen denotes the ap- proach in which external model first generates reasoning and summaries, followed by DUME to obtain the corresponding embeddings.
Discriminative Embeddings vs. Self-Generated Generative Embeddings. We further investigate approach where an external reasoning model generates reasoning and summaries, subsequently encoded by discriminative embedding model to obtain representations. We evaluate whether this approach enhances performance and compare it with proposed self- generated method. Concretely, we evaluate the 2B model on previously extracted test set, employing the 9B GLM-4.1 V-Thinking (Hong al. 2025al the external reasoning model. As shown in Fig-
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ure incorporating an external model can enhance discriminative embeddings on certain tasks, with improvements of 19.7 and 3.9 observed on K700 and MSVD, respectively. However, this approach may also degrade performance, exemplified by 12.3 12.3-point drop on CIRR. Importantly, UME-RI consistently outperforms DUnE+ indicating that self-generated reasoning and summaries are more efficient and effective than even stronger external model for producing high-quality embed- ding representations.
RELATED WORK
5.1 MULTIMODAL LARGE LANGUAGE MODEL
Multimodal large language models (MLLMs) OpenAl 2023 Liu al.
2023 Chen et al. 2023
Li et al. 2024; Wang et al. 2024) have achieved remarkable progress across a wide range of mul- timodal understanding tasks (Li et al. 2023 Lan al.) 2025al Lin et al.
2025b). The emergence 2024) and DeepSeek-RI
of Large Reasoning Models (LRMs), exemplified by GPT-4o (Hurst et al.
(Guo al., 2025), has catalyzed the development of various strategies
elicit Shain-offtthougee
(CoT) reasoning (Wei et al. 2022b Muennighoff et al., [025 Zhang et al.
2026) within MLLMs.
Among the most prominent the use reinforcement learning with verifiable reward signals to en- hance visual reasoning (Zhou et al. 2025a Zhan et al. 2025 Liu et al. 2025b Shen et al. (225a))
However, to our knowledge, no prior work has applied reinforcement learning with verifiable re- ward to embedding tasks, primarily due to such tasks are non-generative and do not have definitive answers.
5.2 UNIVERSAL MULTIMODAL EMBEDDINGS
Universal multimodal embedding models aim to encode inputs of various modalities into vector rep- resentations, facilitating range of multimodal tasks such as image-text retrieval (Wu et al., [021] Zhang et al. 2024a), automatic evaluation (Hessel et al., (021). and ettrievl-aaugmente generation (RAG) (Zhao et al. 2023). Early vision-language models (VLMs) (Radford al. 2021 Jia al.,
2021 Zhai et al.) 2023) primarily used a dual-encoder architecture and were trained with contrastive learning on large-sceu scale image-text datasets. Although these models exhibited strong representa- tional capabilities, they still suffered from deficiencies such as poor understanding of interleaved image-text inputs and tendency behave like bag-of-words (Yüksekgönül et al. 2023).
To address these issues, VLM2Vec (Jiang et al. 2025) and MM-Embed (Lin et al., 2025a con- vert MILMs into multimodal embedding models through contrastive learning, leveraging MLLMs' strong multimodal understanding and inherent advantages in handling interleaved image-tex inputs. Given the limited scale of existing multimodal embedding datasets, MegaPairs (Zhou et al.) 2025c) and GME (Zhang et al., 2025b) introduce automated data synthesis pipelines to generate large-scale pairs, thereby further improving the performance of MLLM- MaLsed multimodal embedding models. On the other hand, some works focus negative sample selection learning, for example, UniME (Gu et al. 2025a) filters out false negatives and easy negatives during training based on similarity, while LLavE (Lan et 2025b) and QQMM (Xue al.) (025)) estimate negative difficulty and weight negatives accordingly. Furthermore, B3 (Thirukovalluru et al. 2025) introduces a hard neg- ative mining method that leverages community detection to construct training batches enriched with
in-batch negatives.
Additionally, some studies explore how to preserve MLLMs' generative strengths when converting them from generative to discriminative models. VladVA (Ouali et al. (202)) and CAFe (Yu et al.) 2025a) combine contrastive objective with autoregressive language modeling to prevent catas- trophic forgetting of the models' generative abilities while enhancing their discriminative capabili- ties. Moreover, Ju & Lee (2025) design hierarchical prompts to elicit powerful discriminative em- beddings from generative models in zero-shot manner. Despite these advances, existing MLLM- based embedding models remain limited to producing discriminative embeddings and therefore do not exploit MLLMs' generative and reasoning capabilities. In contrast, UME-RI generate dis- criminative or reasoning-drise generative embeddings on demand, demonstrating the substantial potential of harnessing MILMMs reasoning power for embedding tasks.
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CONCLUSION
In this work, we pioneer the exploration of reasoning-driven generative embeddings and propose UME-RI, a universal multimodal embedding framework that unifies discriminative and reasoning- driven generative embeddings. To support this, we construct an SFT dataset by augmenting existing multimodal embedding benchmarks with reasoning and summaries produced by cankiig--apabbee MLLM. Fine-tuning on this dataset enables the model to produce both embedding types. We further apply reinforcement learning with reward function that incorporates similarity gaps and ranking, encouraging reasoning trajectories that enhance paasoinggddriver generative embeddings. Experi- ments on MMEB- V2, spanning 78 tasks across video, image, and visual document domains, show that reasoning driven generative embeddings yield significant gains over discriminative ones. Fi- nally, oracle and inference-time analyses suggest that UME-RI holds substantial headroom for fur- ther improvement.
Our work highlights three promising directions for future research: 1) developing mechanisms that allow the model to adaptively decide whether to produce discriminative reasoning driven gen- erative embeddings based on the input; 2) constructing more challenging RI. datasets or designing more effective RI. training strategies encourage the model to produce reasoning and summaries that more conducive to embedding quality; and 3) exploring inference-time scaling techniques to further enhance the quality of reasoning-die generative embeddings. In general, UME-RI estab- lishes a new direction for reasoning-driven generative multimodal embeddings and lays a foundation for future research.
ACKNOWLEDGMENTS
The project was supported by National Key R&D Program of China (No. 2022ZD0160501), Natural Science Foundation of Fujian Province of China (No. 2024J011001), and the Open Competition for Innovative Projects of Xiamen (No.3300722221011)) We also thank the reviewers for their insightful comments.
ETHICS STATEMENT
This work complies with the ICLR Code of Ethics and does not involve the collection of new human subject data or any personally identifiable information. All datasets used in this study are publicly available and widely adopted in the research community. Additionally, the constructed data in our experiments is derived from existing models and datasets, without introducing any new sensitive, private, proprietary content. We have carefully ensured that our methodology and experiments comply with relevant ethical standards, including fairness, transparency, and reproducibility.
REPRODUCIBILITY STATEMENT
To facilitate reprodueibility, we will release code, datasets, and trained models used in this work. The code has already been included in the supplementary materials submitted with this paper. Detailed descriptions of the dataset construction, model architectures, and training procedures are provided in both the main text and the appendix. These resources are intended enable other researchers to reproduce the results reported in this work and build upon our methods.
USE OF LARGE LANGUAGE MODELS
In the preparation of this paper, we use Large Language Models (LLMs) solely to aid in writing and polishing the text, including improving clarity, grammar, and readability. LLMs are not used for generating scientific content, experimental design, analysis, or conclusions. All technical ideas, experiments, and results reported in this paper are entirely the work of the authors.
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Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chun- yuan Li, Alexander G. Hauptmann, Yonatan Bisk, and Yiming Yang. Direct preference optimiza- tion of video large multimodal models from language model reward. In Luis Chiruzzo, Alan Ritter, and Lu Wang (eds.), Proceedings of the 2025 Conference of the Nations of the Ameri- cas Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2025 Volume 1: Long Papers, Albuquerque, New Mexico, USA, April 29 - May 4, 2025, pp. 694-717. Association for Computational Linguistics, 2025a.
Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li, Ziqi Dai, Dingkum Long, Pengjun Xie, Meis- han Zhang, Wenjie Li, and Min Zhang. Bridging modalities: Improving universal multimodal retrieval by multimodal large language models. In IEEE/CVF Conference on Computer Vision and Puttern Recognition, CVPR 2025, Nashville, TN, USA, June 11-15, 2025, pp. 9274-9285. Computer Vision Foundation IEEE, 2025b.
Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Do Xuan Long, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, and Shafiq Joty. Retrieving multimodal information for augmented generation:
survey. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Findings of the Association for Computational Linguistics: EMNIP 2023, Singapore, December 6-10, 2023, pp. 4736-4756. Association for Computational Linguisties, 2023.
Hengguang Zhou, Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, and Cho-Jui Hsieh. RI- zero's aha moment" in visual reasoning 2b non-sft model. CoRR, abs/2503.05132. 2025a.
Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, and Defu Lian. Megapairs: Massive data synthesis for universal multimodal retrieval. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), ACI. 2025, Vienna, Austria, July 27 August 1, 2025, pp. 19076-19095. Association for Computational Linguistics, 2025b.
Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, and Defu Lian. Megapairs: Massive data synthesis for universal multimodal retrieval. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), ACI. 2025, Vienna, Austria, July 27 August 1, 2025, pp. 19076-19095. Association for Computational Linguistics, 2025c.
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answer- ing in images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4995-5004, 2016.
15

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Published as a conference paper at ICLR 2026
A LIMITATIONS OF UME-R1
Although UME-R1 demonstrates that reasoning-driven generative embeddings exhibit stronger per- formance and greater potential than discriminative embeddings, they incur higher training and in- ference costs due to the generation of long CoT and summaries. However, this also opens a new avenue for improving embedding performance beyond scaling model size, namely scaling compu- tation. Moreover, while our oracle upper- bound analysis empirically shows the complementarity between discriminative and reasoning-drivee generative embeddings, designing a practical router to select between the two in real-world applications remains an open problem. Finally, there is still room for further performance improvement in our current RI. setup, for example, by constructing harder negative examples for RI. training or scaling up the training instances.
B TRAINING AND INFERENCE COST
In this section, we discuss the training cost of UME-R1 well as the inference overhead of reasoning-driven generative embeddings compared to discriminative embeddings.
Under the training configuration, DUME requires 1487 H20 GPU-hours for fine-tuning, whereas UME-RI incurs 2336 H20 GPU-hours in the SFT stage and 1344 H20 GPU-hours in the RL stage.
Table 4: Comparison of inference speed between discriminative and reasoning- driven generative embeddings across different datasets. The embedding type produced is indicated in parentheses.
Model
CIRR
FashIQ
K700
MSVD
UME-RI (Generative)
1.48 samples/s
1.14 samples/s
0.50 samples/s
1.10 samples/s
UME-RI (Discriminative)
20.0 samples/s
19.1 samples/s
1.59 samples/s
28.0 samples/s
As for inference cost, we evaluate inference speed on CIRR, FashionIQ, K700, and MSVD using single L40s GPU under the vL..M framework. The batch size is set to for image modalities and 4 for video modalities. As shown in Table 4 reasoning-driven generative embeddings indeed introduce a noticeably higher inference overhead, especially when the input token length is short. The speed gap narrows the input token length increases. Nevertheless, the stronger performance, better interpretability. and the ability to scale computation to further enhance embedding quality make the cost of reasoning-driver generative embeddings well justified.
C EXAMPLE OF DATA CONSTRUCTION
The prompt template for SFT CoT annotation is provided as follows:
Prompt Template for Reasoning Annotation
(query/candiaate,
The above input is a query/candidate for retrieval. Carefully examine and analyze the above input (which may include text, images, videos, or any combination). Identify and describe the key elements present the input, such as the main topic, important entities, relationships, context, and any notable features or details that contribute the overall meaning. Finally, synthesize your analysis and reflection into single word or concise sentence that best captures the essence of the input for retrieval purposes. If the input is phrase or word, the summary is that word itself.
We present examples of our constructed cold-start dataset in Figures 5 6 and to illustrate the typical query-target pairs it contains. For RL training, we sampled roughly equal numbers of in- stances from each modality while ensuring a balanced numbers across different datasets within each modality. In particular, for the image modality, pairs were drawn only from OK-VQA (Marino et al. 2019). ChartQA (Masry et al. 2022). CIRR (Liu et al. (02T). A-OKVQA Marino et al.19019). and Visual7W (Zhu al. 2016), the tasks in the other image datasets relatively simple.
16

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Published as a conference paper at ICLR 2026
No: 230705
Query:
Given an image, find a similar everyday image with the described changes: Shows a similar dog touching noses with a smaller light brown and white dog. <disc_emb>
Represent the above input text, images, videos, or any combination of the three as embeddings. First output the thinking process in <think?> </think> tags and then summarize the entire input in a word or sentence. Finally, use the <gen emb> tag to represent the entire input.
<think> So, let''s analyze the input. The query is asking for a similar everyday image with dog touching noses with a smaller light brown and white dog. First, identify key elements: main topic is a dog image with interaction (nose touching) with smaller dog of specific colors (light brown and white). Important entities: dogs, touching noses, smaller light brown and white dog. Relationships: interaction between two dogs, one larger (similar to original dog maybe), one smaller with light brown and white. Context: everyday image, so normal scene, not a special setting. Notable features: size difference, color of the smaller dog, the action of touching noses. Now, synthesize into concise sentence for retrieval purposes. Maybe something like "Dog touching noses with smaller light brown and white dog in everyday setting" or shorter version. Wait, the task says single word concise sentence. Let''s make it concise. "Dog interacting with smaller light brown and white dog by nose touching" or even shorter. Hmm. Let'ss check the original image: the first image is white dog on a leash. The query is for similar image (so maybe same type setting, park maybe) with two dogs, larger dog (similar to original maybe) touching noses with smaller light brown and white one. So the essence is finding an image
dog (probably similar to first dog or same context) touching noses with smaller, light brown and white dog. So the summary could be a sentence that captures the key elements: "Dog touching nose with smaller light brown and white dog." " But need to make sure it's concise. A Alternativeyy, "Similar image of dog touching noses with smaller light brown and white dog. " Wait, the original input query for retrieval, so the output should be a concise summary. Let me think again. The input is query asking for an image of dog touching noses with a smaller light brown and white dog (similar to an everyday image). key elements: dog, touch noses, smaller, light brown and white, everyday image. So the summary could be "dog touching nose with smaller light brown and white dog" or a brief description. Since it's a retrieval query, maybe "similar dog touching smaller light brown and white dog with noses". But need to be concise. Let's go with "dog touching noses with smaller light brown and white dog" as that captures all key elements, </think> <answer>>-aaawdeaar touching noses with smaller light brown and white doginsgen emb> Target:
Represent the given image. <dise emb>>
Represent the above input text, images, videos, any combination of the three as embeddings. First output the thinking process in <hhinke> </think> tags and then summarize the entire input a word or sentence. Finally, use the <gen emb> tag to represent the entire input.
<think>So, So, let's analyze the image. There are two dogs touching noses, one white and fluffy, the other tan and white (like a corgi). The setting is grassy area. The main elements are the two dogs interacting, their breeds/color, and the outdoor env environment. So the key elements are the interaction between the two dogs, their appearance, and the grassy background. The essence would be two dogs touching noses in grassy area. <answer>-- Two dogs nose-to-nose on (Interpreen the image: two dogs, one white fluffy breed and one lan/white corgi-like, touching noses in grassy setting, Syncretized essence for retrieval: two dogs nose-to-nose on grass.) Jnn _emb>
Figure 5: Example from the constructed cold-start dataset (Case 1). The orange part represents the original data, the blue part denotes the added prompt, the black part indicates the reasoning content, and the green part shows the summary. orange segments correspond to the original data, blue segments represent the added prompts, black segments capture the reasoning process, and green segments provide the summaries.
17

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Published as a conference paper at ICLR 2026
R.J.
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Query:
Represent the given image with the following question: What is the Account Name? <dise emb>> Represent the above input text, images, videos, or any combination of the three as embeddings. First output the thinking process in <think> </think> tags and then summarize the entire input word or sentence. Finally, use the <gen emb> lag to represent entire inpul.
<think>So, let''s look at the image. The question is asking for the Account Name. In the document, under "Account" it has "Janeto''s Super Market" written. So we need to extract that. The key element here is the account name, which is clearly labeled next to "Account". </think>> Saamsserr>JJnno'' Super Marketin<gen_ennt
Target:
Janeto's Super Market-dise_ene emb>>
Represent the above inpul text, images, videos, or any combination of the three as embeddings. First output the thinking process in <think> </think> tags and then summarize the entire input a word or sentence. Finally, use the <gen emb> tag to represent the entire input.
<think>So, So, lett's analyze the input. The input says "Janeto''s Super Market" as candidate for retrieval. need to identify key elements:in'' Main topic: Janeto''s Super Market (which is likely a store name). Important entities: "Janetol's" ( (Likely business name, super market as the type). Relationships: It's a business (super market) owned named "Janeto's". Context: This seems be name for supermarket business. Notable features: It's presented as a candidate for retrieval, so it''s a name that might be used for identification. l'nnSnthesiize this into single word or concise sentence. The main essence is supermarket business named Janeto''s. So the summary would be the business name, a concise description. But since the task says "single word or concise sentence", "Janeto''s Super Market" is the key, but maybe shorter. Alternatively, "Janeto'ss Supermarket" as the essence. Wait, the input is "Juneto''s Super Market" (with an upostrophe), the essence is the supermarket with that name, <answer>>> Janeto''s Super Market (essence: supermarket named (snettos)innCoomii summary for retrieval: "Janeto''s super market" or "Janeto''s Super Market") gen emb>>
Figure 6: Example from the constructed cold-start dataset (Case 2).
18

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Published as a conference paper at ICLR 2026
Joan Possos
Query:
Find a video that contains the following visual content: This video captures the heart- pound-po moment an airplane, with a visibly blue and white body probably belonging to a commercial airline, flying at low altitude over an overhead green sign. The sign reads "João Pessou" in while capital letters, it appears to be naming a city or a specific area. It'ss made of reflective material allowing high visibility even darker conditions. This particular sign is prominently installed on metal pillars with a series of lights to illuminate it, suggesting is likely situated near busy motorway highway. As the airplane ascends, its undercarriage is still visible, highlighting that it has likely just taken off from a nearby airport. We do not have clear visibility of the airplanel's livery or any markings that could identify the specific airline or model of the plane. The sky is clear, with scarce, fluffy clouds scatlered, suggesting good weather conditions. No other objects subjects are visible in the video other than the stationary overhead sign and the airplane making its ascent, <disc emb>
Represent the above input texl, images, videos, any combination of the three embeddings. First output the thinking process in <think </think> tags and then summarize the entire input in a word or sentence. Finally, use the <'gen_emb>> tag to represent the entire input.
<think>Su So, let''s analyze the inpul. The visual content, Let's break down the key Demments'nnnMMann topic: A video capturing an airplane (commercial, blue and white) flying low over a "João Pessoa" sign. 'n'nlmportant entities: 'n- Airplane: blue/white, low altitude lukcoff,, undercarriage visible, livery/turss unclear Sign: "João Pessoa" (white letters, green background, reflective, on metal pillars with lights), near motorway/highway.h Context: clear
looking for specific video with detailed description of the
sky, low cloud, no other objects. n'neeaationshis: Airplune is flying over the sign which is near motorway.
The sign''s purpuse to name city/area, the uirplane is ascending (justice lakeoff). 'n'nNotable features: Color of airplane, visibility of sign, sky conditions, absence of other summary. The essence is video of commercial airplane flying low over "Joãu Pessoa" sign near motorway with clear sky. <<hinkk><nnswer>>>>>>>>>>>>>> <answer>> <awsserr-oommeeeeeeee Commercial sirplane flies low over "João Pessoa" sign near molorway, clear sky.insgen >nggeenem>>>>>>>>->e
wiabetts.iinSSyteesiiiii into a concise
Target:
Understand the content of the provided video.<dia <disc_emb>
Represent the above input text, images, videos, any combination of the three as embeddings. First output the thinking process in <think> </think> tags and then summarize the entire input in word or sentence. Finally, use the <gen emb> tag to represent the entire input.
<think= Got it, let'ss analyze the input. The user provided multiple images (though text describes them as video frames maybe) showing green highway signs with "João Pessoa" and airplanes in some. Let''s identify key elements: green highway signs, Jodo Pessoa as a place name, airplanes (aviation elements), the signs! structure (metal frames), sky, trees, etc. The main topic is highway signs for João Pessoa with airplanes (maybe airport area). Important entities: "João Pessoa" signs, airplanes, highway infrastructure. Relationships: signs are for directing to João Pessoa, airplanes indicate proximity to an airport. Context: outdoor setting, clear sky, urban/airport infrastructure. Notable features: repeated green sign with João Pessoa, airplanes flying above signs. In'nSynthesize into single word or concise sentence. The core is highway signs for João Pessoa with airplanes, so maybe "João Pessou highway signs with airplanes" but make it concise. Or "João Pessoa highway signage with aircraft". But need to capture essence. Alternatively, </think>> <answer>>=sseroooo Pessoa highway signs with aircraftin( Condensed to capture the key elements: the main subject "João Pessou" on highway signs plus the included aircraft.) Jinn eenb>
"João Pessoa highway signs and airplanes" ",
Figure 7: Example from the constructed cold-start dataset (Case 3).
19

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Published as a conference paper at ICLR 2026
D DETAILED SCORES OF MMEB- V2
Table 5: The detailed results of the baselines and UMB-RI the full MMEB-v2 benchmark. We only include the best version of each series of previous models in the table. Numbers in parentheses represent the task count for each category.
O4Pdisi CME-78
VUMENN-TB
VLMDNe-NN20
CAFe-7B
DUMB-28
DUME-7B
UNE-RI17B
oxde)
97.5
45.4
52.5
160
60.6
52.1
55.9
66L1
Ang Tmage talks, H1x011
55.0
655
64.9
67.6
62.5
06.5
Aeg- Vikeo tadks,
35.4
33.7
41A
35.2
42.1
Ang-
(1)
40.3
51.7
51.7
62.7
62.9
63.6
56.
642
66.3
67.1
F-JA 1315
11.5
34.7
36.1
56.0
76.3
6377
56.0
$7.11
62.4
66.3
(82)
712
66.3
6911
66.
07.0
V-C1S
V-0A (1)
52.
320
51.0
V-KET (5)
21.5
23.4
25.2
230
16.
34
17.1
$2.9
38.2
V-MR (3)
25.3
31.0
264
35.5
500
25.0
50.3
ViD-Videee 1106
7577
67.
12A
VD-Vakee-V22
49
VD-000 (5)
462
411
36.1
Isnachic--J IK
424
646
369
108
31.3
76.6
75.6
13.3
804
N24Nlos
83.2
VOC207
89.8
SUNI3T
69.5
71.
Plaxats
27.5
321
374
375
150
13.01
41.
4L.
12.
163
A
16.9
413
363
181
474
55.2
412
533
IrageNes-K
935
OAjectNer
23.
24.5
22.
VGJA
322
360
365
21.5
63.5
62.4
11.
A CIKVAA
6.6
350
361
13A
618
959
51.
967
DeeVIQA
11.3
41.
297
192
360
95.5
021
Intigremoocigg
3055
200
535
192
Vis ITW
22.2
32.
5255
ScaneeijA
2601
313
65.
VirWa
35.6
350
320
200
43.3
438
420
443
66.
116
CO4
76.9
374
351
649
65.7
103
369
673
(83
453
210
758
T8A
CIRR
549
VavalNowo 121
5011
755
905
14.5
695
71.2
76.
11.
163
47.
763
12
13.7
79A
725
13.
D6.
130
MSCOCO.S
712
65.
222
75.
75.4
70.1
75.1
71
220
114
2111
713
667
WARQA
183
900
Fasbient))
31.8
33.4
151
193
11.
325
17.1
W5 NQ
R05
782
670
16.
612
356
67.0
13.5
OVEN
300
75.1
143
405
652
205
16.
T14
EDNS
95.0
352
141
712
180
RaCoco
600
250
55.1
7611
76A
55.5
51.
12A
15.
512
610
16.3
105
MAA
T1.
M.3
401
22.
121
17.9
435
469
SS4
SEA
LCFI0l
40.4
347
530
618
100
356
55.
45.
67.2
100
100
145
261
238
168
156
15.9
31.
35.7
456
326
295
137
45.9
47.4
403
563
460
393
417
EguSibeera
100
250
25.
45.4
22.5
51.3
65.9
25.6
22.
46.5
233
523
204
350
375
0.00
51.2
57.5
22A
400
MSR-VTI
315
750
355
102
14.
43.7
460
23.8
55.4
OVHigilihh
183
595
609
513
100
10A
494
584
1256
4013
29A
57.5
12.9
545
Chanaee-STA
2010
2411
1558
168
197
1611
15K
2144
219
MinserSeeker
27.6
37.6
31.
293
103
41.
34.
41.
411
ViDeR:
33.
57.5
315
56.5
27.4
VID.R..idoop
54.9
916
723
204
53.7
806
72.5
762
503
VID-RR. alD-R:
969
916
661
282
192
802
89.
861
902
vitloMi.saazz sadza
241
270
11.5
25.1
405
53.0
ViDolk: ewayy
instigence
96.7
25.
65.1
16.5
545
65A
83.1
153
Vmn- wr..ommmraaaeeggr
91.6
123
150
25.0
SED
111
VIDuR: aaldsere sadater
053
933
83.8
225
1222
$5A
181
$40
10L5
634
907
Jackem st
VID-R2 aqgas aankliil
342
54.
vitlokz www.prre....sateeeeee st.raatiinga
53.
110
425
41.
42.
VERAGL
503
874
373
53.6
760
340
356
71.5
74.3
505
7233
351
66.
377
55.
13.7
473
43.
M60
550
YaRAi MINDSEVQA
937
900
1961
35.1
35.1
518
155
VERAD IavVQA
819
VERAIUTRRQA
26.4
437
754
364
060
ViTiSaaii page
16.1
37.
187
165
361
325
310
313
213
313
ViD-Sedi--de
67.5
935
653
494
190
138
055
732
759
153
MNN Willonglleachgpgg page
25.9
15.4
135
164
183
114
852
288
436
354
360
20

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Published as a conference paper at ICLR 2026
E AMEB-VI BENCHMARK SCORES
Since MMEB-VI has been widely adopted prior work, this section we also report the perfor- mance of UME-RI alongside other baseline models on MMEB-VI. The results in Table 6 demon- strate that UME-RI achieves the best overall score among models of the same size.
Table 6: Results on the MMEB-VI benchmark, which comprises total of 36 image embedding tasks. IND represents the in-distribution dataset, and OOD represents the out-of-distribution dataset. In UnilR, the FF and SF subscripts under CLIP or BLIP represent feature-level fusion and score- level fusion, respectively. CAFe-VI indicates that the model is trained solely on the MMEB-V training data (contains only image data), whereas C2 denotes that the model is trained on the MMEB-V training data. The best results are marked bold, and the second-best results are underlined.
Meta-Task
Average Score
Model
Classification
VQA
Retrieval
Grounding
IND
OOD
Overall
# of Datasets
10
10
12
20
16
36
Baseline Models
CLIP RRdford
2021
42.8
9.1
53.0
518
37.1
38.7
37.8
et 2025
27.0
47.0
25.1
SigLIP Zhai et al.
40.3
8.4
31.6
59.5
32.3
38.0
34.8
OpenCLII Cherti
2023
47.8
10.9
52.3
53.3
39.3
40.2
39.7
UnilR (BLIP (rr) Wei ct al.
2024
42.1
15.0
60.1
62.2
44.7
40.4
42.8
UnilR (CI [Psp)) Wei al 2024
44.3
16.2
61.8
65.3
47.1
41.7
44.7
Magiclens
Zhang et
2024h
38.8
8.3
35.4
26.0
31.0
23.7
27.8
MLLM-bosed Baseline Models
ES-V (iang et
2024
21.8
4.9
19.0
14.9
VeM2 (Qwen2 2B)
Jiang al. 2025
59.0
49.4
65.4
73.4
66.0
52.6
60.1
VLM2Vee (Qwen2-VL---)
Jang et al
2025
62.6
57.8
69.9
81.7
72.2
57.8
65.8
VLM2Ve: V2 (Qwen2-VV VL-
7B1
Diang et 2025
62.9
56.3
69.5
77.3
68.8
59.9
64.9
MMRc+17B Zhou et al.
2025b
56,0
57.4
69.9
83.6
68.0 0
59.1
64.1
Yu CARc-V1-7B
al.
2025a
65.2
65.6
70.0
91.2
75.8
62.4
69.8
Yu CARe-V2-7B
2025a
63.6
61.7
69.1
87.6
72.8
61.1
67.6
mmES-11B
Chen
2025
67.6
62.8
89.7
72.3
LLaVE-2B
Lan
2025b
62.1
60.2
65.2
84.9
Lan
65.7
70.9
75.0
UniME-4B
al.
2025b
54.8
55.9
64.5
81.8
68.2
52.7
64.2
UniME-78 (u al.
2025b
66.8
66.6
70.6
90.9
74.6
65.8
70.7
Ourv
UME-RI-2B
64,8
62.8
67.6
77.2
71.5
60.4
66.6
UME-RI M-BBR1-7B
67.1
84.9
Oracte
UME- R1-2B
67.6
67.5
71.2
80.1
75.3
63.8
70.2
UME-RI-7B
69.1
73.2
74.8
87.4
79.2
67.9
74.2
21

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Published as a conference paper at ICLR 2026
F COMPARATIVE EXAMPLES OF REASONING-DRIVEN GENERATIVE AND DISCRIMINATIVE EMBEDDINGS
Figures 8 TO and 11 present several comparative examples of reasoning-driven generative and discriminative embeddings. It can be observed that reasoning-driven generative embeddings are ca- pable of producing effective reasoning and summaries, thereby facilitating the generation of higher- quality embeddings. For example, as shown in Figure []] UME-RI first engages in reasoning. The intermediate thought process includes: "Wait, think the key that the food question the hot dog so the name of the food not white is the hot dog." As a result, the final summary directly produces "hol dog", yielding a higher-quality embedding and enabling correct retrieval results.
Query:
Represent the given
image with the following question: What is the name of the food that is not white?
Target: The food hot dog.
UME-R1:
<image>RRpresee the given image with the following question: What is the name of the food that is white?<disc emb>
Represent the above imput lext, images, videos, or any combination of as embeddings. First output the thinking process in <think>> </think> tags and then summarize the entire input in word or sentence. Finally, use the iign_eemb> tag represent the entire input.
<think>So, let's analyze the input. The question is asking for the food name that's not white. Looking at the image, the food is white plate with a hot dog (let's check). Wait, plate is white, but the food-wait, wait, no, food a hot dog with toppings, but let's confirm. Wait, the image shows a hot dog on a bun, which is common food item. But wait, the question is "the food that is not white". Let's check the bread bun, which white? Wait, bun probubly white. Wait, no, foud's main body might be... Wait, the food in question could be a hot dog, But the plate is white. Wait,
The food is hot dog.
the question asking for the food name that's not white. So identify the food item. food the image a hot dog (with toppings like mustard, ketchup), but bun is white, the meat is maybe red or brown...
Hmm. Alternatively, maybe the food is "sausage" but that's not. Wait, think the key
that food question the hot dog (as the food item, not bun or sausage), so the name of the food not white is the hot dog. So the final summary is the food that's not white, "hot dog </thinn**
dogn emb>
DUME:
<'mage>RRpreseee Represent the given image with the following question: What is the name of the food that is not white?<disc emb-
The food is bread.
X
Figure 8: A comparison of reasoning-drivent generative and discriminative embeddings is shown (Case 1). Green highlights denote content that positively impacts retrieval performance. UME- RI demonstrates the results of reasoning-driven generative embeddings, while DUME is trained using standard discriminative contrastive learning. In this example, UME-RI produces "hot dog" directly during both the reasoning and summarization steps, which helps generate higher-quality embedding.
22

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Published as a conference paper at ICLR 2026
Query:
Find an image match the fashion image and style note: white and less revealing and is white and same longth all around bottom.
Turget: Represent the given image.
UME-RI:
<image>Finng Find image to match the fashion image und style note: Is white und less revealing and is white and same length around bottom. <dise emb> Represent the above input text, images, videos, or any combination of the three as embeddings, First oulput the thinking process in <think> </think> lags and then summarize the entire input in word sentence. Finally, use the <gen emb> lag represent the entire input.
<think>Got it, let's analyze the input. The query is usking to find fashion image that matches two conditions: white and less revealing maybe sleeveless shorter, or more fitting), and white and same length all around the bottom. Wait, but the image provided is black sleeveless dress with high-low hem. Let's break down the key elements. The main topic is fashion retrieval, specifically looking for dress. The important entities are "white" (color), "less revealing" (style). "sleeveless" (fashion style), "same length all around bottom" (design element, maybe hem). The context is fashion design. Now, the image shows black sleeveless dress, possibly more form-fitting or shorter. The relationship is between the image (black dress) and the required style. So the essence is finding white sleeveless dress without sleeves but same length all around the bottom. But the summary needs be single word concise sentence. Maybe "fashion sleeveless white dress". Wait, let's check again. The imput is retrieval query asking for fashion image similar to the provided one (black sleeveless dress) with conditions: white (less revealing style), white and same length bottom/around So the key elements are fashion image with white sleeveless dress, same length all around the bottom. So the summary could be "white sleeveless dress" "fashion white sleeveless dress". But need concise sentence. Wait, the user says "synthesize into a single word or concise sentence". Let's see the image is a black sleeveless dress, so the core sleeveless white dress. So the white
summary "sleeveless white dress". <ithink---nswerrsleer dress<gen__end emb-
DUME:
<image>-Fin an image to match the fashion image and style note: Is white and less revealing and is white and same length all around bottom. <dise emb>
Figure 9: A comparison of reasoning-driven generative and discriminative embeddings is shown (Case 2). In this example, UME-R1 reasons and summarizes that it needs to find white sleeveless dress with a skirt length matching the provided image, whereas DUME retrieves results based only on the input image and instruction, which do not fully satisfy the requirements.
23

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Published as a conference paper at ICLR 2026
bape
Query: Recognize the category of the video content.
Target: doing nails
UME-R1:
Roloss-Rccogiie the category video content. <dise embe
Represent the above input text, images, videos, or any combination the three embeddings.
oulput the thinking process in <think> </think> tags word sentence. Finally, use <gen embe tug represent entire input.
summarize the entire input
tutorial where someone painting design nail using brush, The "Passeuma sohre adesivo" is present, likely part of the instructions. The key clements nail demenstration, using base and adhesives, hands applying polish/paint nail. The essence demonstration. So the concise summary would about nuil art tutorial. </thinkk
Sthink>So, let's analyze the input. The video shows
doing nails
process tutorial demonstrationegen. emb-
DUME:
Siddes>Revognizz category
content. <disc__eiee
tying
X
Figure 10: comparison of reasoning-drived generative and discriminative embeddings is shown (Case 3). In this example, UME-RI summarizes the video as "Nail art tutorial demonstration." providing crucial information for correct retrieval.
Query: Recognize the category of video content.
Target: news anchoring
UME-RI:
<vides-RReognin the category video content. <dise embe
Represent above input text, images, videos, any combination First output the thinking process in <think </think> tags word <think*So, let's analyze input. The image shows news studio with sitting at desk, backdrop with "WUFT NEWS" and "WUFTS" branding. holding papers and notebooks, so the key elements news studio, news presenters, studio setup with logos, the muin topic (news unchor set), the people (two anchors), the network (WUFT 5 News), and the presentation materials. The essence is
embeddings. summarize entire input tag represent the input.
Finally,
<gen
(reporters)
news anchoring
broadcast
The key elements include the studio setting
studio presentation. So the concise sentence would be "News studio broadcast with two journalists. aattinnk-nnsswe--eWww studio broadcast with two journalists (analysis: the image shows studio with two women desks, wearing professional attire, holding documents, under the "WUFTS 5 NEWS" and "WUFT5" brunding. indicating news broadcast setting. Key elements include the news studio environment, the presenters, their attire, and the media branding, so the essence news studio broadcast featuring two journalists, >rgen_neeennnnneee emb--
DUME:
presenting
Recoeo=Recognize the category of the video content. <dise emb>
weather
X
forecast
Figure 11: comparison of reasoning-driven generative and discriminative embeddings is shown (Case 4). In this example, UME-RI identifies key elements such as "WUFT News" during rea- soning, preventing incorrect retrieval weather forecast.
24

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Published as a conference paper at ICLR 2026
G EXAMPLE OF REPEATED SAMPLING
Figures 12 13 and 14 illustrate the impact different samplings on the reasoning and summa- rization of reasoning-driver ddrivev generative embeddings. Interestingly, we observe that for correctly retrieved examples, the generated reasoning and summaries tend to be more effective. This sug- gests that reasoning-driven generative embeddings offer more interpretable approach, potentially allowing the quality of embeddings to be assessed through the generated reasoning and summaries.
Query:
Represent the given image with the following question: Which side photo knife on?
Target:
The knife is the left of the image
Reasoning 1:
<think-So, let's analyze the input. The question is "Which side of the photo is the knife on?" First, look at the image. In the left side of the photo, near the utensils, there's knife. Wait, the image shows utensils on table, and the knife is on the left side? Wait, the image has various utensils: there's a knife or a similar tool near the left edge. Let me check again. The left side of the photo has utensils, probably the knife on the left side? Wait, the utensils (fork, knife maybe) are on the left table urea. So the key is identifying which side of the photo the knife is.
The knife is on the left image
The image shows utensils, with the knife on the left side of the table. Then, the question is asking which side of the photo the knife is, so the answer would be left side (or maybe right?
Wait, no, wait. Wait, in the image, the utensils are arranged on the left and right. The knife might be on the left side? Wait, no, let's check the image. The table has utensils: fork, knife maybe, and the spoon. The knifé is on the left side of the dining table area, as the utensils are spread out. So key element the location of the knife's position. The question about identifying
of the photo where the is. So synthesizing, the answer would "left side photo's table surface. Wait,
side" or maybe since the knife image the utensils on the left, the knife the left side sidesgeen embo-
Reasoning 2:
Sthink-So, let's analyze the ioput. The question is asking which side of the photo the knife is on. First, look at the image: there's knife maybe on the right side? Wait, let's check. The image shows plate with food, bowl, and knife neur the right edge. But the key is "which side". So the knife is on the right side of the photo. Now, the task is to synthesize into single word or concise sentence. The essence is identifying the knife's position.</hinkk-ne
nupkin the right of the plate.
X
cmb-
Reasoning 3:
<think=So, let's analyze the input. The question is "Which side of the photo is the knife on?" Wait, first, look the image. Wait, the image shows plate with food, bowl of what might be scrambled eggs maybe on the left or right? Wait, the key is to find the knife's position. The image hus a knife and fork the right of the plate. So need to identify which side. Let's check the image: the utensils are on the right side of the plate. Wait, the fork is on the left side of the plate, the knife is on the right, next to the bowl of food. So the answer would be right, but the question is "Which side of the photo the knife on?" So the knife is on the right side, maaink--anssee-rrighh
dish, and there's fork and knife. Wait, the knife is near the plate,
The napkin to the right of the plate.
emb-
An example showing how repeated sampling leads to variations in model-generated reasoning and summaries, resulting in different retrieval outcomes (Case 1). The green segments indicate correct reasoning or summaries, while the red segments highlight incorrect ones. It can be observed that in correctly retrieved examples, the summaries more accurate. For instance, when the summary is "left side." the retrieval results include "left of." whereas when the summary "right." the retrieval results include "right of."
Figure 12:
25

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Published as a conference paper at ICLR 2026
Query:
Find an image match the fashion image and style note: Is a black dress with shade of grey and is similar in black.
Target: Represent the given image.
Reasoning 1:
<think-Got it, let's analyze the input. Thee query is about finding black dress with shade of grey (similar to a black) that's like the one provided. Let's break down the key clements:
Main topic: Fashion dress (specificully dress, which clothing item)
Important entities: Black dress, shude of grey, similar in black
Relationship: The query is asking for dress that matches a style (like the original dress) but with a specific color scheme (black and grey, similar to the original).
Context: It's a retrieval query for an image of dress with these color and style attributes. Notable features: The original image shows a yellow dress, so the target dress should be a black dress (or similar color family) with grey-like shade, matching the style.
Now, synthesize this into concise sentence or single for retrieval. essence is about finding dress similar to the one provided black and grey pollosssthhinkk*aaaaa black dress with grey shade similar the one shown.sgen embo
Reasoning 2:
-think-Got let's analyze the inpur. The user is asking find a black dress (shade of grcy) similar in color to given one? Wait, first, the input mentions "a black dress with u shade of grey and similar in black." Wait, maybe black dress is being usked for, and the context involves finding similar black dress? Wait, let's check the key elements. Main topic: fashion (clothing, dresses) retrieval. Important entities: dress (style note), black, grey shade, similar in black. Context is about finding a similar black-dress. Notable features: the request for black dress with grey-like tone (shading) and similar black. Now, the task is to summarize into a concise idea for retrieval. The core is identifying a black dress similar in color, so the summary would capture that they're looking for a black (with grey tonc) dress similar in color tathnkk <<asswerr>>A query to find black dress with a grey toned shude identicul in color to a given black dress. Summary: Find black dress similar in color to another. <gen embo-
Reasoning 3:
<think=Got it, let's analyze the input. The query is asking to find black dress with a shude of grey, similar to given onc. Wait, no no-wait, the input says "is a black dress with a shade of grey and is similar in black." Wait, first, the main topic is fashion item, specifically dress. Importunt entities: black dress, shade of grey (so light or darker shade of grey), similar in black (meaning the dress has a greyish shide, maybe matching). Context is about matching specific dress (with those color attributes and style similurity). The process is to identify key elements: the object dress, its color (black, with grey shade), and the task is to find similar one (so same style, collor-elated), Then, synthesize into concise sentence for retrieval. Wait, the input about retrieving a dress similar color and style to a black dress with grey shade. So key elements: dress, black, shade of grey, similar. Then, the summary should capture that, Maybe "black dress with grey shade similar" </think>>answer>> black dress with greyish shade (similar in black) for retrieval, <gen <gen_embe-
embe-
An example showing how repeated sampling leads to variations in model-generated rea- soning and summaries, resulting in different retrieval outcomes (Case 2). In this example, correctly retrieved cases are able to reason and summarize that the item needs to match the original style, whereas in incorrectly retrieved cases, the reasoning and summarization only consider the color.
Figure 13:
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Published as a conference paper at ICLR 2026
Query: Recognize the category of the video content.
Target: plastering
Reasoning
<think=So, let's analyze input. input description sandals holding a trowel similar tool) with bucket, working near wall. Key elements: child (toddler), activity (using tools wall work), setting (indoor, possibly construction or repair), actions (holding trowel, moving near wall). The main topic child working with tools for construction. To capture the essence, concise phrase would be "toddler doing construction work" similar. Wait, the video shows toddler using tool key elements the child, tool, and construction-reke relatted action. So concise summary could be "toddler constructing with tools" or "child doing construction work". But need a single word or concise sentence. Let's check: main topic is a child (infant/oodller) engaging in construction repair, using tool near wall....
clip a loddler
wall, the
plustering
So the final summary should capture the main action: toddler using tool for construction. So "toddler constructing" or child doing construction work". I think "toddler construction" is a concise way, or "child working on wall". Wait, let's check the video: the toddler has a bucket and a tool (trowel) near a wall, So the action is related to building or fixing wall. So the best summary is a sentence that captures the child's action. "Toddlers working on wall with tool" but maybe too long. The question says "single word or concise sentence". So "toddler construction" works, or "child building" but "toddler" is specific. So "toddler constructing" might be good (ttink=<Snsswr>>>> Toddler constructing with tool near wall (Summary for retrieval: "toddler constructing with tool" or similar, capturing the key elements of a toddler, tool, and construction wign) emb>
Reasoning 2:
<thnn-~So, let's analyze the input. The input description of video clip showing child playing with a broom in room. Key elements: main topic is child playing with a broom important entities are young child, broom, an indoor setting (with concrete wall, some items on the floor), actions like swoeping or playing with the broom. Context is domestic environment, possibly home play. Notable features: the child's activity with the broom, the setting So the essence is about a child playing with broom in a room, child playing with broom indoors (essence: a child playing with broom in coom)--gen eemb
moving child
X
Reasoning 3:
<think> So, let's analyze the input. The input content, The video shows child using a shovel to dig (or mayhe play with sand/mud), with another child and construction worker in the background. The key elements are child engaged in digging with shovel, another child watching, and an adult assisting. The context is likely playful or instructional activity, maybe in a casual setting (like a home or construction site). So the main topic is child playing with a shovel, and the context is simple activity with others around. The essence would be child digging with a whthelll-hikk---ammaa <gen_emma> emb-
recognize the cutegory of the video
moving child
x
child using a shovel to dig at location with other people.
An example showing how repeated sampling leads to variations in model model-generated reasoning and summaries, resulting in different retrieval outcomes (Case 3). In this example, only Reasoning Path correctly identifies that the video depicts a child building, leading to the correct retrieval, while the other reasoning paths mainly focus on "playing."
Figure 14:
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Published as a conference paper at ICLR 2026
H REWARD AND COMPLETION LENGTH VISUALIZATION
In this section, we present visualizations in Figures 15 and T6 illustrating the evolution of reward and completion length throughout training. We observe that for both the 2B and 7B models, the lowest reward value increases as training progresses. However, unlike other tasks, our reward does exhibit strictly increasing trend. This is because our RL dataset consists of data from multiple modalities and sources, and follows the VLM2 within each batch avoid overly trivial negatives. Due to substantial differences in similarity and difficulty across datasets, the rewards vary considerably between batches: rewards are relatively high when the batch easier, but lower when the batch is more challenging. Consequently, the reward curve does not follow strictly monotonic upward trajectory. In addition, observe that the completion length the 2B model decreases as training progresses. This trend is consistent with the findings of Chen al. (2025c), Chen et al. (2025b), and Peng al. (2025) small-scale MLLMs. possible explanation is that the reasoning capacity of the 2B model is limited, and excessively long reasoning may even impair its performance.
strategy of using data from the same source
0.47
Query Completion Length
Positive Completion Length
Completion Length
800
Embod Reward
575
0.45
550
525
0.44
500
Query Embed Reward
475
Positive Reward
450
Step
Step
Figure 15: Evolution of reward and generated completion length of UME-R1-2B during training.
0.515
6:0
Query Completion Lengthh
Positive Completion Length
0.510
Completion Length
620
Embed Reward
0.505
0.500
580
0.495
560
0.490
Query Embed Rewerrd
540
Positive Reward
0.480
Step
Step
Figure 16: Evolution of reward and generated completion length of UME-R1-7B during training.
28


Reviews:
[{"review_id": "NcsGhJbtls", "reviewer": "Reviewer_DYSt", "summary": "This paper introduces UME-R1, a universal multimodal embedding framework that produces embeddings conditioned on chain-of-thought reasoning. The approach consists of a two-stage training strategy: supervised fine-tuning augments query-target pairs with intermediate reasoning and summaries generated by a thinking-capable MLLM, followed by reinforcement learning that optimizes a reward function based on both ranking and similarity gaps to further enhance embedding quality. The model can flexibly produce either discriminative embeddings (extracted directly from input tokens) or generative embeddings (extracted from tokens following generated reasoning and summaries). Evaluated on the MMEB-V2 benchmark spanning 78 tasks across video, image, and visual documents, UME-R1 demonstrates significant improvements over conventional discriminative embedding models, with oracle analysis revealing substantial complementarity between the two embedding types.", "questions": "My comments are constructive, containing both my critique as well as approaches to resolve the concern.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The core contribution is novel and well-executed. To my knowledge, this is the first work to systematically incorporate chain-of-thought reasoning into multimodal embedding generation, demonstrating that reasoning-conditioned embeddings can substantially outperform standard discriminative embeddings. The idea of having models generate intermediate reasoning before producing embeddings is intuitive and well-motivated.\n\n2. The two-stage training framework is well-designed and clearly presented. The supervised fine-tuning stage effectively teaches the model to produce both embedding types while developing reasoning capabilities, and the reinforcement learning stage introduces a clever reward design that addresses the challenge of applying RL to embedding tasks which lack ground-truth answers. The combined ranking and similarity gap reward is particularly elegant, addressing the zero gradient problem that would arise from using fixed similarity thresholds.\n\n3. The experimental results are strong and comprehensive. UME-R1 achieves consistent improvements across multiple modalities (images, videos, visual documents) and task types. The ablation studies are thorough, demonstrating the value of each component including the RL stage, the dual reward design, and the generative training objective. The oracle analysis showing 3.6-4.3 point improvements reveals interesting complementarity between discriminative and generative embeddings.\n\n4. The paper includes valuable analysis beyond standard benchmarking. The inference-time scaling experiments (pass@k analysis) reveal an intriguing property of reasoning-conditioned embeddings, and the comparison with external reasoning models demonstrates that self-generated reasoning is more effective than externally-provided reasoning. These insights extend beyond simply showing performance improvements.\n\n5. The writing is generally clear and the paper is well-structured, making it easy to follow the methodology and experimental design.", "weaknesses": "1. The terminology and framing claims in the abstract and introduction are too broad and potentially misleading. The paper repeatedly claims to \"pioneer the exploration of generative embeddings\" and positions UME-R1 as introducing the first \"generative\" embeddings. However, the term \"generative embedding\" is overloaded and could reasonably describe several existing approaches. For instance, LamRA and UniIR extract embeddings from generative models during the generation process, which some would consider \"generative embeddings.\" What UME-R1 actually introduces are embeddings conditioned on chain-of-thought reasoning that the model generates before producing the final embedding. This is a more specific contribution than introducing \"generative embeddings\" broadly. The authors should revise their claims throughout the abstract, introduction, and conclusion to more precisely describe their contribution as \"reasoning-driven\" or \"CoT-conditioned\" or \"reasoning-conditioned\" embeddings rather than claiming primacy on all \"generative embeddings.\"\n\n2. The distinction between discriminative and generative embeddings as defined in this paper needs clearer justification. The paper defines discriminative embeddings as those extracted from the last input token and generative embeddings as those extracted after generating reasoning and summaries. However, this distinction is somewhat arbitrary - both involve forward passes through the model and both produce vector representations. The key difference is really whether intermediate reasoning is generated, not whether the embedding process is \"discriminative\" vs \"generative\" in any fundamental sense. The paper would benefit from more precise terminology that doesn't overload these already-loaded terms from the broader machine learning literature. \n\n3. The computational costs and practical implications of the approach deserve more discussion. Generative embeddings require generating potentially lengthy reasoning chains (up to 8,192 tokens according to the experimental setup), which substantially increases inference cost compared to discriminative embeddings. Although, I think the performance improvements and potential interpretability make the approach \"worth it\", it would be nice to consider these ancillary factors in the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iSI2s9RrAS", "reviewer": "Reviewer_b8RW", "summary": "The paper introduces UME-R1, a multimodal embedding framework that unifies discriminative and generative embeddings within a single model. Specifically, it incorporates reasoning supervision during training and extends the traditional discriminative embedding pipeline to produce generative embeddings through a reasoning-augmented generation process. Moreover, it employs reinforcement learning to further enhance representation quality. Experiments on MMEB-V2 demonstrate consistent improvements over baselines.", "questions": "1. The authors should discuss the weaknesses mentioned above to provide a constructive view of the method’s effectiveness.\n2. What is the additional inference-time cost of generating reasoning and summary tokens compared with standard discriminative embedding extraction?\n3. Have the authors examined how varying the loss weighting among contrastive and generative objectives during the SFT stage affects final performance?\n4. Have the authors considered implementing a learned or heuristic selector to approximate the oracle results in practice?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The integration of generative embeddings into the existing embedding learning paradigm represents a conceptually meaningful extension beyond prior MLLM-based approaches.\n- The application of reinforcement learning with verifiable rewards is a well-considered adaptation for optimizing embedding quality.\n- Experiments show consistent and notable improvements on the MMEB-V2 benchmark across multiple modalities.", "weaknesses": "1. The improvement achieved by the RL stage is marginal (around one point) relative to its additional computational cost.\n2. While the oracle setting illustrates the upper bound of the framework, the resulting scores are overly idealized and offer limited practical relevance without an implementable selector.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PNuoAa0Mi8", "reviewer": "Reviewer_Lg7o", "summary": "This paper propose UME-R1, a novel universal multimodal embedding framework which unifies the discriminative and generative embeddings. A two-stage training strategy applies a cold-start supervised fine-tuning for the model with reasoning capabilities. Extensive experiment is conducted across various visual tasks and the proposed method outperforms conventional discriminative embedding models and provide a future direction of more interpretable, reasoning-driven generative multimodal embeddings.", "questions": "1. See weakness.\n\n2. The training cost and inference speed between the proposed method and baselines may require further investigation.\n\nOverall, I think this work is solid. Although it is an essential module in MLLM work, multimodal embeddings is a relatively unfamiliar research field to me compared to MLLM, which calls for a better presentation in several respects on this work. So I tend to adjust my score and confidence based on the author's response and other reviewers' opinions.", "limitations": "", "rating": 6, "confidence": 1, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Generative embeddings are worth studying and have certain commonalities for multimodal tasks. This work explores a reasoning-driven method that is brave and novel.\n\n2. The experiment result is solid with a newly constructed cold-start supervised fine-tuning dataset for embedding training with intermediate reasoning and summaries.\n\n3. Convincing visualization examples are provided to prove the effectiveness of the proposed method.", "weaknesses": "1. Lack of a comparison framework between the traditional multimodal embeddings and the proposed method of generative embeddings. Providing this may help better presentation.\n\n2. Visual examples require relatively detailed explanations; otherwise, they may feel complex and difficult to determine the more efficient aspects of the proposed method when viewed directly.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xtzEM2qBYS", "reviewer": "Reviewer_1Cy2", "summary": "This paper introduces UME-R1, a universal multimodal embedding framework that bridges discriminative and generative paradigms, allowing embeddings to be produced either directly or through reasoning-driven token generation. The method involves a two-stage training process: a supervised stage with chain-of-thought reasoning and summary annotations, followed by reinforcement learning leveraging a novel reward based on template adherence and embedding quality. Evaluations on the MMEB-V2 benchmark demonstrate strong improvements over discriminative embedding models.", "questions": "1. How is the generated embedding used in different MMEB-V2 tasks?\n2. How would one deploy a system that decides between discriminative and generative embeddings in practical applications? Is there any mechanism—heuristic or learned—that enables adaptive switching?\n3. UME-R1 doesn't beat the previous SOTA work on the VisDoc workload. Can you provide some potential solutions to improve performance for this specific task?\n4. In the ablation study, the author claims that the RL stage improves the performance substantially. However, the improvement seems marginal according to Table 2.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Thorough testing across 78 diverse tasks spanning three visual modalities provides strong empirical validation.\n2. Generative embeddings provide interpretable reasoning paths, potentially allowing quality assessment through generated explanations.\n3. This paper claims some insights about the relationship between generative and discriminative embeddings as well as their impact on the model's performance.", "weaknesses": "1. Generating reasoning and summaries significantly increases inference time and computational requirements compared to traditional discriminative embeddings.\n2. The selection mechanism between discriminative and generative embeddings for different tasks is not clearly defined, and there is a gap from the oracle baseline.\n3. UME-R1's performance doesn't seem to be comparable with the SOTA baseline on the VisDoc workload.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)