Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs
Authors: Yi Zhang
Bolin Ni
Xin-Sheng Chen
Hengrui Zhang
Yongming Rao
Houwen Peng
Qinglin Lu
Han Hu
Meng-Hao Guo
Shi-min Hu
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal Large Language Models
Dataset
Fully Open
Reasoning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: By building a massive, clean, and reasoning-enhanced dataset, we trained a new state-of-the-art open MLLM that significantly closes the gap with semi-open competitors.

Abstract: Fully open multimodal large language models (MLLMs) currently lag behind proprietary counterparts, primarily due to a significant gap in data quality for supervised fine-tuning (SFT). 
Existing open-source datasets are often plagued by widespread noise and a critical deficit in complex reasoning data, such as Chain-of-Thought (CoT), which hinders the development of advanced model capabilities.
Addressing these challenges, 
our work makes three primary contributions.
First, we introduce Honey-Data-15M, a new SFT dataset comprising approximately 15 million QA pairs, processed through multiple cleaning techniques and enhanced with a novel dual-level (short and long) CoT enrichment strategy.
Second, we introduce HoneyPipe, the data curation pipeline, and its underlying framework DataStudio, providing the community with a transparent and adaptable methodology for data curation that moves beyond static dataset releases. 
Finally, to validate our dataset and pipeline, we train Bee-8B, an 8B model on Honey-Data-15M. Experiments show that Bee-8B establishes a new state-of-the-art (SOTA) for fully open MLLMs, achieving performance that is competitive with, and in some cases surpasses, recent 
semi-open models such as InternVL3.5-8B. A comprehensive ablation study further dissects the impact of our data curation process, revealing that each stage provides significant performance gains across a wide range of benchmarks.
Our work delivers to the community a suite of foundational resources, including: the Honey-Data-15M corpus; the full-stack suite comprising HoneyPipe and DataStudio; training recipes; an evaluation harness; and the model weights. This effort demonstrates that a principled focus on data quality is a key pathway to developing fully open MLLMs that are highly competitive with their semi-open counterparts.

Published as a conference paper at ICLR 2026
BEE: A HIGH-QUALITY CORPUS AND FULL-STACK SUITE TO UNLOCK ADVANCED FULLY OPEN MLLMS
Yi Zhang".? Bolin Ni², Xin-Sheng Chen', Heng-Rui Zhang', Yongming Rao",
Houwen Peng"*, Qinglin Lu², Han Hu², Meng-Hao Guo SShi-Min Hu Tsinghua University, Tencent Hunyuan Team
https://open-best wit..u.......tbbt....... io
ABSTRACT
Fully open multimodal large language models (MLLMs) currently lag behind proprietary counterparts, primarily due to significant gap in data quality for supervised fine-tuning (SFT). Existing open-source datasets are often plagued by widespread noise and critical deficit in complex reasoning data, such Chain-of-Thought (Co'T), which hinders the development of advanced model ca- pabilities. Addressing these challenges, our work makes three primary contribu- tions. First, we introduce Honey-Data-15M, a new SFT dataset comprising ap- proximately 15 million QA pairs. processed through multiple cleaning techniques and enhanced with novel dual-level (short and long) CoT enrichment strategy. Second, we introduce HoneyPipe, the data curation pipeline, and its underlying framework DataStudio, providing the community with transparent and adaptable methodology for data curation that moves beyond static dataset releases. Finally, validate our dataset and pipeline, train Bee- an 8B model on Honey-Date 15M. Experiments show that Bee-8B establishes a new state-of-the-aa (SOTA) for fully open MILMss, achieving performance that is competitive with, and in some cases surpasses, recent semi-open models such as Intern VL.35-88. A compre- hensive ablation study further dissects the impact of our data curation process, revealing that each stage provides significant performance gains across a wide range of benchmarks. Our work delivers to the community a suite of foundational resources, including: the Honey- Daayy-aaaasa 15M corpus; the full-stack suite comprising HoneyPipe and DataStudio; training recipes; an evaluation harness; and the model weights. This effort demonstrates that a principled focus on data quality is key pathway to developing fully open MILMSs that are highly competitive with their semi-open counterparts.
INTRODUCTION
Massive datasets have been comnersoone of the success seen in today's powerful multimodal large language models (MLLMs) (Li et al., 2025a; Liu et al., 2023c; Wang et al., 2024b; Hurst et al., 2024; Guo et al., 2025b). However, as the field matures, a new consensus is emerging that data quality is as critical as data quantity, especially for the supervised fine-tuning (SFT) stage (Li et al., 2024d; Wang et al., 2023b). The success top-tier proprietary models such as Gemini 2.5 (Comanici et al., 2025) and GPT-5 (OpenAl, 2025) relies heavily highly curated and refined SFT datasets. Such private resources pose a formidable barrier to entry-ore one that has split the MLLMs field into a distinctly tiered structure: (1) top proprietary models (Guo et al., 2025b; Jaech et al., 2024; Comanici et al., 2025), (2) semi-open MILMS that release weights but keep data private (Yue et al., 2025b; Bai et al., 2025), and (3) the fully open MILMS (Li et al., 2025a; Guo al., 2025c; Deitke et al., 2025), which lag behind the former two. For the fully open community, competing on sheer data volume is not an appropriate strategy. Therefore, focusing on data quality is the most viable path forward.
Project lead. Contact: henryllpeng @tencent.com, gmh @ tsinghua.edu.cn Corresponding author. Contact: shimin @tsinghua.cdu.co

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Published as a conference paper at ICLR 2026
However, existing open-source SFT datasets suffer from fundamental quality issues that hinder model development. The first of these is widespread data noise (Chen et al., 2024d; Guo et al,, 2025c). Existing datasets are frequently contaminated with not only content-level issues such as fac- tual inaccuracies and image-instruction mismatches, but also structural and format-level flaws, such as excessive text repetition, incorrect tags in instructions, and low-quality images with improper sizes or aspect ratios, During training, this noisy data misleads the model into learning spurious cor- relations, which systematically undermines its core capabilities. This manifests not only as factual hallucinations (Bai et al., 2024) but also as degraded reasoning, poor estructioonfollowwee ity, and a tendency to generate low-quality responses. Another critical issue the gap in complex problem-solving abilities. The capacity of model for complex reasoning has become a key deter- minant of its overall capability and a primary manifestation of its advanced intelligence (Kahneman, 2011; Guo et al., 2025e;d; Jaech et al., 2024; Guo et al., 2025a; Yue et al., 2025b). This is clearly demonstrated by leading proprictary and semi-open models, which excel at handling complex in- structions, often leveraging long Chain-of- however, lags significantly in this area. This weakness is rooted in data deficiencies: the community not only lacks large-scale, high-quality long CoT datasets, but it also remains difficult to identify which instructions actually require these deep, multistep reasoning paths (Kojima et al., 2022). This data deficit represents the primary bottleneck preventing fully open MLLMS from developing the advanced problem-solving skills necessary to compete.
abil-
(CoT) processes. The fully open community,
The root of these data quality issues lies not only in the raw data but also in the lack of transparent and reproducible data curation pipelines. Proprictary models benefit from sophisticated, undisclosed similar opacity
recipes for data filtering and cleaning (Guo al., 2025b; Yue et al., 2025b). exists within the open-source community. Previous work has focused on sharing the final dataset rather than the methodologies behind its creation. Pioneering projects (Li et al., 2025a; Guo al., 2025c), despite their contributions, often release static datasets. Their curation pipelines-the code, prompts, and filtering logic remain opaque black boxes. This one-off release model is critically flawed. Proprietary efforts constantly refine their internal data recipes. To truly compete, the open- source community needs access to evolving methodologies. The absence of shared, adaptable data curation methods is fundamental roadblock.
To address these multifaceted challenges, we introduce Honey-Data-15M, a large-scale, high- quality SFT dataset designed serve new cornerstone resource for the fully open MILM community. Its construction is guided by two core principles. First, we conducted a comprehensive data refinement process, collecting from diverse projects and systematically cleaning the corpus to purge widespread data noise, thereby significantly enhancing overall data quality and reliability. Second, we implemented nuanced, dual-level CoT enrichment strategy that tailors response depth to instruction complexity. For instructions requiring moderate reasoning, we constructed short CoT responses, creating a massive corpus of 12.1M 1M instruction-response pairs. For the most complex instructions, we generated detailed long CoT responses, yielding a high-quality set of 2.9M pairs. This targeted, dual-level approach provides tailored reasoning depth across the entire dataset and inherently solves the critical challenge of identifying which instructions warrant more elaborate, multi-step solutions. Honey-Data- 15M was created using our data pipeline, HoneyPipe which is an instantiation of our self- delf-developed data curation framework, DataStudio. This pipeline leverages MLLMs to automate the entire curation workflow, from cleaning to enrichment. As a scalable and economical alternative to costly human annotation, this model-driven process makes high-quality data construction feasible for the open-source community.
To validate the effectiveness of curated Honey-Data- 15M, we also contribute new model to the fully open MLLM ecosystem. Experiments with our final model, Bee-8B, trained on the full Honey-Data- 15M dataset, establish new state-of-the-art (SOTA) among fully open MLLMs. Its performance is highly competitive, standing on par with, and in some cases surpassing, several recent semi-open models such as VL3.5-B (Wang et al.. 2025b). This significant leap is directly attributable to our data curation strategy. A comprehensive ablation study quantifies this impact, showing that our curation process yields significant improvements across multiple beneh- marks compared to using the original, unprocessed data. These results confirm that our focus on data quality is a crucial strategy for closing the performance gap between fully open MLLMS and recent semi-open models.
In summary, our key contributions are threefold:
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Overview of the HoneyPipe. After initial data preparation and filterin,, the foundational path generates short CoT responses, which are then checked by Fidelity Verification. Complex instructions that fail this check are routed to the long CoT Enrichment loop. Here, a more powerful model generates detailed long CoT response, which then undergoes the same Fidelity Verification. This dual-level architecture systematically builds the Honey-Data-15M0
Figure I:
Honey-Data-l5M: A dataset with 15M QA pairs, systematically cleaned of noise and enriched with a dual-level CoT reasoning, serving new cornerstone for the open community.
HoneyPipe: The data curation pipeline and its underlying framework, DataStudio, offering the community transparent and adaptable methodology that moves beyond static dataset releases. Bee-8B: An 8B model trained on Honey-Data Maney-Data-15MM 15M that achieves SOTA among fully open mod- els and competes with semi-open counterparts, validating data quality and pipeline effectiveness.
2 HONEY-DATA 15M AND HONEYPIPE
To construct our Honey-Data- 15M dataset, we introduce the data pipeline, HoneyPipe, as shown in Fig. 1. It is an automated and reproducible workflow designed to systematically address the twin challenges of widespread data noise and the gap in complex reasoming abilities identified as critical roadblocks for the open-source community. The entire pipeline is constructed from the modular components of our DataStudio. Its architecture features nuanced, dual-level reasoning enrichment strategy, comprising a foundational path for large-scale short CoT enrichment and a specialized loop for generating long CoT responses to the most complex instructions. This structure allows us to transform a vast, raw data pool into a high-quality, dual-level CoT supervised fine-tuning (SFT) dataset. By offering the community transparent and adaptable methodology. move beyond static dataset releases. Each stage of this pipeline is detailed below.
2.1 STAGE I: DATA AGGREGATION AND PREPARATION
The pipeline begins by assembling an initial pool of approximately 24 million image-text pairs from diverse community datasets, including LLaVA-One Vision (Li et al., 2025a). PixMo (Deitke al., 2025), and MAmmoth-VL (Guo et al., 2025c), etc., where content overlap is a key challenge. To maximize data diversity and enhance processing efficiency, we performed rigorous deduplication at the pair level. Specifically, sample was considered a duplicate and removed only if both its image (represented by a perceptual hash) and its textual instruction (represented by a simhash) were iden- tical to those of another sample. This critical process significantly reduced redundancy, yielding instruction-respeeeee
clean and unique set of each sample was assigned domain label (e.g.. General, Chart, OCR, STEM). Instead of perform- ing computationally expensive per-instance classification, we employed an efficient coarse- coarse-grained strategy at the data source level. Specifically, for each aggregated data source, manually in-
triplets. Finally, to guide subsequent processing,
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spected approximately five representative image-text pairs determine single overarching topic label, which was then uniformly assigned to all samples originating from that source.
2.2 STAGE 2: NOISE AND IRRELEVANCE FILTERING
This stage designed to purge the widespread data noise endemic open-source datasets by inte- grating both rule-based and model- based filtering operators. The rule-based operators address spe- cific formatting issues, such as removing samples with very small images, extreme aspect ratios, or repeated text in instructions. Orchestrated alongside these is model-based filtering operator, which leverages the powerful Qwen2.5-VL-725 (Bai et al., 2025) model to ensure image- image-instruction con- sistency. This operator is prompted to assess whether an instruction is logical and answerable, and whether is semantically related to the visual content. For example, it will flag an instruction such as "solve the function problem" irrelevant to image containing only oranges.
By integrating these different types of operators within this stage, we effectively pruned flawed samples, producing a clean set of inaage-nsttructio pairs ready for enrichment.
2.3 STAGE 3: SHORT COT ENRICHMENT AND VERIFICATION
With clean data foundation, this stage constitutes the foundational path of our dual-level CoT enrichment strategy. targets instructions that require moderate reasoning by generating explicit, step-by-step CoT explanations. The process begins with data triage before proceeding to two main phases: enrichment and verification.
First, we identify data sources unsuitable for reasoning enhancement, such as samples from com- puter vision tasks like OCR or object detection. These samples bypass the enrichment process and are added to the final dataset. For all other samples, we proceed as follows:
Short CoT Enrichment. For samples slated for enrichment, we begin by preprocessing their in- structions. We remove any head or tail prompts that might discourage detailed reasoning (e.g,, "Answer directly") to elicit the model to produce a comprehensive, step-by-step response. We then use powerful open-source MILMs sponses into detailed reasoning paths. Notably, we do not add extra system prompts. This is because the models are already adept generating multi-step responses, and a prompt could constrain their output diversity. This step is the primary source of the approximately 12.1 million short CoT samples in our dataset.
to transform the simple, short-form
Fidelity Verification. To ensure the fidelity of the generated content, we employ a verification stage that operates on the "LLM-as-a-Judge' (Gu et al., 2024: Zheng al., 2023). We use verifier model S-wen2.5-VL-7221
to perform a semantic comparison between the final conclusion of the newly generated CoT and the original response. The evaluation is twofold: for factual queries (objective questions), the final responses must match precisely; for descriptive open-ended queries (subjective questions), thematic relevance and semantic consistency are required. Samples that pass this check are added to the final dataset. Samples that fail are not discarded but are instead routed to the long CoT enrichment loop for more specialized enrichment.
2.4 STAGE 4: LONG COT ENRICHMENT LOOP
This loop constitutes the second level of the enrichment strategy, designed specifically for the most complex instructions that demand deep, multi-step problem-solving. This secondary path processes three primary types of inputs: 1) the samples that failed the fidelity verification in the previous stage Sec. 2.3; 2) samples from select data sources identified as inherently complex during our initial classification (e.g., Weblnsteettt (Jia et al., 2025)), for which we proactively generate a long CoT version in addition to their short CoT counterpart; and 3) samples from datasets that have been validated in prior research (c.g., Vision-RI (Huang et al., 2025)) as being particularly suitable for generating deep reasoning chains.
We leverage the top proprietary MLLMs to generate a more detailed solution. When tasked with the instructions, the model first generates deep reasoning, often structured with tags like < think >< /think >, before outputting the final response. This specialized reasoning level can handle complex instructions beyond the reach of the initial models.
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Following this enrichment, each long CoT response undergoes the same fidelity verification described in Sec. 2.3. This final validation ensures the correctness the enriched response. The samples that successfully pass this check constitute the approximately 2.9 million long CoT data points Honey-Data-15M, We discard any sample that fails this final verification, assuming is erroneous, unsolvable, too costly annotate, even if top proprietary models cannot solve it.
2.5 HONEY-DATA-15M: HIGHI QUALITY CORPUS WITH THE DUAL-LEVEL CoT
The primary output our pipeline Honey-Data Sarey--Dta-Ble Matta-15M, large-scale, multimodal SFT dataset com- prising 15 million meticulously curated samples. Our primary objective in developing Honey-Data- 15M furnish the research community with a high-quality, reproducible resource that can serve as new comerstone for the fully open MLLM community. The compositional breakdown, illus- trated in Fig. 2, reveals a diverse amalgamation data sources, strategically chosen to cover a wide spectrum of domains and complexities. A more granular description of the source-specific statistics can be found in Fig. 3.
A defining feature of Honey-Data-15M is its enrichment with dual-level CoT reason- ing, which forms the backbone of the dataset. We have integrated approximately 12.1 million short CoT samples, designed instill founda- tional, step-by-step logical inference. Comple- menting these are 2.9 million long CoT sam- ples, which challenge models with more intri- cate, multi-step reasoning problems that require deeper synthesis of information. This dual- pronged approach ensures that models trained on Honey-Data- DDM can develop both preci- sion in concise reasoning and depth in complex problem-solving.
OCR
Grounding
Counting
Document
General
STEM
36,8%
Honey-Data 15M
15.1%
Caption
24.0%
To forge powerful and well-rounded models, Honey-Data- 15M applies its dual-level reason- ing across a spectrum of critical domains, such "General" for foundational visual under- standing and "STEM" for symbolic reasoning, thereby ensuring comprehensive skill develop- ment.
Figure 2: Distributions of Honey- Daayy--taa1155
BEE-SB TRAINING RECIPE
To validate the effectiveness our Honey-Data-15M, we developed Bee-8B, an 8B multimodal large language model trained with multi-stage recipe. This training process intended showcase the strengths of our Honey-Data-I553 15M dataset, particularly its capacity to foster advanced complex reasoning.
3.1 MODEL ARCHITECTURE
Our model, Bee-8B, adopts proven and effective MLLM architecture, drawing inspiration from leading open-source models like LLaVA-OneVisse (Li et al., 2025a). It is built upon the powerful Qwen3-8B (Yang et al., 2025a) large language model, which serves as the foundation for reason- ing and text generation. For visual understanding, we employ the open-source SigLIP2-so440m- patch14-384 (Tschannen et al., 2025) as our core vision encoder. To effectively handle images of varying resolutions and preserve fine-grained details, simple two-layer MLP with a GELU activation function serves as the projector, mapping these aggregated visual features into the L.LM's token embedding space.
the Anyres strategy (Liu et al., 2024b).

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Figure 3: Data collection of Honey-Dater 15M. detailed breakdown of our dataset's composition across seven major categories. The number of samples (in thousands) is listed for each source. The * denotes that the data contains the long CoT response.
3.2 MODEL TRAINING
Our training methodology is a five-stage process, detailed in Tab. 1, which progressively builds the model's capabilities from basic perception to complex reasoning. The overarching goal is to first establish robust vision-language foundation, then instill deep reasoning abilities using our Honey-Data-155 15M dataset, and finally polish the model's outputs for enhanced reliability.
Table 1: Detailed configuration for cach training stage of Bee-8B.
Stages
Stage
Stage 2
Stage
Stage
Stage
MLP Warmup
Vision- Vision-Langueen Language
Multimodal SFT
Efficient Refinement SFT
Policy Optimization RL (GRPO)
Purpose
Alignment
Batch size
512
256
256
256
512
Learning Rate
le-3
4c-5
5c-5
3e-5,5c-6
2c-6
Datuset Items
IM
14M
15M
1M
50K
Packed Sequence Length
8192
16384
16384
16384
Training Epochs
1
1
1
Trainable Components
MI.P
All
All
A1l
All
The training begins with two foundational stages. The first stage consists of an MLP warmup, where only the projector is trained to efficiently align the visual and language feature spaces without altering the pretrained backbones. This is followed by a full-parameter vision-language alignment stage, where the model is trained on large corpus of image-text pairs and text-only data to build core multimodal capabilities while preserving the LLM's intrinsic cognitive abilities.
The subsequent three stages focus on advanced instruction tuning and refinement. The pivotal third stage is a large-scale SFT on the entire Honey-Data- dataset, designed to instill the complex reasoning patterns from our dual-level CoT data. The fourth stage is an efficient refinement SFT on Honey-Data- 1M, a subset of Honey-Data- further polishing the model's capabilities. Finally, the fifth stage employs Group Relative Policy Optimization (GRPO) to improve response quality and reliability by mitigating common generation issues like text repetition. The efficacy of this final optimization phase hinges on the high-qualit model produced by the SFT stages, which in turn validates our curated data. Further details on the data composition, sources, and specific configurations for each stage provided in Appx. F.
curated for a more rational topic distribution, thereby

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4 EXPERIMENTS
To validate the effectiveness of our Honey-Data-15M, we conduct a comprehensive experimental evaluation of Bee-8B, beginning with our evaluation setup (Sec. 4.1). We then benchmark Bee- 8B against leading fully open and semi-open MLLMs to demonstrate its capabilities (Sec. 4.2) and conclude with ablation studies validate the high quality of our Honey-Data 15M and quantify the impact of our data curation strategy (Sec. 4.3).
4.1 EVALUATION SETUP
We evaluated our model using a customized VLMEvalKit (Duan et al., 2024), which we adapted to enable LLM-based judging on benchmarks like DocVQA. Our model was evaluated with the thinking mode and maximum response length of 16,384 tokens. For LLM-based judging, we employed Qwen3-32B (Yang et al., 2025a) in non-thinking mode, Additional details are provided in the Appx. H.
4.2 EVALUATION OF MODEL. CAPABILITY
As shown in the comprehensive benehmark results in Tab. 2, Bee-8B achieves across-the-boan performance improvements over existing fully open models (Li et al., 2025a; Deitke et al., 2025) and stands as highly competitive alternative to recent semi-open models (Bai et al., 2025; Team et al., 2025c; Wang et al., 2025b). Its most significant advantages are observed in factual accuracy and complex multi-step reasoning, directly reflecting the strengths of our Honey-Data- More results for all benchmarks are available in Appx. A.1. Key results are detailed as follows:
dataset.
General VQA Tasks:
Bee-8B showcases robust and well-rounded performance across wide ray of general visual question answering tasks. On comprehensive, mulli-domain benchmarks such as MMMU (Yue et al., 2024) and MMStar (Chen et al., 2024c), it achieves highly competitive scores of 66.8 and 71.4, respectively, demonstrating its strong general knowledge base. The model's 's supe- rior performance becomes particularly apparent benchmarks that test specifie VQA capabilities. For example, on MMMU-Pro (Yue et al., 2025a), a benchmark for professional-leve knowledge, Bee-8B obtains top score of 50.7. This establishes commanding lead of 3.6% over the next- best competitor. Qwen2.5-VL-7B (Bai et al., 2025), underscoring its advanced cognitive abilities. Similarly, on CountBench (Paiss et al., 2023), it ranks first with an exceptional score of 93.0. This leading performance extends to other tasks, including top score of 83.9 on the MMVet (Yu et al., 2024b). Furthermore, it demonstrates superior real-world knowledge by securing the top rank on RealWorldQA (xAI, 2024) with a score of 73.1. This suite of results confirms that Bee-8B not only possesses a broad understanding of general multimodal information but also excels in a variety of core visual skills.
Document, Chart, and OCR Tasks: In tasks involving structured visual content like documents, tables, and charts, Bee-8B demonstrates strong performance. Its skill in chart analysis is evident on ChartQA (Masry et al., 2022), where achieves a highly competitive score of 86.7, confirming its robust data parsing abilities. This proficiency is most pronounced in the challenging area of scientific document analysis. On the CharXiv benchmark (Wang et al., 2024c), Bee-8B secures the top rank in both descriptive questions (DQ) and reasoning questions (RQ) with scores of 84.8 and 57.3, respectively. For the reasoning task, its score establishes an impressive lead of nearly 12% over the closest competitor, Keye-VL (45.4) (Team al., 2025c), underscoring its advanced capacity for deep semantic inference. These results validate Bee-8B's 's's powerful abilities to not only extract precise information but also to deeply comprehend the context of structured visual data.
The most significant advancements delivered by Bee-8B are in com- plex math and reasoning, directly validating the effectiveness of our CoT-enriched Honey- 15M dataset. It consistently delivers exceptional performance across suite of benchmarks designed to test quantitative and logical problem-solving Verse (Zhang et al., 2024b), where the RL-tuned version scores top-ranking 67.0. This represents a clear improvement of 5.5% over the strong semi-open model Intern VL3.5-8B (Wang et al., 2025b), showcasing its superior mastammmateeatttcal skills. Furthermore, LogicVista Vista (Xiao et al., 2024), Bee-8B also achieves the top score of 61.3, surpassing the next-best competitor by 4% and demon- strating its versatile reasoning abilities. Its robustness is further confirmed DynaMath (Zou al.,
Math and Reasoning Tasks:
The model's leadership is most evident on Math-
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Table 2: Evaluation of Bee-8B against other MLLMs. We distinguish between fully open (*) and semi-open (f) models. The top and second-best scores for each benchmark are highlighted.
LLaVA One Vision-BB
Molmo -BB-D"
Qwen2.5 -VL-7B
Keye-VL -88
In.ernV1.3.5 -8B
Bee-$B -SFT
Bee-8B
Task
Benchmark
-RL
A12D
81.4
81.0
34.3
86.7
84.0
83.8
85.3
BLINKest
48.2
59.5
52.5
55.0
CountBench
84.8
74.1
78.0
90.55
93.0
HallusionRenchaa
MMBennchCNN.
81.3
92.0
81.2
84.2
MMBeach-ENaa
80.8
82.1
91.5
83.0
85.5
MMMU.ul
48.8
45.3
58.6
71.4
73.4
66.8
66.1
General
MMMU
AMMUU-PDDesaddar PrDetandard
29.5
34.7
47.1
50.4
50.7
VQA
MMStar
61.7
56.1
63.9
75.5
69.3
69.0
71.4
MMTT-Bench.ai
59.3
56.3
63.6
63.9
66.7
64.6
67.0
MMVer
83,9
MMVP
73.3
79.0
80.7
82.0
POPE
RealWoridQA
66.3
70.7
68.5
67.7
67.5
70.1
73.1
VisuLogiic
VLMs are Blind
39.2
37.4
57.1
55.8
56,5
CharXiveq
73.9
77.7
72.2
84.7
84.8
CharXivaq
Table Chart OCR
ChartQAuss
80.0
84.1
87.3
86.3
86.7
86.7
86.1
DocVQAual
87.2
87.0
InfoVQAosl
81.4
67.4
72.3
72.9
OCRBench
65.6
85.1
84.0
83.1
82.5
SEED SEED-Bench2-PRD
65.4
67.6
70.4
69.4
70.8
67.7
68.5
DynaMathworrt
9.0
21.0
37.3
37.7
41.3
40,5
LosicVista
33.3
44.1
54.8
$7.3
56.8
61.3
Math Reasoning
MathVerse, SealtVerre,ssono.. veln
26.2
4.2
25.1
59.8
61.5
61.9
67.0
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2025), where it again secures the top of 41.3. This consistent pattern of leading performance on challenging reasoning tasks confirms that our high-quality data strategy has successfully instilled the model with complex reasoning capabilities.
In summary, Bee-8B establishes a new performance bar for fully open models, particularly in factual accuracy and complex reasoning, and proves highly competitive with recent semi-open models. These findings confirm our core thesis: focus on high-quality data curation is critical for creating models that can rival leading semi-open models.
4.3 ABLATION STUDY
Ablation for HoneyPipe: To precisely isolate the effects our data curation pipeline, we con- ducted ablation study with three data subsets, visualized in Fig. 4. We established a baseline with Draw (1.2M raw samples). Our main curated set Deurased (960K samples after full filtering and short CoT enrichment). To isolate the impact of the enriched responses themselves, DoT, an identical 960K set to Deurated, but with the new CoT responses replaced by the original, simple responses. The results reveal clear hierarchy (Dearned > Dno-CoT Draw). The significant improvement from Daw to Dmo-CoT demonstrates the large combined benefit of noise filtering and data selection process. More importantly, the performance leap from DRo-Cor Deurated clearly demonstrates the direct impact of the CoT enrichment itself. The superior performance, especially reasoning- heavy benchmarks like Math Vista and CharXiv-RQ. is therefore directly attributable to training on detailed, step-by-step rationales. This study provides powerful evidence that data cleaning and CoT enrichment are critical.
created
Ablation for Honey-Data-IM 1M: To validate the effectiveness of our curated 1M data subset, we conducted an ablation study with results visualized in Fig. 5. We compare the original Qwen2.5-VL- 7B base model against two variants fine-tuned from our Stage 2 checkpoint: one trained on Random- 1M (a randomly sampled 1M subset) and another on Honey-Date 1M (our meticulously curated IM subset). The results clearly demonstrate the superiority of selection strategy, as the model fine-tuned on Honey-Date 1M outperforms the variant trained on the randomly sampled Random-
1M. Notably, fine-tuning on just this 1M curated subset sufficient for model to surpass the
8

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Figure 4: Visual comparison of model perfor- mance in our ablation study. The radar chart il- lustrates the step-by-step impact of our curation pipeline. The performance lift from Dew (base- line) to Dio-CoT shows the benefit of noise filter- ing, while the larger leap to Deurated highlights the critical contribution Chain-of- Thought enrichment, especially in reasoning-hean do- mains.
Figure 5: Performance comparison of models fine-tuned on different 1M data subsets. The model trained on our curated Honey-Data-IN consistently and outperforms both the Qwen2.5- VL-7B base model and the model trained on a Random- 1M subset, highlighting the efficiency of our data selection strategy.
original Qwen2.5-VL-7B nearly half of the evaluated benchmarks. This study provides powerful evidence for both the high quality of our data and the effectiveness of our selection strategy in cultivating advanced reasoning skills.
RELATED WORK
Multimodal Large Language Models (MLLMs) have demonstrated immense potential across diverse real-world scenarios (Li et al., 2026; Xu al., 2026; Ning et al., 2026; Tang al., 2025a; Wei al., 2025; Tang et al., 2025b). The modern wave of MLLMs began with GPT-4V (OpenAI, 2023), which showed that general LLM (OpenAI, 2023; Touvron et al., 2023; Bai et al., 2023a) could process visual inputs. In the open-source community, LLaVA (Liu et al., 2023c) established standard paradigm of frozen vision encoder (Radford et al., 2021; Tschannen et al., 2025; Zhai et al., 2023), lightweight connector, and visual instruction tuning, proving that GPT-generated instructions were sufficient for building a practical multimodal assistant. Subsequently. the field's focus shifted to data, which has produced significant data gap. Proprietary models (OpenAI, 2025; Comanici al., 2025; Guo et al., 2025b) leverage massive, ever-evolving private datasets. Semi-open models (Wang al., 2024b; Yao et al., 2024; Zhang et al., 2025a: Bai et al., 2025: 2023b; Yue al., 2025b; Team et al., 2025c;b; Yang et al., 2025b; Team et al., 2025a) retain an edge through private data curation, even with released weights. In contrast, fully open models (Liu et al., 2024b; Wang al., 2025c; Guo et al., 2025c; Deitke et al., 2025; Li et al., 2025a) are constrained in both the scale and quality of their public data. This performance hierarchy stems more from data access and pipelines than from architectural tweaks. Accordingly, work centers not on novel architectures but high-quality, reproducible data curation pipeline designed to narrow this gap for fully open models at a reasonable cost.
AVAILABILITY
We are committed to open seience and the reproducibility our results. We will publicly release the full suite of resources developed in this work, including:

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Published as a conference paper at ICLR 2026
Dataset: The complete Honey-Data-155 15M corpus.
Model: The weights for the final Bee-8B model.
Codebase: The source code for our data curation pipeline (HoneyPipe) and the underlying framework (DataStudio). And all seripts, configurations, and the code required to train and evaluate.
Training Resources: To support detailed analysis and community research, we will also release the intermediate model checkpoints for each stage of our training recipe, along with the specific training data subsets used for each stage.
7 CONCLUSION
In this work, we addressed the critical data quality gap that hinders the progress of open-source MLLMs. We introduced 10Me 15M-sample SFT dataset systematically cleaned of noise and enriched with a dual-level Chain-of-Thought (Co')) reasoning structure, all constructed using open-source, model-driven pipeline. HoneyPipe. To validate approach, trained Bee-8B, which establishes a new state-of-the-ane among fully open models and proves highly com- petitive with several leading semi-open models. Our experiments reveal standout performance in complex math and reasoning, a direct outcome of our targeted CoT enrichment, with ablation stud- ies confirming the significant impact of our curation process. Ultimately, our work suggests clear path forward for the community: prioritizing data quality through transparent, reproducible meth- ods is more effective strategy than competing on data volume. The full-stack suite of our dataset, pipeline, and model aims to facilitate this data-centric approach and empower further innovation within the open-source ecosystem. By open-soureing not just our dataset but the entire curation methodology, we hope to provide comnersoone for a new wave of collaborative and competitive open-source MILLM development.
Acknowledgement. This work was supported by Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (No. JYB2025XDXM101, the National Natural Science Foundation of China (project No. 62495060, 623B2057), and the Research Grant of Tsinghua- Tsinghua-Tencent Tencent Joint Laboratory for Internet Innovation Technology.
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APPENDIX
A ADDITIONAL EXPERIMENTS
In this section, we provide series of additional experiments to offer a more granular understanding of our model's performance and the key components of our training methodology. We investigate the specific contributions different training stages and analyze the model's behavior under various inference conditions. These analyses serve to validate design choices and highlight the critical factors that enable the model's 's advanced reasoning capabilities.
A.1 ABLATION OF DIFFERENT REASONING MODES AND STAGES
To further analyze our model's performance and the impact of our training methodology, we con- ducted comprehensive ablation study comparing the model at three key checkpoints: the initial SFT model (Stage 3), the model after refinement SFT (Stage 4), and the final model after reinforcement learning (Stage 5). For the SFT models, also assessed performance across both short CoT (N-T) and long CoT (T) inference modes. This multifaceted analysis aims to understand the progressive impact of each training stage and the model's behavior across different reasoning complexities.
As detailed in Tab. 3 to 6, we assessed performance across a wide array benchmarks in two distinct inference modes:
Table 3: Performance comparison of our model after Stage 3, Stage 4, and Stage on general VQA benchmarks (Part l). The top and second-best scores for each benchmark are highlighted.
BLINK
Count Bench
Hallusion Benchavg
MMBench -CNaev
MMBench -ENaev
MMMU
MMMU
Model
Mode
A12D
Proscandard
Stage3
N-T
82.7
53.3
93.2
54.2
79.6
81.3
58.9
43.2
Stage3
T
83.7
52.6
91.4
57.0
83.0
81.9
66.7
48.5
Stage4
N-T
81.2
54.9
92.4
56.8
80.7
82.7
59.8
43.9
Stage4
T
83,8
52.5
90.5
59.8
81.2
83.0
66.8
50.4
Stage5
T
85.3
55.0
93.0
58.2
84.2
85.5
66.1
50.7
Short CoT (N-T) mode: We prompt the model with <think> (nwn</thinkee to elicit concise reasoning path with fewer steps (short CoT).
Long CoT (T) mode: We the prompt <think> An to encourage the model to produce more detailed and in-depth reasoning process (long CoT).
The model exhibits strong performance across both inference modes, reflecting effective align- ment between its vision module and the dual-level reasoning capabilities inherent the Qwen3 L.M.. The results show clear progression of improvements. First, the Stage 4 refinement yields
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Table Performance comparison of model after Stage 3, Stage 4, and Stage on general VQA benchmarks (Part 2). The top and second-best scores for each benchmark are highlighted.
MMT -Benchyll
POPE
VLMs
Model
Mode
MMStar
MMVet
MMVP
RealWorIdQA
VisuLogic
Blind
ovg
Stage3
62.3
69.3
70.7
Stage3
68.0
63.8
84.1
72.5
24.0
Stage4
N-T
67.5
63.2
69.2
79.0
86.2
70.5
24.2
52.9
Stage4
69.0
64.6
83.3
80.7
84.0
70.1
24.4
55.8
Stage5
71.4
67.0
83.9
82.0
84.8
73.1
26.5
56.5
Table Performance comparison on benchmarks for table, chart, and document understanding. The top and second-best scores for each benchmark are highlighted.
CharXiv
CharXiv
ChartQA
DocVQA
InfoVQA
SEED Bennch2-Plus
Model
Mode
OCRBench
DQ
ToQ
Stage3
81.7
47.0
82.1
86.3
84.2
66.7
Stage3
53.4
86.8
84.1
67.7
Stage4
N-T
48.3
79.4
86.3
66.7
83.0
67.2
Stage4
84.7
55.3
86.7
87.2
72.3
83.1
67.7
Stage5
84.8
57.3
86.1
87.0
72.9
82.5
68.5
discernible gains, which we attribute to the high quality of the curated IM subset. Subsequently, the Stage reinforcement learning provides further significant boost. This stage primarily enhances the model's s reliability by mitigating common generation issues such as response repetition, which in turn elevates its final performance on the majority of benchmarks, particularly in complex math and reasoning. This finding underscores that our multi-stage recipe, combining high-quality data curation with final policy optimization, critical for unlocking model's most advanced reasoning abilities.
A.2 SENSITIVITY ANALYSIS OF JUDGE MODELS
As primary evaluation pipeline utilizes Qwen-based judge (Qwen3-32BB to assess Qwen- based model (Bee-8B), there a potential risk of "self-preference" bias. To rigorously quantify the sensitivity of our results to the choice of the evaluator, we conducted cross-family evaluation using GLM-4,5-FPS (Zeng et al., 2025) as an independent judge across all 29 benchmarks.
Tab. presents comprehensive comparison of the performance of both Bee-8B-SFF and Bee-8B- RL when evaluated by the original Qwen judge versus the GLM judge. The results indicate a high degree of consistency between the evaluators.
While we observe discrepancies specific benchmarks, most notably in ChartQA, Doc and InfoVQA, where the GLM judge tends be stricter, the overall impact the global average is minimal. For Bee-8B-RL, the Global Average score shifts only slightly by -0.3 points (from 70.2 with the Qwen judge 69.9 with the GLM judge). This minimal deviation confirms that while family preference may exist to a minor degree or judges may have different calibration thresholds
Table 6: Performance comparison mathematical and logical reasoning benchmarks. The top and second-best scores for each benchmark are highlighted.
DynaMath worat.
Math Verse vision Siilon_only
Math Vista
Model
Mode
Logic
Math
WeMath
Stage3
N-T
34.3
52.6
61.9
37.8
78.1
43.1
Stage3
T
35.9
54.1
63.3
42.8
78.8
52.7
Stage4
N-T
31.1
52.1
62.9
40.6
78.6
45.5
Stage4
T
41.3
56.8
61.9
46.8
78.6
55.0
Stage5
T
40.5
61.3
67.0
50.0
81.4
59.8
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Table 7: Sensitivity analysis of Bee-8B performance using different LLM judges (Qwen3-32B GLM-4,5-FP8), We report the scores for both SFT and RL stages. The Average row shows the mean score across benchmarks. The minimal delta in the global average confirms the robustness of our evaluation.
-B--88-SFT
Bee-8B-RI.
Task
Benchmark
Qwen Judge
GLM Judge
A
Qwen Judge
GLM Judge
A
A12D BLINK
83.8
83.7
-0.1
85.3
85.3
0.0
52.5
52.7
+0.2
55.0
55.2
+0,2
CountBench
90.6
90.3
-0.3
93.0
92.8
-0.2
HallusionBencharg
59.8
59.4
-0.4
58.2
58.6
+0.4
MMBench-CNN
81.2
81.2
0.0
84.2
84.2
0.0
MMBench-EHl -aanchh--eerrrr
83.0
83.0
0.0
85.5
85.7
+0.2
MMMU vni
66.8
66.0
-0.8
66.1
65.6
-0.5
General VQA
MMMU-Pro. pronsndaddd
50.5
49.0
-1.5
50.7
50.3
-0.4
MMStar
69.0
68.9
-0.1
71.4
71.4
0.0
MMT-Bencheal
64.6
64.6
67.0
67.1
+0.1
MMVet
83.3
85.0
+1.7
83.9
83.7
-0.2
MMVP
80.7
80.0
-0.7
82.0
82.0
0.0
POPE
84.0
84.0
0.0
84.8
84.8
0.0
Real WarldQA
70.1
70.1
0.0
73.1
72.8
-0.3
VisuLogic
24.4
24.4
0.0
26.5
26.5
0.0
VLMs are Blind
55.8
55.8
0.0
56.5
56.5
0.0
CharXivpQ
84.7
83.6
84.8
84.8
0.0
CharXivRQ
55.3
54.0
-1.3
57.3
55.8
-1.5
CharlQA
86.7
84.2
-2.5
86.1
83.8
-2.3
Table & Chart & OCR
VocVQA
87.2
83.6
-3.6
87.0
83.4
-3.6
IQAoVQAval
72.3
69,9
-2.4
72.9
70.7
-2.2
OCRBench
83.1
83.1
0.0
82.5
82.5
0.0
SHHD-BBnchh2 Bench2-Plus
67.7
67.8
+0.1
68.5
68.5
0.0
DynaMath
41.3
41.5
+0.2
40.5
40.5
0.0
Logic
56,8
56.2
-0.6
61.3
60.9
-0.4
Math & Reasoning
Math Vath
61.9
61.5
-0.4
67.0
66.9
-0.1
MathVision
46.8
47.6
+0.8
50.0
50.2
+0.2
Vistamstamiii
78.6
78.7
+0.1
81.4
81.4
0.0
WeMath
55.0
54.8
-0.2
59.8
60.0
+0.2
Average
All Benchmarks
68.6
68.2
-0.4
70.2
69.9
-0.3
for specific tasks, our model's strong performance is fundamentally robust and not artifact of specific evaluator.
A.3 EVALUATION STABILITY ANALYSIS
To verify the stability of our results, we repeated the evaluation for both Bee-8B-SFT and Bee-8B- RL across 5 independent inference runs. We report the scores for each individual run, the calculated mean, and the standard deviation (Std) Tab. 8. We also provide the average score across all 29 benchmarks for each The extremely low standard deviation values across all benchmarks and the stable global average demonstrate that the model's performance is highly consistent and reproducible.
A.4 HUMAN EVALUATION DATA QUALITY
To verify that our automated curation aligns with human standards, conducted rigorous blind evaluation. We recruited valid evaluators to perform 532 pairwise comparisons between Original and Enriched responses across 38 randomly sampled questions. The assessment covered four key di- mensions: Accuracy (factual correctness), Reasoning & Explanation (logical depth), Instruction Following (constraint adherence), and Expression Style (fluency and structure).
The results, summarized in Tab. reveal strong alignment with human preferences. The dominant Tie rate (83.65%) in Accuracy validates that our Fidelity Verification module effectively maintains
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Table 8: Robustness analysis of Bee- (SFT and RL) across 5 independent runs. The table reports the score for each run, the mean, and the standard deviation.
See-8B-SFT (5 Runs)
Bee-8B-RL,
Runs)
Task
Benchmark
R1
R2
R3
R4
R5
Mcan
Std
RI
Mcan
Std
A12D
83.8
83.5
83.7
84.1
84.0
83.8
0.2
85.3
85.1
85.7
84.8
84.5
85.1
0.4
BLINKval
52.5
53.1
51.8
51.8
50.3
$1.9
1.0
55.0
55.1 56.1
55.0
53.2
54.9
1.0
CountBench
90.6 92.2 91.0
92.2
91.4
91.5
0.7
93.0
92.2 91.8
93.8
93.4
92.9
0.9
HallusionBenchavg
59.8 57.9
59.0
59.3
59.9
59.2
0.8
58.2
58.5 60.6
59.7
60.9
59.6
1.2
MMBench-CNN dew
81.2 82.2 81.8
82.0
81.7
81.8
0.4
84.2 83.8
84.4
83.8
84,1
84.1
0.3
MMBench-ENNe dev
83.0 82.6 82.7
83.4
83.9
83.1
0.6
85.5 84.6 85.3
84.6
84.6
84,9
0.5
MMMU
66.8 65.3 65.4
65.7
64.8
65.6
0.7
66.1 68.0 67.7
67.9
67.1
67.4
0.8
General
MMMU-PM Lranddee
50.5 48.8 50.0
50.0
48.7
49.6
0.8
50.8
50.8
0.8
VQA
MMStar
69.0 68.7 70.3
68.5
69.4
69.2
0.7
71.4 72.9
70.9
71.5
MMT-Bench.al
64.6 65.0 65.2
64.6
65.6
65.0
0.4
67.0
66.6
67.3
67.5
67.2
67.1
0.3
MMVet
83.3 81.7 82.6
78.9
82.8
81.8
1.8
83.9
85.9 83.6
82.1 82.2
83.5
1.5
MMVP
80.7 82.7 80.7
79.3
79.3
80.5
1.4
82.0
80.7 83.0
84.0
79.7
81.9
1.7
POPE Eave
84.0 83.3 84,2
83.3
84.1
83.8
0.5
84.8
85.0 84.7
84,9
84.4
84.8
0.2
Real RealWorldQA
70.1 68.6 70.8
70.2
69.9
69.9
0.8
73.1
71.0 70.8
73.6
72.9
72.3
1.3
VisuLogic
24.4 25.4 23.7
22.7
24.8
24.2
1.0
26.5
25.4
25.3
26.1
26.6
26.0
0.6
VL.Ms are Blind
55.8 53.7
54.0
54.5
54.5
54.5
0.8
56.5
57.3
57.5
56.6
56.6
56.9
0.5
CharXivpo
84.7 84,1
84.6
84,2
84,9
84.5
0.4
84.8
84.7
84.8
84.8
84.8
84.8
0.1
CharXivaq
55.3 56.4
53.4
56.3
55.1
55.3
1.2
57.3
57.2 54.6
55.8
56.6
56.3
1.1
ChartQagert
86.7 86.6
87.4
86.9
86.5
86.8
0.3
86.1
87.3 87.0
87.1
87.4
87.0
0.5
Table Chart
DocVQA
87.2 87.2
87.5
87.7
87.2
87.4
0.3
87.0
87.2 87.0
87.3
87.0
87.1
0.2
OCR
InfoVQA.u.
72.3 71.8 72.1
72.2
72.3
72.1
0.2
72.9
73.5 72.8
72.5
72.5
72.9
0.4
OCRBench
83.1 82.9 83.5
83.5
82.8
83.2
0.3
82.5
83.3
83.6
82.4
83.7
83.1
0.6
SEED-Bencce Beeii--Bench--luss
67.7 67.3
67.7
67.2
68.4
67.7
0.5
68.5
68.6
69.1
68.3
68.8
68.7
0.3
DynaMathoorst
41.3 40.3 39.9
38.7
40.1
40.1
0.9
40.5
39.5
41.3
40.9
40.7
40.6
0.7
LogicVista
56.8 56.6 60.0
58.2
59.1
58.1
1.4
61.3
60.2 57.7
59.3
57.7
59.2
1.6
Math & Reasoning
MathVerse, Verrsecisiiua..oo cisica_only
61.9 63.7 62.6
64.5
64.0
63.3
1.0
67.0
67.1 66.2
65.4
66.6
66.5
0.7
Math Vision
46.8 46.9 47.8
47.7
47.4
47.3
0.4
50.0
50.0 50.4
50.4
50.1
50.2
0.2
Math Vistamini
78.6 78.7 79.0
79.1
78.7
78.8
0.2
81.4
79.6
79.9
79.9
81.3
80.4
0.9
WeMath
55.0 55.6 53.6
54.5
56.2
55.0
1.0
59.8
58.5
57.3
60.6
57.8
58.8
1.4
Average
All Benchmarks
68.6 68.5 68.6
68.5
68.7
68.6
0.1
70.2
70.2
70.0
70.2
70.0
70.1
0.1
factual consistency with the ground truth. Crucially, evaluators overwhelmingly preferred the en- riched data in Reasoning (72.74%) and Expression Style (69.92%). These findings confirm that our pipeline successfully transforms raw data into high-utility, logically structured content that meets human expectations for advanced reasoning tasks.
Table 9: Blind human evaluation results comparing Original vs. Enriched responses (532 pairwise comparisons). The high Tie rate in Accuracy confirms fidelity, while the strong preference for Enriched in Reasoning and Style demonstrates that our data aligns better with human standards for high-quality responses.
Metric
Accuracy
Reasoning & Explanation
Instruction Following
Expression Style
Prefer Enriched
42 (7.89%)
(72.74%)
(30.64%)
372 (69.92%) (16.35%)
Prefer Original
45 (8.46%)
75 10%%
45 (8.46%)
Tie
445 (83.65 %)
70 16%%
324 (60.90%)
73 (13.72%)
A.5 DATA CONTAMINATION ANALYSIS
To rigorously assess potential data leakage, performed comprehensive decontamination anal- ysis against all 66,682 samples in our evaluation sets. We utilized Perceptual Hashing (pHash) for visual content and SimHash for textual instructions, calculating Hamming distances to identify matches ranging from exact duplicates (Distance = 0) to high-similarity near-duplicates (Distance 3).
As detailed in Tab. 10, the detected leakage extremely minimal. Even under the relaxed threshold, only 29 samples (< 0.05%) were identified as potential overlaps, with merely 2 exact matches. No-
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tably, these overlaps are concentrated in specific benchmarks like Vision, while our primary comprehensive benchmarks innchmarks-including MMMU, MMMU-Pro, MMStar, and CharXiv remain completely free of any overlap.
We conclude that the identified overlap of 29 samples across all evaluation sets is statistically neg- ligible and insufficient to influence the performance metrics the validity of our conclusions. We strictly clarify that evaluation data was never intentionally included our training set. Given that our dataset aggregates open-source collections, these few overlapping samples likely originated from the web-crawled nature of the upstream data sources.
Table 10: Data decontamination analysis results. We report the number of overlapping samples between Honey-Data- 15M and the evaluation sets (total 66,682 samples) at different Hamming Dis- tance thresholds.
Threshold
Overlaps
Ratio
Benchmarks with Overlap (Count)
Exact Match (=
2
0.003%
Math Vision (1), Math (1)
Diff' < 1 Diff / 2
4
0.006%
Math Vision (1), Vista (1), DocVQA (1), InfoVQA (I)
14
0.021 %
Math Vision (10), DocVQA (2). MathVista (1), InfoVQA (1)
Math Vision (19), Vista (4), Doc (3).
Diff
29
0.043%
ChartQA (1), Info (1), OCRBench (1)
A.6
AGREEMENT ON VERIFICATION
To validate the reliability of our data curation pipeline and address potential concerns regarding the bias of using a single verifier model, we conducted a comprehensive experiment evaluating inter- model agreement using diverse set of verifier models. We sampled random subset of approxi- mately 83,800 raw samples from our pool and performed the Fidelity Verification process indepen- dently using three different models with varying scales and capabilities: the original verifier utilized in our pipeline (Qwen2.5-VLL. V-L-722B, a smaller, efficiency-focused variant (Qwen2.5-VLL-3) VLL-2B) and a significantly larger Hixuure-of-Exper model V0W203--L--23BB-A222 Insttrcct) serving as a strong reference capability upper bound.
The number of samples retained by each model and the intersection of samples retained by all three models are reported in Tab. 11. The results demonstrate high degree of inter- model consistency. The intersection of 63,203 samples constitutes 96.5% of the data retained by the powerful reference model -0een3--LL225B--A222
and 91.3% % by our chosen verifier. This significant over- lap across model scales (spanning from 32B to 235B) confirms that the identified inconsistencies are largely objective flaws rather than artifacts of specific model biases. These findings validate Qwen2.5-VL: V-- as a robust and reliable verifier for our pipeline.
Table 11: Inter-model agreement analysis for Fidelity Verification across distinct model scales. The high intersection count demonstrates that our filtering criteria are robust and not heavily biased by specific model.
Metric
Qwen2.5-VL-72B (Ours)
Qwen2.5-VL-32B
Qwen3-VL-2255
Intersection (All 3)
Retained Samples
69,198
65,054
65,468
63,203
B THE USE OF LARGE LANGUAGE MODELS
In preparing this manuscript, we utilized Large Language Models (LLMs) as general-purpose as- sistive tool. Specifically, we employed LLMs to refine and polish the language, improve clarity, and perform comprehensive grammar checks. The models were also used to ensure that our phrasing and word choices received assistance from the LLM for minor LaTeX formatting adjustments and engaged in discus- sions with it to brainstorm and select appropriate for dataset and model.
idiomatic and aligned with standard scientific discourse. Additionally,
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C REPRODUCIBILITY STATEMENT
We are committed to the full reproducibility of our work and will publicly release our full-stack suite. This includes the Honey-Data- 15M corpus, the final Bee-8B model weights, the intermediate checkpoints from our training recipe, the specific training data used for each intermediate stage, and the complete code for our HoneyPipe data pipeline and DataStudio framework.
D ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics and involved no human or animal subjects. Our work relies public academic datasets and MILMs. We acknowledge that Honey-Data- 155M may inherit biases from its public sources, despite our HoneyPipe pipeline's ' design to systematically filter noise, factual errors, and image-instruction mismatches. Users should be aware of these potential limita- tions. Similarly, Bee-8B may generate inaccurate or harmful content. We are publicly releasing our full suite (dataset, pipeline, model weights, etc.) to promote open academic research and strongly discourage malicious use. We are committed to transparency, releasing our full methodology to help the community understand, build upon, and mitigate the limitations of such models.
E LIMITATIONS
E LIMITATION OF FIDELITY VERIFICATION
Our data curation pipeline employs an automated "I.LM "Ls-M-as-a-ludge" mechanism for Fidelity Veri- fication. We explicitly acknowledge that this design necessitates strategic trade-off between recall (the coverage of valid samples) and computational cost.
Specifically, relying on single-pass model-based verifier to enforce consistency with the original ground truth functions as a strict filter. High-quality, logically correct CoT samples may inadver- tently discarded if they conflict with the original answer due valid variations (e.g., formatting dif- ferences, unit conversions, or synonyms) that the verifier fails to align. While massive-scale human annotation extensive rejection sampling (e.g.. generating and verifying dozens of candidates per sample) could theoretically resolve this ambiguity and achieve near-perfect recall, such approaches prohibitively expensive and computationally infeasible at the scale of tens of millions of samples.
Therefore, we made a conscious design choice to prioritize precision (ensuring the retained data is factually consistent) and cost-effectivenes over recall. This pragmatic approach sacrifices a fraction of valid enriched data but ensures that the pipeline remains scalable, reproducible, and accessible to the broader academic community without requiring industrial-scale resources.
Furthermore, regarding the potential bias of using a single verifier model, we acknowledge that an ensemble of diverse judges could theoretically offer higher robustness. However, for the specific task of checking factual consistency against ground truth, strong MLLMs generally exhibit a high degree of inter-model agreement. The inconsistencies identified by our verifier typically reflect ob- jective flaws or significant deviations rather than model-specific artifacts. Thus, employing single, strong open-source model as a verifier represents an optimal balance point, minimizing engineering complexity while maintaining high data quality standards.
E.2 LIMITATION OF EVALUATION PARADIGM
As MILMss evolve from simple pattern matchers to models capable of complex CoT reasoning, traditional deterministic evaluation methods like Regex have become insufficient. Advanced models now generate verbose outputs that standard extraction scripts often fail to parse correctly, leading to false negatives even when the reasoning is sound. Consequently, adopting "LLM-as-a-Judge"" to perform semantic consistency checking has become necessary evolution for the field.
However, this shift introduces a new layer of uncertainty: evaluation becomes inherently non- deterministic and dependent on the chosen judge's capabilities. Unlike rigid code-based metries, an LLM judge introduces subjectivity regarding formatting strictness and semantic equivalence, and may exhibit subtle biases such as self-preference We acknowledge that performance metrics in the
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of reasoning models are longer absolute but are bounded by the subjectivity of the evaluator. Establishing standardized, objective, and automated metrics for rapidly evolving reasoning models remains critical open challenge for the community.
F DETAILED TRAINING STAGE CONFIGURATIONS
This section provides an expanded description of the five-stage training recipe for Bee-8B, as in- troduced in Sec. 3. Our methodology follows progressive curriculum, starting with foundational vision-language alignment, advancing to complex instruction-based SFT, and concluding with tar- geted refinement and reinforcement learning. For the purpose of clarity and reproducibility, the following subsections detail the specific data sources, and key configurations for each of the five training stages.
F.1 STAGE MLP WARMUP
This initial stage is dedicated to bridging the visual and language modalities. For this, train only the MLP projector on curated dataset of approximately one million image-caption pairs, keeping the vision encoder and LLM backbone frozen. This training set is carefully assembled collection, combining roughly 560K samples from LLaVA-OneVision (Li et al., 2025a) with a high- quality subset of around 440K samples derived from COYO (Byeon et al., 2022). To generate this high-quality subset, we enriched the original responses by recaptioning them with the powerful Qwen2.5-VI. VLLL72B (Bai et al., 2025), followed by rule-based filtering. This entire process efficiently maps visual features into the language model's 's token space without disturbing the powerful pre- trained weights the core components.
F.2 STAGE VISION-LANGUACA ALIGNMENT
In the second stage, unfreeze all model components build foundational multimodal capa- bilities. The training data is a large-scale composite resource, mixing approximately 12.6 million vision-language pairs with 1.43 million text-only samples teach visual understanding while pre- serving language skills. The vision-language component is a meticulously curated collection from three main sources: a high-quality filtered subset of LAION (Schuhmann et al., 2022) (-6.9M), refined selection from COYO (Byeon et al., 2022) (-5,4M), and additional specialized data from the stage 1.5 of LLaVA- One Vision (Li al., 2025a) (-300K). All rision-languas data was constructed using the same recaptioning and filtering methodology detailed in Appx. F.1. Crucially, to preserve the LLM's intrinsic reasoning abilities and mitigate catastrophic forgetting, we integrate a blend of text-only data from the Nemotron dataset (Bercovich et al., 2025; Nathawani et al., 2025). This blend includes long CoT samples for deep, multi-step problem-solving. sourced from reasoning- focused subsets like Momntron-STEM. (-458K), Nemotron- Math (-229K), and reasoning -intoinnnintensveee ver- sion of Nemoteon- (-376K) to maintain broad conversational proficiency. This mixed-modality approach enables the model to learn robust visual- language correlations while ensuring its core reasoning engine remains intact for subsequent stages.
(-367K). This is complemented by the short CoT Nemotron-Chie subset
F.3 STAGE 3: MULTIMODAL SET
In this stage, we train the model on the entire Honey-Data-15M, to develop its advanced instruction- following and reasoning capabilities with vision. detailed breakdown of the dataset's composition, including its sources and dual-level CoT distribution, is provided in Fig. 3 and Tab. 12. Training for one full epoch ensures complete exposure to this diverse data distribution. This critical for learning from the rarer but more complex long CoT samples and allowing the model to learn their intricate reasoning patterns.
F.4 STAGE 4: EFFICIENT REFINEMENT SFT
This final stage of SFT is designed for dual purposes: conduct targeted refinement of the model's capabilities and to provide an accessible, efficient training option for researchers with limited com- putational resources. To this end, curated high-quality 1M subset from our full 15M dataset
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Table 12: A detailed breakdown of the datasets used in our collection, categorized by task.
Task
Dataset
SVIT-mix--665 (Zhao et ul., 2023a), ALLaVA (Chen et al., 2024a). TQA (Kembhavi et al., 2017).
LLaVA-NeXT-Data (Liu et al., 2024c), IconQA (Lu et al., 2021b), Objects365 (Shao et al., 2019).
Vision FLAN (Xu et al., 2024), idefics375k (Laurengon et al., 2023), ViQuAE (Lemer al., 2022).
Co-Instruct (Wu al., 2024), Insttuunt (Wang al., 2023a), DreamSim (Fu et al., 2023),
SVTT-core- 150K (Zhao al., 2023a), Visual7W (Zhu al., 2016) ShareGPTAV(AAM)
2025a).
Cambrian (Filter) (Tong et al., 2024a), PapMo (Deitke el al., 2025), HQ-Edit (Hui et al. 2025),
PicMo-AskModde/Ayytinng (Deitke et al., 2025). ShareGPT4o (Cui ct al., 2024), IDK (Cha et 2024).
EST- (Wang al., 2020). COCO (Lin et al., 2014), PixMo- 1on----Point-xpllaattooo (Deitke et al., 2025).
General
Birds-to-Wordd (Forbes et al., 2019). GQA (Hudson Manning, 2019), NexQQ (Xiao al., 2021).
ALFWorld (Shridhar et al., 2021), Cauldron(mulberrr)) (Laurençon et 2024b), VSR VSR eLiu 2023a),
A-OKVQA (Schwenk et al., 2022), KVQA (Shah el al. 2019). ContrastiveCaption (Jiang et al., 2024),
WebQA (Chang et al., 2022b), VildVision (Lu et al., 2024b), New Yorker Caption (llessel et al., 2023),
Sew.V---S-IB--apiiai (Chen et al., 2023; 2024c). ShareGPTaV(Knoweeee LLaVA-Instmuct-000 (Liu et al., 2023c). VIST (Huang al., 2016), LRV Normal (Liu al., 2023b),
(Chen al., 2024b),
KonIQ-10k (Hosu et al., 2020), Hateful Memes (Kiela et al., 2020), ARTS00K (Man et al., 2017).
NLVR2 (Suhr ct al., 2019), ScanQA (Azuma ct al., 2022), MMChat Twitter- (Li et al., 2024b).
nuScenes (Caesar et al., 2020). VlintsooneSSV (Gupta al.. 2018), MagicBrush (Zhang al., 2023a)
TinyChart (Zhang et al,, 2024a), DVQA (Kafle et al., 2), UniChart (Masry et al., 2023),
CoSyn(chart, tahle, diagram, graphic) (Yang et al., 2025c), ArxivQA (I.i et al., 2024e),
FigureQA (Kahou ct al., 2018), MMTab (Zheng ct al., 2024). PlotQA (Methani ct al., 2020).
UReaderr QA (Ye al., 2023), RobuT WikiSQL (Zhao et al., 2023b), TabMWP (Lu et al., 2023).
Chart
RobuT WTQ (Zhao et al., 2023b). URcader KG (Ye al., 2023). ChartZText (Obeid & Hoque, 2020).
ChariQA (Masry el al., 2022), MMC-Insiruction (Liu al., 2024a), RobuT SQA (Zhao et al., 2023b),
MAVIS-Function (Zhang el al., 2025b), VisTex/ (Tang el al., 2023), Multiliert (Zhao et al., 2022),
SciTSR (Chi el al., 2019), LRV Chart (Liu et al., 20236), SimCChrl9K (Xia et al., 2023),
Infographic (Mathew al., 2022), HiTab (Cheng al., 2022)
PixMo-Cap (Deilke et al. 2025), WTT (Srinivasan el al., 2021), ST-VQA (Biten el al., 2019b),
Caption
CoYO-Recaption (Byeon et al., 2022), Sherlock (lessel et al., 2022)
VisualWeblnsttnuc(filleeeedd (Li et al., 2025a), MapQA (Chang et al., 2022a), VizWiz (Gurari et al., 2018),
MetuMathQA (Yu 2024a), Geo170K (Gao al., 2025), VisualWebInstruct (Jia et al., 2025).
MathV360K(TQA) (Shi et al., 2024), A12D (Kembhavi et al., 2016). Geom (Kazemi al., 2023).
GeoQA+ (Chen et al., 2021), MAVIS-Geo (Zhang et al., 2025h), CMM Math (I.iu et al., 2024d),
STEM
CoSyn(math, music, chemical, circuit) (Yang et al., 2025c), MAVIS-Metagen (Zhang et al., 2025h),
PMC-VQA (Zhang al., 2023b), Vat (He al., 2020), InterGPS (I. et al., 2021a), VQA-RAD (T.au et al., 2018), RAVEN (Zhang et al., 2019a), AT2D(GPT4V) (Kembhavi et al., 20,
Geometry3K (Lu al., 2021a) AI2D(IntemVL)) (Kembhavi et al., 2016). MMChem (Li et al., 2025b),
WebSight (Laurençon ct al., 2024c), UniGeo (Chen ct al., 2022), ScienceQA (Lu et al., 2022)
Ureader Chart (Ye al., 2023), OCR-VQA (Mishra al., 2019), InfographicVQA (Mathew al., 2022),
CoSyn(document, nutrition) (Yang
2025c), POIE (Kuang ct al., 2023), EATEN (Guo al., 2019),
FinTabNet (Zheng ct al., 2021), UreaderOCR (Ye ct al., 2023), InfoVQA (Mathew et al., 2022),
Document
Docmatix (Laurençon ct al., 2024a), DQC (Mathew ct al., 2021), SereenQA (Hsiao et al., 2025),
TexIVQA (Singh et al., 2019), DocReason (Ilu et al., 2024), VisualMRC (Tanaka el al., 2021),
VLR GPT4 (Zhang et al., 2023c)
CLEVR (Johnson et al., 2017), TallyQA (Acharya et al., 2019), VisualGenome (Krishna ct al., 2017),
Grounding
TQA (Kembhavi et al., 2017), MovieNet (Huang et al., 2020), MathV360K (VQAAQAAA (Shi et al., 2024),
& Counting
CLEVR-Math (Lindström & Abraham, 2022). Super-CLEVR (Li et al., 2023b),
IconQA (Lu al., 2021b), CLEVR-Change (Park et al., 2019)
K12 Printing (I.i ct al., 2025a), ArXiv OCR (Zanichelli, 2024), HME (Yuan et al., 2022),
VCR-Wiki (Zhang et al. 2024c). Sroie (Huang et al., 2021). 11T 5K (Mishra et al., 2012).
ICDAR-LSVT-zh (Sun et al., 2019), TexOCR (Singh et al.. 2021), ReCTs (Zhang al., 2019b).
OCR
Orand-Car-A (Russakovsky et al., 2015), Rendered Text (Wendler, 2023). ICDAR2017 (Shi al., 2017),
Chrome Writing (Wendler, 2023), MTWI()h (He et al., 2018), TAM (Marti & Bunke, 2002),
ICDAR2019 (Biten et al., 2019a), CTW (Yuan el al., 2019)
through a meticulous, multi-faceted selection strategy. Our process began by defining target pro- portions for various topics to ensure balanced and comprehensive training corpus. We established a more rational distribution among topics such as STEM, Chart, Document, Grounding, and OCR, among others, while also maintaining substantial portion for General. Furthermore, aimed for
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an approximate 1:1 ratio between long- long-chain and short-chain conversations to balance depth and breadth.
The core of our selection methodology signed a quality score (on a scale of I to 5) to each data source. This score was the primary factor used to determine each source's proportional contribution towards the overall target for its topic. This approach ensured that higher-quality sources contributed a proportionally larger number of samples, while still guaranteeing that every source was represented in the final subset to preserve diversity. Within each source's assigned quota, we employed a hybrid sampling strategy to balance difficulty and variety. Specifically, 60% of the data was selected by prioritizing conversations with the longest responses, based on the hypothesis that longer responses often correlate with more com- plex and challenging user queries. The remaining 40% was chosen via random sampling from the source to maintain broad diversity and prevent overfitting on specific types of difficult instructions. Through this stratified approach, we successfully constructed a 1M subset that is not only compu- tationally efficient for training but also features a more rational topic distribution, preserving the difficulty and diversity of the original, larger dataset.
quality-drivee quota system. We first manually
F,5 STAGE 5: REINFORCEMENT LEARNING WITH GRPO
In this final stage, we employ the Group Relative Policy Optimization (GRPO) (Shao et al., 2024) algorithm to address persistent SFT issues such as text repetition, incomplete responses, and im- proper formatting. The reinforcement learning (RL) process is implemented with the verl frame- work (Sheng et al., 2025), where each rollout uses a batch size of 512, and the policy model updates its gradients with a batch size of 128.
The training data was constructed using prompts from the open-source MMKI 12 (Meng et al., 2025) and ViRL39K (Wang et al., 2025a) datasets. To improve data quality, the ViRL39K dataset was preprocessed by removing multi- multi-image samples and randomly splitting 95% of the remaining data as the training set. For each prompt, we generated set of candidate responses, enabling GRPO to refine the model's policy by learning to distinguish high-quality responses from flawed ones.
To guide this optimization, we adopt rule-based reward function consisting of two components: a format reward (with weight of 0.2) that enforces the presence of \boxed() in the final output, and an accuracy reward (with a weight of 0.8) that evaluates whether the extracted content inside \boxed ( } matches the ground-truth answer. This targeted reinforcement learning step provides final polish, significantly improving the model's overall reliability and output quality.
G DATA LICENSING AND GOVERNANCE
To ensure rigorous compliance and promote responsible adoption within the community, we have established transparent licensing framework and governance policy for Honey-Date D5Ma Our ap- proach is modeled after industry standards, such as The Cauldron Dataset (Laurençon et al., 2024b), to clarify rights and usage boundaries. Honey-Data- 15M is primarily aggregated from established, large-scale open-source collections, including LLa Vision and MAmmoth-Va VL, which typ- ically operate under permissive licenses like Apache 2.0. While we accessed data through these aggregates, we performed fine-grained tracing to generate the source distribution statistics, ensuring maximum credit given the original content creators.
We implement a clear dual-layered licensing structure for the repository. First, we explicitly state that Money-Data-1555 15M is composite collection where each sub-dataset remains governed by its specific original license. Users are strictly mandated to adhere to the terms and restrictions of each original source. Second, to the extent that we hold rights in the modified prompts, structural format- ting, and the newly generated CoT responses, these contributions are licensed under CC-BY-NC-4.0 (Creative Commons Attribution-None NonCommercial 4.0 International).
Regarding specific materials such as OCRed textbooks or K-12 content, we clarify that we did not independently digitize physical materials; we utilized them strictly as they exist in public academic datasets. For Personally Identifiable Information (PII), we primarily rely the anonymization measures performed by the original dataset curators (e.g., SROIE explicitly addresses privacy). To address any potential future concerns regarding copyright or privacy, have implemented strict
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Notice and Takedown Policy. We have established a dedicated communication channel for these concerns, and upon receiving a valid notice regarding infringement or Pll exposure, we are commit- ted to promptly reviewing the request and removing the contested content from our distribution.
From Tab. 13 to Tab. 19 provides a detailed enumeration of the license terms, usage restrictions (e.g., Non-Commerciall for the constituent source categories. For instances where definitive licens- ing information remained unverifiable despite exhaustive search, entries are left blank to maintain accuracy.
Table 13: Detailed overview of the licensing terms, usage restrictions for the General Category
Dataset
License
Restrictions
Appropriate credit must be given when sharing or adapting the work.
SVIT-mix-665K
CC BY 4.0
Appropriate credit must be given when sharing or adapting
SVIT-core-150K
BY 4.0
the work.
Appropriate credit must be given and no commercial
ALLaVA
CC
permitted when sharing or adapting the work. Appropriate credit must be given and any modified versions must be licensed under the same terms when sharing
LLaVA- -eXt--NeT
Apache-2.0
adapting work.
Appropriate credit must be given when sharing or adapting
VLaVA--Intmuct-000
CC 4.0
the work.
Vision FLAN
Appropriate credit must he given when sharing or adapting the work.
idefics375k
CC BY 4.0
Appropriate credit must be given when using, sharing,
PixMo-CapQ
ODC-By 1.0
creating works from the datahase.
Appropriute credit must be given when using, sharing, or
PisMo-AskKooddlAttyhinngg
ODC-By 1.0
creating works from the database.
Appropriate credit must he given when using, sharing, or creating works from the database.
L.MMo-Pinn--xplanca
ODC-By 1.0
IVIS-Instruc44
Appropriate credit must be given and any modified versions must be licensed under same terms w when sharing or
ShaeeGPT4V(SAM)
Apache-2.0
adapting the work.
Appropriate credit must be given and any moditied versions must be licensed under the same terms when sharing
Cambrian (Filter)
Apache-2.0
adapting the work.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
ShareGPT4o
MIT
NLVR2
GQA
Appropriate credit must be given, notices and the author's 's name cannot be used for endorsement. Appropriate credit must be given and no commercial use is
retained,
LRV Normal
BSD-3-Clause
HQQ-Bdit
CC BY-NC 4.0
permitted when sharing adapting the work.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
ALFWorld
MIT
Appropriate credit must be given and the copyright and permission notice must be included when distributing the
Visual7W
MIT
work.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the
Co-Instruct
MIT
work.
(uulerro//ulberry))
Appropriate credit must be given and any modified versions must be licensed under the same terms when sharing adapting the work. Continued on next page
A-OKVQA
Apache-2.0
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Table 13- continudd from previous page
Dataset
License
Restrictions
Appropriate credit must be given, no commercial use is permitted, and adaptations must be shured under the same license.
CC BY-NCSA 4.0
IconQA
VIST
KVQA
Appropriate credit must be given and any modified versions must be licensed under the same terms w when sharing or adapting the work.
ContrastiveCaption
Apache-2.0
FlintstonesSV
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
IntenVL-SA-IB-Caption
MIT
Apprupriate credit must be given, notices musl retained, and author's name cannot used for endorsement. Appropriate credit must be given when sharing or adapting the work.
IDK
BSD-3-Clause
COCO
CC
EST-VQA
Appropriate credit must be given and adaptations must be
Binds-to-Wort Words
BC-SA
shared under the same license.
Use limited to non-commercial research, no redistribution or commercial exploitation is permitted.
ARTS00K
Appropriate credit must be given and the copyright and permission notice must be included when distributing the
DreamSim
MIT
work.
Appropriate credit must be given, no commercial use is permitted, and adaptations must be shared under the same
CC BY-NC-SA 3.0
ScanQA
license.
Appropriate credit must be given when sharing or adapting
MagicBrush
CC BY 4.0
the work.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
KonIQ-10k
MIT
Hateful Memes
No restrictions imposed, and the work may be used freely for any purpose.
WebQA
CCO-1.0
Appropriate credit must be given, no commercial use is permitted. and adaptations must be shared under the same
CC BY-NC-SA
nuScenes
4.0
license.
Appropriate credit must be given when sharing or adapting the work.
Objects365
CC BY 4.0
Appropriate credit must be given when sharing or adapting
MMChat-TWiteer-Pon
CC BY 4.0
the work.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
NexIQA
MIT
Appropriate credit must be given when sharing or adapting
VSR
CC BY 4.0
the work.
Appropriate credit must he given when sharing adapting the work.
New Yorker Caption
CC BY 4.0
Appropriate credit must he given when sharing or adapting the work.
ViQuAE
CC BY 4.0
Appropriate credit must be given and no commercial permitted when sharing adapting the work.
TQA
CC 3.0
Appropriate credit must be given and no commercial use is permitted when sharing adapting the work. Appropriate credit must he given when sharing or adapting the work.
ShareGPTaV/Knoolllee))
CC
Wilddision
CC BY 4.0
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Table 14: Detailed overview of the licensing terms, usage restrictions for the Chart Category
Dataset
License
Restrictions
Appropriate credit must given and any modified versions must be licensed under the same terms when sharing or
TinyChart
Apuche-2.0
adapting the work.
Appropriate credit must be given and no commercial use is permitted when sharing adapting the work.
DVQA
CC BY-NC 4.0
UniChart
Appropriate credit must be given when using. sharing, or creating works from the database.
CoSyn(chart)
ODC-By 1.0
Appropriate credit must be given when using, sharing. or creating works from the databasc.
CoSyn(table)
ODC-By 1.0
Appropriate credit must be given when using, sharing,
CoSyn(dingram)
ODC-By 1.0
creating works from the databasc.
Appropriate credit must be given when using, sharing, or
CoSyn(graphic)
ODC-By 1.0
creating works from the database.
Appropriate credit must be given and adaptations must
ArxivQA
CC BY-SA 4.0
shared under the same license.
Appropriate credit must be given and the copyright and
FigureQA
MIT
permission notice must be included when distributing the work.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
MMTab
MIT
Appropriate credit must be given and the copyright and permission notice must be included when distrihuting the work.
PlotQA
MIT
Appropriate credit must be given and any modified versions must he licensed under the same terms when sharing
UReader QA
Apache-2.0
adapting the work.
UReader KG
Appropriate credit must be given and the copyright and permission notice must be included when distributing the
RobuT WikiSQL
MIT
work.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
RobuT SQA
MIT
Appropriate credit must be given and the copyright and permission notice must be included when distributing the
RobuT WTQ
MIT
work.
Appropriate credit must be given, no commercial use is permitted, and adaptations must be shared under the same
CC 4.0
TahMWP
license.
Chart2Text
Appropriate credit must be given and any modified versions must be released under the same license when distributing the
ChartQA
GPL-3.0
work.
Appropriate credit must be given and adaptations must
MMC-Instruction
CC BY-SA 4.0
shured under the same license.
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
MAVIS-
MIT
Appropriate credit must be given and any modified versions GPL-330
VisText
must be released under the same license when distributing the
work.
Appropriate credit must be given and the copyright and permission nolice must be included when distributing the
SciTSR
MIT
work.
Appropriate credit must be given and the copyright and
Multillirrti
MIT
permission notice must be included when distributing the
work.
Continucd on next page
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Table 14 continued from previous page
Dataset
License
Restrictions
SimChart9K
Use is limited computational purposes, attribution must be retained, and any redistributed data must remain under the
HiTab
C-UDA 1.0
same terms.
Appropriate credit must he given, notices must be retained, and the author's cannot be used endorsement.
LRV Chart
BSD-3-Clause
Infographic
Table 15: Detailed overview the licensing terms, usage restrictions for the Caption Category
Datasel
License
Restrictions
Appropriate credit must be given when sharing or adapting
COYO-Rccaption
CC 4.0
the work.
Appropriate credit must be given when using, sharing, creating works from the datahasc.
PixMo-Cap
ODC-By 1.0
Appropriate credit must be given and adaptations must be shared under the same license.
WIT
BY-SA
Appropriate credit must be given when sharing or adapting the work.
Sherlock
CC BY 4.0
ST-VQA
Table 16: Detailed overview of the licensing terms, usage restrictions for the STEM Category
Dataset
License
Restrictions
Appropriate credit must be given and any modified versions must licensed under the same terms when sharing
Visua/Webdnsrrne((fftrrree))
Apache-2.0
adapting the work.
Appropriate credit must be given, no commercial use is permitted, and adaptations must be shared under the same
BY-NC-AA 4.0
MapQA
license.
Appropriate credit must be given and any modified versions must be licensed under the same terms when sharing or
Visua/WebInstrutt
Apachc-2.0
adapting the work.
Appropriate credit must be given and the copyright permission notice must be included when distributing the work.
MetaMathQA
MIT
Geo170K
Appropriate credit must be given and the copyright and permission notice must be included when distributing the
MAVIS-Metagen
MIT
work.
GenQA+
Appropriate credit must be given and the copyright and permission notice must be included when distributing the
MAVIS-Gen
MIT
work.
Appropriate credit must be given when using, sharing. or
CoSyn(math)
ODC-By 1.0
creating works from the database.
Appropriate credit must be given when using, sharing.
CoSyn(music)
ODC-By 1.0
creating works from the databasc.
Appropriate credit must be given when using, sharing, or
CoSyníchemical)
ODC-By 1.0
creating works from the databasc.
Appropriate credit must be given when using, sharing, creating wurks from database.
CoSyn(circuit)
ODC-By 1.0
A12D
Appropriate credit must be given and adaptations must be shared under the same license.
PMC-VQA
CC
Continued on nexi page
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Table 16 continued from previous page License
Dataset
Restrictions
Appropriate credit must be given and any moditied versions must be released under the same license when distributing the work.
RAVEN
GPL-3.0
Appropriate credit must be given und the copyright and permission notice must be included when distributing the work.
PathVQA
MIT
Appropriate credit must given and any modified versions must be licensed under the same terms when sharing or
MathV360K(TQA)
Apachc-2.0
adapting the work.
Appropriate credit be given and adaptations must
ScienceQA
CC BY-SA 4.0
shared under the same license.
Apprupriate credit must be given and the copyright and permission notice must be included when distributing the work.
Geometry3K
MIT
AI2D(IntemVL)
MMChem
Appropriate credit must be given when sharing or adapting the work.
WebSight
CC BY 4,0
UniGeo
GeomVerse
A(2(GPP44)
Appropriate credit must be given when sharing or adapting the work.
VizWiz
CC BY 4.0
No restrictions are imposed, and the work may used freely for any purpose.
VQA-RAD
CCO-1.0
Appropriate credit must he given, notices must be retained, the author's name cannot used endorsement. Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
CMM-Math
BSD-3-Clause
InterGPS
MIT
Table 17: Detailed overview of the licensing terms, usage restrictions for the Document Category
Dataset
License
Restrictions
Appropriate credit must be given and any modified versions must licensed under same terms when sharing adapting the work.
Ureader Chart
Apache-2.0
OCR-VQA
Appropriate credit must be given when using, sharing, creating works from the databasc.
CuSyn(document)
ODC-By 1.0
Appropriate credit must be given when using, sharing, creating works from the datahase.
CoSyn(nutrition)
ODC-By
Appropriate credit must be given when sharing or adapting the work.
ScreenQA
CC CC BY 4.0
FinTabbNet
Appropriate credit must he given when sharing or adapting the work.
TexIVQ
CC BY 4.0
EATEN
DocVQA
LLLVAR GPT4
CC-BY-SA 4.0
Appropriate credit must be given and the copyright and permission notice must be included when distributing the work.
Docmatix
MIT
InfoVQA
Appropriate credit must be given and any modified versions must licensed under same terms when sharing adapting the work.
UreaderOCR
Apache-2.0
Continued on next page
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Table continud from previous page
Dataset
License
Restrictions
Appropriate credit must be given and any modified versions must licensed under sume terms when sharing adapting work.
DocReason
Apuche-2.0
InfographicVQA
VisualMRC.
POIE
Table 18: Detailed overview of the licensing terms, usage restrictions for the Grounding & Counting Category
Dataset
License
Restrictions
Appropriate credit must be given when sharing or adapting
CLEVR
CC BY 4.0
the work.
Appropriate credit must be given and any modified versions must be licensed under the same terms when sharing or
TallyQA
Apachc-2.0
adapting the work.
Appropriate credit must be given when sharing or adapting the work. Appropriate credit must be given, no commercial use is permitted, and adaptations must be shared under the same
VisualGenome
BC 4.0
CC BY-NC-SA 4.0
IconAA
license.
Appropriate credit must be given and no commercial use permitted when sharing adapting the work.
TQA
CCBNNNC.
MovieNet
Appropriate credit must be given when sharing or adapting
CLEVR-Math
BC
the work.
Appropriate credit must be retained when redistributing the work. Appropriate credit must be given and any modified versions must be licensed under the same terms when sharing adaptingg work.
given and the copyright notice
Super-CLEER
BSD
Math
2.0
CLEVV- -Change
Table 19; Detailed overview of the licensing terms, usage restrictions for the OCR Category
Dataset
License
Restrictions
Appropriate credit must be given and any modified versions must be licensed under the same terms when sharing adapting the work.
K12 Printing
Apache 2.0
ArXiv OCR
HME
Appropriate credit be given and adaptations must shared under the license.
VCR-Wiki
CC BY-SA 4.0
Appropriate credit must be given when sharing or adapting the work. Appropriate credit be given and the copyright and permission notice must included when distributing the work.
Tox1CR
CC
Sroie
MIT
ICDAR-LSVT-sh
ReCTs
Appropriate credit must be given, no commercial use is permitted, and adaptations must be shared under the same license.
CC CCBY-NC-SA
CTW
4.0
Rendered Text
ICDAR2017
Chrome- Writing
MTWT(z)
Continued on next page
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Table 19 continudd from previous page
Dataset
License
Restrictions
Appropriate credit must be given and the copyright and permission notice must included when distributing the work.
IAM
MIT
Appropriate credit must be given und the copyright and permission notice must be included when distributing the work.
ICDAR2019
MIT
CC BY-NC-DD 4.0
Appropriate credit must be given, no commercial use permitted, and no adaptations are allowed.
Orand-Car-A
IIT 5K
H EVALUATION
We comprehensively evaluate our model across wide range of benchmarks, which are grouped into three main categories: General VQA tasks, Table & Chart & OCR tasks, and Math & Reasoning tasks. The full list of benchmarks and their corresponding categories presented in Tab. 20.
H.1 EVALUATION DETAIL.
Our model evaluated under two distinct inference configurations to assess its capabilities under different conditions. For the non-thinking mode, we employ deterministic setting with the tem- perature set to and a maximum output length of 8,192 tokens. Conversely, for the thinking mode, which is designed to elicit more detailed reasoning, set the temperature to 0.6 and increase the maximum token length to 16,384.
All experiments are conducted using customized version of the VLMEvalKit (Duan et al., 2024) framework. To ensure a more comprehensive and accurate evaluation, we introduced sev- eral key modifications. First, extended the framework's ' L.LM-based judging capabilities to eral benchmarks that originally lacked this support, including ChartQA (Masry et al., 2022), In- foVQA (Mathew al., 2022), VQA (Mathew et al., 2021), and CountBench (Paiss al., 2023). Second, for the Math Verse (Zhang et al., 2024b) benchmark, we identified and addressed issues of inaccurate answer extraction and judging errors present in the original implementation. To enhance its robustness, we supplemented its judge with a broader set of test cases, thereby improving the reliability of the evaluation for this benchmark.
H.2 PROMPT FOR LLM-RASED EVALUATION
Finally, to ensure transparency and reproducibility in our evaluation process, we provide the exact prompt from VLMEvalkit official (Duan et al., 2024) used for our LLM-based judging across the aforementioned benchmarks. This prompt constitutes a rigorous instruction set that directs the judge model to strictly evaluate the correctness of the candidate's 's final answer against the standard answer, ignoring intermediate reasoning errors and accommodating various valid output formats. The full prompt is detailed below:
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Prompt for LLM--based Answer Grading
Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. Here are some evaluation criteria:
1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's ' answer is consistent with the standard answer according to the form of the question. THE STANDARD ANSWER IS ALWAYS CORRECT AND THE QUESTION IS PERFECTLY VALID. NEVER QUESTION THEM.
2. ONLY compare the FINAL ANSWER COMPLETELY IGNORE any potential errors in the REASONING PROCESSES.
3. Some answers may be expressed in different ways, such as some answers may be mathematical expression, some answers may be textual description, long as the meaning expressed is the same. Before making judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct. If the standard answer does not specify a unit, but the candidate's answer includes a unit that is correct for the value given, consider correct.
4. Some answers may consist of multiple items, such as multiple-choice questions, multiple- select questions, all-in-the-blande questions, etc. Regardless the question type, the final answer will be considered correct long it matches the standard answer, regardless of whether the reasoning process correct. For multiple-select questions and multi-blank fill-in-hhebllan questions, all corresponding options blanks must be answered correctly and match the standard answer exactly to be deemed correct.
5. If the prediction is given with \boxed please ignore the \boxed() | and only judge whether the candidate's 's answer is consistent with the standard answer.
6. If the candidate's 's answer invalid (e.g., incomplete (cut off mid-response), lots of unnormal repetitive content, or irrelevant to the question, saying it can't answer the question because some irresistible factors, like ethical issues, no enough information, etc.), select option C (INVALID).
Please judge whether the following answers are consistent with the standard answer based the above criteria. Grade the predicted answer of this new question as one of:
A: CORRECT
B: INCORRECT
C: INVALID
Just return the letters "A", "B", or "C", with no text around it. Here your task. Simply reply with either CORRECT, INCORRECT, or INVALID. Don't apologize or correct yourself if there was mistake; we are just trying to grade the answer.
<Original Question Begin>: [question]
< <Original Question End>
<
Standard Answer Begin>:
<Standard Answer End>
Canndidttes Answer Begin>:
[llm_ |
<Candidate's Answer End>>
Judging the correctness of the candidate's 's answer:
H.3 BENCHMARKS
We comprehensively evaluate model across wide range of benchmarks. which grouped into three main categories: General VQA tasks, Table & Chart OCR tasks, and Math Reasoning tasks. The full list of benchmarks and their corresponding categories is presented in Tab. 20.
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Table 20: The evaluation benchmarks used in our study, grouped into three main categories: General VQA, Table & Chart & OCR, and Math & Reasoning.
Task
Benchmark
MMMU (Yuc al., 2024), A12D (Kembhavi et al., 2016), MMStar (Chen al., 2024c),
MMVet (Yu al., 2024b), HallusionBench (Guan al., 2024), MMBench (Liu 2024e). MMMU-Pro (Yue et al., 2025a), MMVP (Tong et al., 2024b), POPE (Li al., 2023a)
General VQA
VisuLogic (Xu et al,, 2025), RealWorldQA (xA1, 2024), CountBench (Paiss et ul., 2023).
BL.NK (Fu al., 2024), MMT-Bench (Ying et al., 2024),
VL.Ms are Blind (Rabbnanzadehgervi al., 2024)
Table Chart
DocVQA (Mathew et al., 2021), InfoVQA (Mathew et al., 2022), CharXiv (Wang et al., 2024c).
& OCR
SEED- CGED-Bench2-Plun (Li ct al., 2024a). CharQA (Masry et al., 2022), OCRBench (Liu et al., 2024f)
Math
Math (Lu al., 2024a), Vathiision (Wang al., 2024a). Verse (Zhang et al. 2024b),
Reasoning
LisicVista (Xiao al., 2024), WeMath (Qiao al., 2025). DynaMath (Zou al., 2025)
PROMPT
In this section, present the complete prompts used in our HoneyPipe. Specifically, two distinct prompts are utilized: one for the Noise and Irrelevance Filtering process in Sec. 2.2, and the other for the Fidelity Verification process in Sec. 2.3.
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Prompt for Noise and Irrelevance Filtering
Given the image and the [QUESTION x]. your task is to determine:
1. Whether the image and question are relevant to each other;
2. Whether they form an appropriate question;
3. Whether there are any obvious issues with the question.
Below some examples of evaluation scenarios:
1. **Relevance Check**; Is the image related to the question?
Example 1: If the question is about "the dog in the photo," the image should clearly depict dog or something directly related to dogs.
the question asks about the diameter of a circle in the image, but the image does not contain a circle + filter out.
Example
Example 3: If the image completely unrelated the question filter out.
2. ** Ambiguuus or Vague Question**; **; If the question is too vague, unclear, or lacks sufficient context to establish a clear connection with the image - filter out.
Example 1: "What do you think about this?" without specifying what "this" refers to in the image's s context.
Example 2: Ambiguous question (e.g., unclear referents) - filter out.
Example 3: The question is not a question. (c.g., list of numbers, a table, etc.) - filter out.
3. L*aaguaage Filter**; If the question contains languages other than Chinese or English filter out.
After evaluating the image and question, provide a clear decision:
false: If the image and question relevant, the relationship is clear, and issues are found. true: If the image and question do not match, contain issues (c.g., ambiguity, contradiction, irrelevance, language errors), or fail any evaluation criteria.
** *utppt Format(json format)**: json
"q0": "true",
"q0 reason": "Briefly explain the reason for filtering",
where the returned result index starts from O. If there N questions, return the results of all questions(qO, .... qN). Here are the specific [QUESTION x] and image:
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Prompt for Fidelity Verification
** Evaluate whether the new answer JANSWER x] should be retained or filtered compared to the original answer [ORLANSWER x] for [QUESTION x] according to the following guidelines. Provide the evaluation results in JSON format (where an index starting from 0).
** **Evaluation Guidelines:**
1. Bewe--enddeedeeppiiiiii Questions**
**Rule:** Answers inherently diverse and subjective. Retain the new answer unless it is
clearly irrelevant to the question.
2. ** **Precise Answer Questions*
**Rule:** Retain the new answer if it is numerically equivalent to the original answer (e.g.,
different formats, minor rounding errors within 0.1, valid unit conversions). Filter it if there is fundamental conflict in numerical values.
**Factual Questions**
***Rule*** Retain the new answer if it conveys the same fact (rephrasing is allowed). Filter it if it introduces fabricated or irrelevant content.
4. **Conceptual Common Sense Questions**
**Rule:** Retain the new answer if the final conclusion or reasoning result is consistent,
regardless of wording. Filter it if there is logical contradiction opposite judgment. **Chart Analysis Questions**
**Rule:** Retain the answer if both reference consistent content in the chart. Filter if conflicts with the factual data in the chart.
6. Logical Reasoning Hypothetical Questions**
**Rule:*** Retain the new answer if the final conclusion is consistent, even if the steps differ.
Filter it if the results are contradictory or contain logical errors.
7. aaarriig/ProrityCCooprriiii Questions**
** **Rule*** ** Retain the new answer if the relative order or judgment is consistent, Filter if
the new answer reverses or distorts the sorting/compaarioo result.
**Judgment Criteria:
**false:** The new answer meets consistency requirements, supplements information, and should be retained.
**true:** The new answer is inconsistent, irrelevant, or contains factual errors, and should be filtered.
** **Uttput Format (JSON)*** json
"q0": "false".
"q0 "00_reason": "The new answer effectively rephrases the same factual information.",
"qN": "true", "gN_reeson": "The new answer introduces numerical values conflicting with the original precise answer."
""Explanation***
"false" indicates that the new answer should be retained (consistent), and "true" indicates that the new answer should be filtered (inconsistent or erroneous).
Thee index starts from and must cover all questions (q0, gl..... qN).
Here are the specific (QUESTION x]. [ANSWER x] and ANSWER x]:
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J NOISE DATA CASE STUDY
J.1 FORMAT FLAW
Some data exhibit format flaws, such missing instructions or responses. Below is example of data filtered out due to a missing response.
Case: Format flaw
User Question:
What might be the implications of this lighting for photographer?
Response:
(None)
Filtering Reason:
Only half conversation.
J.2 LOw QUALITY IMAGE
Low-quality images, such as those with excessively low resolution, overly large aspect ratios, or blurriness, are detrimental to the model's ability to extract high-quality visual features and semantic information, thereby impairing the model's performance and effectiveness. Below is example of an image filtered out due to excessively low resolution.
J.3 TEXT REPETITION
Some responses generated by MLLMs may suffer from the issue of text repetition. Below is example of data filtered out due to the inclusion of repeatedly occurring segments in the response. The first occurrence of repeatedly appearing pattern is marked in red, and the second occurrence is marked in blue. More repeated occurrences have been omitted.
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Case: Low-quality image
User Question:
In this task, you will classify traffic signs in the given image. The image is in color and contains various traffic signs captured in real-world conditions. Your goal is to accurately identify the type of traffic sign present in the image. Consider factors such the sign's shape, color, and symbols to ensure correct classification. A sample output should look like this: 'Speed limit (80km/h)' Options: (a) Go straight right (b) Right-of-way the intersection (c) Dangerous curve to the left (d) Bicycles crossing (e) Speed limit (30km/h) (f) Speed limit (120km/h) (g) Speed limit (50km/h) (h) End of speed limit (80km/h) (i) Pedestrians (j) Ahead only
Response:
(j) Ahead only
Filtering Reason:
Image size too small: [26, 27]
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Case: Text repetition
X + 11
User Question:
3y 9
Hint: Please answer the question and provide the cor- rect option letter, e.g., A, B, C, D, at the end.
x + 2
Question: Determine the value of
2y 6
Options: (A) 12 (B) 15
(C) 18
(D) 21
Response (part):
To determine the value of X, we need to analyze the given geometric configuration. The image shows triangle with segments marked with expressions involving and y.
156
However, this result seems incorrect based on the provided options. Let's re-evaluate the steps. Revisiting the substitution step:
3r
12
+
11
9
Multiply every term by 6:
10x
66
9x 36 54
156
This result is still incorrect. Let's re-evaluate the initial setup and equations. Revisiting the initial equations:
x +
11
3y 9
5
3z 12 9
11
Multiply every term by 6:
10x + 66 = 9r 36 54
156
This result is still incorrect. Let's re-evaluate the initial setup and equations. Revisiting the initial equations:
11+11- 3y 9
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J.4 IMAGE-INSTRUCTIO MISMATCH
Some data contain instructions irrelevant to the associated images, which is detrimental to the MLLM's ability to align visual features with semantic information. Below is example of data filtered out due to the irrelevance between the image and its corresponding instruction.
Case: Image-instruction mismatch
bloke.
User Question:
What natural elements contribute to the sense of tranquility in this landscape, and how does the play of light enhance the overall atmosphere?
Response:
The natural elements that contribute the tranquility include the lush green meadow, vibrant wildflowers, and the gently flowing stream. These elements evoke sense of peace and harmony with nature. The play of light, with sunlight filtering through the tree branches, creates dynamic interplay of shadows and highlights, further enhancing the serene atmosphere by adding depth and visual interest to the landscape.
Filtering Reason:
The image shows person on an escalator with bag labeled 'bloke," which is unrelated natural elements tranquility in landscape. The question and image not relevant to each other.
J.5 UNANSWERABLE INSTRUCTION
Some data contain overly vague instruction descriptions, or the image-instruction pairs lack suffi- cient information, rendering the instructions unanswerable. However, it is important to note that well-articulated open-ended instructions will still be retained. Below an example of an image- instruction pair that was filtered out due to insufficient information caused by partial corruption of the image.
J.6 Low QUALITY INSTRUCTION
Some data contain images that are relevant to their corresponding instructions, but the instructions can be answered without extracting information from the images. Such data only leverages the LLM component of MLLMs and provides benefit to the training of the vision encoder and projector. Below an example of data filtered out because the instruction can be answered without extracting information from the image.
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Case: Unanswerable instruction
User Question:
Based on the pie chart representing the distribution of parliamentary seats in Italy, which party has the second-largest share, and what percentage seats does it hold?
Response:
M5S, 25%
Filtering Reason:
The image is pie chart, but it lacks labels legend to identify which colors correspond to which parties. Without this information, impossible to determine which party has the second-largest share the percentage of seats it holds.
Case: Low quality instruction
User Question:
Who discovered the moon of Saturn called Titan in 1655? Answer the question using a single word or phrase.
Response:
Huygens
Filtering Reason:
The image shows Saturn, but the question is about the discovery Titan, which is not de- picted in the image. The image does not provide any information relevant to answering the question.
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K QUALITATIVE ANALYSIS OF HONEY-DATA-15M
K.1 FIDELITY VERIFICATION- FAILED DATA
The fidelity verification process Sec. 2.3 filters out data where there are conflicts between the enriched response and the original response. Below is an example of factual query being filtered out because the enriched response differs from the original response.
Case: Factual inaccuracy
User Question:
B
Hint: Please answer the question and provide the final answer at the end. Question: What is the radius of the cirele O the length of are AB is 4 4pp and the measure of angle AOB is 40 degrees?
Original Response:
The answer is 9
Rewritten Response:
To find the radius of circle O, use the formula for the length of an arc. The for- mula for the length an are s is:
0
2Tr
360
where is the central angle in degrees, and r is the radius of the circle.
We know that the length of are AB is 4T and the central angle LAOB is 40 Plugging these values into the formula:
4T 40
2πr
360
First, simplify
10
:
360
1
4T
x 2nr
9
Simplify the right-hand side:
4x 2mr
To solve for multiply both sides of the equation by 9:
36m 2πr
Then divide both sides by 2m:
36
18
2m
Final answer: The radius of circle O is 18.
Filtering Reason:
The new answer introduces numerical value 18 that conflicts with the original precise 9.
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K.2 SHOWCASE OF HIGH-QUALITY ENRICHED DATA
In this section, showcase data from diverse topics within Honey-Data-15M, A subset of these responses with long Chain-of- Thouuhht reasoning. The enriched responses incorporate more in-depth knowledge, feature more accurate responses, and present more detailed reasoning processes.
Case: Caption (with short Co'T)
IOMPOUES
Wait The Lord
JOSHUA
OCTORtT
User Question:
Can you describe the scene depicted in the image?
Original Response:
The image shows tree in the desert with a sand dune in the background.
Enriched Response:
The image depicts serene and somewhat desolate desert with the following ele- ments:
Desert Landscape:
The background features rolling sand dunes with a warm, orange hue, characteristic of desert environment.
TTe sky is clear and light, suggesting calm and open atmosphere.
2. Tree:
In the foreground, there is a bare, leafless tree with twisted branches. The tree appears to be struggling or enduring harsh conditions, symbolizing resilience waiting.
3. Text Overlay:
The text "Wait on the Lord" is prominently displayed in the center of the image, written in clean, white font.
Below the main text, there is additional information: "Joshua 3 October 11th, 2020," indicating reference to biblical passage and specific date, likely for church service or event.
In the top left corner, the text "Immanuel Baptist Church" is visible, suggesting the image related to church event sermon.
Interpretation:
The scene conveys sense of patience, endurance, and faith, as the desert and the bare tree symbolize challenging conditions. The text "Wait on the Lord" reinforces the theme of waiting and trusting in divine guidance, aligning with the biblical reference to Joshua 3. The overall tone reflective and spiritual, encouraging viewers to find strength and hope in difficult times.
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Case: General (with short CoTT)
User Question:
Can you tell what the people are doing?
Original Response:
The people appear to be either walking or standing on the street, perhaps waiting to cross the intersection attending public event.
Enriched Response:
The image shows large group of people gathered in public space, likely on street near a building. Here's what can be inferred about their activities:
The people are standing attending an event or gathering. The density of the crowd indicates it might be public or organized event.
Crowd Gathering:
large group, suggesting they are
The presence of traffic cones, barricades, and the organized nature of the crowd suggest this could be parade, festival, public demonstration. The "BEGIN" sign on the pole might indicate the start of route or event.
Event or Parade:
3. Traffic Management:
The traffic lights and signs (e.g., "ONLY," "NO TURN ON RED") indicate that the area is being managed control traffic flow, likely accommodate the event.
4. Casual Attire: The people are dressed in casual clothing, which is typical for outdoor public events.
5. Balloons and Decorations: The balloons visible in the background suggest festive or celebratory occasion.
Overall, the people appear to be participating in observing public event, such as pa- rade, festival, or community gathering. The setup indicates careful planning to manage the crowd and traffic.
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Case: Grounding Counting (with short Co'T)
User Question:
Think out loud before giving the final conclusion. Is there a large blue cube made of the same material the small sphere?
Original Response:
yes
Enriched Response:
To answer the question, let's analyze the image step by step:
Identify the objects in the image:
There are two red cylinders.
There is red metallic cube.
There small red sphere.
There a brown matte cube.
There is a large blue cube.
Examine the material of the small sphere:
The small sphere red and appears have matte finish. This can be inferred from its lack of reflective properties and its uniform color.
3. Examine the material of the large blue cube:
The large blue cube also appears to have matte finish. It does not reflect light in way that suggests metallic glossy surface.
4. Compare the materials:
The small red sphere has matte finish.
The large blue cube also has matte finish.
Since both objects share the same matte appearance, it is reasonable conclude that they are made of the same material.
Final conclusion:
There large blue cube, and it appears to be made of the same material the small red sphere.
Final Answer: Yes, there is a large blue cube made of the same material as the small sphere.
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Case: Chart (with long CoT)
Accuracy of algorithms different
datasets
datasets
16
bird
14
poet
12
Accuracy
10
artery
steel
User Question:
Which algorithm has the smallest accuracy summed across all the datasets? Please clude your answer as Answer: xxx at the end if possible.
Enriched Response (Part):
< think > So, let's solve this problem. First, we need to find the accuracy of each algo- rithm (artery and steel) across all datasets (bird and poet), sum them, and then compare. First, look at the "artery" algorithm. The black bar (bird dataset) has a height of 1, and the gray bar (poet dataset) has height of 7 (since total for artery is 7, and bird 1, so poet is 7-1=6... < //tink >> To determine which algorithm has the smallest accuracy summed across all datasets, we analyze the two algorithms (artery and steel) by calculating the total accuracy across both datasets (bird and poet) for each:
Artery algorithm:
Accuracy on the bird dataset: The height of the black bar is
Accuracy on the poet dataset: The height of the gray bar - 1 = 6 (since the total height of the artery bar is 7).
Sum of accuracies:
1 -
2. Steel algorithm:
Accuracy on the bird dataset: The height of the black bar is
Accuracy on the poet dataset: The height of gray bar 17 - (since
the total height of the steel bar is 17).
Sum of accuracies:
9
17.
Comparing the two sums (7 for artery and for steel), the smaller sum belongs to the artery algorithm.
Answer: artery
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Case: STEM (with long CoTT)
Tye
4
D
B
User Question:
In the right triangle ABC, ZACB -00, CD is the height from to side AB, CE is the median to side AB, = CE = 5, then CD () A.
B. 4 D. 2/3.
Enriched Response (Part):
< think Got it, let's try to solve this problem step by step. First, let's recall some prop- erties of right triangles, medians, and altitudes.
We have right triangle ABC with LACB 90" CE is the median to AB, so right triangle, the median to the hypotenuse is half the hypotenuse. That's key property: in a right triangle, the median from the right angle to the hypotenuse is equal half the hypotenuse. So CE AB, We know CE = 5, so AB = 2 CE 10... < /think > To solve the problem, we can use properties right triangles, medians, and altitudes. Let's break down the steps:
Step 1: Use the property of the median in a right triangle In right triangle, the median to the hypotenuse is half the length of the hypotenuse.
Given AABC is a right triangle with ZACB = 90", and CF the median to A AB we have:
CE = AB
2
Since CE= 5, substitute to find AB:
1
5 AB
AB 10
2
Step 2: Analyze segments on AB
E is the midpoint of AB (because C. is median), so AE = BE AB We know AD 2, the segment DE (between D and E) is: 2
5.
DE AAE AD = 5- 3
Step 3: Use the Pythagorean theorem in ACDE CD is the altitude from C to AB, so CD 1 AB, meaning <CDE 90". Thus, ACDE is right triangle.
In ACDE:
Leg DE 3.
Hypotenuse CE 5 (given)
LLeg CD (unknown, let's call it .))
By the Pythagorean theorem:
CD2 DE = CE
Substitute DE =3 3 and CE = 5:
2 32 52 -
2 + 25
2 = 4=4
Thus, CD 4, and the answer is C.
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Case: Document (with long CoTT)
Monthly Subscription Summary Report
Spotily Premtan
Selor Novrtbl
Addle
Cload
Detec Sovenber
Moanden 1,75
Change CT
Releocal Date Sowerder 2014
skpp aiie
Amarnn Frime
behee
(ed-aaporrta))
Canoolaten Loac wwwe betor novratle
Charpoo $103.45
User Question:
Which service has the next renewal date after Spotify?
Enriched Response:
<. think So, let's look at the renewal dates for each service. First, Spotify's 's renewal date is November 1, 2023, Now we need to find the next one after that. Let's list all renewal dates:
Spotify Premium: November 1, 2023
Netflix Standard Plan: November 7, 2023
Hulu (Ad-Supported): November 12, 2023
Adobe Creative Cloud: November 15, 2023
Amazon Prime: November 20, 2023
So after November 1, the next date is November 7, which Netflix Standard Plan.
< /think
To determine which service has the next renewal date after Spotify, we first identify Spo- tify's renewal date and then compare with the renewal dates of other services:
1. Locate Spotify Premium's 's renewal date: November 1, 2023.
2. List the renewal dates of all other services:
Netflix Standard Plan: November 7, 2023
Hulu (Ad Supported): November 12, 2023
Adobe Creative Cloud: November 15, 2023
Amazon Prime: November 2023
Identify the earliest date after November 2023: November 2023, which corresponds to
Netflix Standard Plan.
Thus, the service with the next renewal date after Spotify is Netflix Standard Plan.
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L
QUALITATIVE SHOWCASE OF BEE-8B
Case: STEM
B
C
M
N
User Question:
Given: In AABC, the perpendicular bisectors of AB and AC intersect BC at points M and N, respectively. AB = 4, AC 7, BC 10. Find the perimeter of AAMN.
Bee-8B Response:
< think Got it, let's try to solve this problem step by step. First, let's recall what a per- pendicular bisector is. A perpendicular bisector of segment is line that is perpendicular to the segment and divides it into two equal parts. So, if M is on the perpendicular bisec- tor of AB, that means MA = MB. Similarly, N is the perpendicular bisector of AC, NA NC.
Now, need to find the perimeter of triangle AMN. The perimeter of triangle is the sum its sides, so that would be AM MN + AN. Let's substitute the equal segments we replace AM with MB and AN with
just found. Since MA MB and NA NC, NC. So the perimeter becomes MB + MN + NC... < /think To solve the problem, perpendicular bisector of a segment is equidistant from the segment's two endpoints.
the property of perpendicular bisectors: any point the
Step 1: Analyze the Perpendicular Bisectors
.M lies on the perpendicular bisector of AB, MA MB (by the perpendicular bisector theorem).
N lies on the perpendicular bisector AC, so N NC (by the perpendicular bisector theorem).
Step 2: Express the Perimeter of AAMN The perimeter of AAMN is AM + MN + AN. Substitute AM = MB and AN = NC (from Step 1):
Perimeter of AAMN MB + MN + NC
Step 3: Relate BC
Points M and N lie on BC, so MB + MN + NC is exactly the length of BC. We know BC 10 (given). Thus:
Perimeter of AAMN BC 10
Answer: The perimeter of AAMN is 10.
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Case: General
User Question:
This is an illusion image. What kind of visual illusions can it make people perceive? And why able to produce such effects?
Bee-8B Response:
< think >... < //thnk
To analyze the visual illusions in this image and their underlying mechanisms, let's break it down:
1. Types of Visual Illusions Perceived
This image triggers ambiguous shape perception (a type of multistable illusion). Viewers can perceive the same visual contour in two distinct ways:
Interpretation 1: Two face profiles
The left and right sides of the gray shape resemble the side profiles of two human faces (from forehead to chin). The central "connection" between the two profiles creates the illusion of two faces facing each other.
Interpretation 2: A goblet (chalice) shape
The central curve and the extended top/bottom of the gray shape can also be read the outline of goblet: the central curve as the goblet's bowl, and the top/bottom as the rim and stem.
2. Why Produces These Effects The illusion arises from cognitive biases and visual perception principles, rooted in how the brain processes shape and familiarity:
Shape Similarity & Prior Experience
Human faces and goblets share structural similarities in their eurved contours (e.g., the smooth, continuous curves of face's 's profile match the curve of goblet's bowl). The brain prioritizes interpreting ambiguous shapes using prior experience- evolved recognize faces (a "biologically significant" shape for survival) and are also trained to recognize everyday objects like goblets. When a single contour matches both sets of familiar shapes, the brain generates multiple plausible interpretations.
Gestalt Psychology: "Ambiguity" and "Continuity"
Gestalt principles explain how the brain organizes visual information into coherent wholes. Here, the continuity of the gray shape's curve is split into competing "wholes": one resembling two faces (due to the brain's 's bias for face-like patterns) and one resembling goblet (due to the brain's bias for object-like patterns). This split creates the illusion of simultaneous, conflictigg interpretations.
In short, the image leverages one contour's ability to fit two familiar shapes (faces goblet) and the brain's innate tendency prioritize familiar, evolutionarily important shapes (like faces) to create a multistable, ambiguous perception.
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Case: Chart
OCR
Counting
Decomeot
5.7%
STEM
Gencral
User Question:
7.8%
If you want to build new dataset based on this dataset, how would you adjust the distribution of the data?
Honey-Data 15M
BIS
Capten
24.5%
Chart
Bee-8B Response:
< think So, let's tackle this question. First, I need to understand the current distribu- tion from the pie chart. The Honey-Data 15M dataset has segments: General (36.8%), Chart (24.6%), Caption (15.1%), STEM (7.6%), Document (5.9%), Grounding & Count- ing (5.1%). OCR (4.9%)... /think >>
To adjust the data distribution when building new dataset based on Honey-Data 15M, follow this structured approach, which accounts for the current distribution (from the pie chart) and the task's requirements:
1. Analyze the Current Distribution
2.
Define the New Dataset's Purpose
The distribution adjustment depends on the task the new dataset will support. For exam- ple:
Niche application: If the task targets rare category (e.g., OCR for document analysis), prioritize increasing that category's share.
Balanced general-purpose: If the task requires broad coverage, rebalance dominant categories (General, Chart) and enhance smaller ones.
Multi-modal focus: If the task combines categories (e.g., Chart Caption for image-text models), balance those segments.
3. Select Categories to Adjust
Determine which categories need oversampling (more data) undersampling (less data):
Oversample underserved categories: If category is critical but underrepresented (e.g., OCR at 4.9%), increase its proportion.
Undersample overrepresented categories: dominant category (General 36.8%) is less relevant to the task, reduce its share.
4. Implement Adjustment Methods
5. Validate the Adjusted Distribution
Example: Adjusting for an OCR-Focused Task
If the dataset targets OCR (currently 4.9%):
Oversample OCR: Collect OCR samples (e.g., scanned documents, handwritten notes) and augment existing ones (e.g., add noise, change fonts).
Undersample General: Reduce the General segment to free up space for OCR (e.g., scale General to 25% instead of 36.8%).
3. Balance remaining categories: Adjust other segments (e.g., Chart to 25% Caption to 10%) fit the new total (100%).
By aligning the distribution with the task's goals and using data-driven methods, the new dataset will better support the intended application while maintaining robustness.
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Case: Grounding Counting
User Question:
How many monitors there?
Bee-8B Response:
think >... < /hhink
To determine the number of monitors, we analyze the image by counting each distinct monitor from left to right:
The first monitor (leftmost, with textured black front).
2. The second monitor (black).
3. The third monitor (silver).
4. The fourth monitor (white bordered).
The fifth monitor (black, behind the white bordered one).
The sixth monitor (black).
The seventh monitor (black).
8. The eighth monitor (rightmost, black).
Answer: 8
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Reviews:
[{"review_id": "OKCIUpqDjE", "reviewer": "Reviewer_yEFr", "summary": "This paper addresses the issue of open-source multimodal models (MLLMs) lagging due to a lack of high-quality data. It introduces a high-quality fine-tuning dataset containing 15 million QA pairs. This dataset has undergone noise filtering and was enhanced using a \"dual-level CoT\" strategy. The paper also opens up the transparent data curation pipeline, HoneyPipe.", "questions": "Besides massive data and data cleaning, what other factors contribute to the model's performance?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "* Excellent performance, comprehensive, and open-sourced.\n\n* A transparent and reproducible data curation pipeline.", "weaknesses": "* The description \"meticulously cleaned of noise\" is somewhat of an overstatement. The method relies on strong models, or LLM-as-a-Judge, meaning its effectiveness is still constrained by these models' capabilities.\n\n* Experiments may be designed to validate the performance of the model-based filtering operator (Qwen2.5-VL-72B).\n\n* Regarding Stage 1 (Data Aggregation), it appears to be an \"exploration\" of the novelty, diversity, and coverage of existing datasets. However, it lacks sufficient \"novelty,\" as it is primarily a large-scale aggregation of existing, public community datasets (e.g., LLaVA, PixMo). The domain classification is coarse and relies on manual inspection, creating a significant engineering burden for reproducibility.\n\n* Regarding Stage 3 & 4, the process seems more like \"utilizing\" existing information to construct questions & answers. A good utilization process should be able to discover difficult problems. In this process, it's necessary to correct the noise introduced during the exploration phase. However, this paper directly uses an LLM to judge if the newly generated CoT answer is consistent with the original one. This can cause high-quality, correct CoT samples to be filtered out simply because they \"conflict with the original precise answer\".\n\n* The \"long CoT\" generation relies on \"top proprietary MLLMs\". A \"good\" utilization process should actively identify which problems are \"difficult\" and worth deep mining. In this paper, Stage 4 (Long CoT) largely passively receives samples that failed Stage 3 verification. This feels more like a \"remedy\" than a principled \"hard-case discovery\" mechanism.", "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": "Iu4303lLA3", "reviewer": "Reviewer_yRMp", "summary": "This paper addresses the performance gap between fully open multimodal large language models (MLLMs) and their proprietary counterparts, attributing this gap primarily to a deficit in high-quality Supervised Fine-Tuning (SFT) data. The authors argue existing open datasets are noisy and lack complex reasoning data, such as Chain-of-Thought (CoT).\n\nThey make a  new, large-scale SFT dataset of approximately 15 million QA pairs, open source the data platform, and train an 8B model. Authors also benchmark Bee-8B on a wide array of tasks, claiming it establishes a new state-of-the-art (SOTA) for fully open MLLMs and achieves performance competitive with semi-open models , particularly on reasoning-heavy benchmarks like MMMU , MMStar , and CharXiv. Ablation studies are provided to demonstrate that both the noise filtering and the CoT enrichment stages contribute significantly to these performance gains", "questions": "- How sensitive are your results to the choice of LLM judge? Please report metrics with (at least) two families of judges\n- Where applicable, please report some CI / confidence intervals for the figures in Table 2\n- For greater adoption in the community, for each source category in Figure 3, please provide a table with license terms, known restrictions (e.g., non‑commercial), and PII removal strategy. For OCRed textbooks/receipts and K‑12 materials, what steps ensure copyright compliance?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "The paper's methodology for data curation is extensive, and the five-stage training recipe for the validation model is thorough. The ablation studies convincingly isolate the benefits of data filtering and CoT enrichment which supports the central thesis.\n\nPaper is also well-written, clearly structured, and logically organized. The core thesis is stated upfront and defended throughout.\n\nThe primary bottleneck for open-source MLLM development is widely recognized as access to high-quality, large-scale SFT data. This paper delivers not only a 15-million-sample dataset specifically designed to address this (with a principled dual-CoT approach) but also the entire pipeline to create it.", "weaknesses": "- Both short‑ and long‑CoT enrichment hinge on model‑generated content; “fidelity verification” uses a Qwen‑family judge. The paper acknowledges Qwen3‑32B as the evaluator for some tasks (e.g., DocVQA), while Bee‑8B is trained on Qwen3‑8B, raising potential brand/self‑preference risks and evaluation coupling (same family, same stylistic priors). Empirically, LLM judges exhibit position, verbosity, and self‑enhancement biases; they correlate with humans but not uniformly across domains. A careful judge‑robustness analysis (randomized order, multiple independent judges, human spot‑checks, reporting agreement) is needed to reinforce the claims. For instance, the yourbench paper employs an ensemble of judges in order to reduce self-preference and increase reliability. A similar method, with multiple judges, can be employed to further strengthen the claims\n\n- Although many benchmarks have automatic metrics or references, the paper also “enables LLM‑based judging on DocVQA” using a specific setup. Given known instabilities of LLM‑as‑judge (prompt sensitivity, order effects), stronger reporting (prompt template, seed settings, inter‑judge agreement, and correlation to human labels on a held‑out slice) is warranted.\n- Given the breadth of sources and the emphasis on chart/document benchmarks, the paper should report automatic overlap/de‑dup statistics (hashes, text similarity, synthetic perturbed matching) between Honey‑Data‑15M and every evaluation set (MMMU/MMMU‑Pro, MMStar, LogicVista, DynaMath, CharXiv, OCRBench, etc.). Without this, some gains could come from near‑duplicates or stylistic priming. (The chosen benchmarks are widely used and public, which increases leakage risk.). If the authors have indeed already performed this, then they should report this.", "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": "Rda8KX9wSp", "reviewer": "Reviewer_ctd5", "summary": "The authors tackle the problem of quality in SFT datasets, where previous contributions often have noise in the form of a lack of instruction following, factual inaccuracies, etc., for multimodal LLMs. The authors propose a data collection pipeline, contribute the data collected from the same, and train a model on the data.\n\n## Honey 15M and HoneyPipe\n\nThe 15M dataset is curated using the proposed (automated) HoneyPipe pipeline. Stage 1 - the authors collect 24M image-text pairs from publicly available datasets and deduplicate them (using image hashing and text hashing for instructions - both approximate techniques). With the final image-instruction-response tuples, they assign a domain label (unclear how many tuples exist / how manual tagging was done for a million scale). Stage 2 - heuristics-based filtering with rules and using a powerful model for logical consistency filtering. Stage 3 - (partially applicable to the collected tuples) CoT is promoted and ensured by removing phrases discouraging it, and LLM as Judge to compare the new CoT and the original ground truth response. Stage 4 - Long CoT generated with different models. The authors end with the total Honey 15M. Figure 3  has a breakdown by categories.\n\n## Bee-8B \n\nAuthors train a Qwen3-8B + SigLip2 model. The training gradually moves from warmup to alignment to SFT to post-training RL.\n\n# Results\n\nThe trained model performs really well across benchmarks, with Math & Reasoning gaining a lot of performance. Ablations suggest curation helped with math-based tasks and did not help specific performance in MMBench (with some loss). No-CoT also seems to give strong performance on DocVQA.", "questions": "/ Please look at the questions in the weaknesses and summary above on unclear/missing details. I'm willing to bump my score to 8 once authors provide clear details.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. Solid contributions, clear presentation, and clean analysis from the authors; I really appreciate the details in the appendix.\n2. The gains in performance and ablations clearly suggest the strengths of the data, especially with math/reasoning style benchmarks.", "weaknesses": "1. Lack of release details: it is unclear what the authors are releasing publicly, given that a lot of this work has strongly benefitted from public work. I strongly encourage authors to clarify this here and also encourage/ask if the authors can release checkpoints for each of the different stages - releasing these checkpoints would strongly improve the contributions beyond the final model/data and improve open science beyond improving model performance.\n2. [Minor] Lack of reproducibility and ethics statements. The authors are strongly encouraged to write these statements.", "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": "GWdZxJr8gI", "reviewer": "Reviewer_vE6P", "summary": "- The paper introduces Bee, a long-context evaluation corpus focused on improving benchmark reliability and diagnostic granularity for long-context LLMs. To reduce any artifacts or contaminations from using synthetic or web-mined long-context datasets, they provide human-written and length-controlled contexts.This corpus covers four task types and also fine-grained position control (of beginning, middle, and end).\n- The authors evaluate 15 open and closed LLMs on Bee and find that models generally retain strong short-context ability but degrade beyond 64K tokens. Also, it confirms the “lost-in-the-middle” phenomenon showing middle-context reasoning is worst and LLMs hallucinate under dense distractor settings, even when retrieval accuracy remains high.", "questions": "- The context range (8K-200K) for the Bee corpus is motivated by “scaling to extreme contexts” according to the authors but is there any theoretical justifications for this? Is 200K a representative threshold for real tasks?\n- How many human annotators were used per instance? Also, what was the inter-annotator agreement or validation accuracy before filtering?\n- Have the authors tested whether models fine-tuned on Bee generalize to other long-context corpora publicly availably, and conversely, how well do Bee-trained models perform on out-of-domain texts? (Or any future experimental plans?)\n- [Minor] I’m not sure if the current primary area or the “datasets and benchmarks” area might be a better fit for the paper.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- In terms of originality, the authors introduce a long-context corpus named Bee, combining human-authored text, controlled length scaling and multiple evidence positions. Bee also seems to provide a strong diagnostic resource for evaluating context fidelity in long-context settings. In terms of domain the dataset covers, since it includes multiple domains, including policy, law, science, manuals, it would serve as a value source in the long-context research.\n- The data construction pipeline is clearly explained and includes human validation at every stage—authors filter GPT-generated distractors and manually verify evidence spans.", "weaknesses": "- While the authors emphasize “human-authored and contamination-free,” the process for verifying that test instances aren’t part of model pretraining data is not sufficiently detailed. No automatic overlap detection (e.g., MinHash or embedding-based retrieval) against known training corpora is reported to mitigate this and also the “human written” claim might not fully guarantee model-agnostic contamination, especially given the high proportion of UN and government reports in the pretraining datasets.\n- Although the Bee corpus supports entity tracking and multi-hop reasoning, most experiments laid in the main paper seems to focus on factual recall and summarization accuracy while limited analysis of higher-order reasoning. This makes the contribution weaker and Bee is rather more shown to be retrieval-heavy than reasoning-intensive benchmark.\n- The evaluation has rather heavy reliance on LLM-as-a-judge, which might introduce biases since the same model being evaluated also serves as evaluator.\n- Also, in terms of reporting the LLM-as-judge performance, there is no inter-model agreement check or human correlation study. Although the authors seem to partially mitigate this by including ROUGE-L and BLEU, these metrics are weak at measuring factual alignment for long-form outputs.", "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)