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must have wings (Step 6), and it must be a bird (Step 4): this step incorrectly utilizes rule fly -> birds to derive rock must be a bird. Step 8: Given the disjunction in Step 7, the only consistent option is that Rock is a bird, because if Rock can fly, it must be a bird, and if Rock cannot breathe, it contradicts the... | https://arxiv.org/abs/2505.15817v1 |
then it has wings. if pet.can_fly is True and pet.has_wings is not True: pet.has_wings = True changed = True # Premise 7: Rock is Peter’s pet. # This does not change the state of the pet but indicates that the pet is Rock. # Premise 8: Rock can fly, or Rock is a bird, or Rock cannot breathe. if pet.name == "Rock": if p... | https://arxiv.org/abs/2505.15817v1 |
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation Yunjia Xi1, Jianghao Lin1∗, Menghui Zhu2, Yongzhao Xiao1, Zhuoying Ou1, Jiaqi Liu1, Tong Wan1, Bo Chen2, Weiwen Liu1, Yasheng Wang2, Ruiming Tang2, Weinan Zhang1, Yong Yu1 1Shanghai Jiao Tong University,2Huawei Noah’s Ark Lab {xiy... | https://arxiv.org/abs/2505.15872v2 |
Agentic RAG should be to assess the effectiveness of agentic information seeking. Rigorous benchmarking and evaluation are essential to quantify these improvements, identify potential weaknesses, and guide the development of more capable agentic systems. However, existing RAG benchmarks are inadequate for this purpose,... | https://arxiv.org/abs/2505.15872v2 |
evaluation metrics and protocols that assess information seeking effectiveness in dynamic environments. •We provide a comprehensive comparison of agents under different LLMs, search engines, and question types, identifying their limitations and outlining directions for future research. 2 Related Work Agentic RAG . RAG ... | https://arxiv.org/abs/2505.15872v2 |
goal of Agentic RAG is to acquire a set of evidence C= {c1, c2,···, cnq}of length nqby iteratively searching and browsing within an open environment, and to generate an response ˆyqthat closely approximates the groundtrue answer yq. Following the three-stage framework of RAG [ 42],i.e., retrieval, augmentation, and gen... | https://arxiv.org/abs/2505.15872v2 |
Appendix B for more details. 4.1 Criteria for Query Determinacy and Verifiability . Unlike static RAG settings with a fixed corpus and information, real-world environments have constantly changing information. Thus, questions in this context must preserve stability and verifiability to allow consistent and reliable eva... | https://arxiv.org/abs/2505.15872v2 |
validate both factual correctness and answer uniqueness. Expand from Anchor Knowledge. During the data collection, we observed that many seemingly complex questions, involving multi-hop reasoning, emerging facts, temporal sensitivity, or false premises, could still be solved by LLMs based on their knowledge or single-r... | https://arxiv.org/abs/2505.15872v2 |
groundtrue answer yq, that is ACC =P q∈QI(ϕ(q, O) =yq)/|Q|, where I(·)is indicator function to determine whether ϕ(q, O)andyqare the same, implemented by a judge LLM in Section 5.2. This is a coarse-grained correctness metric without considering augmentation stage. Information Accuracy (IA@k) measures quality of eviden... | https://arxiv.org/abs/2505.15872v2 |
false-premise and other questions to encourage evaluators to condition their judgment appropriately. In our experiments, this strategy improves LLMs’ evaluation accuracy from 95.57% to 99.29% compared with human-eval. See Appendix C.2 for more details. 6 Benchmarking Agentic Information Seeking 6.1 Experiment Setup We ... | https://arxiv.org/abs/2505.15872v2 |
we observe a trend of initial improvement followed by a decline. This is likely due to the influence of irrelevant or distracting information from later retrieved sources, highlighting the importance of effective augmentation in selecting relevant evidence. 7 Effective Evidence Utilization (EEU) is mostly below 1, indi... | https://arxiv.org/abs/2505.15872v2 |
Figures 3(a) and (b) show the performance of different LLMs under DuckDuckGo, while Figure 3(c) presents results of DeepSeek-V3 with different search engines. More results are available in Appendix D.2. Firstly, LLMs and search engines consistently perform better on simpler attributes ,e.g., false premise, time sensiti... | https://arxiv.org/abs/2505.15872v2 |
that low-quality or tangentially relevant web content can often override or dilute the model’s internal confidence, leading to degraded performance. To mitigate this issue, future systems should explore methods to preserve model confidence in accurate internal knowledge and develop more precise retrieval strategies tha... | https://arxiv.org/abs/2505.15872v2 |
https://gemini.google/overview/ deep-research/ , Accessed on 2025-5-5. 10 [10] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948... | https://arxiv.org/abs/2505.15872v2 |
Tang, Yong Yu, and Weinan Zhang. Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation. In Proceedings of the ACM Web Conference 2024 , pages 3497–3508, 2024. 11 [25] Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong Liu, Chuhan Wu, Xiang... | https://arxiv.org/abs/2505.15872v2 |
Perplexity Team. Introducing perplexity deep research, 2025. https://www.perplexity. ai/hub/blog/introducing-perplexity-deep-research , Accessed on 2025-5-5. [45] Qwen Team. Qwen3 technical report, 2025. https://github.com/QwenLM/Qwen3/blob/ main/Qwen3_Technical_Report.pdf , Accessed on 2025-05-14. [46] Arun James Thir... | https://arxiv.org/abs/2505.15872v2 |
al. A survey of ai agent protocols. arXiv preprint arXiv:2504.16736 , 2025. 13 [60] yoheinakajima. Babyagi, 2025. https://github.com/yoheinakajima/babyagi , Accessed on 2025-05-06. [61] Weinan Zhang, Junwei Liao, Ning Li, Kounianhua Du, and Jianghao Lin. Agentic information retrieval. arXiv preprint arXiv:2410.09713 , ... | https://arxiv.org/abs/2505.15872v2 |
. . . . . . . . . . 20 B.2.2 Final Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 B.3 Data Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 B.4 Data Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 C Evaluation 24 C.1... | https://arxiv.org/abs/2505.15872v2 |
LLMs . The LLM serves as the agent’s central reasoning engine. It is responsible for interpreting the user query, generating search plans, selecting tools, reflecting on past actions, filtering retrieved content, and synthesizing the final answer. Our framework supports a variety of LLMs, including both API-accessible ... | https://arxiv.org/abs/2505.15872v2 |
deems the collected information sufficient or when the step limit Tis reached. The output of this stage is a sequence of retrieved observations O={o1, o2,···, oT}, representing the raw web content gathered during the search process. The detailed prompt of the retrieval stage is listed as follows: 16 You are a {agent_na... | https://arxiv.org/abs/2505.15872v2 |
In the previous interactions, you have already found several webpages in response to the user’s query. Now, you need to consolidate this information and select the {max_webpage_num} most relevant webpages, then rank them. 1. A webpage consists of a URL and webpage summary or information extracted from the webpage that ... | https://arxiv.org/abs/2505.15872v2 |
as follows: 1.Anchor knowledge identification . Annotators begin by selecting an underrepresented domain or language. They search authoritative sources such as Wikipedia, reputable news sites, fact-checking platforms, expert forums, and academic databases to extract candidate facts—particularly long-tail or distracting... | https://arxiv.org/abs/2505.15872v2 |
ensure the reliability and robustness of each constructed question, we implement a multi-stage human verification process involving two independent annotators (verifiers). Each verifier is required to evaluate the question from multiple perspectives. The validation process includes: 1.Content Verification : Check the c... | https://arxiv.org/abs/2505.15872v2 |
the actual target is Jimmy Morales, former president of Guatemala. His English Wikipedia entry ( https://en.wikipedia.org/wiki/Jimmy_Morales ) only notes that he holds a master’s degree in strategic studies with a specialization in security and defense from Mariano Gálvez University. Only by consulting less-common Span... | https://arxiv.org/abs/2505.15872v2 |
cover a wide range of topics. In total, queries span 14 broad domains, including history, geography, film & TV , science & technology, literature & art, politics, education, music, news, sports, humanities, entertainment, games, and social sciences. For visualization, closely related subfields (e.g., astronomy, biology... | https://arxiv.org/abs/2505.15872v2 |
can be provided. 2. If there are multiple items in the groundtrue answer, the candidate answer must also contain all the items to be correct. 3. If the groundtrue answer mentions that the premise of the question is wrong, such as some assumptions of the question are wrong, the candidate answer must point out the wrong ... | https://arxiv.org/abs/2505.15872v2 |
to the consistency of time, place, person, and quantity), but more details can be provided. 2. If there are multiple contents/events/persons in the groundtrue answer, the candidate answer must also contain all the contents/events/persons to be considered correct. 3. The given question does not have a wrong premise, and... | https://arxiv.org/abs/2505.15872v2 |
like 26 BabyAGI [ 60], AutoGPT [ 41], and KwaiAgent [ 34]. Unless otherwise specified, our default search engine is DuckDuckGo. For the implementation of the search engine, we first use the API to retrieve results. If the API fails to return the correct results, we then use a web scraper to fetch the results. Our evalu... | https://arxiv.org/abs/2505.15872v2 |
a noticeable en- hancement in both ACC and IA@k as the maximum number of retrieval steps increases. This scaling effect highlights the agent’s ability to refine its search and gather more evidence with additional computation time. However, the performance improvements for long-tail and distracting information questions... | https://arxiv.org/abs/2505.15872v2 |
Gemini-2.5-Flash consistently shows a higher EEU compared to DeepSeek-V3, particularly at 3, 5, and 10 rounds. IC (Information Compactness) remains relatively stable for both models across different retrieval rounds, with Gemini-2.5-Flash maintaining a slightly better performance compared to DeepSeek-V3. D.4 More Resul... | https://arxiv.org/abs/2505.15872v2 |
the model’s confidence in its own accurate knowledge, reducing its tendency to override correct internal answers when external information contradicts it. This could involve enhancing the model’s self-reflection capabilities or providing additional confidence scores for internally generated answers before querying exte... | https://arxiv.org/abs/2505.15872v2 |
Model Language ACC IA@1 IA@2 IA@3 IA@4 IA@5 EEU IC DeepSeek-V3Chinese 8.98 5.71 7.35 9.39 9.39 10.20 1.136 3.926 English 13.47 12.24 11.84 11.84 11.84 12.65 0.939 4.032 Pred. Lang. 17.14 11.02 15.92 17.96 17.55 17.96 1.048 3.919 GPT-4oChinese 10.20 9.39 8.16 9.39 8.57 8.98 0.920 3.878 English 11.02 8.16 9.80 10.61 11.0... | https://arxiv.org/abs/2505.15872v2 |
sure about, but do not call the same tool with the same parameters repeatedly. 6. You can flexibly switch the language of the search term to get more information. You can choose to search in Chinese, English, or the language related to the entity involved in the question (for example, if the question involves a French ... | https://arxiv.org/abs/2505.15872v2 |
observe that the performance difference between the Original and Fixed configurations is relatively small. However, fixed LLM configurations generally perform slightly worse. This may be because the information seeking LLM is aware of the knowledge gaps it has and selects corresponding documents to serve as evidence. D... | https://arxiv.org/abs/2505.15872v2 |
arXiv:2505.15874v1 [cs.IR] 21 May 2025Text-to-Pipeline: Bridging Natural Language and Data Preparation Pipelines Yuhang Ge, Yachuan Liu, Yuren Mao, Yunjun Gao Zhejiang University yuhangge@zju.edu.cn, liuyachuan@zju.edu.cn, yuren.mao@zju.edu.cn, gaoyj@zju.edu.cn Abstract Data preparation (DP) transforms raw data into a ... | https://arxiv.org/abs/2505.15874v1 |
/gid00008/gid00019/gid00016/gid00022/gid00017/gid00003/gid00026/gid00023/gid00032/gid00045/gid00036/gid00033/gid00036/gid00032/gid00031/gid00001/gid00016/gid00048/gid00047/gid00043/gid00048/gid00047 /gid00021/gid00042/gid00043/gid00012Figure 1: Task overview of Text-to-Pipeline .To bridge the gap, we introduce Text-to-... | https://arxiv.org/abs/2505.15874v1 |
extensively studied how to translate NL into executable programs. Specifically, Text-to-SQL methods [ 46,22,25,23] map NL queries to SQL statements, emphasizing semantic understanding and schema linking. These approaches are typically benchmarked on datasets such as WikiSQL [ 52], Spider [ 46], Spider 2.0 [ 22], and BI... | https://arxiv.org/abs/2505.15874v1 |
In summary, each instance of this Text-to-Pipeline can be represented as a five-tuple (x, ℓ, p, c, y ). Pipeline Structure. Each program p∈ P is represented as a left-to-right chain of ksymbolic operators: p=o1◦o2◦ ··· ◦ ok, where each operator oi∈ O is drawn from a core set covering common DP actions: O={groupby ,sort... | https://arxiv.org/abs/2505.15874v1 |
2 to 120 columns, and include both wide and long formats. Notably, 34% involve multi-table compositions. We standardize column names, normalize cell formats, and align schema types to ensure downstream compatibility. Algorithm 1 Operator Chain Construction Require: Curated table x, transition matrix P, validator S Ensu... | https://arxiv.org/abs/2505.15874v1 |
of realistic operator chains and reflects common transformation motifs such as select –filter –groupby (see Fig. 3, right). Stage 4: Rule-Based Code Compilation. Each DSL {oi}is deterministically compiled into ex- ecutable Pandas code via a rule engine. The compiler fills parameter templates and maintains a running sch... | https://arxiv.org/abs/2505.15874v1 |
2: Operation distribution.Table 2: Statistics of the PARROT. Statistics Number Total Tasks 17,168 (100%) Single Tab. 11,327 (66.0%) Multi-Tab. 5,841 (34.0%) Train / Dev / Test 14,388 / 1,387 / 1,393 Input Table Avg. Columns Per Tab. 6.7 Avg. Row Count 134.2 Num / Cat / Mixed 44.3% / 42.5% / 13.2% Chain Complexity Easy ... | https://arxiv.org/abs/2505.15874v1 |
set of LLMs, encompassing both zero-shot inference capabilities of proprietary models and the performance of fine-tuned open-source models. These models represent the current state-of-the-art in code generation and natural language understanding: (1) Zero-shot LLMs: We utilize several leading API-based models, includin... | https://arxiv.org/abs/2505.15874v1 |
backbone unless specified otherwise. For Text-to-Pandas, the DSL-to-Pandas compilation is rule-based and deterministic (see App.C.2). For Text-to-SQL, the generated SQL is subsequently executed via the SQLite engine. We have provided partial comments for operator types that are not supported by SQL. More experimental d... | https://arxiv.org/abs/2505.15874v1 |
in PARROT. Impact of Structured Target Generation. Our proposed PARROT approach, which generates transformation programs in a DSL, substantially outperforms direct code and SQL generation. As shown in Tab. 4, it achieves an overall EA of 62.88% , exceeding Text-to-Code (Pandas, 33.80% ) by +29.08 points and Text-to-SQL... | https://arxiv.org/abs/2505.15874v1 |
- GPT-4o 77.70 78.21 67.44 76.17 96.62 88.33 82.56 89.82 78.04 72.32 65.91 72.92 - Deepseek-V3 66.89 60.70 48.84 60.49 91.21 78.99 70.93 81.26 68.47 58.18 47.53 59.42 gation to non-numeric fields or filtering without proper type casting. A further 16.7% of errors are due to incorrect column or index references—often re... | https://arxiv.org/abs/2505.15874v1 |
our work advocates a paradigm shift in data preparation, from manual processing to intelligent, agent-driven systems. By grounding pipeline synthesis in NL and execution feedback, we pave the way toward autonomous DP agents capable of planning, adaptation, and self-correction, moving closer to fully automated data mana... | https://arxiv.org/abs/2505.15874v1 |
arXiv:1803.00701 , 2018. [20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv:1412.6980 , 2014. [21] Eugenie Y Lai, Yeye He, and Surajit Chaudhuri. Auto-prep: Holistic prediction of data preparation steps for self-service business intelligence. arXiv:2504.11627 , 2025. [22] Fangyu Lei,... | https://arxiv.org/abs/2505.15874v1 |
Ka-Wei Lee, and Ee-Peng Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. arXiv:2305.04091 , 2023. [40] Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, et al. ... | https://arxiv.org/abs/2505.15874v1 |
task description, table schema, and sampled rows (up to 10) to provide contextual grounding. Additionally, we adopt a few-shot prompting format with three curated demonstrations at the hard level to control the generation style during instruction refinement. All prompts used for data synthesis are listed in App. E.9. T... | https://arxiv.org/abs/2505.15874v1 |
target schema, often relying on implicit table constraints like functional dependencies and keys to constrain the search space. Auto-Tables [27] is a benchmark dataset designed to evaluate systems that automatically synthesize multi-step transformations to convert non-relational tables into standard relational formats ... | https://arxiv.org/abs/2505.15874v1 |
style. Below, we provide examples for each DSL operator along with their corresponding expressions. Table 6: Examples of DSL operators and their corresponding rewritten expressions. Operator Intent Expression pivot Pivot the resulting table with the station names ( STN_NAM ) as the index, the data provider ( DATA_PVDR ... | https://arxiv.org/abs/2505.15874v1 |
operator categories and schema dependencies. For instance, transitions like groupby →dropna are pruned unless dropna targets non-aggregated columns, while sort is allowed to follow most aggregation or integration steps. •Sampling integration. During Markov-based operator chain synthesis, we use Pas a dynamic prior, fur... | https://arxiv.org/abs/2505.15874v1 |
100.5 tokens Tokenized instruction (LLM-generated) Unique instruction vocabulary 192.3 Total distinct tokens across all instructions Average columns per input table 6.7 Captures schema size Average rows per input table 134.2 Captures table content scale Tables with non-English column names 3.19% Includes latin, code-mi... | https://arxiv.org/abs/2505.15874v1 |
df2]) 2198 Structural Reconstruction groupby by, agg df.groupby("region").sum() 9271 pivot index, columns, values df.pivot("id", "type", "score") 2933 unpivot id_vars, value_vars df.melt(id_vars=["id"], value_vars=["value"]) 4365 explode column df.explode("tags") 2135 transpose - df.transpose() 2271 wide_to_long stubna... | https://arxiv.org/abs/2505.15874v1 |
textual columns and reduced numeric fields, reflecting structural transformations such as pivoting or aggregations that change schema layouts. These statistics collectively ensure that PARROT presents operational and Figure 8: Column type distributions in input vs. output tables. structural diversity, challenging model... | https://arxiv.org/abs/2505.15874v1 |
Chile 253 125 2014 Pontifical Catholic University of Chile 202 118 2014 University of Concepción 108 46 2013 University of Chile 74 33 2015 Pontifical Catholic University of Chile 69 31 2015 Output Table (M001) Name Number of Contestants Pontifical Catholic University of Chile 69 University of Chile 74 Example (Hard) {... | https://arxiv.org/abs/2505.15874v1 |
1931 Uusikaupunki, Finland 23 Decommissioned 1960 7 8 1932 Turku, Finland 23 Lost (burned and sunk) 1933 Output Table (H001) category velocity in nautical miles vessel identifier Cargo ship 7 4.875 Battle ship 4 5.25 C.7 Operator-Level Execution Characteristics. To quantify the computational characteristics of differen... | https://arxiv.org/abs/2505.15874v1 |
instruction-tuned LLMs, including Qwen2.5-Coder-1.5B-Instruct , Qwen2.5-Coder-3B-Instruct , and Qwen2.5-Coder-7B-Instruct . The goal is to improve the model’s ability to interpret natural language instructions and generate accurate transformation plans. Training Environment Training is conducted on a server equipped wi... | https://arxiv.org/abs/2505.15874v1 |
a high-level transformation plan as a sequence of operations (e.g., filter →groupby→sort), and then executes each operation step-by-step via tool invocation. Chain-of-Tables. The Chain-of-Tables Agent operates by tracking and updating intermediate tabular states, enabling stepwise manipulation of evolving data frames. ... | https://arxiv.org/abs/2505.15874v1 |
by: List[str] aggregations: List[AggItem] E.7 Error Analysis As shown in Fig. 14, type errors consistently account for the largest proportion across all models, ranging from 41.2% (GPT-4o-mini) to 54.5% (DeepSeek-R1). These errors typically stem from applying transformations to columns with incompatible data types, suc... | https://arxiv.org/abs/2505.15874v1 |
logically inconsistent rows (e.g., the buyer “Bob” appears with an amount below 1000, contradicting the filtering intent). This case exemplifies several challenges specific to Text-to-Pipeline : •Instruction grounding : The model fails to fully map the natural language intent to a faithful operator sequence, omitting t... | https://arxiv.org/abs/2505.15874v1 |
the target table is obtained by transforming the input tables. Based on the transformation relationship between them, please generate a clear natural language instruction that describes how to transform the input tables into the target table.,→ ,→ ,→ ,→ The transformation operations and their detailed parameters are as... | https://arxiv.org/abs/2505.15874v1 |
that have a notable number of students associated with them. 7. Remove any duplicate entries from the filtered dataset to ensure that each allergen-student ID combination is unique. 8. Finally, perform a sort on the deduplicated data by student ID in ascending order and allergen in descending order to achieve the desir... | https://arxiv.org/abs/2505.15874v1 |
the "NAV CANADA" column to a string data type and ensure that there are no rows where "NAV CANADA" is equal to itself. Finally, deduplicate the data based on the "NAV CANADA" column, keeping the last occurrence of each duplicate entry.,→ ,→ ,→ ,→ ,→ ,→ ,→ ,→ ,→ ,→ --- Task Description: First, reshape the data from the ... | https://arxiv.org/abs/2505.15874v1 |
serves as the basis for consistent evaluation across different zero-shot LLMs. Prompts for Zero-shot LLMs You are a data expert with extensive knowledge in data preparation pipelines. ,→ Your task is to select operators based on user intent and use them to transform the source tables. ,→ Important notes: - After select... | https://arxiv.org/abs/2505.15874v1 |
"source_table_name", "subset": ["List or single column name to determine duplicates"], "keep": ["first", "last"] }, "description": "Remove duplicate rows, keeping either the first or last occurrence in each group" ,→ }, { "name": "topk", "pandas_equivalent": "head(k)", "parameters": { "source_table": "source_table_name... | https://arxiv.org/abs/2505.15874v1 |
arXiv:2505.15877v1 [cs.CV] 21 May 2025Highlighting What Matters: Promptable Embeddings for Attribute-Focused Image Retrieval Siting Li University of Washington sitingli@cs.washington.eduXiang Gao IIIS, Tsinghua University x-gao22@mails.tsinghua.edu.cn Simon Shaolei Du University of Washington ssdu@cs.washington.edu Abs... | https://arxiv.org/abs/2505.15877v1 |
and build COCO-F ACET for benchmarking various retrievers. (Below) We show that using promptable image embeddings enhances performance on such queries, and propose two acceleration strategies to improve its applicability. we build a new benchmark, COCO-F ACET with 9,112 attribute-focused queries of 8 types1, based on e... | https://arxiv.org/abs/2505.15877v1 |
[ 35], OVEN [ 14], Wiki-SS-NQ [ 38]. We note that these subsets either focus on the global semantics of the images or pre-select images with a single main subject, though in-the-wild images can be visually crowded with many attributes. Visual Genome [ 24] considers such complexity by annotating the images with detailed... | https://arxiv.org/abs/2505.15877v1 |
T2I retrievers on attribute- focused queries in Section 3.1, and analyze their performance in Section 3.2. We reveal that current retrievers fall short of such queries, even though they involve less image-text matching than long MSCOCO-style captions with multiple attributes. 3 Table 1: Model details of T2I retrievers ... | https://arxiv.org/abs/2505.15877v1 |
rows feature the CLIP family (CLIP [ 47], EV A-CLIP [ 10,11], SigLIP [ 65], SigLIP2 [ 53], and BLIP2 finetuned on COCO [ 28]) and MagicLens [ 67], which use unimodal encoders. Although MagicLens accepts image+text as the input through a fusion module, we follow their T2I retrieval protocol and use the finetuned text an... | https://arxiv.org/abs/2505.15877v1 |
observe that MagicLens has the best overall performance in the first section despite its small scale, output dimension, and image resolution. Universal multimodal embedders have better performance than unimodal embedders, with MMRet-MLLM-S2 in a relatively large scale outperforming other models. Still, the performance ... | https://arxiv.org/abs/2505.15877v1 |
we hypothesize that general image embeddings might be inefficient for attribute-specific queries. We therefore focus on highlighting the important part in the embeddings. For this purpose, we propose to use promptable image embeddings for different attributes—conditioning image embeddings on textual prompts, which is e... | https://arxiv.org/abs/2505.15877v1 |
image embed- dings. The improvement indicates that our pipeline generalizes effectively to these im- age pools. Place365 SUN397 Recall@1 VLM2Vec-Phi-3.5-V 30.0 57.0 w/ GPT prompt 33.9 61.9 Recall@5 VLM2Vec-Phi-3.5-V 58.2 85.2 w/ GPT prompt 66.7 88.9Furthermore, the same set of prompts generalizes to other universal mul... | https://arxiv.org/abs/2505.15877v1 |
be efficiently handled by the FAISS library [ 9]. Let vdenote the cost of computing a single image embedding, and tthe cost for a single text embedding, using CLIP-like models. Let Frepresent the cost of a single forward pass through the base model of our multimodal embedder (e.g., Phi-3.5-V with a CLIP vision encoder)... | https://arxiv.org/abs/2505.15877v1 |
VLM2Vec-Phi-3.5-V 95.5 69.8 74.4 14.4 6.3 5.5 4.3 9.8 44.5 w/ selected GPT prompt 90.9 81.4 75.5 72.7 25.8 18.4 14.4 10.5 52.8 w/ gt GPT prompt 90.7 81.4 75.5 72.7 25.8 18.4 14.4 15.7 53.4 Recall@5 VLM2Vec-Phi-3.5-V 99.3 90.7 90.7 36.6 18.2 12.9 19.2 27.1 58.9 w/ selected GPT prompt 99.1 95.9 92.0 92.1 48.8 82.4 36.5 2... | https://arxiv.org/abs/2505.15877v1 |
2024. [2]A. Asai, T. Schick, P. Lewis, X. Chen, G. Izacard, S. Riedel, H. Hajishirzi, and W.-t. Yih. Task-aware retrieval with instructions. arXiv preprint arXiv:2211.09260 , 2022. [3]H. Caesar, J. Uijlings, and V . Ferrari. Coco-stuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on comput... | https://arxiv.org/abs/2505.15877v1 |
Lerer, A. P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276 , 2024. [18] G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V . Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, a... | https://arxiv.org/abs/2505.15877v1 |
Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In Computer vision–ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13 , pages 740–755. Springer, 2014. [33] W. Lin, J. Chen, J. Mei, A. Coca, and B. Byrne. Fine-grained late-i... | https://arxiv.org/abs/2505.15877v1 |
Schuster, R. Krishna, A. Chang, L. Fei-Fei, and C. D. Manning. Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In A. Belz, L. Coheur, V . Ferrari, M.-F. Moens, K. Pastra, and I. Vuli ´c, editors, Proceedings of the Fourth Workshop on Vision and Language , pages 70–80... | https://arxiv.org/abs/2505.15877v1 |
arXiv:2412.03561 , 2024. [62] M. Yasunaga, A. Aghajanyan, W. Shi, R. James, J. Leskovec, P. Liang, M. Lewis, L. Zettle- moyer, and W.-t. Yih. Retrieval-augmented multimodal language modeling. arXiv preprint arXiv:2211.12561 , 2022. [63] P. Young, A. Lai, M. Hodosh, and J. Hockenmaier. From image descriptions to visual ... | https://arxiv.org/abs/2505.15877v1 |
in Table 7. In short, we came up with the most proper division for every image in every fixed criteria. The information needed for the classification might come from the whole picture or a part of it. Each test case consists of a query text (e.g., “ Find me an everyday image showing some object or surface made of stone... | https://arxiv.org/abs/2505.15877v1 |
14, “crawl”: 1 with more precise phrases to avoid mislabeling images based on captions like “A building sits between.” Finally, we manually check the images to ensure that they meet our criteria. A.2.2 Materials of Objects or Surfaces •Idea: Although different materials may have similar uses, they often exhibit differe... | https://arxiv.org/abs/2505.15877v1 |
that it is generally not warm when it is raining). Among the last four categories (“snowy”, “warm”, “cold”, “chilly”), “snowy”, “cold”, and “chilly” often co-occur and are therefore not used as positive and negative samples together. •Sampling: We first extract questions containing weather conditions from the Visual7W ... | https://arxiv.org/abs/2505.15877v1 |
object is a crucial feature in the picture is really clear. To be clear, we do not involve animals or people in this classification, for they have totally different norms. • Categories: “bicycle”, “bus”, “light”, “backpack”, etc. (3849 samples in total) •Avoidance: The COCO set gives quite comprehensive content about t... | https://arxiv.org/abs/2505.15877v1 |
zero-shot instructions for embeddings. E5-V: E5-V [ 19] adapts an MLLM to generate universal multimodal embeddings. Unlike traditional models trained on image-text pairs, E5-V leverages MLLM’s capabilities to represent multimodal information effectively, demonstrating significant potential in various retrieval tasks. W... | https://arxiv.org/abs/2505.15877v1 |
w/ CLIP-style text 98.4 79.1 72.7 13.3 14.4 9.9 16.4 19.1 47.6 B.3 Promptable Image Embeddings We list the obtained GPT-written prompts for eight categories of our COCO-F ACET benchmark in Table 10. We also test human-written prompts listed in Table 11. The results are shown in Table 12, where we find that human-writte... | https://arxiv.org/abs/2505.15877v1 |
question: When is the image taken? /cloud-sunWeathers <|image_1|> Represent the given image with the following question: What is the weather in the image? ♂prayGestures <|image_1|> Represent the given image with the following question: What is the person doing in the image? 21 Table 12: Recall@1 and Recall@5 of text-to... | https://arxiv.org/abs/2505.15877v1 |
and Objects . (2) The semantic understanding of attributes, like “ 0 people ” and “ made of .” (3) The visual grounding of attributes, like “ sports arena ” and “ jumping ”. Current retrievers may not ground these attributes precisely. (4) Simple visual reasoning. In the Count of People category, 22 USER: <image> Which... | https://arxiv.org/abs/2505.15877v1 |
prompt 98.7 95.9 92.0 92.1 48.8 82.4 36.5 39.3 75.5 E Limitations First, COCO-F ACET is built on existing annotations of COCO images. Although we have conducted manual checking detailed in the Appendix, there might still contain inaccurate and ambiguous annotations. For example, some small animals or objects might not ... | https://arxiv.org/abs/2505.15877v1 |
arXiv:2505.15879v1 [cs.CV] 21 May 2025GRIT: Teaching MLLMs to Think with Images Yue Fan1Xuehai He1Diji Yang1Kaizhi Zheng1 Ching-Chen Kuo2Yuting Zheng2Sravana Jyothi Narayanaraju2 Xinze Guan2Xin Eric Wang1 1UC Santa Cruz2eBay https://grounded-reasoning.github.io {yfan71,xwang366}@ucsc.edu Group- normalised advantage Inp... | https://arxiv.org/abs/2505.15879v1 |
"reasoning chain" which comprises a detailed, step-by-step deliberation and a final answer. Recent studies have shown that such trained reasoning models are superior in reasoning than models directly prompted in the zero-shot Chain-of-Thought (CoT) [ 6] manner, where off-the-shelf models are simply guided to "think alo... | https://arxiv.org/abs/2505.15879v1 |
need for data with reasoning chain annotations or explicit bounding box labels. As a result, we find that the GRIT method is extremely data efficient: it enables MLLMs to acquire the grounded reasoning ability with very few data samples sourced from existing VQA datasets using only image-query-answer triplets. With the... | https://arxiv.org/abs/2505.15879v1 |
trace. Models trained with GRIT produce interleaved chains of natural language and bounding box coordinates, enabling a dynamic interplay where visual evidence informs textual logic, and vice-versa. While proprietary systems such as ChatGPT-o3/4 [ 17] have shown similar "thinking with images" capabilities, GRIT offers ... | https://arxiv.org/abs/2505.15879v1 |
proposed grounded reasoning paradigm encourages the model to unify these existing faculties to form the new grounded reasoning ability. Compared to alternative approaches such as generating pixel-level attention masks or adding the image region indicated by bounding boxes as additional input for multi-turn generation, ... | https://arxiv.org/abs/2505.15879v1 |
160, 250, 270], [280, 160, 380, 270], [390, 160, 490, 270], [500, 160, 600, 270], [610, 160, 710, 270], [720, 160, 820, 270] Thinking… Answer: Pure natural language reasoning Thinking… Answer: There are nine eggs in the nest. Input image Model <think> I need to check …located at [123,456,234,567 ] … [ 321,432,654,543 ]... | https://arxiv.org/abs/2505.15879v1 |
spatial questions, MME [ 26] on diverse visual tasks including counting and position, MathVista-mini [ 27] on mathematical reasoning in visual contexts, and position subset of OVDEval [ 28] on open-vocabulary object grounding. For assessing the quality of bounding boxes generated during grounded reasoning outputs, we l... | https://arxiv.org/abs/2505.15879v1 |
GPT-aided answer-accuracy reward during training (Section 3.2); (2) the accuracy of the grounding coordinates, measured by grounding IoU , the average Intersection over Union (IoU) between the union of all bounding boxes generated by the model (i.e., within its reasoning chain or as the direct answer for tasks like OVD... | https://arxiv.org/abs/2505.15879v1 |
The bounding box coordinates for the knife would be outside the image area. </rethink> <answer> No Ground truth answer : no (iii) Model correctly handles queries about non-existent entities without any grounding action. Figure 3: Inference examples of Qwen2.5-VL-GRIT. Results. The results are summarized in Table 1. In ... | https://arxiv.org/abs/2505.15879v1 |
seamlessly to diverse queries. Specifically, as shown in Figure 3i, GRIT-trained models sometimes directly provide an answer while then reflecting the answer in subsequent reasoning; while in other instances, as in Figure 3ii, they perform the ground actions to identify visual evidence initially and subsequently analyz... | https://arxiv.org/abs/2505.15879v1 |
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