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are needed to enhance o3’s instruction-following ability. Table 3: Error analysis of chart understanding failures on ChartQAPro for o1, o3, and o4-mini. Model Perception ErrorInstruction Following Error o1 48 12 o3 47 22 o4-mini 46 8 Table 4: Performance of o3 using different levels of reasoning effort. Reasoning Effor... | https://arxiv.org/abs/2505.17473v2 |
54.29 ±0.63 - - - - T-Rex2 - 21.36 - - - - - - DINO-X 38.17 - - - - - - -Traditional ModelsFaster R-CNN - - - - 22.95±7.38 26.67 ±4.31 34.75 ±3.32 87.57 ±0.07 YOLOv3 - - - - 26.01±1.54 30.68 ±1.54 36.56 ±3.68 61.81 ±0.47 RTMDet - - - - 56.76±2.27 63.70 ±5.13 70.22 ±1.04 83.80 ±0.42 Co-DETR - - - - 66.74±11.12 74.94 ±5.... | https://arxiv.org/abs/2505.17473v2 |
Joty, “Chartqapro: A more diverse and challenging benchmark for chart question answering,” 2025. [Online]. Available: https://arxiv.org/abs/2504.05506 [2]A. F. Biten, R. Tito, A. Mafla, L. Gomez, M. Rusinol, E. Valveny, C. Jawahar, and D. Karatzas, “Scene text visual question answering,” in Proceedings of the IEEE/CVF i... | https://arxiv.org/abs/2505.17473v2 |
R. Xia, S. Mao, X. Yan, H. Zhou, B. Zhang, H. Peng, J. Pi, D. Fu, W. Wu, H. Ye, S. Feng, B. Wang, C. Xu, C. He, P. Cai, M. Dou, B. Shi, S. Zhou, Y . Wang, B. Wang, J. Yan, F. Wu, and Y . Qiao, “Docgenome: An open large-scale scientific document benchmark for training and testing multi-modal large language models,” 2024.... | https://arxiv.org/abs/2505.17473v2 |
arXiv:2505.17481v1 [cs.CL] 23 May 2025MARCO : Meta-Reflection with Cross-Referencing for Code Reasoning Yusheng Zhao1, Xiao Luo2, Weizhi Zhang3, Zhiping Xiao4, Wei Ju1, Philip S. Yu3, Ming Zhang1 1Peking University,2University of California, Los Angeles, 3University of Illinois Chicago,4University of Washington yusheng... | https://arxiv.org/abs/2505.17481v1 |
Python function: lambda x: x + ".0" Rule: For integer inputs, add ".0" at the end of it. For float numbers, return the input directly. Python function: ... Problem i ResponseCase 3 check failed. Input "12.0" , expect "12.0" , got "12.0.0" Rule: For inputs end with ".0" , directly return it. Otherwise add ".0" at the en... | https://arxiv.org/abs/2505.17481v1 |
. 2 Preliminary 2.1 Problem Setup The execution of programs can be written as: IF− → O , forming a triplet of ⟨I,F,O⟩, where Iis the input, Fis the function written in code, and Ois the output of program execution. The goal of 2 Rule: To map the given inputs to their corresponding outputs, we need to convert all the le... | https://arxiv.org/abs/2505.17481v1 |
when dealing with a sequence of problems {Xi}N i=1. In comparison, we propose to incorporate both inter-problem and intra-problem information into the problem-solving process, i.e. Yi j,t∼ A j(Y | Pi j,t−1,inter-problemz }| { {{Pi′ j,T}i−1 i′=1}M j=1,intra-problemz }| { {Pi j′,t−1}j′∈{1,···,M}\{j},Tj). (2) 2.2 Problems... | https://arxiv.org/abs/2505.17481v1 |
for inter-problem knowledge accumulation and cross-referencing for intra-problem lesson sharing, as shown in Figure 3. MARCO maintains a knowledge bank that stores summarized experiences of previous reasoning processes, with knowledge accumulating throughout the whole problem-solving process. When the LLM agents are gi... | https://arxiv.org/abs/2505.17481v1 |
lesson sharing, allowing the agents to learn from the faults of others. Specifically, when agent Ajis given a problem Xi, it falls into "proposal-feedback" iterations, where it first proposes a solution and receives feedback of it through the code interpreter. At iteration t−1, it receives feedback Bi j,t−1of the propo... | https://arxiv.org/abs/2505.17481v1 |
Among these datasets, RobustFill and DeepCoder use specially designed domain-specific languages (DSLs, with details in Appendix ??). For the deductive reasoning sub-task, we use two datasets: CRUXEval-O and LiveCodeBench-O that are built upon the CRUXEval [ 20] and LiveCodeBench [ 30] datasets by excluding the outputs.... | https://arxiv.org/abs/2505.17481v1 |
the inputs that lead to the cor- responding outputs with the given program (function). In many cases, this would be harder than deductive code reasoning, and we can see from the results in Table 2 that the ab- ductive reasoning accuracies are gen- erally lower than deductive reason- ing. Despite the difficulty, the pro... | https://arxiv.org/abs/2505.17481v1 |
: performance under different iterations and condensation periods in terms of accuracy and problem accuracy on the ListFunction dataset. Right : the comparison of absolute improvements of MARCO and the baseline in both the first half and the second half of the datasets. 7 A concrete strategy is to clearly separate and ... | https://arxiv.org/abs/2505.17481v1 |
weaker adaptability causes a mild decrease in performance. 4.5 Further Analysis Effects of Meta-Reflection in the Problem-Solving Process. To better understand the effect of inter-problem knowledge accumulation in meta-reflection, we measure the absolute improvement (in accuracy) of MARCO compared to CoT in both the fi... | https://arxiv.org/abs/2505.17481v1 |
via task decomposition or solution searching, we take a cognitive-evolving perspective that uses meta-reflection for inter-problem knowledge accumulation and cross-referencing for intra- problem lesson sharing to enhance the code ability of the LLM itself. LLM Reflection. The ability to perform reflection is an essenti... | https://arxiv.org/abs/2505.17481v1 |
In Proceedings of the 31st International Conference on Computational Linguistics , pages 1964–1982, 2025. [5]Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate pro... | https://arxiv.org/abs/2505.17481v1 |
on Artificial Intelligence , volume 38, pages 22031–22039, 2024. [20] Alex Gu, Baptiste Rozière, Hugh Leather, Armando Solar-Lezama, Gabriel Synnaeve, and Sida I Wang. Cruxeval: A benchmark for code reasoning, understanding and execution. arXiv preprint arXiv:2401.03065 , 2024. [21] Qiuhan Gu. Llm-based code generation... | https://arxiv.org/abs/2505.17481v1 |
Prin Phunyaphibarn, Donghyun Ahn, and Sundong Kim. Playgrounds for abstraction and reasoning. In NeurIPS 2022 Workshop on Neuro Causal and Symbolic AI (nCSI) , 2022. [35] Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, et al. T... | https://arxiv.org/abs/2505.17481v1 |
Large language model enhanced incremental heuristic search on path planning. arXiv preprint arXiv:2407.02511 , 2024. [49] Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, and Yasuhiro Sogawa. Enhancing reasoning capabilities of llms via principled synthetic logic corpus. Advances in Neural Information Processing Syste... | https://arxiv.org/abs/2505.17481v1 |
Mi, Furong Huang, and Dong Yu. Towards self-improvement of llms via mcts: Leveraging stepwise knowledge with curriculum preference learning. arXiv preprint arXiv:2410.06508 , 2024. [66] Yifei Wang, Yuheng Chen, Wanting Wen, Yu Sheng, Linjing Li, and Daniel Dajun Zeng. Unveiling factual recall behaviors of large languag... | https://arxiv.org/abs/2505.17481v1 |
arXiv:2505.17482v1 [cs.AI] 23 May 2025From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark Chao Lei, Nir Lipovetzky, Krista A. Ehinger, Yanchuan Chang School of Computing and Information Systems, The University of Melbourne, Australia clei1@student.unimelb.edu.au, {kris.ehinger, nir.lipovetzky, ... | https://arxiv.org/abs/2505.17482v1 |
[1, 1, 0]][[0, 2, 2], [0, 0, 2], [0, 0, 0][[0, 0, 0], [0, 2, 2], [0, 0, 2]][[2, 0, 0], [2, 0, 0], [0, 0, 0]][[0, 0, 0], [2, 0, 0], [2, 0, 0]](a) Image Visualization(b) Matrix RepresentationFigure 1: An ARC problem example ( 25ff71a9 ) with image visualizations (a), including three input- output pairs in the training in... | https://arxiv.org/abs/2505.17482v1 |
the solver backbone. Motivated by the success of manually defined priors in ARC solvers [ 9,10], we propose Knowledge Augmentation for Abstract Reasoning (KAAR) for solving ARC tasks using reasoning-oriented LLMs. KAAR formalizes manually defined priors through a lightweight ontology that organizes priors into hierarch... | https://arxiv.org/abs/2505.17482v1 |
s. We encode the solution sin different forms, as a solution plan p, or as Python code c, optionally guided by p. We denote each ARC problem description, comprising Irand{ii|(ii, io)∈It}, asQ. 3 ARC Solver Backbone LLMs have shown promise in solving tasks that rely on ARC-relevant priors [ 30–33]. We initially assume t... | https://arxiv.org/abs/2505.17482v1 |
detail, KAAR adopts fundamental abstraction methods from GPAR to enable objectness. Objects are typically defined as components based on adjacency rules and color consistency (e.g., 4-connected or 8-connected components), while also including the entire image as a component. KAAR further introduces additional abstracti... | https://arxiv.org/abs/2505.17482v1 |
counting priors. This enables fine-grained control, for example, applying color changes only to black components conditioned on the maximum or minimum size: from black (value 4 ARC solver backboneARC solver backboneARC solver backboneGoal-directedness Objectness (b) Augmentation process in KAAR When we consider 4-conne... | https://arxiv.org/abs/2505.17482v1 |
instance, when the entire image is treated as a component, relation priors are excluded, and actions such as movement and color change are omitted, whereas symmetry and size attributes are retained and actions such as flipping and rotation are considered. In contrast, 4-connected and 8-connected abstractions include al... | https://arxiv.org/abs/2505.17482v1 |
as percentages. The best results for IrandIr&Itare in bold; the highest for Itis in red. and refinement. We also report accuracy on IrandIr&It, measuring the percentage of problems whose solutions solve Irand both IrandIt. See Appendix A.4 for parameter settings. Table 1 reports the performance of nine ARC solvers acro... | https://arxiv.org/abs/2505.17482v1 |
test instances are solved by the row model that are also solved by the column model, across four LLMs.We compare relative problem cov- erage across evaluated LLMs under RSPC and KAAR based on successful solutions on test instances. As shown in Figure 5, each cell (i, j)represents the proportion of problems solved by th... | https://arxiv.org/abs/2505.17482v1 |
139(100,225] Total: 129(225,400] Total: 51(400,625] Total: 39(625,900] Total: 23 Average Image Size Interval (width x height)01020304050607080Accuracy on It (%) 73.75.3 48.95.0 24.84.7 11.85.9 5.1 4.342.115.8 23.711.5 8.56.2 9.82.0GPT-o3-mini RSPC GPT-o3-mini KAAR QwQ-32B RSPC QwQ-32B KAARFigure 7: Accuracy on test ins... | https://arxiv.org/abs/2505.17482v1 |
augment LLMs, per- form stage-wise reasoning, and improve solution accuracy. Compared to augmentation at once and non-stage-wise reasoning, KAAR consistently yields superior accuracy, as detailed in Appendix A.6. 6 Discussion ARC and KAAR . ARC serves as a visual abstract reasoning benchmark, requiring models to infer ... | https://arxiv.org/abs/2505.17482v1 |
than other solvers, though the issue persists. One reason is that solutions include low-level logic for the training pairs, thus failing to generalize. See Appendix A.9 for examples. Another reason is the usage of incorrect abstractions. For example, reliance solely on 4-connected abstraction leads RSPC to solve only I... | https://arxiv.org/abs/2505.17482v1 |
Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th Advances in Neural Information Processing Systems , NeurIPS, pages 24824–24837, 2022. [12] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira ... | https://arxiv.org/abs/2505.17482v1 |
2025. URL https://openai.com/index/openai-o 3-mini/ . Accessed: 2025-03-22. [26] Google DeepMind. Gemini 2.0 flash thinking. Google DeepMind , 2024. URL https: //deepmind.google/technologies/gemini/flash-thinking/ . Accessed: 2025-03-22. [27] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qi... | https://arxiv.org/abs/2505.17482v1 |
675–718, 2023. [41] Wenhao Li, Yudong Xu, Scott Sanner, and Elias Boutros Khalil. Tackling the abstraction and reasoning corpus with vision transformers: the importance of 2d representation, positions, and objects. arXiv preprint arXiv:2410.06405 , 2024. [42] John Raven. The raven’s progressive matrices: change and sta... | https://arxiv.org/abs/2505.17482v1 |
to solving the abstraction and reasoning corpus (arc) challenge. arXiv preprint arXiv:2306.03553 , 2023. [57] John Chong Min Tan and Mehul Motani. Llms as a system of multiple expert agents: An approach to solve the abstraction and reasoning corpus (arc) challenge. In Proceedings of the 2024 IEEE Conference on Artifici... | https://arxiv.org/abs/2505.17482v1 |
from predefined abstractions can improve LLM performance on ARC tasks. Recent advances in code generation by LLMs [ 18,17,14] highlight their potential to replace search-based program synthesis, addressing efficiency limitations. Tan and Motani [57] evaluated LLM performance on the ARC benchmark by generating Python pr... | https://arxiv.org/abs/2505.17482v1 |
The highlighted priors that capture per-component color diversity are used exclusively for 4/8-connected-multi-color-non-background abstractions, while priors tailored to a single-color component, such as components with same color ,components with most frequent color , andcomponents with least frequent color , are exc... | https://arxiv.org/abs/2505.17482v1 |
passedCount ←SolverBackbone (M,P,Q,t); 7 ifresult̸=failure then 8 return result 9 attributePriors ←GenerateAttributePriors( Q,abs); 10 AugmentKnowledge( M, attributePriors); 11 relationPriors ←GenerateRelationPriors( Q,abs); 12 AugmentKnowledge( M, relationPriors); 13 numberPriors ←GenerateNumbersCountingPriors( Q,abs)... | https://arxiv.org/abs/2505.17482v1 |
and others categories in the public evaluation set. In the movement example, components are shifted to the image boundary in directions determined by their colors. The extension example is more complex, requiring LLMs to find the shortest path between two red pixels while avoiding obstacles, which presents challenges f... | https://arxiv.org/abs/2505.17482v1 |
enable objectness. KAAR begins with the simplest abstraction, no abstraction , where KAAR degrades to RSPC. As a result, it generates the same solution as RSPC and terminates without attempting other abstractions, since the solution already solves Irand is then evaluated on It, resulting in overfitting. A.10 Problem Co... | https://arxiv.org/abs/2505.17482v1 |
sampling with planning-aided code generation yields a correct solution, whereas its refinement variant fails to correct the initial erroneous code, and the flawed logic persists across subsequent refinements when using GPT-o3-mini. Previous studies have shown that refinement can benefit from control flow graph informat... | https://arxiv.org/abs/2505.17482v1 |
order is given by the table from top to bottom, and making 8-connected abstraction to follow that of the corresponding 4-connected abstraction at the end of the sequence. Abstractions highlighted in red are exclusive to KAAR. 20 (0,25] Total: 19(25,100] Total: 139(100,225] Total: 129(225,400] Total: 51(400,625] Total: ... | https://arxiv.org/abs/2505.17482v1 |
and CountingGoal-directedness0K10K20K30K40K50KTokens 11K40K 19K 12K24K31K 20K29K43K 15K37K 18KGPT-o3-mini Gemini-2.0 QwQ-32B DeepSeek-R1-70B Figure 14: Average token cost for augmenting priors at each level across four LLMs. K is 103. 23 Abstractions Geometry and Topology Numbers and Counting Goal-directedness whole im... | https://arxiv.org/abs/2505.17482v1 |
top_active = active_pattern second_active = active_pattern else: # Use the "softer-border" pattern: top_active = [0, 8, 0, 0, 8] second_active = [8, 8, 0, 8, 8] # The blank row (middle row) is always all zeros. blank = [0, 0, 0, 0, 0] # Construct the final 5x5 output image. # The output consists of: # - The first activ... | https://arxiv.org/abs/2505.17482v1 |
Refinement C Refinement PC QwQ-32BDirect Generation P Direct Generation C Direct Generation PC Repeated Sampling P Repeated Sampling C Repeated Sampling PC Refinement P Refinement C Refinement PC1.00 0.53 0.37 0.68 0.71 0.74 0.68 0.71 0.74 0.69 1.00 0.45 0.79 0.93 0.86 0.72 0.86 0.86 0.61 0.57 1.00 0.70 0.78 0.91 0.61 ... | https://arxiv.org/abs/2505.17482v1 |
6], [ 6, 6, 6, 6, 6] ] elif digit 2: return [ [ 2, 2, 2, 2, 2], [ 2, 2, 2, 2, 2], [ 2, 0, 2, 2, 2], [ 2, 2, 2, 2, 2], [ 2, 2, 2, 2, 2] ] elif digit 1: # For the test image the drawn digit is 1. # We choose a pattern that follows a similar style to the others. return [ [ 1, 1, 1, 1, 1], [ 1, 0, 1, 0, 1], [ 1, 1, 1... | https://arxiv.org/abs/2505.17482v1 |
+ i][block * 3 + j] = input_image[i][j]Code SolutionRepeated sampling with standalone code generation Repeated sampling with planning-aided code generationIncorrect condition.Figure 22: ARC problem 15696249 , where repeated sampling with standalone code generation produces an incorrect solution, whereas repeated sampli... | https://arxiv.org/abs/2505.17482v1 |
persists across subsequent refinements when using GPT-o3-mini. 32 A.13 Prompts for LLMs We include all prompts used by KAAR and nine ARC solvers described in Section 3. We adopt a bash-like notation for input arguments within the prompts, such as ${test_inputs} denotes the test input 2D matrices. A brief description of... | https://arxiv.org/abs/2505.17482v1 |
output. Each image can be represented as a 2 D matrix: ${matrix} Please note that each number in the matrix corresponds to a pixel, and its value represents the color. Derive a text transformation plan (not Python code) that maps each given input image (2D matrix) to its corresponding output image (2D matrix). Ensure t... | https://arxiv.org/abs/2505.17482v1 |
= 2 # Explore neighbors (up, down, left, right) dfs(r - 1, c) # Up dfs(r + 1, c) # Down dfs(r, c - 1) # Left dfs(r, c + 1) # Right # Traverse the image to find all components with ’1’ for r in range(rows): for c in range(cols): if input_image[r][c] == 1: dfs(r, c) return input_image [End Program] [Assert Statement] ass... | https://arxiv.org/abs/2505.17482v1 |
color. [end problem description] The INCORRECT text transformation plan fails to solve some example training input and output pairs in the above problem! [start incorrect transformation plan] ${plan} [end incorrect transformation plan] The incorrect output(s) generated by the incorrect plan: [start incorrect output] ${... | https://arxiv.org/abs/2505.17482v1 |
== "__main__": block. Prompt 7: Refinement with planning-aided code - refinement on both generated plan and code. ================================ System ================================ As an expert in analyzing grid-based image processing tasks, your objective is to refine your transformation plan and program based o... | https://arxiv.org/abs/2505.17482v1 |
input and output images, where the left image in each pair represents the input and the right image represents the corresponding output. Please note that the test instance(s) only contains input image(s). Each image is represented as a 2D matrix: ${matrix} Please note that each number in the matrix corresponds to a pix... | https://arxiv.org/abs/2505.17482v1 |
ending with [ end category], numbering each category selected. For example, if the task belongs only to "color change", your response should be: [start category] 1. color chang [end category] If the task belongs to both "selecting" and "extension", your response should be: [start category] 1. selecting 2. extension [en... | https://arxiv.org/abs/2505.17482v1 |
arXiv:2505.17485v1 [cs.CL] 23 May 2025keepitsimple at SemEval-2025 Task 3: LLM-Uncertainty based Approach for Multilingual Hallucination Span Detection Saketh Reddy Vemula IIIT Hyderabad saketh.vemula@research.iiit.ac.inParameswari Krishnamurthy IIIT Hyderabad param.krishna@iiit.ac.in Abstract Identification of halluci... | https://arxiv.org/abs/2505.17485v1 |
unsup- ported, compromising their validity for real-world usage. Farquhar et al. (2024) proposed a technique employing semantic entropy to identify such con- fabulations through uncertainty estimation in the semantic space of model outputs. This method cal- culates uncertainty at the meaning level as opposed to actual ... | https://arxiv.org/abs/2505.17485v1 |
text from the LLM. hard_labels List of pairs (si, ei)rep- resenting hallucination spans (start-inclusive, end-exclusive). soft_labels List of dictionaries, each containing: •start : Start in- dex of hallucina- tion span. •end: End index of hallucination span. •prob : Probability of the span being a hallucination. Table... | https://arxiv.org/abs/2505.17485v1 |
repetitive patterns of text, we use a 3- gram repetition penalty. We produce 20 candidate responses with a maximum of 64 tokens per input query. The model is executed in mixed-precision using FP16 to save memory, with memory con- sumption limited to 6GB GPU memory and 8GB CPU memory via gradient offloading. 5.2 Hyperpa... | https://arxiv.org/abs/2505.17485v1 |
as they show its effective- ness in coping with varied linguistic forms and in yielding precise hallucination span detection. Our error analysis also informs on typical failure in- stances, presenting potential for additional refine- ments. Although our approach is strong, it has limita- tions, specifically in exploiti... | https://arxiv.org/abs/2505.17485v1 |
even further. With LLMs still evolving, creating scalable and accurate methods of halluci- nation detection remains a critical step to maintain the integrity of AI-produced text across real-world use cases. Limitations Our method does not employ supervised learning for predicting the exact spans. Under-utilization of t... | https://arxiv.org/abs/2505.17485v1 |
arXiv:2505.17492v1 [cs.AI] 23 May 2025PD3: A Project Duplication Detection Framework via Adapted Multi-Agent Debate Dezheng Bao Zhejiang University baodezheng@zju.edu.cnYueci Yang Zhejiang University baodezheng@zju.edu.cnXin Chen Zhejiang University xin.21@intl.zju.edu.cn Zhengxuan Jiang Zhejiang University mystery_jia... | https://arxiv.org/abs/2505.17492v1 |
constructing an icing fault early warning model driven by monitoring, forecasting, and historical failure data ..., developing intelligent de-icing and ice measurement devices, and realizing precise and efficient de-icing operations through drone-mounted devices. ...Compared to Project C, I have decided that Project B ... | https://arxiv.org/abs/2505.17492v1 |
prevents them from prioritizing core project content in duplication detection. To address these limitations, other researchers propose vector distance-based methods by encoding full-text semantics into vectors. However, their static semantic representations lack task- specific adaptability. Although some embedding mode... | https://arxiv.org/abs/2505.17492v1 |
distance-based methods help narrow the candidate pool, radically reducing the num- ber of candidates affects final performance by prematurely eliminating relevant projects, failing to fundamentally solve the issue. To overcome these limitations, we propose PD3, aProject Duplication Detection framework via adapted multi... | https://arxiv.org/abs/2505.17492v1 |
the score domain and the score function f: (Pu, R)→ D evaluates the overall duplication level of Puagainst R. As illustrated in Figure 3, PD3framework comprises four key components. We first briefly outline each component before delving into detailed discussions of core components in subsequent chapters: 1 Data Pre-pro... | https://arxiv.org/abs/2505.17492v1 |
on the discourse. Once all group competitions finish, we aggregate results via voting (inspired by voting for knowledge-based tasks in [ 13]) to select the global top-5. Another advantage of this format is that group competitions are independent and can run in parallel, significantly reducing execution time. In summary... | https://arxiv.org/abs/2505.17492v1 |
2024), with an average length of 957 tokens, sourced from the State Grid Corporation of China (SGCC). These projects span 22 broad domains, including dispatching, dis- tribution networks, transmission and transformation, digitalization, and informatization. Common topics include AI-based power consumption forecasting, ... | https://arxiv.org/abs/2505.17492v1 |
| 0.8882 213 | 0.6435 96 | 0.2900 21 | 0.0636 2 | 0.0060 gte-7B as reranker 0.3897 298 | 0.9003 212 | 0.6405 105 | 0.3172 29 | 0.0876 1 | 0.0030 jina as reranker 0.2798 275 | 0.8308 141 | 0.4260 42 | 0.1269 4 | 0.0121 1 | 0.0030 DeepSeek V3 LLM0.3686 297 | 0.8973 194 | 0.5861 88 | 0.2659 27 | 0.0816 4 | 0.0121 DeepSeek... | https://arxiv.org/abs/2505.17492v1 |
calculates the results that overlap with the expert selection greater than or equal to K. Where I(·)is the indicator function (1 if true, else 0). Particularly, Match@1 ≡Hit Rate@5 . For Match@K , we additionally report its ratio to the size of test set in the form Match@K | (Match@K /Size of test set . In Task 2, we u... | https://arxiv.org/abs/2505.17492v1 |
(8.00%) particularly indicates that our method is more aligned with the preferences of human review experts. Under "Method Retrieval" setting, performance gains increase to 7.00% and 9.00%, confirming that better retrieval enhances final scoring quality and validating PD3’s effectiveness. Notably, when incorporating si... | https://arxiv.org/abs/2505.17492v1 |
and consider Project 10 as an alternative. The current selection (1/4/6/12/13) already covers the full chain of technical needs from monitoring to warning..ExpertAgentB … I acknow ledge the analysis of experts B and C on the advantages of project 13 in data fusion and spatial visualization,... If it is necessary to tak... | https://arxiv.org/abs/2505.17492v1 |
debate rounds and agents facilitate multi-perspective analysis and comprehen- sive reasoning. (2) Excessive rounds or agents introduce diminishing returns due to error propagation and context overload, as discussed in [9]. While our PD3implementation does not adopt the empirically optimal configuration (3 agents + 3 ro... | https://arxiv.org/abs/2505.17492v1 |
arXiv preprint arXiv:2308.07201 , 2023. [4]Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216 , 2024. [5]Xi Chen, Mao Mao, Shuo Li, and Ha... | https://arxiv.org/abs/2505.17492v1 |
estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 , 2013. [21] Chau Pham, Boyi Liu, Yingxiang Yang, Zhengyu Chen, Tianyi Liu, Jianbo Yuan, Bryan A Plummer, Zhaoran Wang, and Hongxia Yang. Let models speak ciphers: Multiagent debate through embeddings. In The Twelfth International Confer... | https://arxiv.org/abs/2505.17492v1 |
system for automatically detecting academic misconduct literature , cn101833579b edition, 2012. [37] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Informa... | https://arxiv.org/abs/2505.17492v1 |
agents share training data [9]. For example, Wang et al. [31] finds that well-prompted single agents can match MAD with demonstrations, while Estornell and Liu [9]shows debates often converge to majority opinions, reinforcing misconceptions. These limitations underscore the need for careful role diversity and consensus... | https://arxiv.org/abs/2505.17492v1 |
conduct additional experiments beyond those reported in the main text. Due to space limications, Table 1 presents only the top-performing method from each category, while Table 4 in the appendix provides complete experimental results. Within each category, the bold entries indicate the methods selected for Table 1 base... | https://arxiv.org/abs/2505.17492v1 |
4,416,494.5 Average per Debate (Average per Project /15)290,567.7 3,865.2 294,432.9 Feedback Average per Project 31753.2 3607.2 35,360.4 A.6 Prompt Templates Here provides several prompt templates use in PD3. Prompttemplatefor MAD round -robin retrieval – initial round You are an expe rt in the field of power cond ucti... | https://arxiv.org/abs/2505.17492v1 |
{project_under_detection_info} ## Candidate Relevant Reference Project Information: {candi dates_project_info} ## Records of debate: {debate_records} Please analyze the experts' cons ensus, and finally, output the list of project numbers in order of relevance after [RESULT].Figure 8: Prompt template used to set senior ... | https://arxiv.org/abs/2505.17492v1 |
arXiv:2505.17495v1 [cs.LG] 23 May 2025ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs Landon Butler∗ Department of EECS UC Berkeley landonb@berkeley.eduAbhineet Agarwal∗ Department of Statistics UC Berkeley aa3797@berkeley.eduJustin Singh Kang∗ Department of EECS UC Berkeley just... | https://arxiv.org/abs/2505.17495v1 |
O(nd)interactions quickly becomes infeasible for even small nandd. Kang et al. [4]recently introduced SPEX, the first interaction attribution method capable of scaling up to n= 1000 features. SPEX scales with nby observing that LLM outputs are driven by a small number of interactions. It exploits this sparsity by utili... | https://arxiv.org/abs/2505.17495v1 |
modern ML problems that often consist of hundreds of features. Fourier transforms and deep learning explainability. Several works theoretically study the spectral properties of transformers. Ren et al. [18] show transformers have sparse spectra and Hahn and Rofin [19], Abbe et al. [20] establish that they are low degre... | https://arxiv.org/abs/2505.17495v1 |
as follows: Transform: F(T) =1 2nX S⊆[n](−1)|S∩T|f(S), Inverse: f(S) =X T⊆[n](−1)|T∩S|F(T).(1) The parameters F(T)are known as Fourier coefficients and capture the importance of an interaction of features in a subset T. Equation (1) represents an orthonormal transform onto a parity (XOR) basis [36]. For the rest of the... | https://arxiv.org/abs/2505.17495v1 |
Tan et al. [42] proved that decision trees learn “staircase” functions, e.g., f=x1+x1x2+x1x2x3, effectively due to their greedy construction procedure. We empirically confirm this by comparing the performance of various proxy models on a synthetic hierarchical function (i.e., sum of staircase functions resembling Figur... | https://arxiv.org/abs/2505.17495v1 |
2 4 6 8 Inference Multiplier ( α)0.00.20.40.6Faithfulness ( R2) +14%+16%+18% +19%DROP 2 4 6 8 Inference Multiplier ( α)0.00.10.20.3Faithfulness ( R2) +11%+16%+21%+26%HotpotQA 2 4 6 8 Inference Multiplier ( α)0.00.20.40.60.8Faithfulness ( R2) +4%+14%+18% +20%MS-COCO LASSO ProxySPEX (Ours) SPEXFigure 6: Comparison of fai... | https://arxiv.org/abs/2505.17495v1 |
Apart from the sentiment analysis task (top left), SPEX does not collect enough training masks to out-perform LASSO. 4.1 Faithfulness We compare attribution method faithfulness by varying the number of training masks. For each sample with nfeatures, we generate α·nlog2(n)masks, varying α∈ {2,4,6,8}, to normalize diffic... | https://arxiv.org/abs/2505.17495v1 |
via Non-Linear Datamodels Data attribution for classification is the problem of understanding how fitting a model gθon a subset Sof training samples affects the prediction of a test point zof class c. This problem can be converted into our framework by defining an appropriate value function f, f(S)≜(logit for conz)−(hi... | https://arxiv.org/abs/2505.17495v1 |
of MMLU . (6) Pruning results. We use the LASSO and PROXY SPEX to identify the most important heads for various sparsity levels ( i.e., the number of retained heads) across different sets of layers. We also compare to a Best-of- Nbaseline, where we take the best of N= 5000 different randomly chosen S, further details a... | https://arxiv.org/abs/2505.17495v1 |
DGE-2146752. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work used NCSA DeltaAI at UIUC through allocation CIS250245 from the Advanced Cyberin- frastructure Coordination... | https://arxiv.org/abs/2505.17495v1 |
, Jul 2020, pp. 9259–9268. [Online]. Available: https://proceedings.mlr.press/v119/sundararajan20a.html [16] S. Bordt and U. von Luxburg, “From shapley values to generalized additive models and back,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2023, pp. 709–745. [17] F. Fumagalli, M. ... | https://arxiv.org/abs/2505.17495v1 |
mechanistic interpretability,” Advances in Neural Information Processing Systems , vol. 36, pp. 16 318–16 352, 2023. [33] A. Syed, C. Rager, and A. Conmy, “Attribution patching outperforms automated circuit discov- ery,” arXiv preprint arXiv:2310.10348 , 2023. [34] A. R. Hsu, G. Zhou, Y . Cherapanamjeri, Y . Huang, A. ... | https://arxiv.org/abs/2505.17495v1 |
in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 2368–2378. [50] P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, and E. Hüllermeier, “Svarm-iq: Effi- cient approximation of... | https://arxiv.org/abs/2505.17495v1 |
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 B.1.2 Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 B.1.3 HotpotQA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 B.1.4 DROP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... | https://arxiv.org/abs/2505.17495v1 |
work. All remaining conversions are derived in Appendix C of [4]. A.2 Fourier Extraction The exact Fourier transform of a decision tree can be computed recursively [ 5,59,60]. Due to the linearity of the Fourier transform, the Fourier transform of each boosted tree can be computed separately and added together. Algorit... | https://arxiv.org/abs/2505.17495v1 |
the sentiment of each movie review, we utilize a DistilBERT model [ 46] fine-tuned for sentiment analysis [ 47]. When masking, we replace the word with the [UNK] token. We construct an value function over the output logit associated with the positive class. B.1.3 HotpotQA We consider 50examples from the HotpotQA [48] d... | https://arxiv.org/abs/2505.17495v1 |
step-wise or "staircase" manner. TheStrong Hierarchy Rate (SHR (f, k)) is defined as: SHR (f, k) =1 kX S∈Fk1{∀S′⊆S, S′∈ Fk},where Fkdenotes the klargest Fourier coefficients of f.(11) TheSHR is a stricter measure, quantifying the proportion of top- kcoefficients F(S)for which all subsets of S(not just initial subsets, ... | https://arxiv.org/abs/2505.17495v1 |
indicated in the main text, GBTs have been shown to vastly outperform other proxy models, including random forests, particularly because random forests are less effective at learning hierarchical functions. GBT-like algorithms, on the other hand, are adept at disentangling sums of these hierarchical components. Figure ... | https://arxiv.org/abs/2505.17495v1 |
Analysis dataset across varying α. (c) Faithfulness on a synthetic dataset with a sparse, non-hierarchical peak function, across varying α, illustrating a limitation of GBTs for non-hierarchical structures. 0 25 50 75 100 125 150 Wall Clock Time (sec.)0.700.750.80Faithfulness ( R2) 3.7×speed-up2.7×speed-up2.7×speed-up ... | https://arxiv.org/abs/2505.17495v1 |
Horse Dog Dog Frog Frog Frog Dog Bird Bird Deer Horse Horse Cat Cat Cat Bird Airplane Truck Automobile Truck Truck Airplane Airplane Airplane Truck Automobile Automobile Ship Ship Ship Horse Dog Dog Frog Frog Frog Dog Dog Dog Deer Deer Deer Cat Bird Dog Bird Bird Bird Automobile Ship Ship Airplane Bird CatFigure 14: Fo... | https://arxiv.org/abs/2505.17495v1 |
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