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and text prompt finetuning. This structured progression enables the model to first learn robust tactile- visual representations, then align them with textual descriptions, and finally optimize response generation, enhancing VTV-LLM’s capability for cross-modal understanding and tactile reasoning. In the following, we d... | https://arxiv.org/abs/2505.22566v1 |
flow computation to ensure scale invariance across different sequences. After that, we utilize the backward warping [ 52,53] to generate the temporal consistent masking map based on the keyframe and mask the corresponding video frames. The masked visuo-tactile frames Vm={Mt}T t=0are fed into the VTV encoder-decoder arc... | https://arxiv.org/abs/2505.22566v1 |
and 14B parameters). All experiments are conducted on 4 NVIDIA 7 Question: Among these three videos: a) <a.mp4> , b) <b.mp4> , c) <c.mp4> , identify the softestobject after describing each one.VTV-LLM: a) It presents a extremely hard and strong protrusion surface with no elasticity and strong friction. b) It presents a... | https://arxiv.org/abs/2505.22566v1 |
video and a question prompt, VTV-LLM generates descriptions of the four key tactile attributes. The results presented in Tab. 1 demonstrate that our method consistently outperforms all baseline models across both individual attribute and combined attribute settings. The performance gap is particularly notable in the co... | https://arxiv.org/abs/2505.22566v1 |
at 51.7%, confirming that independent datasets for each stage significantly improve model robustness. 5 Conclusion In this work, we presented VTV-LLM, the first multi-modal large language model for universal visuo- tactile video understanding. We contributed VTV150K, a comprehensive dataset of visuo-tactile videos acro... | https://arxiv.org/abs/2505.22566v1 |
et al. Anytouch: Learning unified static-dynamic representation across multiple visuo-tactile sensors. In The Thirteenth International Conference on Learning Representations , 2025. [14] Jialiang Zhao, Yuxiang Ma, Lirui Wang, and Edward Adelson. Transferable tactile transformers for representation learning across diver... | https://arxiv.org/abs/2505.22566v1 |
for self-supervised video pre-training. Advances in neural information processing systems , 35:10078–10093, 2022. [28] Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, and Yu Qiao. Videomae v2: Scaling video masked autoencoders with dual masking. In Proceedings of the IEEE/CVF conference ... | https://arxiv.org/abs/2505.22566v1 |
Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation. arXiv preprint arXiv:2410.07864 , 2024. [44] Joshua Jones, Oier Mees, Carmelo Sferrazza, Kyle Stachowicz, Pieter Abbeel, and Sergey Levine. Beyond sight: Finetuning general... | https://arxiv.org/abs/2505.22566v1 |
arXiv:2505.22571v1 [cs.CL] 28 May 2025Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems Hoang Pham, and Khac-Hoai Nam Bui* Viettel Artificial Intelligence and Data Services Center, Viettel Group, Vietnam {hoangpv4, nambkh}@viettel.com.vn Abstract This paper pre... | https://arxiv.org/abs/2505.22571v1 |
this approach is only suitable when the types of queries are predefined (in specific domains or custom benchmark datasets), which might lack flex- ibility and scalability in terms of various real-world applications. Recently, LLM Agent, by leverag- ing LLMs to execute complex tasks, emerged as a promising approach to e... | https://arxiv.org/abs/2505.22571v1 |
the answers in a single document (i.e., single-hop queries (Ram et al., 2023)). Mean- while, the recent emerging Modular RAG has been introduced to go beyond the two aforementioned RAG paradigms, which requires iterative accesses to both LLMs and retrievers multiple times (i.e., multi-hop queries (Trivedi et al., 2023)... | https://arxiv.org/abs/2505.22571v1 |
each stage where external knowledge is re- quired ( Action: Search ), the agent interacts with the Knowledge Base through the Search Tool by for- mulating a search query generated by the Planning Module. The purpose of querying external knowl- edge is to ground the reasoning process in reliable and up-to-date informati... | https://arxiv.org/abs/2505.22571v1 |
agent triggers a ’Final answer’ ac- tion to aggregate all collected evidence and provide the final answer. 4 SynAgent-RAG Dataset While the framework is fully compatible with big- ger LLMs (e.g., GPT-4), deploying it with smaller LLMs necessitates an additional training process to maintain stability across each step. T... | https://arxiv.org/abs/2505.22571v1 |
the original question qi, each step t, GPT-4 is required to perform two tasks repli- cating the real-world RAG scenario when the pro- cess of retrieving external knowledge is needed: i) provide a short rationale on how to utilize the Search Tool to address the question (Thought rt i) and formulate a detailed search que... | https://arxiv.org/abs/2505.22571v1 |
necessitates two supporting passages. This design ensures that our dataset reflects practical demands while ac- commodating varying complexities of user queries. Moreover, to the best of our knowledge, our dataset is the first dataset to integrate Chain of Thought (COT) reasoning, offering enhanced guidance for the age... | https://arxiv.org/abs/2505.22571v1 |
indexing docu- ments into a corpus, we focus on measuring the model’s reasoning capability under optimal re- trieval conditions. Here, we leave out the perfor- mance of retrieval systems and assume that the retrieved documents are correct and relevant to the original question by directly returning the reference documen... | https://arxiv.org/abs/2505.22571v1 |
Evidence Reflector No Limit 12 / No 20.2 29.9 21.4 49.4 59.9 52.2 51.2 57.94 53.2 w/o Planning 1 12 / No 10.2 15.5 11.4 37.4 43.2 37.6 36.8 43.5 37.6 Table 2: Results on different multi-hop benchmark datasets. * are taken from Jeong et al. (2024) with GPT-3.5 as the backbone LLM for both previous approaches. Bold texts... | https://arxiv.org/abs/2505.22571v1 |
approach with previous methods, as shown in Table 1 for single-hop datasets and Table 2 for multi-hop datasets. Notably, Agent-UniRAG, which leverages a small open-source LLM as its backbone, demonstrates competitive performance relative to recent state-of-the-art models that utilize significantly larger LLMs. A key st... | https://arxiv.org/abs/2505.22571v1 |
queries with unnecessary computational complexity or fail to address com- plex multi-hop queries. This study, inspired by the emerging LLM agent technologies, presents a novel approach for unified RAG systems with en- hanced effectiveness and interpretability. Further- more, we introduce SynAgent-RAG, a synthetic datas... | https://arxiv.org/abs/2505.22571v1 |
Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong Park. 2024. Adaptive-rag: Learn- ing to adapt retrieval-augmented large language mod- els through question complexity. In Proceedings of the2024 Conference oftheNorth American Chapter oftheAssociation forComputational Linguistics: Human Language Technologies (V o... | https://arxiv.org/abs/2505.22571v1 |
multi-step questions. In Proceedings ofthe61st Annual Meeting oftheAssociation forComputational Linguistics (V olume 1:Long Papers) , pages 10014–10037, Toronto, Canada. As- sociation for Computational Linguistics. Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen,... | https://arxiv.org/abs/2505.22571v1 |
Input : Jerry Falwell beliefs about the Antichrist ###Evidence : In 1999, Jerry Falwell professed that the Antichrist would likely emerge within a decade, with the prediction that this figure would be Jewish, which led to allegations of anti-Semitism and an ensuing apology from Falwell. He believed the Antichrist and C... | https://arxiv.org/abs/2505.22571v1 |
answer . 2) You will be given sources of articles, your job is to generate a multi-hop question and then provide the answer for the question based on the provided sources. ### Sources [ { "article_title": "actual article title", "section_title": "actual section title", "content": "actual content" },... ] ### Notes: 1) ... | https://arxiv.org/abs/2505.22571v1 |
an external knowledge base to find text evidence for the provided search query .", "tool_note": "Y ou can not search for multiple information at once. Please provide one clear and detailed query for a small piece of evidence you want at a time.", "tool_input": [ { "param_name": "search_query", "param_type": "string", "... | https://arxiv.org/abs/2505.22571v1 |
liquidate? Reference Answer: Claridge's company liquidated on November 10, 1917. Response: {"list_of_short_extracted_answers": ["November 10, 1917", "10 November 1917", "November 1917", "1917"] Question: Do both Icehouse pieces and El Grande come from the same game? Reference Answer: No, Icehouse pieces and El Grande d... | https://arxiv.org/abs/2505.22571v1 |
arXiv:2505.22572v1 [cs.CL] 28 May 2025Fusion Steering: Prompt-Specific Activation Control Waldemar Chang Johns Hopkins University wchang58@jh.eduDr. Alhassan Yasin Johns Hopkins University ayasin1@jhu.edu Abstract We present Fusion Steering , an activation steering methodology that improves factual accuracy in large la... | https://arxiv.org/abs/2505.22572v1 |
methods that derive latent steering vectors from generic prompts or contrastive pairs, without integrating explanatory context as a conditioning input [7, 9, 10, 11, 12]. Second , our approach employs a synergistic combination of interpolated activation patching and additive steering, executed concurrently across alltr... | https://arxiv.org/abs/2505.22572v1 |
subset both due to computational constraints and because it represents the most challenging cases—each labeled INCORRECT by the LLM grader, making it ideal for stress-testing the effectiveness of steering interventions. 2 To confirm that our experimentation subset covers a representative range of topics, we compared th... | https://arxiv.org/abs/2505.22572v1 |
This allows for finer-grained control over how different representational stages of the model are influenced. We aim to understand the trade-offs between globally uniform steering and more targeted, segment-aware modulation of internal activations. 2.4 Prompt-specific parameter optimization To ensure optimal parameter ... | https://arxiv.org/abs/2505.22572v1 |
primary focus is on factual correctness and reliability, both of which benefit from deterministic evaluation. Future work may extend this setup to sample-based decoding to assess the robustness and adaptability of activation-level steering under uncertainty. 2.6 Evaluation metrics 2.6.1 Composite metric (token overlap ... | https://arxiv.org/abs/2505.22572v1 |
overlap scores (Figure 3b), highlighting its consistency across diverse QA topics. Notably, the SimpleQA benchmark spans a wide range of domains like science, politics, art, and history, suggesting that these gains generalize across semantically distinct question types. We further validate factual correctness using a s... | https://arxiv.org/abs/2505.22572v1 |
the model’s internal representations. Although both steered outputs improve contextual grounding and explanation detail relative to the baseline, segmented steering demonstrates superior factual alignment. Table 1: Comparison of baseline, full-layer, and segmented steering outputs for a factual question. All outputs ar... | https://arxiv.org/abs/2505.22572v1 |
generating the token most strongly associated with correctness (“7”), despite a lack of coherent context. This case vividly illustrates the seemingly counterintuitive pattern discussed earlier: segmented steering methods frequently yield higher token overlap with ground truth, indicative of improved factual alignment, ... | https://arxiv.org/abs/2505.22572v1 |
work on plug-and-play steering mechanisms that do not require full reference completions. One promising direction is to integrate Fusion Steering with emerging neuron- level semantic representations, such as those produced by sparse autoencoders (e.g., Neuronpedia) [25] or crosscoder-based alignment methods [ 6], which... | https://arxiv.org/abs/2505.22572v1 |
al. On the biology of a large language model. Transformer Circuits Thread , 2025. [5]Transformer Circuits Team. Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet, 2024. [6]Transformer Circuits Team. Sparse crosscoders: Simplifying circuit analysis with cross-layer superposition, 2024. [7]A... | https://arxiv.org/abs/2505.22572v1 |
of the following grades: - "CORRECT" (fully contains correct information, no contradictions) - "INCORRECT" (contains incorrect, misleading, or contradictory information) - "NOT_ATTEMPTED" (fails to answer or provides irrelevant information) ### Example Grades: CORRECT: - Question: "Who discovered gravity?" - Gold targe... | https://arxiv.org/abs/2505.22572v1 |
Tell me Habibi, is it Real or Fake? Kartik Kuckreja,λParul Gupta,ξInjy Hamed,λ Thamar Solorio,λMuhammad Haris Khan,λAbhinav Dhallξ λMBZUAIξMonash University {kartik.kuckreja,injy.hamed,thamar.solorio,muhammad.haris}@mbzuai.ac.ae {parul,abhinav.dhall}@monash.edu Abstract Deepfake generation methods are evolving fast, ma... | https://arxiv.org/abs/2505.22581v1 |
bot. Here is the transcript of an audio. Please use the provided operations to modify the transcript to change its sentiment…. ###Input### "input":{"language":"ar","change_mode":"meaning_only","tokens": "[الطقس", "ليس", "ً ائيس", ]"."….###Output###"operations": [{"operation": "delete","old_word": "ليس",”, new_word": nu... | https://arxiv.org/abs/2505.22581v1 |
Tasks Manip. Modality Method #Total Multilingual Code Switching Google DFD Nick and Andrew [2019] 2019 Cla V FS 3,431 ✗ ✗ DFDC Dolhansky et al. [2020b] 2020 Cla A V FS 128,154 ✗ ✗ DeeperForensics Jiang et al. [2020] 2020 Cla V FS 60,000 ✗ ✗ Celeb–DF Li et al. [2020] 2020 Cla V FS 6,229 ✗ ✗ KoDF Kwon et al. [2021] 2021 ... | https://arxiv.org/abs/2505.22581v1 |
i) Train , ii)Valand iii) Test split. The outer layer shows the split between various combinations of Text-to-Speech and Lip-Sync models used for audio-visual manipulation. The middle layer shows the distribution of language in the original transcript, which is either Ar(Arabic) or CSW (Code-Switched English-Arabic). T... | https://arxiv.org/abs/2505.22581v1 |
the language as it is, second, meaning + dialect , which involves changing the meaning of the word and changing its language to another Arabic variant (either MSA or any dialect), and lastly, meaning + translation , which asks the model to change the meaning of the word, and then translate it to English. Table 2 summar... | https://arxiv.org/abs/2505.22581v1 |
real versus fake transcripts under two open-source LLMs; Jais-3B (Sengupta et al. [2023]) and Qwen-2.5-7B (Qwen et al. [2025]), across the data splits. The minimal difference in perplexity shows that our fake transcripts remain fluent and natural, despite major changes in meaning. This balance between altered content a... | https://arxiv.org/abs/2505.22581v1 |
of human-reported reasons for labellng a video as fake in the user study. (a) Visual quality metrics. Dataset PSNR(dB) ↑SSIM ↑FID↓ FF++ 24.40 0.812 1.06 DFDC — — 5.69 FakeA VCeleb 29.82 0.919 2.29 LA V-DF 33.06 0.898 1.92 A V-Deepfake1M 39.49 0.977 0.49 ArEnA V 37.70 0.971 0.68(b) Distribution of top reasons. Reason Pe... | https://arxiv.org/abs/2505.22581v1 |
to 1) watch the video, and 2) answer 3 questions, i) Is the video real of fake, ii) If it is fake, localize where they think the fake region is, iii) Whether the given video contains Arabic-English code-switching or not, iv) Give a reason for labelling the video (if they have) as a deepfake. The results in Table 7 reaf... | https://arxiv.org/abs/2505.22581v1 |
a zero-shot setting (pre-trained on A V-1M; Cai et al. [2023a]) and after fine-tuning on our dataset. For image-based classifiers (Meso4, MesoInception4; 7 Afchar et al. [2018] and Xception; Chollet [2017]), we aggregate frame-level predictions to segments following Cai et al. [2023a]. For benchmarking deepfake detecti... | https://arxiv.org/abs/2505.22581v1 |
0.18 0.04 0.01 4.11 4.11 4.11 4.11 4.08 Xception V 22.50 10.26 2.29 0.58 19.13 19.13 19.13 19.13 19.13 BA-TFD (ZS) A V 0.17 0.01 0.00 0.00 9.72 5.20 3.07 1.46 0.73 BA-TFD+ (ZS) A V 0.11 0.00 0.00 0.00 5.77 2.95 2.09 0.87 0.37 BA-TFD A V 2.42 0.55 0.01 0.00 22.30 10.31 3.41 2.54 1.67 BA-TFD+ A V 3.74 1.10 0.06 0.01 30.7... | https://arxiv.org/abs/2505.22581v1 |
[2023a,b], indicating that the proposed dataset is an important asset for building the next-generation of multilingual deepfake localization methods. As future work, we will evaluate LLM-based detectors after fine-tuning them on the dataset. Limitations . Similar to other deepfake datasets, ArEnA V exhibits a misbalanc... | https://arxiv.org/abs/2505.22581v1 |
and localization. Computer Vision and Image Understanding , 236:103818, November 2023b. ISSN 1077-3142. Zhixi Cai, Abhinav Dhall, Shreya Ghosh, Munawar Hayat, Dimitrios Kollias, Kalin Stefanov, and Usman Tariq. 1m-deepfakes detection challenge, 2024. URL https://arxiv.org/abs/2409. 06991 . Edresson Casanova, Julian Web... | https://arxiv.org/abs/2505.22581v1 |
You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae. KoDF: A Large-Scale Korean DeepFake Detection Dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 10744–10753, 2021. Chunyu Li, Chao Zhang, Weikai Xu, Jingyu Lin, Jinghui Xie, Weiguo Feng, Bingyue Peng, Cunjian Chen, and Weiwei ... | https://arxiv.org/abs/2505.22581v1 |
Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https://arxiv.org/abs/2412.15115 . Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recogni... | https://arxiv.org/abs/2505.22581v1 |
Language Model for Video Understanding, June 2023. arXiv:2306.02858 [cs, eess]. 13 A Real Perturbations Table 11: List of video and audio perturbation types with descriptions. Category Perturbation Type Description Video PerturbationsGaussian Blur Applies Gaussian smoothing to simulate out-of-focus capture. Salt and Pe... | https://arxiv.org/abs/2505.22581v1 |
task) Edit: اﻟﻨﺎس → friends اﻟﻨﺎس أﻧﺎ ﺑﺮوح ﻗﺎﺑﻞ [ orig] Arabic EN EN Change meaning (I go meet people ) + change language to English friends أﻧﺎ ﺑﺮوح ﻗﺎﺑﻞ[ aug] (I go meet friends ) 15 C Prompt for Text Perturbation Prompt for Fake Transcript Generation. ###SYSTEM MESSAGE### You are a controlled text-perturbation bot. ... | https://arxiv.org/abs/2505.22581v1 |
arXiv:2505.22583v1 [cs.SE] 28 May 2025GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git Tobias Lindenbauer1,2*, Egor Bogomolov1, Yaroslav Zharov1 1JetBrains Research 2School of Computation, Information and Technology, Technical University of Munich Correspondence: tobias.lindenbauer@jetbrains.co... | https://arxiv.org/abs/2505.22583v1 |
end-to-end manner. Furthermore, typical Git tasks such as Interactive Rebase ( IR) are time- consuming and distinct from raw code-generation. IRrequires reasoning over the Git history and an in-depth understanding of dependencies between the commits constituting the history. To stimulate innovation in the direction of ... | https://arxiv.org/abs/2505.22583v1 |
Git LFS heavy repositories Repository stars ≥1000 Heuristic for repository quality Repository is not archived Heuristic for repository quality Repository is not forked Avoid duplication Last commit within a month of May, 31st 2024 Avoid stale repositories Repository has permissive license Ensure legal compliance Reposi... | https://arxiv.org/abs/2505.22583v1 |
These samples target the use-case of (1) cleaning up the local Git his- tory before pushing new commits to the remote (e.g.,git rebase -i HEAD~5 , and (2) construct- ing a clean git history given a set of changes for the IRandICC scenario, respectively. To tailor these samples toward evaluating an aspect of Git distinc... | https://arxiv.org/abs/2505.22583v1 |
ground truth Git history in both cases, we count a sample as solved. For details on the prompt see Appendix B.4.Scenario Success Rate Solve Rate IR 93.33 26.67 ICC 93.33 23.33 MCR 76.67 13.33 Total 88 21.11 Table 2: Success and solve rates (%) by scenario type, rounded to two decimal places. We observe the high complex... | https://arxiv.org/abs/2505.22583v1 |
agent to sequentially resolving conflicts. It may only specify a patch for resolving the current conflict. 5 Baseline Results In Table 2, we see that our baseline implementation succeeds in 88% and solves 21.11% of scenarios in GitGoodBench Lite3overall. Even with significant scaffolding support the LLM is unable to so... | https://arxiv.org/abs/2505.22583v1 |
allow more accu- rate handling of multi-file changes. Furthermore, enabling commit content modification during IR would allow handling more complex IRscenar- ios, including ones during which a merge conflict occurs. Additionally, for FCC samples our eval- uation methodology may introduce bias, as it is LLM -based. We s... | https://arxiv.org/abs/2505.22583v1 |
, pages 560–564. Aleksandra Eliseeva, Yaroslav Sokolov, Egor Bogo- molov, Yaroslav Golubev, Danny Dig, and Timofey Bryksin. 2023. From Commit Message Generation to History-Aware Commit Message Completion. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . S. R. P. van Hal, M. Post,... | https://arxiv.org/abs/2505.22583v1 |
the 41st International Conference on Machine Learning , pages 50208–50232.John Yang, Kilian Leret, Carlos E. Jimenez, Alexan- der Wettig, Kabir Khandpur, Yanzhe Zhang, Binyuan Hui, Ofir Press, Ludwig Schmidt, and Diyi Yang. 2025. SWE-smith: Scaling Data for Software Engi- neering Agents. arXiv preprint . ArXiv:2504.217... | https://arxiv.org/abs/2505.22583v1 |
The Scenario Field In this section we provide further details regarding the contents of the scenario for the two sample types in our datasets. A.2.1 Contents For FCC Samples In Table 8 we show the structure of the scenario field for FCC samples. Furthermore, Table 11 provides an exemplary FCC datapoint’s scenario field... | https://arxiv.org/abs/2505.22583v1 |
Description file The relative path of the file this sample refers to. branch The branch name from which this FCC originates. times_seen_consecutively The number of times this particular file was modified in succession. purity ∈[0; 1] . Ratio between changes in the file and the total changes in all files of a FCC scenar... | https://arxiv.org/abs/2505.22583v1 |
in these scenarios. B.4 LLM-as-a-Judge Evaluation In Figures 10 and 11 we provide the prompt our system uses when evaluating the Git histories gen- erated by the agent in FCC samples. First, we pro- vide detailed instructions regarding the dimensions based on which the LLM should assess the qualityof a history. Next, w... | https://arxiv.org/abs/2505.22583v1 |
temporal relationship of the branches that are being merged and the intent of the junior developer, with respect to which side of the conflict contains the local and which the incoming changes. The intent of the developer is to merge the incoming changes into the local changes. You must only use the following tools and... | https://arxiv.org/abs/2505.22583v1 |
newline character to avoid introducing accidental changes. - You must always fill all parameters of the provided tools. This includes the "reason" parameter. Figure 4: Our MCR prompt continued. Interactive Rebase (IR) Prompt - Part 1 You are a staff software engineer with expertise in {programming_language} and Git. Yo... | https://arxiv.org/abs/2505.22583v1 |
ensure to always have exactly one item per commit. update_rebase_todo_list(rebase_todo_list_items=[ '{{"commit_index": 0, "command": "pick"}} ', '{{"commit_index": 2, "command": "squash", "commit_msg": "ADD: Define interfaces and test cases for ShoppingBasketService"}} ', '{{"commit_index": 1, "command": "pick"}} ' ], ... | https://arxiv.org/abs/2505.22583v1 |
clear and concise commit message for each commit following the format provided in the example usages. Key Requirements: - Avoid apply all changes in a single commit unless you are absolutely sure this will yield the best possible git history. - You must always fill all parameters of the provided tools. This includes th... | https://arxiv.org/abs/2505.22583v1 |
history 2 are more concise and introduce logically coherent changes. The changes are introduces in such a way that they are unlikely to break the system as the commits are self- contained with respect to the part of the system that they affect and correctly propagate changes throughout the system. Thus I chose history ... | https://arxiv.org/abs/2505.22583v1 |
arXiv:2505.22591v1 [cs.CL] 28 May 2025Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning Erxin Yu1, Jing Li1,2*, Ming Liao1, Qi Zhu3, Boyang Xue4, Minghui Xu3, Baojun Wang3,Lanqing Hong3,Fei Mi3,Lifeng Shang3 1Department of Computing, The Hong Kong Polytechnic University 2Research Centre for ... | https://arxiv.org/abs/2505.22591v1 |
vidual cases. For example, in Figure 1, the left subfigure displays various error types of Qwen2.5- Math. We enhanced its mathematical reasoning by generalizing the data according to these error types, which is depicted in the right subfigure. To the best of our knowledge, we are the first to explore data synthesis and... | https://arxiv.org/abs/2505.22591v1 |
ex- traction and clustering, then performs data general- ization for each cluster. •Experiments show that our method efficiently generalizes data based on error types, enhancing mathematical reasoning skills and validating the effectiveness of our data selection strategy. 2 Related Work 2.1 Mathematical Reasoning With ... | https://arxiv.org/abs/2505.22591v1 |
the mathemati- cal reasoning ability of the target model Mtarget by identifying its weaknesses, referred to as bad cases, on an existing mathematical training dataset Dtrain. These bad cases are analyzed to guide the synthesis of targeted training data that directly addresses the model’s specific shortcomings. By progr... | https://arxiv.org/abs/2505.22591v1 |
trains for the Olympics. He trains twice a day for 4 hours each time for all but 2 days per week. How many hours does he train a year? SyntheticDataSelectedDataSection 3.3: One-shot Learning SelctionSection 3.4: Iterative Training Optimization Ignored Calculations and Omissions2...Figure 2: An overview of our Self-Erro... | https://arxiv.org/abs/2505.22591v1 |
mance on good cases originally answered correctly, preserving its effectiveness across challenging and straightforward examples. First, we randomly sam- ple a subset of bad cases and good cases to create a validation set, Ddev. Next, we evaluate each sam- ple in DSEIby measuring the number of cases in Ddevthat can be r... | https://arxiv.org/abs/2505.22591v1 |
of the synthetic data from each part and combine them into a uni- fied dataset for training. Over three iterations, we generate a total of 30,000 data points and select 1,500 for training. We also compared two meth- ods for training the target model: iterative training, which starts from the model trained in the previo... | https://arxiv.org/abs/2505.22591v1 |
detailed dataset statistics are provided in Table 1. We evaluated the models on these datasets us- ing greedy decoding in a zero-shot setting, with the maximum generation length set to 2048. Per- formance was measured using Exact Match (EM), where answers were extracted from the generated reasoning paths and compared t... | https://arxiv.org/abs/2505.22591v1 |
through Rand, LESS, and one-shot ICL methods. by comparing them against the GSM8K and MATH training and test datasets. For rand selection, data is proportionally sam- pled from each error type, with more samples drawn from types with more bad cases. For LESS, following the original setting, we randomly select 10 exampl... | https://arxiv.org/abs/2505.22591v1 |
Testset (EM Score) GSM8K MATH GSM8K MATH Iter-0 (ori) 0 0 55.50 32.32 Iter-1 29.98 23.17 79.48 57.21 Iter-2 38.01 39.44 84.70 58.19 Iter-3 39.13 40.57 87.79 59.18 Table 4: Bad Case Fix Rate of Qwen2.5-Math on GSM8K and MATH during iterative improvement, along with its performance on the test sets. Bad cases refer to th... | https://arxiv.org/abs/2505.22591v1 |
reasoning by generalizing training data based on error types rather than individual bad cases. Our method en- hances data diversity and mitigates overfitting by analyzing errors, clustering them into categories, and synthesizing targeted data using a self-instruct approach. Experiments on LLaMA3-8B-Instruct, Qwen2.5-Ma... | https://arxiv.org/abs/2505.22591v1 |
Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-V oss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gra... | https://arxiv.org/abs/2505.22591v1 |
Sa- hana Chennabasappa, Sanjay Singh, Sean Bell, Seo- hyun Sonia Kim, Sergey Edunov, Shaoliang Nie, Sha- ran Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhosale, Shun Zhang, Simon Van- denhende, Soumya Batra, Spencer Whitman, Sten Sootla, Stephane Collot, Suchin Gururangan, Syd- ney Borodinsky, Tamar Herm... | https://arxiv.org/abs/2505.22591v1 |
Keneally, Miao Liu, Michael L. Seltzer, Michal Valko, Michelle Restrepo, Mihir Pa- tel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey, Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Munish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata Bawa, Nayan Singhal, Nick Egebo, Ni... | https://arxiv.org/abs/2505.22591v1 |
of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages 7602–7635, Mexico City, Mexico. Association for Computational Linguistics. Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Jun- ha... | https://arxiv.org/abs/2505.22591v1 |
tuning. InInternational Conference on Machine Learning (ICML) . Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, and Carl Yang. 2024. Knowledge-infused prompting: Assessing and advancing clinical text data generation with large language models. In Find- ings of the Ass... | https://arxiv.org/abs/2505.22591v1 |
Lima: Less is more for alignment. Preprint , arXiv:2305.11206. A Overview of Prompts Used A.1 Prompt for Training and Inference For all the models, we use the built-in chat tem- plates for training and inference. Figure 7 illus- trates the one-shot learning prompt for the Qwen2.5 model, where the model generates a resp... | https://arxiv.org/abs/2505.22591v1 |
Generation Prompt:Based on the given mathematical problem, identify the step where the model made an error in its reasoning process. Analyze the reason for this error and summarize it using a keyphrase. The input consists of a math question, the correct answer, and the model's incorrect answer. Please output the result... | https://arxiv.org/abs/2505.22591v1 |
as a string in this structure: [{{"question":,"solution":}}, {{"question":,"solution":,}},...]. Ensure no additional output beyond the specified structure. Output in JSON format.4. The reasoning process for each step should be provided in the answer.5. The final answer should be marked with \\boxed{{}}When generating m... | https://arxiv.org/abs/2505.22591v1 |
arXiv:2505.22597v1 [cs.AI] 28 May 2025HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI Gym Ngoc La1, Ruaridh Mon-Williams2,andJulie A. Shah1 1MIT 2University of Edinburgh ntmla@mit.edu, ruaridh.mw@ed.ac.uk, julie.a.shah@csail.mit.edu Abstract In recent years, reinforcement lear... | https://arxiv.org/abs/2505.22597v1 |
these resources are sourced from the hier- archical task network tracks of the International Planning Competitions (IPC-HTN) (IPC 2023 HTN Tracks). HDDL’s intuitive and flexible design also allows users to define or modify problem-solving approaches by adjusting the hierar- chical task networks to suit their specific n... | https://arxiv.org/abs/2505.22597v1 |
al. 2020) is an extension of PDDL (Mc- Dermott et al. 1998). H ¨oller et al. (2020) define the domain and problem as follows. Definition of Planning Domain: A planning domain Dis a tuple (L, TP, TC, M)defined as follows. •Lis the underlying predicate logic. •TPandTCare finite sets of primitive and compound tasks, respe... | https://arxiv.org/abs/2505.22597v1 |
action-drive city-1 task-deliver package city-0 ⇒ method-deliver ⇒ task-load package ⇒ method-load package ⇒ action-pickup package task-deliver package city-0 ⇒ method-deliver ⇒ task-get-to city-0 ⇒ method-drive-to city-0 ⇒ action-drive city-0 t = 0 t = 1 t = 2 t = 3 t = 4(a) Transport scenario Hierarchy of chef-1 t-ma... | https://arxiv.org/abs/2505.22597v1 |
H ¨oller et al. (2020). The agent-centric extension only includes changes to the HDDL domain. The agent- centric planning domain is defined below: Definition 1. An agent-centric planning domain Dis a tuple D=⟨ta, L, T P, TC, M⟩, where: •tais an agent type hierarchy in the domain. •L is the underlying predicate logic. •... | https://arxiv.org/abs/2505.22597v1 |
?destination) ) The remaining components in the tuple, LandM, are con- sistent with the standard HDDL formulation as defined by H¨oller et al. (2020). Note that to facilitate the modification and verification of HDDL domains for compatibility with HDDLGym, the codebase includes an interactive platform featuring autonom... | https://arxiv.org/abs/2505.22597v1 |
portant method in the Agent class is the update hierarchy function U. This function checks whether any tasks or ac- tions in the agent’s hierarchy Hhave been completed by comparing their effects with the current state of the world. Once tasks or actions are completed, they are removed from both the agent’s hierarchy Ha... | https://arxiv.org/abs/2505.22597v1 |
3 G. predicates 35 79 269 937,158 1,186,066 G. dynamic pred. 13 38 132 90 101 G. operators 184 1570 11,979 200,597 300,860 G. actions 58 610 4,093 175,791 263,668 L. operators 14 14 14 18 18 L. actions 4 4 4 5 5 Objects 8 13 23 18 19 Table 1: Dimensions of Lifted and Grounded Represen- tations in Transport and Overcook... | https://arxiv.org/abs/2505.22597v1 |
their hierarchies. Within this loop, an empty list, OA, is initialized to store the valid operators of all agents in A(line 3). Next, the for-loop from lines 4 to 17 iterates to find all valid operators Oafor each agent a, for a∈ A. To do this, the algorithm first checks if ais in Done , meaning its hierarchy is comple... | https://arxiv.org/abs/2505.22597v1 |
valid operators for a 10: ifOais empty then 11: Remove the last operator of agent ahierar- chy from its hierarchy 12: If no more operator from a’s hierarchy to remove, add none action to Oa 13: end if 14: end while 15: end if 16: AddOatoOA 17: end for 18: C←Combinations of joint operators from OA 19: Remove any invalid... | https://arxiv.org/abs/2505.22597v1 |
better illustrate the ap- plications of these agent-centric environments, we provide a detailed discussion on Transport domain as follows. Transport domain The goal of a Transport problem is to deliver one or more packages from their original locations to designated locations. The resulting action hierarchy of an 1-age... | https://arxiv.org/abs/2505.22597v1 |
found from the codebase. Additionally, Figure 1b shows an example of a hierarchy of an agent and its belief about the other agent’s hierarchy. The following videos help visualize the result of combin- ing HDDLgym in task planning and use A∗for motion plan- ning in various Overcooked layouts: Bottleneck — https://tinyur... | https://arxiv.org/abs/2505.22597v1 |
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