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Olsen, Jeffrey R. Leonard, David M. Jaffe, Julie C. Leonard, and for the Pediatric Emergency Care Applied Research Network (PECARN) Cervical Spine Study Group. Utility of Plain Radiographs in Detecting Traumatic Injuries of the Cervical Spine in Children. Pediatric Emergency Care , 28(5):426, May 2012. [43] Nancy Qian.... | https://arxiv.org/abs/2505.18223v1 |
Thirteenth International Conference on Learning Representations , October 2024. [55] Bin Yu and Rebecca L. Barter. Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making . MIT Press, 2024. [56] Daoguang Zan, Zhirong Huang, Wei Liu, Hanwu Chen, Linhao Zhang, Shulin Xin, Lu Chen, Qi Liu, Xi... | https://arxiv.org/abs/2505.18223v1 |
constructed problem sequences, rather than deriving from real-world notebook flows. This approach lacks the dynamic, subjective user interaction central to our benchmark. DS-1000, sourced from StackOverflow, tests code snippet completion for libraries like Pandas in a single-turn fashion, not interactive analysis [38].... | https://arxiv.org/abs/2505.18223v1 |
often requires synthesizing multiple test results into a composite score, guided by guidelines derived from extensive clinical trials; a single abnormal indicator is rarely sufficient [ 2]. In atmospheric science, predicting the state of water (ice crystal vs. liquid) is significantly improved by incorporating features... | https://arxiv.org/abs/2505.18223v1 |
and create a submission file using the provided dataset. The dataset contains csv files saved at '/app/datasets '. The data is split into train.csv (with targets) for training, and test.csv (without targets) for evaluation. Based on your model 's predictions for the test set, create a submission.csv file matching the f... | https://arxiv.org/abs/2505.18223v1 |
in rainfall - Consider that the dataset likely has strong geographic and seasonal dependencies that should be incorporated into feature engineering C.2 System Prompt for Simulated User You are a user interacting with a data analysis agent. {project_context} Rules: - **Do not** ask the agent to generate data visualizati... | https://arxiv.org/abs/2505.18223v1 |
an existing file in the current directory. - Break down complex tasks into small, manageable steps. Execute code in small blocks, print information, then continue based on results. - Make plans with as few steps as possible while ensuring accuracy and completeness. 6. Summary Output Format: Summarize your work briefly ... | https://arxiv.org/abs/2505.18223v1 |
instructions* step-by-step. 2. Decide if they are contradictory. 3. If contradictory, draft a follow-up instruction that steers the user back toward the reference instructions, matching the user’s tone. **OUTPUT FORMAT -- VISIBLE TO USER** Return *only*the following JSON object (no additional text): {{ "thought": <the ... | https://arxiv.org/abs/2505.18223v1 |
to parse multiple columns, the user might suggest parsing only one, or focus solely on exploration when a complete pipeline is specified. The gatekeeper identifies these partial approaches and encourages comprehensive task completion through supportive follow-up instructions. •Feature engineering variations (2 cases). ... | https://arxiv.org/abs/2505.18223v1 |
later. 3. [Feature Engineering] Parse the 'Cabin'column to extract the Deck (letter), Number (numeric portion), and Side (P/S indicating port/starboard) as separate features. 4. [Feature Engineering] Alternatively, test both the decomposed cabin features and the original cabin encoding to determine which provides bette... | https://arxiv.org/abs/2505.18223v1 |
information, termed “shards.” These shards represent individual facts, constraints, or preferences that a user might reveal gradually. The generated shards are then stored for the shard user to utilize during the interaction workflow. Prompt of the shard generation. We present the prompt to generate sharded instruction... | https://arxiv.org/abs/2505.18223v1 |
interactions – persist even under dramatically different interaction conditions with comprehensive guidance. This consistency across interaction paradigms validates that the performance bottlenecks are inherent to current agent architectures rather than artifacts of specific user interaction patterns. 6used in the main... | https://arxiv.org/abs/2505.18223v1 |
ground truth values. This same function is applied to the human baseline submission derived from the original notebook. These evaluation functions, though varying by task, can be categorized into two types: 1.Accuracy metrics : Higher values signify better performance. These metrics typically possess a theoretical uppe... | https://arxiv.org/abs/2505.18223v1 |
LLM agents. “ Avg Score ” is the average of the per-task score among all runs with valid submission. “ Scores > -5% Proportion ” reports the percentage of runs that achieve a score higher than −5%. In addition, to provide a more granular view, Figure 6 visualizes the performance score for each task-model pair. In this ... | https://arxiv.org/abs/2505.18223v1 |
MB 252 8 19.64 KB 15.02 KB 9 3 5.19 MB 34 22 43.38 KB 14.63 KB 10 8 25.56 KB 24 21 11.90 KB 3.90 KB 11 21 236.31 KB 31 6 22.78 KB 19.23 KB 12 18 2.81 MB 33 7 16.49 KB 10.05 KB 13 24 14.09 MB 80 4 9.04 KB 7.44 KB 14 12 93.08 KB 61 14 24.86 KB 14.86 KB 15 12 1.12 MB 70 15 8.79 KB 7.04 KB 16 10 380.07 KB 45 8 21.82 KB 9.7... | https://arxiv.org/abs/2505.18223v1 |
This timeframe was chosen to significantly reduce the likelihood of data contamination, as these notebooks would be new to most large language models. A rule-based filtering system was then applied. We only keep Python notebooks, as they are the most common format on Kaggle. We excluded notebooks designed to run on GPU... | https://arxiv.org/abs/2505.18223v1 |
Features: 33 –Complex Function Calls: Frequency of calls to advanced data analysis functions such as pivot_table ,groupby ,apply ,merge ,join ,concat ,agg, and logical operators ( &,|). –Function Definitions: A moderate number of function definitions (1-5, based on corpus statistics) was considered indicative of comple... | https://arxiv.org/abs/2505.18223v1 |
# NotebookInfo parameters votes: int, copy_and_edit: int, views: int, comments: int, runtime: int, input_size: float, prize: str | None, # CodeInfo parameters num_pivot_table: int, num_groupby: int, 34 num_apply: int, num_def: int, num_for: int, num_and: int, num_or: int, num_merge: int, num_concat: int, num_join: int,... | https://arxiv.org/abs/2505.18223v1 |
increasing score up to 60 seconds resource_score = runtime / 12 else: # runtime > 300 # Decreasing score for runtimes over 5 minutes resource_score = 5.0 - (runtime - 300) / 180 # heavily penalize too many plots plot_penalty = max(num_plots - 15, 0) * 20.0 # Compute final score as a weighted sum of individual scores sc... | https://arxiv.org/abs/2505.18223v1 |
in this file. This typically involves a **performance metric** (e.g., accuracy, RMSE, MAE, MSE, RMLSE) used to evaluate predictions of a specific **response variable**. **Never use R^2 as the evaluation metric if there are other metrics** - Determine the name of the **response variable** 's column in the dataset (e.g.,... | https://arxiv.org/abs/2505.18223v1 |
visualization-focused 2. Create a cleaned version of the markdown file that: - Retains all section headers and code blocks necessary to reproduce the final result - Completely removes sections that don 't affect the final numerical output (like plotting, data exploration, or checks that don 't lead to modifications) - ... | https://arxiv.org/abs/2505.18223v1 |
purpose of each logical code block, aiding in understanding how the code processes data step-by-step to arrive at the final result. Concurrently, it attempted to distill any underlying design decisions or domain knowledge that might be implicit in the code, such as the reasons for choosing a particular data processing ... | https://arxiv.org/abs/2505.18223v1 |
with positive weekly sales; Check the dimensions of the resulting dataframe; Convert the date column to datetime format and extract week, month, and year components into new columns; Create Super Bowl, Labor Day, Thanksgiving, and Christmas indicator columns based on the dates. Replace all missing values with zeros; Co... | https://arxiv.org/abs/2505.18223v1 |
isn 't artificially inflated by predicting the majority class. - The hyperparameter tuning focuses on tree-based models with parameters that control model complexity (depth, samples per split) and ensemble strength (number of estimators, learning rate), suggesting a balance is needed between fitting the training data a... | https://arxiv.org/abs/2505.18223v1 |
date, which is 90 days before the maximum date. 45 3. Create `X_train_val `by taking all rows from `train_df `where the date is less than the split date. 4. Create `X_val`by taking all rows from `train_df `where the date is greater than or equal to the split date. 5. Print the date range (min and max date) and shape fo... | https://arxiv.org/abs/2505.18223v1 |
before proceeding, leading to redundant exchanges that ultimately exceed the allowed number of interaction rounds. G.3 Overclaims In the task case with id “jimmyyeung-spaceship-titanic-xgb-top5”, Claude-3.7 claims that the hyper- parameters are optimized despite only choosing one set of hyperparameters. We present the ... | https://arxiv.org/abs/2505.18223v1 |
and y (target) properly first. ### Agent: <think> Okay, let 's figure out what 's going wrong here. The user is trying to predict 'Listening_Time_minutes 'using a linear regression model. They 've encountered a ValueError about feature names not matching, specifically mentioning 'Listening_Time_minutes 'as an unseen fe... | https://arxiv.org/abs/2505.18223v1 |
user wants to analyze electric vehicle data using the train.csv and test.csv files. The goal is to create a submission.csv file similar to the sample provided. Let me start by understanding the data structure. ...(omitted, the agent printed the columns of train.csv and test.csv, and assumed that the task is to predict ... | https://arxiv.org/abs/2505.18223v1 |
arXiv:2505.18232v1 [cs.LG] 23 May 2025ELDeR: Getting Efficient LLMs through Data-Driven Regularized Layer-wise Pruning Mingkuan Feng1∗, Jinyang Wu1∗Siyuan Liu2∗, Shuai Zhang1B, Hongjian Fang1, Ruihan Jin1, Feihu Che1, Pengpeng Shao1, Zhengqi Wen1, Jianhua Tao1 1Tsinghua University,2Peking University Abstract The deploy... | https://arxiv.org/abs/2505.18232v1 |
cause the model’s performance to collapse, limiting the acceleration effect. To address these limitations, we present a new pruning paradigm that first apply regularization and followed by pruning. The regularization process dynamically transfers crucial information from the layers to be pruned to the remaining layers,... | https://arxiv.org/abs/2505.18232v1 |
accelerate [ 30], the requirement for extreme sparsity to achieve considerable acceleration [ 31], and are not suitable for RFT on downstream tasks. Structured pruning methods, including channel-wise pruning [ 15] and layer-wise pruning [ 32]. Channel-wise pruning methods typically create a metric to assess the importa... | https://arxiv.org/abs/2505.18232v1 |
the input data embedding, where bis the batch size and nis the number of tokens. The input to the ith layer is denoted asXi in∈Rb×n×d, and the output from the ith layer is denoted as Xi out∈Rb×n×d. A learnable weight S[i]is assigned to the ith transformer layer, and the set of all layer weights is denoted as S. Algorit... | https://arxiv.org/abs/2505.18232v1 |
the pruned layers on the Transformer Layers𝐗𝐢𝐧𝐢 Residual𝐗𝐢𝐧𝐢+𝟏 Residual Regularize( 𝐗𝐨𝐮𝐭𝐢, 𝐗𝐢𝐧𝐢)𝐗𝐨𝐮𝐭𝐢𝑆𝑖∙𝐗𝐨𝐮𝐭𝐢𝐗𝐨𝐮𝐭𝐢+𝟏𝑆𝑖+1∙𝐗𝐨𝐮𝐭𝐢+𝟏 𝑖+1∉𝑃 𝑖∈𝑃 Figure 2: Details of layer weight learning and regularization. Pis the set of prunable layers, identi- fied iteratively using the met... | https://arxiv.org/abs/2505.18232v1 |
WinoGrande(%) HellaSwag(%) ARC-e(%) ARC-c(%) Avg_Acc(%) Phi-2Dense 0% 5.28 79.11 75.77 73.83 78.32 54.18 72.24 SLEB 25% 7.65 68.85 63.63 50.96 49.38 30.79 52.72 ShortGPT 25% 7.15 69.67 65.19 51.26 52.46 33.89 54.49 LaCo 25% 7.38 68.53 63.76 50.39 51.28 33.45 53.48 Shortened LLaMA 25% 7.74 66.88 62.19 51.45 51.47 32.66 ... | https://arxiv.org/abs/2505.18232v1 |
to perform RFT leveraging LoRA [ 58] on the baselines after pruning. The aforementioned 128 samples and the 1,000 samples have no overlap. Because ELDeR is finetuning- free, we did not perform RFT on ELDeR. The pruning ratio was 25% which means 25% of the transformer layers in a model will be pruned. As shown in Table ... | https://arxiv.org/abs/2505.18232v1 |
on WikiText-2. Alpaca WikiText-2 C4 PTB Different Calibration Datasets0.05.010.015.020.0Perplexity8.58.910.312.4 8.9 9.111.313.5 9.19.512.114.5 9.4 9.613.515.9 6.67.19.511.7ShortGPT LaCo Shortened LLaMA SLEB ELDeRFigure 4: Comparison of perplexity on different calibration datasets at a pruning ratio of 25% on LLaMA2-7B... | https://arxiv.org/abs/2505.18232v1 |
25% and conduct experiments on LLaMA2-7B (32 layers), keeping all settings consistent with Section 4.2 except for the method used to determine layer weights. The perplexity and average accuracy of the pruned models are shown in Table 4. It can be observed that, compared to using a greedy strategy to iteratively learn l... | https://arxiv.org/abs/2505.18232v1 |
decreases, meaning that, after regularization, the input undergoes greater changes when passing through these layers than before. This demonstrates that the regularization process weakens the influence of the regularized layers and enhances the influence of the unregularized parts, suggesting that regularization proces... | https://arxiv.org/abs/2505.18232v1 |
pp. 38087–38099, PMLR, 2023. [10] C.-Y . Hsieh, C.-L. Li, C.-k. Yeh, H. Nakhost, Y . Fujii, A. Ratner, R. Krishna, C.-Y . Lee, and T. Pfister, “Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes,” in Findings of the Association for Computational Linguistics: AC... | https://arxiv.org/abs/2505.18232v1 |
Conference on Empirical Methods in Natural Language Processing , pp. 4163–4181, 2022. [25] Y . Zhang, H. Bai, H. Lin, J. Zhao, L. Hou, and C. V . Cannistraci, “Plug-and-play: An effi- cient post-training pruning method for large language models,” in The Twelfth International Conference on Learning Representations , 202... | https://arxiv.org/abs/2505.18232v1 |
of the Royal Statistical Society Series B: Statistical Methodology , vol. 58, no. 1, pp. 267–288, 1996. [40] S. Boyd and L. Vandenberghe, Convex optimization . Cambridge university press, 2004. [41] C. Tao, L. Hou, H. Bai, J. Wei, X. Jiang, Q. Liu, P. Luo, and N. Wong, “Structured prun- ing for efficient generative pre... | https://arxiv.org/abs/2505.18232v1 |
2022. [55] M. Javaheripi, S. Bubeck, M. Abdin, J. Aneja, S. Bubeck, C. C. T. Mendes, W. Chen, A. Del Giorno, R. Eldan, S. Gopi, et al. , “Phi-2: The surprising power of small language models,” Microsoft Research Blog , vol. 1, no. 3, p. 3, 2023. [56] Z. Yao, R. Yazdani Aminabadi, M. Zhang, X. Wu, C. Li, and Y . He, “Ze... | https://arxiv.org/abs/2505.18232v1 |
LoRA Rank Scaling Factor Max Sequence Length Batch Size Learning Rate Early Stop Threshold Min Delta FP16 32 10 2048 64 2e-5 5 1e-4 fair comparison between ELDeR and other methods, we maintain consistency in the data used across all approaches. Specifically, the data used by ELDeR for learning layer weights and perform... | https://arxiv.org/abs/2505.18232v1 |
significantly outperforms existing unstructured pruning methods with a sparsity of 2:4 on generative and zero- shot tasks, demonstrating the powerful performance of ELDeR. The reason ELDeR outperforms unstructured pruning in comparisons lies in iteratively learning layer weights and the regularization process before pr... | https://arxiv.org/abs/2505.18232v1 |
measuring changes in perplexity across various generative task datasets, including WikiText- 2, Alpaca, PTB, and C4, under pruning rates of 10%, 20%, 30%, 40%, 50%, and 60%. The detailed results, presented in Table 9, indicate that ELDeR exhibits greater robustness as model scale increases, suggesting that the proposed... | https://arxiv.org/abs/2505.18232v1 |
60.58 54.42 31.14 56.39 10% 72.35 60.75 51.53 52.73 29.15 53.30 20% 71.47 60.46 48.72 51.95 28.04 52.13 30% 66.73 58.53 44.36 50.02 26.58 49.24 40% 63.57 55.32 41.63 47.79 25.24 46.71 50% 58.48 52.29 40.52 46.26 21.32 43.77 60% 52.85 49.59 38.46 43.24 16.07 40.04 OPT-6.7BDense 76.39 65.19 67.16 60.14 34.64 60.70 10% 75... | https://arxiv.org/abs/2505.18232v1 |
will not be perceived well by the reviewers. •The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. •It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper... | https://arxiv.org/abs/2505.18232v1 |
in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material. • Theorems and Lemmas that the proof relies upon should be properly referenced. 4.Experimental result reproducibility Question: Does the paper fully disclose all the information needed to reproduce the main e... | https://arxiv.org/abs/2505.18232v1 |
we will release our reproducible code after the author notification. Guidelines: • The answer NA means that paper does not include experiments requiring code. •Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details. •While we encourage the rel... | https://arxiv.org/abs/2505.18232v1 |
than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified. •For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates). •If error bars are reported in tabl... | https://arxiv.org/abs/2505.18232v1 |
of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. •The authors should consider possible harms that could arise when the ... | https://arxiv.org/abs/2505.18232v1 |
Are new assets introduced in the paper well documented and is the documentation provided alongside the assets? Answer: [NA] Justification: We totally use public benchmarks. Guidelines: • The answer NA means that the paper does not release new assets. •Researchers should communicate the details of the dataset/code/model... | https://arxiv.org/abs/2505.18232v1 |
arXiv:2505.18237v1 [cs.CL] 23 May 2025Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens Xixian Yong1Xiao Zhou123∗Yingying Zhang4Jinlin Li1 Yefeng Zheng5Xian Wu4∗ 1Gaoling School of Artificial Intelligence, Renmin University of China 2Beijing Key Laboratory of Resear... | https://arxiv.org/abs/2505.18237v1 |
may even reduce final accuracy. Detailed calculation methods and analysis are provided in §3. enhance robustness against perturbations [Min et al., 2022]. However, once the reasoning length exceeds the model’s reasoning capacity—an analogue to channel capacity—additional redundancy ceases to improve accuracy and instea... | https://arxiv.org/abs/2505.18237v1 |
non-mathematical reasoning tasks. Finally, we conduct an in-depth analysis (§5.3) of when and how much reasoning a model should perform. 2 Related Work Information-Theoretic Perspectives Information theory has long served as a lens for analyzing machine learning systems, particularly in studying generalization bounds [... | https://arxiv.org/abs/2505.18237v1 |
trajectory. 3.2 Response-Level: Measuring Information Bias in Entire Trajectories While Scaptures the model’s observable reasoning path, we posit the existence of a latent, ideal trajectory T={t1, t2,···, tm}representing the correct reasoning steps for a given question Q. This ideal trajectory may correspond to a human... | https://arxiv.org/abs/2505.18237v1 |
details). Results on GSM8K (Figure 2) reveal two key observations: Findings 1: Cumulative InfoBias with Increased Reasoning Length. We observe a consistent monotonic trend: longer reasoning chains tend to accumulate deviation from the correct reasoning path, suggesting that additional tokens often introduce noise rathe... | https://arxiv.org/abs/2505.18237v1 |
biases toward the correct choice, possibly due to extensive prior exposure during training. Findings 5: Task-Specific Reasoning Dynamics. In CommonsenseQA, entropy drops rapidly at the early stages, suggesting that commonsense questions can often be resolved with minimal reasoning. Notably, No-Think mode yields higher ... | https://arxiv.org/abs/2505.18237v1 |
decoding [Team, 2025a]. However, we find that using the following prompt more effectively encourages the model to adopt a non-reasoning mode when generating its response. <think> This is a simple question that I will answer directly without deep thinking. </think> (c) Gated Think. This setting represents a hybrid of th... | https://arxiv.org/abs/2505.18237v1 |
7 5.2 Main Results We conduct experiments on two math reasoning benchmarks with different difficulty levels: GSM8K (standard) and AIME2025 (challenging), as shown in Table 1. First, reasoning models outperform non-reasoning ones significantly. For instance, on AIME2025, QwQ-32B with Vanilla Think achieves 70.67% accura... | https://arxiv.org/abs/2505.18237v1 |
control halts early once confidence suffices, reducing costs with minimal accuracy impact. 8 Table 2: Performance and efficiency comparison on four other reasoning benchmarks. Models Think ModeMMLU-Pro MuSR ProntoQA CommonsenseQA Acc↑ #Token ↓ Acc↑ #Token ↓ Acc↑ #Token ↓ Acc↑ #Token ↓ Non-Reasoning Models Llama-3.1-8B-... | https://arxiv.org/abs/2505.18237v1 |
to significant accuracy drops (e.g., from 99.96% to 71.60% on ProntoQA and from 77.14% to 60.00% on MMLU-Pro). These tasks demand deeper reasoning to resolve ambiguity or recall fine-grained knowledge. In contrast, soft-reasoning tasks such as CommonsenseQA and MuSR exhibit greater robustness to early stopping. Due to ... | https://arxiv.org/abs/2505.18237v1 |
2024. Zeyu Gan, Yun Liao, and Yong Liu. Rethinking external slow-thinking: From snowball errors to probability of correct reasoning. arXiv preprint arXiv:2501.15602 , 2025. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentiv... | https://arxiv.org/abs/2505.18237v1 |
scaling. arXiv preprint arXiv:2501.19393 , 2025. OpenAI. Learning to reason with llms, 2024. URL https://openai.com/index/ learning-to-reason-with-llms/ . Jiabao Pan, Yan Zhang, Chen Zhang, Zuozhu Liu, Hongwei Wang, and Haizhou Li. DynaThink: Fast or slow? a dynamic decision-making framework for large language models. ... | https://arxiv.org/abs/2505.18237v1 |
. Qwen Team. Qwq-32b: Embracing the power of reinforcement learning, March 2025b. URL https://qwenlm.github.io/blog/qwq-32b/ . 12 Jean-Francois Ton, Muhammad Faaiz Taufiq, and Yang Liu. Understanding chain-of-thought in llms through information theory. arXiv preprint arXiv:2411.11984 , 2024. Luong Trung, Xinbo Zhang, Z... | https://arxiv.org/abs/2505.18237v1 |
. . 16 C Implementation Details 16 C.1 Models and Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 C.2 Information Bias Calculation Settings . . . . . . . . . . . . . . . . . . . . . . . 17 C.3 Information Gain Calculation Settings . . . . . . . . . . . . . . . . . . . . . . 18 C.4 Detailed C... | https://arxiv.org/abs/2505.18237v1 |
[Farquhar et al., 2024]. Semantic Entropy Probes (SEPs) further improve efficiency by estimating this uncertainty directly from intermediate hidden states, without requiring multiple generations [Kossen et al., 2024]. Beyond output quality, entropy has also been used to investigate the internal reasoning process of LLM... | https://arxiv.org/abs/2505.18237v1 |
unnecessary reasoning steps and sequence length during inference. This stands in fundamental contrast to model -based efficient reasoning, which seeks to compress full -length reasoning models into more concise variants or to train inherently efficient reasoning architectures from scratch. While Adaptive Think provides... | https://arxiv.org/abs/2505.18237v1 |
comprehension) Reasoning Type: Soft, multi-step reasoning over long contexts Description: MuSR (Multi-step Soft Reasoning) features long-form ( 1,000 words) natural language narratives, requiring the model to reason about evolving relationships, causality, and world knowledge. Each instance poses a series of questions ... | https://arxiv.org/abs/2505.18237v1 |
the final answer space at a specific stage of reasoning—as well as the corresponding entropy—we concatenate the question Q, the intermediate reasoning steps up to step i, denoted as s1:i, and an answer-prompting suffix. This composite prompt is then used to compute the token-level probabilities of subsequent model outp... | https://arxiv.org/abs/2505.18237v1 |
answer is: \\ boxed{{ For the visualization method described in §3.4.2, we first normalize the number of reasoning steps for each question in the Vanilla Think mode to a 0–1 range and plot the line chart of metric changes across all questions. We then fit separate curves for correctly and incorrectly answered questions... | https://arxiv.org/abs/2505.18237v1 |
proofs). •It involves recursive reasoning, mathematical induction, or constructing coun- terexamples. 4. Non-trivial strategy or non-unique solution •The question requires evaluating multiple potential solutions and choosing the optimal one . •There may be multiple valid approaches, requiring deep analysis and compari-... | https://arxiv.org/abs/2505.18237v1 |
by incorporating two additional models—Llama 3.1 -8B-Instruct and Phi -4—and uncovers the same systematic pattern identified in Figure 2. Specifically, as the length of the reasoning chain increases, the generated outputs progressively diverge from the ground -truth solution, highlighting a clear trade -off: each extra... | https://arxiv.org/abs/2505.18237v1 |
the answer here since it’s more specific. Metaphysics (B) comes to mind; I remember that metaphysics deals with the fundamental nature of being and the world. Ontology (C) is a part of philosophy that studies being, existence, and reality. So that’s very close. Phenomenology (D) is about structures of experience and co... | https://arxiv.org/abs/2505.18237v1 |
of metaphysics. For example, when people say metaphysics deals with the fundamental nature of reality, existence, space, time, etc. Ontology is a major branch of metaphysics. So the broader term is metaphysics. Therefore, the correct answer should be B. Hmm, but maybe the question is tricky. Let me think again. Wait, t... | https://arxiv.org/abs/2505.18237v1 |
correct response from MMLU-Pro based on the Adaptive Think Question: Same as above. 23 Model Answer: <think> Okay, so I need to figure out which term refers to the study of reality in the broadest sense, focusing on the elemental nature of the universe and its contents. First, I remember that philosophy has several bra... | https://arxiv.org/abs/2505.18237v1 |
additional discovery, Winston found a newspaper article, along with some concert footage showing Letti using a stun gun effectively for self-defense during a music event. This was all coming together. Next, he turned towards a report stating Letti had recently signed up for self-defense classes - the kind that teaches ... | https://arxiv.org/abs/2505.18237v1 |
of her confession. Winston didn’t say anything. He simply nodded before standing up, thanking Deborah for her time and promising to be in contact. He left the house, lost in his thoughts. There was much to ponder over in this investigation. After all, the puzzle of the case was only just beginning to reveal itself. Who... | https://arxiv.org/abs/2505.18237v1 |
in the way? Alternatively, maybe Letti wanted to steal something or there was a personal conflict? Other clues: Letti was seen at Charlotte’s for a dinner invitation. So they were together alone. That’s a perfect opportunity. Deborah was supposed to drive Charlotte that day. If she drove her there, maybe after the dinn... | https://arxiv.org/abs/2505.18237v1 |
of a stun gun and being a security guard gives her knowledge. But Letti’s recent purchase and training show intent. The concert footage shows Letti using one effectively, which supports her capability. The fact that Deborah was the last to see Charlotte might imply she found the body, but maybe she did it and lied abou... | https://arxiv.org/abs/2505.18237v1 |
access as Charlotte’s driver, and had the stun gun. The fact that Deborah was the one who found the body might be a cover-up. If she killed Charlotte, she might have staged it or was present when it happened. The timing matches since she was supposed to drive Charlotte that day. Another angle: The "stuffing" comment. T... | https://arxiv.org/abs/2505.18237v1 |
letters. Let me check the options again. The question might expect one answer. Wait, the options are in singular? The user didn’t specify if multiple answers are allowed, but the way the question is phrased, perhaps only one is correct. The options are A to E. The question is from a quiz maybe. So which is more essenti... | https://arxiv.org/abs/2505.18237v1 |
But perhaps the best answer is D and E, but since it’s multiple-choice with single answer, maybe the question expects D. Or perhaps the answer is E. Alternatively, thinking of "needs"—maybe "lots of attention" is more about companionship and mental health, while walks ensure physical activity. Both are needed, but whic... | https://arxiv.org/abs/2505.18237v1 |
answer. Because it’s a physical need beyond food and water, whereas attention is more about social needs. But both are important. Alternatively, maybe "lots of attention" is a better answer since it’s more general. But I’m not entirely certain. Another approach: checking the options. If "charm" is B, that’s unrelated. ... | https://arxiv.org/abs/2505.18237v1 |
sense. Charm isn’t a physical or emotional need for a dog. Maybe "charm" refers to something else, but I can’t think of any standard dog need here. C. Petted: Being 30 petted is affection, which is good, but is it a necessity? Probably part of attention. D. Lots of attention: Yes, dogs need social interaction and atten... | https://arxiv.org/abs/2505.18237v1 |
or area ratios? But the problem mentions reflections and a specific area given for quadrilateral DEGF, which is 288. Let me see if coordinate geometry is feasible here. Let me proceed with coordinates. Let’s set point A as (0,0). Since AB is along the x-axis, point B is at (28,0). Now, point D is 4 units from A, so D i... | https://arxiv.org/abs/2505.18237v1 |
me check if all those points are defined properly. A, F, N: F is on AC, N is the reflection of G over E which is at (40,-65). B is on AB, C is vertex, E is on AB, and M is the reflection over F on the other side. Hmm. So the heptagon includes vertices along AC, jumping out to N, then going to B, then up to C? Maybe, bu... | https://arxiv.org/abs/2505.18237v1 |
DE=16, etc., are along AB, but AC isn’t necessarily aligned with an axis. So perhaps coordinate system isn’t the best way? Wait, let me think again. Maybe if I set coordinates with A at (0,0), B at (28,0), and C at (0, something). But then according to my previous calculation, DEGF area was 624 instead of 288, so that’... | https://arxiv.org/abs/2505.18237v1 |
Half of that is 624. So same result. Hmm. The problem states it’s 288. So maybe my coordinate system is wrong in assuming right angle at A? Ah, perhaps the triangle isn’t right-angled. So placing C at (0, c) assumes a right angle, but if the triangle isn’t right-angled, then coordinates must be set differently. Hmm, so... | https://arxiv.org/abs/2505.18237v1 |
C are (p,42), then AC has length 91 units, which is the distance from A(0,0) to (p,42): sqrt(p² +42²) = 91 Thus, p² + 1764 = 91² = 8281 Therefore, p² = 8281 -1764 = 6517 Wait, 8281 - 1764: Let me calculate that correctly. 8281 - 1000 = 7281 7281 - 764 = 7281 -700 -64 = 6581 -64= 6517. Yes. Thus, p = sqrt(6517). Hmm. No... | https://arxiv.org/abs/2505.18237v1 |
heptagon AFNBCEM. Let me try to visualize the heptagon. Its vertices are A, F, N, B, C, E, M. Starting from A(0,0), going to F( p/7, 42/7 ), since F is 1/7 along AC. Then to N, which is reflection of G through E. Wait, need coordinates for N and M. Earlier, when I assumed coordinates with C at (0,91), I miscalculated b... | https://arxiv.org/abs/2505.18237v1 |
Wait, but that can’t be. The area of the heptagon is equal to the entire triangle’s area? Because the triangle’s area was 588. That would mean the heptagon covers the whole triangle, but clearly, the heptagon is missing some regions, like quadrilateral DEGF which has area 288. Therefore, this suggests that my calculati... | https://arxiv.org/abs/2505.18237v1 |
so the heptagon’s area should be less than 588. Therefore my error must be in the ordering or selection of vertices. Looking at the vertices of the heptagon: AFNBCEM. The points go A to F (on AC), then to N (outside the triangle?), then to B, then to C, to E, then to M (somewhere else), then back to A. Depending on whe... | https://arxiv.org/abs/2505.18237v1 |
might include regions outside the triangle. Hmm, this complicates things. Alternatively, perhaps the heptagon is within the triangle? If N is outside, but maybe the problem counts area algebraically? Alternatively, maybe my coordinate approach is not the best. Let me think of another method using reflections and area r... | https://arxiv.org/abs/2505.18237v1 |
states points are on the sides AB and AC, but M is the reflection of D through F, which is on AC, so if F is between A and C, reflecting D would place M outside the triangle on the opposite side of F from D. Similarly, N is reflection over E, so since G is closer to C than to A, reflecting over E (which is on AB) might... | https://arxiv.org/abs/2505.18237v1 |
is flawed because of assuming the reflection points extend the polygon to cover the entire triangle? Hmm. Alternatively, maybe the problem is designed so that the area of the heptagon is Area of ABC minus DEGF plus areas of the mirrored parts. Wait, area of the reflections? Reflecting D over F to get M creates a symmet... | https://arxiv.org/abs/2505.18237v1 |
All terms involving p canceled except in the terms for M’s coordinates? Wait Mxis (2p/7 -4), so in Term6, x7 is (2p/7 -4), but Term6 was 20*12 - (terms involving M’s coordinates in Term7? Wait Term7 involves x7*y8 which is (2p/7 -4)*0 and y7*x8 which is 12*0, so Term7 is zero. Therefore, the entire computation of the t... | https://arxiv.org/abs/2505.18237v1 |
that the problem says it’s 288, perhaps the actual coordinate’s y-axis is scaled down by half? Then the total area of ABC would be (28 * 42)/2 = 588, but DEGF area in that coordinate system was twice as big. Hmm. Alternatively, the problem uses the concept of similar triangles or area ratios. Note that along AB, the po... | https://arxiv.org/abs/2505.18237v1 |
(28,0) B to C: up to (80.73,42) C to E: left and down to (20,0) E to M: right a bit upward to (19.06,12)? Wait E is at (20,0), M is (19.06,12). So leftwards to M. M to A: down to (0,0). This forms a heptagon that loops around the triangle but includes the lower left area below AB and the main triangle. The shoelace for... | https://arxiv.org/abs/2505.18237v1 |
be the intersection of the reflections with the triangle? So subtract areas outside the triangle. Calculate the area within the triangle: The heptagon includes regions inside the triangle and outside. To find the area within the triangle, subtract the area below the x-axis (from point N) and any other external areas. C... | https://arxiv.org/abs/2505.18237v1 |
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