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multi-agent system’s output generated by the critic mechanism f , we perform prompt opti- mization for participation agents inside the system. Specifically, we first use a locator L to identify the underperforming agents, and , that are not performing well and generate explanations of their failures, utilizing textual ... | https://arxiv.org/abs/2505.16086v1 |
online setting, we use the optimized prompts at the current step to derive the solution code so that agents must interact with the environment (the critic) to retrieve real-time feedback. However, for offline setting, we use the default initial agent prompts to derive solution codes and retrieve feedback for all traini... | https://arxiv.org/abs/2505.16086v1 |
and docstrings in the solution code as the scalar score. For code style violation dimension, we uti- lize an external tool, pycodestyle2, to check the software code against style conventions in PEP 8. We define the total number of violations and the corresponding explanations the checker identifies as score and feedbac... | https://arxiv.org/abs/2505.16086v1 |
20.33 3.24 Table 1: Evaluation scores under all optimization settings across all evaluation dimensions for baselines and our proposed two-step optimization pipeline. Bold numbers indicate best-performing results, and underlined numbers indicate second-best results. Note that the score range for the first three dimensio... | https://arxiv.org/abs/2505.16086v1 |
choose 7 0% 20% 40% 60% 80% 100%24681012 Optimization StepsEvaluation Scores ( ↓)Online Multi-Pass Online One-Pass Offline Multi-Pass Offline One-Pass Figure 3: Training curve analysis for code style violation evaluation dimension under major optimization settings. one-pass optimization for higher efficiency without sa... | https://arxiv.org/abs/2505.16086v1 |
shown in green text. How- ever, we also notice a current problem: as shown in the red text, the optimized prompts might con- tain instance-specific content that does not apply to general software development tasks even though we deliberately prompt the optimizer to think gen- erally instead of focusing on the current t... | https://arxiv.org/abs/2505.16086v1 |
through multi-agent debate. In The Twelfth International Conference on Learning Representa- tions . Harrison Chase. 2022. LangChain. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé, Jared Kaplan, Harrison Ed- wards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Mich... | https://arxiv.org/abs/2505.16086v1 |
Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) . Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2024. Met... | https://arxiv.org/abs/2505.16086v1 |
Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, and Michael Zeng. 2023. Automatic prompt op- timization with “gradient descent” and beam search. InProceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing , pages 7957–7968, Singapore. Association for Computa- tional Linguistics.... | https://arxiv.org/abs/2505.16086v1 |
ICLR 2024 Workshop on Large Language Model (LLM) Agents . Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, and Chi Wang. 2024b. Mathchat: Con- verse to tackle challenging math problems with LLM agents. In ICLR 2024 Workshop on Large Language Model (LLM) Agents... | https://arxiv.org/abs/2505.16086v1 |
R, provide feedback F={ri t=Ri(Yt)|Ri∈ R} regarding solution Ytto the solver agent. Finally, the solver agent refines its solution based on the feedback Yt+1=S(X, Y t,F). Such a review it- eration can go on for a few rounds. In the current case study, we use a total of 2 reviewer agents and a single review iteration. T... | https://arxiv.org/abs/2505.16086v1 |
by step in ```python markdown quotes. Figure 4: System and user prompt for solver agent whose responsibility is to write the main solution code given a software task description. System Prompt : You are ${role description}. You are in a multi-agent collaboration environment aiming to solve a given software development ... | https://arxiv.org/abs/2505.16086v1 |
for your selection. For example, if you recruit programmer, chief technology officer, and chief creative officer, you should output: 6,4,9 Figure 6: System and user prompt we use for the role selection agent for selecting concrete roles for solver and reviewer agents. 14 System Prompt : You are a professional and stric... | https://arxiv.org/abs/2505.16086v1 |
role description} Agent 2: ${agent 2 role description} Agent 3: ${agent 3 role description} ... ${high-level responsibilities of each agents} [/Agents with their role descriptions] [Agents communication trajectory] ${communication trajectory} [/Agents communication trajectory] [Feedback to final solution code] ${evalua... | https://arxiv.org/abs/2505.16086v1 |
IMPROVE> tag in the system prompt, make sure you ONLY output the improved prompt. DO NOT output anything else, such as the <TO IMPROVE> tag. Figure 10: System and user prompt for the optimizer. 18 Functionality As a Programmer, your task is to provide a comprehensive solution to the given software task. Your solution s... | https://arxiv.org/abs/2505.16086v1 |
evaluation dimensions. Green text indicates agent prompts are being optimized towards the evaluation dimension; however, we observe a current problem in that optimization tends to generate instance-specific prompts, which is not generally useful across the entire dataset, as shown in the red text. 19 Functionality As a... | https://arxiv.org/abs/2505.16086v1 |
arXiv:2505.16088v2 [cs.CL] 25 May 2025Date Fragments: A Hidden Bottleneck of Tokenisation for Temporal Reasoning Gagan Bhatia1Maxime Peyrard2Wei Zhao1 1University of Aberdeen2Université Grenoble Alpes & CNRS {g.bhatia.24,wei.zhao}@abdn.ac.uk Abstract Modern BPE tokenisers often split calen- dar dates into meaningless f... | https://arxiv.org/abs/2505.16088v2 |
forecasting (Carriero et al., 2024; Bhatia et al., 2024), and automated cur- riculum scheduling (Vasileiou and Yeoh, 2024), the brittleness introduced by subword fragmentation poses a risk of propagating temporal biases and inaccuracies into downstream scientific discoveries and decision-making systems (Tan et al., 202... | https://arxiv.org/abs/2505.16088v2 |
“sub-word” dogma with SuperBPE , a curriculum that first learns subwords and then merges them into cross-whitespace “super- words”, cutting average sequence length by 27 %. Complementary studies expose and correct system- atic biases introduced by segmentation (Phan et al., 2024) and propose trans-tokenisation to trans... | https://arxiv.org/abs/2505.16088v2 |
is presented in ten different representa- tions, spanning slash-, dash-, and dot-delimited for- mats, both zero-padded and minimally notated, to stress-test format invariance under maximal tokeni- sation drift. This produces 1,500 targeted examples of pure format robustness. We also have exampleswhere the dates are not... | https://arxiv.org/abs/2505.16088v2 |
parameters: five open- source Qwen 2.5 models (0.5 B, 1.5 B, 3 B, 7 B, 14 B) (Yang et al., 2024), two Llama 3 mod- els (3 B, 8 B) (Touvron et al., 2023), and two OLMo (Groeneveld et al., 2024) models (1 B, 7 B). For comparison with state-of-the-art closed models, we also query the proprietary GPT-4o and GPT-4o-mini end... | https://arxiv.org/abs/2505.16088v2 |
model sizes, aiming to test our hypothesis: larger models would recover calendar-level semantics from fragmented tokens at earlier stages, i.e., tokenisation compensation is accomplished at early layers, as illustrated in Figure 2. Causal attention-hop analysis. We introduce a framework intended to understand in which ... | https://arxiv.org/abs/2505.16088v2 |
32.34 47.77 Qwen2.5 0.5B 10.23 88.95 31.32 43.50 OLMo-2-1B 9.26 90.09 25.90 41.75 LLama3.2 3B 9.51 88.45 23.66 40.54 Table 3: Average accuracies per task. Context Rlt stands for context based resolution, Fmt Switch refers to format switching, and Date Arth. refers to date arithmetic. Cross-temporal performance. Table 2... | https://arxiv.org/abs/2505.16088v2 |
0.1 0.2 0.3 0.4 0.5 0.6 Date Fragmentation Ratio354045505560657075Date Resolution Accuracy (%)Fragmentation vs. Accuracy by T emporal Regime Past Near Past Present FutureBestfit (r=0.61) Figure 3: Date fragmentation ratio versus date resolu- tion accuracy, stratified by four time periods and six LLMs: OLMo, Llama 3, GP... | https://arxiv.org/abs/2505.16088v2 |
much better on separator-rich formats compared to the date formats without separators. In sum- mary, model accuracy is correlated to how cleanly a model can tokenise the string into interpretable tokens: more visual structure (slashes or dashes) means lower fragmentation, which suggests more straightforward reasoning, ... | https://arxiv.org/abs/2505.16088v2 |
human readers parse dates by immedi- ately mapping each component to a coherent tem- poral schema: “03” is March, “12” is day of month, “2025” is year, and then checking whether the day falls within the calendar bounds of that month. Humans bring rich world knowledge of calendars and leap-year rules to bear in parallel... | https://arxiv.org/abs/2505.16088v2 |
systems, all of which may influence temporal interpretation; further, the fragmentation ratio met- ric, though straightforward and interpretable, is not rigorously evaluated. Lastly, the core idea of our causal framework is inspired by Lindsey et al. (2025); however, our extension to temporal reason- ing is not evaluat... | https://arxiv.org/abs/2505.16088v2 |
Craig Citro, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall, Hoagy Cun- ningham, Thomas Henighan, Adam Jermyn, Andy Jones, and 8 others. 2025. On the biology of a large language model. Transformer Circuits Thread . Alisa Liu, Jonathan Hayase,... | https://arxiv.org/abs/2505.16088v2 |
for temporal knowledge graph reasoning. arXiv preprint arXiv:2405.14170 . Yang Wang and Hassan A Karimi. 2024. Exploring large language models for climate forecasting. arXiv preprint arXiv:2411.13724 . Jason Wei, Nguyen Karina, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, and William F... | https://arxiv.org/abs/2505.16088v2 |
from the appropriate range and rendering them in two randomly chosen, distinct formatting patterns ( Ymdvsd/m/Y ). Exactly half of each set are “YES” examples (identical dates under differ- ent formats), which are our positive examples, and half are “NO” (different dates), which are our neg- ative examples. All three d... | https://arxiv.org/abs/2505.16088v2 |
k−2. •Gap penalty : to discourage large jumps in po- sition, we compute the mean gap ¯gand apply a small multiplier λ= 0.1: Sgap= 1−λ¯g, S gap≤1. (6) This is done to encourage model paths to think step by step instead of directly jumping to the conclusion (yes/no).•Final confidence : We compute the position of the most... | https://arxiv.org/abs/2505.16088v2 |
additional text or punctuation. Example template Question: {question} Gold target: {target} Predicted answer: {predicted_answer} Now grade: AorBorC Table 7: LLM-as-Judge prompt used for comparing model and gold answers in the three DateAugBench tasks. Human Evaluation Context-based resolution Prompt : Who was the chair... | https://arxiv.org/abs/2505.16088v2 |
arXiv:2505.16090v1 [cs.AI] 22 May 2025CANAI R EAD BETWEEN THE LINES ? B ENCHMARKING LLM S ONFINANCIAL NUANCE Dominick Kubicaa+, Dylan T. Gordona+, Nanami Emuraa+, Derleen Sainia+, and Charlie Goldenberga+ aDepartment of Business Analytics, Santa Clara University - Leavey School of Business, Santa Clara, California 9505... | https://arxiv.org/abs/2505.16090v1 |
250 Rows) 3.Analyzing results to identify optimization opportunities and assess how sentiment correlates with actual stock performance. The results were both encouraging and eye-opening: while LLMs significantly outperformed traditional tools in grasping nuanced sentiment, they still face performance challenges. This p... | https://arxiv.org/abs/2505.16090v1 |
challenges for Copilot across all tested versions. The models occasionally struggled to interpret structured data accurately, and converting CSVs to plain text was often necessary to improve reliability. Instances of hallucination were observed during formatting and post-processing, resulting in inconsistent outputs. I... | https://arxiv.org/abs/2505.16090v1 |
tone of each earnings call. To visualize this relationship, the above SHAP (SHapley Additive exPlanations) beeswarm plot was created to map sentiment direction against stock movement (Figure 4). Each dot represents a business segment within a specific quarter. Red indicates high positive sentiment, while blue indicates... | https://arxiv.org/abs/2505.16090v1 |
Transparency: In some configurations, tasks were routed to traditional tools like TextBlob rather than handled directly by the LLM, but users were not clearly informed when this occurred. Clear indicators of fallback behavior can help users understand what the system is doing. In addition, documentation that explains c... | https://arxiv.org/abs/2505.16090v1 |
arXiv:2505.16094v1 [cs.LG] 22 May 2025A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization Ziqing Wang1*Kexin Zhang1*Zihan Zhao1Yibo Wen1 Abhishek Pandey2Han Liu1Kaize Ding1† 1Northwestern University2AbbVie {ziqingwang2029, zihanzhao2026, yibowen2024}@u.northwe... | https://arxiv.org/abs/2505.16094v1 |
al., 2024) either primarily focus on the general chemical domain or include smaller language mod- els lacking the emergent capabilities characteristic of the LLMs central to this survey. Our survey addresses this critical gap by providing the first overview specifically focused on LLMs as generators in molecular discov... | https://arxiv.org/abs/2505.16094v1 |
to mod- ify or edit a given input molecule, aiming to en- hance one or more of its properties while often preserving essential structural characteristics. As illustrated in Fig. 2, for both tasks, the input prompt provided to the LLM typically comprises three key components: (1) Instruction ( I): A tex- tual component ... | https://arxiv.org/abs/2505.16094v1 |
primary learning paradigms: Without LLM Tuning: These methods utilize pre- trained LLMs directly, guiding their behavior solely through the input prompt Iwithout modifying the model’s weights. This paradigm primarily encom- passes strategies like Zero-Shot Prompting , where the LLM operates based on instructions alone,... | https://arxiv.org/abs/2505.16094v1 |
limited for highly special- ized or complex generation tasks. SFT addresses this by adapting pre-trained LLMs specifically for molecule generation on labeled datasets, typically pairs of instructions and target molecular represen- tations. Although early explorations demonstrated the viability of SFT using smaller PLMs... | https://arxiv.org/abs/2505.16094v1 |
LLM to assign higher like- lihoods to the preferred candidates based on com- parative evaluations. SmileyLlama (Cavanagh et al., 2024) utilizes DPO after SFT to significantly improve adherence to specified property constraints by learning from preferences between correctly and incorrectly gen- erated molecules. Mol-MoE... | https://arxiv.org/abs/2505.16094v1 |
2025b) structures molecule optimization into a multi-step pipeline: interaction analysis, design, and reflec- tion. Each step is guided by prompts derived from interaction profiles, and during the design step, pre- vious designs and reflections are provided to make better modifications. Both LLM-EO (Lu et al., 2024) an... | https://arxiv.org/abs/2505.16094v1 |
molecules, reconstructs SMILES based on scaffold-property prompts and modu- lates them during inference for property optimiza- tion. The research agent (GPT-4o) extracts struc- ture–property patterns from literature and ranks candidates using regression-based scoring, ensur- ing consistency with domain-specific knowled... | https://arxiv.org/abs/2505.16094v1 |
Importantly, during fine-tuning, the use of graph en- coders and fingerprint integration ensures that the model captures both global topology and substruc- tural details, which are essential for chemically valid optimization. It indicates that fine-tuning the LLM to establish multi-modal models shows better performance... | https://arxiv.org/abs/2505.16094v1 |
quantify the variety and novelty of the generated outputs (e.g., uniqueness rate, novelty rate). Property-Based Metrics gauge how well molecules meet desired functional cri- teria, applied for (1) Single-Property evaluation focusing on individual targets like Quantitative Estimate of Drug-likeness (QED), Lipophilicity ... | https://arxiv.org/abs/2505.16094v1 |
molecule generation and molecule optimization. These tasks represent the most di- rect applications of LLMs in molecular design and are the primary scope of current research. We are aware that LLMs can also significantly impact other important areas of molecular science (Sun et al., 2025), such as reaction prediction, ... | https://arxiv.org/abs/2505.16094v1 |
expertise. Nicola De Cao and Thomas Kipf. 2018. Molgan: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 . Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of th... | https://arxiv.org/abs/2505.16094v1 |
agents. arXiv preprint arXiv:2503.22164 .Bowen Gao, Yanwen Huang, Yiqiao Liu, Wenxuan Xie, Wei-Ying Ma, Ya-Qin Zhang, and Yanyan Lan. 2025b. Pushing the boundaries of structure-based drug design through collaboration with large language mod- els.arXiv preprint arXiv:2503.01376 . Miguel García-Ortegón, Gregor NC Simm, A... | https://arxiv.org/abs/2505.16094v1 |
Mis- tral 7b. arXiv preprint arXiv:2310.06825 . Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2020. Multi-objective molecule generation using inter- pretable substructures. In International Conference on Machine Learning , pages 4849–4859. PMLR. Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gin- dulyte, Jia He, Siqian... | https://arxiv.org/abs/2505.16094v1 |
Xinfeng Li, Jiayi Zhang, Jinlin Wang, Tan- jin He, Sirui Hong, Hongzhang Liu, Shaokun Zhang, Kaitao Song, Kunlun Zhu, and 1 others. 2025a. Ad- vances and challenges in foundation agents: From brain- inspired intelligence to evolutionary, collaborative, and safe systems. arXiv preprint arXiv:2504.01990 . Gang Liu, Micha... | https://arxiv.org/abs/2505.16094v1 |
of the Association for Computational Linguistics , pages 311–318. Vilfredo Pareto. 1919. Manuale di economia politica con una introduzione alla scienza sociale , volume 13. Società editrice libraria. Jinyeong Park, Jaegyoon Ahn, Jonghwan Choi, and Jibum Kim. 2025. Mol-air: Molecular reinforce- ment learning with adapti... | https://arxiv.org/abs/2505.16094v1 |
Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alan Aspuru-Guzik, Kirill Neklyudov, and Chao Zhang. 2025. Efficient evolutionary search over chemi- cal space with large language models. In The Thirteenth International Conference on Learning Representations . Y . Wang, ... | https://arxiv.org/abs/2505.16094v1 |
2024b. Llasmol: Advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset. arXiv preprint arXiv:2402.09391 . Jiajun Yu, Yizhen Zheng, Huan Yee Koh, Shirui Pan, Tianyue Wang, and Haishuai Wang. 2025. Collaborative expert llms guided multi-objective molecular... | https://arxiv.org/abs/2505.16094v1 |
e∈Ecorrespond to chemical bonds. Node and edge features may encode atom types, bond or- ders, aromaticity, and other topological attributes. While not directly token-based, 2D graphs can be integrated via hybrid models that combine lan- guage and graph encoders, or serialized (e.g., via adjacency lists or graph travers... | https://arxiv.org/abs/2505.16094v1 |
2D graph view, and its 3D ball-and-stick model. fine-tuning chemical LLMs, containing 7 million instruction query-response pairs. Derived from various online structural datasets like PubChem and ChEMBL, it encompasses a broad range of chemical domain knowledge and is frequently used for tasks in molecule understanding,... | https://arxiv.org/abs/2505.16094v1 |
captioning task; Docking denotes molecule docking (a way to find correct molecule binds for proteins); Translation denotes the translation from textual knowledge to molecular features; Conversion denotes the translation between different representations of a molecule’s identity; Prediction denotes property prediction, ... | https://arxiv.org/abs/2505.16094v1 |
manually curated dictionary of molec- ular entities concentrated on small chemical com- pounds. It includes over 20,000 molecules rep- resented by SMILES strings, natural language descriptions, and ontology terms, widely em- ployed in molecule generation and instruction- based tasks requiring chemical understanding. •C... | https://arxiv.org/abs/2505.16094v1 |
the context of LLM-centric molecular generation and optimization, evaluation metrics are commonly grouped into categories based on molecular struc- ture, physicochemical properties, and optimization success, each reflecting distinct aspects of molecu- lar quality and model performance. This appendix details these metri... | https://arxiv.org/abs/2505.16094v1 |
the explo- ration of chemical space for discovering novel and relevant molecules. •Uniqueness: Uniqueness quantifies the propor- tion of valid generated molecules that are distinct from each other within a given set. It reflects the model’s capacity to generate diverse struc- tures rather than redundant outputs. This i... | https://arxiv.org/abs/2505.16094v1 |
score (Jin et al., 2020) aggregates multiple individual prop- erty scores into a single scalar objective, often through a weighted sum or other combination rules. This allows optimization frameworks (e.g., evolutionary algorithms, reinforcement learning) to be guided by a unified fitness metric. The weights can be adju... | https://arxiv.org/abs/2505.16094v1 |
et al., 2024a)Arxiv InstructionInternLM2- 7B-BaseChemData N/A ICMA (Li et al., 2024b)TKDEInstruction + Few shotMistral-7BPubChem & ChEBI-20N/A MolReFlect (Li et al., 2024d) ArxivInstruction + Few shotMistral-7B ChEBI-20 Link ChatMol (Fan et al., 2025)Arxiv Instruction LLaMA-3-8B ZINC Link PEIT-LLM (Lin et al., 2025)Arx... | https://arxiv.org/abs/2505.16094v1 |
arXiv:2505.16100v1 [cs.AI] 22 May 2025BIODSA-1K: Benchmarking Data Science Agents for Biomedical Research Zifeng Wang∗Benjamin Danek∗Jimeng Sun University of Illinois Urbana-Champaign Correspondence: {zifengw2,jimeng}@illinois.edu https://ryanwangzf.github.io/projects/biodsa Abstract Validating scientific hypotheses is... | https://arxiv.org/abs/2505.16100v1 |
Bubble plot illustrating the diverse range of biomedical data tables in BIODSA -1K, showing each data table’s number of rows (x-axis, log-scale) versus number of columns (y-axis, log-scale). benchmark dataset that captures the breadth and complexity of data science tasks in biomedical research. The following challenges... | https://arxiv.org/abs/2505.16100v1 |
metrics •Precision•RecallCode quality AnalyzeObserve Executability rate ∈0,1 PlanProgramBenchmark curation Publication Hypothesis Hypothesis HypothesisFigure 2: Overview of BIODSA -1K. a, Benchmark curation: Scientific publications linked to biomedical datasets are parsed to extract hypotheses and their corresponding s... | https://arxiv.org/abs/2505.16100v1 |
the benchmarks. Benchmark Domain Task Levels Task Sources Avg. # Tables Avg. # Columns* # Tasks DS-1000 [19] General Analysis Stackoverflow 1 - 1000 MLAgentBench [20] General Analysis Publications 1 47 13 DSBench [21] General Analysis Kaggle - - 466 BLADE [22] General Hypothesis and analysis 31 Publications 1 13 12 Sci... | https://arxiv.org/abs/2505.16100v1 |
one or more evidence entries per hypothesis, each corresponding to a distinct data analysis performed in the study. Each evidence entry is annotated with the following fields: •Analysis plan: a concise description of the statistical or computational procedure used (e.g., frequency analysis, correlation test, clustering... | https://arxiv.org/abs/2505.16100v1 |
hypotheses are curated by taking claims from other publications that reference unrelated datasets. An ideal agent should classify such hypotheses as Non-verifiable due to the absence of relevant data. Let H=Non-verifiable be the ground truth and ˆHbe the predicted label. We report the non-verifiable detection accuracy ... | https://arxiv.org/abs/2505.16100v1 |
publication types. In nearly every setting, the Type II error rate ( EII), which measures the frequency of missed relevant findings, is consistently higher than the Type I error rate ( EI), which reflects the incidence of false positives. For example, in the Biomarkers category, CodeGen (gpt-4o) exhibits a Type II erro... | https://arxiv.org/abs/2505.16100v1 |
As shown in Figure 5, the evidence alignment scores remain modest across all methods, typically ranging from 0.20 to 0.25, regardless of whether the hypothesis being validated is ultimately True or False. Among the evaluated methods, ReAct-based agents exhibit marginally higher alignment scores compared to code generat... | https://arxiv.org/abs/2505.16100v1 |
reject them. In this setting, the correct model behavior is to respond with “Not Verifiable”; any decision of “True” or “False” reflects overconfidence or hallucination. The ability to correctly identify these cases, quantified as the true positive rate (TPR) for the “Not Verifiable” class, varies substantially across ... | https://arxiv.org/abs/2505.16100v1 |
the propagation of false findings under the guise of data-driven analysis. Ensuring transparency, interpretability, and human-in-the-loop validation will be critical to responsibly deploying these tools in high-stakes scientific domains. In this work, we present BIODSA -1K, a benchmark designed to evaluate AI agents on... | https://arxiv.org/abs/2505.16100v1 |
Xinyun Chen, and Mark B Gerstein. BioCoder: a benchmark for bioinformatics code generation with large language models. Bioin- formatics , 40(Supplement_1):i266–i276, 2024. [13] Kexin Huang, Ying Jin, Ryan Li, Michael Y Li, Emmanuel Candès, and Jure Leskovec. Automated hypothesis validation with agentic sequential falsi... | https://arxiv.org/abs/2505.16100v1 |
Friedman. Code generation with alphacodium: From prompt engineering to flow engineering. arXiv preprint arXiv:2401.08500 , 2024. [27] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In International Conference on Lea... | https://arxiv.org/abs/2505.16100v1 |
a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. •The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low l... | https://arxiv.org/abs/2505.16100v1 |
dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpo... | https://arxiv.org/abs/2505.16100v1 |
were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [Yes] Justification: [NA] Guidelines: • The answer NA means that the paper does not include experiments. •The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the resu... | https://arxiv.org/abs/2505.16100v1 |
deviation from the Code of Ethics. •The authors should make sure to preserve anonymity (e.g., if there is a special consid- eration due to laws or regulations in their jurisdiction). 10.Broader impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work pe... | https://arxiv.org/abs/2505.16100v1 |
NA means that the paper does not use existing assets. • The authors should cite the original paper that produced the code package or dataset. •The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset. •For ... | https://arxiv.org/abs/2505.16100v1 |
to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. •For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review. 16.Declaration of LLM usage Question: Does the paper describe the usage of LLMs if it is a... | https://arxiv.org/abs/2505.16100v1 |
p < 0.05), supporting the association." , "analysis_variables" : [ "MGMT_promoter_methylation_status" , "mutation_rate" ], "result_variable" : "mean_mutation_rate_difference" , "result_variable_value" : "15 mutations/sample, p < 0.05" } ] }, { "hypothesis" : "The PIK3R1 gene is frequently mutated in glioblastoma sample... | https://arxiv.org/abs/2505.16100v1 |
primarily those with filenames beginning with data_ . For each dataset, we extracted high-level metadata including dataset ID, cancer type, and description. Within each dataset directory, we identified all data tables and parsed their contents while ignoring comment lines (those beginning with “#”). The first non-comme... | https://arxiv.org/abs/2505.16100v1 |
and their supporting evidences that can be tested through data analysis. Requirements for hypotheses and evidences: 1. Each hypothesis must be testable using statistical analysis or machine learning methods 23 2. All evidence must include specific, measurable quantities or statistical relationships 3. Result values mus... | https://arxiv.org/abs/2505.16100v1 |
studies combine data from multiple omics layers—such as genomics, transcriptomics, and proteomics—to provide a comprehensive view of tumor biology. They aim to interconnect disparate data types into coherent biological insights. Translational: This class emphasizes bridging the gap between research and clinical applica... | https://arxiv.org/abs/2505.16100v1 |
user evaluate the hypothesis. # IMPORTANT: CODE OUTPUT REQUIREMENTS You must import all the necessary libraries at the beginning of your code. You must use explicit print() statements for ALL outputs you want to see or analyze. Simply writing expressions like ’df.head()’ will NOT show results in the execution log. Alwa... | https://arxiv.org/abs/2505.16100v1 |
code iteratively to evaluate a scientific hypothesis. Note: - You must execute and refine the given analysis plan iteratively until you have enough evidence to support the hypothesis. - You must always write a single Python code block that can be executed directly based on the analysis plan. - Use ‘print()‘ statements ... | https://arxiv.org/abs/2505.16100v1 |
arXiv:2505.16102v2 [cs.CL] 23 May 2025Continually Self-Improving Language Models for Bariatric Surgery Question–Answering Yash Kumar Atri atri@virginia.edu School of Data Science University of Virginia Charlottesville, VA, USA Thomas Shin thomas.shin@uvahealth.org Department of Surgery University of Virginia School of ... | https://arxiv.org/abs/2505.16102v2 |
is a clear need for scalable, accessible, and continually updated educational and decision-support tools tailored to the unique needs of MBS pa- tients—spanning from preoperative preparation to long-term postoperative management (P et al., 2024). Traditional patient education materials—whether delivered in print, via s... | https://arxiv.org/abs/2505.16102v2 |
to any domains where patient questions can be collected, relevant literature exists, and model answers can be validated. 2. Related Work The landscape of digital health interventions for bariatric care across the entire surgical spectrum-from preoperative preparation to long-term postoperative management has ex- panded... | https://arxiv.org/abs/2505.16102v2 |
topics reappear under different linguistic formulations. In contrast, our proposed framework, bRAGgen , introduces an adaptive self-updating mechanism that not only monitors response confidence but actively integrates validated, up-to-date clinical information into the model itself. By embedding uncertainty detection a... | https://arxiv.org/abs/2505.16102v2 |
Does a Pre-Hospital Patient Education Program improve Outcomes of Bariatric Surgery? 2. Does perioperative care map improve compliance with best practices for the morbidly obese? Surgical & Medical Info 1. Is laparoscopic gastric bypass superior to laparoscopic gastric banding for treatment of morbid obesity? 2. Is pot... | https://arxiv.org/abs/2505.16102v2 |
with new data and interactions, enabling the framework to improve over time and adapt to evolving clinical guidelines. By combining these components, our framework offers a dynamic, evidence-based deci- sion support system that remains adaptable and effective over time. Each module addresses specific challenges, ensuri... | https://arxiv.org/abs/2505.16102v2 |
relevance score computed using the BM25 ranking function to assess how well the document sanswers the query q. The retrieval process is powered by the DuckDuckGo API, which allows for broad web searches while prioritizing authoritative sources through URL domain filtering. The BM25 scoring system is applied to rank the... | https://arxiv.org/abs/2505.16102v2 |
the expectation over a mini-batch Bof query-document pairs, pθ(d|q) is the predicted probability of document dgiven query q, and λis the regulariza- tion parameter that controls the magnitude of the model’s parameters. The term ∥ΘA∥2 F represents the Frobenius norm of the model’s adaptation parameters Θ A, which serves... | https://arxiv.org/abs/2505.16102v2 |
relevance and factual accuracy of the retrieved information. (iii) Llama3-8B (Grattafiori et al., 2024) is a 8-billion-parameter large language model eval- uated under two configurations: (a) Zero-shot , where the model responds using only its pre-trained knowledge; and (b) Context-prompted , where external context ret... | https://arxiv.org/abs/2505.16102v2 |
3.37 Phi-3 2.37 2.15 2.25 2.26 Mistral instruct 2.23 2.18 2.14 2.18 Context prompted Llama3-8B 3.82 3.91 4.34 4.02 Phi-3 2.64 2.75 2.42 2.60 Mistral instruct 2.69 2.37 3.35 2.80 bRAGgen with Llama3-8B 4.18 4.58 4.76 4.51 Phi-3 2.87 3.17 2.60 2.88 Mistral instruct 2.95 2.71 3.63 3.09 Table 3: Evaluation of various model... | https://arxiv.org/abs/2505.16102v2 |
three axes: Factuality , Clinical Relevance , and Comprehensiveness , using a 5-point Likert scale. Table 4 summarizes the performance of baseline systems and our proposed bRAGgen across these dimensions. Among the baselines, MedGraphRAG and context-prompted Llama3-8B show relatively strong performance, achieving avera... | https://arxiv.org/abs/2505.16102v2 |
and efficiency of bRAGgen during real-time knowledge integration in the context of bariatric care. (a) Confidence Change Distribution. The histogram in Fig. 2.a illustrates the distribution of confidence score changes triggered by the adaptive retrieval mechanism. Most examples exhibit moderate confidence gains (bins 0... | https://arxiv.org/abs/2505.16102v2 |
Mustafa Nasir-Moin, Xujin Chris Liu, Lavender Yao Jiang, Michal A Mankowski, Dorry L Segev, Yindalon Aphinyanaphongs, Howard A Riina, John G Golfinos, Daniel A Orringer, Douglas Kondziolka, and Eric Karl Oermann. Medical large language models are vulnerable to data-poisoning attacks. Nat. Med., 31(2):618–626, February ... | https://arxiv.org/abs/2505.16102v2 |
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