{"total":204,"rows":[{"id":"2503.19786","title":"Gemma 3 Technical Report","created":"2025-03-25","cited_by_count":1824,"area":null},{"id":"2507.02833","title":"Generalizing Verifiable Instruction Following","created":"2025-07-03","cited_by_count":194,"area":null},{"id":"2511.04703","title":"Measuring what Matters: Construct Validity in Large Language Model Benchmarks","created":"2025-11-03","cited_by_count":119,"area":null},{"id":"2504.10415","title":"LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models","created":"2025-04-14","cited_by_count":71,"area":null},{"id":"2509.09677","title":"The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs","created":"2025-09-11","cited_by_count":64,"area":null},{"id":"2503.05336","title":"Toward an Evaluation Science for Generative AI Systems","created":"2025-03-07","cited_by_count":45,"area":null},{"id":"2502.17521","title":"Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation","created":"2025-02-23","cited_by_count":38,"area":null},{"id":"2502.03814","title":"Large Language Models for Multi-Robot Systems: A Survey","created":"2025-02-06","cited_by_count":27,"area":null},{"id":"2508.13144","title":"Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation","created":"2025-08-18","cited_by_count":26,"area":null},{"id":"2411.00625","title":"Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization","created":"2024-11-01","cited_by_count":24,"area":null},{"id":"2503.16460","title":"Beyond Final Answers: Evaluating Large Language Models for Math Tutoring","created":"2025-02-23","cited_by_count":23,"area":null},{"id":"2505.11581","title":"Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis","created":"2025-05-16","cited_by_count":22,"area":null},{"id":"2604.03444","title":"Olmo Hybrid: From Theory to Practice and Back","created":"2026-04-03","cited_by_count":22,"area":null},{"id":"2510.02263","title":"RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems","created":"2025-10-02","cited_by_count":21,"area":null},{"id":"2507.12379","title":"Probing for Arithmetic Errors in Language Models","created":"2025-07-16","cited_by_count":18,"area":null},{"id":"2411.19504","title":"TQA-Bench: Evaluating LLMs for Multi-Table Question Answering","created":"2024-11-29","cited_by_count":17,"area":null},{"id":"2508.16546","title":"RL Is Neither a Panacea Nor a Mirage: Understanding Supervised vs. Reinforcement Learning Fine-Tuning for LLMs","created":"2025-08-22","cited_by_count":17,"area":null},{"id":"2505.23061","title":"DINGO: Constrained Inference for Diffusion LLMs","created":"2025-05-29","cited_by_count":16,"area":null},{"id":"2506.18110","title":"RL for Reasoning by Adaptively Revealing Rationales","created":"2025-06-22","cited_by_count":16,"area":null},{"id":"2509.21519","title":"Provable Scaling Laws of Feature Emergence from Learning Dynamics of Grokking","created":"2025-09-25","cited_by_count":15,"area":null},{"id":"2510.01218","title":"Control the Temperature: Selective Sampling for Diverse and High-Quality LLM Outputs","created":"2025-09-20","cited_by_count":15,"area":null},{"id":"2507.12547","title":"Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models","created":"2025-07-16","cited_by_count":14,"area":null},{"id":"2410.15512","title":"Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?","created":"2024-10-20","cited_by_count":13,"area":null},{"id":"2410.13502","title":"MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs","created":"2024-10-17","cited_by_count":12,"area":null},{"id":"2502.19402","title":"General Intelligence Requires Reward-based Pretraining","created":"2025-02-26","cited_by_count":12,"area":null},{"id":"2509.21882","title":"Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards","created":"2025-09-26","cited_by_count":12,"area":null},{"id":"2412.15184","title":"Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning","created":"2024-12-19","cited_by_count":11,"area":null},{"id":"2505.11770","title":"Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors","created":"2025-05-17","cited_by_count":11,"area":null},{"id":"2506.11343","title":"From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review","created":"2025-06-12","cited_by_count":11,"area":null},{"id":"2503.05543","title":"Pi-GPS: Enhancing Geometry Problem Solving by Unleashing the Power of Diagrammatic Information","created":"2025-03-07","cited_by_count":10,"area":null},{"id":"2503.13401","title":"Levels of Analysis for Large Language Models","created":"2025-03-17","cited_by_count":10,"area":null},{"id":"2504.00285","title":"When Do Large Language Models Exhibit Unsolicited Deception?","created":"2025-03-31","cited_by_count":10,"area":null},{"id":"2411.17066","title":"Relations, Negations, and Numbers: Looking for Logic in Generative Text-to-Image Models","created":"2024-11-26","cited_by_count":9,"area":null},{"id":"2412.11698","title":"On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?","created":"2024-12-16","cited_by_count":9,"area":null},{"id":"2502.15823","title":"InductionBench: LLMs Fail in the Simplest Complexity Class","created":"2025-02-20","cited_by_count":9,"area":null},{"id":"2504.01201","title":"Medical large language models are easily distracted","created":"2025-04-01","cited_by_count":9,"area":null},{"id":"2504.05262","title":"Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic","created":"2025-04-07","cited_by_count":8,"area":null},{"id":"2604.06802","title":"Riemann-Bench: A Benchmark for Moonshot Mathematics","created":"2026-04-08","cited_by_count":8,"area":null},{"id":"2412.15557","title":"MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems","created":"2024-12-20","cited_by_count":7,"area":null},{"id":"2506.00309","title":"Evaluation of LLMs for mathematical problem solving","created":"2025-05-30","cited_by_count":7,"area":null},{"id":"2506.11135","title":"Large Language Models and Emergence: A Complex Systems Perspective","created":"2025-06-10","cited_by_count":7,"area":null},{"id":"2507.07207","title":"Scaling can lead to compositional generalization","created":"2025-07-09","cited_by_count":7,"area":null},{"id":"2508.17380","title":"Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery","created":"2025-08-24","cited_by_count":7,"area":null},{"id":"2510.06824","title":"Efficient numeracy in language models through single-token number embeddings","created":"2025-10-08","cited_by_count":7,"area":null},{"id":"2603.03305","title":"The Hidden Cost of Structured Generation in LLMs: Draft-Conditioned Constrained Decoding","created":"2026-02-08","cited_by_count":7,"area":null},{"id":"2502.00943","title":"Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale","created":"2025-02-02","cited_by_count":6,"area":null},{"id":"2506.01734","title":"Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs","created":"2025-06-02","cited_by_count":6,"area":null},{"id":"2510.09259","title":"Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models","created":"2025-10-10","cited_by_count":6,"area":null},{"id":"2510.15749","title":"SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior","created":"2025-10-17","cited_by_count":6,"area":null},{"id":"2602.08864","title":"Understanding Dynamic Compute Allocation in Recurrent Transformers","created":"2026-02-09","cited_by_count":6,"area":null},{"id":"2410.15490","title":"Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence","created":"2024-10-20","cited_by_count":5,"area":null},{"id":"2411.11465","title":"Re-examining learning linear functions in context","created":"2024-11-18","cited_by_count":5,"area":null},{"id":"2505.23851","title":"ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark","created":"2025-05-28","cited_by_count":5,"area":null},{"id":"2506.09433","title":"Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training","created":"2025-06-11","cited_by_count":5,"area":null},{"id":"2507.03285","title":"Memory Mosaics at scale","created":"2025-07-04","cited_by_count":5,"area":null},{"id":"2509.21791","title":"Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference","created":"2025-09-26","cited_by_count":5,"area":null},{"id":"2512.16902","title":"In-Context Algebra","created":"2025-12-18","cited_by_count":5,"area":null},{"id":"2601.19924","title":"OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling","created":"2026-01-09","cited_by_count":5,"area":null},{"id":"2412.16177","title":"Mining Math Conjectures from LLMs: A Pruning Approach","created":"2024-12-09","cited_by_count":4,"area":null},{"id":"2503.19449","title":"VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations","created":"2025-03-25","cited_by_count":4,"area":null},{"id":"2504.09763","title":"Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems","created":"2025-04-14","cited_by_count":4,"area":null},{"id":"2504.12585","title":"Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models","created":"2025-04-17","cited_by_count":4,"area":null},{"id":"2506.08002","title":"Aligning Text, Images, and 3D Structure Token-by-Token","created":"2025-06-09","cited_by_count":4,"area":null},{"id":"2507.19232","title":"Event-Driven Storytelling with Multiple Lifelike Humans in a 3D Scene","created":"2025-07-25","cited_by_count":4,"area":null},{"id":"2602.17316","title":"Same Meaning, Different Scores: Lexical and Syntactic Sensitivity in LLM Evaluation","created":"2026-02-19","cited_by_count":4,"area":null},{"id":"2503.10542","title":"Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More","created":"2025-03-13","cited_by_count":3,"area":null},{"id":"2504.10036","title":"DataPuzzle: Breaking Free from the Hallucinated Promise of LLMs in Data Analysis","created":"2025-04-14","cited_by_count":3,"area":null},{"id":"2504.16760","title":"Lightweight Latent Verifiers for Efficient Meta-Generation Strategies","created":"2025-04-23","cited_by_count":3,"area":null},{"id":"2505.11004","title":"Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context Learning","created":"2025-05-16","cited_by_count":3,"area":null},{"id":"2508.07111","title":"Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution","created":"2025-08-09","cited_by_count":3,"area":null},{"id":"2510.00496","title":"Agent-ScanKit: Unraveling Memory and Reasoning of Multimodal Agents via Sensitivity Perturbations","created":"2025-10-01","cited_by_count":3,"area":null},{"id":"2601.08847","title":"Scalable and Reliable Evaluation of AI Knowledge Retrieval Systems: RIKER and the Coherent Simulated Universe","created":"2025-12-22","cited_by_count":3,"area":null},{"id":"2601.22510","title":"Shattered Compositionality: Counterintuitive Learning Dynamics of Transformers for Arithmetic","created":"2026-01-30","cited_by_count":3,"area":null},{"id":"2602.01075","title":"ConvexBench: Can LLMs Recognize Convex Functions?","created":"2026-02-01","cited_by_count":3,"area":null},{"id":"2411.01049","title":"Exploratory Models of Human-AI Teams: Leveraging Human Digital Twins to Investigate Trust Development","created":"2024-11-01","cited_by_count":2,"area":null},{"id":"2411.02639","title":"Active Prompt Tuning Enables Gpt-40 To Do Efficient Classification Of Microscopy Images","created":"2024-11-04","cited_by_count":2,"area":null},{"id":"2411.06269","title":"AI's Spatial Intelligence: Evaluating AI's Understanding of Spatial Transformations in PSVT:R and Augmented Reality","created":"2024-11-09","cited_by_count":2,"area":null},{"id":"2411.16917","title":"Are Transformers Truly Foundational for Robotics?","created":"2024-11-25","cited_by_count":2,"area":null},{"id":"2412.14708","title":"Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey","created":"2024-12-19","cited_by_count":2,"area":null},{"id":"2412.15501","title":"Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models","created":"2024-12-20","cited_by_count":2,"area":null},{"id":"2501.05465","title":"Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)","created":"2025-01-03","cited_by_count":2,"area":null},{"id":"2501.13779","title":"Not Every AI Problem is a Data Problem: We Should Be Intentional About Data Scaling","created":"2025-01-23","cited_by_count":2,"area":null},{"id":"2503.04756","title":"Peeking Behind Closed Doors: Risks of LLM Evaluation by Private Data Curators","created":"2025-02-09","cited_by_count":2,"area":null},{"id":"2505.04528","title":"Beyond Theorem Proving: Formulation, Framework and Benchmark for Formal Problem-Solving","created":"2025-05-07","cited_by_count":2,"area":null},{"id":"2505.13398","title":"A Minimum Description Length Approach to Regularization in Neural Networks","created":"2025-05-19","cited_by_count":2,"area":null},{"id":"2505.15242","title":"Adaptive Plan-Execute Framework for Smart Contract Security Auditing","created":"2025-05-21","cited_by_count":2,"area":null},{"id":"2506.23964","title":"Making Logic a First-Class Citizen in Generative ML for Networking","created":"2025-06-30","cited_by_count":2,"area":null},{"id":"2508.19563","title":"Robustness is Important: Limitations of LLMs for Data Fitting","created":"2025-08-27","cited_by_count":2,"area":null},{"id":"2509.10414","title":"Is In-Context Learning Learning?","created":"2025-09-12","cited_by_count":2,"area":null},{"id":"2509.15651","title":"Toward Efficient Influence Function: Dropout as a Compression Tool","created":"2025-09-19","cited_by_count":2,"area":null},{"id":"2509.19880","title":"Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2510.08569","title":"ArenaBencher: Automatic Benchmark Evolution via Multi-Model Competitive Evaluation","created":"2025-10-09","cited_by_count":2,"area":null},{"id":"2512.05439","title":"BEAVER: An Efficient Deterministic LLM Verifier","created":"2025-12-05","cited_by_count":2,"area":null},{"id":"2604.01418","title":"Cost-Efficient Estimation of General Abilities Across Benchmarks","created":"2026-04-01","cited_by_count":2,"area":null},{"id":"2604.02863","title":"EMS: Multi-Agent Voting via Efficient Majority-then-Stopping","created":"2026-04-03","cited_by_count":2,"area":null},{"id":"2604.08782","title":"MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation","created":"2026-04-09","cited_by_count":2,"area":null},{"id":"2605.11209","title":"Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2605.12421","title":"Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2605.15393","title":"LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling","created":"2026-05-14","cited_by_count":2,"area":null},{"id":"2410.14979","title":"Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology","created":"2024-10-19","cited_by_count":1,"area":null}],"next_cursor":"100"}