{"total":26,"rows":[{"id":"2601.18577","title":"Self-Refining Video Sampling","created":"2026-01-26","cited_by_count":15,"area":null},{"id":"2605.00365","title":"Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity","created":"2026-05-01","cited_by_count":9,"area":null},{"id":"2602.06791","title":"Rare Event Analysis of Large Language Models","created":"2026-02-06","cited_by_count":8,"area":null},{"id":"2605.02427","title":"The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling","created":"2026-05-04","cited_by_count":6,"area":null},{"id":"2604.16453","title":"Sampling for Quality: Training-Free Reward-Guided LLM Decoding via Sequential Monte Carlo","created":"2026-04-07","cited_by_count":5,"area":null},{"id":"2601.15609","title":"When Sharpening Becomes Collapse: Sampling Bias and Semantic Coupling in RL with Verifiable Rewards","created":"2026-01-22","cited_by_count":4,"area":null},{"id":"2602.15829","title":"Operationalising the Superficial Alignment Hypothesis via Task Complexity","created":"2026-02-17","cited_by_count":4,"area":null},{"id":"2606.27359","title":"When are likely answers right? On Sequence Probability and Correctness in LLMs","created":"2026-06-25","cited_by_count":4,"area":null},{"id":"2512.10309","title":"Tracking large chemical reaction networks and rare events by neural networks","created":"2025-12-11","cited_by_count":3,"area":null},{"id":"2606.10587","title":"Towards Diverse Scientific Hypothesis Search with Large Language Models","created":"2026-06-09","cited_by_count":3,"area":null},{"id":"2602.18292","title":"Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers","created":"2026-02-20","cited_by_count":2,"area":null},{"id":"2603.18363","title":"PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching","created":"2026-03-19","cited_by_count":2,"area":null},{"id":"2605.11128","title":"Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2606.12360","title":"Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal","created":"2026-06-10","cited_by_count":2,"area":null},{"id":"2601.10267","title":"In-Context Source and Channel Coding","created":"2026-01-15","cited_by_count":1,"area":null},{"id":"2601.14456","title":"On the Generalization Gap in LLM Planning: Tests and Verifier-Reward RL","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2605.11813","title":"Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2606.04272","title":"RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training","created":"2026-06-02","cited_by_count":1,"area":null},{"id":"2608.14420","title":"More Correct Mass, Worse Answers: Why Power Sampling Can Fail and How to Fix It","created":"2026-08-14","cited_by_count":1,"area":null},{"id":"2608.26120","title":"Recipes for Steering and Scaling LLMs via Sampling","created":"2026-06-19","cited_by_count":1,"area":null},{"id":"2604.04855","title":"The Role of Generator Access in Autoregressive Post-Training","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2607.23854","title":"Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2608.00019","title":"Uncertainty-Aware Simulation-Based Inference for Operations Research with Large Language Models","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2608.30689","title":"CANVAS: Consistency-Aware Navigation via Visual Adaptive Sampling for Long-Context Text-to-SVG Generation","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.34992","title":"Composable Decoding on the Probability Simplex: Theory and Implementation","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.05817","title":"Nash Equilibrium Text: A Game-Theoretic Decoding Framework for Text Generation","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}