{"total":75,"rows":[{"id":"2405.15943","title":"Transformers represent belief state geometry in their residual stream","created":"2024-05-24","cited_by_count":77,"area":null},{"id":"2404.09516","title":"State Space Model for New-Generation Network Alternative to Transformers: A Survey","created":"2024-04-15","cited_by_count":59,"area":null},{"id":"2407.14414","title":"System-1.x: Learning to Balance Fast and Slow Planning with Language Models","created":"2024-07-19","cited_by_count":35,"area":null},{"id":"2408.11865","title":"How Susceptible are LLMs to Influence in Prompts?","created":"2024-08-17","cited_by_count":33,"area":null},{"id":"2504.15266","title":"Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction","created":"2025-04-21","cited_by_count":29,"area":null},{"id":"2412.05152","title":"Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation","created":"2024-12-06","cited_by_count":25,"area":null},{"id":"2407.13934","title":"Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models","created":"2024-06-01","cited_by_count":22,"area":null},{"id":"2412.04703","title":"Transformers Struggle to Learn to Search","created":"2024-12-06","cited_by_count":19,"area":null},{"id":"2505.10518","title":"Multi-Token Prediction Needs Registers","created":"2025-05-15","cited_by_count":16,"area":null},{"id":"2510.14751","title":"Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries","created":"2025-10-16","cited_by_count":15,"area":null},{"id":"2410.17241","title":"Frontiers in Intelligent Colonoscopy","created":"2024-10-22","cited_by_count":14,"area":null},{"id":"2502.11075","title":"Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models","created":"2025-02-16","cited_by_count":14,"area":null},{"id":"2505.12629","title":"Enhancing Latent Computation in Transformers with Latent Tokens","created":"2025-05-19","cited_by_count":14,"area":null},{"id":"2411.02083","title":"Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Models","created":"2024-11-04","cited_by_count":13,"area":null},{"id":"2410.23506","title":"The Belief State Transformer","created":"2024-10-30","cited_by_count":12,"area":null},{"id":"2412.05117","title":"Transformers Can Navigate Mazes With Multi-Step Prediction","created":"2024-12-06","cited_by_count":11,"area":null},{"id":"2412.10675","title":"Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation","created":"2024-12-14","cited_by_count":11,"area":null},{"id":"2505.05755","title":"Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions","created":"2025-05-09","cited_by_count":11,"area":null},{"id":"2602.14233","title":"Evaluating LLMs in Finance Requires Explicit Bias Consideration","created":"2026-02-15","cited_by_count":11,"area":null},{"id":"2503.21801","title":"Efficient Joint Prediction of Multiple Future Tokens","created":"2025-03-24","cited_by_count":10,"area":null},{"id":"2409.11143","title":"Semformer: Transformer Language Models with Semantic Planning","created":"2024-09-17","cited_by_count":9,"area":null},{"id":"2412.04318","title":"The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation","created":"2024-12-05","cited_by_count":9,"area":null},{"id":"2410.13779","title":"The Mystery of the Pathological Path-star Task for Language Models","created":"2024-10-17","cited_by_count":8,"area":null},{"id":"2510.01274","title":"TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models","created":"2025-09-30","cited_by_count":8,"area":null},{"id":"2512.15605","title":"Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction","created":"2025-12-17","cited_by_count":8,"area":null},{"id":"2405.16847","title":"TokenUnify: Scaling Up Autoregressive Pretraining for Neuron Segmentation","created":"2024-05-27","cited_by_count":7,"area":null},{"id":"2509.23186","title":"Understanding and Enhancing the Planning Capability of Language Models via Multi-Token Prediction","created":"2025-09-27","cited_by_count":6,"area":null},{"id":"2510.20280","title":"Context-level Language Modeling by Learning Predictive Context Embeddings","created":"2025-10-23","cited_by_count":6,"area":null},{"id":"2604.12493","title":"Latent Planning Emerges with Scale","created":"2026-04-14","cited_by_count":6,"area":null},{"id":"2502.13533","title":"Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models","created":"2025-02-19","cited_by_count":5,"area":null},{"id":"2504.11336","title":"Looking beyond the next token","created":"2025-04-15","cited_by_count":5,"area":null},{"id":"2508.12903","title":"A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models","created":"2025-08-18","cited_by_count":4,"area":null},{"id":"2510.02224","title":"Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models","created":"2025-10-02","cited_by_count":4,"area":null},{"id":"2512.17065","title":"XLM: A Python package for non-autoregressive language models","created":"2025-12-18","cited_by_count":4,"area":null},{"id":"2605.23454","title":"ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning","created":"2026-05-22","cited_by_count":4,"area":null},{"id":"2409.07889","title":"BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding","created":"2024-09-12","cited_by_count":3,"area":null},{"id":"2412.07629","title":"Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering","created":"2024-12-10","cited_by_count":3,"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":"2508.19228","title":"Predicting the Order of Upcoming Tokens Improves Language Modeling","created":"2025-08-26","cited_by_count":3,"area":null},{"id":"2607.01218","title":"The State-Prediction Separation Hypothesis","created":"2026-07-01","cited_by_count":3,"area":null},{"id":"2411.00863","title":"Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation","created":"2024-10-30","cited_by_count":2,"area":null},{"id":"2501.06382","title":"Dynamics of Spontaneous Topic Changes in Next Token Prediction with Self-Attention","created":"2025-01-10","cited_by_count":2,"area":null},{"id":"2503.20102","title":"Extendable Planning via Multiscale Diffusion","created":"2025-03-25","cited_by_count":2,"area":null},{"id":"2506.07833","title":"Improving Large Language Models with Concept-Aware Fine-Tuning","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2602.00533","title":"Convergent World Representations and Divergent Tasks","created":"2026-01-31","cited_by_count":2,"area":null},{"id":"2602.00906","title":"Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing","created":"2026-01-31","cited_by_count":2,"area":null},{"id":"2505.11183","title":"On Next-Token Prediction in LLMs: How End Goals Determine the Consistency of Decoding Algorithms","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2506.22516","title":"Can \"consciousness\" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis","created":"2025-06-26","cited_by_count":1,"area":null},{"id":"2507.18382","title":"Towards Consistent Long-Term Pose Generation","created":"2025-07-24","cited_by_count":1,"area":null},{"id":"2509.24435","title":"Alternatives To Next Token Prediction In Text Generation -- A Survey","created":"2025-09-29","cited_by_count":1,"area":null},{"id":"2509.25260","title":"Internal Planning in Language Models: Characterizing Horizon and Branch Awareness","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2511.13371","title":"Cognitive Maps in Language Models: A Mechanistic Analysis of Spatial Planning","created":"2025-11-17","cited_by_count":1,"area":null},{"id":"2601.22108","title":"Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2604.02910","title":"Analysis of Optimality of Large Language Models on Planning Problems","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2604.22939","title":"Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge","created":"2026-04-24","cited_by_count":1,"area":null},{"id":"2605.15394","title":"Representation Without Reward: A JEPA Audit for LLM Fine-Tuning","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2605.19975","title":"Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction","created":"2026-05-19","cited_by_count":1,"area":null},{"id":"2608.05806","title":"Hierarchical Latent Prediction for Language Models","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2409.06518","title":"Medal Matters: Probing LLMs' Failure Cases Through Olympic Rankings","created":"2024-09-10","cited_by_count":0,"area":null},{"id":"2410.18160","title":"Future Token Prediction -- Causal Language Modelling with Per-Token Semantic State Vector for Multi-Token Prediction","created":"2024-10-23","cited_by_count":0,"area":null},{"id":"2502.00197","title":"Learning Model Successors","created":"2025-01-31","cited_by_count":0,"area":null},{"id":"2505.17288","title":"Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2507.17220","title":"PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models","created":"2025-07-23","cited_by_count":0,"area":null},{"id":"2508.07702","title":"What am I missing here?: Evaluating Large Language Models for Masked Sentence Prediction","created":"2025-08-11","cited_by_count":0,"area":null},{"id":"2508.20978","title":"Scaling Neuro-symbolic Problem Solving: Solver-Free Learning of Constraints and Objectives","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2510.05856","title":"How to model Human Actions distribution with Event Sequence Data","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2511.05044","title":"Medical Referring Image Segmentation via Next-Token Mask Prediction","created":"2025-11-07","cited_by_count":0,"area":null},{"id":"2512.07818","title":"Provable Long-Range Benefits of Next-Token Prediction","created":"2025-12-08","cited_by_count":0,"area":null},{"id":"2603.14087","title":"Understanding the Emergence of Seemingly Useless Features in Next-Token Predictors","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.11912","title":"How Transformers Learn to Plan via Multi-Token Prediction","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2606.20714","title":"A Generalized Formalism of Auto-Regressive Decoding for Speech Processing","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2608.05050","title":"The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.29376","title":"Evaluating Tiny Recursive Models Across Training for Code Generation","created":"2026-08-29","cited_by_count":0,"area":null},{"id":"2609.34731","title":"Information-Theoretic Analysis of Next-Token Prediction under Markovian Data","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.06031","title":"LightMTP: Lightweight Latent Multi-Token Prediction","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}