{"total":75,"rows":[{"id":"2506.13585","title":"MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention","created":"2025-06-16","cited_by_count":263,"area":"lr"},{"id":"2506.04761","title":"Log-Linear Attention","created":"2025-06-05","cited_by_count":40,"area":null},{"id":"2505.19488","title":"Understanding Transformer from the Perspective of Associative Memory","created":"2025-05-26","cited_by_count":34,"area":null},{"id":"2512.20569","title":"Distilling to Hybrid Attention Models via KL-Guided Layer Selection","created":"2025-12-23","cited_by_count":20,"area":null},{"id":"2504.05646","title":"Lattice: Learning to Efficiently Compress the Memory","created":"2025-04-08","cited_by_count":19,"area":null},{"id":"2505.17761","title":"Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence Models","created":"2025-05-23","cited_by_count":18,"area":null},{"id":"2509.22284","title":"Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models","created":"2025-09-26","cited_by_count":16,"area":null},{"id":"2506.02475","title":"Comba: Improving Bilinear RNNs with Closed-loop Control","created":"2025-06-03","cited_by_count":15,"area":null},{"id":"2507.16577","title":"Scaling Linear Attention with Sparse State Expansion","created":"2025-07-22","cited_by_count":13,"area":null},{"id":"2602.17634","title":"Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting","created":"2026-02-19","cited_by_count":13,"area":null},{"id":"2506.00744","title":"Blending Complementary Memory Systems in Hybrid Quadratic-Linear Transformers","created":"2025-05-31","cited_by_count":12,"area":null},{"id":"2511.21016","title":"Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression","created":"2025-11-26","cited_by_count":11,"area":null},{"id":"2603.01959","title":"The Expressive Limits of Diagonal SSMs for State-Tracking","created":"2026-03-02","cited_by_count":9,"area":null},{"id":"2506.10918","title":"Sequential-Parallel Duality in Prefix Scannable Models","created":"2025-06-12","cited_by_count":8,"area":"ssm"},{"id":"2508.08435","title":"Fast weight programming and linear transformers: from machine learning to neurobiology","created":"2025-08-11","cited_by_count":8,"area":null},{"id":"2603.14360","title":"M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling","created":"2026-03-15","cited_by_count":8,"area":null},{"id":"2604.21100","title":"Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences","created":"2026-04-22","cited_by_count":7,"area":null},{"id":"2605.20670","title":"LT2: Linear-Time Looped Transformers","created":"2026-05-20","cited_by_count":7,"area":null},{"id":"2505.21785","title":"Born a Transformer -- Always a Transformer? On the Effect of Pretraining on Architectural Abilities","created":"2025-05-27","cited_by_count":6,"area":null},{"id":"2602.04852","title":"On State Reduction in Linear Attention","created":"2026-02-04","cited_by_count":6,"area":null},{"id":"2602.14814","title":"Learning State-Tracking from Code Using Linear RNNs","created":"2026-02-16","cited_by_count":6,"area":null},{"id":"2602.07672","title":"Debugging code world models","created":"2026-02-07","cited_by_count":5,"area":"wm"},{"id":"2606.26560","title":"Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention","created":"2026-06-25","cited_by_count":5,"area":"moe"},{"id":"2504.00492","title":"ParallelFlow: Parallelizing Linear Transformers via Flow Discretization","created":"2025-04-01","cited_by_count":4,"area":null},{"id":"2510.01450","title":"Local Linear Attention: An Optimal Interpolation of Linear and Softmax Attention For Test-Time Regression","created":"2025-10-01","cited_by_count":4,"area":null},{"id":"2605.05838","title":"MDN: Parallelizing Stepwise Momentum for Delta Linear Attention","created":"2026-05-07","cited_by_count":4,"area":null},{"id":"2605.07755","title":"Rethinking State Tracking in Recurrent Models Through Error Control Dynamics","created":"2026-05-08","cited_by_count":4,"area":null},{"id":"2607.14144","title":"The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale","created":"2026-07-14","cited_by_count":4,"area":null},{"id":"2607.25380","title":"Memory for Large Language Models","created":"2026-07-28","cited_by_count":4,"area":null},{"id":"2604.17121","title":"The Topological Trouble With Transformers","created":"2026-04-18","cited_by_count":3,"area":null},{"id":"2506.11997","title":"pLSTM: parallelizable Linear Source Transition Mark networks","created":"2025-06-13","cited_by_count":2,"area":"ssm"},{"id":"2506.17671","title":"TPTT: Transforming Pretrained Transformers into Titans","created":"2025-06-21","cited_by_count":2,"area":null},{"id":"2510.11602","title":"Deconstructing Attention: Investigating Design Principles for Effective Language Modeling","created":"2025-10-13","cited_by_count":2,"area":null},{"id":"2604.01577","title":"Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization","created":"2026-04-02","cited_by_count":2,"area":null},{"id":"2604.18058","title":"Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity","created":"2026-04-20","cited_by_count":2,"area":"wm"},{"id":"2606.23670","title":"Tapered Language Models","created":"2026-06-22","cited_by_count":2,"area":null},{"id":"2608.12435","title":"MARCH: Scaling Recurrent Memory with Content-Routed State Anchors","created":"2026-08-12","cited_by_count":2,"area":null},{"id":"2508.07395","title":"Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs","created":"2025-08-10","cited_by_count":1,"area":"ssm"},{"id":"2510.09389","title":"Design Principles for Sequence Models via Coefficient Dynamics","created":"2025-10-10","cited_by_count":1,"area":null},{"id":"2602.18417","title":"Subgroups of $U(d)$ Induce Natural RNN and Transformer Architectures","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2603.05573","title":"Why Depth Matters in Parallelizable Sequence Models: A Lie Algebraic View","created":"2026-03-05","cited_by_count":1,"area":"ssm"},{"id":"2603.22325","title":"Hybrid Associative Memories","created":"2026-03-20","cited_by_count":1,"area":null},{"id":"2605.08587","title":"Kaczmarz Linear Attention","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2605.12770","title":"WriteSAE: Sparse Autoencoders for Recurrent State","created":"2026-05-12","cited_by_count":1,"area":"ssm"},{"id":"2605.29157","title":"Parallax: Parameterized Local Linear Attention for Language Modeling","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2605.30100","title":"Chess-World-Model: A 10M-Game Benchmark for Exact State Tracking from Chess Move Sequences","created":"2026-05-28","cited_by_count":1,"area":"wm"},{"id":"2606.12364","title":"On Subquadratic Architectures: From Applications to Principles","created":"2026-06-10","cited_by_count":1,"area":null},{"id":"2608.16844","title":"Proteus: Incremental Memory Activation for Long-Context Sequence Modeling","created":"2026-08-17","cited_by_count":1,"area":null},{"id":"2609.00092","title":"Safin-1: Safety from Within through Memory-Native State Evolution","created":"2026-08-31","cited_by_count":1,"area":null},{"id":"2609.12259","title":"The Rank the Task Demands: A Causal Rank Law for Matrix Memories Trained on Group Composition","created":"2026-09-10","cited_by_count":1,"area":null},{"id":"2609.12814","title":"RunningTensor: Generalizing Linear Attention to Higher-Order Recurrent States","created":"2026-09-11","cited_by_count":1,"area":null},{"id":"2508.11921","title":"ENA: Efficient N-dimensional Attention","created":"2025-08-16","cited_by_count":0,"area":null},{"id":"2509.25223","title":"Enhancing Linear Attention with Residual Learning","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2602.12021","title":"Improved state mixing in higher-order and block diagonal linear recurrent networks","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2603.19198","title":"Extending SSMs with the Exponentially Weighted Signature","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2605.16640","title":"Provably Shorter Scratchpads in Hybrid DeltaNet-Attention Decoders","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.19150","title":"Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.28507","title":"Universal Time Series Generation with Neural Controlled Differential Equations","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2605.31163","title":"Memory by Design: Bayesian Layers for Sequence Modeling","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2609.11132","title":"How Wrong Can a Good Predictor Be? Diverging Updates with Vanishing Predictive KL","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.17594","title":"When the Gradient Sees Rank: Provable Necessity, Causal Recruitment, and Composition in Trained Matrix Memories","created":"2026-09-12","cited_by_count":0,"area":null},{"id":"2609.18966","title":"The Automaton Underneath: The Additive Input Pathway Is a Parasitic Attractor for State Tracking in Householder Linear RNN","created":"2026-07-16","cited_by_count":0,"area":null},{"id":"2609.28273","title":"Non-Commutative State Tracking with Input-Dependent Low-Rank Updates in Mamba-3","created":"2026-09-23","cited_by_count":0,"area":"ssm"},{"id":"2609.32447","title":"Length-Independent State Tracking Under a Parallel Scan","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.33465","title":"GSM: Efficient Language Modeling with Shared Global State","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.36259","title":"CyFA: Linear Sequence Modeling with Relative-Time-Partitioned Memory","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36529","title":"Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.38166","title":"LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.38598","title":"Reinforcement Learning with Complex (valued) Memories","created":"2026-09-29","cited_by_count":0,"area":"deeprl"},{"id":"2609.39661","title":"The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.03858","title":"Retrieval-Centric Deep Learning in Growing Nonparametric Neural Networks","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.12582","title":"One Model to Loop Them All: Elastic Latent Recurrence for Language Models","created":"2026-10-08","cited_by_count":0,"area":"moe"},{"id":"2610.14030","title":"Rotate the Loop: Blockwise Conformality for Looped Transformers","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.15715","title":"History Replays Itself: Unleashing Long-Context Capability in Linear Attention","created":"2026-10-11","cited_by_count":0,"area":null},{"id":"2610.18809","title":"Depth Memory","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}