{"total":25,"rows":[{"id":"2601.14525","title":"Towards Execution-Grounded Automated AI Research","created":"2026-01-20","cited_by_count":28,"area":null},{"id":"2602.24281","title":"Memory Caching: RNNs with Growing Memory","created":"2026-02-27","cited_by_count":15,"area":null},{"id":"2607.20519","title":"Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readouts","created":"2026-07-08","cited_by_count":12,"area":null},{"id":"2512.18634","title":"From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers","created":"2025-12-21","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":"2604.20817","title":"Convergent Evolution: How Different Language Models Learn Similar Number Representations","created":"2026-04-22","cited_by_count":6,"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":"2602.12601","title":"HyperMLP: An Integrated Perspective for Sequence Modeling","created":"2026-02-13","cited_by_count":3,"area":null},{"id":"2603.16039","title":"Residual Stream Duality in Modern Transformer Architectures","created":"2026-03-17","cited_by_count":3,"area":null},{"id":"2604.17121","title":"The Topological Trouble With Transformers","created":"2026-04-18","cited_by_count":3,"area":null},{"id":"2607.17624","title":"Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks","created":"2026-07-20","cited_by_count":2,"area":null},{"id":"2603.03583","title":"ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer","created":"2026-03-03","cited_by_count":1,"area":null},{"id":"2606.25156","title":"ATMA: Long-Context Language Modeling via Polar Attention and Gated-Delta Compression Memory","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2606.25342","title":"Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention","created":"2026-06-24","cited_by_count":1,"area":null},{"id":"2606.29858","title":"Smooth Scaling Laws Hide Stepwise Token Learning","created":"2026-06-29","cited_by_count":1,"area":null},{"id":"2607.07706","title":"The Key to Going Linear: Analysis-Driven Transformer Linearization","created":"2026-07-08","cited_by_count":1,"area":null},{"id":"2602.01845","title":"No Generation without Representation: Efficient Causal Protein Language Models Enable Zero-Shot Fitness Estimation","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.11128","title":"Efficient Approximation to Analytic and $L^p$ functions by Height-Augmented ReLU Networks","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2605.11287","title":"Beyond Similarity: Temporal Operator Attention for Time Series Analysis","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.26099","title":"Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2605.29152","title":"Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.03825","title":"Dynamic Short Convolutions Improve Transformers","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2609.38764","title":"Lasting Effects of Abstract Pretraining Beyond Perplexity","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00820","title":"On-the-fly Weight Generation: A Hypernetwork Proof of Concept on ARC-1D","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.19552","title":"Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}