arXiv Machine Learning
Jun 24
Selective Rotary Position Embedding
arXiv:2511. 17388v3 Announce Type: replace-cross Abstract: Position information is essential for language modeling.
Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings
arXiv:2509. 10534v3 Announce Type: replace-cross Abstract: The attention mechanism in a Transformer architecture matches key to query based on both content -- the what -- and position in a sequence -- the where.
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arXiv AI
Jul 23
AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally
arXiv:2607. 19363v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a uniform frequency schedule and scaling across all attention heads.
arXiv Machine Learning
Jul 14
LeRoPE: Learnable RoPE Frequencies Improve Language Modeling
arXiv:2607. 10134v1 Announce Type: new Abstract: Rotary Positional Encodings (RoPE) are currently the most popular positional encodings used in modern language models.
arXiv Machine Learning
Jul 9
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization
arXiv:2607. 07678v1 Announce Type: new Abstract: Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly.
arXiv AI
Aug 7
Beyond Sequence Order: Syntax-Informed Positional Embeddings for Transformers
arXiv:2608. 06111v1 Announce Type: cross Abstract: Positional embeddings (PE) in Transformers encode token distance and order but are largely agnostic to \textit{syntactic structure}.
arXiv Machine Learning
Jul 22
A Better Start for Language Models: Domain-Conditional Position Offsets
arXiv:2607. 18302v1 Announce Type: new Abstract: Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior.
arXiv Machine Learning
Jun 25
ATMA: Length-Invariant Language Modeling via Polar Attention and Gated-Delta Compression Memory
arXiv:2606. 25156v1 Announce Type: new Abstract: Modern large language models based on softmax scaled-dot-product attention are constrained by their training sequence length: as the key-value sequence grows, softmax probability mass can dilute across a wider distribution, inducing activation shift and long-context performance collapse.
arXiv Machine Learning
Jul 1
InfoFlow KV: Information-Flow-Aware KV Recomputation for Long Context
arXiv:2603. 05353v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) for long-context question answering is bottlenecked by inference-time prefilling over large retrieved contexts.
arXiv AI
Aug 11
ATMA: Long-Context Language Modeling via Polar Attention and Gated-Delta Compression Memory
arXiv:2606. 25156v3 Announce Type: replace-cross Abstract: Native length extrapolation remain a weakly solvable problem in language modeling due to trade-off balancing between exact retrieval fidelity, long-document likelihood, and inference efficiency.
arXiv Machine Learning
Jul 30
Dense Local Dependencies Induce Attention-Logit Explosion and Training Instability During Long-Sequence Transformer Training
arXiv:2505. 15548v2 Announce Type: replace Abstract: Autoregressive transformer language models frequently exhibit training instability when trained on long sequences, particularly under low-precision arithmetic.
arXiv AI
Jul 24
Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models
arXiv:2607. 20524v1 Announce Type: new Abstract: Mean cross-positional attention degradation is widely reported in transformer interpretability, yet whether it causally limits contextual retrieval remains untested.
Hugging Face Trending Papers
Aug 12
Task- and dataset-specific information in protein language models
Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on large corpora of protein sequence data, are widely used to translate amino acid sequences into latent-space embeddings, ready for use in diverse downstream tasks (DTs).