arXiv AI
Jun 16
Long-Context Modeling via GSS-Transformer Hybrid Architecture with Learnable Mixing
arXiv:2606. 16093v1 Announce Type: cross Abstract: Modeling long-range dependencies remains a central challenge in natural language processing.
FON: A Draft-Revise Dual-Organ Language Model without Sequence-Level Quadratic Attention
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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 Computation and Language
2d ago
Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules
arXiv:2610.19210v1 Announce Type: new Abstract: Transformers are efficient to pre-train at scale because attention permits parallel computation across sequence positions, but long-context inference r...
arXiv AI
Oct 2
MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs
MWOP (Modality-aware Width-wise Operation Pruning) is a method that independently prunes visual‑to‑visual, text‑to‑visual, and text‑to‑text attention paths within each layer of multimodal large language models, and separately selects feed‑forward network channels for visual and textual inputs. It uses a first‑order Taylor criterion to guide pruning, re‑evaluates FFN importance after attention pruning, and applies LoRA‑based recovery training. The approach is paired with path‑sparse Triton attention kernels and compact visual‑side FFN execution to achieve practical acceleration, preserving token sequences while reducing computation. "whyItMatters":"MWOP achieves a 1.6× prefill speedup on LLaVA‑OneVision‑7B while retaining 99.7% performance, and further boosts token‑compression methods to 2.9× and 2.7× speedups, demonstrating its effectiveness across architectures."
arXiv AI
Jul 10
Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing
arXiv:2607. 07953v1 Announce Type: cross Abstract: Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at long context.
arXiv AI
5d ago
Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers
Orthros introduces a phase‑aware heterogeneous attention mechanism that dynamically switches between linear‑complexity prefilling and full‑attention decoding, reducing the asymptotic cost compared to standard Transformers in long‑context scenarios. The method supports efficient Transformer‑to‑Orthros conversion, achieving higher performance than the original model and delivering a 21.9× prefilling speedup while maintaining strong in‑context recall, including 128K‑scale needle‑in‑a‑haystack tasks. These results demonstrate Orthros’s potential as a more efficient and flexible alternative for industrial Transformer applications.
arXiv AI
2d ago
History Replays Itself: Unleashing Long-Context Capability in Linear Attention
arXiv:2610.15715v1 Announce Type: new Abstract: Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support ong...
arXiv Computation and Language
Sep 4
Margins, Not Windows: Training-Free Per-Step Lossy Speculative Decoding
The paper introduces AdaptiveSpec, a training‑free speculative decoding method that simultaneously adapts the per‑step verification rule and the draft‑tree shape using signals generated during decoding. It replaces the fixed token‑match rule with a margin‑based threshold and adjusts tree depth, width, and node count based on draft confidence and recent acceptance history, allowing the total draft count to vary. Experiments on SGLang show up to 56% throughput gains over EAGLE‑3 while maintaining 93% of lossless task accuracy on GSM8K, MATH‑500, and HumanEval across three models.
arXiv Machine Learning
Jun 17
Olmo Hybrid: From Theory to Practice and Back
arXiv:2604. 03444v4 Announce Type: replace Abstract: Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention.
arXiv AI
Jul 21
Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones
arXiv:2607. 17419v1 Announce Type: cross Abstract: Linear attention promises constant-time recurrent inference but degrades sharply on associative recall.
arXiv AI
Jul 23
LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning
arXiv:2607. 19358v1 Announce Type: new Abstract: Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths under the test-time scaling paradigm.
arXiv AI
Jun 29
The Context-Ready Transformer
arXiv:2606. 27538v1 Announce Type: cross Abstract: We introduce the context-ready transformer, a new recurrent neural network architecture built from a D-layer transformer block that pre-contextualizes each token before it enters the block.