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Release of Ling-3.0-tiny, a hybrid reasoning model with 7.9B total parameters and only 1.3B active per token, free for a week.
This paper proposes PLAN, a lightweight parallel liquid-inspired approximation network for efficient representation learning in flexible job shop scheduling, achieving better makespan and lower inference latency with fewer parameters than state-of-the-art baselines.
DeepGrove releases Maple-Preview, an open-source 20B-A1B ternary-weight reasoning LLM with SOTA reasoning for its weight class, capable of 200+ tokens/sec on a Mac mini M4 and competitive with larger models.
inclusionAI released Ling-3.0-flash, a native hybrid reasoning model with 124B total/5.1B active parameters using a hybrid linear attention architecture (KDA+MLA) and sparse MoE. It matches or outperforms its 1T-class predecessor Ring-2.6-1T while being far more compute-efficient, with built-in agentic and long-context optimizations.
SeDeM is a selective decompression framework that stores long-context hidden states in a compact memory bank and decompresses only query-relevant blocks for decoder conditioning, improving QA accuracy and efficiency over compression baselines.
Discusses Apple's AFM3 20B model architecture, which uses Instruction-Following Pruning to activate only ~20% of MLP layers per prompt and keep the model in flash storage for efficient on-device inference.
This paper introduces Structured All-Mask Prediction and STAMPlus, a method for MLLM-based segmentation that jointly predicts all target masks in one non-autoregressive pass, resolving the trilemma of high segmentation performance, preserved dialogue ability, and fast inference.
EdgeRazor is a lightweight framework for compressing large language models using entropy-guided mixed-precision quantization-aware distillation, achieving 1.88 bits per parameter while preserving teacher model competence and requiring no changes to inference implementations like llama.cpp. The method is demonstrated on small models such as MobileLLM and Qwen variants.
This paper introduces CoMem, a method that exploits the depth-wise division of labor in LLMs to cache intermediate residual tensors and recompute only upper layers for retrieval, enabling bounded read compute and memory independent of stored-context length. Evaluated on Qwen3-8B, CoMem achieves strong long-context performance with significant memory savings and prefill speedups.
ReTopK is a training-free method that accelerates dynamic Top-K sparse attention for long-context LLMs by reusing historical query–support pairs, avoiding full-context scoring and global Top-K selection. It achieves up to 3.07x speedup over Exact Top-K with only 0.50% perplexity increase at 128K context.
ARC improves retrieval efficiency and accuracy in long-context AI agents by separating append-only storage from bounded citation views, achieving near-perfect recall with lower latency and bandwidth.
GLIDE introduces a layer-wise adaptive mechanism that strategically integrates sliding-window softmax attention with linear recurrent aggregation for efficient LLM inference, reducing KV cache I/O and latency for long contexts without compromising quality.
SpecPrefetch proposes a parameter-efficient expert prefetching framework for sparse MoE models, using a lightweight adapter to predict next-layer experts for asynchronous transfer while preserving native routing semantics. It achieves up to 20% decoding throughput improvement on a Snapdragon 8 Elite device, demonstrating practical benefits for memory-constrained deployment.
TurboVLA introduces a new Vision-Language-Action paradigm that directly maps vision and language to action, achieving 97.7% success on LIBERO with only 0.2B parameters and real-time inference at 32 Hz on consumer GPUs, significantly reducing computational cost.
Liquid AI released LFM2.5-2.6B, a 2.6B-parameter hybrid model optimized for on-device deployment with 128K context, agentic post-training, and fast inference (220 tok/s on Apple M5 Max) under 2.5GB memory.
Neutrino-1 8B is an 8.19B-parameter transformer with a proprietary ternary weight format, enabling it to fit on 8GB GPUs and serve multiple platforms from a single 3.88GB artifact with high decode speed.
This paper presents a frozen 12B model that uses a persistent memory of verified solutions to achieve 100% accuracy on solved problem families with zero generation tokens, and demonstrates a 6M-token movable window on a single 46GB GPU.
Proposes AutoThinkSQL, a framework that integrates an auto-thinking mechanism into SFT and DPO for Text-to-SQL, enabling the model to dynamically skip reasoning for simple queries and invoke deep CoT for complex ones, achieving gains on Spider and BIRD benchmarks while reducing output tokens by 24.6% and latency by 17.1%.
CausalGate introduces a method that uses causal interventions to measure the importance of transformer sub-layers and distills this into static scalar gates for efficient inference without runtime overhead, outperforming existing pruning and routing methods.
A frozen 12B parameter model augmented with a persistent memory of verified solutions achieves 100% accuracy on over 180 instances across nine problem families with zero generation tokens, bit-exact deterministic outputs, outperforming frontier models on those solved tasks while using negligible compute.