The research paper presents PageANN, a novel framework engineered to overcome the severe latency and scalability limitations facing existing **disk-based Approximate Nearest Neighbor Search (ANNS)** methods used in vector databases. Current systems suffer from inefficient search paths and a crucial misalignment between logical graph node size and the **physical I/O granularity of Solid-State Drives (SSDs)**. PageANN introduces a core innovation: a **page-node graph structure** that directly maps logical graph nodes to physical SSD pages, significantly shortening I/O traversal paths and maximizing data utility during retrieval. This is supported by a co-designed **disk data layout** that embeds compressed neighbor vectors within each page and a dynamic **memory management strategy** utilizing lightweight indexing for fast query routing. According to experimental results, PageANN consistently **outperforms state-of-the-art techniques**, achieving substantial gains in throughput and latency across diverse datasets and memory constraints while maintaining comparable recall accuracy.

Source:

https://arxiv.org/pdf/2509.25487

This research presents a novel method for efficient long-context modeling in Large Language Models (LLMs) by tackling the quadratic complexity of attention mechanisms through KV cache compression. The core discovery is a fundamental **local KV cache asymmetry**, which reveals that adjacent attention keys exhibit high structural homogeneity, while their associated value vectors possess distinct, heterogeneous distributions. To capitalize on this finding, the authors propose **AsymKV**, a training-free compression framework that shifts information loss from heterogeneous values to homogeneous keys. AsymKV operates by applying **homogeneity-based merging to keys** using a mathematically derived optimal vector, paired with a **lossless value representation scheme** utilizing cardinality-aware normalization to preserve vital information. Extensive empirical results on benchmarks like LongBench, across diverse models such as LLaMA3.1-8B, confirm that **AsymKV consistently surpasses state-of-the-art long-context methods** in terms of accuracy and information retention, offering improved performance with practical inference efficiency.

Source:

https://arxiv.org/pdf/2506.05410

The research systematically investigates the effects of integrating various gating mechanisms into the standard softmax attention layer, comparing over thirty configurations across dense and Mixture-of-Experts Large Language Models. The central finding demonstrates that applying an elementwise, head-specific sigmoid gate immediately following the Scaled Dot-Product Attention (SDPA) output consistently yields the most substantial improvement in overall performance metrics. This successful gating method also provides superior training stability, allowing models to converge effectively under larger learning rates and mitigating disruptive loss spikes during optimization. The improved efficacy is attributed to two factors: introducing essential non-linearity into the low-rank attention mapping and generating input-dependent sparse gating scores. Crucially, this sparsity acts to normalize attention dynamics, eliminating the 'attention sink' problem where initial tokens dominate attention scores, thereby facilitating notably better long-context extrapolation. These demonstrated benefits led to the incorporation of this specific gated attention design into the forthcoming Qwen3-Next models.

Source:

https://openreview.net/pdf?id=1b7whO4SfY

This research paper introduces LLaDA, an 8-billion parameter language model based on the masked diffusion model (MDM) architecture, specifically developed to challenge the assumption that core Large Language Model (LLM) capabilities are exclusive to autoregressive models (ARMs). Unlike ARMs that predict the next token sequentially, LLaDA employs a generative approach featuring a forward token-masking process and a reverse process that simultaneously predicts masked tokens using a Transformer network. Trained and evaluated from scratch, LLaDA demonstrates strong scalability and achieves performance comparable to advanced ARM baselines like LLaMA 3 8B across various benchmarks covering general knowledge, math, and code generation. Crucially, the non-autoregressive nature enables bidirectional modeling, which allows LLaDA to effectively address the reversal curse and outperform contemporary models, including GPT-4o, on complex reversal reasoning tasks. These findings confirm that fundamental generative modeling principles, rather than dependence on sequential ARMs, underpin essential LLM capabilities. The work concludes that diffusion models offer a promising new paradigm for building robust, large-scale language models.

Source:

https://openreview.net/pdf?id=KnqiC0znVF

This research examines the data efficiency of Reinforcement Learning with Verifiable Reward (RLVR) when applied to large language models for mathematical reasoning tasks. The paper's most significant finding is the success of 1-shot RLVR, showing that comparable performance to using a large training dataset can be achieved using just a single, carefully selected example. This result suggests that RLVR is effective primarily because it activates the strong latent reasoning capabilities already present in the base model, rather than imparting new domain knowledge. An interesting phenomenon observed during training is "post-saturation generalization," where the model's test performance continues to rise long after training accuracy has saturated and the model has begun overfitting the single example. Ablation studies indicate that while policy gradient loss is the main source of improvement, entropy loss is essential for encouraging the exploration needed to realize this enhanced long-term generalization.

Source:

https://openreview.net/pdf?id=IBrRNLr6JA

The research proposes Parallel Scaling (PARSCALE) as a novel, efficient strategy to enhance Large Language Model (LLM) capacity by increasing parallel computation rather than merely growing the parameter count. This method reuses existing model parameters by feeding multiple parallel input streams (differentiated by learned prefixes) and dynamically combining their outputs into a single prediction. Through extensive testing, the paper develops a new scaling law, showing that scaling computation by a factor of P provides performance gains roughly equivalent to scaling parameters by a factor of O(N logP). PARSCALE demonstrates particular effectiveness in boosting performance on reasoning-intensive tasks like coding and mathematics problems. Critically, this scaling technique offers superior efficiency during inference, requiring significantly less memory and time increase than traditional parameter scaling, thereby making it highly suitable for low-resource edge deployment.

Source:

https://openreview.net/pdf?id=dEi1S731lk

The academic paper introduces Self-play Reinforcement Learning (SeRL), a framework engineered to enhance the reasoning capabilities of Large Language Models (LLMs) specifically in scenarios lacking extensive, high-quality labeled data. SeRL consists of two core, complementary modules: the self-instruction module generates new and diverse training problems from a small seed dataset, ensuring data quality and appropriate difficulty via an online filtering strategy. Simultaneously, the self-rewarding module bypasses the need for external supervision by estimating response rewards using a stable majority-voting mechanism among sampled outputs. This integrated approach facilitates sustained, unsupervised reinforcement learning across multiple training iterations. Experiments demonstrate that SeRL is highly effective, consistently outperforming existing self-play methods and matching the performance levels achieved by models trained on full datasets with verifiable rewards.

Source:

https://openreview.net/pdf?id=ZF93vyH9He

The provided text outlines DYNAACT, a new framework intended to enhance sequential reasoning in Large Language Models (LLMs) by dynamically managing the available actions during complex problem-solving. This approach targets the inefficiency of current methods that either rely on manually defined and restrictive action spaces or utilize unstructured spaces that prove computationally prohibitive for exhaustive searches. DYNAACT addresses this by first estimating a broad action space from a corpus and then using a greedy algorithm to select an optimal, compact action space for each step. The core of the method is a submodular function that ensures the selected subset of actions maintains a balance between high utility (relevance to the current state) and sufficient diversity (avoiding redundant actions). Extensive evaluation on six benchmarks confirms that DYNAACT significantly improves problem-solving accuracy—especially in math and complex reasoning tasks—while also maintaining efficient inference compared to baseline methods.

Source:

https://openreview.net/pdf?id=R24ZqNwoDz

The academic paper introduces KGGen, a novel text-to-knowledge-graph generator designed to overcome the scarcity and poor quality of automatically extracted knowledge graphs (KGs). KGGen utilizes Language Models for initial triple extraction but innovates by employing an iterative clustering and de-duplication process that resolves duplicate entities and relations to reduce sparsity in the final graph representation. To properly assess KG extraction performance, the authors release a new two-part benchmark called Measure of Information in Nodes and Edges (MINE), which evaluates both short-text information retention and knowledge retrieval capabilities in RAG systems. Results on this new benchmark demonstrate that KGGen outperforms competitors like OpenIE and Microsoft's GraphRAG in crucial metrics, including information capture and scaling efficiency across large corpora. The study concludes that KGGen successfully generates KGs with more concise, generalizable entities and relations, which is essential for maximizing utility in downstream applications like embeddings and information retrieval.

Source:

https://openreview.net/pdf?id=YyhRJXxbpi

The academic paper presents the Self-Adapting LLM (SEAL) framework, designed to allow large language models to overcome their static nature by transforming and generating their own fine-tuning data. This mechanism involves the model producing a "self-edit," which consists of natural-language instructions that specify synthetic data, tool invocations, or optimization hyperparameters for adaptation. Training is managed by an outer reinforcement learning (RL) loop that rewards the model based on the improved performance achieved after the self-edit results in persistent weight updates via supervised fine-tuning. Evaluations show that SEAL significantly enhances both knowledge incorporation of new factual data and few-shot generalization on abstract reasoning tasks. Ultimately, the authors propose this work as a viable strategy for enabling models to pursue self-directed, continual learning in preparation for a future where traditional human-generated data sources are exhausted.

Source:

https://openreview.net/pdf?id=JsNUE84Hxi