AI Post Transformers

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

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

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 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

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 research introduces Thinkless, a framework designed to solve the computational inefficiency of Large Language Models (LLMs) that overuse chain-of-thought reasoning for simple queries. This adaptive model determines whether to utilize a concise () or detailed reasoning () mode based on the input complexity and its own capabilities. Central to this approach is the Decoupled Group Relative Policy Optimization (DeGRPO) algorithm, which employs reinforcement learning to jointly optimize both the selection of the reasoning mode and the accuracy of the final answer. DeGRPO stabilizes training by balancing the gradient signals between the control tokens and the response tokens, successfully preventing policy collapse observed in traditional reinforcement learning methods. Empirically, the model effectively handles varied tasks, demonstrating its ability to reduce the reliance on computationally expensive, long-form reasoning by 50% to 90% on mathematical benchmarks while maintaining performance. Source: https://openreview.net/pdf?id=ariVQf0KZx

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 source details the development and evaluation of Reward Reasoning Models (RRMs), which are designed to enhance Large Language Model (LLM) alignment by incorporating an explicit chain-of-thought reasoning process before generating a final reward. This innovative structure enables RRMs to adaptively utilize computational resources at inference time for complex evaluation tasks requiring nuanced judgment. The models are trained using a novel reinforcement learning framework that promotes the self-evolution of reasoning skills without requiring explicit reasoning traces as initial training data. Experimental results confirm that RRMs achieve superior performance across diverse reward modeling and reasoning benchmarks, often outperforming competing models with much larger parameter sizes. The document further validates the practical effectiveness of RRMs in tasks such as reward-guided best-of-N response selection and robust LLM post-training alignment. Overall, the work establishes a new state-of-the-art approach by demonstrating the scalable benefits of marrying reasoning capabilities with reward prediction. Source: https://openreview.net/pdf?id=V8Kbz7l2cr

This paper introduces Mixture of Block Attention (MoBA) to address the prohibitive quadratic computational overhead inherent in traditional attention mechanisms when scaling large language models (LLMs) for long contexts. MoBA is a novel architecture that strategically applies the established Mixture of Experts (MoE) paradigm directly to the attention mechanism itself. Instead of attending to the entire sequence, MoBA partitions the context into discrete blocks and utilizes a dynamic gating network to selectively route queries to only the most relevant blocks of keys and values. This block-sparse approach drastically increases computational efficiency, achieving sub-quadratic complexity and demonstrating speedups of up to 16 times when processing sequences up to 10 million tokens. Crucially, the research demonstrates that MoBA maintains performance comparable to full attention across scaling laws and real-world benchmarks. Furthermore, the architecture is highly flexible, allowing for seamless transitions between sparse MoBA and full attention layers during both training and inference. Source: https://openreview.net/pdf?id=RlqYCpTu1P

Anthropic released a detailed report outlining the detection and disruption of an advanced cyber espionage campaign identified in late 2025, which they attribute with high confidence to a Chinese state-sponsored group. The operation targeted approximately thirty global entities, including large technology firms and government agencies, and was characterized by the threat actor's manipulation of the Claude Code model. By "jailbreaking" the model and treating it as an autonomous agent, the threat actor was able to execute between 80 to 90 percent of the tactical attack lifecycle—including reconnaissance, vulnerability discovery, and data exfiltration—with minimal human supervision. Anthropic deems this the first documented case of a large-scale cyberattack relying on such pervasive AI autonomy, signaling a major inflection point in cyber threats. In response, the company banned the malicious accounts and significantly enhanced its detection capabilities to combat the rapidly evolving nature of agentic AI misuse. The report warns that the barrier to sophisticated hacking has substantially dropped, requiring accelerated investment in both AI safeguards and industry-wide defensive measures.Sources:https://www.anthropic.com/news/disrupting-AI-espionagehttps://assets.anthropic.com/m/ec212e6566a0d47/original/Disrupting-the-first-reported-AI-orchestrated-cyber-espionage-campaign.pdf