AI Post Transformers

This September 20 2025 paper introduce a novel, efficient architecture for training retrieval models used in retrieval-augmented generation (RAG) systems. This architecture addresses the inefficiency of fine-tuning large models by combining adapters for soft embeddings with a Classifier-as-Retriever (CaR) approach. The soft embeddings, created by lightweight layers in a frozen small language model...

This Meta September 24 2025 paper provides an extensive overview of Code World Model (CWM), a 32-billion-parameter dense decoder-only Transformer designed for coding and reasoning tasks, highlighting its architecture and multi-stage training process. Training involves pre-training, mid-training, and post-training stages which include supervised fine-tuning (SFT) and joint reinforcement learning (R...

The September 25 2025 paper introduces Tree-based Group Relative Policy Optimization (Tree-GRPO), a new reinforcement learning (RL) method designed to enhance the agentic capabilities of large language models (LLMs) in multi-turn tasks where supervision is typically sparse. Tree-GRPO addresses the challenges of sparse rewards and heavy rollout costs associated with existing chain-based RL by emplo...

The September 24 2025 paper is a technical report from ByteDance Seed detailing the Seedream 4.0 system, an advanced multimodal image generation model. This single framework efficiently unifies text-to-image synthesis, image editing, and multi-image composition. The core innovation is an efficient diffusion transformer and powerful VAE that facilitates fast generation of high-resolution images (up...

The September 25 2025 dated sources introduce GDPval, a novel benchmark created by OpenAI to evaluate the performance of AI models on economically valuable, real-world tasks. This evaluation spans 44 knowledge work occupations across the top nine sectors contributing to the U.S. GDP, using tasks meticulously crafted by experienced industry professionals. Results indicate that the best frontier mod...

The September 24 2025 paper introduces EmbeddingGemma, a novel, lightweight text embedding model developed by Google DeepMind, built upon the Gemma 3 language model family. The paper details the innovative training methodology, which involves encoder-decoder initialization and geometric embedding distillation from larger models like Gemini Embedding, alongside a "spread-out" regularizer...

The September 25 2035 paper introduces a novel reinforcement learning (RL) algorithm, Controlling Entropy via Gradient-Preserving Policy Optimization (CE-GPPO), designed to fine-tune large language models (LLMs) for complex reasoning tasks. The authors analyze how policy entropy, which represents the balance between exploration and exploitation, becomes unstable in existing methods like Proximal P...

The September 2025 academic paper introduces LLM-Interleaved (LLM-I), a novel, flexible framework for interleaved image-text generation that reframes the task as a tool-use problem to overcome the "one-tool" limitation of unified models. Authored by researchers from Zhejiang University and ByteDance, BandAI, the system uses a central Large Language Model (LLM) or Multimodal LLM (MLLM) ag...

These 14 research papers provide an overview of various compression techniques for Large Language Models (LLMs), primarily focusing on reducing the size and computational overhead of the Key-Value (KV) cache to handle long contexts more efficiently. Several novel methods are detailed, including GVote, an adaptive compression algorithm using query sampling and voting to find an optimal cache budget...

This September 2025 paper describes THOR (Tool-Integrated Hierarchical Optimization via RL), a novel approach designed to enhance the mathematical reasoning and code generation capabilities of Large Language Models (LLMs) by integrating external code-execution tools. The methodology introduces TIRGen, a pipeline for creating high-quality Tool-Integrated Reasoning (TIR) data, which is crucial for t...

The "Anthropic Economic Index report" documents the rapid and uneven adoption of Artificial Intelligence (AI), specifically using data from the company's Claude.ai consumer platform and its enterprise API. The report highlights that AI adoption is geographically concentrated in high-income regions and that the speed of adoption surpasses previous technologies like the internet. Anal...

This September 2025 paper introduces Single-stream Policy Optimization (SPO), a new reinforcement learning algorithm for training Large Language Models (LLMs) developed by Tencent researchers. SPO challenges the prevailing group-based optimization methods like Group Relative Policy Optimization (GRPO), which suffer from high computational waste due to "degenerate groups" and synchronizat...

This September 2025 paper introduces SearchInstruct, a novel framework designed to enhance Supervised Fine-Tuning (SFT) of large language models (LLMs) by constructing high-quality, domain-specific instruction datasets. This approach overcomes challenges like data scarcity and outdated model knowledge by dynamically retrieving external, up-to-date documents to generate accurate, context-grounded a...

This September 2025 paper introduces FlowRL, a novel reinforcement learning (RL) algorithm for large language models (LLMs) that shifts the optimization objective from reward maximization to reward distribution matching via flow balancing. Traditional RL methods like PPO and GRPO tend to over-optimize high-reward paths, leading to limited solution diversity and mode collapse, particularly in compl...

This September 2025 paper source is a research paper from Tencent AI Lab and academic collaborators that introduces EVOL-RL, an Evolution-Oriented and Label-free Reinforcement Learning framework for Large Language Models (LLMs). The paper addresses a critical flaw, termed entropy collapse, in existing label-free self-improvement methods like Test-Time Reinforcement Learning (TTRL), where reliance...

This September 2025 academic paper, titled "REFRAG: Rethinking RAG based Decoding," appears on the alphaXiv pre-print server. It focuses on Reframing Retrieval-Augmented Generation (RAG) within the context of decoding processes. The research likely explores new approaches or improvements to how RAG mechanisms are integrated and utilized during the generation of text. By "rethinking&...

How can pre-computing and reusing Key-Value (KV) caches accelerate inference for Retrieval-Augmented Generation and other long-context LLM tasks? The provided sources identify the same core problem—high latency in Large Language Model (LLM) inference due to processing long, repetitive contexts—and converge on a unified solution: leveraging pre-computed Key-Value (KV) caches. Each source then contr...

This paper published on Nature on September 17 2025, "DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning," details the development of DeepSeek-R1-Zero and DeepSeek-R1, two large language models (LLMs) engineered to enhance reasoning capabilities. The authors explain how reinforcement learning (RL) is used to enable emergent advanced reasoning patterns like self-ref...

This September 2025 paper introduces WebSailor-V2, an open-source deep research agent developed by Alibaba Group's Tongyi Lab. The paper details a post-training pipeline that uses a novel synthetic data construction scheme, SailorFog-QA-V2, and a dual-environment reinforcement learning framework. WebSailor-V2, built on the Qwen3-30B-A3B model, demonstrates state-of-the-art performance among o...

This May 2025 paper introduces TailorKV, a novel hybrid framework designed to optimize Key-Value (KV) cache management in large language models (LLMs) for long-context inference. It addresses challenges like high GPU memory consumption and inference latency that arise from the linear growth of KV cache size with sequence length. TailorKV categorizes Transformer layers into quantization-friendly an...

This April 2025 paper introduces ShadowKV, an innovative inference system for long-context Large Language Models (LLMs) designed to significantly enhance throughput and support larger batch sizes without compromising accuracy. It achieves this by strategically managing the Key-Value (KV) cache: specifically, it compresses the low-rank pre-Rotary Position Embedding (RoPE) key cache on the GPU and o...

This September 2025 paper describes QuantAgent, a novel multi-agent large language model (LLM) framework designed for high-frequency quantitative trading based solely on price-derived market signals. The system decomposes trading decisions into four specialized agents—IndicatorAgent, PatternAgent, TrendAgent, and DecisionAgent—which analyze market dynamics from complementary perspectives and commu...

This July 2025 paper discusses advanced memory optimization techniques for Large Language Models (LLMs), particularly focusing on KV cache management in multi-tenant serving environments. The primary subject, MIRAGE, introduces parameter remapping, a novel method that dynamically repurposes GPU memory allocated for model parameters to expand KV cache capacity, outperforming traditional CPU-offload...

This September 2025 paper introduces LoFT, a novel framework designed to improve Long-Tailed Semi-Supervised Learning (LTSSL) by leveraging parameter-efficient fine-tuning of pre-trained foundation models. The core idea is to enhance confidence calibration and generate more reliable pseudo-labels, which are crucial for addressing the imbalance inherent in long-tailed datasets. Furthermore, the pap...

This April 2025 paper introduces Infini-gram, a novel engine designed to scale n-gram language models to an unprecedented 5 trillion tokens and support unbounded n (∞-gram LMs). Unlike traditional methods that rely on pre-computed count tables, Infini-gram leverages suffix arrays for efficient, low-latency calculation of n-gram and ∞-gram probabilities, even for extremely long contexts. The author...