AI Post Transformers

The provided sources explore advanced techniques for optimizing large language model (LLM) inference, specifically by addressing the memory bottlenecks of the Key-Value (KV) cache. KVQuant introduces a high-precision quantization framework that utilizes per-channel scaling, non-uniform datatypes, and sparse outlier handling to compress activations to sub-4-bit precision with minimal accuracy loss....

The Deepmind February 3, 2023 paper "Accelerating Large Language Model Decoding with Speculative Sampling introduced speculative sampling, a novel algorithm designed to increase the speed of Large Language Model (LLM) decoding without altering the final output. The researchers utilize a smaller, faster draft model to predict multiple potential tokens, which are then verified in parallel by a...

The January 26, 2026 Stanford research paper introduces Agentic Plan Caching (APC), a novel framework designed to reduce the high operational costs of Large Language Model (LLM) agents. Traditional caching methods often fail because agent workflows are highly dynamic and dependent on external environments, making simple input-output storage ineffective. The APC framework solves this by extracting...

This February 2026 research paper introduces Bidaw, a novel system designed to optimize the performance of interactive Large Language Model (LLM) serving. The authors address inefficiencies in existing key–value (KV) caching methods, where the separation of computational engines and storage layers leads to high latency and redundant data processing. Bidaw implements bidirectional awareness, allowi...

This FAST26 February 24, 2026 paper introduces CacheSlide, an innovative system designed to accelerate Large Language Model (LLM) serving by improving KV cache reuse. The researchers identify that complex agent-based workflows often cause positional misalignment, where cached data becomes unusable when text segments shift in absolute position. To resolve this, they propose Relative-Position-Depend...

The February 3, 2026 research paper in collaboration between the National University of Singapore, USTC, University of Toronto and the Sea AI Lab introduces Cortex, a specialized caching system designed to address high latency and financial costs in LLM agent applications. Unlike standard models, agents frequently perform repetitive external data retrievals that lead to significant delays and expe...

The January 29, 2026 research collaboration between Stanford University, SambaNova Systems, Inc and UC Berkeley introduce ACE (Agentic Context Engineering), a novel framework designed to improve how large language models learn and adapt through context rather than weight updates. Unlike traditional methods that suffer from brevity bias or context collapse by summarizing information too aggressivel...

The global AI agents market is experiencing explosive growth, with projections suggesting it could reach nearly $183 billion by 2033. This surge is fueled by a transition from simple bots to autonomous systems capable of executing complex, multi-step workflows across sectors like finance, healthcare, and retail. North America currently leads the industry, supported by heavy investments from tech g...

This December 2025 research introduces Contextual Sample Efficiency (CSE), a novel algorithm designed to improve zero-shot generalization in reinforcement learning using minimal training data. Standard methods often require expensive, diverse simulations to prepare agents for new environments, but this approach leverages linear approximations of environmental dynamics to bypass extensive sampling....

Leading researchers propose a shift away from agentic AI, which autonomously pursues goals and poses catastrophic risks such as deception and loss of human control. To mitigate these dangers, they introduce the concept of Scientist AI, a non-agentic framework designed for understanding the world rather than acting within it. This system utilizes a probabilistic world model to generate causal theor...

The 2025 research introduces PPDS, an innovative dialogue system designed to solve character inconsistency in open-domain AI conversations. Researchers developed a persona extraction model based on the T5 architecture to automatically build a massive, diverse dataset from millions of social media comments. The primary dialogue engine utilizes a unified Transformer (UniLM) backbone, which efficient...

This December 2025 paper introduces SGI-Bench, a comprehensive framework designed to evaluate the capabilities of autonomous scientific agents across diverse research workflows. The benchmark spans multiple disciplines, including chemistry, materials science, and astronomy, by challenging models with tasks like experimental design, numerical modeling, and data interpretation. Through a series of s...

This 2022 paper introduces Persona-Adaptive Attention (PAA), a specialized framework designed to improve dialogue systems by better integrating persona descriptions and conversational history. The researchers address the challenge of balancing these two information sources by using a weighting and masking mechanism that dynamically prioritizes relevant details while filtering out redundant data. T...

The 2023 researchers introduce PersonaPKT, a novel framework designed to create personalized dialogue agents that maintain a consistent personality without needing explicit, private user descriptions. By utilizing parameter-efficient transfer learning, the system represents individual personas as continuous vectors or "prefixes," which adds less than 0.1% of new trainable parameters to a...

Researchers have developed an automated pipeline to identify persona vectors, which are linear directions in a language model's activation space that correspond to specific personality traits like evil, sycophancy, or hallucination. These vectors allow developers to monitor and control a model's behavior during both deployment and training by projecting internal states onto these identif...

This research article introduces a computational analysis pipeline designed to identify objective electrophysiological biomarkers for schizophrenia and bipolar disorder. By utilizing multi-electrode array recordings from patient-derived cerebral organoids and two-dimensional neurons, the study establishes a method for distinguishing psychiatric conditions through neural network dynamics. The resea...

This July 2025 research article investigates the use of Large Language Model (LLM) embeddings to predict Big Five personality traits using data from Reddit. The study demonstrates that custom-trained deep learning models using these embeddings significantly outperform zero-shot inference and traditional linguistic feature engineering. Through psychometric validation, the authors confirm that these...

Bloom is an open-source agentic framework designed to automate the development and execution of behavioral evaluations for frontier AI models. Unlike traditional static benchmarks, it utilizes a four-stage pipeline—Understanding, Ideation, Rollout, and Judgment—to generate diverse, targeted scenarios that quantify specific traits like sycophancy, sabotage, and bias. The tool is highly configurable...

This research paper analyzes the labor market effects of generative artificial intelligence using high-frequency payroll data through mid-2025. The authors identify a 13 percent relative decline in employment for early-career workers, specifically those aged 22–25, in occupations with high AI exposure. While overall national employment remains robust, the study finds that entry-level hiring has st...

The 2019 Microsoft paper introduced UNILM, and it never really took off because GPT2 followed through without the need of any encoder and GPT3 pushed forward further showing scale gives generalization. It's still an interesting historical footnote. UNILM is a new unified language model designed by Microsoft researchers to handle both natural language understanding and generation. Unlike previ...

The 2019 OpenAI Procgen Benchmark is a suite of 16 procedurally generated environments created to measure the generalization and sample efficiency of reinforcement learning agents. Unlike traditional benchmarks with fixed layouts, these games use algorithmic randomization to ensure agents develop robust skills rather than simply memorizing specific trajectories. Research using this tool reveals th...

This February 13, 2026 Tencent research introduces Generalized On-Policy Distillation (G-OPD), a framework that refines how smaller AI models learn from larger or specialized teachers. By establishing a mathematical link between distillation and reinforcement learning, the authors demonstrate that traditional methods are limited by a rigid weighting of rewards. They propose ExOPD, a technique usin...

This technical report published on February 17, 2026 introduces GLM-5, a next-generation flagship language model developed to master agentic tasks, complex coding, and autonomous reasoning. To achieve state-of-the-art efficiency, the architecture utilizes a Mixture-of-Experts (MoE) framework combined with DeepSeek Sparse Attention and specialized Multi-token Prediction. The model excels at end-to-...

The research published on February 15, 2026 in a joint collaboration between University of Southern California, Microsoft and University of Pennsylvania introduces Experiential Reinforcement Learning (ERL), a novel training framework designed to help language models learn from their own interactions more effectively than standard reinforcement learning. Unlike traditional methods that rely solely...

The 2021 Google Research, Brain Team paper "Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning" introduces Policy Similarity Embeddings (PSEs), a novel framework designed to help reinforcement learning (RL) agents apply their skills to unfamiliar tasks. Traditional methods often struggle with generalization, failing when minor visual changes occur i...