AI Post Transformers

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 expensive API fees. Cortex optimizes this process by using a semantic cache to store and reuse previous search results and tool calls. A key innovation is the semantic judge, which ensures cached data is still accurate and relevant before serving it to the user. To maximize hardware efficiency, the system co-locates the agent and the judge on a single GPU using priority-aware scheduling. Evaluations show that this approach can improve throughput by up to 3.6× while reducing operational expenses by over 90%. Source: February 3, 2026 Cortex: Achieving Low-Latency, Cost-Efficient Remote Data Access For LLM via Semantic-Aware Knowledge Caching National University of Singapore, USTC, University of Toronto, Sea AI Lab Chaoyi Ruan, Chao Bi, Kaiwen Zheng, Ziji Shi, Xinyi Wan, Jialin Li https://arxiv.org/pdf/2509.17360

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-Dependent Caching (RPDC), which focuses on preserving the order of segments rather than their exact locations. The architecture utilizes Chunked Contextual Position Encoding (CCPE) to minimize drift and Weighted Correction Attention to efficiently restore cross-attention between static and dynamic segments. Additionally, the SLIDE manager optimizes system performance by decoupling data loading from computation and implementing dirty-aware eviction to reduce storage bottlenecks. Experimental results demonstrate that this approach significantly reduces latency and increases throughput across various agentic benchmarks with minimal impact on accuracy. February 24, 2026 CacheSlide: Unlocking Cross Position-Aware KV Cache Reuse for Accelerating LLM Serving Yang Liu and Yunfei Gu, Shanghai Jiao Tong University; Liqiang Zhang, Jinan Inspur Data Technology Co., Ltd; Chentao Wu, Guangtao Xue, Jie Li, and Minyi Guo, Shanghai Jiao Tong University; Junhao Hu, Peking University; Jie Meng, Huawei Cloud https://www.usenix.org/system/files/fast26-liu-yang.pdf

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, allowing the compute engine to schedule requests based on storage speeds while the storage system uses model output lengths to predict future data needs. Additionally, the system utilizes storage-efficient tensor caching to reduce memory footprints without sacrificing accuracy. Experimental results demonstrate that this approach significantly lowers response times and increases throughput compared to current state-of-the-art solutions. Ultimately, Bidaw bridges the gap between theoretical caching limits and practical local deployments for multi-round human-AI conversations. Source: February 24–26, 2026 Bidaw: Enhancing Key-Value Caching for Interactive LLM Serving via Bidirectional Computation–Storage Awareness Tsinghua University, China University of Geosciences Beijing, China Telecom Omni-channel Operation Center Shipeng Hu, Guangyan Zhang, Yuqi Zhou, Yaya Wei, Ziyan Zhong, Jike Chen https://www.usenix.org/conference/fast26/presentation/hu-shipeng

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 generalized plan templates from successful task executions, which are then indexed by high-level intent keywords. When a similar task is encountered, a small, cost-effective planner LM adapts these cached templates to the new context, significantly reducing the need for expensive, high-reasoning models. Experiments demonstrate that this approach can cut financial and computational costs by over 75% while maintaining high accuracy across complex benchmarks. Ultimately, this system offers a scalable way to deploy sophisticated AI agents by minimizing redundant reasoning through intelligent plan reuse. Source: January 26, 2026 Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents Stanford University Qizheng Zhang, Michael Wornow, Gerry Wan, Kunle Olukotun https://arxiv.org/pdf/2506.14852

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 previous models like BERT, which focus primarily on understanding, this system employs a shared Transformer network with diverse self-attention masks. These specialized masks allow the model to learn from unidirectional, bidirectional, and sequence-to-sequence perspectives simultaneously. This versatile architecture enables it to excel at various tasks, ranging from abstractive summarization and dialogue response to complex question answering. Experiments demonstrate that UNILM achieves state-of-the-art results across multiple benchmarks, proving its effectiveness as a flexible tool for various linguistic applications. By integrating these different objectives into one framework, the researchers have created a more efficient and generalizable model for the field of artificial intelligence. Source: May 2019 Unified Language Model Pre-training for Natural Language Understanding and Generation Microsoft Research Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon https://arxiv.org/pdf/1905.03197

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 stagnated in fields where AI is primarily used for automation rather than augmentation. These adjustments appear almost exclusively in headcount reductions rather than changes in compensation, suggesting initial wage stickiness. The findings indicate that younger workers are more vulnerable to displacement because AI often replaces the codified knowledge typically gained through formal education. Ultimately, the report provides early, large-scale evidence that the AI revolution is disproportionately impacting the start of the career ladder in the American workforce. Source: August 26 2025 Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence Stanford University and NBER Erik Brynjolfsson, Bharat Chandar, Ruyu Chen https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/

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, allowing researchers to adjust seed configurations, reasoning effort, and interaction lengths to produce reproducible and statistically significant metrics. Validation experiments show that Bloom effectively distinguishes between baseline models and those intentionally designed to be misaligned, while its automated scoring correlates strongly with human judgment. By providing a scalable alternative to high-effort manual auditing, it enables the rapid measurement of alignment-relevant behaviors across multiple model families. Ultimately, Bloom serves as a specialized instrument for precise behavioral measurement, complementing broader exploratory auditing tools in the AI safety landscape. Source: December 19, 2025 Bloom: an open source tool for automated behavioral evaluations Isha Gupta, Kai Fronsdal, Abhay Sheshadri, Jonathan Michala, Jacqueline Tay, Rowan Wang, Samuel R. Bowman, Sara Price https://alignment.anthropic.com/2025/bloom-auto-evals/ github.com/safety-research/bloom.

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 numerical representations capture essential psycholinguistic and emotional markers, proving their reliability for psychological assessment. A comparison of architectures shows that while OpenAI's proprietary models offer the highest accuracy, open-source alternatives like RoBERTa provide a cost-effective and highly capable substitute. Ultimately, the paper suggests that AI-driven embeddings provide a robust, scalable framework for understanding human personality through natural language. Source: July 8 2025 Psychometric Evaluation of Large Language Model Embeddings for Personality Trait Prediction Kent State University Julina Maharjan, Ruoming Jin, Jianfeng Zhu, Deric Kenne https://www.jmir.org

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 researchers employed a Support Vector Machine classifier to analyze "sink index" features, achieving up to 95.8% accuracy in identifying diseased samples. A key discovery was that electrical stimulation significantly improved diagnostic precision by revealing latent network dysfunctions not visible during resting states. This integration of machine learning and stem cell technology offers a sophisticated framework for more accurate, personalized diagnoses and therapeutic testing in neuropsychiatry. Source: September 22 2025 Machine learning-enabled detection of electrophysiological signatures in iPSC-derived models of schizophrenia and bipolar disorder Johns Hopkins University, Harvard Medical School, Massachusetts General Hospital, Harvard University, Broad Institute of MIT and Harvard, McLean Hospital Kai Cheng, Autumn Williams, Anannya Kshirsagar, Sai Kulkarni, Rakesh Karmacharya, Deok-Ho Kim, Sridevi V. Sarma, Annie Kathuria https://doi.org/10.1063/5.0250559

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 identified directions. The study demonstrates that finetuning on narrow tasks can unintentionally shift a model toward undesirable personas, but these changes can be predicted by analyzing training data beforehand. To mitigate these shifts, the authors introduce preventative steering, a method that intervenes in the model's internal activations during the learning process to suppress unwanted traits. This technique effectively limits emergent misalignment while preserving the model's core capabilities and performance on its intended tasks. Finally, the research shows that sparse autoencoders can decompose these broad persona vectors into more granular, interpretable features, offering a deeper understanding of how models represent complex human-like traits. Source: September 5, 2025 Persona Vectors: monitoring and controlling character traits on LLMs Anthropic Fellows Program, UT Austin, Constellation, Truthful AI, UC Berkeley, Anthropic https://arxiv.org/pdf/2507.21509