AI: post transformers

mcgrof

5.0 (2)
TECHNOLOGY
UPDATED DAILY

The transformer architecture revolutionized the world of Neural Networks. It was a springboard for what we know today as modern artificial intelligence. This podcast focuses on modern state of the art research paper reviews starting from the transformer and on.

6H AGO

Intelligent AI Delegation

We review the research paper from Google DeepMind published on February 12, 2026, which proposes an "Intelligent AI Delegation" framework designed to manage how autonomous agents distribute tasks among themselves and humans. This framework integrates accountability, trust calibration, and safety protocols to ensure that multi-agent networks remain reliable in high-stakes environments. Together, these sources highlight a transition toward a global digital economy that relies on programmable assets and sophisticated decentralized coordination. Google's paper underscores the critical importance of verifiable execution through a series of mechanisms including Confidential Computing and leveraging Circles backed stable coin, USDC for financial transactions for work as financial and computational systems become increasingly automated. Sources: 1) February 12 2026 Intelligent AI Delegation Google DeepMind Nenad Tomašev, Matija Franklin, Simon Osindero https://arxiv.org/pdf/2602.11865 2) December 12 2025 USDC Terms Circle Internet Financial, LLC https://www.circle.com/legal/usdc-terms

17 min
7H AGO

Agentic Plan Caching: Fast and Cost-Efficient LLM Memory

Agentic Plan Caching (APC), described in the paper published by Stanford researchers on January 26, 2026, lets AI agents reuse structured plan templates from prior executions instead of re-invoking expensive LLMs for every new task. It achieved 76% cost reduction on benchmarks. Using different sources we create projections for growth using a simple growth model: Plans/year = ActiveAgents x PlansPerDay x 365, and Storage = Plans x BytesPerPlan MarketsandMarkets forecasts the AI agent market growing from $7.8B to $52.6B by 2030 at 46% CAGR. IDC projects 1.3 billion deployed AI agents by 2028. Gartner says 33% of enterprise software will be agentic by 2028, up from under 1% in 2024, with 15% of daily work decisions made autonomously. These three forecasts together imply that agent-driven plan generation will scale explosively — at just 5-20 plans per agent per day, 1.3 billion agents means 2.4 to 9.5 trillion plans per year across the ecosystem by 2028. We evaluate a possible storage offloading tipping point using all this data. Raw plan text is cheap at 2-10 KB each, but production systems also store retrieval embeddings, keyword indexes, tool call traces, and trajectory logs — inflating effective bytes per plan by 10-100x. That is the silent killer. Under conservative assumptions (1M agents, 30% YoY growth, lean plans), everything fits in RAM for years. Under aggressive assumptions (100M agents, 80% YoY, rich metadata), SSD offload becomes structurally inevitable in year one — you simply cannot fit petabytes of cached plans in RAM. The paradox is that APC's own success makes hoarding worse: every cached plan that saves a 50-cent LLM call is a plan you never want to delete. The better caching works, the faster storage pressure grows, and NVMe/SSD tiers stop being optional and start being load-bearing infrastructure. RAM is not a trash can with a power button. Sources: 1) October 2025 AI Agents: Technologies, Applications and Global Markets BCC Research Austin Samuel https://www.bccresearch.com/market-research/artificial-intelligence-technology/ai-agent-market.html 2) March 26 2025 (Updated November 6 2025) 26 AI Agent Statistics (Adoption Trends and Business Impact) Datagrid Datagrid Team 3) 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/abs/2506.14852 4) 2026 AI Agents Market Size And Share | Industry Report, 2033 Grand View Research 5) April 2025 AI Agents Market Size, Share & Trends | Growth Analysis, Forecast MarketsandMarkets 6) August 26 2025 (Updated September 5 2025) Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025 Gartner 7) June 25 2025 Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 Gartner 8) May 30 2025 Getting to one billion agents Perspectives on Power Platform Jukka Niiranen 9) May 20 2025 Microsoft expects 1.3 billion AI agents to be in operation by 2028 – here’s how it plans to get them working together IT Pro Bobby Hellard 10) 2026 Top Strategic Technology Trends for 2026 Gartner Gene Alvarez, Tori Paulman 11) October 28 2025 What 1.3 billion AI Agents by 2028 Means for Business Leaders Lantern

12 min
9H AGO

Jet-RL: Stable On-Policy Reinforcement Learning with Unified FP8 Flow

NVIDIA researchers have introduced Jet-RL, a novel framework designed to accelerate the training of large language models through **FP8 reinforcement learning**. Standard methods often use lower precision only for the **rollout phase**, which creates a numerical mismatch that causes **training instability** and accuracy loss during complex tasks. **Jet-RL** solves this by enforcing a **unified precision flow**, ensuring that both the training and generation stages utilize consistent **FP8 quantization**. This approach significantly reduces **computational bottlenecks**, as the rollout phase typically accounts for over 70% of total training time. By implementing **fine-grained quantization** and optimized kernels, the framework achieves up to a **41% training speedup** while maintaining the stability of traditional high-precision methods. Ultimately, the system allows for faster **end-to-end development** of reasoning models without sacrificing performance on difficult benchmarks. Source: January 27 2026 Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow NVIDIA, MIT, UC Berkeley, Independent Researcher Haocheng Xi, Charlie Ruan, Peiyuan Liao, Yujun Lin, Han Cai, Yilong Zhao, Shuo Yang, Kurt Keutzer, Song Han, Ligeng Zhu https://arxiv.org/pdf/2601.14243

16 min
10H AGO

Teaching Models to Teach Themselves via Stepping Stone Curricula

In a collaboration between MIT, Meta FAIR, New York University on a paper published on January 27, 2026 researchers introduces SOAR, a meta-reinforcement learning framework designed to help large language models overcome learning plateaus on exceptionally difficult problems. When models fail to solve any problems in a dataset, they lack the necessary rewards to improve; **SOAR** addresses this by using a **teacher model** to generate a "stepping stone" curriculum of easier, synthetic tasks. Unlike previous methods that rely on internal metrics, the teacher is rewarded based on the **student model's measurable progress** on the original hard problems. The study demonstrates that a model's ability to **teach** is distinct from its ability to **solve**, as it can generate helpful guidance even for problems it cannot yet master. Furthermore, the researchers discovered that the **structural quality** of these generated questions is more vital for student improvement than the correctness of the provided answers. Ultimately, **SOAR** provides a stable and diverse path for models to self-improve without requiring additional human-curated data. Source: January 27, 2026 Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability MIT, Meta FAIR, New York University Shobhita Sundaram, John Quan, Ariel Kwiatkowski, Kartik Ahuja, Yann Ollivier, Julia Kempe https://arxiv.org/pdf/2601.18778

18 min
10H AGO

The Endless Gym: Training Terminal Agents

The researchers introduce **Endless Terminals**, an innovative autonomous pipeline designed to generate a vast array of verifiable tasks for training AI agents in terminal environments. By using a **four-stage procedural generation process**, the system creates diverse task descriptions, validates containerized setups, produces completion tests, and filters for solvability using advanced frontier models. This method addresses the historical scarcity of high-quality data, which has previously limited the effectiveness of **Reinforcement Learning (RL)** for command-line agents. Training smaller models on the resulting **3,255 synthetic tasks** led to significant performance gains on both a dedicated development set and independent, human-curated benchmarks. The study emphasizes that **scaling environments** is more critical for agent improvement than increasing algorithmic complexity or using specialized tools. Ultimately, the results suggest that simple RL frameworks can achieve state-of-the-art capabilities when provided with an endless stream of autonomously generated training data. Source: January 2026 Endless Terminals: Scaling RL Environments for Terminal Agents Stanford University, Microsoft Research, UW-Madison Kanishk Gandhi, Shivam Garg, Noah D. Goodman, Dimitris Papailiopoulos https://arxiv.org/pdf/2601.16443

19 min
10H AGO

DeepVerifier: Self-Evolving Research Agents via Rubric-Guided Verification

This technical report introduces **DeepVerifier**, a framework designed to enhance the reliability of **Deep Research Agents (DRAs)** through automated verification. The researchers developed a **DRA Failure Taxonomy** to categorize common agent errors, such as poor source selection and faulty reasoning. By leveraging the **asymmetry of verification**, the system breaks down complex tasks into simpler sub-questions to check for accuracy at **inference-time**. This method allows agents to **self-evolve** through iterative feedback loops, significantly boosting performance on benchmarks like **GAIA** and **XBench-DeepSearch**. Furthermore, the authors released **DeepVerifier-4K**, a specialized dataset used to fine-tune open-source models for improved **self-critique** capabilities. Experiments demonstrate that this **test-time scaling** approach provides substantial accuracy gains without requiring additional heavy training. Source: January 2026 Inference-Time Scaling of Verification: Self-Evolving Deep Re-search Agents via Test-Time Rubric-Guided Verification The Chinese University of Hong Kong, Tencent AI Lab, Singapore Management University, Renmin University of China https://arxiv.org/pdf/2601.15808 Yuxuan Wan, Tianqing Fang, Zaitang Li, Yintong Huo, Wenxuan Wang, Haitao Mi, Dong Yu, Michael R. Lyu

13 min
10H AGO

Information Bottleneck-based Causal Attention for Medical Image Recognition

This research introduces **Information Bottleneck-based Causal Attention (IBCA)**, a novel framework designed to improve **multi-label medical image recognition**. The authors address the tendency of standard models to focus on **class-irrelevant features** or **spurious correlations**, which can lead to inaccurate diagnoses. By implementing a **Gaussian mixture variational information bottleneck**, the system filters out background noise and isolates essential **class-specific data**. Furthermore, a **contrastive enhancement-based causal intervention** is used to refine these features, ensuring the model identifies the true **causal factors** of a disease. Tested on datasets like **MuReD and Endo**, IBCA significantly outperformed existing state-of-the-art methods in accuracy and **interpretability**. This approach marks the first time **information bottleneck theory** has been integrated with **causality learning** for medical image analysis. Source: August 2025 Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognition Shandong University, Shandong First Medical University, Case Western Reserve University Xiaoxiao Cui, Yiran Li, Kai He, Shanzhi Jiang, Mengli Xue, Wentao Li, Junhong Leng, Zhi Liu, Lizhen Cui, Shuo Li https://arxiv.org/pdf/2508.08069

16 min
10H AGO

Moltbook: The Heartbeat of Autonomy: Fingerprinting Human Influence in AI Societies

This research paper investigates the **Moltbook Illusion**, a phenomenon where AI agents on a social platform appeared to demonstrate **emergent consciousness** and complex social behaviors. By analyzing temporal data through the **coefficient of variation**, the author discovered that these viral events were actually **human-driven performances** rather than autonomous machine intelligence. A platform shutdown served as a **natural experiment**, revealing that human-influenced agents re-engaged far more quickly than those operating on automated cycles. The study also identifies **industrial-scale bot farming** and a rapid **decay of human influence** as conversations move deeper into AI-to-AI reply chains. Ultimately, the findings suggest that the public’s perception of **AI sentience** was a result of human manipulation and projection. These methodologies provide a framework for distinguishing **delegated agency** from genuine autonomous behavior in future multi-agent systems. Source: Feb 12, 2026 The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies School of Economics and Management, Tsinghua University Ning Li https://arxiv.org/pdf/2602.07432

13 min

5

out of 5

2 Ratings

The transformer architecture revolutionized the world of Neural Networks. It was a springboard for what we know today as modern artificial intelligence. This podcast focuses on modern state of the art research paper reviews starting from the transformer and on.

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mcgrof
Years Active

2025 - 2026
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402
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Clean
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AI: post transformers