shaan_tyagi_victor_blogs

 source :  https://arxiv.org/abs/2609.11911 From Claude, Here's the same paper, but in plain simple language, with each point broken into two short lines. 1. The Problem AI agents used to just do one task and stop. Now they run continuously, so we need a way to decide when to keep going, stop, or change course. 2. The Idea: "Artificial Id" The researchers created a new part called an "artificial id." Its job is to be the agent's internal drive that decides continue/stop/change — automatically, not hand-coded. 3. Why "Id" and "Ego"? They borrowed the words from Freud, but only as labels. "Id" = the drive, "Ego" = the reasoning brain (like an LLM). No deep psychology claim intended. 4. The Test Setup They built a tiny simulated world, like a digital "Petri dish." A very simple 20-number controller lived in it, with no instructions or rewards given. 5. How It Learned (No Rewards!) Controll...

 Here is the **fully commented code** with detailed explanations for every logical block, along with **simulated but realistic results** based on the paper’s benchmarks and standard LLM performance in 2026. You can copy this directly into your blog or Python file. --- ## 1. The Complete Code with Exhaustive Comments ```python """ ================================================================================ COMPLETE COMPARISON: NRI vs GPT-4o vs Gemini vs DeepSeek vs Perplexity Sonar Zero-Shot Logical Rule Induction HOW TO RUN: 1. Install dependencies: pip install openai google-generativeai numpy pandas tabulate python-dotenv 2. Clone NRI: git clone https://github.com/phuayj/neural-rule-inducer.git && cd neural-rule-inducer && pip install -e . 3. Create a .env file with your API keys (OPENAI_API_KEY, GEMINI_API_KEY, etc.) 4. Run this script: python compare_models.py ================================================================================ "...

 Here is the plain-text version of the provided content. All code blocks, file tags, and markdown formatting have been removed. The charts are referenced but not included; their key observations and data are fully described in text. USING GEMINI AND DEEPSEEK --- # Plain‑Text Performance Evaluation and Comparison ## 1. Step‑by‑Step Performance Evaluation Protocol To rigorously compare models **without** the paper’s principles versus models **with** the paper’s principles (*“Iterative Finetuning is Mostly Idempotent”*, arXiv:2605.01130), a recursive N‑cycle fine‑tuning pipeline is used. **Pipeline Overview** - Start from a base model checkpoint (M₀). - Two parallel paths are followed for N cycles (N = 0 to 5): **Without Paper Principles (Naive Continual Loop)**   - The model’s weights are updated continuously across cycles.   - At each cycle i, the current model Mᵢ generates synthetic outputs Dᵢ.   - The model is then trained directly on its own generate...

Here is the content formatted as clean, simple plain text suitable for copying into Google Blogs (no special formatting, no backticks, no markdown). FROM DEEP SEEK AND GEMINI : Bayesian Self-Escalation in Hierarchical LLM Agents: Key Algorithms and Benchmark Results Source Information - Paper Title: Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents - Author: Nadeem Shaikh - arXiv ID: 2608.24087 - Repository & Code: github.com/nadeem-shaikh/llm-self-escalation (search on Google) Core Idea in Simple Terms Current LLM agent systems route tasks in two ways: 1. Static Pre-Routing: Deciding whether a query goes to a small model or a large model before generation starts. 2. Post-Hoc Retries: Generating the full response with a small model first, verifying it afterwards, and retrying with a large model if it fails. This paper introduces Mid-Generation Bayesian Self-Escalation. The agent monitors its own confidence token-by-token during generation. As soon as...

  European Management Review (EMR) Best Paper Award Paper Camparison. Using DeepSeek and Gemini :  Here is the content formatted for a Google Blog post. I have broken down the dense text into scannable sections with clear headings, bullet points, and callouts to make it engaging for a business audience. --- **Title:** The Bottom Line: Are Award-Winning Management Theories Actually Profitable? **Subtitle:** A 10-year financial autopsy of the EMR Best Paper winners (2016-2025). --- ### Executive Summary For years, management frameworks have promised to revolutionize the workplace. But do they actually deliver returns, or are they just academic theory? We analyzed the financial performance of firms that adopted the **EMR Best Paper award-winning concepts** over the last decade. The verdict is clear: **Most of these frameworks are highly profitable.** However, there is one notable exception that carries a "penalty" for investors. Let’s dive into the data. --- ### 1. The Scorecard...