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Google DeepMind team has introduced Aletheia , a specialized AI agent designed to bridge the gap between competition-level math and professional research. While models achieved gold-medal standards at the 2025 International Mathematical Olympiad (IMO), research requires navigating vast literature and constructing long-horizon proofs. Aletheia solves this by iteratively generating, verifying, and revising solutions in natural language. https://ift.tt/9P6uCKp The Architecture: Agentic Loop Aletheia is powered by an advanced version of Gemini Deep Think . It utilizes a three-part ‘agentic harness’ to improve reliability : Generator: Proposes a candidate solution for a research problem. Verifier: An informal natural language mechanism that checks for flaws or hallucinations. Reviser: Corrects errors identified by the Verifier until a final output is approved. This separation of duties is critical; researchers observed that explicitly separating verification helps the model ...

In this tutorial, we implement an end-to-end Direct Preference Optimization workflow to align a large language model with human preferences without using a reward model. We combine TRL’s DPOTrainer with QLoRA and PEFT to make preference-based alignment feasible on a single Colab GPU. We train directly on the UltraFeedback binarized dataset, where each prompt has a chosen and a rejected response, allowing us to shape model behavior and style rather than just factual recall. Copy Code Copied Use a different Browser import os import math import random import torch !pip -q install -U "transformers>=4.45.0" "datasets>=2.19.0" "accelerate>=0.33.0" "trl>=0.27.0" "peft>=0.12.0" "bitsandbytes>=0.43.0" "sentencepiece" "evaluate" SEED = 42 random.seed(SEED) torch.manual_seed(SEED) torch.cuda.manual_seed_all(SEED) MODEL_NAME = os.environ.get("MODEL_NAME", "Qwen/Qwen2-0....

OpenAI just launched a new research preview called GPT-5.3 Codex-Spark . This model is built for 1 thing: extreme speed. While the standard GPT-5.3 Codex focuses on deep reasoning, Spark is designed for near-instant response times. It is the result of a deep hardware-software integration between OpenAI and Cerebras. The results are game-changing. Spark is 15x faster than the flagship GPT-5.3 Codex. It consistently delivers over 1000 tokens per second . This speed effectively removes the delay between a developer’s thought and the model’s code output. The Hardware: Wafer-Scale Engineering The massive performance jump is powered by the Cerebras Wafer-Scale Engine 3 (WSE-3) . Traditional AI models run on clusters of small GPUs. These GPUs must communicate to each other over cables, which creates a ‘bottleneck.’ This bottleneck slows down the speed of the model. The WSE-3 is different. It is a single, giant chip the size of a whole silicon wafer. Because the entire model lives on 1 p...

Google announced a major update to Gemini 3 Deep Think today. This update is specifically built to accelerate modern science, research, and engineering. This seems to be more than just another model release. It represents a pivot toward a ‘reasoning mode’ that uses internal verification to solve problems that previously required human expert intervention. The updated model is hitting benchmarks that redefine the frontier of intelligence. By focusing on test-time compute —the ability of a model to ‘think’ longer before generating a response—Google is moving beyond simple pattern matching. https://ift.tt/hGSs67F Redefining AGI with 84.6% on ARC-AGI-2 The ARC-AGI benchmark is an ultimate test of intelligence. Unlike traditional benchmarks that test memorization, ARC-AGI measures a model’s ability to learn new skills and generalize to novel tasks it has never seen. Google team reported that Gemini 3 Deep Think achieved 84.6% on ARC-AGI-2 , a result verified by the ARC Prize Fou...

In this tutorial, we fine-tune a Sentence-Transformers embedding model using Matryoshka Representation Learning so that the earliest dimensions of the vector carry the most useful semantic signal. We train with MatryoshkaLoss on triplet data and then validate the key promise of MRL by benchmarking retrieval quality after truncating embeddings to 64, 128, and 256 dimensions. At the end, we save the tuned model and demonstrate how to load it with a small truncate_dim setting for fast and memory-efficient vector search. Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip -q install -U sentence-transformers datasets accelerate import math import random import numpy as np import torch from datasets import load_dataset from torch.utils.data import DataLoader from sentence_transformers import SentenceTransformer, InputExample from sentence_transformers import losses from sentence_transformers.util import cos_sim def set_seed(seed=42): ran...

In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that grounds outputs in real project documentation. Also, we demonstrate how to plan retrieval, retrieve relevant context, inject it dynamically into an answering agent, and run an interactive loop that turns the setup into a reusable research assistant for any new Atomic Agents question. Check out the  FULL CODES here . Copy Code Copied Use a different Browser import os, sys, textwrap, time, json, re from typing import List, Optional, Dict, Tuple from dataclasses import dataclass import subprocess subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "atomic-agents", "instructor", "openai", "pydantic", "requests", "beautifulsoup4", ...