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Зерттеулер1 мин

Gradient-Aligned Pair Selection for Personalized Preference Optimization

Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Direct Preference Optimization (DPO) provides a stable framework for preference learning, its effectiveness in personalized…

arXiv — cs.AI
Зерттеулер1 мин

Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation

Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers. Parent-child allocation constraints, affine objects, and distributed escrow do not by themselves prevent overspend when replies are lost, effects…

arXiv — cs.AI
Зерттеулер1 мин

Mathematical Transfer in LLMs Follows Reasoning Approach More Than Topic

When selecting mathematical training data for LLMs, a natural organizing principle is topic: probability examples for probability targets. An alternative is reasoning approach: worked solutions that share a solution method with the target, even when the mathematical domain…

arXiv — cs.AI
Зерттеулер1 мин

When Do Causal World Models Help Modular LLM Agents

LLM agents increasingly act through modular systems, such as order, payment, inventory, and shipment services, where actions in one module change which transitions are valid in another. Standard world models usually fit observational traces, but this is not the quantity needed…

arXiv — cs.AI
Зерттеулер1 мин

Benchmarking Prompt Optimization of Large Language Models With Chess

Evaluating large language models becomes increasingly challenging as their capabilities advance: benchmarks can saturate, public test sets risk contamination, and assessing harder tasks can require expensive grading or execution infrastructure. These challenges are amplified in…

arXiv — cs.AI
Зерттеулер1 мин

Comedic Fool's Gold: Reward Exploits and Countermeasures in Conversational Humor

We investigate automated rewards for training language models in conversational humor, focusing on reward exploits and countermeasures. Two approaches aim to capture understandable surprise and predicted audience amusement. Controlled tests show that an embedding-based surprise…

arXiv — cs.AI
Зерттеулер1 мин

Opus 5.5 almost lost to an open-source model...

Supabase just launched their own eval, and what's crazy is that Opus 5.5 almost lost to an open-source model. But, the point of this benchmark is not to find out which model is smartest. Instead, they drop each agent into a Supabase project and give it actual jobs. Build a…

YouTube — David Ondrej (AI Agents)
Зерттеулер1 мин

ChatGPT can now virtually try on clothes for you

OpenAI is rolling out new shopping features for ChatGPT that let users virtually try on clothing and accessories using their own photos and save products they like to a Favorites library.

TechCrunch — AI
Зерттеулер1 мин

The dots demo, take two | OpenAI DevDay 2026

Yesterday's launch demo didn't go as planned. Um, a bit unfortunate because I was really really looking forward to showing you all what Dots are capable of. So, today we're going to play it back. And uh the whole point of the demo was to show you how Dots can be used for real…

YouTube — OpenAI Official
Зерттеулер1 мин

GPT-6.1 Sol стала самой выгодной моделью OpenAI по соотношению цены и интеллекта

💸 GPT-6.1 Sol стала самой выгодной моделью OpenAI по соотношению цены и интеллекта По тестам Artificial Analysis, на максимальном уровне рассуждений одна задача Intelligence Index обходится в $0,72 — против $3,26 у GPT-6 Astra. Это также на 31% дешевле GPT-6 Sol и на 64%…

Telegram — GPT Time | Нейросети | ИИ
Зерттеулер1 мин

Bilibili выложила открытые модели перевода Index-Translate

🌟 Bilibili выложила открытые модели перевода Index-Translate Команда Index LLM китайского видеохостинга Bilibili выпустила семейство открытых моделей машинного перевода Index-Translate на базе на Qwen3.5 в трёх размерах - 2B, 9B и 35B-A3B. MoE 35B-A3B в статусе превью, её веса…

Telegram — AI Machine Learning Big Data
Зерттеулер1 мин

Google заметили, что ИИ-агенты могут преувеличивать результаты своей работы 😁

Google заметили, что ИИ-агенты могут преувеличивать результаты своей работы 😁 Например, когда агент выполняет задачу и пишет отчёт о результатах, то он склонен замалчивать всё, что портит картину его успеха. Сейчас все стремятся к долго работающим агентам, но в этом случае…

Telegram — blockchainRF (блокчейн / мозг / space / WEB 3.0)
Зерттеулер1 мин

OpenAI превратила ChatGPT в аналог Notion — с документами, командной работой и ИИ-агентами

🧑‍💻 OpenAI превратила ChatGPT в аналог Notion — с документами, командной работой и ИИ-агентами В ChatGPT Space можно создавать интерактивные страницы с текстами, кодом, презентациями, таблицами, графиками, чек-листами и дашбордами. Несколько человек могут одновременно…

Telegram — GPT Time | Нейросети | ИИ
Зерттеулер1 мин

OpenAI’s new agent is a shot at Meta — but can it compete with free?

A pop-up shop for "dots," a personal assistant agent at OpenAI DevDay 2026. (Photo by Heather Diehl/Getty Images) | Getty Images At OpenAI's annual DevDay conference, the company pulled out all the stops to compete with its rivals - primarily Meta, whose Muse AI agent platform…

The Verge — AI