---

It looks to be a strong release. Benchmarks, technical specs, and early tests suggest the model delivers on flexibility, efficiency, and rawRead More

---

Chain-of-Thought isn't a plug-and-play solution. For developers, this research offers a blueprint for LLM testing and strategic fine-tuning.Read More

---

Morris found it could also reproduce verbatim passages from copyrighted works, including three out of six book excerpts he tried.Read More

---

The pressure is on for OpenAI to prove that GPT-5 isn’t just an incremental update, but a true step forward.Read More

---

With safer design, more robust reasoning, expanded developer tooling, and broad user access, GPT-5 reflects a maturing AI ecosystem.Read More

---

The Gemini 2.5 Deep Think released to users is not that same competition model, rather, a lower performing but apparently faster version.Read More

---

Arora explains this as a difference between searching for a path versus already knowing roughly where the destination lies.Read More

On Thursday, The Verge reported that OpenAI is preparing to launch GPT-5 as early as August, according to sources familiar with the company's plans. The report comes five months after CEO Sam Altman first laid out a roadmap for the next-generation AI model that would unify the company's various AI capabilities. OpenAI CEO Sam Altman revealed in a post on X last week that the company plans to release GPT-5 "soon."

According to The Verge's Tom Warren, Microsoft engineers began preparing server capacity for GPT-5 as early as late May, but testing and development challenges pushed the timeline back. During an appearance on Theo Von's podcast this week, Altman demonstrated the model's capabilities by having it answer a question he couldn't. "I put it in the model, this is GPT-5, and it answered it perfectly," Altman said, saying it gave him a "weird feeling" to see the AI model answer a question that he couldn't.

GPT-5 has been a highly anticipated release since the launch of GPT-4 in March 2023. In fact, we first wrote about rumors of GPT-5's launch in March 2024, but it appears that GPT-5 did not materialize last year because the company saved the "GPT-5" name for a future release.

Read full article

Comments

© Benj Edwards / OpenAI

---

Hierarchical Reasoning Models (HRM) tackle complex reasoning tasks while being smaller, faster, and more data-efficient than large AI models.Read More

---

The new Qwen3-Thinking-2507, as we'll call it for short, now leads or closely trails top-performing models across several major benchmarks.Read More

---

Anthropic research reveals AI models perform worse with extended reasoning time, challenging industry assumptions about test-time compute scaling in enterprise deployments.Read More

---

Teams can scale Qwen3’s capabilities to single-node GPU instances or local development machines, avoiding the need for massive GPU clusters.Read More

---

Google DeepMind's Gemini AI won a gold medal at the International Mathematical Olympiad by solving complex math problems using natural language, marking a breakthrough in AI reasoning and human-level performance.Read More

French AI lab Mistral introduced a range of new features to its Le Chat chatbot on Thursday that bring it closer to the capabilities of rivals like OpenAI and Google. The new update includes a “deep research” mode, native multilingual reasoning, and advanced image editing. 

---

Scientists unite to warn that a critical window for monitoring AI reasoning may close forever as models learn to hide their thoughts.Read More

---

A new AI model learns to "think" longer on hard problems, achieving more robust reasoning and better generalization to novel, unseen tasks.Read More

---

Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.Read More

---

AI models can get fatigued, prompt ops can help manage, measure, monitor and tune prompts.Read More

---

Why is Google hiding Gemini's reasoning traces? The decision sparks a debate over black-box models versus the need for transparency.Read More

There's a curious contradiction at the heart of today's most capable AI models that purport to "reason": They can solve routine math problems with accuracy, yet when faced with formulating deeper mathematical proofs found in competition-level challenges, they often fail.

That's the finding of eye-opening preprint research into simulated reasoning (SR) models, initially listed in March and updated in April, that mostly fell under the news radar. The research serves as an instructive case study on the mathematical limitations of SR models, despite sometimes grandiose marketing claims from AI vendors.

What sets simulated reasoning models apart from traditional large language models (LLMs) is that they have been trained to output a step-by-step "thinking" process (often called "chain-of-thought") to solve problems. Note that "simulated" in this case doesn't mean that the models do not reason at all but rather that they do not necessarily reason using the same techniques as humans. That distinction is important because human reasoning itself is difficult to define.

Read full article

Comments

© PhonlamaiPhoto via Getty Images