GoKawiil
Tech News
A group of recently dismissed OpenAI researchers has sent a letter to the company's board, requesting that ongoing AI development efforts do not compromise the ability to understand and monitor AI reasoning processes. They emphasize the importance of transparency for safety and oversight.
Thore Graepel, who helped build AlphaGo, has left Google DeepMind and published an opinion piece arguing that large language models lack genuine reasoning ability. He contrasts this with AlphaGo's 2016 victory over Lee Sedol, which he credits to the system's capacity for creative, reasoned decision-making. Separately, a MIT Technology Review writer reports joining a six-month competition involving roughly 500 participants aiming to reduce their biological age, tracked via a leaderboard.
An opinion piece argues that large language models operate like fast, associative 'System 1' thinking, predicting tokens without genuine step-by-step reasoning. The authors contrast this with AlphaGo's famous move 37 against Lee Sedol, which they say resulted from deliberate tree search rather than intuition alone, combining a policy network's hunches with explicit lookahead through thousands of possible game branches.
OpenAI says a coordinated campaign attempted to extract its models' hidden 'chain of thought' reasoning in July, with actors associated with China-based Moonshot AI at the center. Activity started July 1 and spiked to 16,000 extraction-pattern requests from over 4,000 users on July 24-25, before OpenAI says it fully disrupted the campaign by July 28. OpenAI says the encryption protecting this reasoning data was not broken and no user conversation database was compromised.
Anthropic's Claude Opus 5.5, running in Adaptive Reasoning Max Effort mode, scored 58 on the Artificial Analysis Intelligence Index against a median of 25 among comparable models. The model handles text and image input with text output and a 1M token context window, but is priced at $4.00 per 1M input tokens and $20.00 per 1M output tokens, both above the reported medians of $2.00 and $10.00. Evaluating it on the Intelligence Index generated 260M tokens and cost $8,708.20 in total.
A developer testing an AI model at maximum effort settings found that most requests produced little or no chain-of-thought reasoning tokens. Even when extended reasoning occurred, it fell well short of the levels seen in the model's published benchmark results.
Major AI labs have moved their attention from building ever-larger models to running inference—the process of using trained models to generate text, code, and images. This shift is driven by growing real-world use of large language models, the rise of reasoning models that repeatedly reprompt themselves, and autonomous AI agents that run continuously rather than just responding to single queries. Amazon Web Services, for instance, has split inference tasks between its Trainium chips and Cerebras's wafer-scale hardware.
A follow-up experiment tested four open models—DeepSeek V4 Flash, Inkling, Kimi K3, and Qwen3.8 A95B—by inserting the first 1% of GPT-5.5 Pro's reasoning trace into each model's own reasoning channel before letting it generate answers freely. Researchers then measured how much of GPT-5.5 Pro's visible answer text overlapped with each model's output. Qwen3.8 showed the largest jump, with overlap rising from 33.92% unprefilled to 54.50% with the GPT-5.5 Pro prefill, a 20.58 percentage-point increase, while other models showed much smaller shifts.
Google has introduced a new slash command, /boost, in Antigravity 2.0 and the Antigravity CLI that triggers a multi-agent reasoning pipeline for difficult coding problems like race conditions, complex refactors, and subtle bugs. The feature works through a three-phase process where an orchestrator agent breaks down the problem and dispatches specialized subagents to isolated workstreams, then verifies results across multiple rounds. The command is restricted to paid plan subscribers.
Autonomous Technologies Group, a Y Combinator-backed AI lab building frontier reasoning systems for financial markets, has posted openings for engineers via its careers page. The company's flagship product, Autonomous, is described as an agentic wealth strategist that applies these AI systems to personal finance and investing.