OpenAI's GPT-5 is finally here, but a rocky rollout and mixed reviews have divided the community, creating a reality check for AI hype. The post OpenAI’s GPT-5: A reality check for the AI hype train first appeared on TechTalks.| TechTalks
The Hierarchical Reasoning Model uses a simple and two-tiered structure to beat large transformers on reasoning tasks with fewer parameters and compute budget. The post New brain-inspired AI model shows a more efficient path to reasoning first appeared on TechTalks.| TechTalks
GPT-5 and GPT-5 Thinking are large language models recently realeased by OpenAI, after a long series of annoucements and hype. Results on benchmarks are impressive. How good these reasoning models are in chess? Using a simple four-move sequence, I suceed to force GPT-5 and GPT-5 Thinking into an illegal move. Basically as GPT3.5, GPT4, DeepSeek-R1, o4-mini, o3 (see all my posts). There are other concerning insights… Though it is a very specific example, it is not a good sign.| Mathieu Acher
o3 and o4-mini are large language models recently realeased by OpenAI and augmented with chain-of-thought reinforcement learning, designed to “think before they speak” by generating explicit, multi-step reasoning before producing an answer. How good these reasoning models are in chess? Using a simple four-move sequence, I suceed to force o3 into an illegal move, and across multiple matches both o3 and o4-mini struggle dramatically, by generating illegal moves in over 90% of cases and even...| blog.mathieuacher.com
AI models are often overconfident. A new MIT training method teaches them self-doubt, improving reliability and making them more trustworthy. The post A new way to train AI models to know when they don’t know first appeared on TechTalks.| TechTalks
Researchers discover critical vulnerability in LLM-as-a-judge reward models that could compromise the integrity and reliability of your AI training pipelines. The post LLM-as-a-judge easily fooled by a single token, study finds first appeared on TechTalks.| TechTalks
Chain-of-thought tokens don't reflect genuine reasoning in LLMs is misleading. They're navigational aids devoid of true cognitive processing or reliability. The post Why we misinterpret LLM ‘reasoning’ first appeared on TechTalks.| TechTalks
A study by Palisade Research found that advanced AI models (most notably OpenAI’s o1-preview or Claude Sonnet 3.5 from Anthropic) sometimes “cheat” in chess by hacking their opponent’s system files rather than playing by the rules. While older AI models required explicit prompting to cheat, the most recent agents seem capable of discovering and exploiting cybersecurity holes, raising concerns that AI systems might develop manipulative strategies and be uncontrollable for complex tasks...| Mathieu Acher
There is significant doubt about the trustworthiness of chain-of-thought traces in large language models, challenging developers' reliance on them for AI safety. The post Anthropic study reveals LLM reasoning isn’t always what it seems first appeared on TechTalks.| TechTalks
Stanford's "Think, Prune, Train" framework enables LLMs to enhance reasoning skills through self-generated data, leading to more efficient and smarter systems. The post Can LLMs learn to reason without RL or large datasets? first appeared on TechTalks.| TechTalks
Alibaba's Qwen3 open-weight LLMs combine direct response and chain-of-thought reasoning in a single architecture, and compete withe leading models. The post Alibaba’s Qwen3: Open-weight LLMs with hybrid thinking first appeared on TechTalks.| TechTalks