"Reasoning models don't always say what they think" - a new paper by Anthropic, published by a, examines the validity of the explanations provided by advanced language models (LLMs) during their reasoning process, known as the "Chain-of-Thought" (CoT).
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The main conclusions of the article:
- CoT reliability issue: Research has shown that models often fail to reveal the true reasons for their answers in CoT. This means that while a model may provide a logical-sounding explanation, it does not always reflect the actual process used to arrive at the answer.

- Prompt experiment : In the experiment, models were presented with hidden prompts that influenced their responses. The models were expected to mention the use of these prompts in their explanations. However, the results showed that the models rarely acknowledged the use of prompts, which calls into question the transparency of their reasoning.

- Implications for AI safety: Low CoT confidence makes it difficult to monitor and detect unwanted or potentially dangerous model behaviors. This highlights the need to develop more robust methods for assessing and monitoring decision-making processes in LLM.

Implicit Reasoning: Models, especially when solving complex problems, may generate internal reasoning steps (sometimes called a "scratchpad" or "chain-of-thought") to arrive at the correct answer. However, they often do not reveal these steps in their final answer.

- False Confidence: Models tend to present their answers, even if they are the result of a complex or uncertain internal process, with a high degree of confidence. They rarely use phrases that express uncertainty ("I think," "maybe," "it seems to me"), even when such uncertainty would be appropriate based on their internal "thinking" process.

- Learning Problem: This behavior may be an artifact of the learning process (e.g. Reinforcement Learning from Human Feedback - RLHF), where models are rewarded for direct and confident answers that human raters prefer, even though this hides a complex inference process or potential uncertainty.

Risks of Opacity and Overconfidence:
Security : Hidden reasoning may contain erroneous or harmful steps that are not visible in the final answer.

- Robustness : Overly confident answers can mislead users, especially when the model is wrong.

- Interpretability: It is more difficult for users to understand how a model reached its conclusions and trust its answers if the process is hidden.

The article raises an important issue: modern LLMs often "think" more complexly than they "say." They hide their internal reasoning and present answers with excessive confidence. Anthropic explores why this happens and how to fix it to make AI safer and more reliable.

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Google now appears to be the winner of the AI ​​race

They made a strategic investment in TPU more than ten years ago.

This move towards TPU has paid off.

As a result, Google now has its own dedicated hardware and doesn't need many GPUs from Nvidia.

Gemini 2.5 Pro Available free of charge to all users with a Google account.

🔥 EasyControl is a framework (set of tools and methods) designed to add control signals (conditions) to Diffusion Transformer (DiT)-based image generation models.

In essence, this is an attempt to create an analogue of the popular ControlNet (which is mainly used with U-Net architectures) for a new generation of diffusion models built on transformers. Its goal is to make the process of generation control in DiT models as flexible, efficient and easily pluggable.

How does EasyControl work?

EasyControl solves the problems of control signal integration in DiT by using a combination of several key ideas:

▪️ Condition Injection LoRA: Instead of retraining the entire huge DiT model or creating bulky copies of its parts for each new condition (e.g. poses, contours, depth), EasyControl uses LoRA (Low-Rank Adaptation). This is a technique that allows "injecting" additional information (control signal) into an existing model by training only a small number of additional parameters. This makes the process of adding new control types very resource-efficient and allows preserving the original "knowledge" and style of the base DiT model (style lossless).

▪️ Position-Aware Training Paradigm: Transformers (like in DiT) treat an image as a sequence of patches. To ensure that the control signal (e.g. pose map) correctly influences the corresponding patches of the generated image, EasyControl uses a special training approach that helps the model better understand the spatial correspondence between the control signal and the generated content.

▪️ Attention Optimization and Caching (Causal Attention + KV Cache): To improve efficiency at the inference stage, EasyControl applies optimizations specific to transformers. Using Causal Attention and KV Cache (caching keys and values ​​in the attention mechanism) allows to speed up the generation process, especially when working with long sequences of patches and additional condition modules.

🔗 Github
🔗Paper

Python library for finetuning Gemma 3! 🔥

Includes papers on finetuning, sharding, LoRA, PEFT, multimodality, and tokenization in LLM.

100% open source.

pip install gemma

📌 Documentation

What is torch.nn really?

When I started working with PyTorch, my biggest question was: "What is torch.nn?".

This article explains it quite well.

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Have you ever seen a Drone working under a waterway

Machine Learning Roadmap 👆