Maligned - October 23, 2025

AI news without the BS

Here’s what actually matters in AI today. No fluff, no hype - just 5 developments worth your time.

Today’s Top 5 AI Developments

1. Ring-1T: Open-Source Trillion-Parameter Reasoning Giant 🤯

Ring-1T just dropped, the first open-source MoE model with a trillion total parameters, actively using about 50 billion per token. This isn’t just big; it’s a breakthrough in democratizing advanced reasoning, smashing benchmarks like IMO-2025 (silver medal level) by tackling RL scaling hurdles head-on. Expect this model to set a new standard for what’s possible in accessible, large-scale AI.

Source: arXiv Link: http://arxiv.org/abs/2510.18855v1

2. SeeTok: LLMs Ditch Tokens for Visual Reading 👁️‍🗨️

Forget subword tokenization, SeeTok is here to revolutionize how LLMs process text by letting them “see” words as images. This vision-centric approach cuts token usage by over 4x and FLOPs by 70%, boosting cross-lingual generalization and robustness to typos. It’s a fundamental shift towards more human-like, visually-grounded language understanding.

Source: arXiv Link: http://arxiv.org/abs/2510.18840v1

3. LightMem: Finally, Efficient LLM Memory 🧠💨

LLMs get a serious memory upgrade with LightMem, a new system that mimics human memory to handle historical interactions efficiently. It filters irrelevant info, organizes topics, and updates offline, leading to up to 10.9% accuracy gains while drastically cutting token usage (117x), API calls (159x), and runtime (12x). This is a game-changer for stateful, long-context LLM applications.

Source: arXiv Link: http://arxiv.org/abs/2510.18866v1

4. Grasp Any Region: Multimodal LLMs Get Laser-Focused Precision 🎯

Multimodal LLMs (MLLMs) just got a lot smarter at understanding visual details. Grasp Any Region (GAR) allows MLLMs to precisely interpret specific areas within complex scenes, not just isolated objects, by leveraging global context and handling multiple region prompts. This pushes MLLMs beyond passive description, enabling truly interactive and fine-grained visual reasoning.

Source: arXiv Link: http://arxiv.org/abs/2510.18876v1

5. Critique-Post-Edit: LLMs Learn Your Preferences Without the BS 🤝

Tired of generic LLM responses? Critique-Post-Edit is a new RL framework for personalizing LLMs that actually works, avoiding the “reward hacking” common in standard RLHF. It uses a generative reward model for multi-dimensional critiques, allowing LLMs to revise outputs based on feedback, leading to significantly more faithful and controllable personalization. This means LLMs can truly learn your style.

Source: arXiv Link: http://arxiv.org/abs/2510.18849v1

That’s it for today. Stay aligned. 🎯

Maligned - AI news without the BS