ARE
Metal SuperIntelligence Labs presents a research platform and benchmark for building and stress-testing agent systems in realistic, time-driven environments. The paper introduces a modular simulator (ARE) and a mobile-style benchmark (Gaia2) that emphasize asynchronous events, verification of write actions, and multi-agent coordination in noisy, dynamic settings.
ATOKEN
ATOKEN introduces a single transformer tokenizer that works for images, videos, and 3D assets. It encodes all inputs into a shared sparse 4D latent space with 4D RoPE, trains without adversarial losses, and supports both continuous and discrete tokens. The paper reports strong reconstruction quality and solid semantic alignment, enabling both generation and understanding across modalities.
Code World Model
Meta FAIR releases CWM, a 32B open-weights coder trained to model code execution and to act inside containers. It mid-trains on Python interpreter traces and agentic Docker trajectories, then upgrades with multi-turn RL across SWE, coding, and math. CWM is both a strong coder and a testbed for world-model-style reasoning in software environments.
Teaching LLMs to Plan
A training recipe that teaches LLMs to plan in Planning Domain Definition Language (PDDL) by making them write explicit state–action–state chains and checking each step with an external verifier (VAL). The result: big jumps in plan validity on PlanBench domains, especially when feedback explains why an action failed rather than just saying it failed.
LLM-JEPA
A JEPA-style training objective is adapted to LLMs by treating paired views of the same underlying content (for example, text and code) as prediction targets in embedding space, added on top of the usual next-token loss. The result consistently improves fine-tuning and shows promising pretraining gains, while being more resistant to overfitting.
ARK-V1
ARK-V1 is a lightweight agent that helps language models answer questions by actively walking through a knowledge graph instead of relying only on memorized text. This is especially useful for long-tail entities (less common stuff) where the model’s pretraining knowledge falls short.
Language Models that Think, Chat Better
A simple recipe, RL with Model-rewarded Thinking, makes small open models “plan first, answer second” on regular chat prompts and trains them with online RL against a preference reward. On Llama-3.1-8B and Qwen-2.5-7B, this consistently beats standard RLHF on chat, creative writing, and general knowledge, with the best 8B model topping some frontier systems on WildBench and AlpacaEval2.
Embodied AI: From LLMs to World Models
This paper surveys embodied AI through the lens of LLMs and World Models (WMs). It highlights how LLMs enable semantic reasoning and task decomposition, while WMs provide predictive, physics-grounded interaction, and argues for a joint MLLM-WM architecture to advance real-world embodied cognition and applications.
GDPval
GDPval is a new benchmark of 1,320 real-world tasks across 44 occupations in 9 major GDP sectors, graded by industry experts with a 220-task gold set. It shows frontier models improve roughly linearly and are nearing expert parity, with Claude Opus 4.1 preferred or tied 47.6% of the time, while GPT-5 leads in accuracy. Model-plus-human workflows can reduce time and cost, and adding reasoning effort and prompt scaffolding further raises scores, with an open gold set and automated grader available for researchers.
Automating the Search for Artificial Life with Foundation Models
ASAL uses vision-language foundation models to automatically search across ALife substrates for simulations that match prompts, sustain open-ended novelty, or maximize diversity, reducing manual trial-and-error. It discovers new Lenia and Boids life-forms and lifelike CAs with strong open-endedness, and leverages FM embeddings to quantify emergent behaviors in a substrate-agnostic way.
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