AI Model2026
Uses reinforcement learning to improve task generation, solving strategies, and solution rollouts in a continuous loop. This 122B MoE model targets coding, reasoning, and agent tasks, supports a 262K context window, and can run on one 80GB GPU.
AI Agent Papers2026
Provides a training-free multimodal reasoning harness that combines heterogeneous visual tools, persistent multimodal memory, and code execution so MLLMs can iteratively acquire and compose grounded evidence. Improves accuracy on 22 benchmarks by about 9.7–12.6 points while cutting comparative token costs.
AI Agent Papers2026
Helps general coding agents turn 3D perception readings into spatial conclusions using persistent scene memory and plain-language interpretation. It improves open- and closed-model results on MMSI-Bench and ReVSI.
AI Model2026
Provides GGUF quantizations of Flint for local coding-agent inference with llama.cpp and Unsloth Desktop. Multiple quantization levels trade model size against output fidelity.
AI Model2026
Helps coding agents investigate codebases, fix bugs, implement features, and write tests. Its mixture-of-experts design activates about 2.5 billion parameters per token and supports reasoning and multi-turn tool use.
Handles coding and general tasks through command-line tools, with multi-stage training, reinforcement learning, and distillation aimed at reliable tool use and long-horizon execution.
AI Model2026
Handles agentic coding, tool use, and professional workflow tasks in a 33B dense model with up to 512K context. Its model card reports leading results on many benchmarks against open models of similar size.