Search Results for "planning"
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Showing 13 open source projects for "planning"
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GLM-5
From Vibe Coding to Agentic Engineering
GLM-5 is a next-generation open-source large language model (LLM) developed by the Z .ai team under the zai-org organization that pushes the boundaries of reasoning, coding, and long-horizon agentic intelligence. Building on earlier GLM series models, GLM-5 dramatically scales the parameter count (to roughly 744 billion) and expands pre-training data to significantly improve performance on complex tasks such as multi-step reasoning, software engineering workflows, and agent orchestration... -
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MiniMax-M2.1
MiniMax M2.1, a SOTA model for real-world dev & agents.
MiniMax-M2.1 is an open-source, state-of-the-art agentic language model released to democratize high-performance AI capabilities. It goes beyond a simple parameter upgrade, delivering major gains in coding, tool use, instruction following, and long-horizon planning. The model is designed to be transparent, controllable, and accessible, enabling developers to build autonomous systems without relying on closed platforms. MiniMax-M2.1 excels in real-world software engineering tasks, including multilingual development and complex workflow automation. It demonstrates strong generalization across agent frameworks and consistently improves upon its predecessor, MiniMax-M2. ... -
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MiMo-V2-Flash
MiMo-V2-Flash: Efficient Reasoning, Coding, and Agentic Foundation
...It uses an MoE setup where a very large total parameter count is available, but only a smaller subset is activated per token, which helps balance capability with runtime efficiency. The project positions the model for workflows that require tool use, multi-step planning, and higher throughput, rather than only single-turn chat. Architecturally, it highlights attention and prediction choices aimed at accelerating generation while preserving instruction-following quality in complex prompts. The repository typically serves as a launch point for running the model, understanding its intended use cases, and reproducing or extending its evaluation on reasoning and agent-style tasks. ... -
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HY-World 2.0
A Multi-Modal World Model for Reconstructing, Generating, Simulation
...For text and single-image inputs, it generates high-fidelity 3D Gaussian Splatting scenes through a multi-stage pipeline that includes panorama generation, trajectory planning, world expansion, and world composition. The system also improves reconstruction from multi-view images and video by upgrading its feed-forward 3D prediction components and its memory-aware view generation process. Another major part of the project is WorldLens, a rendering platform designed for interactive exploration with an engine-agnostic architecture, automatic image-based lighting, collision detection, and support for character interaction. -
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PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
...Instead of relying only on static training data, the agent performs searches, visits pages, and extracts evidence before forming answers to complex queries. It is built to operate end-to-end: planning a research strategy, gathering sources, reasoning over conflicting claims, and writing a grounded response. The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. ... -
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Kimi k1.5
Scaling Reinforcement Learning with LLMs
Kimi-k1.5 is an advanced open-source multimodal large-language model project that explores scaling reinforcement learning with long-context chains of thought, achieving performance that rivals or surpasses state-of-the-art models on benchmarks like LiveCodeBench, AIME, and MATH-500. The project emphasizes a simplistic yet powerful framework where the context window scales up to 128k tokens, enabling reasoning that resembles planning, reflection, and correction over a much longer sequence of data than typical models. By using techniques like partial rollouts to improve training efficiency and applying sophisticated policy optimization methods, the developers demonstrate that strong ability can emerge without relying on complex solutions like Monte Carlo tree search or value functions. ... -
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MiniMax-M2
MiniMax-M2, a model built for Max coding & agentic workflows
...The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real repositories and diverse programming languages. It is also optimized for multi-step agent tasks, planning and executing long toolchains that span shell commands, browsers, retrieval systems, and code runners. Benchmarks show that it achieves highly competitive scores on a wide range of intelligence and agent benchmarks, including SWE-Bench variants, Terminal-Bench, BrowseComp, GAIA, and several long-context reasoning suites. -
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CogVLM
A state-of-the-art open visual language model
CogVLM is an open-source visual–language model suite—and its GUI-oriented sibling CogAgent—aimed at image understanding, grounding, and multi-turn dialogue, with optional agent actions on real UI screenshots. The flagship CogVLM-17B combines ~10B visual parameters with ~7B language parameters and supports 490×490 inputs; CogAgent-18B extends this to 1120×1120 and adds plan/next-action outputs plus grounded operation coordinates for GUI tasks. The repo provides multiple ways to run models... -
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MuJoCo MPC
Real-time behaviour synthesis with MuJoCo, using Predictive Control
...MJPC integrates a high-performance GUI and multiple predictive control algorithms, including iLQG, gradient descent, and Predictive Sampling — a competitive, derivative-free method that achieves robust real-time control. The system supports multi-shooting optimization, enabling precise motion planning across diverse domains like quadruped locomotion, humanoid tracking, and dexterous manipulation. In addition to its C++ core, MJPC includes an experimental Python API, enabling integration with custom models and MuJoCo tasks for flexible scripting and experimentation. -
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Nemotron 3 Super
Open language model developed by NVIDIA as part of Nemotron-3 family
...Its architecture combines Transformer attention layers with Mamba state-space components to balance long-context reasoning, memory efficiency, and high-quality language generation. The model is optimized for building AI agents that must perform complex tasks such as planning, tool usage, coding assistance, and multi-step reasoning. -
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Nex-N2-mini
Compact agentic model for coding, tools, and productivity tasks
...Built on Qwen3.5-35B-A3B-Base, it offers a lighter latency and deployment profile than Nex-N2-Pro while preserving the core Nex-N2 “Agentic Thinking” framework. This framework unifies requirement understanding, planning, code implementation, environmental feedback, debugging, evaluation, and iteration into a closed loop. It uses adaptive thinking to decide when deeper reasoning is needed and coherent thinking to keep reasoning consistent across tasks and modalities. Nex-N2-mini supports image-text-to-text workflows, explicit reasoning traces, robust function calling, and deployment through Transformers, vLLM, SGLang, Docker, and quantized local apps. ... -
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Nex-N2-Pro
Large agentic model for coding, tools, research, and execution
Nex-N2-Pro is Nex AGI’s larger open-source agentic model, built for real-world productivity, coding, deep research, tool calling, and long-horizon terminal execution. It uses the Nex-N2 “Agentic Thinking” framework, which connects requirement understanding, planning, implementation, environmental feedback, debugging, evaluation, and iteration into a single closed loop. The model is built on Qwen3.5-397B-A17B and is designed as the high-quality counterpart to Nex-N2-mini, trading higher compute needs for stronger reasoning and agent performance. It supports image-text-to-text workflows, explicit reasoning traces, robust function calling, and deployment through Transformers, vLLM, SGLang, Docker, and quantized local apps. ... -
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Mistral Large 3 675B Base 2512
Frontier-scale 675B multimodal base model for custom AI training
...It is trained from scratch using 3000 H200 GPUs, making it one of the most advanced and compute-intensive open-weight models available. As the base version, it is not fine-tuned for instruction following or reasoning, making it ideal for teams planning their own domain-specific finetuning or custom training pipelines. The model is engineered for reliability, long-context comprehension, and stable performance across many enterprise, scientific, and knowledge-intensive workloads. Its architecture includes a powerful language MoE and a 2.5B-parameter vision encoder, enabling multimodal understanding out of the box. ...
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