Evaluates multimodal LLMs' ability to reconstruct past observations and act in controllable non-Markov games. Introduces RNG-Bench with two games (Matching Pairs, 3D Maze), three controllable difficulty axes, a head-to-head duel protocol, and a Memory Gap metric to separate forgetting from action errors.
AI Dataset2026
Provides a deduplicated 2.0M-row corpus of FABLE.5 / Mythos agent traces with row-level provenance and session-limit rows removed. Includes canonical Parquet and gzip JSONL exports, SHA256 row hashes, and provenance fields for tracing first-source datasets.
AI Dataset2026
Benchmark for evaluating procedural skill evolution in LLM agents: isolates reusable skill bodies, role-specific work surfaces, and hidden oracle assets to measure whether skill refinements transfer across tasks, roles, and model backbones. Includes 382 workplace tasks, 22 skills, and a controlled evaluation protocol.
AI Model2026
A 9B reasoning LLM fine-tuned from Qwen3.5 that ships with a 1,048,576-token context, native function-calling and tool-use, and notable benchmark gains (+34 MMLU, +30 gsm8k-strict).
AI Dataset2026
Provides 319 newline-delimited JSON agent session traces captured from GLM-5.2 using Teich for training agentic models. Preserves reasoning-first assistant fragments, tool-call events, and a dataset-level training-ready tool schema; convertible to OpenAI-style JSONL for SFT/distillation.
AI Dataset2026
Provides agentic instruction‑tuning trajectories for software‑engineering tasks, formatted for supervised fine‑tuning and agent training. Contains multi‑file edits, tests, docs and structured agent traces (≈5,115 records, 1.9 GiB). Intended for commercial use; licensed CC‑BY 4.0 with additional permissive licenses.
AI Model2026
A dense ~9B reasoning LLM optimized for agentic coding and tool-calling that emits explicit chain-of-thought (<think>) blocks and well-formed tool calls. Designed to run on a single 80GB GPU (~19GB bf16), uses self-scaffolding RL and exposes an OpenAI-compatible API.
AI Model2026
A 35B mixture-of-experts LLM specialized for agentic coding and tool-enabled code generation, fine-tuned with self-scaffolding reinforcement learning. Supports very long contexts, OpenAI-compatible tool calls, and multiple serving runtimes under an MIT license.
AI Model2026
35B Mixture-of-Experts agent model for long-horizon, multi-domain agent workflows; trained with a knowledge–action infrastructure that produces ~45K-token trajectories and supports native tool calling and function integration for research and deployment.
AI Model2026
Simulates agentic environments and predicts next environment states from actions and interaction history using a language-based world model across seven domains. Trained via a CPT→SFT→RL pipeline with an MoE architecture and very long context; intended for environment simulation and agent research.
AI Dataset2026
Provides 2,170 reference-grounded evaluation samples across seven agent domains (MCP, Search, Terminal, SWE, Android, Web, OS) to score language world models on Format, Factuality, Consistency, Realism and Quality. Includes per-domain JSONL files, judge prompts and an evaluation script for reproducible scoring.
AI Model2026
Provides an open-source Mixture-of-Experts coding LLM (397B) optimized for agentic, tool-enabled coding workflows with a 262,144-token context window, OpenAI-compatible API, serving recipes (vLLM/SGLang), and published coding-benchmark results.