Turns fragile, implicit search progress into explicit, persistent, shared state for multi-agent information seeking — externalizes progress as Frontier Task, Evidence Graph, Coverage Map and Failure Memory, and uses pipeline-parallel scheduling plus a middleware harness to avoid repeated failed searches and improve utilization and throughput.
Guides an LLM agent to build persistent, editable DAG-based data pipelines via typed, incremental mutations instead of free-form scripts. Combines DataFlow-Skills, a Model Context Protocol exposing live operator registry and pipeline state, and a synchronized Web UI; achieves 93.3% end-to-end pass rate on a 12-task benchmark while cutting cost and latency versus script baselines.
Provides verified, model-attested end-to-end agent coding and debugging trajectories (JSONL). Each whole-session trace was produced by moonshotai/kimi-k3 on the pi/openrouter runtime, passed acceptance tests and independent model screening — useful for SFT, distillation, and analyzing tool-use behavior.
Models long-horizon interactive literary simulation where characters and world co-evolve; introduces an open‑schema framework with a Character Agent and an LLM-based World Model, plus seven trainable tasks and a dataset from 57 books for benchmarking persistent narrative state.
Provides newline-delimited JSON agent session traces (5 files) generated with Teich for moonshotai/kimi-k3, including recovered and embedded tool-schema snapshots so traces remain training-ready even when tools weren't invoked; includes guidance for Teich data preparation and conversion.
Provides under-1K JSON agent-trace records documenting model refusal responses and forensic metadata — useful for evaluating refusal-detection, audit pipelines, and safety analysis; small size limits large-scale statistical studies.
1B-parameter text-generation model tuned for conversational and agentic workflows with a focus on security and vulnerability-detection; suited for low-cost or on-prem/edge deployments and terminal-agent integrations.
A compact evaluation dataset and harness for testing agentic AI on safety-critical robotics tasks. Includes multimodal episodes in parquet format, task-specific eval scripts (gauge reading, human safety monitoring, VLA estimators), and TFDS/Hugging Face integration for reproducible safety evaluations.
Compact 3B-scale agentic LLM for multi-step tool use and reasoning, using a Looped Transformer to increase capacity without adding parameters; built for local deployment with configurable "thinking" modes and benchmark gains vs larger open models.
A 250B-parameter mixture-of-experts LLM that activates 15B parameters per token to lower inference cost for agentic tasks—tool calling, long-context reasoning, and coding. Uses a hybrid softmax+linear-attention stack with 1M-token context and supports English, Korean, and Japanese; requires H200/B200-class GPUs to run efficiently.
An open-weight LLM checkpoint post-trained for agentic deep search: Qwen-compatible reasoning and tool-call formats optimized for web browsing, multi-source evidence aggregation, long-horizon planning and recovery from failed interactions; typically paired with the AxisAgentic harness.
An open-weight, Qwen-derived thinking model optimized for agentic deep web search and long-horizon planning. Provides Qwen-compatible reasoning and tool-call formats for English/Chinese browsing, multi-source evidence aggregation, source verification, and recovery from failed environment interactions.