AI Model2026
Provides a 27B-class Qwen3.6-derived language model in GGUF with end-to-end ternary weights (Q2_0_g128), reducing deployed footprint to ~7.2 GB while retaining ~95% of FP16 reasoning ability and enabling on-device 262K-token context inference.
AI Model2026
Runs a full 27B-class Qwen3.6-derived language model in a ~3.9 GB 1-bit GGUF pack for on-device inference with a 262K-token context; true 1.125 bits/weight binary representation, DSpark speculative drafter, and llama.cpp (CUDA/Metal/CPU) support.
AI Model2026
Runs a full 27B-class Qwen3.6-derived LLM in a ~7.2 GB ternary/2‑bit format for on-device or single‑GPU text generation, retaining ~95% of FP16 performance and supporting a 262K‑token context. Designed for laptop/GPU deployment; exceeds typical phone memory limits.
Transfers RL-induced policy shifts from a smaller 'weak' teacher to a stronger target by using the teacher's post-/pre-RL log-ratio as a dense implicit reward applied on the student's on-policy states. Enables reuse of RL supervision without running RL rollouts on the target, improving sample/time efficiency.
AI Dataset2026
Provides ~5M model-generated reasoning chains (within 5k sequence length) with structured fields for supervised fine-tuning, reasoning distillation, and instruction tuning. Includes separate fields for prompt, reasoning trace, final answer and a ChatML view; streaming access recommended for large-scale use.
AI Dataset2026
Provides ~5M tokens of chain-of-thought reasoning traces generated by many LLMs (DeepSeek, Qwen, Gemma, etc.) for training and evaluating reasoning SLMs — includes repo_id, tok_len, user, thought_trace, assistant and ChatML fields; sequences limited to 5k.
AI Dataset2026
Simulates a hospital LIMS to benchmark agentic clinical reasoning: agents inspect demographics, medications, lab orders/results and then submit ICD‑10 diagnostic reports scored by deterministic, context‑aware graders. Ships as an OpenEnv/FastAPI runtime with 8 scenarios, step‑level rewards and trajectory capture for RL, tool‑use and evaluation.
AI Dataset2026
Evaluates agents inside a structured hospital workflow via a downloadable FastAPI runtime that enforces role-specific tool permissions, evidence-before-treatment discipline, deterministic grading, dense process rewards, and full trajectory logging. Designed for RL, offline policy learning, multi-agent workflow research and process-supervision datasets; not for real patient care.
AI Model2026
A 9B-parameter Qwen3.5-based multimodal model tuned to preserve chain-of-thought reasoning while eliminating repetition loops; restores native multi-token prediction, supports 1,048,576-token context, and targets research/red-team use.
Proposes Riemannian Isometric Policy Optimization (RIPO) to fix exploration collapse in PPO-style RL for LLMs by aligning policy updates with the policy manifold's Riemannian geometry, improving exploration–exploitation balance and optimization stability across competition benchmarks.
AI Agent Papers2026
Unifies high-level visual-language reasoning and low-level control for visual navigation by decoupling cognition and control: a slow vision-language reasoner produces pixel goals with explicit chain-of-thought, and a fast action expert converts those anchors into continuous waypoints for robust urban and indoor navigation.
AI Model2026
27B multimodal reasoning model built on Qwen3.5-27B that preserves the base model's native multi-token-prediction head, full vision tower, and a 1,048,576-token YaRN context window. Designed for agentic tool use, long-context reasoning, and research deployments; released under Apache-2.0.