AI Dataset2026
Provides a bilingual Chinese–English corpus for LLM training covering pretraining, capability-oriented midtraining (16K–256K long contexts), and supervised fine-tuning. Includes ~4.2T pretrain tokens, ~600B midtrain tokens, and ~4.57M SFT samples; sources span web, PDFs/OCR, code, math, QA, and agentic trajectories under mixed upstream licenses.
AI Model2026
Compact causal LLM for on-device assistants, coding agents and long-context tool use — ~2.52B parameters with a 131,072-token context, trained with SFT + RL + OPD and released with its UltraData training corpora and multi-format deployment checkpoints.
AI Dataset2026
Provides ~86K verifiable-reward RL training samples across Math, Knowledge (STEM), Long-Context, and Code for post-training LLMs; each sample includes a ground-truth and verifier-friendly JSONL format for stable reward signals. Note: code tasks require an external sandbox to execute tests.
Trains LVLMs to improve 3D spatial reasoning using a synthetic block-stacking dataset of 15,000 problems that cover 3D→2D projection, viewpoint transforms, and structural combination; includes color-anchored cues and provides code and data.
Evaluates a weak teacher's RL-induced policy shift on the student's own rollouts and amplifies verifier-supported updates so stronger models can learn from weaker supervisors and surpass them. It rescales only verifier-supported policy-gradient components to preserve optimization fixed points while accelerating learning, reducing student updates versus standard RL or distillation.
AI Agent Papers2026
Unifies continuous streaming perception, full‑duplex real‑time interaction, and agentic tool use across video, audio, and text. Proposes a Cerebellum–Brain split—streaming Thinker‑Talker cerebellum for low‑latency interaction plus a Brain for complex reasoning—and is released with models, code, and data.
AI Model2026
A reasoning‑efficient fine-tune of Qwen3.8-27B that penalizes overthinking tokens to shorten internal reasoning traces — about 58.3% fewer thinking tokens with <1% accuracy loss and ~1.95× speedup; designed for long-context, multimodal and quantized deployments.
AI Model2026
Multimodal agentic model for long-horizon computer and browser tasks, with visual self-correction and function-calling. The Pro variant is a 397B Mixture-of-Experts (≈17B active) model with a 262,144-token context window, Docker deployment recipes, and weights currently marked “coming soon.”
Adds a Next Concept Prediction objective that predicts discrete, product-quantized latent concepts spanning multiple tokens alongside next-token prediction, yielding faster pretraining convergence and measurable downstream gains with an 8.9B-parameter latent-space LLM.
AI Model2026
Open-weights preview checkpoint for a multimodal reasoning model that generates text from text and image/video inputs, exposes adjustable reasoning effort and tool-calling, and supports an extended 262,144-token context for long-horizon tasks and agent-style workflows.
AI Model2026
F16 GGUF release packaged for llama.cpp and similar runtimes that applies UkisAI's Swift adapter to Qwen3.8-27B. Cuts reasoning/thinking tokens by ~58% with <1% performance loss, includes multimodal projector, MTP layers, multiple GGUF quant tiers, and is distributed under the Swift Open License v1.0.
Presents an open 7B dense foundation model for mathematical reasoning and tool-assisted web search, supporting 256K-token context. Key features: hybrid gated sliding-window + global attention for efficient long-context training, FP8 Muon optimizer, progressive 16K→64K→256K curriculum, and open-source weights, data, and recipes.