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.
Provides a realtime audio–language foundation model for fully duplex spoken interaction that listens, interprets acoustic cues, reasons, and speaks in parallel. Key features include Deep Perception for acoustic understanding, Seamless Duplex for synchronized streams, and Think‑While‑Speaking for parallel private reasoning; reports strong benchmark results.
AI Model2026
A pretrained autoregressive foundation LLM from Yandex that uses a hybrid MoE design (80B params, 3B active) and supports up to 262,144-token context. Notably strong on Russian factual knowledge, math and coding benchmarks; intended for research and fine-tuning rather than out-of-the-box production.
AI Agent Papers2026
Presents Atria Dawn Preview, an agentic foundation LLM trained with a Verifiable Experience Pipeline to connect tool-mediated interactions to executable environments; evaluated across 16 real-world research and engineering benchmarks with top scores on five, and a human–AI collaboration analysis of 769 task records.
Surveys how foundation models reshape AI across the game lifecycle, organizing literature into six roles (play & act; model players & games; design; build & maintain; runtime generation & adaptation; test & evaluate). Emphasizes cross-role links, limits to transfer, and gaps in evaluation and persistent state.
AI Model2026
Runs locally on constrained devices to turn text into guaranteed-parsable JSON tool calls, typed structured extractions, or sentence embeddings. Delivered as a single compact weights file (8–29 MB) with a laddered 2–20-layer design, low-bit quantisation and calibrated confidence scores for on-device apps.
MLOps2026
Provides a programming model and distributed runtime that preserves parent–child lineage and ordered variable-cardinality expansions for 1→M→1 dataflow pipelines, enabling completion-driven cross-input GPU batching and ordered gathers for foundation-model data preparation. Demonstrates multi-GPU scaling speedups and lower end-to-end time versus Ray Data and Daft.
Describes DeepSeek-V4.1-Flash, a 552B-parameter multimodal Mixture-of-Experts LLM that supports up to 1M-token contexts and reduces KV cache footprint using Compressed Sparse Attention 2, FP4 KV caching, and SWA Bounded Replay to lower HBM/SSD and prefill costs.
Provides an open family of K–12 foundation models and a capability-oriented instruction-tuning corpus that teaches models to solve curriculum problems, map items to curriculum concepts, diagnose learner errors, and produce scaffolded pedagogical responses.
Jiahao Lu, Minghao Yin +6The Hong Kong University of Science and Technology, ARC Lab, Tencent IEG +2
Compresses a geometry-foundation model’s multi-level features into a compact latent that decodes jointly to RGB, depth, cameras and point maps — enabling a conditional flow to generate 3D-consistent video and novel views with measurably improved coherence.
AI Video Papers2026
Transforms user prompts into shot-level cinematic directions for text-to-video generation, using a 397B prompt-enhancer trained on 1.05M videos and SC-GRPO to preserve semantic consistency across shots; evaluated on WanPEval (5–30s) with large human-preference gains.
Embodied AI2026
Lets general-purpose vision-language models directly command robots via a compact mid-level action interface and asynchronous monitoring, enabling zero-shot manipulation without task-specific policy training; demonstrates strong sim benchmarks and real xArm6 transfer.