Generates unified embeddings for text, images, video, visual documents and interleaved multimodal inputs with configurable output dimensions and Matryoshka truncation to trade accuracy for cost. Model weights and code are released under Apache-2.0; the 9B variant scores 80.6 on MMEB-v2.
AI Dataset2026
A 10‑billion‑document retrieval benchmark with per‑document 768‑dim unit‑norm dense embeddings and mGTE sparse embeddings, FineWeb text/metadata, and exact top‑1000 MS MARCO ground truth for ~120k queries. Built for large‑scale evaluation of dense/sparse/hybrid retrieval, filtered search, indexing, ANNS algorithms, and embedding compression.
AI Agent Papers2026
Synthesizes, repairs, and self-evolves task-adaptive agent harnesses on demand for off-the-shelf LLM agents, using a trainable harness-intelligence model that distills signals from past configurations. Demonstrates consistent performance gains across benchmarks and model families by producing four-module, composable harnesses.
AI Model2026
Provides a GGUF-quantized build of GLM-5.3-Flash for local text-generation and inference. Key features: 320B total / 18B active parameters, hybrid sparse+linear attention, native multimodal pretraining and Unsloth Dynamic quantization. Best for developers running GGUF local inference workflows.
AI Model2026
Integrates a pretrained vision–language model with a BEV perception head and a Planning Expert to provide 3D perception, driving VQA and motion planning for autonomous driving while keeping the base VLM architecture unchanged.
AI Agent Papers2026
Analyzes how to generate useful interaction data for LLM agents and proposes the ACE lens — Accuracy, Complexity, divErsity — while factorizing agentic data as (E, q, τ, v). Surveys verification, difficulty calibration, and coverage strategies and outlines implications for training and benchmarks.
A zero-data self-evolution framework that co-trains a Challenger, Solver, and Judge so LLMs can iteratively improve on both verifiable and unverifiable tasks without human labels. Uses role-asymmetry and subtask-amplification preference pairs to train the Judge and sustain improvement.
AI Model2026
A 770B-parameter Mixture-of-Experts instruct model from Tencent that natively supports 1,048,576-token contexts, Gated DSA attention, and speculative MTP decoding; open-sourced under Apache-2.0 with BF16 and FP8 weights for deployable inference.
AI Video Papers2026
Measures whether video generators reproduce the correct distribution of possible physical behaviors under repeated rollouts. Introduces PAWBench and PAWEval to convert repeated generations into outcome-level empirical distributions and quantify probabilistic alignment; evaluates 50 scenarios and 11 models and finds no model consistently matches reference probabilities.
AI Model2026
Provides per-tensor non-uniform GGUF quantizations of Qwen3.8-27B using GSQ and RCO, delivering high accuracy at 2.5–3.5 bits and including a BF16 vision projector for multimodal use. Optimized to run unmodified in llama.cpp, Ollama, and LM Studio.
AI Dataset2026
A living, crowdsourced dataset of hard-to-translate examples (text, images, audio, video) paired with handcrafted verification rules that flag concrete MT failures. LTBv1 contains 3,456 peer-reviewed examples across many language pairs and accepts ongoing contributions.
AI Dataset2026
Provides a human-verified benchmark of 1,927 heterogeneous articulated 3D objects with part-level articulation semantics and intrinsic physical-property annotations for evaluating physical grounding and simulation readiness. Includes URDF assemblies, aligned point clouds, per-part JSON annotations, and a curated evaluation protocol; licensed CC BY-NC 4.0 (non-commercial).