AI Model2026
GGUF build of Ornith-1.5's 35B mixture-of-experts model (A3B) for local inference — activates ~3B params per token, supports up to 262,144 tokens, emits separate reasoning traces and OpenAI-style tool calls, optimized for agentic coding and long-context use cases.
AI Model2026
A 9B dense reasoning LLM optimized for single‑GPU deployment and terminal-based coding agents, with long-context support (up to 262,144 tokens) and GGUF/quantized builds for edge/mobile. Strong on coding and agentic benchmarks.
AI Dataset2026
Converts 200+ hours of expert Figma screen recordings into 3,469 Playwright-MCP action trajectories for training and evaluating vision-language and GUI agents; includes 126 long‑horizon tasks, phase labels, a 10‑skill taxonomy, and is CC‑BY‑4.0 licensed.
AI Dataset2026
Provides 1,021.64 hours across 597 CAD/BIM workflows with synchronized screen recordings and interaction logs; each workflow includes video, timestamped input events, task specs, source files, final outputs, and evaluation rubrics for training or evaluating desktop CAD agents.
AI Video Papers2026
Continues a live or ongoing video stream while applying user-specified edits on the fly using a lightweight edit-ignition adapter. The adapter injects edits only in chunks where requests arrive and uses history cross-attention and temporal causal self-attention to preserve continuity and stability for unbounded streaming edits.
AI Video Papers2026
Xianyun Sun, Chaoyou Fu +7Affiliation: Project Leader & Corresponding AuthorProject Page: https://xianyunsun.github.io/OmniAssistBench/, Affiliation: Nankai University +1
Benchmarks assistant-style, multi-turn interaction for omni-modal LLMs on real-time video by reverse-engineering Internet clips into guided multi-turn interactions. It provides predefined priors and segment-level constraints so models must follow exact routes while being evaluated on answer correctness, timing, visual-prompt handling, and context retention.
AI Agent Papers2026
Proposes “Graph Engineering”: using explicit, dynamic graphs to represent tasks, agents, tools, and system state so LLM-based agent systems can coordinate, persist, and evolve. Surveys principles, methods, applications, and curates related resources.
AI Model2026
Provides FP8-quantized Hugging Face weights and config for Qwen3.8-Flash-Next (block size 128), preserving near-original performance. Compatible with Transformers, vLLM, SGLang and TokenSpeed; intended for efficient deployment of a 125B multimodal causal LM with very long context support.
AI Model2026
Experimental open-weight multimodal LLM preview designed for long-context, agentic workloads. It introduces hybrid sparse attention (QSA), gated residual streams, and large offloadable n‑gram embeddings (51B) alongside a high-sparsity MoE (125B total, 6B active) to trade memory for runtime efficiency and improved long-horizon reasoning.
AI Video Papers2026
Generates enterable omnimodal world-model rollouts that follow continuous 6-DoF camera control while jointly producing 720p video, environmental sound, music and speech. Uses dataset-level motion calibration, a specialized data engine, progressive training and autoregressive post-training to support long-horizon first- and third-person interaction.
AI Agent Papers2026
Assesses mobile planning agents' ability to call tools, plan long-horizon workflows, and coordinate sub-agents in realistic, interactive phone scenarios via a stateful executable sandbox. Covers 13 domains, 212 tools, evidence-based verification, and tests memory, skill usage, permission and runtime constraints.
AI Agent Papers2026
Sungho Park, Wonjoong Kim +11Affiliation: KAIST, Affiliation: Southern University of Science and Technology +1
Automatically optimizes runtime harnesses for LLM agents by diagnosing failure traces and iteratively applying structured, generalizable patches. Combines batch-based failure diagnosis, code-like patch generation across prompts/tools/middleware, and validation-aware selection to raise long-horizon task success on multiple benchmarks.