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AI Video Papers·2026
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SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation

Keyu Tu, Zhuowei Chen +5·University of Science and Technology of China, FrameX.AI +1

Introduces SemComp-Bench: a benchmark and VLM-based evaluation protocol for measuring outcome achievement and task-relevant semantic grounding in instruction-driven video generation. Ships with SemComp-Data, curated image–instruction–outcome triplets and OA/GR scoring.

#video#vision#evaluation#benchmark#benchmarks+4
Hugging Face
AI Model·2026
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Qwen3.8-27B — OBLITERATED

OBLITERATUS, Pliny the Prompter +1

An uncensored, weight-modified variant of Qwen3.8-27B that surgically removes the model's refusal directions to produce 0% refusals while aiming to preserve or improve capability. Uses complementary abliteration blending (SVD + LEACE blend) and ships with recommended greedy inference settings; intended for AI-safety research and red‑teaming, not for causing harm.

#qwen#llm#huggingface#safetensors#gguf+5
Hugging Face
AI Dataset·2026
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datapointai/text-2-image-human-preferences-2m

Datapoint AI

Contains ~2 million human pairwise preference judgments comparing images generated from text prompts; each example pairs two images with a preferred/tie label and is formatted for preference learning, reward-model training, and evaluation.

#image#ai-image#evaluation#benchmark#parquet+4
AI Agent Papers·2026
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FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

Kou Shi, Zun Wang +11·Affiliation: Shanghai AI Laboratory, Affiliation: Fudan University[0.25em] +2

Proposes FACET, a framework that synthesizes verifiable terminal tasks by reconstructing scenario intent and grounding instruction, solution, and verifier in a shared executable container state. Key features include environment-first generation, execution-based validation, and targeted repair to preserve source intent and cross-artifact consistency.

#terminal#agent-skills#ai-agent#evaluation#benchmark+4
AI Agent Papers·2026
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SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?

Zhipeng Xu, Jiahao Lu +3

Evaluates whether coding agents can modify real scientific software while preserving domain-specific scientific contracts. Contains 119 repository-level tasks across 98 GitHub projects and 20 scientific domains, measures reproducible edits in pinned Docker images, and analyzes recurring failure modes.

#benchmark#coding-agents#science#software-engineering#evaluation+3
AI Video Papers·2026
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VGI-Bench: Probing Visual Intelligence in Video Generation Models

Xuan He, Cong Wei +21·University of Illinois Urbana Champaign, Tsinghua University +8

Evaluates visual reasoning in video generation models using 27 photorealistic tasks (810 instances), a two-level taxonomy of domains and skill tags, and task designs that enforce valid intermediate trajectories and calibrated difficulty.

#video#vision#benchmark#evaluation#ai-video+4
AI Video Papers·2026
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OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs

Xianyun Sun, Chaoyou Fu +7·Affiliation: 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.

#multimodal#video#vision#llm#benchmark+5
AI Agent Papers·2026
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MobilePA-Bench: Benchmarking Mobile Planner Agents on Complex Real-World Tasks

Yi Zhu, Xiongwei Wu +9

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.

#benchmark#benchmarks#mobile#ai-agent#agent-skills+4
Hugging Face
AI Dataset·2026
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ThinkingBox-Bench

Microsoft

Evaluates whether tool-using LLM agents reliably complete stateful business workflows via 507 executable agent–tool–user tasks across retail, travel, auto insurance, neobank, and IT/HR consulting. Provides browsable Parquet tables for tasks, scenarios, and agent instructions; v1.0 is intended for evaluation-only.

#benchmark#evaluation#ai-agent#mcp-server#mcp+4
AI Agent Papers·2026
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Apodex 1.1: Scaling Agentic Intelligence for Complex Work

Apodex Team, B. An +69

Develops methods to scale agentic AI for sustained, verifiable execution of complex long-horizon work by expanding executable environments and training coordinated agents with a shared execution harness (AgentOS) to maintain state, provenance, and failure recovery.

#ai-agent#long-horizon#agent-skills#ai-workflow#llm+2
AI Agent Papers·2026
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FrontierChallenge: Evaluating Scientific Workflow Completion

Liangcai Su, Zhaopeng Feng +14

Evaluates AI agents' ability to complete end-to-end scientific workflows by releasing and assessing 97 tasks from a 300-task FrontierChallenge suite across chemistry, materials, life science, and electrochemistry. Finds that top agent configurations achieved only a 20.6% pass rate despite high partial scores, revealing a gap between partial progress/confident completion claims and actual complete scientific deliverables.

#benchmark#benchmarks#evaluation#agent-skills#ai-agent+6
Hugging Face
AI Model·2026
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GLM-5.3-Flash

Z.ai (zai-org), GLM-5 Team

A natively multimodal model for text and image→text generation, long-context reasoning, and complex coding/agent workloads. Uses 320B total / 18B active params with a hybrid sparse+linear attention and manifold-constrained hyper-connections to reduce long-context serving cost.

#transformers#multimodal#fp8#safetensors#huggingface+7
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