AI Agent Papers2026
Yunhao Chen, Xin Wang +7Affiliation: Fudan University, Affiliation: Shanghai Artificial Intelligence Laboratory +1
Evolves persistent, stateful environments to red-team tool-using AI agents — provides 10K+ validated scenarios across 50 domains and a feedback-driven attack policy (EMHA) to surface long‑horizon safety failures.
AI Agent Papers2026
Reformulates long-horizon agent execution as explicit task-state management: a manager defines bounded subtasks, fresh-context executors run them, and read-only auditors verify outcomes. Shows large performance gains on WeaveBench, Terminal-Bench and OSWorld.
AI Dataset2026
Provides per-decision training samples for RL-driven command-line LLM agents: each record pairs a task prompt plus terminal history with a teacher's next-action in Terminus-2 JSON. Around 31k verifier-passing samples from 630 ATCB tasks, formatted for NeMo Gym's terminus_judge and licensed CC-BY-4.0.
AI Agent Papers2026
Enables a coding agent to self-develop by evolving its harness, prompts, tools, and core code via reviewed commits — supporting recursive free evolution and experience-driven evolution. Demonstrated a 161-day live lineage and state-of-the-art scores on multiple coding benchmarks while foregrounding operational safety.
AI Model2026
A MoE causal large language model for long-horizon agents, coding, and multi-step reasoning: 2.4T parameters (95B activated), native 262,144-token context (extensible to 1,010,000), multi-token prediction, and configurable thinking-mode reasoning controls.
AI Model2026
FP8-quantized checkpoint of the Qwen3.8 text-only causal LLM (2.4T params, 95B activated) for text-generation; preserves near-original performance, supports very long contexts (262k–1M), Mixture-of-Experts architecture, and is compatible with vLLM/SGLang/TokenSpeed. Thinking mode and preserve_thinking are enabled by default.
AI Model2026
A 29.6B-parameter multimodal causal language model with a dedicated ViT-G/14 perception encoder for running agentic, tool-using, multimodal reasoning locally on consumer hardware. Offers 4-bit quantized weights and a DFlash drafter for speculative decoding to reduce memory and speed up generation.
AI Model2026
A GGUF release of Meta's Muse Glimmer 30B optimized for local multimodal agent inference; includes two quantized text builds, a perception encoder for image input, and an optional DFlash drafter for speculative decoding—fits on 24–32 GB VRAM.
AI Dataset2026
Provides a curated benchmark of 170 real-world, multilingual code-refactoring instances to evaluate AI coding agents on large-scale, behavior-preserving, cross-file refactors. Each task includes rewritten issue descriptions and manually reviewed test suites to avoid over- and under-constraining evaluations.
AI Model2026
Open-weight 30B-parameter Mixture-of-Experts LLM with 3B active params, NVFP4-quantized checkpoint, and speculative-decoding support for long-context (up to 1M tokens) agentic, chat, reasoning and tool-calling workloads optimized for NVIDIA GPUs.
Zhuoyang Qian, Biao Wu +7Affiliation: Vast Intelligence Lab
University of Technology Sydney, Affiliation: Equal Contribution
Corresponding Authorhttps://github.com/Spark-To-Paper-Skills/[email protected]
Generates full publication-format research papers from a short idea by composing 13 coding-assistant skills; it retrieves literature, plans and runs feasible experiments, produces editable vector figures, and enforces deterministic integrity checks so claims are revised to match measured evidence.
AI Model2026
Provides an FP8-post-trained 27B multimodal causal language model with a native vision encoder, large-context support (262,144 native, extensible to 1,000,000), controllable thinking-mode reasoning, and compatibility with common inference engines for deployment.