AI Model2026
A 124B hybrid-linear Mixture-of-Experts language model optimized for instruction following, long-context reasoning and agentic workflows, activating ~5.1B parameters per token. Key features include a 256K native context (extendable to 1M), alternating KDA/MLA attention layers, and vLLM/SGLang inference support.
AI Agent Papers2026
Alternates targeted research and constraint-wise audits to recursively improve long-horizon answers: an inner loop gathers evidence and drafts solutions, an outer loop audits unresolved claims and launches focused follow-ups. Trains 4B dense and 122B-A10B MoE agents with long-horizon RL and agentic mid-training, outperforming comparable-scale baselines on multi-step research benchmarks.
Presents Skill Self-Play (Skill-SP), a co-evolutionary training loop where a proposer, solver, and dynamic skill controller generate, solve, and verify tasks conditioned on reusable skills — balancing verifiable execution with open-ended task diversity to boost LLM tool-use and reasoning.
AI Agent Papers2026
Drives long‑horizon desktop agents by reading and manipulating program state (files, DOM, backends) instead of relying on screenshots. The main agent uses code for actions and structural verification while a lightweight GUI subagent handles rare screenshot-click steps, improving success rates and lowering per-task cost versus screenshot-only approaches.
Transforms open-ended LLM optimization into self-verifiable reinforcement learning by turning tasks into proxy environments that produce deterministic, rule-based rewards. Proposes RLSVR and SpyRL — an information-asymmetric self-play scheme where agents vote to identify a preassigned spy, yielding verifiable rewards without human annotation. Demonstrated on summarization, creative writing and mathematical reasoning.
AI Dataset2026
Provides a 57,937-row, quality-filtered multi-teacher SFT distillation corpus combining outputs from Qwen3.8-Max, GLM-5.2 and Kimi K3 across math, code, reasoning, tool-use and dialogue. Includes 24 parquet training views (including a pre-tokenized GLM-4.7 view), configurable sampling weights (sft_balanced), and explicit tool-call trajectories for agent training.
AI Model2026
An open-weight LLM focused on deep reasoning, native agentic tool use, and repository-scale code understanding — Mixture-of-Experts architecture with an extended context window and permissive licensing.
AI Model2026
Generates text from text, image, or audio inputs using a native multimodal, Mixture-of-Experts autoregressive transformer (276B total / 12B active) with up to 1M-token context; targeted at conversational, agentic, coding and multimodal applications.
Turns document relevance into an execution prior for agentic corpus interaction: orders documents for sequential ripgrep traversal, seeds promising entry points with query-relevant paragraphs, and reranks grep matches to surface informative excerpts. Improves the accuracy–efficiency frontier on browse QA and reasoning-intensive retrieval.
AI Agent Papers2026
Bridges the proprietary-to-open-source gap in agentic search by converting multi-step retrieval and reasoning traces into a structured, style-normalized JSON protocol and using it for joint distillation + RL. Produces denser supervision that improves student success rates while reducing style drift.
AI Video Papers2026
Real-time streaming multimodal foundation model that uses a codec-native tokenizer (Mage-ViT) to encode motion- and residual-rich regions from video I/P frames, reducing visual token usage by over 75% and enabling up to ~3.5× wall-clock inference speedup after training on ~560M images and 100M video frames.
AI Dataset2026
Generated instance set (seed 31415) for ASI‑Bench: includes four matched prompt variants, agent-visible inputs, reference artifacts, and instance metadata for 60 project-scale scientific research tasks across 11 domains; intended for evaluating autonomous research agents. Licensed Apache‑2.0.