Discover the Best AI Resources
Curated essentials, no noise — just what matters
Native multimodal model for image/text/video→text tasks with million‑token context support. Uses a sparse-attention operator to cut long‑context compute and latency, and targets agentic, coding, and long-horizon conversational workloads.
Enables agents to proactively discover multiple hidden problems in a user context and pair each with supporting evidence and concrete actions. Uses iterative discovery (batch rounds conditioned on prior finds) and reusable "thought templates" to expand coverage and ground claims.
Provides ~1M synthetic Salvadoran‑Spanish personas (148k records, ~300M tokens) grounded in 2024 census distributions for demographics, occupations and locations; intended for training/evaluating localized LLMs and synthetic-data workflows. CC BY 4.0, adults only.
Agentic LLM for long-horizon, environment-driven workflows: decomposes goals, generates and executes code/tool calls, evaluates outputs, and iterates. The Pro variant emphasizes coding and terminal execution and is published for use with sglang and multi-node H100 deployment.
Generates synthetic coding-agent session traces by pairing remotely hosted open agent models with local llama.cpp user models across real open-source codebases. Each trace records read/write/edit/bash actions and tool use; the dataset is a reproducible cartesian product (20×3×20×20 = 24,000 sessions) under an MIT license.
Large-scale training corpus for knowledge- and reasoning-intensive video understanding: 315K video reasoning examples over 145K CC-licensed expert-domain videos, with human-in-the-loop chain-of-thought rationales to strengthen post-training for video reasoning. ([arxiv.org](https://arxiv.org/abs/2606.05259))
Around 80K short audio clips paired with transcripts in JSON, organized for easy loading with the Hugging Face datasets ecosystem—designed for short-form speech tasks (ASR, TTS, fine-tuning) and quick prototyping with common Python data tools.
Represents episodic memory as a Cue–Tag–Content graph and integrates LLM reasoning into active retrieval so agents iteratively reconstruct and prune evidence paths for long-horizon questions. Reports up to 23% gains on LoCoMo / LongMemEval while reducing token and runtime costs.
Generates repository-specific LoRA adapters via a hypernetwork to inject repo-level knowledge into code LMs with zero inference-time token overhead. Provides a Static snapshot mode and an Evo mode that updates adapters per commit; evaluated on the 604-repo RepoPeftBench.
Benchmark for evaluating proactive LLM mediators in realistic, multi-domain conflict scenarios by constructing cases from real disputes, probing five socio-cognitive adaptation axes, and using a topic-localized evaluator that achieves 0.82 alignment with human experts.
Open-weight frontier LLM for agentic reasoning and long-context analysis (up to 1M tokens). Uses a LatentMoE + Mamba-2 hybrid with Multi-Token Prediction and NVFP4 efficiency (550B total / 55B active). Suited for multilingual agents, RAG, and heavy tool-use workloads.
Converts text into expressive conversational speech across 100+ languages with zero-shot voice cloning and inline control tokens for emotion, style, prosody, pauses, and sound effects. Released under a research/non-commercial license; commercial use requires separate licensing.