Discover the Best AI Resources
Curated essentials, no noise — just what matters
AI Model2026
Multilingual frontier LLM optimized for long-context reasoning and agentic workflows, combining a LatentMoE (Mamba-2 + MoE) hybrid architecture with Multi-Token Prediction and NVFP4 quantization; targeted for NVIDIA GPU deployments and governed by the OpenMDW-1.1 license.
AI Dataset2026
Provides 600,000 synthetic Vietnamese persona texts (100,000 records, 6 personas per record) aligned to Vietnam's 2024 census and surveys for training and evaluating NLP / text-generation models; includes 21 demographic and persona fields, CC BY 4.0, single train split.
AI Agent Papers2026
Benchmark that measures an agent's ability to discriminate fine-grained relational structure in long-term memories. It embeds relation-controlled memory variants into realistic user–agent histories and tests downstream recovery and reasoning, highlighting where current memory systems fail.
Evaluates whether role-playing language agents follow a character's evolving psychological arc rather than a fixed persona, using ArcANE — an automatically constructed benchmark spanning 17 novels and 80 principal characters. Tests both in-text and out-of-text scenarios and compares context strategies and fine-tuned models.
AI Model2026
Provides compact, agentic text-generation for long-horizon, tool-enabled workflows — trading some peak capability for lower latency and easier on-prem deployment. Key features: adaptive/coherent thinking traces, function-calling support, and sglang/docker-ready serving.
AI Dataset2026
End-to-end evaluation framework for conversational voice agents that runs bot-to-bot audio simulations and scores agents on task accuracy (EVA-A) and interaction experience (EVA-X). Includes per-scenario backend state, accent/noise perturbations, and 213 scenarios across airline, healthcare HR, and enterprise IT domains.
AI Agent Papers2026
Dynamic interactive benchmark that tests whether LLM agents can adaptively plan and re-plan when world and user constraints are progressively revealed. Built on 307 household tasks with a multi-turn protocol that exposes hidden constraints only after plan violations, emphasizing iterative revision and constraint inference.
Trains LLMs with reinforcement learning using a surface chrF reward so models learn to extract and apply linguistic signals from rich context for translating completely unseen languages. Demonstrates better zero-shot translation than in-context learning or supervised fine-tuning, framing outcome-based RL as a meta-skill for language learning from context.
AI Dataset2026
Provides a complete, lightly-processed export of AI Village's >1-year multi-agent data: per-agent computer sessions (with screenshots), turn-by-turn computer-use logs, group chats, agent memories, goals, and daily summaries for research into agentic behaviour, multi-agent dynamics, long-horizon memory, and AI safety. Access is manually reviewed.
AI Model2026
Provides a GGUF-ready QAT (Q4_0) quantized build of Gemma 4 12B that preserves near-bfloat16 quality while reducing memory footprint for local inference; compatible with Transformers-based and GGUF runtimes.
AI Agent Papers2026
Measures how coding agents explore repositories by asking them to return a ranked, line-level list of code regions relevant to an issue under a fixed line budget. Covers 848 issues across 203 repos and 10 languages; evaluates coverage, ranking, and context-efficiency to isolate exploration quality.
AI Model2026
A surgically modified Gemma 4 (12B) that removes refusal behavior while preserving benchmark parity; released as an uncensored research artifact with GGUF quantizations for local inference and red‑team/alignment evaluation.