Discover the Best AI Resources
Curated essentials, no noise — just what matters
AI Dataset2026
Evaluates retrieval-augmented generation by decomposing user queries into sub-queries and answers into atomic claims, scoring retrieval by query coverage and generation by claim verifiability. Reference-free benchmark with 800 queries, inlined retrieved chunks, and answers from multiple RAG systems; runs locally without API keys.
AI Dataset2026
Contains 244 Cursor agent sessions recorded from Claude Fable‑5, formatted for training and research. Sessions include multi-turn assistant/tool interactions and are Teich-compatible; several rows exceed one million characters, so apply explicit tokenization and oversize policies before training.
AI Agent Papers2026
Proposes treating game development as a recursive data engine and introduces RLHEV (Reinforcement Learning with Human-Engine Verification) to combine dense engine checks (collision, physics, navigability) with human acceptance feedback, producing trajectory data and rewards for post-training world models.
AI Model2026
GGUF-quantized build of Qwen3.8-Flash-Next for image-text-to-text inference and local deployment. Ships with Unsloth Dynamic 3.0 quantization, thinking-mode controls (preserve_thinking, reasoning_effort), and native long-context support (262k, extensible to 1M with YaRN).
Provides a streaming dual-brain memory for real-time speech agents: an informational left brain for factual retrieval and an affective right brain for persona/emotion, achieving high top-5 accuracy while keeping retrieval latency within VAD budgets (~134 ms).
AI Dataset2026
Kunal Handa, Miranda Zhang +22Anthropic, Social and Language Technologies (SALT) Lab, Stanford University +2
Provides aggregated, privacy-preserving cluster outputs from three external research teams' analyses of ~250k Claude/Claude Code conversations; includes per-team CSVs (Stanford, Oxford, METR) for studying human–AI collaboration and model behavior without raw conversations.
AI Agent Papers2026
Synthesizes, repairs, and self-evolves task-adaptive agent harnesses on demand for off-the-shelf LLM agents, using a trainable harness-intelligence model that distills signals from past configurations. Demonstrates consistent performance gains across benchmarks and model families by producing four-module, composable harnesses.
AI Model2026
Provides a GGUF-quantized build of GLM-5.3-Flash for local text-generation and inference. Key features: 320B total / 18B active parameters, hybrid sparse+linear attention, native multimodal pretraining and Unsloth Dynamic quantization. Best for developers running GGUF local inference workflows.
AI Model2026
Integrates a pretrained vision–language model with a BEV perception head and a Planning Expert to provide 3D perception, driving VQA and motion planning for autonomous driving while keeping the base VLM architecture unchanged.
AI Agent Papers2026
Analyzes how to generate useful interaction data for LLM agents and proposes the ACE lens — Accuracy, Complexity, divErsity — while factorizing agentic data as (E, q, τ, v). Surveys verification, difficulty calibration, and coverage strategies and outlines implications for training and benchmarks.
A zero-data self-evolution framework that co-trains a Challenger, Solver, and Judge so LLMs can iteratively improve on both verifiable and unverifiable tasks without human labels. Uses role-asymmetry and subtask-amplification preference pairs to train the Judge and sustain improvement.
Performs causal, bounded‑memory streaming 3D reconstruction by caching KV features from only the preceding 11 frames, predicting a per‑frame point map and adjacent relative pose, and composing these local predictions into a global trajectory; includes a lightweight rotation refiner and composition‑aware loss to limit drift.