AI Infra2026
Indexes chemistry literature as provenance-bearing atomic claims and provides a faceted taxonomy, evidence graph, and REST/SDK/MCP APIs so researchers and AI agents can retrieve verifiable, claim-level findings across papers; live index contains 2.4M claims from 147K papers.
AI Dataset2026
Provides a CC0-licensed corpus of 11,045,085 Turkish court decisions (1962–2026) in Parquet: 31.5 billion characters, 5.5 GB—designed for retrieval, summarization, classification and RAG workflows.
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 Agent Papers2026
Trains LLM agents to proactively edit and manage their working context for long-horizon tasks using an expanded toolset (planning, long-term memory, soft offloading) and a fine-grained RL algorithm that identifies critical edits and assigns action-level credit. Improves accuracy while keeping contexts compact on long-context QA and deep search.
Teaches how to build and evaluate AI agents, from hand-written RAG and tool loops to memory, optimization, and safety. Two applied assignments and a quarter-long project connect design decisions to measured reliability.
AI Model2026
Generates unified 768‑dimensional embeddings for text (including code), images, video and audio to enable cross‑modal semantic search and retrieval. Supports task instruction prefixes, Matryoshka truncation to 128/256/512/768 dims, and modular encoders for on‑device use under an Apache‑2.0 license.
AI Video Papers2026
Surveys memory mechanisms for autoregressive video generation, framing memory as persistent historical information that influences future generation. Organizes work by Forms, Functions, Operations, Learning, and Evaluation, and synthesizes challenges for long-horizon consistency and memory-aware learning.
AI Dataset2026
Synthetic, clinician-verified ChatML dataset of 2,194 doctor–patient encounters covering 2,194 unique human diseases; each JSONL record includes 20 structured fields, verified PubMed references, realistic vitals/labs, and is intended for RAG and model fine-tuning (not medical advice).
AI Dataset2026
Provides 130,000 typed RAG claim-verification decisions that pair source-derived reference labels with jev-1.13.0 model predictions. Includes sentence/span grounding, hallucination-type presence, whole-response grounding and response-quality decisions with retained provenance. Suited for grounding error analysis, prototype evidence-checkers and confidence-threshold studies; license: other.
AI Agent Papers2026
Analyzes how proposer–solver loops in self-evolving search agents can develop shared errors (co-cheating) that inflate internal rewards; introduces Multi-Sample Verification and CrossFit (cross-fitted scoring with partitioned sources) to reduce false agreement and improve downstream search performance.
AI Dataset2026
Aggregated, screened corpus of 55,050 normalized Indian public-information text bodies and 65,209 source records for retrieval and question-answering. Exports include deduplicated CSV/Parquet with provenance, topic labels, extraction quality flags and a private SQLite backup.
Adaptive-granularity post-retrieval evidence compressor for multimodal RAG that selects variable-sized regions from retrieved text, tables, images, and videos to retain necessary context while cutting irrelevant content. Key features include a hierarchy-based node encoder with parent-relative refinement and a critic that issues targeted follow-up retrievals; yields higher QA accuracy across five benchmarks and reduces reader-input tokens by ~14–28%.