Performs native structural reasoning for proteins, small molecules and inorganic crystals by tokenizing coordinates, topologies and periodic connectivities into a unified structure-aware vocabulary. Treats structural tokens as addressable evidence to produce interpretable prediction traces and improves accuracy across biology, chemistry and materials benchmarks.
Provides IdeaGene-Bench, a dataset and evaluation suite for scientific-lineage reasoning and lineage-grounded idea generation, representing papers as minimal, typed Idea Genome objects and GenomeDiffs that record inheritance, mutation, loss, import and novel insertion. Includes 1,961 lineage traces, IG-Exam (42 task types) and IG-Arena with a Population-Evolution Score for generation.
AI Agent Papers2026
Provides a terminal-style benchmark of 46 long-horizon tasks decomposed into fine-grained graded subtasks to produce dense intermediate rewards and partial credit, enabling evaluation of long-horizon planning, long-context management, and iterative debugging. Tasks typically require hundreds of episodes and minutes-to-hours of execution; baseline evaluations report high token and episode consumption with low pass rates, highlighting evaluation headroom.
AI Dataset2026
Simulates a hospital LIMS to benchmark agentic clinical reasoning: agents inspect demographics, medications, lab orders/results and then submit ICD‑10 diagnostic reports scored by deterministic, context‑aware graders. Ships as an OpenEnv/FastAPI runtime with 8 scenarios, step‑level rewards and trajectory capture for RL, tool‑use and evaluation.
AI Dataset2026
Evaluates agents inside a structured hospital workflow via a downloadable FastAPI runtime that enforces role-specific tool permissions, evidence-before-treatment discipline, deterministic grading, dense process rewards, and full trajectory logging. Designed for RL, offline policy learning, multi-agent workflow research and process-supervision datasets; not for real patient care.
AI Agent Papers2026
Evaluates proactive, multimodal agents on 400 bilingual real‑world tasks across five capability axes (Skill Usage, Exploration, Long‑Context Reasoning, Multimodal Understanding, Cross‑Platform Coordination) using live Docker‑based, stepwise closed‑loop evaluation to separate base model skills from framework design.
Proposes Riemannian Isometric Policy Optimization (RIPO) to fix exploration collapse in PPO-style RL for LLMs by aligning policy updates with the policy manifold's Riemannian geometry, improving exploration–exploitation balance and optimization stability across competition benchmarks.
AI Agent Papers2026
Provides a deliberative Agent OS layer for robots that handles scene-conditioned planning, context-isolated skill execution, multi-stage verification, persistent multi-modal graph memory, and edge–cloud collaboration. Introduces EmbodiedWorldBench (16 scenes, 200+ tasks) and a failure-driven self-evolution loop; shows improved task success and strong memory benchmark scores.
Uses pretrained multimodal LLMs as zero-shot, training-free reward models for text-to-image RL by scoring how well the original text prompt can be recovered from a generated image via image-conditioned prompt log-likelihood; includes a Self-SpectraReward closed-loop variant.
AI Agent Papers2026
Acquires repository knowledge via a targeted QA loop before generating patches, decoupling knowledge acquisition from repair. A Questioner and Answerer produce evidence-grounded QA pairs that a Resolver uses to generate fixes; improves Pass@1 on SWE-bench Verified with modest overhead.
AI Dataset2026
Evaluates retrievers and search agents on synthetic multi-hop questions that require assembling a complete set of supporting evidence. Provides English and Russian variants (395 questions each), a fixed dense index embedded with Qwen3-Embedding-8B, and BrowseComp-Plus evaluation integrations.
AI Dataset2026
Provides 324 Russian short-answer web-search tasks with gold supporting documents to evaluate fixed-index retrievers and search agents. Tasks span eight topical categories and five retrieval challenge types (multihop, structured evidence, temporal, entity disambiguation, comparative) and use a Qwen3-Embedding-8B index for evaluation.