Model Detail
Auto Router
—Auto Router is a multimodal model released by Openrouter. And supports text+image+file+audio+video->text+image inputs.
Auto Router is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
Auto Router is best fit for mixed text-and-image reasoning tasks such as document understanding, and long-context tasks such as full-codebase analysis or book-length summarization (2000K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Robust Asynchronous Planning via Auto-Formalization
arXiv:2606.00981v2 Announce Type: replace Abstract: LLMs can plan by generating schedules directly as a Planner, or by translating a task into a formal specification for a solver as a Formalizer. Most real-world tasks are asynchronous, with non-uniform durations, concurrency, and execution-time chan
PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience
arXiv:2606.18060v2 Announce Type: replace Abstract: As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important. Otherwise, such systems may rapidly generate plausible yet misleading studies that contaminate academic
Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding
arXiv:2607.04383v5 Announce Type: replace-cross Abstract: Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection (SED) attains frame-level precision only over a closed label set. At the intersection
How Far Are We From True Auto-Research?
arXiv:2605.19156v2 Announce Type: replace-cross Abstract: Recent auto-research systems can produce complete papers, but feasibility is not the same as quality, and the field still lacks a systematic study of how good agent-generated papers actually are. We introduce ResearchArena, a minimal scaffold
