Papersby GlyphAIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents research paper by Meta, 2026
Meta · Applied AI · Feb 6, 2026 · 30 citations · 83 upvotes · unverified
What it shows
AIRS-Bench presents a comprehensive benchmark suite for evaluating LLM agents across diverse scientific domains, demonstrating current limitations while providing open-source resources for advancement.
By Alisia Lupidi, Bhavul Gauri, Thomas Simon Foster and 34 more · arXiv 2602.06855 · PDF · Code
UnverifiedHugging Face's summary; not yet checked by hand.
More from Meta
All 40InteractiveWorld: A Verifiable Human-Centric Environment for Building Better Interactive MLLM AssistantsInteractiveWorld provides a verifiable simulation environment and benchmark to evaluate whether multimodal language models can reliably guide humans through physical tasks.
Meta46 upvotesRead →Native Action-Prior Learning from Videos for World Action ModelsNAVA-WAM learns robot action priors directly from observation-only videos, improving control performance without needing action-labeled data for pretraining.Meta88 upvotesRead →Sharpening Tax in Post-TrainingThis paper introduces a metric to quantify the loss in solution coverage after reinforcement learning and proposes a sampler to mitigate it.Meta1 citations111 upvotesRead →World Action Modeling with Progressive Visual PlanningProWAM improves robotic control by predicting sparse visual sub-goals to guide action generation, avoiding the need for dense video generation.Meta1 citations98 upvotesRead →Context Language ModelsContext Language Models manage their own context by treating it as an editable file, improving performance and efficiency.Meta5 citations47 upvotesRead →In-Flight KV Cache with Clean Anchors for Faster Autoregressive Video DiffusionFlashForward accelerates video diffusion by reusing in-flight KV caches and using sparse clean anchors to maintain structural consistency.Meta1 citations34 upvotesRead →WearableQA: A Benchmark for Health Reasoning over Real-World Wearable DataWearableQA is a benchmark of multiple-choice questions derived from real longitudinal wearable data that evaluates large language model reasoning across data and health dimensions.Meta43 upvotesRead →Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and RecipesControlled experiments on when text, image understanding and image generation help or compete when trained together.Meta7 citations63 upvotesRead →
Other applied ai papers
TopicSciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?SciPredict benchmark reveals that large language models struggle to accurately predict scientific experiment outcomes and cannot reliably assess prediction confidence, unlike human experts who show better calibration and performance when experiments are deemed predictable.
Scale AI4 citations4 upvotesRead →AIM: Agentic Idea Management for Automated ResearchAIM is an autonomous framework that manages and explores research directions by using surrogate models to guide experimental budget allocation.
Google2 citations53 upvotesRead →Agensh: Scaling Organizational Intelligence to 1,024 AgentsAgensh is a scalable multi-agent harness that uses self-organized cooperation to coordinate up to 1,024 agents without a central orchestrator.
Microsoft2 citations28 upvotesRead →Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic DesignDesigner-RSI improves agentic graphic design by continually refining a procedural memory of skills based on user traffic and execution feedback.
Adobe1 citations34 upvotesRead →BI-Agent and BI-Bench: Towards Automating End-to-End Business IntelligenceBI-Agent automates end-to-end business intelligence tasks by decomposing workflows into subtasks like data search, join, and transformation.Microsoft1 citations30 upvotesRead →StudentSim: Training LLM-based Student SimulatorsStudentSim trains per-student simulators that answer like a given learner and change their answers under a tutor's guidance.Microsoft1 citations371 upvotesRead →
About this paper
- Venue
- arXiv.org
- Citations
- 30, 2 influential · Semantic Scholar
- Upvotes
- 83 · Hugging Face
- Lab
- Meta · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf
Changes
Citations rose from 28 to 30.
Citations rose from 26 to 28.
Citations rose from 25 to 26.
A first count of 2 influential citations.
A first count of 25 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.