…AI-generated summary Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning , reasoning , tool use , and multi-turn interaction with environments. Despite major investment, open…
…To better understand the political rhetoric from various movements, ranging from left to right, we propose a transformer-based model capable of projecting the political orientation of a text on a continuous…
…A Multitask Benchmark and Model for Code Search Published on May 6 Submitted by Ziyin Zhang on May 11 high-quality llm benchmarks Authors: , , , Ziyin Zhang , , , Hang Yu Abstract A new code…
…To address this problem, we propose a new benchmark CUActSpot for evaluating models' capabilities on complex interactions across five modalities: GUI, text, table, canvas, and natural image, as well as a variety…
…We then train a family of capable Claw-style models, termed ClawGym-Agents , through supervised fine-tuning on black-box rollout trajectories , and further explore reinforcement learning via a lightweight pipeline that…
…AI-generated summary In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without…
…AI-generated summary Recent progress in multimodal large language models (MLLMs) has brought AI capabilities from static offline data processing to real-time streaming interaction , yet they still remain far from human…
…AI-generated summary LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom? Researchers operate under different…
…AI-generated summary Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded solutions in open-ended environments, beyond…
…To further enhance practicality, we introduce a reinforcement learning framework that enhances length extrapolation capability while dynamically determining the number of rereading passes based on task complexity, thereby flexibly controlling computational overhead…