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Even the best search agents struggle to exceed 35% accuracy on a benchmark designed to push the limits of long-horizon reasoning.
Code agents struggle with evolving user requirements, revealing a 38-point gap in performance across leading LLMs when faced with iterative feedback.
GUI agents can learn world knowledge more efficiently by internalizing causal relationships during mid-training, rather than relying on implicit learning through action annotations or reward signals in post-training.
Skill0.5 achieves state-of-the-art out-of-distribution generalization in agentic RL by intelligently combining skill internalization and utilization, outperforming methods that rely solely on one or the other.
Long-context LLM rankings dramatically reshuffle when evaluated across a range of context lengths and capabilities, proving that a single headline score is misleading.
Current LLM agents still struggle to infer and leverage user preferences from fragmented, real-world interactions, revealing a substantial gap between their capabilities and the demands of personalized decision-making.
Open-source LongCat-Video-Avatar 1.5 leapfrogs closed-source competitors in audio-driven video generation by prioritizing practical engineering over architectural novelty, delivering commercial-grade quality and speed.
MONA unlocks faster LLM pretraining and superior downstream performance by turbocharging the Muon optimizer with Nesterov-style acceleration, leaving AdamW in the dust.
LLM agents trained with simulated user and tool noise not only become more robust in messy real-world environments, but also surprisingly improve on clean, idealized benchmarks.
Interactive world models still have a long way to go: a comprehensive benchmark reveals that even state-of-the-art models struggle to consistently perform well across video quality, interaction adherence, and physics compliance.
Stop LLMs from drifting to English when reasoning in other languages: language-adaptive RL can guide them to stay consistent without sacrificing performance.
Forget brittle orchestration layers – LLMs can internalize complex reasoning as a learnable "HeavySkill" that rivals external agentic frameworks.
