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Chenxu Wang, Chaozhuo Li, Songyang Liu +10 authors
Multi-agent LLM systems face fundamental limitations in achieving continuous self-improvement while maintaining safety alignment due to inherent statistical blind spots in isolated evolution.
Ailin Huang, Chengyuan Yao, Chunrui Han +90 authors
STEP3-VL-10B achieves superior multimodal performance through unified pre-training with a language-aligned Perception Encoder and Qwen3-8B decoder, combined with scaled post-training and Parallel Coordinated Reasoning for efficient large-scale visual reasoning.
GLM-5 Team, Aohan Zeng, Xin Lv +183 authors
GLM-5 advances foundation models with DSA for cost reduction, asynchronous reinforcement learning for improved alignment, and enhanced coding capabilities for real-world software engineering.
Guobin Shen, Xiang Cheng, Chenxiao Zhao +4 authors
Anti-Self-Distillation reverses the direction of knowledge transfer in self-distillation to improve math reasoning efficiency and accuracy.
Pengfei Zhou, Hexin Wang, Zhengfeiyang Zhang +5 authors
Game engines provide executable verification and long-horizon trajectories for reinforcement learning post-training of spatial world models, motivating a human-engine verification paradigm.
Qianggang Ding, Xingyao Wang, Rui Feng +19 authors
Combodied Agents integrate digital and embodied tools into a closed-loop framework that models individual human-state trajectories over time to provide proportionate, consent-aware support.
Haiwen Diao, Penghao Wu, Hanming Deng +55 authors
Unified vision-language models treat understanding and generation as integrated processes rather than separate tasks, demonstrating strong performance across multiple multimodal capabilities including image synthesis and action reasoning.
Kwai Keye Team, Bin Wen, Changyi Liu +50 authors
Kwai Keye-VL-2.0-30B-A3B is an open-source Mixture-of-Experts multimodal foundation model that enables long-video understanding and agentic intelligence through DeepSeek Sparse Attention and specialized training infrastructure.
Jianshu Zhang, Keliang Wu, Haoran Lu +8 authors
A survey unifies progress reward modeling for robotic learning by analyzing model interfaces, internal construction methods, and evaluation benchmarks.
Jun Han, Shuo Zhang, Wei Li +21 authors
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated experience. To address these challenges, we propose QuantaAlpha, an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors through trajectory-level mutation and crossover operations. QuantaAlpha localizes suboptimal steps in each trajectory for targeted revision and recombines complementary high-reward segments to reuse effective patterns, enabling structured exploration and refinement across mining iterations. During factor generation, QuantaAlpha enforces semantic consistency across the hypothesis, factor expression, and executable code, while constraining the complexity and redundancy of the generated factor to mitigate crowding. Extensive experiments on the China Securities Index 300 (CSI 300) demonstrate consistent gains over strong baseline models and prior agentic systems. When utilizing GPT-5.2, QuantaAlpha achieves an Information Coefficient (IC) of 0.1501, with an Annualized Rate of Return (ARR) of 27.75% and a Maximum Drawdown (MDD) of 7.98%. Moreover, factors mined on CSI 300 transfer effectively to the China Securities Index 500 (CSI 500) and the Standard & Poor's 500 Index (S&P 500), delivering 160% and 137% cumulative excess return over four years, respectively, which indicates strong robustness of QuantaAlpha under market distribution shifts.
Wei Zhou, Jun Zhou, Haoyu Wang +16 authors
LLM-enhanced data preparation methods are transforming data-centric workflows from rule-based pipelines to prompt-driven, context-aware approaches, organized into data cleaning, integration, and enrichment tasks.
Shenghai Yuan, Yuanyang Yin, Zongjian Li +3 authors
Helios is a 14 billion parameter autoregressive diffusion model for video generation that achieves real-time performance and high-quality long-video synthesis without conventional optimization techniques.
Jiaqi Liu, Shi Qiu, Mairui Li +32 authors
AutoResearchClaw is a multi-agent autonomous research system that improves scientific discovery through structured debate, self-healing execution, verifiable reporting, human collaboration, and evolutionary learning, outperforming previous systems on a benchmark while maintaining human oversight.
Kimi Team, Guangyu Chen, Yu Zhang +34 authors
Residual connections with PreNorm are standard in modern LLMs, yet they accumulate all layer outputs with fixed unit weights. This uniform aggregation causes uncontrolled hidden-state growth with depth, progressively diluting each layer's contribution. We propose Attention Residuals (AttnRes), which replaces this fixed accumulation with softmax attention over preceding layer outputs, allowing each layer to selectively aggregate earlier representations with learned, input-dependent weights. To address the memory and communication overhead of attending over all preceding layer outputs for large-scale model training, we introduce Block AttnRes, which partitions layers into blocks and attends over block-level representations, reducing the memory footprint while preserving most of the gains of full AttnRes. Combined with cache-based pipeline communication and a two-phase computation strategy, Block AttnRes becomes a practical drop-in replacement for standard residual connections with minimal overhead. Scaling law experiments confirm that the improvement is consistent across model sizes, and ablations validate the benefit of content-dependent depth-wise selection. We further integrate AttnRes into the Kimi Linear architecture (48B total / 3B activated parameters) and pre-train on 1.4T tokens, where AttnRes mitigates PreNorm dilution, yielding more uniform output magnitudes and gradient distribution across depth, and improves downstream performance across all evaluated tasks.
Zihao Liu, Xiaolong Shen, Zhenglin Zhou +2 authors
Latent-to-4D enables reusable direct 4D generation from video diffusion latents via alignment with a pretrained decoder and spatiotemporal attention, transferring across generators without retraining.
MiroMind Team, S. Bai, L. Bing +41 authors
MiroThinker-1.7 and MiroThinker-H1 are research agents that enhance complex reasoning through structured planning, contextual reasoning, and tool interaction, with MiroThinker-H1 incorporating verification at local and global levels for more reliable multi-step problem solving.
Yujia Zhang, Xiaoyang Wu, Yunhan Yang +6 authors
Utonia enables cross-domain point cloud representation learning through a unified self-supervised transformer encoder, enhancing perception and supporting embodied and multimodal reasoning tasks.
Junlin Yang, Che Jiang, Yu Fu +21 authors
OpenMLE enables recursive self-improvement in machine learning engineering via a full-stack system combining execution-grounded operator learning and long-horizon evolutionary search, significantly boosting benchmark performance with a 35B model.
Ziyu Ma, Hailang Huang, Shun Zou +5 authors
LongHorizon-Harness improves long-horizon agent performance by explicitly tracking verified task states outside context via a manage-execute-audit loop.
Yoav HaCohen, Benny Brazowski, Nisan Chiprut +26 authors
LTX-2 is an open-source audiovisual diffusion model that generates synchronized video and audio content using a dual-stream transformer architecture with cross-modal attention and classifier-free guidance.
Meituan LongCat Team, Anchun Gui, Bei Li +159 authors
A 560-billion-parameter Mixture-of-Experts reasoning model achieves state-of-the-art performance on agentic benchmarks through a unified training framework combining domain-parallel expert training with fusion, along with enhancements for real-world robustness and complex reasoning.
Tengyue Xu, Zhuoyang Qian, Gaoge Liu +16 authors
Offline knowledge construction through structured methodological graphs enables more reliable and scalable autonomous scientific discovery by reducing reliance on real-time literature processing.
Tencent Robotics X, HY Vision Team, Xumin Yu +19 authors
HY-Embodied-0.5 is a foundation model family for embodied agents featuring Mixture-of-Transformers architecture and iterative post-training for enhanced visual perception and reasoning capabilities.
Hanyu Guo, Jiedong Yang, Chao Chen +3 authors
TransitLM dataset enables end-to-end transit route planning using large language models trained on structured transit data, eliminating the need for traditional map-based approaches.
Xuan He, Cong Wei, Yuhao Cheng +20 authors
VGI-bench evaluates visual reasoning in video generation models through 27 tasks, revealing limited reliability and self-correction capabilities.
Zhifei Xie, Jiaqi Lang, Ze An +7 authors
VoiceMem introduces a dual-brain streaming memory architecture for speech language models that improves retrieval accuracy, emotional personalization, and real-time efficiency.
Chenxu Yang, Chuanyu Qin, Qingyi Si +7 authors
RLSD combines reinforcement learning with verifiable rewards and self-distillation to achieve stable training with fine-grained updates and reliable policy direction from environmental feedback.
Heng Wang, Jielin Qiu, Wenting Zhao +7 authors
Random eviction of reasoning tokens matches selective KV cache compression because reasoning traces are self-protecting through redundancy, making scoring unnecessary once prompts are preserved.
Ye Jin, Yangyang Xu, Jun Zhu +1 authors
MemSlides presents a hierarchical memory framework for personalized presentation agents that separates long-term user profiles, working memory for session constraints, and tool memory for reusable execution experiences to enable stable personalization and reliable local edits across multi-turn revisions.
NCP Team, Jiaqi Cao, Chiyu Chen +25 authors
NCP-ArchPreview is a large latent-space language model that jointly trains next-token and next-concept prediction to improve pretraining efficiency and downstream performance.
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