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Jian Yang, Wei Zhang, Jiajun Wu +25 authors
InCoder-32B is a 32-billion-parameter code model trained on industrial datasets with extended context length and execution verification to improve performance in hardware-aware programming tasks.
Hanzhang Zhou, Panrong Tong, Xu Zhang +13 authors
Qwen-UI-Agent is a foundation GUI agent that integrates mobile, computer, web, and search environments with CLI execution, long-horizon online reinforcement learning, and autonomous data generation to achieve state-of-the-art real-world device performance.
Wentao Zhang, Liliana Hotsko, Woojeong Kim +3 authors
Fuzzy-function programming compiles natural-language specifications into compact neural artifacts using a 4B compiler and 0.6B interpreter, achieving efficient, local execution with reduced memory usage and faster inference.
Bing Yan, Gregory Wolfe, Stefano Martiniani +1 authors
Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists. As a result, scientists and AI agents need to locate relevant information, verify their provenance, and assemble cross-paper answers manually. We present AskChem, a claim-centered infrastructure for cross-paper chemistry search. AskChem changes the unit of retrieval from the paper to the provenance-carrying claim: each paper is converted into atomic, typed claims, each grounded by a source DOI and a verbatim quote or an explicit evidence locator. Over this shared claim store, AskChem exposes complementary structures for search and synthesis: a stabilized faceted taxonomy for hierarchical retrieval and browsing, an evidence graph linking claims through relations, and an exploratory living taxonomy that situates indexed papers under scientific principles. AskChem currently indexes 2.4M claims from 147K papers and provides a web interface, as well as REST, SDK, and MCP access for AI agents. On AskChem-Bench, grounding a GPT-5.5 reader in AskChem yields 100% resolvable DOIs, compared with 88.3% without retrieval, and the highest citation density among five tested systems. AskChem is live at https://askchem.org.
Ziyu Ma, Shidong Yang, Yuxiang Ji +5 authors
SkillClaw enables collective skill evolution in multi-user LLM agent systems by aggregating user interactions to autonomously update and improve reusable skills across the ecosystem.
Jie Wu, Zhenru Zhang, Beichen Zhang +11 authors
Terminal-Universe reconstructs executable workspaces from agent trajectories to synthesize diverse training tasks and improves post-training performance through supervised fine-tuning.
Zhuoyang Qian, Biao Wu, Yiran Wang +6 authors
Spark-to-Paper is a lightweight, composable workflow inside coding assistants that generates research papers by separating planning from reporting, enforcing evidence-based claim revision, and using integrity checks to reduce fabrication.
Zehao Chen, Gongxun Li, Tianxiang Ai +8 authors
WMSS is a post-training paradigm that uses weak model checkpoints to identify and fill learning gaps, enabling continued improvement beyond conventional saturation points in large language models.
Xiyuan Yang, Jiaru Zou, Rui Pan +9 authors
RecursiveMAS extends recursive scaling principles from single models to multi-agent systems, enabling collaborative reasoning through iterative latent-space computations with improved efficiency and accuracy.
Shuo Liang, Yixing Ma, Pengfei Zhou +32 authors
A new benchmark for AI-generated video detection reveals that current detectors fail to generalize across realistic crisis-related videos and become less reliable as content spreads socially.
Kimi Team, Tongtong Bai, Yifan Bai +323 authors
Kimi K2.5 is an open-source multimodal agentic model that enhances text and vision processing through joint optimization techniques and introduces Agent Swarm for parallel task execution.
Chengsong Huang, Zifeng Wang, Rujun Han +14 authors
EnvHarness and EnvRigger dynamically reshape static environments via programmable plugins to target agent weaknesses and improve reinforcement learning co-evolution.
Dongsheng Ma, Jiayu Li, Zhengren Wang +8 authors
CiteVQA introduces a benchmark for document vision-language models that evaluates both answer accuracy and correct citation of supporting evidence, revealing significant attribution hallucinations in current models.
Zeyu Zhang, Ziliang Guo, Yihang Sun +14 authors
Metis introduces memory foundation models that embed persistent, dynamically evolving native memory states and autonomous storage procedures directly into foundation models via memory attention and gradient-free updates.
Junxiang Xu, Ruisi Wang, Fanyi Pu +49 authors
VBBR-Pro introduces a closed-loop testbed that enables scalable, verifiable, and controllable native visual reasoning through generation across diverse visual substrates.
Haifeng Wang, Hua Wu, Tian Wu +435 authors
ERNIE 5.0 is a production-scale trillion-parameter autoregressive model that unifies multimodal understanding and generation through sparse MoE architecture and elastic training.
Zixuan Huang, Xin Xia, Yuxi Ren +11 authors
Large reasoning models can implicitly determine optimal stopping points for thinking, which SAGE-RL enhances by incorporating efficient reasoning patterns into pass@1 inference for improved accuracy and efficiency.
Yuxuan Zhang, Yubo Wang, Yipeng Zhu +18 authors
ClawBench presents a comprehensive evaluation framework with 153 real-world tasks across 144 platforms to test AI agents' ability to automate everyday online activities requiring complex multi-step workflows and document processing.
Yuhao Wu, Jingyuan Zhang, Jiajun Shi +16 authors
HarnessDev evaluates agents by measuring their ability to build and iteratively improve execution infrastructure rather than final task outputs, revealing that self-built harnesses vary widely in capability and efficiency and transfer poorly across models.
Yunhao Chen, Xin Wang, Yixu Wang +6 authors
OpenART introduces a scalable red-teaming arena with evolving stateful environments to evaluate long-horizon AI agent safety, using the EMHA attack policy to expose increasing failure rates as task complexity grows.
Zhongzhi Li, Yucheng Shi, Zongxia Li +8 authors
Recursive verified synthesis generates scalable long-horizon terminal-agent training data, substantially improving model performance on terminal benchmarks through supervised fine-tuning and reinforcement learning.
Mengting Chen, Zhengrui Chen, Yongchao Du +16 authors
A commercial-scale virtual try-on system achieves high success rates, photorealistic results, and real-time performance through integrated system design and multi-stage training.
Haozhe Zhao, Shuzheng Si, Zhenhailong Wang +6 authors
Automated systems for generating scientific figures face limitations in handling diverse figure types and conditions, prompting the development of multi-agent frameworks that generalize across different input scenarios and produce editable output formats.
Yifan Yang, Ziyang Gong, Weiquan Huang +12 authors
SkillOpt introduces a systematic text-space optimizer for agent skills that trains skills as external agent state with stable updates and zero deployment inference overhead, achieving superior performance across multiple benchmarks and execution environments.
Weikai Huang, Jieyu Zhang, Sijun Li +14 authors
A unified 3D object detection framework with a large-scale dataset enables open-world detection with multiple prompt types and geometric cue integration.
Tianyu Xie, Jinfa Huang, Yuexiao Ma +9 authors
SocialOmni presents a benchmark for evaluating social interactivity in omni-modal large language models across speaker identification, interruption timing, and natural interruption generation, revealing gaps between perceptual accuracy and conversational competence.
Yuejie Li, Ke Yang, Yueying Hua +4 authors
Spoken query retrieval is an important interaction mode in modern information retrieval. However, existing evaluation datasets are often limited to simple queries under constrained noise conditions, making them inadequate for assessing the robustness of spoken query retrieval systems under complex acoustic perturbations. To address this limitation, we present SQuTR, a robustness benchmark for spoken query retrieval that includes a large-scale dataset and a unified evaluation protocol. SQuTR aggregates 37,317 unique queries from six commonly used English and Chinese text retrieval datasets, spanning multiple domains and diverse query types. We synthesize speech using voice profiles from 200 real speakers and mix 17 categories of real-world environmental noise under controlled SNR levels, enabling reproducible robustness evaluation from quiet to highly noisy conditions. Under the unified protocol, we conduct large-scale evaluations on representative cascaded and end-to-end retrieval systems. Experimental results show that retrieval performance decreases as noise increases, with substantially different drops across systems. Even large-scale retrieval models struggle under extreme noise, indicating that robustness remains a critical bottleneck. Overall, SQuTR provides a reproducible testbed for benchmarking and diagnostic analysis, and facilitates future research on robustness in spoken query to text retrieval.
Inclusion AI, Tiwei Bie, Haoxing Chen +15 authors
LLaDA2.0-Uni is a unified discrete diffusion language model that integrates multimodal understanding and generation through a semantic discrete tokenizer, MoE-based backbone, and diffusion decoder, achieving performance comparable to specialized vision-language models while enabling efficient inference and high-fidelity image generation.
Mind Lab, Song Cao, Vic Cao +51 authors
Parameter-efficient fine-tuning can function as a compact substrate for persistent personal models by enabling small trainable adapters to store instance-specific behaviors on top of strong foundation models.
Jian Yang, Wei Zhang, Jiajun Wu +22 authors
Industrial software development lacks expert reasoning traces for hardware constraints, so a model was trained on error-driven reasoning chains and domain-specific execution traces to generate high-quality code reasoning and performance.
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