Orionnnn/hub-stats huggingface dataset:
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise
  Reasoning
Authors: name: Yihe Deng, name: I-Hung Hsu, name: Jun Yan, name: Zifeng Wang, name: Rujun Han, name: Gufeng Zhang, name: Yanfei Chen, name: Wei Wang, name: Tomas Pfister, name: Chen-Yu Lee
Published: 2025-10-29 22:05:08

Large Language Models (LLMs) often struggle with problems that require
multi-step reasoning. For small-scale open-source models, Reinforcement
Learning with Verifiable Rewards (RLVR) fails when correct solutions are rarely
sampled even after many attempts, while Supervised Fine-Tuning (SFT) tends to
overfit long demonstrations through rigid token-by-token imitation. To address
this gap, we propose Supervised Reinforcement Learning (SRL), a framework that
reformulates problem solving as generating a sequence of logical "actions". SRL
trains the model to generate an internal reasoning monologue before committing
to each action. It provides smoother rewards based on the similarity between
the model's actions and expert actions extracted from the SFT dataset in a
step-wise manner. This supervision offers richer learning signals even when all
rollouts are incorrect, while encouraging flexible reasoning guided by expert
demonstrations. As a result, SRL enables small models to learn challenging
problems previously unlearnable by SFT or RLVR. Moreover, initializing training
with SRL before refining with RLVR yields the strongest overall performance.
Beyond reasoning benchmarks, SRL generalizes effectively to agentic software
engineering tasks, establishing it as a robust and versatile training framework
for reasoning-oriented LLMs.
Emu3.5: Native Multimodal Models are World Learners
Authors: name: Yufeng Cui, name: Honghao Chen, name: Haoge Deng, name: Xu Huang, name: Xinghang Li, name: Jirong Liu, name: Yang Liu, name: Zhuoyan Luo, name: Jinsheng Wang, name: Wenxuan Wang, name: Yueze Wang, name: Chengyuan Wang, name: Fan Zhang, name: Yingli Zhao, name: Ting Pan, name: Xianduo Li, name: Zecheng Hao, name: Wenxuan Ma, name: Zhuo Chen, name: Yulong Ao, name: Tiejun Huang, name: Zhongyuan Wang, name: Xinlong Wang
Published: 2025-10-30 15:11:16

We introduce Emu3.5, a large-scale multimodal world model that natively
predicts the next state across vision and language. Emu3.5 is pre-trained
end-to-end with a unified next-token prediction objective on a corpus of
vision-language interleaved data containing over 10 trillion tokens, primarily
derived from sequential frames and transcripts of internet videos. The model
naturally accepts interleaved vision-language inputs and generates interleaved
vision-language outputs. Emu3.5 is further post-trained with large-scale
reinforcement learning to enhance multimodal reasoning and generation. To
improve inference efficiency, we propose Discrete Diffusion Adaptation (DiDA),
which converts token-by-token decoding into bidirectional parallel prediction,
accelerating per-image inference by about 20x without sacrificing performance.
Emu3.5 exhibits strong native multimodal capabilities, including long-horizon
vision-language generation, any-to-image (X2I) generation, and complex
text-rich image generation. It also exhibits generalizable world-modeling
abilities, enabling spatiotemporally consistent world exploration and
open-world embodied manipulation across diverse scenarios and tasks. For
comparison, Emu3.5 achieves performance comparable to Gemini 2.5 Flash Image
(Nano Banana) on image generation and editing tasks and demonstrates superior
results on a suite of interleaved generation tasks. We open-source Emu3.5 at
https://github.com/baaivision/Emu3.5 to support community research.
Remote Labor Index: Measuring AI Automation of Remote Work
Authors: name: Mantas Mazeika, name: Alice Gatti, name: Cristina Menghini, name: Udari Madhushani Sehwag, name: Shivam Singhal, name: Yury Orlovskiy, name: Steven Basart, name: Manasi Sharma, name: Denis Peskoff, name: Elaine Lau, name: Jaehyuk Lim, name: Lachlan Carroll, name: Alice Blair, name: Vinaya Sivakumar, name: Sumana Basu, name: Brad Kenstler, name: Yuntao Ma, name: Julian Michael, name: Xiaoke Li, name: Oliver Ingebretsen, name: Aditya Mehta, name: Jean Mottola, name: John Teichmann, name: Kevin Yu, name: Zaina Shaik, name: Adam Khoja, name: Richard Ren, name: Jason Hausenloy, name: Long Phan, name: Ye Htet, name: Ankit Aich, name: Tahseen Rabbani, name: Vivswan Shah, name: Andriy Novykov, name: Felix Binder, name: Kirill Chugunov, name: Luis Ramirez, name: Matias Geralnik, name: Hernán Mesura, name: Dean Lee, name: Ed-Yeremai Hernandez Cardona, name: Annette Diamond, name: Summer Yue, name: Alexandr Wang, name: Bing Liu, name: Ernesto Hernandez, name: Dan Hendrycks
Published: 2025-10-30 17:58:04

AIs have made rapid progress on research-oriented benchmarks of knowledge and
reasoning, but it remains unclear how these gains translate into economic value
and automation. To measure this, we introduce the Remote Labor Index (RLI), a
broadly multi-sector benchmark comprising real-world, economically valuable
projects designed to evaluate end-to-end agent performance in practical
settings. AI agents perform near the floor on RLI, with the highest-performing
agent achieving an automation rate of 2.5%. These results help ground
discussions of AI automation in empirical evidence, setting a common basis for
tracking AI impacts and enabling stakeholders to proactively navigate AI-driven
labor automation.
Can Agent Conquer Web? Exploring the Frontiers of ChatGPT Atlas Agent in
  Web Games
Authors: name: Jingran Zhang, name: Ning Li, name: Justin Cui
Published: 2025-10-30 09:35:51

OpenAI's ChatGPT Atlas introduces new capabilities for web interaction,
enabling the model to analyze webpages, process user intents, and execute
cursor and keyboard inputs directly within the browser. While its capacity for
information retrieval tasks has been demonstrated, its performance in dynamic,
interactive environments remains less explored. In this study, we conduct an
early evaluation of Atlas's web interaction capabilities using browser-based
games as test scenarios, including Google's T-Rex Runner, Sudoku, Flappy Bird,
and Stein.world. We employ in-game performance scores as quantitative metrics
to assess performance across different task types. Our results show that Atlas
performs strongly in logical reasoning tasks like Sudoku, completing puzzles
significantly faster than human baselines, but struggles substantially in
real-time games requiring precise timing and motor control, often failing to
progress beyond initial obstacles. These findings suggest that while Atlas
demonstrates capable analytical processing, there remain notable limitations in
dynamic web environments requiring real-time interaction. The website of our
project can be found at https://atlas-game-eval.github.io.
Kimi Linear: An Expressive, Efficient Attention Architecture
Authors: name: Kimi Team, name: Yu Zhang, name: Zongyu Lin, name: Xingcheng Yao, name: Jiaxi Hu, name: Fanqing Meng, name: Chengyin Liu, name: Xin Men, name: Songlin Yang, name: Zhiyuan Li, name: Wentao Li, name: Enzhe Lu, name: Weizhou Liu, name: Yanru Chen, name: Weixin Xu, name: Longhui Yu, name: Yejie Wang, name: Yu Fan, name: Longguang Zhong, name: Enming Yuan, name: Dehao Zhang, name: Yizhi Zhang, name: T. Y. Liu, name: Haiming Wang, name: Shengjun Fang, name: Weiran He, name: Shaowei Liu, name: Yiwei Li, name: Jianlin Su, name: Jiezhong Qiu, name: Bo Pang, name: Junjie Yan, name: Zhejun Jiang, name: Weixiao Huang, name: Bohong Yin, name: Jiacheng You, name: Chu Wei, name: Zhengtao Wang, name: Chao Hong, name: Yutian Chen, name: Guanduo Chen, name: Yucheng Wang, name: Huabin Zheng, name: Feng Wang, name: Yibo Liu, name: Mengnan Dong, name: Zheng Zhang, name: Siyuan Pan, name: Wenhao Wu, name: Yuhao Wu, name: Longyu Guan, name: Jiawen Tao, name: Guohong Fu, name: Xinran Xu, name: Yuzhi Wang, name: Guokun Lai, name: Yuxin Wu, name: Xinyu Zhou, name: Zhilin Yang, name: Yulun Du
Published: 2025-10-30 16:59:43

We introduce Kimi Linear, a hybrid linear attention architecture that, for
the first time, outperforms full attention under fair comparisons across
various scenarios -- including short-context, long-context, and reinforcement
learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA), an
expressive linear attention module that extends Gated DeltaNet with a
finer-grained gating mechanism, enabling more effective use of limited
finite-state RNN memory. Our bespoke chunkwise algorithm achieves high hardware
efficiency through a specialized variant of the Diagonal-Plus-Low-Rank (DPLR)
transition matrices, which substantially reduces computation compared to the
general DPLR formulation while remaining more consistent with the classical
delta rule.
  We pretrain a Kimi Linear model with 3B activated parameters and 48B total
parameters, based on a layerwise hybrid of KDA and Multi-Head Latent Attention
(MLA). Our experiments show that with an identical training recipe, Kimi Linear
outperforms full MLA with a sizeable margin across all evaluated tasks, while
reducing KV cache usage by up to 75% and achieving up to 6 times decoding
throughput for a 1M context. These results demonstrate that Kimi Linear can be
a drop-in replacement for full attention architectures with superior
performance and efficiency, including tasks with longer input and output
lengths.
  To support further research, we open-source the KDA kernel and vLLM
implementations, and release the pre-trained and instruction-tuned model
checkpoints.
The Quest for Reliable Metrics of Responsible AI
Authors: name: Theresia Veronika Rampisela, name: Maria Maistro, name: Tuukka Ruotsalo, name: Christina Lioma
Published: 2025-10-29 22:35:34

The development of Artificial Intelligence (AI), including AI in Science
(AIS), should be done following the principles of responsible AI. Progress in
responsible AI is often quantified through evaluation metrics, yet there has
been less work on assessing the robustness and reliability of the metrics
themselves. We reflect on prior work that examines the robustness of fairness
metrics for recommender systems as a type of AI application and summarise their
key takeaways into a set of non-exhaustive guidelines for developing reliable
metrics of responsible AI. Our guidelines apply to a broad spectrum of AI
applications, including AIS.
Automating Benchmark Design
Authors: name: Amanda Dsouza, name: Harit Vishwakarma, name: Zhengyang Qi, name: Justin Bauer, name: Derek Pham, name: Thomas Walshe, name: Armin Parchami, name: Frederic Sala, name: Paroma Varma
Published: 2025-10-28 23:53:36

The rapid progress and widespread deployment of LLMs and LLM-powered agents
has outpaced our ability to evaluate them. Hand-crafted, static benchmarks are
the primary tool for assessing model capabilities, but these quickly become
saturated. In contrast, dynamic benchmarks evolve alongside the models they
evaluate, but are expensive to create and continuously update. To address these
challenges, we develop BeTaL (Benchmark Tuning with an LLM-in-the-loop), a
framework that leverages environment design principles to automate the process
of dynamic benchmark design. BeTaL works by parameterizing key design choices
in base benchmark templates and uses LLMs to reason through the resulting
parameter space to obtain target properties (such as difficulty and realism) in
a cost-efficient manner. We validate this approach on its ability to create
benchmarks with desired difficulty levels. Using BeTaL, we create two new
benchmarks and extend a popular agentic benchmark tau-bench. Extensive
evaluation on these three tasks and multiple target difficulty levels shows
that BeTaL produces benchmarks much closer to the desired difficulty, with
average deviations ranging from 5.3% to 13.2% -- a 2-4x improvement over the
baselines.
Generative View Stitching
Authors: name: Chonghyuk Song, name: Michal Stary, name: Boyuan Chen, name: George Kopanas, name: Vincent Sitzmann
Published: 2025-10-28 17:59:58

Autoregressive video diffusion models are capable of long rollouts that are
stable and consistent with history, but they are unable to guide the current
generation with conditioning from the future. In camera-guided video generation
with a predefined camera trajectory, this limitation leads to collisions with
the generated scene, after which autoregression quickly collapses. To address
this, we propose Generative View Stitching (GVS), which samples the entire
sequence in parallel such that the generated scene is faithful to every part of
the predefined camera trajectory. Our main contribution is a sampling algorithm
that extends prior work on diffusion stitching for robot planning to video
generation. While such stitching methods usually require a specially trained
model, GVS is compatible with any off-the-shelf video model trained with
Diffusion Forcing, a prevalent sequence diffusion framework that we show
already provides the affordances necessary for stitching. We then introduce
Omni Guidance, a technique that enhances the temporal consistency in stitching
by conditioning on both the past and future, and that enables our proposed
loop-closing mechanism for delivering long-range coherence. Overall, GVS
achieves camera-guided video generation that is stable, collision-free,
frame-to-frame consistent, and closes loops for a variety of predefined camera
paths, including Oscar Reutersv\"ard's Impossible Staircase. Results are best
viewed as videos at https://andrewsonga.github.io/gvs.
GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler
  Research
Authors: name: Xinqi Li, name: Yiqun Liu, name: Shan Jiang, name: Enrong Zheng, name: Huaijin Zheng, name: Wenhao Dai, name: Haodong Deng, name: Dianhai Yu, name: Yanjun Ma
Published: 2025-10-28 03:36:05

We introduce GraphNet, a dataset of 2.7K real-world deep learning
computational graphs with rich metadata, spanning six major task categories
across multiple deep learning frameworks. To evaluate tensor compiler
performance on these samples, we propose the benchmark metric Speedup Score
S(t), which jointly considers runtime speedup and execution correctness under
tunable tolerance levels, offering a reliable measure of general optimization
capability. Furthermore, we extend S(t) to the Error-aware Speedup Score ES(t),
which incorporates error information and helps compiler developers identify key
performance bottlenecks. In this report, we benchmark the default tensor
compilers, CINN for PaddlePaddle and TorchInductor for PyTorch, on computer
vision (CV) and natural language processing (NLP) samples to demonstrate the
practicality of GraphNet. The full construction pipeline with graph extraction
and compiler evaluation tools is available at
https://github.com/PaddlePaddle/GraphNet .
MC-SJD : Maximal Coupling Speculative Jacobi Decoding for Autoregressive
  Visual Generation Acceleration
Authors: name: Junhyuk So, name: Hyunho Kook, name: Chaeyeon Jang, name: Eunhyeok Park
Published: 2025-10-28 09:26:27

While autoregressive (AR) modeling has recently emerged as a new paradigm in
visual generation, its practical adoption is severely constrained by the slow
inference speed of per-token generation, which often requires thousands of
steps to produce a single sample. To address this challenge, we propose MC-SJD,
a training-free, lossless parallel decoding framework designed to accelerate AR
visual generation by extending the recently introduced Speculative Jacobi
Decoding (SJD). Although SJD shows strong potential for accelerating AR
generation, we demonstrate that token instability across iterations
significantly reduces the acceptance rate, a limitation that primarily arises
from the independent sampling process used during draft token generation. To
overcome this, we introduce MC-SJD, an information-theoretic approach based on
coupling, which substantially accelerates standard SJD by maximizing the
probability of sampling identical draft tokens across consecutive iterations,
all while preserving its lossless property. Remarkably, this method requires
only a single-line modification to the existing algorithm, yet achieves
substantial performance gains, delivering up to a ~4.2x acceleration in image
generation and ~13.3x acceleration in video generation compared to standard AR
decoding, without any degradation in output quality.
Reasoning Language Model Inference Serving Unveiled: An Empirical Study
Authors: name: Qi Li, name: Junpan Wu, name: Xiang Liu, name: Yuxin Wang, name: Zeyu Li, name: Zhenheng Tang, name: Yuhan Chen, name: Shaohuai Shi, name: Xiaowen Chu
Published: 2025-10-21 14:25:51

The reasoning large language model (RLLM) has been proven competitive in
solving complex reasoning tasks such as mathematics, coding, compared to
general LLM. However, the serving performance and behavior of RLLM remains
unexplored, which may undermine the deployment and utilization of RLLM in
real-world scenario. To close this gap, in this paper, we conduct a
comprehensive study of RLLM service. We first perform a pilot study on
comparing the serving performance between RLLM and traditional LLM and reveal
that there are several distinct differences regarding serving behavior: (1)
significant memory usage and fluctuations; (2) straggler requests; (3) adaptive
running time; (4) domain preference. Then we further investigate whether
existing inference optimization techniques are valid for RLLM. Our main
takeaways are that model quantization methods and speculative decoding can
improve service system efficiency with small compromise to RLLM accuracy, while
prefix caching, KV cache quantization may even degrade accuracy or serving
performance for small RLLM. Lastly, we conduct evaluation under real world
workload modeled by Gamma distribution to verify our findings. Empirical
results of real world workload evaluation across different dataset are aligned
with our main findings regarding RLLM serving. We hope our work can provide the
research community and industry with insights to advance RLLM inference
serving.
The Principles of Diffusion Models
Authors: name: Chieh-Hsin Lai, name: Yang Song, name: Dongjun Kim, name: Yuki Mitsufuji, name: Stefano Ermon
Published: 2025-10-24 02:29:02

This monograph presents the core principles that have guided the development
of diffusion models, tracing their origins and showing how diverse formulations
arise from shared mathematical ideas. Diffusion modeling starts by defining a
forward process that gradually corrupts data into noise, linking the data
distribution to a simple prior through a continuum of intermediate
distributions. The goal is to learn a reverse process that transforms noise
back into data while recovering the same intermediates. We describe three
complementary views. The variational view, inspired by variational
autoencoders, sees diffusion as learning to remove noise step by step. The
score-based view, rooted in energy-based modeling, learns the gradient of the
evolving data distribution, indicating how to nudge samples toward more likely
regions. The flow-based view, related to normalizing flows, treats generation
as following a smooth path that moves samples from noise to data under a
learned velocity field. These perspectives share a common backbone: a
time-dependent velocity field whose flow transports a simple prior to the data.
Sampling then amounts to solving a differential equation that evolves noise
into data along a continuous trajectory. On this foundation, the monograph
discusses guidance for controllable generation, efficient numerical solvers,
and diffusion-motivated flow-map models that learn direct mappings between
arbitrary times. It provides a conceptual and mathematically grounded
understanding of diffusion models for readers with basic deep-learning
knowledge.
Fortytwo: Swarm Inference with Peer-Ranked Consensus
Authors: name: Vladyslav Larin, name: Ihor Naumenko, name: Aleksei Ivashov, name: Ivan Nikitin, name: Alexander Firsov
Published: 2025-10-27 23:19:48

As centralized AI hits compute ceilings and diminishing returns from
ever-larger training runs, meeting demand requires an inference layer that
scales horizontally in both capacity and capability. We present Fortytwo, a
novel protocol that leverages swarm intelligence principles and distributed
pairwise ranking consensus to achieve superior performance in AI inference. Our
approach reimagines collaboration among AI nodes using swarm inference: a
peer-ranked, reputation-weighted consensus across heterogeneous models that
surfaces the highest-quality responses. Using pairwise ranking with a custom
Bradley-Terry-style aggregation model, we demonstrate that swarm inference
substantially outperforms majority voting, achieving 85.90% on GPQA Diamond
versus 68.69% for majority voting with the same model set - an improvement of
+17.21 percentage points (approximately +25.1% relative). The protocol
incorporates on-chain reputation so node influence adapts to demonstrated
accuracy over time, yielding a meritocratic consensus that filters low-quality
or malicious participants. To resist Sybil attacks, Fortytwo employs
proof-of-capability in its consensus: nodes must successfully complete
calibration/test requests and stake reputation to enter ranking rounds, making
multi-identity attacks economically unattractive while preserving openness.
Across six challenging benchmarks, including GPQA Diamond, LiveCodeBench, and
AIME, our evaluation indicates higher accuracy and strong resilience to
adversarial and noisy free-form prompting (e.g., prompt-injection degradation
of only 0.12% versus 6.20% for a monolithic single-model baseline), while
retaining practical deployability. Together, these results establish a
foundation for decentralized AI systems - democratizing access to high-quality
inference through collective intelligence without sacrificing reliability or
security.
ODesign: A World Model for Biomolecular Interaction Design
Authors: name: Odin Zhang, name: Xujun Zhang, name: Haitao Lin, name: Cheng Tan, name: Qinghan Wang, name: Yuanle Mo, name: Qiantai Feng, name: Gang Du, name: Yuntao Yu, name: Zichang Jin, name: Ziyi You, name: Peicong Lin, name: Yijie Zhang, name: Yuyang Tao, name: Shicheng Chen, name: Jack Xiaoyu Chen, name: Chenqing Hua, name: Weibo Zhao, name: Runze Ma, name: Yunpeng Xia, name: Kejun Ying, name: Jun Li, name: Yundian Zeng, name: Lijun Lang, name: Peichen Pan, name: Hanqun Cao, name: Zihao Song, name: Bo Qiang, name: Jiaqi Wang, name: Pengfei Ji, name: Lei Bai, name: Jian Zhang, name: Chang-yu Hsieh, name: Pheng Ann Heng, name: Siqi Sun, name: Tingjun Hou, name: Shuangjia Zheng
Published: 2025-10-25 14:16:17

Biomolecular interactions underpin almost all biological processes, and their
rational design is central to programming new biological functions. Generative
AI models have emerged as powerful tools for molecular design, yet most remain
specialized for individual molecular types and lack fine-grained control over
interaction details. Here we present ODesign, an all-atom generative world
model for all-to-all biomolecular interaction design. ODesign allows scientists
to specify epitopes on arbitrary targets and generate diverse classes of
binding partners with fine-grained control. Across entity-, token-, and
atom-level benchmarks in the protein modality, ODesign demonstrates superior
controllability and performance to modality-specific baselines. Extending
beyond proteins, it generalizes to nucleic acid and small-molecule design,
enabling interaction types such as protein-binding RNA/DNA and RNA/DNA-binding
ligands that were previously inaccessible. By unifying multimodal biomolecular
interactions within a single generative framework, ODesign moves toward a
general-purpose molecular world model capable of programmable design. ODesign
is available at https://odesign.lglab.ac.cn ,