Orionnnn/hub-stats huggingface dataset:
DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware
  Planning
Authors: name: Boyu Li, name: Siyuan He, name: Hang Xu, name: Haoqi Yuan, name: Yu Zang, name: Liwei Hu, name: Junpeng Yue, name: Zhenxiong Jiang, name: Pengbo Hu, name: Börje F. Karlsson, name: Yehui Tang, name: Zongqing Lu
Published: 2025-06-19 04:13:36

Developing embodied agents capable of performing complex interactive tasks in
real-world scenarios remains a fundamental challenge in embodied AI. Although
recent advances in simulation platforms have greatly enhanced task diversity to
train embodied Vision Language Models (VLMs), most platforms rely on simplified
robot morphologies and bypass the stochastic nature of low-level execution,
which limits their transferability to real-world robots. To address these
issues, we present a physics-based simulation platform DualTHOR for complex
dual-arm humanoid robots, built upon an extended version of AI2-THOR. Our
simulator includes real-world robot assets, a task suite for dual-arm
collaboration, and inverse kinematics solvers for humanoid robots. We also
introduce a contingency mechanism that incorporates potential failures through
physics-based low-level execution, bridging the gap to real-world scenarios.
Our simulator enables a more comprehensive evaluation of the robustness and
generalization of VLMs in household environments. Extensive evaluations reveal
that current VLMs struggle with dual-arm coordination and exhibit limited
robustness in realistic environments with contingencies, highlighting the
importance of using our simulator to develop more capable VLMs for embodied
tasks. The code is available at https://github.com/ds199895/DualTHOR.git.
When Life Gives You Samples: The Benefits of Scaling up Inference
  Compute for Multilingual LLMs
Authors: name: Ammar Khairi, name: Daniel D'souza, name: Ye Shen, name: Julia Kreutzer, name: Sara Hooker
Published: 2025-06-25 15:37:53

Recent advancements in large language models (LLMs) have shifted focus toward
scaling inference-time compute, improving performance without retraining the
model. A common approach is to sample multiple outputs in parallel, and select
one of these as the final output. However, work to date has focused on English
and a handful of domains such as math and code. In contrast, we are most
interested in techniques that generalize across open-ended tasks, formally
verifiable tasks, and across languages. In this work, we study how to robustly
scale inference-time compute for open-ended generative tasks in a multilingual,
multi-task setting.
  Our findings show that both sampling strategy based on temperature variation
and selection strategy must be adapted to account for diverse domains and
varied language settings. We evaluate existing selection methods, revealing
that strategies effective in English often fail to generalize across languages.
We propose novel sampling and selection strategies specifically adapted for
multilingual and multi-task inference scenarios, and show they yield notable
gains across languages and tasks. In particular, our combined sampling and
selection methods lead to an average +6.8 jump in win-rates for our 8B models
on m-ArenaHard-v2.0 prompts, against proprietary models such as Gemini. At
larger scale, Command-A (111B model) equipped with our methods, shows +9.0
improvement in win-rates on the same benchmark with just five samples against
single-sample decoding, a substantial increase at minimal cost. Our results
underscore the need for language- and task-aware approaches to inference-time
compute, aiming to democratize performance improvements in underrepresented
languages.
Is There a Case for Conversation Optimized Tokenizers in Large Language
  Models?
Authors: name: Raquel Ferrando, name: Javier Conde, name: Gonzalo Martínez, name: Pedro Reviriego
Published: 2025-06-23 14:18:46

The computational and energy costs of Large Language Models (LLMs) have
increased exponentially driven by the growing model sizes and the massive
adoption of LLMs by hundreds of millions of users. The unit cost of an LLM is
the computation of a token. Therefore, the tokenizer plays an important role in
the efficiency of a model, and they are carefully optimized to minimize the
number of tokens for the text in their training corpus. One of the most popular
applications of LLMs are chatbots that interact with users. A key observation
is that, for those chatbots, what is important is the performance of the
tokenizer in the user text input and the chatbot responses. Those are most
likely different from the text in the training corpus. So, a question that
immediately arises is whether there is a potential benefit in optimizing
tokenizers for chatbot conversations. In this paper, this idea is explored for
different tokenizers by using a publicly available corpus of chatbot
conversations to redesign their vocabularies and evaluate their performance in
this domain. The results show that conversation-optimized tokenizers
consistently reduce the number of tokens in chatbot dialogues, which can lead
to meaningful energy savings, in the range of 5% to 10% while having minimal or
even slightly positive impact on tokenization efficiency for the original
training corpus.
ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image
  Generation
Authors: name: Junying Chen, name: Zhenyang Cai, name: Pengcheng Chen, name: Shunian Chen, name: Ke Ji, name: Xidong Wang, name: Yunjin Yang, name: Benyou Wang
Published: 2025-06-22 16:51:09

Recent advances in multimodal generative models have unlocked photorealistic,
instruction-aligned image generation, yet leading systems like GPT-4o-Image
remain proprietary and inaccessible. To democratize these capabilities, we
present ShareGPT-4o-Image, the first dataset comprising 45K text-to-image and
46K text-and-image-to-image data, all synthesized using GPT-4o's image
generation capabilities for distilling its advanced image generation abilities.
Leveraging this dataset, we develop Janus-4o, a multimodal large language model
capable of both text-to-image and text-and-image-to-image generation. Janus-4o
not only significantly improves text-to-image generation over its predecessor,
Janus-Pro, but also newly supports text-and-image-to-image generation. Notably,
it achieves impressive performance in text-and-image-to-image generation from
scratch, using only 91K synthetic samples and 6 hours of training on an 8
A800-GPU machine. We hope the release of ShareGPT-4o-Image and Janus-4o will
foster open research in photorealistic, instruction-aligned image generation.
Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large
  Language Models
Authors: name: Jungwoo Park, name: Taewhoo Lee, name: Chanwoong Yoon, name: Hyeon Hwang, name: Jaewoo Kang
Published: 2025-06-24 15:03:57

Extreme activation outliers in Large Language Models (LLMs) critically
degrade quantization performance, hindering efficient on-device deployment.
While channel-wise operations and adaptive gradient scaling are recognized
causes, practical mitigation remains challenging. We introduce Outlier-Safe
Pre-Training (OSP), a practical guideline that proactively prevents outlier
formation rather than relying on post-hoc mitigation. OSP combines three key
innovations: (1) the Muon optimizer, eliminating privileged bases while
maintaining training efficiency; (2) Single-Scale RMSNorm, preventing
channel-wise amplification; and (3) a learnable embedding projection,
redistributing activation magnitudes originating from embedding matrices. We
validate OSP by training a 1.4B-parameter model on 1 trillion tokens, which is
the first production-scale LLM trained without such outliers. Under aggressive
4-bit quantization, our OSP model achieves a 35.7 average score across 10
benchmarks (compared to 26.5 for an Adam-trained model), with only a 2%
training overhead. Remarkably, OSP models exhibit near-zero excess kurtosis
(0.04) compared to extreme values (1818.56) in standard models, fundamentally
altering LLM quantization behavior. Our work demonstrates that outliers are not
inherent to LLMs but are consequences of training strategies, paving the way
for more efficient LLM deployment. The source code and pretrained checkpoints
are available at https://github.com/dmis-lab/Outlier-Safe-Pre-Training.
The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs
Authors: name: Muntasir Adnan, name: Carlos C. N. Kuhn
Published: 2025-06-23 08:40:45

The effectiveness of AI debugging follows a predictable exponential decay
pattern; most models lose 60-80% of their debugging capability within just 2-3
attempts, despite iterative debugging being a critical capability for practical
code generation systems. We introduce the Debugging Decay Index (DDI), a
mathematical framework that quantifies when debugging becomes ineffective and
predicts intervention points. Our strategic fresh start approach shifts from
exploitation to exploration at strategic points in the debugging process,
demonstrating that well-timed interventions can rescue the effectiveness of
debugging. DDI reveals a fundamental limitation in current AI debugging and
provides the first quantitative framework for optimising iterative code
generation strategies.
MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility
  Applications
Authors: name: Aleksandr Algazinov, name: Matt Laing, name: Paul Laban
Published: 2025-06-24 10:40:23

Accessibility remains a critical concern in today's society, as many
technologies are not developed to support the full range of user needs.
Existing multi-agent systems (MAS) often cannot provide comprehensive
assistance for users in need due to the lack of customization stemming from
closed-source designs. Consequently, individuals with disabilities frequently
encounter significant barriers when attempting to interact with digital
environments. We introduce MATE, a multimodal accessibility MAS, which performs
the modality conversions based on the user's needs. The system is useful for
assisting people with disabilities by ensuring that data will be converted to
an understandable format. For instance, if the user cannot see well and
receives an image, the system converts this image to its audio description.
MATE can be applied to a wide range of domains, industries, and areas, such as
healthcare, and can become a useful assistant for various groups of users. The
system supports multiple types of models, ranging from LLM API calling to using
custom machine learning (ML) classifiers. This flexibility ensures that the
system can be adapted to various needs and is compatible with a wide variety of
hardware. Since the system is expected to run locally, it ensures the privacy
and security of sensitive information. In addition, the framework can be
effectively integrated with institutional technologies (e.g., digital
healthcare service) for real-time user assistance. Furthermore, we introduce
ModCon-Task-Identifier, a model that is capable of extracting the precise
modality conversion task from the user input. Numerous experiments show that
ModCon-Task-Identifier consistently outperforms other LLMs and statistical
models on our custom data. Our code and data are publicly available at
https://github.com/AlgazinovAleksandr/Multi-Agent-MATE.
Chain-of-Experts: Unlocking the Communication Power of
  Mixture-of-Experts Models
Authors: name: Zihan Wang, name: Rui Pan, name: Jiarui Yao, name: Robert Csordas, name: Linjie Li, name: Lu Yin, name: Jiajun Wu, name: Tong Zhang, name: Manling Li, name: Shiwei Liu
Published: 2025-06-23 02:15:43

We propose Chain-of-Experts (CoE), a new Mixture-of-Experts (MoE)
architecture that introduces sequential expert communication within each layer.
Unlike traditional MoE models, where experts operate independently in parallel,
CoE processes tokens iteratively across a chain of experts inside a layer. To
support dynamic expert selection across iterations, CoE employs a dedicated
router at each iteration step within a layer. This design allows tokens to
re-evaluate and select different experts during each iteration, rather than
being statically assigned. As a result, CoE introduces a flexible routing
mechanism that increases the diversity of expert combinations and enriches the
model's representational capacity. CoE demonstrates improved performance under
fixed compute: on math reasoning tasks, it reduces validation loss from 1.20 to
1.12 compared to a standard MoE. Beyond performance, CoE offers a new scaling
axis: depth through expert iteration, which complements conventional
width/depth scaling. For example, using 2x iterations matches the performance
of 3x expert selections (in width), while reducing memory usage by 17.6-42%
relative to other scaling strategies. Our analysis reveals that CoE's benefits
stem from its iterative residual structure and enhanced expert specialization
empowered by iterative routing, which together unlock more expressive
representations. Code is available at https://github.com/ZihanWang314/coe.
Improving Progressive Generation with Decomposable Flow Matching
Authors: name: Moayed Haji-Ali, name: Willi Menapace, name: Ivan Skorokhodov, name: Arpit Sahni, name: Sergey Tulyakov, name: Vicente Ordonez, name: Aliaksandr Siarohin
Published: 2025-06-24 17:58:02

Generating high-dimensional visual modalities is a computationally intensive
task. A common solution is progressive generation, where the outputs are
synthesized in a coarse-to-fine spectral autoregressive manner. While diffusion
models benefit from the coarse-to-fine nature of denoising, explicit
multi-stage architectures are rarely adopted. These architectures have
increased the complexity of the overall approach, introducing the need for a
custom diffusion formulation, decomposition-dependent stage transitions,
add-hoc samplers, or a model cascade. Our contribution, Decomposable Flow
Matching (DFM), is a simple and effective framework for the progressive
generation of visual media. DFM applies Flow Matching independently at each
level of a user-defined multi-scale representation (such as Laplacian pyramid).
As shown by our experiments, our approach improves visual quality for both
images and videos, featuring superior results compared to prior multistage
frameworks. On Imagenet-1k 512px, DFM achieves 35.2% improvements in FDD scores
over the base architecture and 26.4% over the best-performing baseline, under
the same training compute. When applied to finetuning of large models, such as
FLUX, DFM shows faster convergence speed to the training distribution.
Crucially, all these advantages are achieved with a single model, architectural
simplicity, and minimal modifications to existing training pipelines.
Orthogonal Finetuning Made Scalable
Authors: name: Zeju Qiu, name: Weiyang Liu, name: Adrian Weller, name: Bernhard Schölkopf
Published: 2025-06-24 17:59:49

Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation
while preventing catastrophic forgetting, but its high runtime and memory
demands limit practical deployment. We identify the core computational
bottleneck in OFT as its weight-centric implementation, which relies on costly
matrix-matrix multiplications with cubic complexity. To overcome this, we
propose OFTv2, an input-centric reformulation that instead uses matrix-vector
multiplications (i.e., matrix-free computation), reducing the computational
cost to quadratic. We further introduce the Cayley-Neumann parameterization, an
efficient orthogonal parameterization that approximates the matrix inversion in
Cayley transform via a truncated Neumann series. These modifications allow
OFTv2 to achieve up to 10x faster training and 3x lower GPU memory usage
without compromising performance. In addition, we extend OFTv2 to support
finetuning quantized foundation models and show that it outperforms the popular
QLoRA in training stability, efficiency, and memory usage.
Scaling Speculative Decoding with Lookahead Reasoning
Authors: name: Yichao Fu, name: Rui Ge, name: Zelei Shao, name: Zhijie Deng, name: Hao Zhang
Published: 2025-06-24 17:48:10

Reasoning models excel by generating long chain-of-thoughts, but decoding the
resulting thousands of tokens is slow. Token-level speculative decoding (SD)
helps, but its benefit is capped, because the chance that an entire
gamma-token guess is correct falls exponentially as gamma grows. This
means allocating more compute for longer token drafts faces an algorithmic
ceiling -- making the speedup modest and hardware-agnostic. We raise this
ceiling with Lookahead Reasoning, which exploits a second, step-level layer of
parallelism. Our key insight is that reasoning models generate step-by-step,
and each step needs only to be semantically correct, not exact token matching.
In Lookahead Reasoning, a lightweight draft model proposes several future
steps; the target model expands each proposal in one batched pass, and a
verifier keeps semantically correct steps while letting the target regenerate
any that fail. Token-level SD still operates within each reasoning step, so the
two layers of parallelism multiply. We show Lookahead Reasoning lifts the peak
speedup of SD both theoretically and empirically. Across GSM8K, AIME, and other
benchmarks, Lookahead Reasoning improves the speedup of SD from 1.4x to 2.1x
while preserving answer quality, and its speedup scales better with additional
GPU throughput. Our code is available at
https://github.com/hao-ai-lab/LookaheadReasoning
Can Large Language Models Capture Human Annotator Disagreements?
Authors: name: Jingwei Ni, name: Yu Fan, name: Vilém Zouhar, name: Donya Rooein, name: Alexander Hoyle, name: Mrinmaya Sachan, name: Markus Leippold, name: Dirk Hovy, name: Elliott Ash
Published: 2025-06-24 09:49:26

Human annotation variation (i.e., annotation disagreements) is common in NLP
and often reflects important information such as task subjectivity and sample
ambiguity. While Large Language Models (LLMs) are increasingly used for
automatic annotation to reduce human effort, their evaluation often focuses on
predicting the majority-voted "ground truth" labels. It is still unclear,
however, whether these models also capture informative human annotation
variation. Our work addresses this gap by extensively evaluating LLMs' ability
to predict annotation disagreements without access to repeated human labels.
Our results show that LLMs struggle with modeling disagreements, which can be
overlooked by majority label-based evaluations. Notably, while RLVR-style
(Reinforcement learning with verifiable rewards) reasoning generally boosts LLM
performance, it degrades performance in disagreement prediction. Our findings
highlight the critical need for evaluating and improving LLM annotators in
disagreement modeling. Code and data at
https://github.com/EdisonNi-hku/Disagreement_Prediction.
Mem4Nav: Boosting Vision-and-Language Navigation in Urban Environments
  with a Hierarchical Spatial-Cognition Long-Short Memory System
Authors: name: Lixuan He, name: Haoyu Dong, name: Zhenxing Chen, name: Yangcheng Yu, name: Jie Feng, name: Yong Li
Published: 2025-06-24 09:00:43

Vision-and-Language Navigation (VLN) in large-scale urban environments
requires embodied agents to ground linguistic instructions in complex scenes
and recall relevant experiences over extended time horizons. Prior modular
pipelines offer interpretability but lack unified memory, while end-to-end
(M)LLM agents excel at fusing vision and language yet remain constrained by
fixed context windows and implicit spatial reasoning. We introduce
Mem4Nav, a hierarchical spatial-cognition long-short memory system
that can augment any VLN backbone. Mem4Nav fuses a sparse octree for
fine-grained voxel indexing with a semantic topology graph for high-level
landmark connectivity, storing both in trainable memory tokens embedded via a
reversible Transformer. Long-term memory (LTM) compresses and retains
historical observations at both octree and graph nodes, while short-term memory
(STM) caches recent multimodal entries in relative coordinates for real-time
obstacle avoidance and local planning. At each step, STM retrieval sharply
prunes dynamic context, and, when deeper history is needed, LTM tokens are
decoded losslessly to reconstruct past embeddings. Evaluated on Touchdown and
Map2Seq across three backbones (modular, state-of-the-art VLN with prompt-based
LLM, and state-of-the-art VLN with strided-attention MLLM), Mem4Nav yields 7-13
pp gains in Task Completion, sufficient SPD reduction, and >10 pp nDTW
improvement. Ablations confirm the indispensability of both the hierarchical
map and dual memory modules. Our codes are open-sourced via
https://github.com/tsinghua-fib-lab/Mem4Nav.
Intelligent Operation and Maintenance and Prediction Model Optimization
  for Improving Wind Power Generation Efficiency
Authors: name: Xun Liu, name: Xiaobin Wu, name: Jiaqi He, name: Rajan Das Gupta
Published: 2025-06-19 07:31:03

This study explores the effectiveness of predictive maintenance models and
the optimization of intelligent Operation and Maintenance (O&M) systems in
improving wind power generation efficiency. Through qualitative research,
structured interviews were conducted with five wind farm engineers and
maintenance managers, each with extensive experience in turbine operations.
Using thematic analysis, the study revealed that while predictive maintenance
models effectively reduce downtime by identifying major faults, they often
struggle with detecting smaller, gradual failures. Key challenges identified
include false positives, sensor malfunctions, and difficulties in integrating
new models with older turbine systems. Advanced technologies such as digital
twins, SCADA systems, and condition monitoring have significantly enhanced
turbine maintenance practices. However, these technologies still require
improvements, particularly in AI refinement and real-time data integration. The
findings emphasize the need for continuous development to fully optimize wind
turbine performance and support the broader adoption of renewable energy.
Matrix-Game: Interactive World Foundation Model
Authors: name: Yifan Zhang, name: Chunli Peng, name: Boyang Wang, name: Puyi Wang, name: Qingcheng Zhu, name: Fei Kang, name: Biao Jiang, name: Zedong Gao, name: Eric Li, name: Yang Liu, name: Yahui Zhou
Published: 2025-06-23 14:40:49

We introduce Matrix-Game, an interactive world foundation model for
controllable game world generation. Matrix-Game is trained using a two-stage
pipeline that first performs large-scale unlabeled pretraining for environment
understanding, followed by action-labeled training for interactive video
generation. To support this, we curate Matrix-Game-MC, a comprehensive
Minecraft dataset comprising over 2,700 hours of unlabeled gameplay video clips
and over 1,000 hours of high-quality labeled clips with fine-grained keyboard
and mouse action annotations. Our model adopts a controllable image-to-world
generation paradigm, conditioned on a reference image, motion context, and user
actions. With over 17 billion parameters, Matrix-Game enables precise control
over character actions and camera movements, while maintaining high visual
quality and temporal coherence. To evaluate performance, we develop GameWorld
Score, a unified benchmark measuring visual quality, temporal quality, action
controllability, and physical rule understanding for Minecraft world
generation. Extensive experiments show that Matrix-Game consistently
outperforms prior open-source Minecraft world models (including Oasis and
MineWorld) across all metrics, with particularly strong gains in
controllability and physical consistency. Double-blind human evaluations
further confirm the superiority of Matrix-Game, highlighting its ability to
generate perceptually realistic and precisely controllable videos across
diverse game scenarios. To facilitate future research on interactive
image-to-world generation, we will open-source the Matrix-Game model weights
and the GameWorld Score benchmark at https://github.com/SkyworkAI/Matrix-Game.