Orionnnn/hub-stats huggingface dataset:
Towards Solving More Challenging IMO Problems via Decoupled Reasoning
  and Proving
Authors: name: Zhenwen Liang, name: Linfeng Song, name: Yang Li, name: Tao Yang, name: Feng Zhang, name: Haitao Mi, name: Dong Yu
Published: 2025-07-07 22:38:49

Automated Theorem Proving (ATP) in formal languages is a foundational
challenge for AI. While Large Language Models (LLMs) have driven remarkable
progress, a significant gap remains between their powerful informal reasoning
capabilities and their weak formal proving performance. Recent studies show
that the informal accuracy exceeds 80% while formal success remains below 8% on
benchmarks like PutnamBench. We argue this gap persists because current
state-of-the-art provers, by tightly coupling reasoning and proving, are
trained with paradigms that inadvertently punish deep reasoning in favor of
shallow, tactic-based strategies. To bridge this fundamental gap, we propose a
novel framework that decouples high-level reasoning from low-level proof
generation. Our approach utilizes two distinct, specialized models: a powerful,
general-purpose Reasoner to generate diverse, strategic subgoal lemmas, and an
efficient Prover to rigorously verify them. This modular design liberates the
model's full reasoning potential and bypasses the pitfalls of end-to-end
training. We evaluate our method on a challenging set of post-2000 IMO
problems, a problem set on which no prior open-source prover has reported
success. Our decoupled framework successfully solves 5 of these problems,
demonstrating a significant step towards automated reasoning on exceptionally
difficult mathematical challenges. To foster future research, we release our
full dataset of generated and verified lemmas for a wide range of IMO problems,
available at https://tencent-imo.github.io/ .
Rethinking Verification for LLM Code Generation: From Generation to
  Testing
Authors: name: Zihan Ma, name: Taolin Zhang, name: Maosong Cao, name: Wenwei Zhang, name: Minnan Luo, name: Songyang Zhang, name: Kai Chen
Published: 2025-07-09 14:58:47

Large language models (LLMs) have recently achieved notable success in
code-generation benchmarks such as HumanEval and LiveCodeBench. However, a
detailed examination reveals that these evaluation suites often comprise only a
limited number of homogeneous test cases, resulting in subtle faults going
undetected. This not only artificially inflates measured performance but also
compromises accurate reward estimation in reinforcement learning frameworks
utilizing verifiable rewards (RLVR). To address these critical shortcomings, we
systematically investigate the test-case generation (TCG) task by proposing
multi-dimensional metrics designed to rigorously quantify test-suite
thoroughness. Furthermore, we introduce a human-LLM collaborative method
(SAGA), leveraging human programming expertise with LLM reasoning capability,
aimed at significantly enhancing both the coverage and the quality of generated
test cases. In addition, we develop a TCGBench to facilitate the study of the
TCG task. Experiments show that SAGA achieves a detection rate of 90.62% and a
verifier accuracy of 32.58% on TCGBench. The Verifier Accuracy (Verifier Acc)
of the code generation evaluation benchmark synthesized by SAGA is 10.78%
higher than that of LiveCodeBench-v6. These results demonstrate the
effectiveness of our proposed method. We hope this work contributes to building
a scalable foundation for reliable LLM code evaluation, further advancing RLVR
in code generation, and paving the way for automated adversarial test synthesis
and adaptive benchmark integration.
Evaluating the Critical Risks of Amazon's Nova Premier under the
  Frontier Model Safety Framework
Authors: name: Satyapriya Krishna, name: Ninareh Mehrabi, name: Abhinav Mohanty, name: Matteo Memelli, name: Vincent Ponzo, name: Payal Motwani, name: Rahul Gupta
Published: 2025-07-07 13:33:35

Nova Premier is Amazon's most capable multimodal foundation model and teacher
for model distillation. It processes text, images, and video with a
one-million-token context window, enabling analysis of large codebases,
400-page documents, and 90-minute videos in a single prompt. We present the
first comprehensive evaluation of Nova Premier's critical risk profile under
the Frontier Model Safety Framework. Evaluations target three high-risk domains
-- Chemical, Biological, Radiological & Nuclear (CBRN), Offensive Cyber
Operations, and Automated AI R&D -- and combine automated benchmarks, expert
red-teaming, and uplift studies to determine whether the model exceeds release
thresholds. We summarize our methodology and report core findings. Based on
this evaluation, we find that Nova Premier is safe for public release as per
our commitments made at the 2025 Paris AI Safety Summit. We will continue to
enhance our safety evaluation and mitigation pipelines as new risks and
capabilities associated with frontier models are identified.
Does Data Scaling Lead to Visual Compositional Generalization?
Authors: name: Arnas Uselis, name: Andrea Dittadi, name: Seong Joon Oh
Published: 2025-07-09 17:59:03

Compositional understanding is crucial for human intelligence, yet it remains
unclear whether contemporary vision models exhibit it. The dominant machine
learning paradigm is built on the premise that scaling data and model sizes
will improve out-of-distribution performance, including compositional
generalization. We test this premise through controlled experiments that
systematically vary data scale, concept diversity, and combination coverage. We
find that compositional generalization is driven by data diversity, not mere
data scale. Increased combinatorial coverage forces models to discover a
linearly factored representational structure, where concepts decompose into
additive components. We prove this structure is key to efficiency, enabling
perfect generalization from few observed combinations. Evaluating pretrained
models (DINO, CLIP), we find above-random yet imperfect performance, suggesting
partial presence of this structure. Our work motivates stronger emphasis on
constructing diverse datasets for compositional generalization, and considering
the importance of representational structure that enables efficient
compositional learning. Code available at
https://github.com/oshapio/visual-compositional-generalization.
NeoBabel: A Multilingual Open Tower for Visual Generation
Authors: name: Mohammad Mahdi Derakhshani, name: Dheeraj Varghese, name: Marzieh Fadaee, name: Cees G. M. Snoek
Published: 2025-07-08 16:19:45

Text-to-image generation advancements have been predominantly
English-centric, creating barriers for non-English speakers and perpetuating
digital inequities. While existing systems rely on translation pipelines, these
introduce semantic drift, computational overhead, and cultural misalignment. We
introduce NeoBabel, a novel multilingual image generation framework that sets a
new Pareto frontier in performance, efficiency and inclusivity, supporting six
languages: English, Chinese, Dutch, French, Hindi, and Persian. The model is
trained using a combination of large-scale multilingual pretraining and
high-resolution instruction tuning. To evaluate its capabilities, we expand two
English-only benchmarks to multilingual equivalents: m-GenEval and m-DPG.
NeoBabel achieves state-of-the-art multilingual performance while retaining
strong English capability, scoring 0.75 on m-GenEval and 0.68 on m-DPG.
Notably, it performs on par with leading models on English tasks while
outperforming them by +0.11 and +0.09 on multilingual benchmarks, even though
these models are built on multilingual base LLMs. This demonstrates the
effectiveness of our targeted alignment training for preserving and extending
crosslingual generalization. We further introduce two new metrics to rigorously
assess multilingual alignment and robustness to code-mixed prompts. Notably,
NeoBabel matches or exceeds English-only models while being 2-4x smaller. We
release an open toolkit, including all code, model checkpoints, a curated
dataset of 124M multilingual text-image pairs, and standardized multilingual
evaluation protocols, to advance inclusive AI research. Our work demonstrates
that multilingual capability is not a trade-off but a catalyst for improved
robustness, efficiency, and cultural fidelity in generative AI.
Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
Authors: name: Xiangru Tang, name: Tianrui Qin, name: Tianhao Peng, name: Ziyang Zhou, name: Daniel Shao, name: Tingting Du, name: Xinming Wei, name: Peng Xia, name: Fang Wu, name: He Zhu, name: Ge Zhang, name: Jiaheng Liu, name: Xingyao Wang, name: Sirui Hong, name: Chenglin Wu, name: Hao Cheng, name: Chi Wang, name: Wangchunshu Zhou
Published: 2025-07-08 17:59:22

As language agents tackle increasingly complex tasks, they struggle with
effective error correction and experience reuse across domains. We introduce
Agent KB, a hierarchical experience framework that enables complex agentic
problem solving via a novel Reason-Retrieve-Refine pipeline. Agent KB addresses
a core limitation: agents traditionally cannot learn from each other's
experiences. By capturing both high-level strategies and detailed execution
logs, Agent KB creates a shared knowledge base that enables cross-agent
knowledge transfer. Evaluated on the GAIA benchmark, Agent KB improves success
rates by up to 16.28 percentage points. On the most challenging tasks, Claude-3
improves from 38.46% to 57.69%, while GPT-4 improves from 53.49% to 73.26% on
intermediate tasks. On SWE-bench code repair, Agent KB enables Claude-3 to
improve from 41.33% to 53.33%. Our results suggest that Agent KB provides a
modular, framework-agnostic infrastructure for enabling agents to learn from
past experiences and generalize successful strategies to new tasks.
Critiques of World Models
Authors: name: Eric Xing, name: Mingkai Deng, name: Jinyu Hou, name: Zhiting Hu
Published: 2025-07-07 16:23:46

World Model, the supposed algorithmic surrogate of the real-world environment
which biological agents experience with and act upon, has been an emerging
topic in recent years because of the rising needs to develop virtual agents
with artificial (general) intelligence. There has been much debate on what a
world model really is, how to build it, how to use it, and how to evaluate it.
In this essay, starting from the imagination in the famed Sci-Fi classic Dune,
and drawing inspiration from the concept of "hypothetical thinking" in
psychology literature, we offer critiques of several schools of thoughts on
world modeling, and argue the primary goal of a world model to be simulating
all actionable possibilities of the real world for purposeful reasoning and
acting. Building on the critiques, we propose a new architecture for a
general-purpose world model, based on hierarchical, multi-level, and mixed
continuous/discrete representations, and a generative and self-supervision
learning framework, with an outlook of a Physical, Agentic, and Nested (PAN)
AGI system enabled by such a model.
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
Authors: name: Aleksandar Jevtić, name: Christoph Reich, name: Felix Wimbauer, name: Oliver Hahn, name: Christian Rupprecht, name: Stefan Roth, name: Daniel Cremers
Published: 2025-07-08 17:59:50

Semantic scene completion (SSC) aims to infer both the 3D geometry and
semantics of a scene from single images. In contrast to prior work on SSC that
heavily relies on expensive ground-truth annotations, we approach SSC in an
unsupervised setting. Our novel method, SceneDINO, adapts techniques from
self-supervised representation learning and 2D unsupervised scene understanding
to SSC. Our training exclusively utilizes multi-view consistency
self-supervision without any form of semantic or geometric ground truth. Given
a single input image, SceneDINO infers the 3D geometry and expressive 3D DINO
features in a feed-forward manner. Through a novel 3D feature distillation
approach, we obtain unsupervised 3D semantics. In both 3D and 2D unsupervised
scene understanding, SceneDINO reaches state-of-the-art segmentation accuracy.
Linear probing our 3D features matches the segmentation accuracy of a current
supervised SSC approach. Additionally, we showcase the domain generalization
and multi-view consistency of SceneDINO, taking the first steps towards a
strong foundation for single image 3D scene understanding.
FAROS: Fair Graph Generation via Attribute Switching Mechanisms
Authors: name: Abdennacer Badaoui, name: Oussama Kharouiche, name: Hatim Mrabet, name: Daniele Malitesta, name: Fragkiskos D. Malliaros
Published: 2025-07-04 17:31:41

Recent advancements in graph diffusion models (GDMs) have enabled the
synthesis of realistic network structures, yet ensuring fairness in the
generated data remains a critical challenge. Existing solutions attempt to
mitigate bias by re-training the GDMs with ad-hoc fairness constraints.
Conversely, with this work, we propose FAROS, a novel FAir graph geneRatiOn
framework leveraging attribute Switching mechanisms and directly running in the
generation process of the pre-trained GDM. Technically, our approach works by
altering nodes' sensitive attributes during the generation. To this end, FAROS
calculates the optimal fraction of switching nodes, and selects the diffusion
step to perform the switch by setting tailored multi-criteria constraints to
preserve the node-topology profile from the original distribution (a proxy for
accuracy) while ensuring the edge independence on the sensitive attributes for
the generated graph (a proxy for fairness). Our experiments on benchmark
datasets for link prediction demonstrate that the proposed approach effectively
reduces fairness discrepancies while maintaining comparable (or even higher)
accuracy performance to other similar baselines. Noteworthy, FAROS is also able
to strike a better accuracy-fairness trade-off than other competitors in some
of the tested settings under the Pareto optimality concept, demonstrating the
effectiveness of the imposed multi-criteria constraints.
Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts
Authors: name: Guokan Shang, name: Hadi Abdine, name: Ahmad Chamma, name: Amr Mohamed, name: Mohamed Anwar, name: Abdelaziz Bounhar, name: Omar El Herraoui, name: Preslav Nakov, name: Michalis Vazirgiannis, name: Eric Xing
Published: 2025-07-06 22:53:41

We introduce Nile-Chat-4B, 3x4B-A6B, and 12B, a collection of LLMs for
Egyptian dialect, uniquely designed to understand and generate texts written in
both Arabic and Latin scripts. Specifically, with Nile-Chat-3x4B-A6B, we
introduce a novel language adaptation approach by leveraging the
Branch-Train-MiX strategy to merge script-specialized experts, into a single
MoE model. Our Nile-Chat models significantly outperform leading multilingual
and Arabic LLMs, such as LLaMa, Jais, and ALLaM, on our newly introduced
Egyptian evaluation benchmarks, which span both understanding and generative
tasks. Notably, our 12B model yields a 14.4% performance gain over
Qwen2.5-14B-Instruct on Latin-script benchmarks. All our resources are publicly
available. We believe this work presents a comprehensive methodology for
adapting LLMs to dual-script languages, addressing an often overlooked aspect
in modern LLM development.
AXLearn: Modular Large Model Training on Heterogeneous Infrastructure
Authors: name: Mark Lee, name: Tom Gunter, name: Chang Lan, name: John Peebles, name: Hanzhi Zhou, name: Kelvin Zou, name: Sneha Bangalore, name: Chung-Cheng Chiu, name: Nan Du, name: Xianzhi Du, name: Philipp Dufter, name: Ruixuan Hou, name: Haoshuo Huang, name: Dongseong Hwang, name: Xiang Kong, name: Jinhao Lei, name: Tao Lei, name: Meng Li, name: Li Li, name: Jiarui Lu, name: Zhiyun Lu, name: Yiping Ma, name: David Qiu, name: Vivek Rathod, name: Senyu Tong, name: Zhucheng Tu, name: Jianyu Wang, name: Yongqiang Wang, name: Zirui Wang, name: Floris Weers, name: Sam Wiseman, name: Guoli Yin, name: Bowen Zhang, name: Xiyou Zhou, name: Danyang Zhuo, name: Cheng Leong, name: Ruoming Pang
Published: 2025-07-07 18:50:58

We design and implement AXLearn, a production deep learning system that
facilitates scalable and high-performance training of large deep learning
models. Compared to other state-of-the-art deep learning systems, AXLearn has a
unique focus on modularity and support for heterogeneous hardware
infrastructure. AXLearn's internal interfaces between software components
follow strict encapsulation, allowing different components to be assembled to
facilitate rapid model development and experimentation on heterogeneous compute
infrastructure. We introduce a novel method of quantifying modularity via
Lines-of-Code (LoC)-complexity, which demonstrates how our system maintains
constant complexity as we scale the components in the system, compared to
linear or quadratic complexity in other systems. This allows integrating
features such as Rotary Position Embeddings (RoPE) into AXLearn across hundred
of modules with just 10 lines of code, compared to hundreds as required in
other systems. At the same time, AXLearn maintains equivalent performance
compared to state-of-the-art training systems. Finally, we share our experience
in the development and operation of AXLearn.
Differential Mamba
Authors: name: Nadav Schneider, name: Itamar Zimerman, name: Eliya Nachmani
Published: 2025-07-08 17:30:14

Sequence models like Transformers and RNNs often overallocate attention to
irrelevant context, leading to noisy intermediate representations. This
degrades LLM capabilities by promoting hallucinations, weakening long-range and
retrieval abilities, and reducing robustness. Recent work has shown that
differential design can mitigate this issue in Transformers, improving their
effectiveness across various applications. In this paper, we explore whether
these techniques, originally developed for Transformers, can be applied to
Mamba, a recent architecture based on selective state-space layers that
achieves Transformer-level performance with greater efficiency. We show that a
naive adaptation of differential design to Mamba is insufficient and requires
careful architectural modifications. To address this, we introduce a novel
differential mechanism for Mamba, empirically validated on language modeling
benchmarks, demonstrating improved retrieval capabilities and superior
performance over vanilla Mamba. Finally, we conduct extensive ablation studies
and empirical analyses to justify our design choices and provide evidence that
our approach effectively mitigates the overallocation problem in Mamba-based
models. Our code is publicly available.
SingLoRA: Low Rank Adaptation Using a Single Matrix
Authors: name: David Bensaïd, name: Noam Rotstein, name: Roy Velich, name: Daniel Bensaïd, name: Ron Kimmel
Published: 2025-07-08 01:11:30

Low-Rank Adaptation (LoRA) has significantly advanced parameter-efficient
fine-tuning of large pretrained models. LoRA augments the pre-trained weights
of a model by adding the product of two smaller matrices that together form a
low-rank matrix update. Recent research has shown that scale disparities
between these two matrices often cause unstable training dynamics, leading to
suboptimal performance. In this paper, we propose SingLoRA, which reformulates
low-rank adaptation by learning the weights update as a decomposition of a
single low-rank matrix multiplied by its transpose. This simple design
inherently removes inter-matrix scale conflicts, ensuring stable optimization,
and roughly halves the parameter count. We analyze SingLoRA within the
infinite-width neural network framework, showing that it guarantees stable
feature learning by construction. Extensive experiments on multiple tasks
validate these benefits. In common sense reasoning, fine-tuning LLama 7B on
MNLI with SingLoRA achieves 91.3% accuracy - surpassing LoRA (89.1%) and LoRA+
(90.2%) - while using only 60% of their parameter budget. In image generation,
fine-tuning Stable Diffusion with SingLoRA significantly improves image
fidelity on DreamBooth, achieving a DINO similarity score of 0.151, compared to
scores of 0.148 and 0.143 for DoRA and LoRA, respectively.
LOOM-Scope: a comprehensive and efficient LOng-cOntext Model evaluation
  framework
Authors: name: Zecheng Tang, name: Haitian Wang, name: Quantong Qiu, name: Baibei Ji, name: Ruoxi Sun, name: Keyan Zhou, name: Juntao Li, name: Min Zhang
Published: 2025-07-07 07:33:24

Long-context processing has become a fundamental capability for large
language models~(LLMs). To assess model's long-context performance, numerous
long-context evaluation benchmarks have been proposed. However, variations in
evaluation settings across these benchmarks lead to inconsistent results,
making it difficult to draw reliable comparisons. Besides, the high
computational cost of long-context evaluation poses a significant barrier for
the community to conduct comprehensive assessments of long-context models. In
this paper, we propose LOOM-Scope, a comprehensive and efficient framework for
long-context evaluation. LOOM-Scope standardizes evaluation settings across
diverse benchmarks, supports deployment of efficient long-context inference
acceleration methods, and introduces a holistic yet lightweight benchmark suite
to evaluate models comprehensively. Homepage: https://loomscope.github.io
StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context
  Modeling
Authors: name: Meng Wei, name: Chenyang Wan, name: Xiqian Yu, name: Tai Wang, name: Yuqiang Yang, name: Xiaohan Mao, name: Chenming Zhu, name: Wenzhe Cai, name: Hanqing Wang, name: Yilun Chen, name: Xihui Liu, name: Jiangmiao Pang
Published: 2025-07-07 17:49:41

Vision-and-Language Navigation (VLN) in real-world settings requires agents
to process continuous visual streams and generate actions with low latency
grounded in language instructions. While Video-based Large Language Models
(Video-LLMs) have driven recent progress, current VLN methods based on
Video-LLM often face trade-offs among fine-grained visual understanding,
long-term context modeling and computational efficiency. We introduce
StreamVLN, a streaming VLN framework that employs a hybrid slow-fast context
modeling strategy to support multi-modal reasoning over interleaved vision,
language and action inputs. The fast-streaming dialogue context facilitates
responsive action generation through a sliding-window of active dialogues,
while the slow-updating memory context compresses historical visual states
using a 3D-aware token pruning strategy. With this slow-fast design, StreamVLN
achieves coherent multi-turn dialogue through efficient KV cache reuse,
supporting long video streams with bounded context size and inference cost.
Experiments on VLN-CE benchmarks demonstrate state-of-the-art performance with
stable low latency, ensuring robustness and efficiency in real-world
deployment. The project page is:
https://streamvln.github.io/{https://streamvln.github.io/}.