Orionnnn/hub-stats huggingface dataset:
OrtSAE: Orthogonal Sparse Autoencoders Uncover Atomic Features
Authors: name: Anton Korznikov, name: Andrey Galichin, name: Alexey Dontsov, name: Oleg Rogov, name: Elena Tutubalina, name: Ivan Oseledets
Published: 2025-09-26 08:10:52

Sparse autoencoders (SAEs) are a technique for sparse decomposition of neural
network activations into human-interpretable features. However, current SAEs
suffer from feature absorption, where specialized features capture instances of
general features creating representation holes, and feature composition, where
independent features merge into composite representations. In this work, we
introduce Orthogonal SAE (OrtSAE), a novel approach aimed to mitigate these
issues by enforcing orthogonality between the learned features. By implementing
a new training procedure that penalizes high pairwise cosine similarity between
SAE features, OrtSAE promotes the development of disentangled features while
scaling linearly with the SAE size, avoiding significant computational
overhead. We train OrtSAE across different models and layers and compare it
with other methods. We find that OrtSAE discovers 9% more distinct features,
reduces feature absorption (by 65%) and composition (by 15%), improves
performance on spurious correlation removal (+6%), and achieves on-par
performance for other downstream tasks compared to traditional SAEs.
LEAML: Label-Efficient Adaptation to Out-of-Distribution Visual Tasks
  for Multimodal Large Language Models
Authors: name: Ci-Siang Lin, name: Min-Hung Chen, name: Yu-Yang Sheng, name: Yu-Chiang Frank Wang
Published: 2025-10-03 17:59:56

Multimodal Large Language Models (MLLMs) have achieved strong performance on
general visual benchmarks but struggle with out-of-distribution (OOD) tasks in
specialized domains such as medical imaging, where labeled data is limited and
expensive. We introduce LEAML, a label-efficient adaptation framework that
leverages both scarce labeled VQA samples and abundant unlabeled images. Our
approach generates domain-relevant pseudo question-answer pairs for unlabeled
data using a QA generator regularized by caption distillation. Importantly, we
selectively update only those neurons most relevant to question-answering,
enabling the QA Generator to efficiently acquire domain-specific knowledge
during distillation. Experiments on gastrointestinal endoscopy and sports VQA
demonstrate that LEAML consistently outperforms standard fine-tuning under
minimal supervision, highlighting the effectiveness of our proposed LEAML
framework.
NuRisk: A Visual Question Answering Dataset for Agent-Level Risk
  Assessment in Autonomous Driving
Authors: name: Yuan Gao, name: Mattia Piccinini, name: Roberto Brusnicki, name: Yuchen Zhang, name: Johannes Betz
Published: 2025-09-30 08:37:31

Understanding risk in autonomous driving requires not only perception and
prediction, but also high-level reasoning about agent behavior and context.
Current Vision Language Models (VLMs)-based methods primarily ground agents in
static images and provide qualitative judgments, lacking the spatio-temporal
reasoning needed to capture how risks evolve over time. To address this gap, we
propose NuRisk, a comprehensive Visual Question Answering (VQA) dataset
comprising 2,900 scenarios and 1.1 million agent-level samples, built on
real-world data from nuScenes and Waymo, supplemented with safety-critical
scenarios from the CommonRoad simulator. The dataset provides Bird-Eye-View
(BEV) based sequential images with quantitative, agent-level risk annotations,
enabling spatio-temporal reasoning. We benchmark well-known VLMs across
different prompting techniques and find that they fail to perform explicit
spatio-temporal reasoning, resulting in a peak accuracy of 33% at high latency.
To address these shortcomings, our fine-tuned 7B VLM agent improves accuracy to
41% and reduces latency by 75%, demonstrating explicit spatio-temporal
reasoning capabilities that proprietary models lacked. While this represents a
significant step forward, the modest accuracy underscores the profound
challenge of the task, establishing NuRisk as a critical benchmark for
advancing spatio-temporal reasoning in autonomous driving.
Apriel-1.5-15b-Thinker
Authors: name: Shruthan Radhakrishna, name: Aman Tiwari, name: Aanjaneya Shukla, name: Masoud Hashemi, name: Rishabh Maheshwary, name: Shiva Krishna Reddy Malay, name: Jash Mehta, name: Pulkit Pattnaik, name: Saloni Mittal, name: Khalil Slimi, name: Kelechi Ogueji, name: Akintunde Oladipo, name: Soham Parikh, name: Oluwanifemi Bamgbose, name: Toby Liang, name: Ahmed Masry, name: Khyati Mahajan, name: Sai Rajeswar Mudumba, name: Vikas Yadav, name: Sathwik Tejaswi Madhusudhan, name: Torsten Scholak, name: Sagar Davasam, name: Srinivas Sunkara, name: Nicholas Chapados
Published: 2025-10-01 17:29:35

We present Apriel-1.5-15B-Thinker, a 15-billion parameter open-weights
multimodal reasoning model that achieves frontier-level performance through
training design rather than sheer scale. Starting from Pixtral-12B, we apply a
progressive three-stage methodology: (1) depth upscaling to expand reasoning
capacity without pretraining from scratch, (2) staged continual pre-training
that first develops foundational text and vision understanding, then enhances
visual reasoning through targeted synthetic data generation addressing spatial
structure, compositional understanding, and fine-grained perception, and (3)
high-quality text-only supervised fine-tuning on curated instruction-response
pairs with explicit reasoning traces spanning mathematics, coding, science, and
tool use. Notably, our model achieves competitive results without reinforcement
learning or preference optimization, isolating the contribution of our
data-centric continual pre-training approach. On the Artificial Analysis
Intelligence Index, Apriel-1.5-15B-Thinker attains a score of 52, matching
DeepSeek-R1-0528 despite requiring significantly fewer computational resources.
Across ten image benchmarks, its performance is on average within five points
of Gemini-2.5-Flash and Claude Sonnet-3.7, a key achievement for a model
operating within single-GPU deployment constraints. Our results demonstrate
that thoughtful mid-training 2 design can close substantial capability gaps
without massive scale, making frontier-level multimodal reasoning accessible to
organizations with limited infrastructure. We release the model checkpoint, all
training recipes, and evaluation protocols under the MIT license to to advance
open-source research.
Triangle Splatting+: Differentiable Rendering with Opaque Triangles
Authors: name: Jan Held, name: Renaud Vandeghen, name: Sanghyun Son, name: Daniel Rebain, name: Matheus Gadelha, name: Yi Zhou, name: Ming C. Lin, name: Marc Van Droogenbroeck, name: Andrea Tagliasacchi
Published: 2025-09-29 17:43:46

Reconstructing 3D scenes and synthesizing novel views has seen rapid progress
in recent years. Neural Radiance Fields demonstrated that continuous volumetric
radiance fields can achieve high-quality image synthesis, but their long
training and rendering times limit practicality. 3D Gaussian Splatting (3DGS)
addressed these issues by representing scenes with millions of Gaussians,
enabling real-time rendering and fast optimization. However, Gaussian
primitives are not natively compatible with the mesh-based pipelines used in VR
headsets, and real-time graphics applications. Existing solutions attempt to
convert Gaussians into meshes through post-processing or two-stage pipelines,
which increases complexity and degrades visual quality. In this work, we
introduce Triangle Splatting+, which directly optimizes triangles, the
fundamental primitive of computer graphics, within a differentiable splatting
framework. We formulate triangle parametrization to enable connectivity through
shared vertices, and we design a training strategy that enforces opaque
triangles. The final output is immediately usable in standard graphics engines
without post-processing. Experiments on the Mip-NeRF360 and Tanks & Temples
datasets show that Triangle Splatting+achieves state-of-the-art performance in
mesh-based novel view synthesis. Our method surpasses prior splatting
approaches in visual fidelity while remaining efficient and fast to training.
Moreover, the resulting semi-connected meshes support downstream applications
such as physics-based simulation or interactive walkthroughs. The project page
is https://trianglesplatting2.github.io/trianglesplatting2/.
A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning
Authors: name: Ruiyi Wang, name: Prithviraj Ammanabrolu
Published: 2025-10-01 17:23:04

We study what actually works and what doesn't for training large language
models as agents via multi-turn reinforcement learning. Despite rapid progress,
existing frameworks and definitions are fragmented, and there is no systematic
formulation or analysis of which design choices matter across tasks. We address
this gap by first breaking down the design space into three inter-related
pillars -- environment, reward, and policy -- and empirically derive a recipe
for training LLM agents in situated textual domains. In particular, we test
TextWorld and ALFWorld, popular domains for testing situated embodied
reasoning, as well as SWE-Gym for more software engineering style tasks. (i)
For the environment, we analyze the impacts of task complexity in terms of
sizes of the state and action spaces as well as optimal solution length,
finding that even simple environments within a domain can provide signal on how
well an agent can generalize to more complex tasks. (ii) For the reward, we
ablate relative reward sparsity, observing that while dense turn-level rewards
accelerate training, performance and stability is highly dependent on the
choice of RL algorithm. (iii) And for the agent's policy, we explore the
interplay between reward sparsity and biased (PPO, GRPO) and unbiased (RLOO)
policy gradient methods in addition to showing how to find the optimal
Supervised Fine-tuning (SFT) to RL training ratio given a fixed budget. We
distill these findings into a training recipe that guides co-design across the
three pillars, facilitating research and practical efforts in multi-turn
agentic RL. Code: https://github.com/pearls-lab/meow-tea-taro
Align Your Tangent: Training Better Consistency Models via
  Manifold-Aligned Tangents
Authors: name: Beomsu Kim, name: Byunghee Cha, name: Jong Chul Ye
Published: 2025-10-01 08:35:18

With diffusion and flow matching models achieving state-of-the-art generating
performance, the interest of the community now turned to reducing the inference
time without sacrificing sample quality. Consistency Models (CMs), which are
trained to be consistent on diffusion or probability flow ordinary differential
equation (PF-ODE) trajectories, enable one or two-step flow or diffusion
sampling. However, CMs typically require prolonged training with large batch
sizes to obtain competitive sample quality. In this paper, we examine the
training dynamics of CMs near convergence and discover that CM tangents -- CM
output update directions -- are quite oscillatory, in the sense that they move
parallel to the data manifold, not towards the manifold. To mitigate
oscillatory tangents, we propose a new loss function, called the manifold
feature distance (MFD), which provides manifold-aligned tangents that point
toward the data manifold. Consequently, our method -- dubbed Align Your Tangent
(AYT) -- can accelerate CM training by orders of magnitude and even out-perform
the learned perceptual image patch similarity metric (LPIPS). Furthermore, we
find that our loss enables training with extremely small batch sizes without
compromising sample quality. Code: https://github.com/1202kbs/AYT
WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents
Authors: name: Yinuo Liu, name: Ruohan Xu, name: Xilong Wang, name: Yuqi Jia, name: Neil Zhenqiang Gong
Published: 2025-10-01 18:34:06

Multiple prompt injection attacks have been proposed against web agents. At
the same time, various methods have been developed to detect general prompt
injection attacks, but none have been systematically evaluated for web agents.
In this work, we bridge this gap by presenting the first comprehensive
benchmark study on detecting prompt injection attacks targeting web agents. We
begin by introducing a fine-grained categorization of such attacks based on the
threat model. We then construct datasets containing both malicious and benign
samples: malicious text segments generated by different attacks, benign text
segments from four categories, malicious images produced by attacks, and benign
images from two categories. Next, we systematize both text-based and
image-based detection methods. Finally, we evaluate their performance across
multiple scenarios. Our key findings show that while some detectors can
identify attacks that rely on explicit textual instructions or visible image
perturbations with moderate to high accuracy, they largely fail against attacks
that omit explicit instructions or employ imperceptible perturbations. Our
datasets and code are released at:
https://github.com/Norrrrrrr-lyn/WAInjectBench.
CoDA: Agentic Systems for Collaborative Data Visualization
Authors: name: Zichen Chen, name: Jiefeng Chen, name: Sercan Ö. Arik, name: Misha Sra, name: Tomas Pfister, name: Jinsung Yoon
Published: 2025-10-03 17:30:16

Deep research has revolutionized data analysis, yet data scientists still
devote substantial time to manually crafting visualizations, highlighting the
need for robust automation from natural language queries. However, current
systems struggle with complex datasets containing multiple files and iterative
refinement. Existing approaches, including simple single- or multi-agent
systems, often oversimplify the task, focusing on initial query parsing while
failing to robustly manage data complexity, code errors, or final visualization
quality. In this paper, we reframe this challenge as a collaborative
multi-agent problem. We introduce CoDA, a multi-agent system that employs
specialized LLM agents for metadata analysis, task planning, code generation,
and self-reflection. We formalize this pipeline, demonstrating how
metadata-focused analysis bypasses token limits and quality-driven refinement
ensures robustness. Extensive evaluations show CoDA achieves substantial gains
in the overall score, outperforming competitive baselines by up to 41.5%. This
work demonstrates that the future of visualization automation lies not in
isolated code generation but in integrated, collaborative agentic workflows.
Efficient Multi-modal Large Language Models via Progressive Consistency
  Distillation
Authors: name: Zichen Wen, name: Shaobo Wang, name: Yufa Zhou, name: Junyuan Zhang, name: Qintong Zhang, name: Yifeng Gao, name: Zhaorun Chen, name: Bin Wang, name: Weijia Li, name: Conghui He, name: Linfeng Zhang
Published: 2025-10-01 04:56:40

Visual tokens consume substantial computational resources in multi-modal
large models (MLLMs), significantly compromising their efficiency. Recent works
have attempted to improve efficiency by compressing visual tokens during
training, either through modifications to model components or by introducing
additional parameters. However, they often overlook the increased learning
difficulty caused by such compression, as the model's parameter space struggles
to quickly adapt to the substantial perturbations in the feature space induced
by token compression. In this work, we propose to develop Efficient MLLMs via
Progressive Consistency Distillation (EPIC), a progressive learning framework.
Specifically, by decomposing the feature space perturbations introduced by
token compression along the token-wise and layer-wise dimensions, we introduce
token consistency distillation and layer consistency distillation,
respectively, aiming to reduce the training difficulty by leveraging guidance
from a teacher model and following a progressive learning trajectory. Extensive
experiments demonstrate the superior effectiveness, robustness, and
generalization capabilities of our proposed framework.
LSPO: Length-aware Dynamic Sampling for Policy Optimization in LLM
  Reasoning
Authors: name: Weizhe Chen, name: Sven Koenig, name: Bistra Dilkina
Published: 2025-10-01 20:57:22

Since the release of Deepseek-R1, reinforcement learning with verifiable
rewards (RLVR) has become a central approach for training large language models
(LLMs) on reasoning tasks. Recent work has largely focused on modifying loss
functions to make RLVR more efficient and effective. In this paper, motivated
by studies of overthinking in LLMs, we propose Length-aware Sampling for Policy
Optimization (LSPO), a novel meta-RLVR algorithm that dynamically selects
training data at each step based on the average response length. We evaluate
LSPO across multiple base models and datasets, demonstrating that it
consistently improves learning effectiveness. In addition, we conduct a
detailed ablation study to examine alternative ways of incorporating length
signals into dynamic sampling, offering further insights and highlighting
promising directions for future research.
Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
Authors: name: Shuai Shao, name: Qihan Ren, name: Chen Qian, name: Boyi Wei, name: Dadi Guo, name: Jingyi Yang, name: Xinhao Song, name: Linfeng Zhang, name: Weinan Zhang, name: Dongrui Liu, name: Jing Shao
Published: 2025-09-30 14:55:55

Advances in Large Language Models (LLMs) have enabled a new class of
self-evolving agents that autonomously improve through interaction with the
environment, demonstrating strong capabilities. However, self-evolution also
introduces novel risks overlooked by current safety research. In this work, we
study the case where an agent's self-evolution deviates in unintended ways,
leading to undesirable or even harmful outcomes. We refer to this as
Misevolution. To provide a systematic investigation, we evaluate misevolution
along four key evolutionary pathways: model, memory, tool, and workflow. Our
empirical findings reveal that misevolution is a widespread risk, affecting
agents built even on top-tier LLMs (e.g., Gemini-2.5-Pro). Different emergent
risks are observed in the self-evolutionary process, such as the degradation of
safety alignment after memory accumulation, or the unintended introduction of
vulnerabilities in tool creation and reuse. To our knowledge, this is the first
study to systematically conceptualize misevolution and provide empirical
evidence of its occurrence, highlighting an urgent need for new safety
paradigms for self-evolving agents. Finally, we discuss potential mitigation
strategies to inspire further research on building safer and more trustworthy
self-evolving agents. Our code and data are available at
https://github.com/ShaoShuai0605/Misevolution . Warning: this paper includes
examples that may be offensive or harmful in nature.
Free Lunch Alignment of Text-to-Image Diffusion Models without
  Preference Image Pairs
Authors: name: Jia Jun Cheng Xian, name: Muchen Li, name: Haotian Yang, name: Xin Tao, name: Pengfei Wan, name: Leonid Sigal, name: Renjie Liao
Published: 2025-09-30 04:32:34

Recent advances in diffusion-based text-to-image (T2I) models have led to
remarkable success in generating high-quality images from textual prompts.
However, ensuring accurate alignment between the text and the generated image
remains a significant challenge for state-of-the-art diffusion models. To
address this, existing studies employ reinforcement learning with human
feedback (RLHF) to align T2I outputs with human preferences. These methods,
however, either rely directly on paired image preference data or require a
learned reward function, both of which depend heavily on costly, high-quality
human annotations and thus face scalability limitations. In this work, we
introduce Text Preference Optimization (TPO), a framework that enables
"free-lunch" alignment of T2I models, achieving alignment without the need for
paired image preference data. TPO works by training the model to prefer matched
prompts over mismatched prompts, which are constructed by perturbing original
captions using a large language model. Our framework is general and compatible
with existing preference-based algorithms. We extend both DPO and KTO to our
setting, resulting in TDPO and TKTO. Quantitative and qualitative evaluations
across multiple benchmarks show that our methods consistently outperform their
original counterparts, delivering better human preference scores and improved
text-to-image alignment. Our Open-source code is available at
https://github.com/DSL-Lab/T2I-Free-Lunch-Alignment.
SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the
  SpineMed-450k Corpus
Authors: name: Ming Zhao, name: Wenhui Dong, name: Yang Zhang, name: Xiang Zheng, name: Zhonghao Zhang, name: Zian Zhou, name: Yunzhi Guan, name: Liukun Xu, name: Wei Peng, name: Zhaoyang Gong, name: Zhicheng Zhang, name: Dachuan Li, name: Xiaosheng Ma, name: Yuli Ma, name: Jianing Ni, name: Changjiang Jiang, name: Lixia Tian, name: Qixin Chen, name: Kaishun Xia, name: Pingping Liu, name: Tongshun Zhang, name: Zhiqiang Liu, name: Zhongan Bi, name: Chenyang Si, name: Tiansheng Sun, name: Caifeng Shan
Published: 2025-10-03 16:32:02

Spine disorders affect 619 million people globally and are a leading cause of
disability, yet AI-assisted diagnosis remains limited by the lack of
level-aware, multimodal datasets. Clinical decision-making for spine disorders
requires sophisticated reasoning across X-ray, CT, and MRI at specific
vertebral levels. However, progress has been constrained by the absence of
traceable, clinically-grounded instruction data and standardized,
spine-specific benchmarks. To address this, we introduce SpineMed, an ecosystem
co-designed with practicing spine surgeons. It features SpineMed-450k, the
first large-scale dataset explicitly designed for vertebral-level reasoning
across imaging modalities with over 450,000 instruction instances, and
SpineBench, a clinically-grounded evaluation framework. SpineMed-450k is
curated from diverse sources, including textbooks, guidelines, open datasets,
and ~1,000 de-identified hospital cases, using a clinician-in-the-loop pipeline
with a two-stage LLM generation method (draft and revision) to ensure
high-quality, traceable data for question-answering, multi-turn consultations,
and report generation. SpineBench evaluates models on clinically salient axes,
including level identification, pathology assessment, and surgical planning.
Our comprehensive evaluation of several recently advanced large vision-language
models (LVLMs) on SpineBench reveals systematic weaknesses in fine-grained,
level-specific reasoning. In contrast, our model fine-tuned on SpineMed-450k
demonstrates consistent and significant improvements across all tasks.
Clinician assessments confirm the diagnostic clarity and practical utility of
our model's outputs.
REPAIR: Robust Editing via Progressive Adaptive Intervention and
  Reintegration
Authors: name: Yisu Wang, name: Ming Wang, name: Haoyuan Song, name: Wenjie Huang, name: Chaozheng Wang, name: Yi Xie, name: Xuming Ran
Published: 2025-10-02 10:35:39

Post-training for large language models (LLMs) is constrained by the high
cost of acquiring new knowledge or correcting errors and by the unintended side
effects that frequently arise from retraining. To address these issues, we
introduce REPAIR (Robust Editing via Progressive Adaptive Intervention and
Reintegration), a lifelong editing framework designed to support precise and
low-cost model updates while preserving non-target knowledge. REPAIR mitigates
the instability and conflicts of large-scale sequential edits through a
closed-loop feedback mechanism coupled with dynamic memory management.
Furthermore, by incorporating frequent knowledge fusion and enforcing strong
locality guards, REPAIR effectively addresses the shortcomings of traditional
distribution-agnostic approaches that often overlook unintended ripple effects.
Our experiments demonstrate that REPAIR boosts editing accuracy by 10%-30%
across multiple model families and significantly reduces knowledge forgetting.
This work introduces a robust framework for developing reliable, scalable, and
continually evolving LLMs.