Orionnnn/hub-stats huggingface dataset:
Scrub It Out! Erasing Sensitive Memorization in Code Language Models via
  Machine Unlearning
Authors: name: Zhaoyang Chu, name: Yao Wan, name: Zhikun Zhang, name: Di Wang, name: Zhou Yang, name: Hongyu Zhang, name: Pan Zhou, name: Xuanhua Shi, name: Hai Jin, name: David Lo
Published: 2025-09-17 07:12:35

While Code Language Models (CLMs) have demonstrated superior performance in
software engineering tasks such as code generation and summarization, recent
empirical studies reveal a critical privacy vulnerability: these models exhibit
unintended memorization of sensitive training data, enabling verbatim
reproduction of confidential information when specifically prompted. To address
this issue, several approaches, including training data de-duplication and
differential privacy augmentation, have been proposed. However, these methods
require full-model retraining for deployed CLMs, which incurs substantial
computational costs. In this paper, we aim to answer the following research
question: Can sensitive information memorized by CLMs be erased effectively and
efficiently?
  We conduct a pioneering investigation into erasing sensitive memorization in
CLMs through machine unlearning - a post-hoc modification method that removes
specific information from trained models without requiring full retraining.
Specifically, we first quantify the memorization risks of sensitive data within
CLM training datasets and curate a high-risk dataset of 50,000 sensitive
memorized samples as unlearning targets. We study two widely used gradient
ascent-based unlearning approaches: the vanilla and constraint-based methods,
and introduce CodeEraser, an advanced variant that selectively unlearns
sensitive memorized segments in code while preserving the structural integrity
and functional correctness of the surrounding code. Extensive experiments on
three families of CLMs, i.e., CodeParrot, CodeGen-Mono, and Qwen2.5-Coder,
validate the effectiveness and efficiency of CodeEraser in erasing targeted
sensitive memorization while maintaining model utility.
Hala Technical Report: Building Arabic-Centric Instruction & Translation
  Models at Scale
Authors: name: Hasan Abed Al Kader Hammoud, name: Mohammad Zbeeb, name: Bernard Ghanem
Published: 2025-09-17 14:19:28

We present Hala, a family of Arabic-centric instruction and translation
models built with our translate-and-tune pipeline. We first compress a strong
ARleftrightarrowEN teacher to FP8 (yielding sim2times higher
throughput with no quality loss) and use it to create high-fidelity bilingual
supervision. A lightweight language model LFM2-1.2B is then fine-tuned on this
data and used to translate high-quality English instruction sets into Arabic,
producing a million-scale corpus tailored to instruction following. We train
Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to
balance Arabic specialization with base-model strengths. On Arabic-centric
benchmarks, Hala achieves state-of-the-art results within both the "nano"
(leq2B) and "small" (7-9B) categories, outperforming their bases. We release
models, data, evaluation, and recipes to accelerate research in Arabic NLP.
SteeringControl: Holistic Evaluation of Alignment Steering in LLMs
Authors: name: Vincent Siu, name: Nicholas Crispino, name: David Park, name: Nathan W. Henry, name: Zhun Wang, name: Yang Liu, name: Dawn Song, name: Chenguang Wang
Published: 2025-09-16 18:36:22

We introduce SteeringControl, a benchmark for evaluating representation
steering methods across core alignment objectives--bias, harmful generation,
and hallucination--and their effects on secondary behaviors such as sycophancy
and commonsense morality. While prior alignment work often highlights
truthfulness or reasoning ability to demonstrate the side effects of
representation steering, we find there are many unexplored tradeoffs not yet
understood in a systematic way. We collect a dataset of safety-relevant primary
and secondary behaviors to evaluate steering effectiveness and behavioral
entanglement centered around five popular steering methods. To enable this, we
craft a modular steering framework based on unique components that serve as the
building blocks of many existing methods. Our results on Qwen-2.5-7B and
Llama-3.1-8B find that strong steering performance is dependent on the specific
combination of steering method, model, and targeted behavior, and that severe
concept entanglement can result from poor combinations of these three as well.
We release our code here:
https://github.com/wang-research-lab/SteeringControl.git.
SAIL-VL2 Technical Report
Authors: name: Weijie Yin, name: Yongjie Ye, name: Fangxun Shu, name: Yue Liao, name: Zijian Kang, name: Hongyuan Dong, name: Haiyang Yu, name: Dingkang Yang, name: Jiacong Wang, name: Han Wang, name: Wenzhuo Liu, name: Xiao Liang, name: Shuicheng Yan, name: Chao Feng
Published: 2025-09-17 14:34:02

We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM)
for comprehensive multimodal understanding and reasoning. As the successor to
SAIL-VL, SAIL-VL2 achieves state-of-the-art performance at the 2B and 8B
parameter scales across diverse image and video benchmarks, demonstrating
strong capabilities from fine-grained perception to complex reasoning. Three
core innovations drive its effectiveness. First, a large-scale data curation
pipeline with scoring and filtering strategies enhances both quality and
distribution across captioning, OCR, QA, and video data, improving training
efficiency. Second, a progressive training framework begins with a powerful
pre-trained vision encoder (SAIL-ViT), advances through multimodal
pre-training, and culminates in a thinking-fusion SFT-RL hybrid paradigm that
systematically strengthens model capabilities. Third, architectural advances
extend beyond dense LLMs to efficient sparse Mixture-of-Experts (MoE) designs.
With these contributions, SAIL-VL2 demonstrates competitive performance across
106 datasets and achieves state-of-the-art results on challenging reasoning
benchmarks such as MMMU and MathVista. Furthermore, on the OpenCompass
leaderboard, SAIL-VL2-2B ranks first among officially released open-source
models under the 4B parameter scale, while serving as an efficient and
extensible foundation for the open-source multimodal community.
Wan-Animate: Unified Character Animation and Replacement with Holistic
  Replication
Authors: name: Gang Cheng, name: Xin Gao, name: Li Hu, name: Siqi Hu, name: Mingyang Huang, name: Chaonan Ji, name: Ju Li, name: Dechao Meng, name: Jinwei Qi, name: Penchong Qiao, name: Zhen Shen, name: Yafei Song, name: Ke Sun, name: Linrui Tian, name: Feng Wang, name: Guangyuan Wang, name: Qi Wang, name: Zhongjian Wang, name: Jiayu Xiao, name: Sheng Xu, name: Bang Zhang, name: Peng Zhang, name: Xindi Zhang, name: Zhe Zhang, name: Jingren Zhou, name: Lian Zhuo
Published: 2025-09-17 15:00:57

We introduce Wan-Animate, a unified framework for character animation and
replacement. Given a character image and a reference video, Wan-Animate can
animate the character by precisely replicating the expressions and movements of
the character in the video to generate high-fidelity character videos.
Alternatively, it can integrate the animated character into the reference video
to replace the original character, replicating the scene's lighting and color
tone to achieve seamless environmental integration. Wan-Animate is built upon
the Wan model. To adapt it for character animation tasks, we employ a modified
input paradigm to differentiate between reference conditions and regions for
generation. This design unifies multiple tasks into a common symbolic
representation. We use spatially-aligned skeleton signals to replicate body
motion and implicit facial features extracted from source images to reenact
expressions, enabling the generation of character videos with high
controllability and expressiveness. Furthermore, to enhance environmental
integration during character replacement, we develop an auxiliary Relighting
LoRA. This module preserves the character's appearance consistency while
applying the appropriate environmental lighting and color tone. Experimental
results demonstrate that Wan-Animate achieves state-of-the-art performance. We
are committed to open-sourcing the model weights and its source code.
GenExam: A Multidisciplinary Text-to-Image Exam
Authors: name: Zhaokai Wang, name: Penghao Yin, name: Xiangyu Zhao, name: Changyao Tian, name: Yu Qiao, name: Wenhai Wang, name: Jifeng Dai, name: Gen Luo
Published: 2025-09-17 17:59:14

Exams are a fundamental test of expert-level intelligence and require
integrated understanding, reasoning, and generation. Existing exam-style
benchmarks mainly focus on understanding and reasoning tasks, and current
generation benchmarks emphasize the illustration of world knowledge and visual
concepts, neglecting the evaluation of rigorous drawing exams. We introduce
GenExam, the first benchmark for multidisciplinary text-to-image exams,
featuring 1,000 samples across 10 subjects with exam-style prompts organized
under a four-level taxonomy. Each problem is equipped with ground-truth images
and fine-grained scoring points to enable a precise evaluation of semantic
correctness and visual plausibility. Experiments show that even
state-of-the-art models such as GPT-Image-1 and Gemini-2.5-Flash-Image achieve
less than 15% strict scores, and most models yield almost 0%, suggesting the
great challenge of our benchmark. By framing image generation as an exam,
GenExam offers a rigorous assessment of models' ability to integrate knowledge,
reasoning, and generation, providing insights on the path to general AGI.
THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical
  Reasoning
Authors: name: Qikai Chang, name: Zhenrong Zhang, name: Pengfei Hu, name: Jiefeng Ma, name: Yicheng Pan, name: Jianshu Zhang, name: Jun Du, name: Quan Liu, name: Jianqing Gao
Published: 2025-09-17 07:16:12

Large Language Models (LLMs) have made remarkable progress in mathematical
reasoning, but still continue to struggle with high-precision tasks like
numerical computation and formal symbolic manipulation. Integrating external
tools has emerged as a promising approach to bridge this gap. Despite recent
advances, existing methods struggle with three key challenges: constructing
tool-integrated reasoning data, performing fine-grained optimization, and
enhancing inference. To overcome these limitations, we propose THOR
(Tool-Integrated Hierarchical Optimization via RL). First, we introduce TIRGen,
a multi-agent actor-critic-based pipeline for constructing high-quality
datasets of tool-integrated reasoning paths, aligning with the policy and
generalizing well across diverse models. Second, to perform fine-grained
hierarchical optimization, we introduce an RL strategy that jointly optimizes
for both trajectory-level problem solving and step-level code generation. This
is motivated by our key insight that the success of an intermediate tool call
is a strong predictor of the final answer's correctness. Finally, THOR
incorporates a self-correction mechanism that leverages immediate tool feedback
to dynamically revise erroneous reasoning paths during inference. Our approach
demonstrates strong generalization across diverse models, performing
effectively in both reasoning and non-reasoning models. It further achieves
state-of-the-art performance for models of a similar scale on multiple
mathematical benchmarks, while also delivering consistent improvements on code
benchmarks. Our code will be publicly available at
https://github.com/JingMog/THOR.
Sound Matching an Analogue Levelling Amplifier Using the Newton-Raphson
  Method
Authors: name: Chin-Yun Yu, name: György Fazekas
Published: 2025-09-12 21:48:30

Automatic differentiation through digital signal processing algorithms for
virtual analogue modelling has recently gained popularity. These algorithms are
typically more computationally efficient than black-box neural networks that
rely on dense matrix multiplications. Due to their differentiable nature, they
can be integrated with neural networks and jointly trained using gradient
descent algorithms, resulting in more efficient systems. Furthermore, signal
processing algorithms have significantly fewer parameters than neural networks,
allowing the application of the Newton-Raphson method. This method offers
faster and more robust convergence than gradient descent at the cost of
quadratic storage. This paper presents a method to emulate analogue levelling
amplifiers using a feed-forward digital compressor with parameters optimised
via the Newton-Raphson method. We demonstrate that a digital compressor can
successfully approximate the behaviour of our target unit, the Teletronix
LA-2A. Different strategies for computing the Hessian matrix are benchmarked.
We leverage parallel algorithms for recursive filters to achieve efficient
training on modern GPUs. The resulting model is made into a VST plugin and is
open-sourced at https://github.com/aim-qmul/4a2a.
Struct-Bench: A Benchmark for Differentially Private Structured Text
  Generation
Authors: name: Shuaiqi Wang, name: Vikas Raunak, name: Arturs Backurs, name: Victor Reis, name: Pei Zhou, name: Sihao Chen, name: Longqi Yang, name: Zinan Lin, name: Sergey Yekhanin, name: Giulia Fanti
Published: 2025-09-12 21:18:13

Differentially private (DP) synthetic data generation is a promising
technique for utilizing private datasets that otherwise cannot be exposed for
model training or other analytics. While much research literature has focused
on generating private unstructured text and image data, in enterprise settings,
structured data (e.g., tabular) is more common, often including natural
language fields or components. Existing synthetic data evaluation techniques
(e.g., FID) struggle to capture the structural properties and correlations of
such datasets. In this work, we propose Struct-Bench, a framework and benchmark
for evaluating synthetic datasets derived from structured datasets that contain
natural language data. The Struct-Bench framework requires users to provide a
representation of their dataset structure as a Context-Free Grammar (CFG). Our
benchmark comprises 5 real-world and 2 synthetically generated datasets, each
annotated with CFGs. We show that these datasets demonstrably present a great
challenge even for state-of-the-art DP synthetic data generation methods.
Struct-Bench also includes reference implementations of different metrics and a
leaderboard, thereby providing researchers a standardized evaluation platform
to benchmark and investigate privacy-preserving synthetic data generation
methods. Further, we also present a case study showing how to use Struct-Bench
to improve the synthetic data quality of Private Evolution (PE) on structured
data. The benchmark and the leaderboard have been publicly made available at
https://struct-bench.github.io.
zELO: ELO-inspired Training Method for Rerankers and Embedding Models
Authors: name: Nicholas Pipitone, name: Ghita Houir Alami, name: Advaith Avadhanam, name: Anton Kaminskyi, name: Ashley Khoo
Published: 2025-09-16 00:44:08

We introduce a novel training methodology named zELO, which optimizes
retrieval performance via the analysis that ranking tasks are statically
equivalent to a Thurstone model. Based on the zELO method, we use unsupervised
data in order train a suite of state-of-the-art open-weight reranker models:
zerank-1 and zerank-1-small. These models achieve the highest retrieval scores
in multiple domains, including finance, legal, code, and STEM, outperforming
closed-source proprietary rerankers on both NDCG@10 and Recall. These models
also demonstrate great versatility, maintaining their 0-shot performance on
out-of-domain and private customer datasets. The training data included 112,000
queries and 100 documents per query, and was trained end-to-end from
unannotated queries and documents in less than 10,000 H100-hours.
RAPTOR: A Foundation Policy for Quadrotor Control
Authors: name: Jonas Eschmann, name: Dario Albani, name: Giuseppe Loianno
Published: 2025-09-15 00:05:40

Humans are remarkably data-efficient when adapting to new unseen conditions,
like driving a new car. In contrast, modern robotic control systems, like
neural network policies trained using Reinforcement Learning (RL), are highly
specialized for single environments. Because of this overfitting, they are
known to break down even under small differences like the Simulation-to-Reality
(Sim2Real) gap and require system identification and retraining for even
minimal changes to the system. In this work, we present RAPTOR, a method for
training a highly adaptive foundation policy for quadrotor control. Our method
enables training a single, end-to-end neural-network policy to control a wide
variety of quadrotors. We test 10 different real quadrotors from 32 g to 2.4 kg
that also differ in motor type (brushed vs. brushless), frame type (soft vs.
rigid), propeller type (2/3/4-blade), and flight controller
(PX4/Betaflight/Crazyflie/M5StampFly). We find that a tiny, three-layer policy
with only 2084 parameters is sufficient for zero-shot adaptation to a wide
variety of platforms. The adaptation through In-Context Learning is made
possible by using a recurrence in the hidden layer. The policy is trained
through a novel Meta-Imitation Learning algorithm, where we sample 1000
quadrotors and train a teacher policy for each of them using Reinforcement
Learning. Subsequently, the 1000 teachers are distilled into a single, adaptive
student policy. We find that within milliseconds, the resulting foundation
policy adapts zero-shot to unseen quadrotors. We extensively test the
capabilities of the foundation policy under numerous conditions (trajectory
tracking, indoor/outdoor, wind disturbance, poking, different propellers).
Stable Part Diffusion 4D: Multi-View RGB and Kinematic Parts Video
  Generation
Authors: name: Hao Zhang, name: Chun-Han Yao, name: Simon Donné, name: Narendra Ahuja, name: Varun Jampani
Published: 2025-09-12 20:39:43

We present Stable Part Diffusion 4D (SP4D), a framework for generating paired
RGB and kinematic part videos from monocular inputs. Unlike conventional part
segmentation methods that rely on appearance-based semantic cues, SP4D learns
to produce kinematic parts - structural components aligned with object
articulation and consistent across views and time. SP4D adopts a dual-branch
diffusion model that jointly synthesizes RGB frames and corresponding part
segmentation maps. To simplify the architecture and flexibly enable different
part counts, we introduce a spatial color encoding scheme that maps part masks
to continuous RGB-like images. This encoding allows the segmentation branch to
share the latent VAE from the RGB branch, while enabling part segmentation to
be recovered via straightforward post-processing. A Bidirectional Diffusion
Fusion (BiDiFuse) module enhances cross-branch consistency, supported by a
contrastive part consistency loss to promote spatial and temporal alignment of
part predictions. We demonstrate that the generated 2D part maps can be lifted
to 3D to derive skeletal structures and harmonic skinning weights with few
manual adjustments. To train and evaluate SP4D, we construct KinematicParts20K,
a curated dataset of over 20K rigged objects selected and processed from
Objaverse XL (Deitke et al., 2023), each paired with multi-view RGB and part
video sequences. Experiments show that SP4D generalizes strongly to diverse
scenarios, including real-world videos, novel generated objects, and rare
articulated poses, producing kinematic-aware outputs suitable for downstream
animation and motion-related tasks.
Phi: Preference Hijacking in Multi-modal Large Language Models at
  Inference Time
Authors: name: Yifan Lan, name: Yuanpu Cao, name: Weitong Zhang, name: Lu Lin, name: Jinghui Chen
Published: 2025-09-15 23:55:57

Recently, Multimodal Large Language Models (MLLMs) have gained significant
attention across various domains. However, their widespread adoption has also
raised serious safety concerns. In this paper, we uncover a new safety risk of
MLLMs: the output preference of MLLMs can be arbitrarily manipulated by
carefully optimized images. Such attacks often generate contextually relevant
yet biased responses that are neither overtly harmful nor unethical, making
them difficult to detect. Specifically, we introduce a novel method, Preference
Hijacking (Phi), for manipulating the MLLM response preferences using a
preference hijacked image. Our method works at inference time and requires no
model modifications. Additionally, we introduce a universal hijacking
perturbation -- a transferable component that can be embedded into different
images to hijack MLLM responses toward any attacker-specified preferences.
Experimental results across various tasks demonstrate the effectiveness of our
approach. The code for Phi is accessible at https://github.com/Yifan-Lan/Phi.
ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic
  Medical Datasets Generation
Authors: name: Salvatore Esposito, name: Matías Mattamala, name: Daniel Rebain, name: Francis Xiatian Zhang, name: Kevin Dhaliwal, name: Mohsen Khadem, name: Subramanian Ramamoorthy
Published: 2025-09-16 15:30:02

Continuum robots are advancing bronchoscopy procedures by accessing complex
lung airways and enabling targeted interventions. However, their development is
limited by the lack of realistic training and test environments: Real data is
difficult to collect due to ethical constraints and patient safety concerns,
and developing autonomy algorithms requires realistic imaging and physical
feedback. We present ROOM (Realistic Optical Observation in Medicine), a
comprehensive simulation framework designed for generating photorealistic
bronchoscopy training data. By leveraging patient CT scans, our pipeline
renders multi-modal sensor data including RGB images with realistic noise and
light specularities, metric depth maps, surface normals, optical flow and point
clouds at medically relevant scales. We validate the data generated by ROOM in
two canonical tasks for medical robotics -- multi-view pose estimation and
monocular depth estimation, demonstrating diverse challenges that
state-of-the-art methods must overcome to transfer to these medical settings.
Furthermore, we show that the data produced by ROOM can be used to fine-tune
existing depth estimation models to overcome these challenges, also enabling
other downstream applications such as navigation. We expect that ROOM will
enable large-scale data generation across diverse patient anatomies and
procedural scenarios that are challenging to capture in clinical settings. Code
and data: https://github.com/iamsalvatore/room.
Optimal Brain Restoration for Joint Quantization and Sparsification of
  LLMs
Authors: name: Hang Guo, name: Yawei Li, name: Luca Benini
Published: 2025-09-14 09:17:19

Recent advances in Large Language Model (LLM) compression, such as
quantization and pruning, have achieved notable success. However, as these
techniques gradually approach their respective limits, relying on a single
method for further compression has become increasingly challenging. In this
work, we explore an alternative solution by combining quantization and
sparsity. This joint approach, though promising, introduces new difficulties
due to the inherently conflicting requirements on weight distributions:
quantization favors compact ranges, while pruning benefits from high variance.
To attack this problem, we propose Optimal Brain Restoration (OBR), a general
and training-free framework that aligns pruning and quantization by error
compensation between both. OBR minimizes performance degradation on downstream
tasks by building on a second-order Hessian objective, which is then
reformulated into a tractable problem through surrogate approximation and
ultimately reaches a closed-form solution via group error compensation.
Experiments show that OBR enables aggressive W4A4KV4 quantization with 50%
sparsity on existing LLMs, and delivers up to 4.72x speedup and 6.4x memory
reduction compared to the FP16-dense baseline.