Orionnnn/hub-stats huggingface dataset:
TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine
  Translation
Authors: name: Armel Zebaze, name: Benoît Sagot, name: Rachel Bawden
Published: 2025-08-12 06:58:02

LLMs have been shown to perform well in machine translation (MT) with the use
of in-context learning (ICL), rivaling supervised models when translating into
high-resource languages (HRLs). However, they lag behind when translating into
low-resource language (LRLs). Example selection via similarity search and
supervised fine-tuning help. However the improvements they give are limited by
the size, quality and diversity of existing parallel datasets. A common
technique in low-resource MT is synthetic parallel data creation, the most
frequent of which is backtranslation, whereby existing target-side texts are
automatically translated into the source language. However, this assumes the
existence of good quality and relevant target-side texts, which are not readily
available for many LRLs. In this paper, we present TopXGen, an
LLM-based approach for the generation of high quality and topic-diverse data in
multiple LRLs, which can then be backtranslated to produce useful and diverse
parallel texts for ICL and fine-tuning. Our intuition is that while LLMs
struggle to translate into LRLs, their ability to translate well into HRLs and
their multilinguality enable them to generate good quality, natural-sounding
target-side texts, which can be translated well into a high-resource source
language. We show that TopXGen boosts LLM translation performance
during fine-tuning and in-context learning. Code and outputs are available at
https://github.com/ArmelRandy/topxgen.
Democratizing Diplomacy: A Harness for Evaluating Any Large Language
  Model on Full-Press Diplomacy
Authors: name: Alexander Duffy, name: Samuel J Paech, name: Ishana Shastri, name: Elizabeth Karpinski, name: Baptiste Alloui-Cros, name: Tyler Marques, name: Matthew Lyle Olson
Published: 2025-08-10 21:07:08

We present the first evaluation harness that enables any out-of-the-box,
local, Large Language Models (LLMs) to play full-press Diplomacy without
fine-tuning or specialized training. Previous work required frontier LLMs, or
fine-tuning, due to the high complexity and information density of Diplomacy's
game state. Combined with the high variance of matches, these factors made
Diplomacy prohibitive for study. In this work, we used data-driven iteration to
optimize a textual game state representation such that a 24B model can reliably
complete matches without any fine tuning. We develop tooling to facilitate
hypothesis testing and statistical analysis, and we present case studies on
persuasion, aggressive playstyles, and performance across a range of models. We
conduct a variety of experiments across many popular LLMs, finding the larger
models perform the best, but the smaller models still play adequately. We also
introduce Critical State Analysis: an experimental protocol for rapidly
iterating and analyzing key moments in a game at depth. Our harness
democratizes the evaluation of strategic reasoning in LLMs by eliminating the
need for fine-tuning, and it provides insights into how these capabilities
emerge naturally from widely used LLMs. Our code is available in the supplement
and will be open sourced.
BiasGym: Fantastic Biases and How to Find (and Remove) Them
Authors: name: Sekh Mainul Islam, name: Nadav Borenstein, name: Siddhesh Milind Pawar, name: Haeun Yu, name: Arnav Arora, name: Isabelle Augenstein
Published: 2025-08-12 11:23:44

Understanding biases and stereotypes encoded in the weights of Large Language
Models (LLMs) is crucial for developing effective mitigation strategies. Biased
behaviour is often subtle and non-trivial to isolate, even when deliberately
elicited, making systematic analysis and debiasing particularly challenging. To
address this, we introduce BiasGym, a simple, cost-effective, and generalizable
framework for reliably injecting, analyzing, and mitigating conceptual
associations within LLMs. BiasGym consists of two components: BiasInject, which
injects specific biases into the model via token-based fine-tuning while
keeping the model frozen, and BiasScope, which leverages these injected signals
to identify and steer the components responsible for biased behavior. Our
method enables consistent bias elicitation for mechanistic analysis, supports
targeted debiasing without degrading performance on downstream tasks, and
generalizes to biases unseen during training. We demonstrate the effectiveness
of BiasGym in reducing real-world stereotypes (e.g., people from a country
being `reckless drivers') and in probing fictional associations (e.g., people
from a country having `blue skin'), showing its utility for both safety
interventions and interpretability research.
DeCRED: Decoder-Centric Regularization for Encoder-Decoder Based Speech
  Recognition
Authors: name: Alexander Polok, name: Santosh Kesiraju, name: Karel Beneš, name: Bolaji Yusuf, name: Lukáš Burget, name: Jan Černocký
Published: 2025-08-12 13:44:50

This paper presents a simple yet effective regularization for the internal
language model induced by the decoder in encoder-decoder ASR models, thereby
improving robustness and generalization in both in- and out-of-domain settings.
The proposed method, Decoder-Centric Regularization in Encoder-Decoder
(DeCRED), adds auxiliary classifiers to the decoder, enabling next token
prediction via intermediate logits. Empirically, DeCRED reduces the mean
internal LM BPE perplexity by 36.6% relative to 11 test sets. Furthermore, this
translates into actual WER improvements over the baseline in 5 of 7 in-domain
and 3 of 4 out-of-domain test sets, reducing macro WER from 6.4% to 6.3% and
18.2% to 16.2%, respectively. On TEDLIUM3, DeCRED achieves 7.0% WER, surpassing
the baseline and encoder-centric InterCTC regularization by 0.6% and 0.5%,
respectively. Finally, we compare DeCRED with OWSM v3.1 and Whisper-medium,
showing competitive WERs despite training on much less data with fewer
parameters.
Towards Affordance-Aware Robotic Dexterous Grasping with Human-like
  Priors
Authors: name: Haoyu Zhao, name: Linghao Zhuang, name: Xingyue Zhao, name: Cheng Zeng, name: Haoran Xu, name: Yuming Jiang, name: Jun Cen, name: Kexiang Wang, name: Jiayan Guo, name: Siteng Huang, name: Xin Li, name: Deli Zhao, name: Hua Zou
Published: 2025-08-12 12:36:01

A dexterous hand capable of generalizable grasping objects is fundamental for
the development of general-purpose embodied AI. However, previous methods focus
narrowly on low-level grasp stability metrics, neglecting affordance-aware
positioning and human-like poses which are crucial for downstream manipulation.
To address these limitations, we propose AffordDex, a novel framework with
two-stage training that learns a universal grasping policy with an inherent
understanding of both motion priors and object affordances. In the first stage,
a trajectory imitator is pre-trained on a large corpus of human hand motions to
instill a strong prior for natural movement. In the second stage, a residual
module is trained to adapt these general human-like motions to specific object
instances. This refinement is critically guided by two components: our Negative
Affordance-aware Segmentation (NAA) module, which identifies functionally
inappropriate contact regions, and a privileged teacher-student distillation
process that ensures the final vision-based policy is highly successful.
Extensive experiments demonstrate that AffordDex not only achieves universal
dexterous grasping but also remains remarkably human-like in posture and
functionally appropriate in contact location. As a result, AffordDex
significantly outperforms state-of-the-art baselines across seen objects,
unseen instances, and even entirely novel categories.
WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface
  Temperature Estimation via Spatio-Temporal Fusion
Authors: name: Sofiane Bouaziz, name: Adel Hafiane, name: Raphael Canals, name: Rachid Nedjai
Published: 2025-08-08 17:49:46

Urbanization, climate change, and agricultural stress are increasing the
demand for precise and timely environmental monitoring. Land Surface
Temperature (LST) is a key variable in this context and is retrieved from
remote sensing satellites. However, these systems face a trade-off between
spatial and temporal resolution. While spatio-temporal fusion methods offer
promising solutions, few have addressed the estimation of daily LST at 10 m
resolution. In this study, we present WGAST, a Weakly-Supervised Generative
Network for Daily 10 m LST Estimation via Spatio-Temporal Fusion of Terra
MODIS, Landsat 8, and Sentinel-2. WGAST is the first end-to-end deep learning
framework designed for this task. It adopts a conditional generative
adversarial architecture, with a generator composed of four stages: feature
extraction, fusion, LST reconstruction, and noise suppression. The first stage
employs a set of encoders to extract multi-level latent representations from
the inputs, which are then fused in the second stage using cosine similarity,
normalization, and temporal attention mechanisms. The third stage decodes the
fused features into high-resolution LST, followed by a Gaussian filter to
suppress high-frequency noise. Training follows a weakly supervised strategy
based on physical averaging principles and reinforced by a PatchGAN
discriminator. Experiments demonstrate that WGAST outperforms existing methods
in both quantitative and qualitative evaluations. Compared to the
best-performing baseline, on average, WGAST reduces RMSE by 17.18% and improves
SSIM by 11.00%. Furthermore, WGAST is robust to cloud-induced LST and
effectively captures fine-scale thermal patterns, as validated against 33
ground-based sensors. The code is available at
https://github.com/Sofianebouaziz1/WGAST.git.
GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via
  General Samples Replay
Authors: name: Yunan Zhang, name: Shuoran Jiang, name: Mengchen Zhao, name: Yuefeng Li, name: Yang Fan, name: Xiangping Wu, name: Qingcai Chen
Published: 2025-08-06 17:42:22

The continual learning capability of large language models (LLMs) is crucial
for advancing artificial general intelligence. However, continual fine-tuning
LLMs across various domains often suffers from catastrophic forgetting,
characterized by: 1) significant forgetting of their general capabilities, and
2) sharp performance declines in previously learned tasks. To simultaneously
address both issues in a simple yet stable manner, we propose General Sample
Replay (GeRe), a framework that use usual pretraining texts for efficient
anti-forgetting. Beyond revisiting the most prevalent replay-based practices
under GeRe, we further leverage neural states to introduce a enhanced
activation states constrained optimization method using threshold-based margin
(TM) loss, which maintains activation state consistency during replay learning.
We are the first to validate that a small, fixed set of pre-collected general
replay samples is sufficient to resolve both concerns--retaining general
capabilities while promoting overall performance across sequential tasks.
Indeed, the former can inherently facilitate the latter. Through controlled
experiments, we systematically compare TM with different replay strategies
under the GeRe framework, including vanilla label fitting, logit imitation via
KL divergence and feature imitation via L1/L2 losses. Results demonstrate that
TM consistently improves performance and exhibits better robustness. Our work
paves the way for efficient replay of LLMs for the future. Our code and data
are available at https://github.com/Qznan/GeRe.
Adversarial Video Promotion Against Text-to-Video Retrieval
Authors: name: Qiwei Tian, name: Chenhao Lin, name: Zhengyu Zhao, name: Qian Li, name: Shuai Liu, name: Chao Shen
Published: 2025-08-09 12:20:13

Thanks to the development of cross-modal models, text-to-video retrieval
(T2VR) is advancing rapidly, but its robustness remains largely unexamined.
Existing attacks against T2VR are designed to push videos away from queries,
i.e., suppressing the ranks of videos, while the attacks that pull videos
towards selected queries, i.e., promoting the ranks of videos, remain largely
unexplored. These attacks can be more impactful as attackers may gain more
views/clicks for financial benefits and widespread (mis)information. To this
end, we pioneer the first attack against T2VR to promote videos adversarially,
dubbed the Video Promotion attack (ViPro). We further propose Modal Refinement
(MoRe) to capture the finer-grained, intricate interaction between visual and
textual modalities to enhance black-box transferability. Comprehensive
experiments cover 2 existing baselines, 3 leading T2VR models, 3 prevailing
datasets with over 10k videos, evaluated under 3 scenarios. All experiments are
conducted in a multi-target setting to reflect realistic scenarios where
attackers seek to promote the video regarding multiple queries simultaneously.
We also evaluated our attacks for defences and imperceptibility. Overall, ViPro
surpasses other baselines by over 30/10/4% for white/grey/black-box settings
on average. Our work highlights an overlooked vulnerability, provides a
qualitative analysis on the upper/lower bound of our attacks, and offers
insights into potential counterplays. Code will be publicly available at
https://github.com/michaeltian108/ViPro.
Aryabhata: An exam-focused language model for JEE Math
Authors: name: Ritvik Rastogi, name: Sachin Dharashivkar, name: Sandeep Varma
Published: 2025-08-12 06:20:07

We present Aryabhata 1.0, a compact 7B parameter math reasoning
model optimized for the Indian academic exam, the Joint Entrance Examination
(JEE). Despite rapid progress in large language models (LLMs), current models
often remain unsuitable for educational use. Aryabhata 1.0 is built by merging
strong open-weight reasoning models, followed by supervised fine-tuning (SFT)
with curriculum learning on verified chain-of-thought (CoT) traces curated
through best-of-n rejection sampling. To further boost performance, we apply
reinforcement learning with verifiable rewards (RLVR) using A2C objective with
group-relative advantage estimation alongwith novel exploration strategies such
as Adaptive Group Resizing and Temperature Scaling.
Evaluated on both in-distribution (JEE Main 2025) and out-of-distribution
(MATH, GSM8K) benchmarks, Aryabhata outperforms existing models in accuracy and
efficiency, while offering pedagogically useful step-by-step reasoning. We
release Aryabhata as a foundation model to advance exam-centric, open-source
small language models. This marks our first open release for community feedback
(https://huggingface.co/PhysicsWallahAI/Aryabhata-1.0{Aryabhata 1.0
on Hugging Face}); PW is actively training future models to further improve
learning outcomes for students.
Train Long, Think Short: Curriculum Learning for Efficient Reasoning
Authors: name: Hasan Abed Al Kader Hammoud, name: Kumail Alhamoud, name: Abed Hammoud, name: Elie Bou-Zeid, name: Marzyeh Ghassemi, name: Bernard Ghanem
Published: 2025-08-12 13:48:03

Recent work on enhancing the reasoning abilities of large language models
(LLMs) has introduced explicit length control as a means of constraining
computational cost while preserving accuracy. However, existing approaches rely
on fixed-length training budgets, which do not take advantage of the natural
progression from exploration to compression during learning. In this work, we
propose a curriculum learning strategy for length-controlled reasoning using
Group Relative Policy Optimization (GRPO). Our method starts with generous
token budgets and gradually tightens them over training, encouraging models to
first discover effective solution strategies and then distill them into more
concise reasoning traces. We augment GRPO with a reward function that balances
three signals: task correctness (via verifier feedback), length efficiency, and
formatting adherence (via structural tags). Experiments on GSM8K, MATH500,
SVAMP, College Math, and GSM+ demonstrate that curriculum-based training
consistently outperforms fixed-budget baselines at the same final budget,
achieving higher accuracy and significantly improved token efficiency. We
further ablate the impact of reward weighting and decay schedule design,
showing that progressive constraint serves as a powerful inductive bias for
training efficient reasoning models. Our code and checkpoints are released at:
https://github.com/hammoudhasan/curriculum_grpo.
OpenCUA: Open Foundations for Computer-Use Agents
Authors: name: Xinyuan Wang, name: Bowen Wang, name: Dunjie Lu, name: Junlin Yang, name: Tianbao Xie, name: Junli Wang, name: Jiaqi Deng, name: Xiaole Guo, name: Yiheng Xu, name: Chen Henry Wu, name: Zhennan Shen, name: Zhuokai Li, name: Ryan Li, name: Xiaochuan Li, name: Junda Chen, name: Boyuan Zheng, name: Peihang Li, name: Fangyu Lei, name: Ruisheng Cao, name: Yeqiao Fu, name: Dongchan Shin, name: Martin Shin, name: Jiarui Hu, name: Yuyan Wang, name: Jixuan Chen, name: Yuxiao Ye, name: Danyang Zhang, name: Dikang Du, name: Hao Hu, name: Huarong Chen, name: Zaida Zhou, name: Yipu Wang, name: Heng Wang, name: Diyi Yang, name: Victor Zhong, name: Flood Sung, name: Y. Charles, name: Zhilin Yang, name: Tao Yu
Published: 2025-08-12 17:52:32

Vision-language models have demonstrated impressive capabilities as
computer-use agents (CUAs) capable of automating diverse computer tasks. As
their commercial potential grows, critical details of the most capable CUA
systems remain closed. As these agents will increasingly mediate digital
interactions and execute consequential decisions on our behalf, the research
community needs access to open CUA frameworks to study their capabilities,
limitations, and risks. To bridge this gap, we propose OpenCUA, a comprehensive
open-source framework for scaling CUA data and foundation models. Our framework
consists of: (1) an annotation infrastructure that seamlessly captures human
computer-use demonstrations; (2) AgentNet, the first large-scale computer-use
task dataset spanning 3 operating systems and 200+ applications and websites;
(3) a scalable pipeline that transforms demonstrations into state-action pairs
with reflective long Chain-of-Thought reasoning that sustain robust performance
gains as data scales. Our end-to-end agent models demonstrate strong
performance across CUA benchmarks. In particular, OpenCUA-32B achieves an
average success rate of 34.8% on OSWorld-Verified, establishing a new
state-of-the-art (SOTA) among open-source models and surpassing OpenAI CUA
(GPT-4o). Further analysis confirms that our approach generalizes well across
domains and benefits significantly from increased test-time computation. We
release our annotation tool, datasets, code, and models to build open
foundations for further CUA research.
NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech
  Modeling with Paralinguistic Vocalizations
Authors: name: Huan Liao, name: Qinke Ni, name: Yuancheng Wang, name: Yiheng Lu, name: Haoyue Zhan, name: Pengyuan Xie, name: Qiang Zhang, name: Zhizheng Wu
Published: 2025-08-06 08:25:26

Paralinguistic vocalizations-including non-verbal sounds like laughter and
breathing, as well as lexicalized interjections such as "uhm" and "oh"-are
integral to natural spoken communication. Despite their importance in conveying
affect, intent, and interactional cues, such cues remain largely overlooked in
conventional automatic speech recognition (ASR) and text-to-speech (TTS)
systems. We present NVSpeech, an integrated and scalable pipeline that bridges
the recognition and synthesis of paralinguistic vocalizations, encompassing
dataset construction, ASR modeling, and controllable TTS. (1) We introduce a
manually annotated dataset of 48,430 human-spoken utterances with 18 word-level
paralinguistic categories. (2) We develop the paralinguistic-aware ASR model,
which treats paralinguistic cues as inline decodable tokens (e.g., "You're so
funny [Laughter]"), enabling joint lexical and non-verbal transcription. This
model is then used to automatically annotate a large corpus, the first
large-scale Chinese dataset of 174,179 utterances (573 hours) with word-level
alignment and paralingustic cues. (3) We finetune zero-shot TTS models on both
human- and auto-labeled data to enable explicit control over paralinguistic
vocalizations, allowing context-aware insertion at arbitrary token positions
for human-like speech synthesis. By unifying the recognition and generation of
paralinguistic vocalizations, NVSpeech offers the first open, large-scale,
word-level annotated pipeline for expressive speech modeling in Mandarin,
integrating recognition and synthesis in a scalable and controllable manner.
Dataset and audio demos are available at https://nvspeech170k.github.io/.
Bridging Theory and Practice in Quantum Game Theory: Optimized
  Implementation of the Battle of the Sexes with Error Mitigation on NISQ
  Hardware
Authors: name: Germán Díaz Agreda, name: Carlos Andres Duran Paredes, name: Mateo Buenaventura Samboni, name: Jhon Alejandro Andrade, name: Sebastián Andrés Cajas Ordoñez
Published: 2025-08-12 16:10:05

Implementing quantum game theory on real hardware is challenging due to
noise, decoherence, and limited qubit connectivity, yet such demonstrations are
essential to validate theoretical predictions. We present one of the first full
experimental realizations of the Battle of the Sexes game under the
Eisert-Wilkens-Lewenstein (EWL) framework on IBM Quantum's ibm sherbrooke
superconducting processor. Four quantum strategies (I, H, R(pi/4), R(pi))
were evaluated across 31 entanglement values gamma in [0, pi] using 2048
shots per configuration, enabling a direct comparison between analytical
predictions and hardware execution. To mitigate noise and variability, we
introduce a Guided Circuit Mapping (GCM) method that dynamically selects qubit
pairs and optimizes routing based on real-time topology and calibration data.
The analytical model forecasts up to 108% payoff improvement over the
classical equilibrium, and despite hardware-induced deviations, experimental
results with GCM preserve the expected payoff trends within 3.5%-12%
relative error. These findings show that quantum advantages in strategic
coordination can persist under realistic NISQ conditions, providing a pathway
toward practical applications of quantum game theory in multi-agent, economic,
and distributed decision-making systems.
VertexRegen: Mesh Generation with Continuous Level of Detail
Authors: name: Xiang Zhang, name: Yawar Siddiqui, name: Armen Avetisyan, name: Chris Xie, name: Jakob Engel, name: Henry Howard-Jenkins
Published: 2025-08-12 16:25:46

We introduce VertexRegen, a novel mesh generation framework that enables
generation at a continuous level of detail. Existing autoregressive methods
generate meshes in a partial-to-complete manner and thus intermediate steps of
generation represent incomplete structures. VertexRegen takes inspiration from
progressive meshes and reformulates the process as the reversal of edge
collapse, i.e. vertex split, learned through a generative model. Experimental
results demonstrate that VertexRegen produces meshes of comparable quality to
state-of-the-art methods while uniquely offering anytime generation with the
flexibility to halt at any step to yield valid meshes with varying levels of
detail.