Orionnnn/hub-stats huggingface dataset:
Lizard: An Efficient Linearization Framework for Large Language Models
Authors: name: Chien Van Nguyen, name: Ruiyi Zhang, name: Hanieh Deilamsalehy, name: Puneet Mathur, name: Viet Dac Lai, name: Haoliang Wang, name: Jayakumar Subramanian, name: Ryan A. Rossi, name: Trung Bui, name: Nikos Vlassis, name: Franck Dernoncourt, name: Thien Huu Nguyen
Published: 2025-07-11 21:19:18

We propose Lizard, a linearization framework that transforms pretrained
Transformer-based Large Language Models (LLMs) into flexible, subquadratic
architectures for infinite-context generation. Transformer-based LLMs face
significant memory and computational bottlenecks as context lengths increase,
due to the quadratic complexity of softmax attention and the growing key-value
(KV) cache. Lizard addresses these limitations by introducing a subquadratic
attention mechanism that closely approximates softmax attention while
preserving the output quality. Unlike previous linearization methods, which are
often limited by fixed model structures and therefore exclude gating
mechanisms, Lizard incorporates a gating module inspired by recent
state-of-the-art linear models. This enables adaptive memory control, supports
constant-memory inference, offers strong length generalization, and allows more
flexible model design. Lizard combines gated linear attention for global
context compression with sliding window attention enhanced by meta memory,
forming a hybrid mechanism that captures both long-range dependencies and
fine-grained local interactions. Moreover, we introduce a hardware-aware
algorithm that accelerates the training speed of our models. Extensive
experiments show that Lizard achieves near-lossless recovery of the teacher
model's performance across standard language modeling tasks, while
significantly outperforming previous linearization methods. On the 5-shot MMLU
benchmark, Lizard improves over prior models by 18 points and shows significant
improvements on associative recall tasks.
DrafterBench: Benchmarking Large Language Models for Tasks Automation in
  Civil Engineering
Authors: name: Yinsheng Li, name: Zhen Dong, name: Yi Shao
Published: 2025-07-15 17:56:04

Large Language Model (LLM) agents have shown great potential for solving
real-world problems and promise to be a solution for tasks automation in
industry. However, more benchmarks are needed to systematically evaluate
automation agents from an industrial perspective, for example, in Civil
Engineering. Therefore, we propose DrafterBench for the comprehensive
evaluation of LLM agents in the context of technical drawing revision, a
representation task in civil engineering. DrafterBench contains twelve types of
tasks summarized from real-world drawing files, with 46 customized
functions/tools and 1920 tasks in total. DrafterBench is an open-source
benchmark to rigorously test AI agents' proficiency in interpreting intricate
and long-context instructions, leveraging prior knowledge, and adapting to
dynamic instruction quality via implicit policy awareness. The toolkit
comprehensively assesses distinct capabilities in structured data
comprehension, function execution, instruction following, and critical
reasoning. DrafterBench offers detailed analysis of task accuracy and error
statistics, aiming to provide deeper insight into agent capabilities and
identify improvement targets for integrating LLMs in engineering applications.
Our benchmark is available at https://github.com/Eason-Li-AIS/DrafterBench,
with the test set hosted at
https://huggingface.co/datasets/Eason666/DrafterBench.
PhysX: Physical-Grounded 3D Asset Generation
Authors: name: Ziang Cao, name: Zhaoxi Chen, name: Linag Pan, name: Ziwei Liu
Published: 2025-07-16 17:59:35

3D modeling is moving from virtual to physical. Existing 3D generation
primarily emphasizes geometries and textures while neglecting physical-grounded
modeling. Consequently, despite the rapid development of 3D generative models,
the synthesized 3D assets often overlook rich and important physical
properties, hampering their real-world application in physical domains like
simulation and embodied AI. As an initial attempt to address this challenge, we
propose PhysX, an end-to-end paradigm for physical-grounded 3D asset
generation. 1) To bridge the critical gap in physics-annotated 3D datasets, we
present PhysXNet - the first physics-grounded 3D dataset systematically
annotated across five foundational dimensions: absolute scale, material,
affordance, kinematics, and function description. In particular, we devise a
scalable human-in-the-loop annotation pipeline based on vision-language models,
which enables efficient creation of physics-first assets from raw 3D assets.2)
Furthermore, we propose PhysXGen, a feed-forward framework for
physics-grounded image-to-3D asset generation, injecting physical knowledge
into the pre-trained 3D structural space. Specifically, PhysXGen employs a
dual-branch architecture to explicitly model the latent correlations between 3D
structures and physical properties, thereby producing 3D assets with plausible
physical predictions while preserving the native geometry quality. Extensive
experiments validate the superior performance and promising generalization
capability of our framework. All the code, data, and models will be released to
facilitate future research in generative physical AI.
Taming generative video models for zero-shot optical flow extraction
Authors: name: Seungwoo Kim, name: Khai Loong Aw, name: Klemen Kotar, name: Cristobal Eyzaguirre, name: Wanhee Lee, name: Yunong Liu, name: Jared Watrous, name: Stefan Stojanov, name: Juan Carlos Niebles, name: Jiajun Wu, name: Daniel L. K. Yamins
Published: 2025-07-11 23:59:38

Extracting optical flow from videos remains a core computer vision problem.
Motivated by the success of large general-purpose models, we ask whether frozen
self-supervised video models trained only for future frame prediction can be
prompted, without fine-tuning, to output flow. Prior work reading out depth or
illumination from video generators required fine-tuning, which is impractical
for flow where labels are scarce and synthetic datasets suffer from a
sim-to-real gap. Inspired by the Counterfactual World Model (CWM) paradigm,
which can obtain point-wise correspondences by injecting a small tracer
perturbation into a next-frame predictor and tracking its propagation, we
extend this idea to generative video models. We explore several popular
architectures and find that successful zero-shot flow extraction in this manner
is aided by three model properties: (1) distributional prediction of future
frames (avoiding blurry or noisy outputs); (2) factorized latents that treat
each spatio-temporal patch independently; and (3) random-access decoding that
can condition on any subset of future pixels. These properties are uniquely
present in the recent Local Random Access Sequence (LRAS) architecture.
Building on LRAS, we propose KL-tracing: a novel test-time procedure that
injects a localized perturbation into the first frame, rolls out the model one
step, and computes the Kullback-Leibler divergence between perturbed and
unperturbed predictive distributions. Without any flow-specific fine-tuning,
our method outperforms state-of-the-art models on real-world TAP-Vid DAVIS
dataset (16.6% relative improvement for endpoint error) and synthetic TAP-Vid
Kubric (4.7% relative improvement). Our results indicate that counterfactual
prompting of controllable generative video models is a scalable and effective
alternative to supervised or photometric-loss approaches for high-quality flow.
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question
  Answering
Authors: name: Costas Mavromatis, name: Soji Adeshina, name: Vassilis N. Ioannidis, name: Zhen Han, name: Qi Zhu, name: Ian Robinson, name: Bryan Thompson, name: Huzefa Rangwala, name: George Karypis
Published: 2025-07-05 18:47:14

Knowledge graph question answering (KGQA) presents significant challenges due
to the structural and semantic variations across input graphs. Existing works
rely on Large Language Model (LLM) agents for graph traversal and retrieval; an
approach that is sensitive to traversal initialization, as it is prone to
entity linking errors and may not generalize well to custom ("bring-your-own")
KGs. We introduce BYOKG-RAG, a framework that enhances KGQA by synergistically
combining LLMs with specialized graph retrieval tools. In BYOKG-RAG, LLMs
generate critical graph artifacts (question entities, candidate answers,
reasoning paths, and OpenCypher queries), and graph tools link these artifacts
to the KG and retrieve relevant graph context. The retrieved context enables
the LLM to iteratively refine its graph linking and retrieval, before final
answer generation. By retrieving context from different graph tools, BYOKG-RAG
offers a more general and robust solution for QA over custom KGs. Through
experiments on five benchmarks spanning diverse KG types, we demonstrate that
BYOKG-RAG outperforms the second-best graph retrieval method by 4.5% points
while showing better generalization to custom KGs. BYOKG-RAG framework is
open-sourced at https://github.com/awslabs/graphrag-toolkit.
Token-based Audio Inpainting via Discrete Diffusion
Authors: name: Tali Dror, name: Iftach Shoham, name: Moshe Buchris, name: Oren Gal, name: Haim Permuter, name: Gilad Katz, name: Eliya Nachmani
Published: 2025-07-11 06:25:49

Audio inpainting refers to the task of reconstructing missing segments in
corrupted audio recordings. While prior approaches-including waveform and
spectrogram-based diffusion models-have shown promising results for short gaps,
they often degrade in quality when gaps exceed 100 milliseconds (ms). In this
work, we introduce a novel inpainting method based on discrete diffusion
modeling, which operates over tokenized audio representations produced by a
pre-trained audio tokenizer. Our approach models the generative process
directly in the discrete latent space, enabling stable and semantically
coherent reconstruction of missing audio. We evaluate the method on the
MusicNet dataset using both objective and perceptual metrics across gap
durations up to 300 ms. We further evaluated our approach on the MTG dataset,
extending the gap duration to 500 ms. Experimental results demonstrate that our
method achieves competitive or superior performance compared to existing
baselines, particularly for longer gaps, offering a robust solution for
restoring degraded musical recordings. Audio examples of our proposed method
can be found at https://iftach21.github.io/
UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New
  Benchmarks
Authors: name: Peiran Wu, name: Yunze Liu, name: Zhengdong Zhu, name: Enmin Zhou, name: Shawn Shen
Published: 2025-07-15 14:08:29

Real-world user-generated videos, especially on platforms like TikTok, often
feature rich and intertwined audio visual content. However, existing video
captioning benchmarks and models remain predominantly visual centric,
overlooking the crucial role of audio in conveying scene dynamics, speaker
intent, and narrative context. This lack of omni datasets and lightweight,
capable models hampers progress in fine grained, multimodal video
understanding. To address these challenges, we introduce UGC-VideoCap, a new
benchmark and model framework specifically designed for detailed omnimodal
captioning of short form user-generated videos. Unlike prior datasets,
UGC-VideoCap emphasizes balanced integration of audio and visual modalities,
featuring 1000 TikTok videos annotated through a structured three stage
human-in-the-loop pipeline covering audio only, visual only, and joint audio
visual semantics. The benchmark also includes 4000 carefully crafted QA pairs
probing both unimodal and cross modal understanding. Alongside the dataset, we
propose UGC-VideoCaptioner(3B), a 3B parameter captioning model distilled from
Gemini 2.5 Flash. Using a novel two-stage training strategy supervised fine
tuning followed by Group Relative Policy Optimization (GRPO), our approach
enables efficient adaptation from limited data while maintaining competitive
performance. Together, our benchmark and model offer a high-quality foundation
and a data-efficient solution for advancing omnimodal video captioning in
unconstrained real-world UGC settings.
Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification
  System with Trust-Aware Orchestration and RAG-Based Reasoning
Authors: name: Konstantinos I. Roumeliotis, name: Ranjan Sapkota, name: Manoj Karkee, name: Nikolaos D. Tselikas
Published: 2025-07-09 16:39:29

Modern Artificial Intelligence (AI) increasingly relies on multi-agent
architectures that blend visual and language understanding. Yet, a pressing
challenge remains: How can we trust these agents especially in zero-shot
settings with no fine-tuning? We introduce a novel modular Agentic AI visual
classification framework that integrates generalist multimodal agents with a
non-visual reasoning orchestrator and a Retrieval-Augmented Generation (RAG)
module. Applied to apple leaf disease diagnosis, we benchmark three
configurations: (I) zero-shot with confidence-based orchestration, (II)
fine-tuned agents with improved performance, and (III) trust-calibrated
orchestration enhanced by CLIP-based image retrieval and re-evaluation loops.
Using confidence calibration metrics (ECE, OCR, CCC), the orchestrator
modulates trust across agents. Our results demonstrate a 77.94\% accuracy
improvement in the zero-shot setting using trust-aware orchestration and RAG,
achieving 85.63\% overall. GPT-4o showed better calibration, while Qwen-2.5-VL
displayed overconfidence. Furthermore, image-RAG grounded predictions with
visually similar cases, enabling correction of agent overconfidence via
iterative re-evaluation. The proposed system separates perception (vision
agents) from meta-reasoning (orchestrator), enabling scalable and interpretable
multi-agent AI. This blueprint is extensible to diagnostics, biology, and other
trust-critical domains. All models, prompts, results, and system components
including the complete software source code are openly released to support
reproducibility, transparency, and community benchmarking at Github:
https://github.com/Applied-AI-Research-Lab/Orchestrator-Agent-Trust
AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Authors: name: Florian Grötschla, name: Luis Müller, name: Jan Tönshoff, name: Mikhail Galkin, name: Bryan Perozzi
Published: 2025-07-11 14:13:22

Large-language models (LLMs) have demonstrated powerful problem-solving
capabilities, in particular when organized in multi-agent systems. However, the
advent of such systems also raises several questions on the ability of a
complex network of agents to effectively self-organize and collaborate. While
measuring performance on standard reasoning benchmarks indicates how well
multi-agent systems can solve reasoning tasks, it is unclear whether these
systems are able to leverage their topology effectively. Here, we propose
AgentsNet, a new benchmark for multi-agent reasoning. By drawing inspiration
from classical problems in distributed systems and graph theory, AgentsNet
measures the ability of multi-agent systems to collaboratively form strategies
for problem-solving, self-organization, and effective communication given a
network topology. We evaluate a variety of baseline methods on AgentsNet
including homogeneous networks of agents which first have to agree on basic
protocols for organization and communication. We find that some frontier LLMs
are already demonstrating strong performance for small networks but begin to
fall off once the size of the network scales. While existing multi-agent
benchmarks cover at most 2-5 agents, AgentsNet is practically unlimited in size
and can scale with new generations of LLMs. As such, we also probe frontier
models in a setup with up to 100 agents.
EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and
  Reasoning Modes
Authors: name: LG AI Research, name: Kyunghoon Bae, name: Eunbi Choi, name: Kibong Choi, name: Stanley Jungkyu Choi, name: Yemuk Choi, name: Kyubeen Han, name: Seokhee Hong, name: Junwon Hwang, name: Taewan Hwang, name: Joonwon Jang, name: Hyojin Jeon, name: Kijeong Jeon, name: Gerrard Jeongwon Jo, name: Hyunjik Jo, name: Jiyeon Jung, name: Euisoon Kim, name: Hyosang Kim, name: Jihoon Kim, name: Joonkee Kim, name: Seonghwan Kim, name: Soyeon Kim, name: Sunkyoung Kim, name: Yireun Kim, name: Yongil Kim, name: Youchul Kim, name: Edward Hwayoung Lee, name: Gwangho Lee, name: Haeju Lee, name: Honglak Lee, name: Jinsik Lee, name: Kyungmin Lee, name: Sangha Park, name: Young Min Paik, name: Yongmin Park, name: Youngyong Park, name: Sanghyun Seo, name: Sihoon Yang, name: Heuiyeen Yeen, name: Sihyuk Yi, name: Hyeongu Yun
Published: 2025-07-15 15:24:51

This technical report introduces EXAONE 4.0, which integrates a Non-reasoning
mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5
and the advanced reasoning abilities of EXAONE Deep. To pave the way for the
agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool
use, and its multilingual capabilities are extended to support Spanish in
addition to English and Korean. The EXAONE 4.0 model series consists of two
sizes: a mid-size 32B model optimized for high performance, and a small-size
1.2B model designed for on-device applications. The EXAONE 4.0 demonstrates
superior performance compared to open-weight models in its class and remains
competitive even against frontier-class models. The models are publicly
available for research purposes and can be easily downloaded via
https://huggingface.co/LGAI-EXAONE.
Planted in Pretraining, Swayed by Finetuning: A Case Study on the
  Origins of Cognitive Biases in LLMs
Authors: name: Itay Itzhak, name: Yonatan Belinkov, name: Gabriel Stanovsky
Published: 2025-07-09 18:01:14

Large language models (LLMs) exhibit cognitive biases -- systematic
tendencies of irrational decision-making, similar to those seen in humans.
Prior work has found that these biases vary across models and can be amplified
by instruction tuning. However, it remains unclear if these differences in
biases stem from pretraining, finetuning, or even random noise due to training
stochasticity. We propose a two-step causal experimental approach to
disentangle these factors. First, we finetune models multiple times using
different random seeds to study how training randomness affects over 30
cognitive biases. Second, we introduce cross-tuning -- swapping
instruction datasets between models to isolate bias sources. This swap uses
datasets that led to different bias patterns, directly testing whether biases
are dataset-dependent. Our findings reveal that while training randomness
introduces some variability, biases are mainly shaped by pretraining: models
with the same pretrained backbone exhibit more similar bias patterns than those
sharing only finetuning data. These insights suggest that understanding biases
in finetuned models requires considering their pretraining origins beyond
finetuning effects. This perspective can guide future efforts to develop
principled strategies for evaluating and mitigating bias in LLMs.
Scaling Laws for Optimal Data Mixtures
Authors: name: Mustafa Shukor, name: Louis Bethune, name: Dan Busbridge, name: David Grangier, name: Enrico Fini, name: Alaaeldin El-Nouby, name: Pierre Ablin
Published: 2025-07-12 21:16:08

Large foundation models are typically trained on data from multiple domains,
with the data mixture--the proportion of each domain used--playing a critical
role in model performance. The standard approach to selecting this mixture
relies on trial and error, which becomes impractical for large-scale
pretraining. We propose a systematic method to determine the optimal data
mixture for any target domain using scaling laws. Our approach accurately
predicts the loss of a model of size N trained with D tokens and a specific
domain weight vector h. We validate the universality of these scaling laws by
demonstrating their predictive power in three distinct and large-scale
settings: large language model (LLM), native multimodal model (NMM), and large
vision models (LVM) pretraining. We further show that these scaling laws can
extrapolate to new data mixtures and across scales: their parameters can be
accurately estimated using a few small-scale training runs, and used to
estimate the performance at larger scales and unseen domain weights. The
scaling laws allow to derive the optimal domain weights for any target domain
under a given training budget (N,D), providing a principled alternative to
costly trial-and-error methods.
Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation
  from Diffusion Models
Authors: name: Tiezheng Zhang, name: Yitong Li, name: Yu-cheng Chou, name: Jieneng Chen, name: Alan Yuille, name: Chen Wei, name: Junfei Xiao
Published: 2025-07-09 17:59:04

Building state-of-the-art Vision-Language Models (VLMs) with strong
captioning capabilities typically necessitates training on billions of
high-quality image-text pairs, requiring millions of GPU hours. This paper
introduces the Vision-Language-Vision (VLV) auto-encoder framework, which
strategically leverages key pretrained components: a vision encoder, the
decoder of a Text-to-Image (T2I) diffusion model, and subsequently, a Large
Language Model (LLM). Specifically, we establish an information bottleneck by
regularizing the language representation space, achieved through freezing the
pretrained T2I diffusion decoder. Our VLV pipeline effectively distills
knowledge from the text-conditioned diffusion model using continuous
embeddings, demonstrating comprehensive semantic understanding via high-quality
reconstructions. Furthermore, by fine-tuning a pretrained LLM to decode the
intermediate language representations into detailed descriptions, we construct
a state-of-the-art (SoTA) captioner comparable to leading models like GPT-4o
and Gemini 2.0 Flash. Our method demonstrates exceptional cost-efficiency and
significantly reduces data requirements; by primarily utilizing single-modal
images for training and maximizing the utility of existing pretrained models
(image encoder, T2I diffusion model, and LLM), it circumvents the need for
massive paired image-text datasets, keeping the total training expenditure
under $1,000 USD.
OpenCodeReasoning-II: A Simple Test Time Scaling Approach via
  Self-Critique
Authors: name: Wasi Uddin Ahmad, name: Somshubra Majumdar, name: Aleksander Ficek, name: Sean Narenthiran, name: Mehrzad Samadi, name: Jocelyn Huang, name: Siddhartha Jain, name: Vahid Noroozi, name: Boris Ginsburg
Published: 2025-07-11 23:35:54

Recent advancements in reasoning-based Large Language Models (LLMs),
particularly their potential through test-time scaling, have created
significant opportunities for distillation in code generation and critique.
However, progress in both areas fundamentally depends on large-scale,
high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a
dataset consists of 2.5M question-solution-critique triples (approx. 35K unique
programming questions), making it nearly twice the size of the previous largest
publicly available code reasoning dataset. In this work, we employ a two-stage
supervised fine-tuning strategy. The first stage focuses on fine-tuning for
code generation, while the second stage involves the joint training of models
for both code generation and critique. Our resulting finetuned Qwen2.5-Instruct
models achieve performance in code generation that either exceeds or equals the
best prior open-weight distilled models. Notably, the integration of our code
generation and critique models leads to significant improvements in competitive
coding performance. Furthermore, we present an extension of the LiveCodeBench
benchmark to specifically support the C++ programming language, thereby
facilitating more comprehensive LLM evaluation using this benchmark.