Orionnnn/hub-stats huggingface dataset:
RectifiedHR: Enable Efficient High-Resolution Image Generation via
  Energy Rectification
Authors: name: Zhen Yang, name: Guibao Shen, name: Liang Hou, name: Mushui Liu, name: Luozhou Wang, name: Xin Tao, name: Pengfei Wan, name: Di Zhang, name: Ying-Cong Chen
Published: 2025-03-04 12:03:26

Diffusion models have achieved remarkable advances in various image
generation tasks. However, their performance notably declines when generating
images at resolutions higher than those used during the training period.
Despite the existence of numerous methods for producing high-resolution images,
they either suffer from inefficiency or are hindered by complex operations. In
this paper, we propose RectifiedHR, an efficient and straightforward solution
for training-free high-resolution image generation. Specifically, we introduce
the noise refresh strategy, which theoretically only requires a few lines of
code to unlock the model's high-resolution generation ability and improve
efficiency. Additionally, we first observe the phenomenon of energy decay that
may cause image blurriness during the high-resolution image generation process.
To address this issue, we propose an Energy Rectification strategy, where
modifying the hyperparameters of the classifier-free guidance effectively
improves the generation performance. Our method is entirely training-free and
boasts a simple implementation logic. Through extensive comparisons with
numerous baseline methods, our RectifiedHR demonstrates superior effectiveness
and efficiency.
LADDER: Self-Improving LLMs Through Recursive Problem Decomposition
Authors: name: Toby Simonds, name: Akira Yoshiyama
Published: 2025-03-02 05:16:43

We introduce LADDER (Learning through Autonomous Difficulty-Driven Example
Recursion), a framework which enables Large Language Models to autonomously
improve their problem-solving capabilities through self-guided learning by
recursively generating and solving progressively simpler variants of complex
problems. Unlike prior approaches that require curated datasets or human
feedback, LADDER leverages a model's own capabilities to generate easier
question variants. We demonstrate LADDER's effectiveness in the subject of
mathematical integration, improving Llama 3.2 3B's accuracy from 1% to 82% on
undergraduate-level problems and enabling Qwen2.5 7B Deepseek-R1 Distilled to
achieve 73% on the MIT Integration Bee qualifying examination. We also
introduce TTRL (Test-Time Reinforcement Learning), where we perform
reinforcement learning on variants of test problems at inference time. TTRL
enables Qwen2.5 7B Deepseek-R1 Distilled to achieve a state-of-the-art score of
90% on the MIT Integration Bee qualifying examination, surpassing OpenAI o1's
performance. These results show how self-directed strategic learning can
achieve significant capability improvements without relying on architectural
scaling or human supervision.
Iterative Value Function Optimization for Guided Decoding
Authors: name: Zhenhua Liu, name: Lijun Li, name: Ruizhe Chen, name: Yuxian Jiang, name: Tong Zhu, name: Wenliang Chen, name: Jing Shao
Published: 2025-03-04 07:49:10

While Reinforcement Learning from Human Feedback (RLHF) has become the
predominant method for controlling language model outputs, it suffers from high
computational costs and training instability. Guided decoding, especially
value-guided methods, offers a cost-effective alternative by controlling
outputs without re-training models. However, the accuracy of the value function
is crucial for value-guided decoding, as inaccuracies can lead to suboptimal
decision-making and degraded performance. Existing methods struggle with
accurately estimating the optimal value function, leading to less effective
control. We propose Iterative Value Function Optimization, a novel framework
that addresses these limitations through two key components: Monte Carlo Value
Estimation, which reduces estimation variance by exploring diverse
trajectories, and Iterative On-Policy Optimization, which progressively
improves value estimation through collecting trajectories from value-guided
policies. Extensive experiments on text summarization, multi-turn dialogue, and
instruction following demonstrate the effectiveness of value-guided decoding
approaches in aligning language models. These approaches not only achieve
alignment but also significantly reduce computational costs by leveraging
principled value function optimization for efficient and effective control.
AppAgentX: Evolving GUI Agents as Proficient Smartphone Users
Authors: name: Wenjia Jiang, name: Yangyang Zhuang, name: Chenxi Song, name: Xu Yang, name: Chi Zhang
Published: 2025-03-04 04:34:09

Recent advancements in Large Language Models (LLMs) have led to the
development of intelligent LLM-based agents capable of interacting with
graphical user interfaces (GUIs). These agents demonstrate strong reasoning and
adaptability, enabling them to perform complex tasks that traditionally
required predefined rules. However, the reliance on step-by-step reasoning in
LLM-based agents often results in inefficiencies, particularly for routine
tasks. In contrast, traditional rule-based systems excel in efficiency but lack
the intelligence and flexibility to adapt to novel scenarios. To address this
challenge, we propose a novel evolutionary framework for GUI agents that
enhances operational efficiency while retaining intelligence and flexibility.
Our approach incorporates a memory mechanism that records the agent's task
execution history. By analyzing this history, the agent identifies repetitive
action sequences and evolves high-level actions that act as shortcuts,
replacing these low-level operations and improving efficiency. This allows the
agent to focus on tasks requiring more complex reasoning, while simplifying
routine actions. Experimental results on multiple benchmark tasks demonstrate
that our approach significantly outperforms existing methods in both efficiency
and accuracy. The code will be open-sourced to support further research.
FR-Spec: Accelerating Large-Vocabulary Language Models via
  Frequency-Ranked Speculative Sampling
Authors: name: Weilin Zhao, name: Tengyu Pan, name: Xu Han, name: Yudi Zhang, name: Ao Sun, name: Yuxiang Huang, name: Kaihuo Zhang, name: Weilun Zhao, name: Yuxuan Li, name: Jianyong Wang, name: Zhiyuan Liu, name: Maosong Sun
Published: 2025-02-20 18:58:10

Speculative sampling has emerged as an important technique for accelerating
the auto-regressive generation process of large language models (LLMs) by
utilizing a draft-then-verify mechanism to produce multiple tokens per forward
pass. While state-of-the-art speculative sampling methods use only a single
layer and a language modeling (LM) head as the draft model to achieve
impressive layer compression, their efficiency gains are substantially reduced
for large-vocabulary LLMs, such as Llama-3-8B with a vocabulary of 128k tokens.
To address this, we present FR-Spec, a frequency-ranked speculative sampling
framework that optimizes draft candidate selection through vocabulary space
compression. By constraining the draft search to a frequency-prioritized token
subset, our method reduces LM Head computation overhead by 75% while ensuring
the equivalence of the final output distribution. Experiments across multiple
datasets demonstrate an average of 1.12times speedup over the
state-of-the-art speculative sampling method EAGLE-2.
SemViQA: A Semantic Question Answering System for Vietnamese Information
  Fact-Checking
Authors: name: Nam V. Nguyen, name: Dien X. Tran, name: Thanh T. Tran, name: Anh T. Hoang, name: Tai V. Duong, name: Di T. Le, name: Phuc-Lu Le
Published: 2025-03-02 16:22:46

The rise of misinformation, exacerbated by Large Language Models (LLMs) like
GPT and Gemini, demands robust fact-checking solutions, especially for
low-resource languages like Vietnamese. Existing methods struggle with semantic
ambiguity, homonyms, and complex linguistic structures, often trading accuracy
for efficiency. We introduce SemViQA, a novel Vietnamese fact-checking
framework integrating Semantic-based Evidence Retrieval (SER) and Two-step
Verdict Classification (TVC). Our approach balances precision and speed,
achieving state-of-the-art results with 78.97\% strict accuracy on ISE-DSC01
and 80.82\% on ViWikiFC, securing 1st place in the UIT Data Science Challenge.
Additionally, SemViQA Faster improves inference speed 7x while maintaining
competitive accuracy. SemViQA sets a new benchmark for Vietnamese fact
verification, advancing the fight against misinformation. The source code is
available at: https://github.com/DAVID-NGUYEN-S16/SemViQA.
UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended
  Language Interface
Authors: name: Hao Tang, name: Chenwei Xie, name: Haiyang Wang, name: Xiaoyi Bao, name: Tingyu Weng, name: Pandeng Li, name: Yun Zheng, name: Liwei Wang
Published: 2025-03-03 09:27:24

Generalist models have achieved remarkable success in both language and
vision-language tasks, showcasing the potential of unified modeling. However,
effectively integrating fine-grained perception tasks like detection and
segmentation into these models remains a significant challenge. This is
primarily because these tasks often rely heavily on task-specific designs and
architectures that can complicate the modeling process. To address this
challenge, we present \ours, a framework that Unifies
Fine-grained visual perception tasks through an Open-ended
language interface. By transforming all perception targets into the language
space, \ours unifies object-level detection, pixel-level segmentation, and
image-level vision-language tasks into a single model. Additionally, we
introduce a novel embedding retrieval approach that relies solely on the
language interface to support segmentation tasks. Our framework bridges the gap
between fine-grained perception and vision-language tasks, significantly
simplifying architectural design and training strategies while achieving
comparable or superior performance to methods with intricate task-specific
designs. After multi-task training on five standard visual perception datasets,
\ours outperforms the previous state-of-the-art generalist models by 12.3 mAP
on COCO instance segmentation and 3.3 mIoU on ADE20K semantic segmentation.
Furthermore, our method seamlessly integrates with existing MLLMs, effectively
combining fine-grained perception capabilities with their advanced language
abilities, thereby enabling more challenging tasks such as reasoning
segmentation. Code and models will be publicly available.
MPO: Boosting LLM Agents with Meta Plan Optimization
Authors: name: Weimin Xiong, name: Yifan Song, name: Qingxiu Dong, name: Bingchan Zhao, name: Feifan Song, name: Xun Wang, name: Sujian Li
Published: 2025-03-04 14:54:45

Recent advancements in large language models (LLMs) have enabled LLM-based
agents to successfully tackle interactive planning tasks. However, despite
their successes, existing approaches often suffer from planning hallucinations
and require retraining for each new agent. To address these challenges, we
propose the Meta Plan Optimization (MPO) framework, which enhances agent
planning capabilities by directly incorporating explicit guidance. Unlike
previous methods that rely on complex knowledge, which either require
significant human effort or lack quality assurance, MPO leverages high-level
general guidance through meta plans to assist agent planning and enables
continuous optimization of the meta plans based on feedback from the agent's
task execution. Our experiments conducted on two representative tasks
demonstrate that MPO significantly outperforms existing baselines. Moreover,
our analysis indicates that MPO provides a plug-and-play solution that enhances
both task completion efficiency and generalization capabilities in previous
unseen scenarios.
Wikipedia in the Era of LLMs: Evolution and Risks
Authors: name: Siming Huang, name: Yuliang Xu, name: Mingmeng Geng, name: Yao Wan, name: Dongping Chen
Published: 2025-03-04 18:58:13

In this paper, we present a thorough analysis of the impact of Large Language
Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through
existing data and using simulations to explore potential risks. We begin by
analyzing page views and article content to study Wikipedia's recent changes
and assess the impact of LLMs. Subsequently, we evaluate how LLMs affect
various Natural Language Processing (NLP) tasks related to Wikipedia, including
machine translation and retrieval-augmented generation (RAG). Our findings and
simulation results reveal that Wikipedia articles have been influenced by LLMs,
with an impact of approximately 1%-2% in certain categories. If the machine
translation benchmark based on Wikipedia is influenced by LLMs, the scores of
the models may become inflated, and the comparative results among models might
shift as well. Moreover, the effectiveness of RAG might decrease if the
knowledge base becomes polluted by LLM-generated content. While LLMs have not
yet fully changed Wikipedia's language and knowledge structures, we believe
that our empirical findings signal the need for careful consideration of
potential future risks.
Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs
Authors: name: Yuzhe Gu, name: Wenwei Zhang, name: Chengqi Lyu, name: Dahua Lin, name: Kai Chen
Published: 2025-03-04 18:20:24

Large language models (LLMs) exhibit hallucinations (i.e., unfaithful or
nonsensical information) when serving as AI assistants in various domains.
Since hallucinations always come with truthful content in the LLM responses,
previous factuality alignment methods that conduct response-level preference
learning inevitably introduced noises during training. Therefore, this paper
proposes a fine-grained factuality alignment method based on Direct Preference
Optimization (DPO), called Mask-DPO. Incorporating sentence-level factuality as
mask signals, Mask-DPO only learns from factually correct sentences in the
preferred samples and prevents the penalty on factual contents in the not
preferred samples, which resolves the ambiguity in the preference learning.
Extensive experimental results demonstrate that Mask-DPO can significantly
improve the factuality of LLMs responses to questions from both in-domain and
out-of-domain datasets, although these questions and their corresponding topics
are unseen during training. Only trained on the ANAH train set, the score of
Llama3.1-8B-Instruct on the ANAH test set is improved from 49.19% to 77.53%,
even surpassing the score of Llama3.1-70B-Instruct (53.44%), while its
FactScore on the out-of-domain Biography dataset is also improved from 30.29%
to 39.39%. We further study the generalization property of Mask-DPO using
different training sample scaling strategies and find that scaling the number
of topics in the dataset is more effective than the number of questions. We
provide a hypothesis of what factual alignment is doing with LLMs, on the
implication of this phenomenon, and conduct proof-of-concept experiments to
verify it. We hope the method and the findings pave the way for future research
on scaling factuality alignment.
ATLaS: Agent Tuning via Learning Critical Steps
Authors: name: Zhixun Chen, name: Ming Li, name: Yuxuan Huang, name: Yali Du, name: Meng Fang, name: Tianyi Zhou
Published: 2025-03-04 02:14:55

Large Language Model (LLM) agents have demonstrated remarkable generalization
capabilities across multi-domain tasks. Existing agent tuning approaches
typically employ supervised finetuning on entire expert trajectories. However,
behavior-cloning of full trajectories can introduce expert bias and weaken
generalization to states not covered by the expert data. Additionally, critical
steps, such as planning, complex reasoning for intermediate subtasks, and
strategic decision-making, are essential to success in agent tasks, so learning
these steps is the key to improving LLM agents. For more effective and
efficient agent tuning, we propose ATLaS that identifies the critical steps in
expert trajectories and finetunes LLMs solely on these steps with reduced
costs. By steering the training's focus to a few critical steps, our method
mitigates the risk of overfitting entire trajectories and promotes
generalization across different environments and tasks. In extensive
experiments, an LLM finetuned on only 30% critical steps selected by ATLaS
outperforms the LLM finetuned on all steps and recent open-source LLM agents.
ATLaS maintains and improves base LLM skills as generalist agents interacting
with diverse environments.
SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and
  Baseline Models
Authors: name: Dmitry Nechaev, name: Alexey Pchelnikov, name: Ekaterina Ivanova
Published: 2025-03-04 18:57:12

Advancing AI in computational pathology requires large, high-quality, and
diverse datasets, yet existing public datasets are often limited in organ
diversity, class coverage, or annotation quality. To bridge this gap, we
introduce SPIDER (Supervised Pathology Image-DEscription Repository), the
largest publicly available patch-level dataset covering multiple organ types,
including Skin, Colorectal, and Thorax, with comprehensive class coverage for
each organ. SPIDER provides high-quality annotations verified by expert
pathologists and includes surrounding context patches, which enhance
classification performance by providing spatial context.
  Alongside the dataset, we present baseline models trained on SPIDER using the
Hibou-L foundation model as a feature extractor combined with an
attention-based classification head. The models achieve state-of-the-art
performance across multiple tissue categories and serve as strong benchmarks
for future digital pathology research. Beyond patch classification, the model
enables rapid identification of significant areas, quantitative tissue metrics,
and establishes a foundation for multimodal approaches.
  Both the dataset and trained models are publicly available to advance
research, reproducibility, and AI-driven pathology development. Access them at:
https://github.com/HistAI/SPIDER
Language Models can Self-Improve at State-Value Estimation for Better
  Search
Authors: name: Ethan Mendes, name: Alan Ritter
Published: 2025-03-04 18:58:11

Collecting ground truth task completion rewards or human demonstrations for
multi-step reasoning tasks is often cost-prohibitive and time-consuming,
especially in interactive domains like web tasks. To address this bottleneck,
we present self-taught lookahead, a self-supervised method that leverages
state-transition dynamics to train a value model capable of effectively guiding
language model-controlled search. We find that moderately sized (8 billion
parameters) open-weight value models improved with self-taught lookahead can
match the performance of using a frontier LLM such as gpt-4o as the value
model. Furthermore, we find that self-taught lookahead improves performance by
20% while reducing costs 37x compared to previous LLM-based tree search,
without relying on ground truth rewards.
MultiAgentBench: Evaluating the Collaboration and Competition of LLM
  agents
Authors: name: Kunlun Zhu, name: Hongyi Du, name: Zhaochen Hong, name: Xiaocheng Yang, name: Shuyi Guo, name: Zhe Wang, name: Zhenhailong Wang, name: Cheng Qian, name: Xiangru Tang, name: Heng Ji, name: Jiaxuan You
Published: 2025-03-03 05:18:50

Large Language Models (LLMs) have shown remarkable capabilities as autonomous
agents, yet existing benchmarks either focus on single-agent tasks or are
confined to narrow domains, failing to capture the dynamics of multi-agent
coordination and competition. In this paper, we introduce MultiAgentBench, a
comprehensive benchmark designed to evaluate LLM-based multi-agent systems
across diverse, interactive scenarios. Our framework measures not only task
completion but also the quality of collaboration and competition using novel,
milestone-based key performance indicators. Moreover, we evaluate various
coordination protocols (including star, chain, tree, and graph topologies) and
innovative strategies such as group discussion and cognitive planning. Notably,
gpt-4o-mini reaches the average highest task score, graph structure performs
the best among coordination protocols in the research scenario, and cognitive
planning improves milestone achievement rates by 3%. Code and datasets are
public available at https://github.com/MultiagentBench/MARBLE.
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory
  Optimization
Authors: name: Xinyi Wan, name: Penghui Qi, name: Guangxing Huang, name: Jialin Li, name: Min Lin
Published: 2025-03-03 09:11:06

Pipeline parallelism (PP) is widely used for training large language models
(LLMs), yet its scalability is often constrained by high activation memory
consumption as the number of in-flight microbatches grows with the degree of
PP. In this paper, we focus on addressing this challenge by leveraging the
under-explored memory offload strategy in PP. With empirical study, we discover
that in the majority of standard configurations, at least half, and potentially
all, of the activations can be offloaded with negligible overhead. In the cases
where full overload is not possible, we introduce a novel selective offload
strategy that decreases peak activation memory in a better-than-linear manner.
Furthermore, we integrate memory offload with other techniques to jointly
consider overall throughput and memory limitation. Our experiments proves that
the per-device activation memory effectively reduces with the total number of
stages, making PP a stronger alternative than TP, offering up to a 19\%
acceleration with even lower memory consumption. The implementation is
open-sourced at
https://github.com/sail-sg/zero-bubble-pipeline-parallelism{this url}.
RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
Authors: name: Yi-Lin Sung, name: Prateek Yadav, name: Jialu Li, name: Jaehong Yoon, name: Mohit Bansal
Published: 2025-03-03 18:46:33

Layer-wise quantization is a key technique for efficiently compressing large
models without expensive retraining. Previous methods typically quantize the
weights of each layer by "uniformly" optimizing the layer reconstruction loss
across all output tokens. However, in this paper, we demonstrate that
better-quantized models can be obtained by prioritizing learning from important
tokens (e.g. which have large attention scores). Building on this finding, we
propose RSQ (Rotate, Scale, then Quantize), which (1) applies rotations
(orthogonal transformation) to the model to mitigate outliers (those with
exceptionally large magnitude), (2) scales the token feature based on its
importance, and (3) quantizes the model using the GPTQ framework with the
second-order statistics computed by scaled tokens. To compute token importance,
we explore both heuristic and dynamic strategies. Based on a thorough analysis
of all approaches, we adopt attention concentration, which uses attention
scores of each token as its importance, as the best approach. We demonstrate
that RSQ consistently outperforms baseline methods across multiple downstream
tasks and three model families: LLaMA3, Mistral, and Qwen2.5. Additionally,
models quantized with RSQ achieve superior performance on long-context tasks,
further highlighting its effectiveness. Lastly, RSQ demonstrates
generalizability across various setups, including different model sizes,
calibration datasets, bit precisions, and quantization methods.