Orionnnn/hub-stats huggingface dataset:
Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV
  Cache Compression
Authors: name: Kunjun Li, name: Zigeng Chen, name: Cheng-Yen Yang, name: Jenq-Neng Hwang
Published: 2025-05-26 07:11:42

Visual Autoregressive (VAR) modeling has garnered significant attention for
its innovative next-scale prediction approach, which yields substantial
improvements in efficiency, scalability, and zero-shot generalization.
Nevertheless, the coarse-to-fine methodology inherent in VAR results in
exponential growth of the KV cache during inference, causing considerable
memory consumption and computational redundancy. To address these bottlenecks,
we introduce ScaleKV, a novel KV cache compression framework tailored for VAR
architectures. ScaleKV leverages two critical observations: varying cache
demands across transformer layers and distinct attention patterns at different
scales. Based on these insights, ScaleKV categorizes transformer layers into
two functional groups: drafters and refiners. Drafters exhibit dispersed
attention across multiple scales, thereby requiring greater cache capacity.
Conversely, refiners focus attention on the current token map to process local
details, consequently necessitating substantially reduced cache capacity.
ScaleKV optimizes the multi-scale inference pipeline by identifying
scale-specific drafters and refiners, facilitating differentiated cache
management tailored to each scale. Evaluation on the state-of-the-art
text-to-image VAR model family, Infinity, demonstrates that our approach
effectively reduces the required KV cache memory to 10% while preserving
pixel-level fidelity.
Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
Authors: name: Junnan Liu, name: Hongwei Liu, name: Linchen Xiao, name: Shudong Liu, name: Taolin Zhang, name: Zihan Ma, name: Songyang Zhang, name: Kai Chen
Published: 2025-05-26 10:52:17

We propose a novel framework for comprehending the reasoning capabilities of
large language models (LLMs) through the perspective of meta-learning. By
conceptualizing reasoning trajectories as pseudo-gradient descent updates to
the LLM's parameters, we identify parallels between LLM reasoning and various
meta-learning paradigms. We formalize the training process for reasoning tasks
as a meta-learning setup, with each question treated as an individual task, and
reasoning trajectories serving as the inner loop optimization for adapting
model parameters. Once trained on a diverse set of questions, the LLM develops
fundamental reasoning capabilities that can generalize to previously unseen
questions. Extensive empirical evaluations substantiate the strong connection
between LLM reasoning and meta-learning, exploring several issues of
significant interest from a meta-learning standpoint. Our work not only
enhances the understanding of LLM reasoning but also provides practical
insights for improving these models through established meta-learning
techniques.
DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning
  System for Multi-Turn Clinical Dialogue
Authors: name: Yichun Feng, name: Jiawei Wang, name: Lu Zhou, name: Yixue Li
Published: 2025-05-26 07:48:14

Large language models (LLMs) have demonstrated excellent capabilities in the
field of biomedical question answering, but their application in real-world
clinical consultations still faces core challenges. Existing systems rely on a
one-way information transmission mode where patients must fully describe their
symptoms in a single round, leading to nonspecific diagnostic recommendations
when complaints are vague. Traditional multi-turn dialogue methods based on
supervised learning are constrained by static data-driven paradigms, lacking
generalizability and struggling to intelligently extract key clinical
information. To address these limitations, we propose DoctorAgent-RL, a
reinforcement learning (RL)-based multi-agent collaborative framework that
models medical consultations as a dynamic decision-making process under
uncertainty. The doctor agent continuously optimizes its questioning strategy
within the RL framework through multi-turn interactions with the patient agent,
dynamically adjusting its information-gathering path based on comprehensive
rewards from the Consultation Evaluator. This RL fine-tuning mechanism enables
LLMs to autonomously develop interaction strategies aligned with clinical
reasoning logic, rather than superficially imitating patterns in existing
dialogue data. Notably, we constructed MTMedDialog, the first English
multi-turn medical consultation dataset capable of simulating patient
interactions. Experiments demonstrate that DoctorAgent-RL outperforms existing
models in both multi-turn reasoning capability and final diagnostic
performance, demonstrating practical value in assisting clinical consultations.
https://github.com/JarvisUSTC/DoctorAgent-RL
B-score: Detecting biases in large language models using response
  history
Authors: name: An Vo, name: Mohammad Reza Taesiri, name: Daeyoung Kim, name: Anh Totti Nguyen
Published: 2025-05-24 06:23:52

Large language models (LLMs) often exhibit strong biases, e.g, against women
or in favor of the number 7. We investigate whether LLMs would be able to
output less biased answers when allowed to observe their prior answers to the
same question in a multi-turn conversation. To understand which types of
questions invite more biased answers, we test LLMs on our proposed set of
questions that span 9 topics and belong to three types: (1) Subjective; (2)
Random; and (3) Objective. Interestingly, LLMs are able to "de-bias" themselves
in a multi-turn conversation in response to questions that seek an Random,
unbiased answer. Furthermore, we propose B-score, a novel metric that is
effective in detecting biases to Subjective, Random, Easy, and Hard questions.
On MMLU, HLE, and CSQA, leveraging B-score substantially improves the
verification accuracy of LLM answers (i.e, accepting LLM correct answers and
rejecting incorrect ones) compared to using verbalized confidence scores or the
frequency of single-turn answers alone. Code and data are available at:
https://b-score.github.io.
Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal
  Large Language Models
Authors: name: Haoyuan Sun, name: Jiaqi Wu, name: Bo Xia, name: Yifu Luo, name: Yifei Zhao, name: Kai Qin, name: Xufei Lv, name: Tiantian Zhang, name: Yongzhe Chang, name: Xueqian Wang
Published: 2025-05-24 06:01:48

Standing in 2025, at a critical juncture in the pursuit of Artificial General
Intelligence (AGI), reinforcement fine-tuning (RFT) has demonstrated
significant potential in enhancing the reasoning capability of large language
models (LLMs) and has led to the development of cutting-edge AI models such as
OpenAI-o1 and DeepSeek-R1. Moreover, the efficient application of RFT to
enhance the reasoning capability of multimodal large language models (MLLMs)
has attracted widespread attention from the community. In this position paper,
we argue that reinforcement fine-tuning powers the reasoning capability of
multimodal large language models. To begin with, we provide a detailed
introduction to the fundamental background knowledge that researchers
interested in this field should be familiar with. Furthermore, we meticulously
summarize the improvements of RFT in powering reasoning capability of MLLMs
into five key points: diverse modalities, diverse tasks and domains, better
training algorithms, abundant benchmarks and thriving engineering frameworks.
Finally, we propose five promising directions for future research that the
community might consider. We hope that this position paper will provide
valuable insights to the community at this pivotal stage in the advancement
toward AGI. Summary of works done on RFT for MLLMs is available at
https://github.com/Sun-Haoyuan23/Awesome-RL-based-Reasoning-MLLMs.
From Tens of Hours to Tens of Thousands: Scaling Back-Translation for
  Speech Recognition
Authors: name: Tianduo Wang, name: Lu Xu, name: Wei Lu, name: Shanbo Cheng
Published: 2025-05-22 17:51:05

Recent advances in Automatic Speech Recognition (ASR) have been largely
fueled by massive speech corpora. However, extending coverage to diverse
languages with limited resources remains a formidable challenge. This paper
introduces Speech Back-Translation, a scalable pipeline that improves
multilingual ASR models by converting large-scale text corpora into synthetic
speech via off-the-shelf text-to-speech (TTS) models. We demonstrate that just
tens of hours of real transcribed speech can effectively train TTS models to
generate synthetic speech at hundreds of times the original volume while
maintaining high quality. To evaluate synthetic speech quality, we develop an
intelligibility-based assessment framework and establish clear thresholds for
when synthetic data benefits ASR training. Using Speech Back-Translation, we
generate more than 500,000 hours of synthetic speech in ten languages and
continue pre-training Whisper-large-v3, achieving average transcription error
reductions of over 30\%. These results highlight the scalability and
effectiveness of Speech Back-Translation for enhancing multilingual ASR
systems.
Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual
  Reasoning from Transit Maps
Authors: name: Sicheng Feng, name: Song Wang, name: Shuyi Ouyang, name: Lingdong Kong, name: Zikai Song, name: Jianke Zhu, name: Huan Wang, name: Xinchao Wang
Published: 2025-05-24 12:33:52

Multimodal large language models (MLLMs) have recently achieved significant
progress in visual tasks, including semantic scene understanding and text-image
alignment, with reasoning variants enhancing performance on complex tasks
involving mathematics and logic. However, their capacity for reasoning tasks
involving fine-grained visual understanding remains insufficiently evaluated.
To address this gap, we introduce ReasonMap, a benchmark designed to assess the
fine-grained visual understanding and spatial reasoning abilities of MLLMs.
ReasonMap encompasses high-resolution transit maps from 30 cities across 13
countries and includes 1,008 question-answer pairs spanning two question types
and three templates. Furthermore, we design a two-level evaluation pipeline
that properly assesses answer correctness and quality. Comprehensive
evaluations of 15 popular MLLMs, including both base and reasoning variants,
reveal a counterintuitive pattern: among open-source models, base models
outperform reasoning ones, while the opposite trend is observed in
closed-source models. Additionally, performance generally degrades when visual
inputs are masked, indicating that while MLLMs can leverage prior knowledge to
answer some questions, fine-grained visual reasoning tasks still require
genuine visual perception for strong performance. Our benchmark study offers
new insights into visual reasoning and contributes to investigating the gap
between open-source and closed-source models.
Error Typing for Smarter Rewards: Improving Process Reward Models with
  Error-Aware Hierarchical Supervision
Authors: name: Tej Deep Pala, name: Panshul Sharma, name: Amir Zadeh, name: Chuan Li, name: Soujanya Poria
Published: 2025-05-26 08:56:36

Large Language Models (LLMs) are prone to hallucination, especially during
multi-hop and reasoning-intensive tasks such as mathematical problem solving.
While Outcome Reward Models verify only final answers, Process Reward Models
(PRMs) score each intermediate step to steer generation toward coherent
solutions. We introduce PathFinder-PRM, a novel hierarchical, error-aware
discriminative PRM that first classifies math and consistency errors at each
step, then combines these fine-grained signals to estimate step correctness. To
train PathFinder-PRM, we construct a 400K-sample dataset by enriching the
human-annotated PRM800K corpus and RLHFlow Mistral traces with
three-dimensional step-level labels. On PRMBench, PathFinder-PRM achieves a new
state-of-the-art PRMScore of 67.7, outperforming the prior best (65.5) while
using 3 times less data. When applied to reward guided greedy search, our model
yields prm@8 48.3, a +1.5 point gain over the strongest baseline. These results
demonstrate that decoupled error detection and reward estimation not only boost
fine-grained error detection but also substantially improve end-to-end,
reward-guided mathematical reasoning with greater data efficiency.
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications
  of Agentic AI
Authors: name: Ranjan Sapkota, name: Konstantinos I. Roumeliotis, name: Manoj Karkee
Published: 2025-05-26 03:00:21

This review presents a comprehensive analysis of two emerging paradigms in
AI-assisted software development: vibe coding and agentic coding. While both
leverage large language models (LLMs), they differ fundamentally in autonomy,
architectural design, and the role of the developer. Vibe coding emphasizes
intuitive, human-in-the-loop interaction through prompt-based, conversational
workflows that support ideation, experimentation, and creative exploration. In
contrast, agentic coding enables autonomous software development through
goal-driven agents capable of planning, executing, testing, and iterating tasks
with minimal human intervention. We propose a detailed taxonomy spanning
conceptual foundations, execution models, feedback loops, safety mechanisms,
debugging strategies, and real-world tool ecosystems. Through comparative
workflow analysis and 20 detailed use cases, we illustrate how vibe systems
thrive in early-stage prototyping and education, while agentic systems excel in
enterprise-grade automation, codebase refactoring, and CI/CD integration. We
further examine emerging trends in hybrid architectures, where natural language
interfaces are coupled with autonomous execution pipelines. Finally, we
articulate a future roadmap for agentic AI, outlining the infrastructure needed
for trustworthy, explainable, and collaborative systems. Our findings suggest
that successful AI software engineering will rely not on choosing one paradigm,
but on harmonizing their strengths within a unified, human-centered development
lifecycle.
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language
  Model via Reinforcement Learning
Authors: name: Liang Chen, name: Hongcheng Gao, name: Tianyu Liu, name: Zhiqi Huang, name: Flood Sung, name: Xinyu Zhou, name: Yuxin Wu, name: Baobao Chang
Published: 2025-05-19 17:54:39

Vision-Language Models (VLMs) excel in many direct multimodal tasks but
struggle to translate this prowess into effective decision-making within
interactive, visually rich environments like games. This ``knowing-doing'' gap
significantly limits their potential as autonomous agents, as leading VLMs
often performing badly in simple games. To address this, we introduce VLM-Gym,
a curated reinforcement learning (RL) environment featuring diverse visual
games with unified interfaces and adjustable, compositional difficulty,
specifically designed for scalable multi-game parallel training. Leveraging
VLM-Gym, we train G0 models using pure RL-driven self-evolution, which
demonstrate emergent perception and reasoning patterns. To further mitigate
challenges arising from game diversity, we develop G1 models. G1 incorporates a
perception-enhanced cold start prior to RL fine-tuning. Our resulting G1 models
consistently surpass their teacher across all games and outperform leading
proprietary models like Claude-3.7-Sonnet-Thinking. Systematic analysis reveals
an intriguing finding: perception and reasoning abilities mutually bootstrap
each other throughout the RL training process. Source code including VLM-Gym
and RL training are released at https://github.com/chenllliang/G1 to foster
future research in advancing VLMs as capable interactive agents.
NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for
  Local Communities
Authors: name: Abdellah El Mekki, name: Houdaifa Atou, name: Omer Nacar, name: Shady Shehata, name: Muhammad Abdul-Mageed
Published: 2025-05-23 21:18:40

Enhancing the linguistic capabilities of Large Language Models (LLMs) to
include low-resource languages is a critical research area. Current research
directions predominantly rely on synthetic data generated by translating
English corpora, which, while demonstrating promising linguistic understanding
and translation abilities, often results in models aligned with source language
culture. These models frequently fail to represent the cultural heritage and
values of local communities. This work proposes a methodology to create both
synthetic and retrieval-based pre-training data tailored to a specific
community, considering its (i) language, (ii) cultural heritage, and (iii)
cultural values. We demonstrate our methodology using Egyptian and Moroccan
dialects as testbeds, chosen for their linguistic and cultural richness and
current underrepresentation in LLMs. As a proof-of-concept, we develop
NileChat, a 3B parameter LLM adapted for Egyptian and Moroccan communities,
incorporating their language, cultural heritage, and values. Our results on
various understanding, translation, and cultural and values alignment
benchmarks show that NileChat outperforms existing Arabic-aware LLMs of similar
size and performs on par with larger models. We share our methods, data, and
models with the community to promote the inclusion and coverage of more diverse
communities in LLM development.
Universal Biological Sequence Reranking for Improved De Novo Peptide
  Sequencing
Authors: name: Zijie Qiu, name: Jiaqi Wei, name: Xiang Zhang, name: Sheng Xu, name: Kai Zou, name: Zhi Jin, name: Zhiqiang Gao, name: Nanqing Dong, name: Siqi Sun
Published: 2025-05-23 06:56:55

De novo peptide sequencing is a critical task in proteomics. However, the
performance of current deep learning-based methods is limited by the inherent
complexity of mass spectrometry data and the heterogeneous distribution of
noise signals, leading to data-specific biases. We present RankNovo, the first
deep reranking framework that enhances de novo peptide sequencing by leveraging
the complementary strengths of multiple sequencing models. RankNovo employs a
list-wise reranking approach, modeling candidate peptides as multiple sequence
alignments and utilizing axial attention to extract informative features across
candidates. Additionally, we introduce two new metrics, PMD (Peptide Mass
Deviation) and RMD (residual Mass Deviation), which offer delicate supervision
by quantifying mass differences between peptides at both the sequence and
residue levels. Extensive experiments demonstrate that RankNovo not only
surpasses its base models used to generate training candidates for reranking
pre-training, but also sets a new state-of-the-art benchmark. Moreover,
RankNovo exhibits strong zero-shot generalization to unseen models whose
generations were not exposed during training, highlighting its robustness and
potential as a universal reranking framework for peptide sequencing. Our work
presents a novel reranking strategy that fundamentally challenges existing
single-model paradigms and advances the frontier of accurate de novo
sequencing. Our source code is provided on GitHub.
Value-Guided Search for Efficient Chain-of-Thought Reasoning
Authors: name: Kaiwen Wang, name: Jin Peng Zhou, name: Jonathan Chang, name: Zhaolin Gao, name: Nathan Kallus, name: Kianté Brantley, name: Wen Sun
Published: 2025-05-23 01:05:07

In this paper, we propose a simple and efficient method for value model
training on long-context reasoning traces. Compared to existing process reward
models (PRMs), our method does not require a fine-grained notion of "step,"
which is difficult to define for long-context reasoning models. By collecting a
dataset of 2.5 million reasoning traces, we train a 1.5B token-level value
model and apply it to DeepSeek models for improved performance with test-time
compute scaling. We find that block-wise value-guided search (VGS) with a final
weighted majority vote achieves better test-time scaling than standard methods
such as majority voting or best-of-n. With an inference budget of 64
generations, VGS with DeepSeek-R1-Distill-1.5B achieves an average accuracy of
45.7% across four competition math benchmarks (AIME 2024 & 2025, HMMT Feb 2024
& 2025), reaching parity with o3-mini-medium. Moreover, VGS significantly
reduces the inference FLOPs required to achieve the same performance of
majority voting. Our dataset, model and codebase are open-sourced.
FuxiMT: Sparsifying Large Language Models for Chinese-Centric
  Multilingual Machine Translation
Authors: name: Shaolin Zhu, name: Tianyu Dong, name: Bo Li, name: Deyi Xiong
Published: 2025-05-20 12:09:17

In this paper, we present FuxiMT, a novel Chinese-centric multilingual
machine translation model powered by a sparsified large language model (LLM).
We adopt a two-stage strategy to train FuxiMT. We first pre-train the model on
a massive Chinese corpus and then conduct multilingual fine-tuning on a large
parallel dataset encompassing 65 languages. FuxiMT incorporates
Mixture-of-Experts (MoEs) and employs a curriculum learning strategy for robust
performance across various resource levels. Experimental results demonstrate
that FuxiMT significantly outperforms strong baselines, including
state-of-the-art LLMs and machine translation models, particularly under
low-resource scenarios. Furthermore, FuxiMT exhibits remarkable zero-shot
translation capabilities for unseen language pairs, indicating its potential to
bridge communication gaps where parallel data are scarce or unavailable.
ScanBot: Towards Intelligent Surface Scanning in Embodied Robotic
  Systems
Authors: name: Zhiling Chen, name: Yang Zhang, name: Fardin Jalil Piran, name: Qianyu Zhou, name: Jiong Tang, name: Farhad Imani
Published: 2025-05-22 21:22:50

We introduce ScanBot, a novel dataset designed for instruction-conditioned,
high-precision surface scanning in robotic systems. In contrast to existing
robot learning datasets that focus on coarse tasks such as grasping,
navigation, or dialogue, ScanBot targets the high-precision demands of
industrial laser scanning, where sub-millimeter path continuity and parameter
stability are critical. The dataset covers laser scanning trajectories executed
by a robot across 12 diverse objects and 6 task types, including full-surface
scans, geometry-focused regions, spatially referenced parts, functionally
relevant structures, defect inspection, and comparative analysis. Each scan is
guided by natural language instructions and paired with synchronized RGB,
depth, and laser profiles, as well as robot pose and joint states. Despite
recent progress, existing vision-language action (VLA) models still fail to
generate stable scanning trajectories under fine-grained instructions and
real-world precision demands. To investigate this limitation, we benchmark a
range of multimodal large language models (MLLMs) across the full
perception-planning-execution loop, revealing persistent challenges in
instruction-following under realistic constraints.
Position of Uncertainty: A Cross-Linguistic Study of Positional Bias in
  Large Language Models
Authors: name: Menschikov Mikhail, name: Alexander Kharitonov, name: Maiia Kotyga, name: Vadim Porvatov, name: Anna Zhukovskaya, name: David Kagramanyan, name: Egor Shvetsov, name: Evgeny Burnaev
Published: 2025-05-22 02:23:00

Large language models exhibit positional bias -- systematic neglect of
information at specific context positions -- yet its interplay with linguistic
diversity remains poorly understood. We present a cross-linguistic study across
five typologically distinct languages (English, Russian, German, Hindi,
Vietnamese), examining how positional bias interacts with model uncertainty,
syntax, and prompting. Key findings: (1) Positional bias is model-driven, with
language-specific variations -- Qwen2.5-7B favors late positions, challenging
assumptions of early-token bias; (2) Explicit positional guidance (e.g.,
correct context is at position X) reduces accuracy across languages,
undermining prompt-engineering practices; (3) Aligning context with positional
bias increases entropy, yet minimal entropy does not predict accuracy. (4) We
further uncover that LLMs differently impose dominant word order in
free-word-order languages like Hindi.