Orionnnn/hub-stats huggingface dataset:
ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in
  Large Language Models
Authors: name: Mingjie Liu, name: Shizhe Diao, name: Ximing Lu, name: Jian Hu, name: Xin Dong, name: Yejin Choi, name: Jan Kautz, name: Yi Dong
Published: 2025-05-30 17:59:01

Recent advances in reasoning-centric language models have highlighted
reinforcement learning (RL) as a promising method for aligning models with
verifiable rewards. However, it remains contentious whether RL truly expands a
model's reasoning capabilities or merely amplifies high-reward outputs already
latent in the base model's distribution, and whether continually scaling up RL
compute reliably leads to improved reasoning performance. In this work, we
challenge prevailing assumptions by demonstrating that prolonged RL (ProRL)
training can uncover novel reasoning strategies that are inaccessible to base
models, even under extensive sampling. We introduce ProRL, a novel training
methodology that incorporates KL divergence control, reference policy
resetting, and a diverse suite of tasks. Our empirical analysis reveals that
RL-trained models consistently outperform base models across a wide range of
pass@k evaluations, including scenarios where base models fail entirely
regardless of the number of attempts. We further show that reasoning boundary
improvements correlates strongly with task competence of base model and
training duration, suggesting that RL can explore and populate new regions of
solution space over time. These findings offer new insights into the conditions
under which RL meaningfully expands reasoning boundaries in language models and
establish a foundation for future work on long-horizon RL for reasoning. We
release model weights to support further research:
https://huggingface.co/nvidia/Nemotron-Research-Reasoning-Qwen-1.5B
MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs
Authors: name: Gabrielle Kaili-May Liu, name: Gal Yona, name: Avi Caciularu, name: Idan Szpektor, name: Tim G. J. Rudner, name: Arman Cohan
Published: 2025-05-30 17:54:08

A critical component in the trustworthiness of LLMs is reliable uncertainty
communication, yet LLMs often use assertive language when conveying false
claims, leading to over-reliance and eroded trust. We present the first
systematic study of faithful confidence calibration of LLMs,
benchmarking models' ability to use linguistic expressions of uncertainty that
faithfully reflect their intrinsic uncertainty, across a
comprehensive array of models, datasets, and prompting strategies. Our results
demonstrate that LLMs largely fail at this task, and that existing
interventions are insufficient: standard prompt approaches provide only
marginal gains, and existing, factuality-based calibration techniques can even
harm faithful calibration. To address this critical gap, we introduce
MetaFaith, a novel prompt-based calibration approach inspired by human
metacognition. We show that MetaFaith robustly improves faithful calibration
across diverse models and task domains, enabling up to 61% improvement in
faithfulness and achieving an 83% win rate over original generations as judged
by humans.
Don't Look Only Once: Towards Multimodal Interactive Reasoning with
  Selective Visual Revisitation
Authors: name: Jiwan Chung, name: Junhyeok Kim, name: Siyeol Kim, name: Jaeyoung Lee, name: Min Soo Kim, name: Youngjae Yu
Published: 2025-05-24 19:30:47

We present v1, a lightweight extension to Multimodal Large Language Models
(MLLMs) that enables selective visual revisitation during inference. While
current MLLMs typically consume visual input only once and reason purely over
internal memory, v1 introduces a simple point-and-copy mechanism that allows
the model to dynamically retrieve relevant image regions throughout the
reasoning process. This mechanism augments existing architectures with minimal
modifications, enabling contextual access to visual tokens based on the model's
evolving hypotheses. To train this capability, we construct v1g, a dataset of
300K multimodal reasoning traces with interleaved visual grounding annotations.
Experiments on three multimodal mathematical reasoning benchmarks -- MathVista,
MathVision, and MathVerse -- demonstrate that v1 consistently improves
performance over comparable baselines, particularly on tasks requiring
fine-grained visual reference and multi-step reasoning. Our results suggest
that dynamic visual access is a promising direction for enhancing grounded
multimodal reasoning. Code, models, and data will be released to support future
research.
Large Language Models for Data Synthesis
Authors: name: Yihong Tang, name: Menglin Kong, name: Lijun Sun
Published: 2025-05-20 13:35:38

Generating synthetic data that faithfully captures the statistical structure
of real-world distributions is a fundamental challenge in data modeling.
Classical approaches often depend on strong parametric assumptions or manual
structural design and struggle in high-dimensional or heterogeneous domains.
Recent progress in Large Language Models (LLMs) reveals their potential as
flexible, high-dimensional priors over real-world distributions. However, when
applied to data synthesis, standard LLM-based sampling is inefficient,
constrained by fixed context limits, and fails to ensure statistical alignment.
Given this, we introduce LLMSynthor, a general framework for data synthesis
that transforms LLMs into structure-aware simulators guided by distributional
feedback. LLMSynthor treats the LLM as a nonparametric copula simulator for
modeling high-order dependencies and introduces LLM Proposal Sampling to
generate grounded proposal distributions that improve sampling efficiency
without requiring rejection. By minimizing discrepancies in the summary
statistics space, the iterative synthesis loop aligns real and synthetic data
while gradually uncovering and refining the latent generative structure. We
evaluate LLMSynthor in both controlled and real-world settings using
heterogeneous datasets in privacy-sensitive domains (e.g., e-commerce,
population, and mobility) that encompass both structured and unstructured
formats. The synthetic data produced by LLMSynthor shows high statistical
fidelity, practical utility, and cross-data adaptability, positioning it as a
valuable tool across economics, social science, urban studies, and beyond.
Harnessing Negative Signals: Reinforcement Distillation from Teacher
  Data for LLM Reasoning
Authors: name: Shuyao Xu, name: Cheng Peng, name: Jiangxuan Long, name: Weidi Xu, name: Wei Chu, name: Yuan Qi
Published: 2025-05-30 17:47:17

Recent advances in model distillation demonstrate that data from advanced
reasoning models (e.g., DeepSeek-R1, OpenAI's o1) can effectively transfer
complex reasoning abilities to smaller, efficient student models. However,
standard practices employ rejection sampling, discarding incorrect reasoning
examples -- valuable, yet often underutilized data. This paper addresses the
critical question: How can both positive and negative distilled reasoning
traces be effectively leveraged to maximize LLM reasoning performance in an
offline setting? To this end, We propose Reinforcement Distillation (REDI), a
two-stage framework. Stage 1 learns from positive traces via Supervised
Fine-Tuning (SFT). Stage 2 further refines the model using both positive and
negative traces through our proposed REDI objective. This novel objective is a
simple, reference-free loss function that outperforms established methods like
DPO and SimPO in this distillation context. Our empirical evaluations
demonstrate REDI's superiority over baseline Rejection Sampling SFT or SFT
combined with DPO/SimPO on mathematical reasoning tasks. Notably, the
Qwen-REDI-1.5B model, post-trained on just 131k positive and negative examples
from the open Open-R1 dataset, achieves an 83.1% score on MATH-500 (pass@1).
Its performance matches or surpasses that of DeepSeek-R1-Distill-Qwen-1.5B (a
model post-trained on 800k proprietary data) across various mathematical
reasoning benchmarks, establishing a new state-of-the-art for 1.5B models
post-trained offline with openly available data.
Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and
  Benchmarking Multimodal LLM Agents
Authors: name: Yaxin Luo, name: Zhaoyi Li, name: Jiacheng Liu, name: Jiacheng Cui, name: Xiaohan Zhao, name: Zhiqiang Shen
Published: 2025-05-30 17:59:55

CAPTCHAs have been a critical bottleneck for deploying web agents in
real-world applications, often blocking them from completing end-to-end
automation tasks. While modern multimodal LLM agents have demonstrated
impressive performance in static perception tasks, their ability to handle
interactive, multi-step reasoning challenges like CAPTCHAs is largely untested.
To address this gap, we introduce Open CaptchaWorld, the first web-based
benchmark and platform specifically designed to evaluate the visual reasoning
and interaction capabilities of MLLM-powered agents through diverse and dynamic
CAPTCHA puzzles. Our benchmark spans 20 modern CAPTCHA types, totaling 225
CAPTCHAs, annotated with a new metric we propose: CAPTCHA Reasoning Depth,
which quantifies the number of cognitive and motor steps required to solve each
puzzle. Experimental results show that humans consistently achieve near-perfect
scores, state-of-the-art MLLM agents struggle significantly, with success rates
at most 40.0% by Browser-Use Openai-o3, far below human-level performance,
93.3%. This highlights Open CaptchaWorld as a vital benchmark for diagnosing
the limits of current multimodal agents and guiding the development of more
robust multimodal reasoning systems. Code and Data are available at this https
URL.
UniGeo: Taming Video Diffusion for Unified Consistent Geometry
  Estimation
Authors: name: Yang-Tian Sun, name: Xin Yu, name: Zehuan Huang, name: Yi-Hua Huang, name: Yuan-Chen Guo, name: Ziyi Yang, name: Yan-Pei Cao, name: Xiaojuan Qi
Published: 2025-05-30 12:31:59

Recently, methods leveraging diffusion model priors to assist monocular
geometric estimation (e.g., depth and normal) have gained significant attention
due to their strong generalization ability. However, most existing works focus
on estimating geometric properties within the camera coordinate system of
individual video frames, neglecting the inherent ability of diffusion models to
determine inter-frame correspondence. In this work, we demonstrate that,
through appropriate design and fine-tuning, the intrinsic consistency of video
generation models can be effectively harnessed for consistent geometric
estimation. Specifically, we 1) select geometric attributes in the global
coordinate system that share the same correspondence with video frames as the
prediction targets, 2) introduce a novel and efficient conditioning method by
reusing positional encodings, and 3) enhance performance through joint training
on multiple geometric attributes that share the same correspondence. Our
results achieve superior performance in predicting global geometric attributes
in videos and can be directly applied to reconstruction tasks. Even when
trained solely on static video data, our approach exhibits the potential to
generalize to dynamic video scenes.
EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic,
  Expressiveness, and Linguistic Challenges Using Model-as-a-Judge
Authors: name: Ruskin Raj Manku, name: Yuzhi Tang, name: Xingjian Shi, name: Mu Li, name: Alex Smola
Published: 2025-05-29 02:36:24

Text-to-Speech (TTS) benchmarks often fail to capture how well models handle
nuanced and semantically complex text. Building on EmergentTTS, we
introduce EmergentTTS-Eval, a comprehensive benchmark covering six
challenging TTS scenarios: emotions, paralinguistics, foreign words, syntactic
complexity, complex pronunciation (e.g. URLs, formulas), and questions.
Crucially, our framework automates both test-case generation and evaluation,
making the benchmark easily extensible. Starting from a small set of
human-written seed prompts, we iteratively extend them using LLMs to target
specific structural, phonetic and prosodic challenges, resulting in 1,645
diverse test cases. Moreover, we employ a model-as-a-judge approach, using a
Large Audio Language Model (LALM) to assess the speech across multiple
dimensions such as expressed emotion, prosodic, intonational, and pronunciation
accuracy. We evaluate state-of-the-art open-source and proprietary TTS systems,
such as 11Labs, Deepgram, and OpenAI's 4o-mini-TTS, on EmergentTTS-Eval,
demonstrating its ability to reveal fine-grained performance differences.
Results show that the model-as-a-judge approach offers robust TTS assessment
and a high correlation with human preferences. We open source the evaluation
https://github.com/boson-ai/EmergentTTS-Eval-public{code} and the
https://huggingface.co/datasets/bosonai/EmergentTTS-Eval{dataset}.
Large Language Models are Locally Linear Mappings
Authors: name: James R. Golden
Published: 2025-05-30 07:08:33

We demonstrate that the inference operations of several open-weight large
language models (LLMs) can be mapped to an exactly equivalent linear system for
an input sequence without modifying the model weights or altering output
predictions. Extending techniques from image diffusion models that exhibit
local or piecewise linearity, we strategically alter the gradient computation
with respect to a given input sequence for a next-token prediction such that
the Jacobian of the model nearly exactly reproduces the forward prediction with
a linear system. We demonstrate this approach across models (Llama 3, Gemma 3,
Qwen 3, Phi 4, Mistral Ministral and OLMo 2, up to Llama 3.3 70B Q4) and show
through the singular value decomposition of the detached Jacobian that these
LLMs operate in extremely low-dimensional subspaces where many of the largest
singular vectors decode to concepts related to the most-likely output token.
This approach also allows us to examine the operation of each successive layer
(and its attention and MLP components) as nearly-exact linear systems and
observe the emergence of semantic concepts. Despite their expressive power and
global nonlinearity, modern LLMs can be interpreted through nearly-exact
locally linear decompositions that provide insights into their internal
representations and reveal interpretable semantic structures in the next-token
prediction process.
EasyText: Controllable Diffusion Transformer for Multilingual Text
  Rendering
Authors: name: Runnan Lu, name: Yuxuan Zhang, name: Jailing Liu, name: Haifa Wang, name: Yiren Song
Published: 2025-05-30 09:55:39

Generating accurate multilingual text with diffusion models has long been
desired but remains challenging. Recent methods have made progress in rendering
text in a single language, but rendering arbitrary languages is still an
unexplored area. This paper introduces EasyText, a text rendering framework
based on DiT (Diffusion Transformer), which connects denoising latents with
multilingual character tokens encoded as character tokens. We propose character
positioning encoding and position encoding interpolation techniques to achieve
controllable and precise text rendering. Additionally, we construct a
large-scale synthetic text image dataset with 1 million multilingual image-text
annotations as well as a high-quality dataset of 20K annotated images, which
are used for pretraining and fine-tuning respectively. Extensive experiments
and evaluations demonstrate the effectiveness and advancement of our approach
in multilingual text rendering, visual quality, and layout-aware text
integration.
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Authors: name: Junyu Zhang, name: Runpei Dong, name: Han Wang, name: Xuying Ning, name: Haoran Geng, name: Peihao Li, name: Xialin He, name: Yutong Bai, name: Jitendra Malik, name: Saurabh Gupta, name: Huan Zhang
Published: 2025-05-30 17:58:36

This paper presents AlphaOne (alpha1), a universal framework for
modulating reasoning progress in large reasoning models (LRMs) at test time.
alpha1 first introduces alpha moment, which represents the scaled
thinking phase with a universal parameter alpha. Within this scaled
pre-alpha moment phase, it dynamically schedules slow thinking transitions
by modeling the insertion of reasoning transition tokens as a Bernoulli
stochastic process. After the alpha moment, alpha1 deterministically
terminates slow thinking with the end-of-thinking token, thereby fostering fast
reasoning and efficient answer generation. This approach unifies and
generalizes existing monotonic scaling methods by enabling flexible and dense
slow-to-fast reasoning modulation. Extensive empirical studies on various
challenging benchmarks across mathematical, coding, and scientific domains
demonstrate alpha1's superior reasoning capability and efficiency. Project
page: https://alphaone-project.github.io/
Enabling Flexible Multi-LLM Integration for Scalable Knowledge
  Aggregation
Authors: name: Zhenglun Kong, name: Zheng Zhan, name: Shiyue Hou, name: Yifan Gong, name: Xin Meng, name: Pengwei Sui, name: Peiyan Dong, name: Xuan Shen, name: Zifeng Wang, name: Pu Zhao, name: Hao Tang, name: Stratis Ioannidis, name: Yanzhi Wang
Published: 2025-05-28 16:24:50

Large language models (LLMs) have shown remarkable promise but remain
challenging to continually improve through traditional finetuning, particularly
when integrating capabilities from other specialized LLMs. Popular methods like
ensemble and weight merging require substantial memory and struggle to adapt to
changing data environments. Recent efforts have transferred knowledge from
multiple LLMs into a single target model; however, they suffer from
interference and degraded performance among tasks, largely due to limited
flexibility in candidate selection and training pipelines. To address these
issues, we propose a framework that adaptively selects and aggregates knowledge
from diverse LLMs to build a single, stronger model, avoiding the high memory
overhead of ensemble and inflexible weight merging. Specifically, we design an
adaptive selection network that identifies the most relevant source LLMs based
on their scores, thereby reducing knowledge interference. We further propose a
dynamic weighted fusion strategy that accounts for the inherent strengths of
candidate LLMs, along with a feedback-driven loss function that prevents the
selector from converging on a single subset of sources. Experimental results
demonstrate that our method can enable a more stable and scalable knowledge
aggregation process while reducing knowledge interference by up to 50% compared
to existing approaches. Code is avaliable at
https://github.com/ZLKong/LLM_Integration
Model-Preserving Adaptive Rounding
Authors: name: Albert Tseng, name: Zhaofeng Sun, name: Christopher De Sa
Published: 2025-05-29 01:53:00

The main goal of post-training quantization (PTQ) is to produced a compressed
model whose output distribution is as close to the original model's as
possible. To do this tractably, almost all LLM PTQ algorithms quantize linear
layers by independently minimizing the immediate activation error. However,
this localized objective ignores the effect of subsequent layers, so reducing
it does not necessarily give a closer model. In this work, we introduce Yet
Another Quantization Algorithm (YAQA), an adaptive rounding algorithm that uses
Kronecker-factored approximations of each linear layer's Hessian with respect
to the full model KL divergence. YAQA consists of two components:
Kronecker-factored sketches of the full layerwise Hessian that can be tractably
computed for hundred-billion parameter LLMs, and a quantizer-independent
rounding algorithm that uses these sketches and comes with theoretical
guarantees. Across a wide range of models and quantizers, YAQA empirically
reduces the KL divergence to the original model by approx 30% while
achieving state of the art performance on downstream tasks.
GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
Authors: name: Manish Shetty, name: Naman Jain, name: Jinjian Liu, name: Vijay Kethanaboyina, name: Koushik Sen, name: Ion Stoica
Published: 2025-05-29 17:14:55

Developing high-performance software is a complex task that requires
specialized expertise. We introduce GSO, a benchmark for evaluating language
models' capabilities in developing high-performance software. We develop an
automated pipeline that generates and executes performance tests to analyze
repository commit histories to identify 102 challenging optimization tasks
across 10 codebases, spanning diverse domains and programming languages. An
agent is provided with a codebase and performance test as a precise
specification, and tasked to improve the runtime efficiency, which is measured
against the expert developer optimization. Our quantitative evaluation reveals
that leading SWE-Agents struggle significantly, achieving less than 5% success
rate, with limited improvements even with inference-time scaling. Our
qualitative analysis identifies key failure modes, including difficulties with
low-level languages, practicing lazy optimization strategies, and challenges in
accurately localizing bottlenecks. We release the code and artifacts of our
benchmark along with agent trajectories to enable future research.
Toward Reliable Biomedical Hypothesis Generation: Evaluating
  Truthfulness and Hallucination in Large Language Models
Authors: name: Guangzhi Xiong, name: Eric Xie, name: Corey Williams, name: Myles Kim, name: Amir Hassan Shariatmadari, name: Sikun Guo, name: Stefan Bekiranov, name: Aidong Zhang
Published: 2025-05-20 16:49:40

Large language models (LLMs) have shown significant potential in scientific
disciplines such as biomedicine, particularly in hypothesis generation, where
they can analyze vast literature, identify patterns, and suggest research
directions. However, a key challenge lies in evaluating the truthfulness of
generated hypotheses, as verifying their accuracy often requires substantial
time and resources. Additionally, the hallucination problem in LLMs can lead to
the generation of hypotheses that appear plausible but are ultimately
incorrect, undermining their reliability. To facilitate the systematic study of
these challenges, we introduce TruthHypo, a benchmark for assessing the
capabilities of LLMs in generating truthful biomedical hypotheses, and KnowHD,
a knowledge-based hallucination detector to evaluate how well hypotheses are
grounded in existing knowledge. Our results show that LLMs struggle to generate
truthful hypotheses. By analyzing hallucinations in reasoning steps, we
demonstrate that the groundedness scores provided by KnowHD serve as an
effective metric for filtering truthful hypotheses from the diverse outputs of
LLMs. Human evaluations further validate the utility of KnowHD in identifying
truthful hypotheses and accelerating scientific discovery. Our data and source
code are available at https://github.com/Teddy-XiongGZ/TruthHypo.