Orionnnn/hub-stats huggingface dataset:
Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model
  Reasoning Ability in VibeThinker-1.5B
Authors: name: Sen Xu, name: Yi Zhou, name: Wei Wang, name: Jixin Min, name: Zhibin Yin, name: Yingwei Dai, name: Shixi Liu, name: Lianyu Pang, name: Yirong Chen, name: Junlin Zhang
Published: 2025-11-09 04:37:36

Challenging the prevailing consensus that small models inherently lack robust
reasoning, this report introduces VibeThinker-1.5B, a 1.5B-parameter dense
model developed via our Spectrum-to-Signal Principle (SSP). This challenges the
prevailing approach of scaling model parameters to enhance capabilities, as
seen in models like DeepSeek R1 (671B) and Kimi k2 (>1T). The SSP framework
first employs a Two-Stage Diversity-Exploring Distillation (SFT) to generate a
broad spectrum of solutions, followed by MaxEnt-Guided Policy Optimization (RL)
to amplify the correct signal. With a total training cost of only $7,800,
VibeThinker-1.5B demonstrates superior reasoning capabilities compared to
closed-source models like Magistral Medium and Claude Opus 4, and performs on
par with open-source models like GPT OSS-20B Medium. Remarkably, it surpasses
the 400x larger DeepSeek R1 on three math benchmarks: AIME24 (80.3 vs. 79.8),
AIME25 (74.4 vs. 70.0), and HMMT25 (50.4 vs. 41.7). This is a substantial
improvement over its base model (6.7, 4.3, and 0.6, respectively). On
LiveCodeBench V6, it scores 51.1, outperforming Magistral Medium's 50.3 and its
base model's 0.0. These findings demonstrate that small models can achieve
reasoning capabilities comparable to large models, drastically reducing
training and inference costs and thereby democratizing advanced AI research.
VideoSSR: Video Self-Supervised Reinforcement Learning
Authors: name: Zefeng He, name: Xiaoye Qu, name: Yafu Li, name: Siyuan Huang, name: Daizong Liu, name: Yu Cheng
Published: 2025-11-09 08:36:40

Reinforcement Learning with Verifiable Rewards (RLVR) has substantially
advanced the video understanding capabilities of Multimodal Large Language
Models (MLLMs). However, the rapid progress of MLLMs is outpacing the
complexity of existing video datasets, while the manual annotation of new,
high-quality data remains prohibitively expensive. This work investigates a
pivotal question: Can the rich, intrinsic information within videos be
harnessed to self-generate high-quality, verifiable training data? To
investigate this, we introduce three self-supervised pretext tasks: Anomaly
Grounding, Object Counting, and Temporal Jigsaw. We construct the Video
Intrinsic Understanding Benchmark (VIUBench) to validate their difficulty,
revealing that current state-of-the-art MLLMs struggle significantly on these
tasks. Building upon these pretext tasks, we develop the VideoSSR-30K dataset
and propose VideoSSR, a novel video self-supervised reinforcement learning
framework for RLVR. Extensive experiments across 17 benchmarks, spanning four
major video domains (General Video QA, Long Video QA, Temporal Grounding, and
Complex Reasoning), demonstrate that VideoSSR consistently enhances model
performance, yielding an average improvement of over 5\%. These results
establish VideoSSR as a potent foundational framework for developing more
advanced video understanding in MLLMs. The code is available at
https://github.com/lcqysl/VideoSSR.
FLEX: Continuous Agent Evolution via Forward Learning from Experience
Authors: name: Zhicheng Cai, name: Xinyuan Guo, name: Yu Pei, name: JiangTao Feng, name: Jiangjie Chen, name: Ya-Qin Zhang, name: Wei-Ying Ma, name: Mingxuan Wang, name: Hao Zhou
Published: 2025-11-09 16:31:39

Autonomous agents driven by Large Language Models (LLMs) have revolutionized
reasoning and problem-solving but remain static after training, unable to grow
with experience as intelligent beings do during deployment. We introduce
Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that
enables LLM agents to continuously evolve through accumulated experience.
Specifically, FLEX cultivates scalable and inheritable evolution by
constructing a structured experience library through continual reflection on
successes and failures during interaction with the environment. FLEX delivers
substantial improvements on mathematical reasoning, chemical retrosynthesis,
and protein fitness prediction (up to 23% on AIME25, 10% on USPTO50k, and 14%
on ProteinGym). We further identify a clear scaling law of experiential growth
and the phenomenon of experience inheritance across agents, marking a step
toward scalable and inheritable continuous agent evolution. Project Page:
https://flex-gensi-thuair.github.io.
Generating an Image From 1,000 Words: Enhancing Text-to-Image With
  Structured Captions
Authors: name: Eyal Gutflaish, name: Eliran Kachlon, name: Hezi Zisman, name: Tal Hacham, name: Nimrod Sarid, name: Alexander Visheratin, name: Saar Huberman, name: Gal Davidi, name: Guy Bukchin, name: Kfir Goldberg, name: Ron Mokady
Published: 2025-11-10 09:25:25

Text-to-image models have rapidly evolved from casual creative tools to
professional-grade systems, achieving unprecedented levels of image quality and
realism. Yet, most models are trained to map short prompts into detailed
images, creating a gap between sparse textual input and rich visual outputs.
This mismatch reduces controllability, as models often fill in missing details
arbitrarily, biasing toward average user preferences and limiting precision for
professional use. We address this limitation by training the first open-source
text-to-image model on long structured captions, where every training sample is
annotated with the same set of fine-grained attributes. This design maximizes
expressive coverage and enables disentangled control over visual factors. To
process long captions efficiently, we propose DimFusion, a fusion mechanism
that integrates intermediate tokens from a lightweight LLM without increasing
token length. We also introduce the Text-as-a-Bottleneck Reconstruction (TaBR)
evaluation protocol. By assessing how well real images can be reconstructed
through a captioning-generation loop, TaBR directly measures controllability
and expressiveness, even for very long captions where existing evaluation
methods fail. Finally, we demonstrate our contributions by training the
large-scale model FIBO, achieving state-of-the-art prompt alignment among
open-source models. Model weights are publicly available at
https://huggingface.co/briaai/FIBO
Reinforcement Learning Improves Traversal of Hierarchical Knowledge in LLMs
Authors: name: Renfei Zhang, name: Manasa Kaniselvan, name: Niloofar Mireshghallah
Published: 2025-11-08 08:56:29

Reinforcement learning (RL) is often credited with improving language model reasoning and generalization at the expense of degrading memorized knowledge. We challenge this narrative by observing that RL-enhanced models consistently outperform their base and supervised fine-tuned (SFT) counterparts on pure knowledge recall tasks, particularly those requiring traversal of hierarchical, structured knowledge (e.g., medical codes). We hypothesize these gains stem not from newly acquired data, but from improved procedural skills in navigating and searching existing knowledge hierarchies within the model parameters. To support this hypothesis, we show that structured prompting, which explicitly guides SFTed models through hierarchical traversal, recovers most of the performance gap (reducing 24pp to 7pp on MedConceptsQA for DeepSeek-V3/R1). We further find that while prompting improves final-answer accuracy, RL-enhanced models retain superior ability to recall correct procedural paths on deep-retrieval tasks. Finally our layer-wise internal activation analysis reveals that while factual representations (e.g., activations for the statement "code 57.95 refers to urinary infection") maintain high cosine similarity between SFT and RL models, query representations (e.g., "what is code 57.95") diverge noticeably, indicating that RL primarily transforms how models traverse knowledge rather than the knowledge representation itself.
Long Grounded Thoughts: Distilling Compositional Visual Reasoning Chains at Scale
Authors: name: David Acuna, name: Chao-Han Huck Yang, name: Yuntian Deng, name: Jaehun Jung, name: Ximing Lu, name: Prithviraj Ammanabrolu, name: Hyunwoo Kim, name: Yuan-Hong Liao, name: Yejin Choi
Published: 2025-11-07 20:50:54

Recent progress in multimodal reasoning has been driven largely by undisclosed datasets and proprietary data synthesis recipes, leaving open questions about how to systematically build large-scale, vision-centric reasoning datasets, particularly for tasks that go beyond visual math. In this work, we introduce a new reasoning data generation framework spanning diverse skills and levels of complexity with over 1M high-quality synthetic vision-centric questions. The dataset also includes preference data and instruction prompts supporting both offline and online RL. Our synthesis framework proceeds in two stages: (1) scale; and (2) complexity. Reasoning traces are then synthesized through a two-stage process that leverages VLMs and reasoning LLMs, producing CoT traces for VLMs that capture the richness and diverse cognitive behaviors found in frontier reasoning models. Remarkably, we show that finetuning Qwen2.5-VL-7B on our data outperforms all open-data baselines across all evaluated vision-centric benchmarks, and even surpasses strong closed-data models such as MiMo-VL-7B-RL on V* Bench, CV-Bench and MMStar-V. Perhaps most surprising, despite being entirely vision-centric, our data transfers positively to text-only reasoning (MMLU-Pro) and audio reasoning (MMAU), demonstrating its effectiveness. Similarly, despite not containing videos or embodied visual data, we observe notable gains when evaluating on a single-evidence embodied QA benchmark (NiEH). Finally, we use our data to analyze the entire VLM post-training pipeline. Our empirical analysis highlights that (i) SFT on high-quality data with non-linear reasoning traces is essential for effective online RL, (ii) staged offline RL matches online RL's performance while reducing compute demands, and (iii) careful SFT on high quality data can substantially improve out-of-domain, cross-modality transfer.
Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for
  Multilingual and Cross-Lingual Tasks
Authors: name: Yauhen Babakhin, name: Radek Osmulski, name: Ronay Ak, name: Gabriel Moreira, name: Mengyao Xu, name: Benedikt Schifferer, name: Bo Liu, name: Even Oldridge
Published: 2025-11-10 12:13:16

We introduce llama-embed-nemotron-8b, an open-weights text embedding model
that achieves state-of-the-art performance on the Multilingual Massive Text
Embedding Benchmark (MMTEB) leaderboard as of October 21, 2025. While recent
models show strong performance, their training data or methodologies are often
not fully disclosed. We aim to address this by developing a fully open-source
model, publicly releasing its weights and detailed ablation studies, and
planning to share the curated training datasets. Our model demonstrates
superior performance across all major embedding tasks -- including retrieval,
classification and semantic textual similarity (STS) -- and excels in
challenging multilingual scenarios, such as low-resource languages and
cross-lingual setups. This state-of-the-art performance is driven by a novel
data mix of 16.1 million query-document pairs, split between 7.7 million
samples from public datasets and 8.4 million synthetically generated examples
from various open-weight LLMs. One of our key contributions is a detailed
ablation study analyzing core design choices, including a comparison of
contrastive loss implementations, an evaluation of synthetic data generation
(SDG) strategies, and the impact of model merging. The llama-embed-nemotron-8b
is an instruction-aware model, supporting user-defined instructions to enhance
performance for specific use-cases. This combination of top-tier performance,
broad applicability, and user-driven flexibility enables it to serve as a
universal text embedding solution.
RLVE: Scaling Up Reinforcement Learning for Language Models with
  Adaptive Verifiable Environments
Authors: name: Zhiyuan Zeng, name: Hamish Ivison, name: Yiping Wang, name: Lifan Yuan, name: Shuyue Stella Li, name: Zhuorui Ye, name: Siting Li, name: Jacqueline He, name: Runlong Zhou, name: Tong Chen, name: Chenyang Zhao, name: Yulia Tsvetkov, name: Simon Shaolei Du, name: Natasha Jaques, name: Hao Peng, name: Pang Wei Koh, name: Hannaneh Hajishirzi
Published: 2025-11-10 17:18:35

We introduce Reinforcement Learning (RL) with Adaptive Verifiable
Environments (RLVE), an approach using verifiable environments that
procedurally generate problems and provide algorithmically verifiable rewards,
to scale up RL for language models (LMs). RLVE enables each verifiable
environment to dynamically adapt its problem difficulty distribution to the
policy model's capabilities as training progresses. In contrast, static data
distributions often lead to vanishing learning signals when problems are either
too easy or too hard for the policy. To implement RLVE, we create RLVE-Gym, a
large-scale suite of 400 verifiable environments carefully developed through
manual environment engineering. Using RLVE-Gym, we show that environment
scaling, i.e., expanding the collection of training environments, consistently
improves generalizable reasoning capabilities. RLVE with joint training across
all 400 environments in RLVE-Gym yields a 3.37% absolute average improvement
across six reasoning benchmarks, starting from one of the strongest 1.5B
reasoning LMs. By comparison, continuing this LM's original RL training yields
only a 0.49% average absolute gain despite using over 3x more compute. We
release our code publicly.
Teaching Pretrained Language Models to Think Deeper with Retrofitted
  Recurrence
Authors: name: Sean McLeish, name: Ang Li, name: John Kirchenbauer, name: Dayal Singh Kalra, name: Brian R. Bartoldson, name: Bhavya Kailkhura, name: Avi Schwarzschild, name: Jonas Geiping, name: Tom Goldstein, name: Micah Goldblum
Published: 2025-11-10 18:43:07

Recent advances in depth-recurrent language models show that recurrence can
decouple train-time compute and parameter count from test-time compute. In this
work, we study how to convert existing pretrained non-recurrent language models
into depth-recurrent models. We find that using a curriculum of recurrences to
increase the effective depth of the model over the course of training preserves
performance while reducing total computational cost. In our experiments, on
mathematics, we observe that converting pretrained models to recurrent ones
results in better performance at a given compute budget than simply
post-training the original non-recurrent language model.
NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS
  Modeling
Authors: name: Muhammad Usama, name: Mohammad Sadil Khan, name: Didier Stricker, name: Muhammad Zeshan Afzal
Published: 2025-11-09 02:45:12

Generating editable 3D CAD models from natural language remains challenging,
as existing text-to-CAD systems either produce meshes or rely on scarce
design-history data. We present NURBGen, the first framework to generate
high-fidelity 3D CAD models directly from text using Non-Uniform Rational
B-Splines (NURBS). To achieve this, we fine-tune a large language model (LLM)
to translate free-form texts into JSON representations containing NURBS surface
parameters (i.e, control points, knot vectors, degrees, and rational
weights) which can be directly converted into BRep format using Python. We
further propose a hybrid representation that combines untrimmed NURBS with
analytic primitives to handle trimmed surfaces and degenerate regions more
robustly, while reducing token complexity. Additionally, we introduce partABC,
a curated subset of the ABC dataset consisting of individual CAD components,
annotated with detailed captions using an automated annotation pipeline.
NURBGen demonstrates strong performance on diverse prompts, surpassing prior
methods in geometric fidelity and dimensional accuracy, as confirmed by expert
evaluations. Code and dataset will be released publicly.
Ariadne: A Controllable Framework for Probing and Extending VLM
  Reasoning Boundaries
Authors: name: Minghe Shen, name: Zhuo Zhi, name: Chonghan Liu, name: Shuo Xing, name: Zhengzhong Tu, name: Che Liu
Published: 2025-11-01 21:19:41

While Vision-Language Models (VLMs) post-trained with Reinforcement Learning
(RL) show impressive general reasoning, their evaluation is often confined to
language-dominant tasks (e.g., math). This raises a critical question: can RL
post-training truly extend the inherent capability boundary of a base VLM,
particularly for visual-centric spatial tasks where it initially fails? To
investigate this, we introduce Ariadne, a framework utilizing synthetic mazes
for multi-step spatial reasoning where task difficulty (e.g., path length,
turns) is precisely controlled. We leverage this controllable environment to
train VLMs using Reinforcement Learning with Verified Rewards (RLVR) in a
difficulty-aware curriculum. Surprisingly, post-RLVR training, the VLM achieves
over 50% accuracy on a problem set where the base model scored 0%,
demonstrating that our approach expands the model's initial capability
boundary. To assess real-world viability, we evaluate out-of-distribution (OOD)
generalization on practical benchmarks. Despite training only on synthetic maze
samples, Ariadne achieves significant zero-shot improvements, averaging 16% on
MapBench (e.g., museum navigation) and 24% on ReasonMap (subway transfer
tasks). These results confirm that our method not only broadens the model's
fundamental limits but also enhances its generalization to real-world spatial
reasoning. We acknowledge our study is limited to the post-training phase,
given the opaqueness of pre-training data, and hope our research motivates
further work on specialized, capability-extending alignment.
Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps
  via Uncertainty Heads
Authors: name: Jingwei Ni, name: Ekaterina Fadeeva, name: Tianyi Wu, name: Mubashara Akhtar, name: Jiaheng Zhang, name: Elliott Ash, name: Markus Leippold, name: Timothy Baldwin, name: See-Kiong Ng, name: Artem Shelmanov, name: Mrinmaya Sachan
Published: 2025-11-09 03:38:29

Solving complex tasks usually requires LLMs to generate long multi-step
reasoning chains. Previous work has shown that verifying the correctness of
individual reasoning steps can further improve the performance and efficiency
of LLMs on such tasks and enhance solution interpretability. However, existing
verification approaches, such as Process Reward Models (PRMs), are either
computationally expensive, limited to specific domains, or require large-scale
human or model-generated annotations. Thus, we propose a lightweight
alternative for step-level reasoning verification based on data-driven
uncertainty scores. We train transformer-based uncertainty quantification heads
(UHeads) that use the internal states of a frozen LLM to estimate the
uncertainty of its reasoning steps during generation. The approach is fully
automatic: target labels are generated either by another larger LLM (e.g.,
DeepSeek R1) or in a self-supervised manner by the original model itself.
UHeads are both effective and lightweight, containing less than 10M parameters.
Across multiple domains, including mathematics, planning, and general knowledge
question answering, they match or even surpass the performance of PRMs that are
up to 810x larger. Our findings suggest that the internal states of LLMs encode
their uncertainty and can serve as reliable signals for reasoning verification,
offering a promising direction toward scalable and generalizable introspective
LLMs.
Omni-AVSR: Towards Unified Multimodal Speech Recognition with Large
  Language Models
Authors: name: Umberto Cappellazzo, name: Xubo Liu, name: Pingchuan Ma, name: Stavros Petridis, name: Maja Pantic
Published: 2025-11-10 16:03:44

Large language models (LLMs) have recently achieved impressive results in
speech recognition across multiple modalities, including Auditory Speech
Recognition (ASR), Visual Speech Recognition (VSR), and Audio-Visual Speech
Recognition (AVSR). Despite this progress, current LLM-based approaches
typically address each task independently, training separate models that raise
computational and deployment resource use while missing potential cross-task
synergies. They also rely on fixed-rate token compression, which restricts
flexibility in balancing accuracy with efficiency. These limitations highlight
the need for a unified framework that can support ASR, VSR, and AVSR while
enabling elastic inference. To this end, we present Omni-AVSR, a unified
audio-visual LLM that combines efficient multi-granularity training with
parameter-efficient adaptation. Specifically, we adapt the matryoshka
representation learning paradigm to efficiently train across multiple audio and
visual granularities, reducing its inherent training resource use. Furthermore,
we explore three LoRA-based strategies for adapting the backbone LLM, balancing
shared and task-specific specialization. Experiments on LRS2 and LRS3 show that
Omni-AVSR achieves comparable or superior accuracy to state-of-the-art
baselines while training a single model at substantially lower training and
deployment resource use. The model also remains robust under acoustic noise,
and we analyze its scaling behavior as LLM size increases, providing insights
into the trade-off between performance and efficiency.
MPJudge: Towards Perceptual Assessment of Music-Induced Paintings
Authors: name: Shiqi Jiang, name: Tianyi Liang, name: Changbo Wang, name: Chenhui Li
Published: 2025-11-10 14:18:27

Music induced painting is a unique artistic practice, where visual artworks
are created under the influence of music. Evaluating whether a painting
faithfully reflects the music that inspired it poses a challenging perceptual
assessment task. Existing methods primarily rely on emotion recognition models
to assess the similarity between music and painting, but such models introduce
considerable noise and overlook broader perceptual cues beyond emotion. To
address these limitations, we propose a novel framework for music induced
painting assessment that directly models perceptual coherence between music and
visual art. We introduce MPD, the first large scale dataset of music painting
pairs annotated by domain experts based on perceptual coherence. To better
handle ambiguous cases, we further collect pairwise preference annotations.
Building on this dataset, we present MPJudge, a model that integrates music
features into a visual encoder via a modulation based fusion mechanism. To
effectively learn from ambiguous cases, we adopt Direct Preference Optimization
for training. Extensive experiments demonstrate that our method outperforms
existing approaches. Qualitative results further show that our model more
accurately identifies music relevant regions in paintings.