Orionnnn/hub-stats huggingface dataset:
The Coverage Principle: A Framework for Understanding Compositional
  Generalization
Authors: name: Hoyeon Chang, name: Jinho Park, name: Hanseul Cho, name: Sohee Yang, name: Miyoung Ko, name: Hyeonbin Hwang, name: Seungpil Won, name: Dohaeng Lee, name: Youbin Ahn, name: Minjoon Seo
Published: 2025-05-26 17:55:15

Large language models excel at pattern matching, yet often fall short in
systematic compositional generalization. We propose the coverage principle: a
data-centric framework showing that models relying primarily on pattern
matching for compositional tasks cannot reliably generalize beyond substituting
fragments that yield identical results when used in the same contexts. We
demonstrate that this framework has a strong predictive power for the
generalization capabilities of Transformers. First, we derive and empirically
confirm that the training data required for two-hop generalization grows at
least quadratically with the token set size, and the training data efficiency
does not improve with 20x parameter scaling. Second, for compositional tasks
with path ambiguity where one variable affects the output through multiple
computational paths, we show that Transformers learn context-dependent state
representations that undermine both performance and interoperability. Third,
Chain-of-Thought supervision improves training data efficiency for multi-hop
tasks but still struggles with path ambiguity. Finally, we outline a
mechanism-based taxonomy that distinguishes three ways neural networks
can generalize: structure-based (bounded by coverage), property-based
(leveraging algebraic invariances), and shared-operator (through function
reuse). This conceptual lens contextualizes our results and highlights where
new architectural ideas are needed to achieve systematic compositionally.
Overall, the coverage principle provides a unified lens for understanding
compositional reasoning, and underscores the need for fundamental architectural
or training innovations to achieve truly systematic compositionality.
Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured
  Multi-Turn Decomposition
Authors: name: Zihao Zeng, name: Xuyao Huang, name: Boxiu Li, name: Hao Zhang, name: Zhijie Deng
Published: 2025-05-26 10:18:57

Large Reasoning Models (LRMs) are criticized for the excessively lengthy
Chain-of-Thought (CoT) to derive the final answer, suffering from high
first-token and overall latency. Typically, the CoT of LRMs mixes multiple
thinking units; each unit attempts to produce a candidate answer to the
original query. Hence, a natural idea to improve efficiency is to reduce the
unit number. Yet, the fact that the thinking units in vanilla CoT cannot be
explicitly managed renders doing so challenging. This paper introduces
Multi-Turn Decomposition (MinD) to decode conventional CoT into a sequence of
explicit, structured, and turn-wise interactions to bridge the gap. In MinD,
the model provides a multi-turn response to the query, where each turn embraces
a thinking unit and yields a corresponding answer. The subsequent turns can
reflect, verify, revise, or explore alternative approaches to both the thinking
and answer parts of earlier ones. This not only makes the answer delivered more
swiftly, but also enables explicit controls over the iterative reasoning
process (i.e., users may halt or continue at any turn). We follow a supervised
fine-tuning (SFT) then reinforcement learning (RL) paradigm to realize MinD. We
first rephrase the outputs of an LRM into multi-turn formats by prompting
another LLM, and then tune the LRM with such data. Observing that the tuned
model tends to consume even more tokens than the original one (probably due to
that the multi-turn formats introduce additional answer tokens), we advocate
leveraging RL algorithms like GRPO to prioritize correct outputs with fewer
turns. Trained on the MATH dataset using R1-Distill models, MinD can achieve up
to ~70% reduction in both output token usage and time to first token (TTFT),
while maintaining competitive performance on reasoning benchmarks such as
MATH-500, AIME24, AMC23, and GPQA-Diamond.
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System
  Collaboration
Authors: name: Hao Zhong, name: Muzhi Zhu, name: Zongze Du, name: Zheng Huang, name: Canyu Zhao, name: Mingyu Liu, name: Wen Wang, name: Hao Chen, name: Chunhua Shen
Published: 2025-05-26 17:34:06

Long-horizon video-audio reasoning and fine-grained pixel understanding
impose conflicting requirements on omnimodal models: dense temporal coverage
demands many low-resolution frames, whereas precise grounding calls for
high-resolution inputs. We tackle this trade-off with a two-system
architecture: a Global Reasoning System selects informative keyframes and
rewrites the task at low spatial cost, while a Detail Understanding System
performs pixel-level grounding on the selected high-resolution snippets.
Because ``optimal'' keyframe selection and reformulation are ambiguous and hard
to supervise, we formulate them as a reinforcement learning (RL) problem and
present Omni-R1, an end-to-end RL framework built on Group Relative Policy
Optimization. Omni-R1 trains the Global Reasoning System through hierarchical
rewards obtained via online collaboration with the Detail Understanding System,
requiring only one epoch of RL on small task splits.
  Experiments on two challenging benchmarks, namely Referring Audio-Visual
Segmentation (RefAVS) and Reasoning Video Object Segmentation (REVOS), show
that Omni-R1 not only surpasses strong supervised baselines but also
outperforms specialized state-of-the-art models, while substantially improving
out-of-domain generalization and mitigating multimodal hallucination. Our
results demonstrate the first successful application of RL to large-scale
omnimodal reasoning and highlight a scalable path toward universally foundation
models.
ARM: Adaptive Reasoning Model
Authors: name: Siye Wu, name: Jian Xie, name: Yikai Zhang, name: Aili Chen, name: Kai Zhang, name: Yu Su, name: Yanghua Xiao
Published: 2025-05-26 17:38:50

While large reasoning models demonstrate strong performance on complex tasks,
they lack the ability to adjust reasoning token usage based on task difficulty.
This often leads to the "overthinking" problem -- excessive and unnecessary
reasoning -- which, although potentially mitigated by human intervention to
control the token budget, still fundamentally contradicts the goal of achieving
fully autonomous AI. In this work, we propose Adaptive Reasoning Model (ARM), a
reasoning model capable of adaptively selecting appropriate reasoning formats
based on the task at hand. These formats include three efficient ones -- Direct
Answer, Short CoT, and Code -- as well as a more elaborate format, Long CoT. To
train ARM, we introduce Ada-GRPO, an adaptation of Group Relative Policy
Optimization (GRPO), which addresses the format collapse issue in traditional
GRPO. Ada-GRPO enables ARM to achieve high token efficiency, reducing tokens by
an average of 30%, and up to 70%, while maintaining performance comparable to
the model that relies solely on Long CoT. Furthermore, not only does it improve
inference efficiency through reduced token generation, but it also brings a 2x
speedup in training. In addition to the default Adaptive Mode, ARM supports two
additional reasoning modes: 1) Instruction-Guided Mode, which allows users to
explicitly specify the reasoning format via special tokens -- ideal when the
appropriate format is known for a batch of tasks. 2) Consensus-Guided Mode,
which aggregates the outputs of the three efficient formats and resorts to Long
CoT in case of disagreement, prioritizing performance with higher token usage.
Towards Holistic Evaluation of Large Audio-Language Models: A
  Comprehensive Survey
Authors: name: Chih-Kai Yang, name: Neo S. Ho, name: Hung-yi Lee
Published: 2025-05-21 19:17:29

With advancements in large audio-language models (LALMs), which enhance large
language models (LLMs) with auditory capabilities, these models are expected to
demonstrate universal proficiency across various auditory tasks. While numerous
benchmarks have emerged to assess LALMs' performance, they remain fragmented
and lack a structured taxonomy. To bridge this gap, we conduct a comprehensive
survey and propose a systematic taxonomy for LALM evaluations, categorizing
them into four dimensions based on their objectives: (1) General Auditory
Awareness and Processing, (2) Knowledge and Reasoning, (3) Dialogue-oriented
Ability, and (4) Fairness, Safety, and Trustworthiness. We provide detailed
overviews within each category and highlight challenges in this field, offering
insights into promising future directions. To the best of our knowledge, this
is the first survey specifically focused on the evaluations of LALMs, providing
clear guidelines for the community. We will release the collection of the
surveyed papers and actively maintain it to support ongoing advancements in the
field.
Which Data Attributes Stimulate Math and Code Reasoning? An
  Investigation via Influence Functions
Authors: name: Siqi Kou, name: Qingyuan Tian, name: Hanwen Xu, name: Zihao Zeng, name: Zhijie Deng
Published: 2025-05-26 13:15:26

Large language models (LLMs) have demonstrated remarkable reasoning
capabilities in math and coding, often bolstered by post-training on the
chain-of-thoughts (CoTs) generated by stronger models. However, existing
strategies for curating such training data predominantly rely on heuristics,
limiting generalizability and failing to capture subtleties underlying in data.
To address these limitations, we leverage influence functions to systematically
attribute LLMs' reasoning ability on math and coding to individual training
examples, sequences, and tokens, enabling deeper insights into effective data
characteristics. Our Influence-based Reasoning Attribution (Infra) uncovers
nontrivial cross-domain effects across math and coding tasks: high-difficulty
math examples improve both math and code reasoning, while low-difficulty code
tasks most effectively benefit code reasoning. Based on these findings, we
introduce a simple yet effective dataset reweighting strategy by flipping task
difficulty, which doubles AIME24 accuracy from 10\% to 20\% and boosts
LiveCodeBench accuracy from 33.8\% to 35.3\% for Qwen2.5-7B-Instruct. Moreover,
our fine-grained attribution reveals that the sequence-level exploratory
behaviors enhance reasoning performance in both math and code, and the
token-level influence patterns are distinct for math and code reasoning: the
former prefers natural language logic connectors and the latter emphasizes
structural syntax.
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis
  Discovery via Hierarchical Search
Authors: name: Zonglin Yang, name: Wanhao Liu, name: Ben Gao, name: Yujie Liu, name: Wei Li, name: Tong Xie, name: Lidong Bing, name: Wanli Ouyang, name: Erik Cambria, name: Dongzhan Zhou
Published: 2025-05-25 16:13:46

Large language models (LLMs) have shown promise in automating scientific
hypothesis generation, yet existing approaches primarily yield coarse-grained
hypotheses lacking critical methodological and experimental details. We
introduce and formally define the novel task of fine-grained scientific
hypothesis discovery, which entails generating detailed, experimentally
actionable hypotheses from coarse initial research directions. We frame this as
a combinatorial optimization problem and investigate the upper limits of LLMs'
capacity to solve it when maximally leveraged. Specifically, we explore four
foundational questions: (1) how to best harness an LLM's internal heuristics to
formulate the fine-grained hypothesis it itself would judge as the most
promising among all the possible hypotheses it might generate, based on its own
internal scoring-thus defining a latent reward landscape over the hypothesis
space; (2) whether such LLM-judged better hypotheses exhibit stronger alignment
with ground-truth hypotheses; (3) whether shaping the reward landscape using an
ensemble of diverse LLMs of similar capacity yields better outcomes than
defining it with repeated instances of the strongest LLM among them; and (4)
whether an ensemble of identical LLMs provides a more reliable reward landscape
than a single LLM. To address these questions, we propose a hierarchical search
method that incrementally proposes and integrates details into the hypothesis,
progressing from general concepts to specific experimental configurations. We
show that this hierarchical process smooths the reward landscape and enables
more effective optimization. Empirical evaluations on a new benchmark of
expert-annotated fine-grained hypotheses from recent chemistry literature show
that our method consistently outperforms strong baselines.
Position: Mechanistic Interpretability Should Prioritize Feature
  Consistency in SAEs
Authors: name: Xiangchen Song, name: Aashiq Muhamed, name: Yujia Zheng, name: Lingjing Kong, name: Zeyu Tang, name: Mona T. Diab, name: Virginia Smith, name: Kun Zhang
Published: 2025-05-26 17:31:36

Sparse Autoencoders (SAEs) are a prominent tool in mechanistic
interpretability (MI) for decomposing neural network activations into
interpretable features. However, the aspiration to identify a canonical set of
features is challenged by the observed inconsistency of learned SAE features
across different training runs, undermining the reliability and efficiency of
MI research. This position paper argues that mechanistic interpretability
should prioritize feature consistency in SAEs -- the reliable convergence to
equivalent feature sets across independent runs. We propose using the Pairwise
Dictionary Mean Correlation Coefficient (PW-MCC) as a practical metric to
operationalize consistency and demonstrate that high levels are achievable
(0.80 for TopK SAEs on LLM activations) with appropriate architectural choices.
Our contributions include detailing the benefits of prioritizing consistency;
providing theoretical grounding and synthetic validation using a model
organism, which verifies PW-MCC as a reliable proxy for ground-truth recovery;
and extending these findings to real-world LLM data, where high feature
consistency strongly correlates with the semantic similarity of learned feature
explanations. We call for a community-wide shift towards systematically
measuring feature consistency to foster robust cumulative progress in MI.
Discrete Markov Bridge
Authors: name: Hengli Li, name: Yuxuan Wang, name: Song-Chun Zhu, name: Ying Nian Wu, name: Zilong Zheng
Published: 2025-05-26 09:32:12

Discrete diffusion has recently emerged as a promising paradigm in discrete
data modeling. However, existing methods typically rely on a fixed rate
transition matrix during training, which not only limits the expressiveness of
latent representations, a fundamental strength of variational methods, but also
constrains the overall design space. To address these limitations, we propose
Discrete Markov Bridge, a novel framework specifically designed for discrete
representation learning. Our approach is built upon two key components: Matrix
Learning and Score Learning. We conduct a rigorous theoretical analysis,
establishing formal performance guarantees for Matrix Learning and proving the
convergence of the overall framework. Furthermore, we analyze the space
complexity of our method, addressing practical constraints identified in prior
studies. Extensive empirical evaluations validate the effectiveness of the
proposed Discrete Markov Bridge, which achieves an Evidence Lower Bound (ELBO)
of 1.38 on the Text8 dataset, outperforming established baselines. Moreover,
the proposed model demonstrates competitive performance on the CIFAR-10
dataset, achieving results comparable to those obtained by image-specific
generation approaches.
Flex-Judge: Think Once, Judge Anywhere
Authors: name: Jongwoo Ko, name: Sungnyun Kim, name: Sungwoo Cho, name: Se-Young Yun
Published: 2025-05-24 08:50:53

Human-generated reward signals are critical for aligning generative models
with human preferences, guiding both training and inference-time evaluations.
While large language models (LLMs) employed as proxy evaluators, i.e.,
LLM-as-a-Judge, significantly reduce the costs associated with manual
annotations, they typically require extensive modality-specific training data
and fail to generalize well across diverse multimodal tasks. In this paper, we
propose Flex-Judge, a reasoning-guided multimodal judge model that leverages
minimal textual reasoning data to robustly generalize across multiple
modalities and evaluation formats. Our core intuition is that structured
textual reasoning explanations inherently encode generalizable decision-making
patterns, enabling an effective transfer to multimodal judgments, e.g., with
images or videos. Empirical results demonstrate that Flex-Judge, despite being
trained on significantly fewer text data, achieves competitive or superior
performance compared to state-of-the-art commercial APIs and extensively
trained multimodal evaluators. Notably, Flex-Judge presents broad impact in
modalities like molecule, where comprehensive evaluation benchmarks are scarce,
underscoring its practical value in resource-constrained domains. Our framework
highlights reasoning-based text supervision as a powerful, cost-effective
alternative to traditional annotation-intensive approaches, substantially
advancing scalable multimodal model-as-a-judge.
Rethinking the Sampling Criteria in Reinforcement Learning for LLM
  Reasoning: A Competence-Difficulty Alignment Perspective
Authors: name: Deyang Kong, name: Qi Guo, name: Xiangyu Xi, name: Wei Wang, name: Jingang Wang, name: Xunliang Cai, name: Shikun Zhang, name: Wei Ye
Published: 2025-05-23 09:15:26

Reinforcement learning exhibits potential in enhancing the reasoning
abilities of large language models, yet it is hard to scale for the low sample
efficiency during the rollout phase. Existing methods attempt to improve
efficiency by scheduling problems based on problem difficulties. However, these
approaches suffer from unstable and biased estimations of problem difficulty
and fail to capture the alignment between model competence and problem
difficulty in RL training, leading to suboptimal results. To tackle these
limitations, this paper introduces Competence-Difficulty
Alignment Sampling (CDAS), which enables accurate
and stable estimation of problem difficulties by aggregating historical
performance discrepancies of problems. Then the model competence is quantified
to adaptively select problems whose difficulty is in alignment with the model's
current competence using a fixed-point system. Experimental results across a
range of challenging mathematical benchmarks show that CDAS achieves great
improvements in both accuracy and efficiency. CDAS attains the highest average
accuracy against baselines and exhibits significant speed advantages compared
to Dynamic Sampling, a competitive strategy in DAPO, which is 2.33
times slower than CDAS.
WINA: Weight Informed Neuron Activation for Accelerating Large Language
  Model Inference
Authors: name: Sihan Chen, name: Dan Zhao, name: Jongwoo Ko, name: Colby Banbury, name: Huiping Zhuang, name: Luming Liang, name: Tianyi Chen
Published: 2025-05-26 02:37:32

The growing computational demands of large language models (LLMs) make
efficient inference and activation strategies increasingly critical. While
recent approaches, such as Mixture-of-Experts (MoE), leverage selective
activation but require specialized training, training-free sparse activation
methods offer broader applicability and superior resource efficiency through
their plug-and-play design. However, many existing methods rely solely on
hidden state magnitudes to determine activation, resulting in high
approximation errors and suboptimal inference accuracy. To address these
limitations, we propose WINA (Weight Informed Neuron Activation), a novel,
simple, and training-free sparse activation framework that jointly considers
hidden state magnitudes and the column-wise ell_2-norms of weight matrices.
We show that this leads to a sparsification strategy that obtains optimal
approximation error bounds with theoretical guarantees tighter than existing
techniques. Empirically, WINA also outperforms state-of-the-art methods (e.g.,
TEAL) by up to 2.94% in average performance at the same sparsity levels,
across a diverse set of LLM architectures and datasets. These results position
WINA as a new performance frontier for training-free sparse activation in LLM
inference, advancing training-free sparse activation methods and setting a
robust baseline for efficient inference. The source code is available at
https://github.com/microsoft/wina.
InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer
  Interaction
Authors: name: Bin Lei, name: Weitai Kang, name: Zijian Zhang, name: Winson Chen, name: Xi Xie, name: Shan Zuo, name: Mimi Xie, name: Ali Payani, name: Mingyi Hong, name: Yan Yan, name: Caiwen Ding
Published: 2025-05-16 05:43:27

This paper introduces InfantAgent-Next, a generalist agent capable
of interacting with computers in a multimodal manner, encompassing text,
images, audio, and video. Unlike existing approaches that either build
intricate workflows around a single large model or only provide workflow
modularity, our agent integrates tool-based and pure vision agents within a
highly modular architecture, enabling different models to collaboratively solve
decoupled tasks in a step-by-step manner. Our generality is demonstrated by our
ability to evaluate not only pure vision-based real-world benchmarks (i.e.,
OSWorld), but also more general or tool-intensive benchmarks (e.g., GAIA and
SWE-Bench). Specifically, we achieve 7.27% accuracy on OSWorld,
higher than Claude-Computer-Use. Codes and evaluation scripts are open-sourced
at https://github.com/bin123apple/InfantAgent.
Learning to Reason without External Rewards
Authors: name: Xuandong Zhao, name: Zhewei Kang, name: Aosong Feng, name: Sergey Levine, name: Dawn Song
Published: 2025-05-26 07:01:06

Training large language models (LLMs) for complex reasoning via Reinforcement
Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on
costly, domain-specific supervision. We explore Reinforcement Learning from
Internal Feedback (RLIF), a framework that enables LLMs to learn from intrinsic
signals without external rewards or labeled data. We propose Intuitor, an RLIF
method that uses a model's own confidence, termed self-certainty, as its sole
reward signal. Intuitor replaces external rewards in Group Relative Policy
Optimization (GRPO) with self-certainty scores, enabling fully unsupervised
learning. Experiments demonstrate that Intuitor matches GRPO's performance on
mathematical benchmarks while achieving superior generalization to
out-of-domain tasks like code generation, without requiring gold solutions or
test cases. Our findings show that intrinsic model signals can drive effective
learning across domains, offering a scalable alternative to RLVR for autonomous
AI systems where verifiable rewards are unavailable. Code is available at
https://github.com/sunblaze-ucb/Intuitor