Orionnnn/hub-stats huggingface dataset:
Masking in Multi-hop QA: An Analysis of How Language Models Perform with
  Context Permutation
Authors: name: Wenyu Huang, name: Pavlos Vougiouklis, name: Mirella Lapata, name: Jeff Z. Pan
Published: 2025-05-16 23:29:47

Multi-hop Question Answering (MHQA) adds layers of complexity to question
answering, making it more challenging. When Language Models (LMs) are prompted
with multiple search results, they are tasked not only with retrieving relevant
information but also employing multi-hop reasoning across the information
sources. Although LMs perform well on traditional question-answering tasks, the
causal mask can hinder their capacity to reason across complex contexts. In
this paper, we explore how LMs respond to multi-hop questions by permuting
search results (retrieved documents) under various configurations. Our study
reveals interesting findings as follows: 1) Encoder-decoder models, such as the
ones in the Flan-T5 family, generally outperform causal decoder-only LMs in
MHQA tasks, despite being significantly smaller in size; 2) altering the order
of gold documents reveals distinct trends in both Flan T5 models and fine-tuned
decoder-only models, with optimal performance observed when the document order
aligns with the reasoning chain order; 3) enhancing causal decoder-only models
with bi-directional attention by modifying the causal mask can effectively
boost their end performance. In addition to the above, we conduct a thorough
investigation of the distribution of LM attention weights in the context of
MHQA. Our experiments reveal that attention weights tend to peak at higher
values when the resulting answer is correct. We leverage this finding to
heuristically improve LMs' performance on this task. Our code is publicly
available at https://github.com/hwy9855/MultiHopQA-Reasoning.
Incorporating brain-inspired mechanisms for multimodal learning in
  artificial intelligence
Authors: name: Xiang He, name: Dongcheng Zhao, name: Yang Li, name: Qingqun Kong, name: Xin Yang, name: Yi Zeng
Published: 2025-05-15 11:08:50

Multimodal learning enhances the perceptual capabilities of cognitive systems
by integrating information from different sensory modalities. However, existing
multimodal fusion research typically assumes static integration, not fully
incorporating key dynamic mechanisms found in the brain. Specifically, the
brain exhibits an inverse effectiveness phenomenon, wherein weaker unimodal
cues yield stronger multisensory integration benefits; conversely, when
individual modal cues are stronger, the effect of fusion is diminished. This
mechanism enables biological systems to achieve robust cognition even with
scarce or noisy perceptual cues. Inspired by this biological mechanism, we
explore the relationship between multimodal output and information from
individual modalities, proposing an inverse effectiveness driven multimodal
fusion (IEMF) strategy. By incorporating this strategy into neural networks, we
achieve more efficient integration with improved model performance and
computational efficiency, demonstrating up to 50% reduction in computational
cost across diverse fusion methods. We conduct experiments on audio-visual
classification, continual learning, and question answering tasks to validate
our method. Results consistently demonstrate that our method performs
excellently in these tasks. To verify universality and generalization, we also
conduct experiments on Artificial Neural Networks (ANN) and Spiking Neural
Networks (SNN), with results showing good adaptability to both network types.
Our research emphasizes the potential of incorporating biologically inspired
mechanisms into multimodal networks and provides promising directions for the
future development of multimodal artificial intelligence. The code is available
at https://github.com/Brain-Cog-Lab/IEMF.
Two Experts Are All You Need for Steering Thinking: Reinforcing
  Cognitive Effort in MoE Reasoning Models Without Additional Training
Authors: name: Mengru Wang, name: Xingyu Chen, name: Yue Wang, name: Zhiwei He, name: Jiahao Xu, name: Tian Liang, name: Qiuzhi Liu, name: Yunzhi Yao, name: Wenxuan Wang, name: Ruotian Ma, name: Haitao Mi, name: Ningyu Zhang, name: Zhaopeng Tu, name: Xiaolong Li, name: Dong Yu
Published: 2025-05-20 17:59:16

Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs)
have achieved impressive reasoning capabilities by selectively activating
experts to facilitate structured cognitive processes. Despite notable advances,
existing reasoning models often suffer from cognitive inefficiencies like
overthinking and underthinking. To address these limitations, we introduce a
novel inference-time steering methodology called Reinforcing Cognitive Experts
(RICE), designed to improve reasoning performance without additional training
or complex heuristics. Leveraging normalized Pointwise Mutual Information
(nPMI), we systematically identify specialized experts, termed ''cognitive
experts'' that orchestrate meta-level reasoning operations characterized by
tokens like ''<think>''. Empirical evaluations with leading MoE-based LRMs
(DeepSeek-R1 and Qwen3-235B) on rigorous quantitative and scientific reasoning
benchmarks demonstrate noticeable and consistent improvements in reasoning
accuracy, cognitive efficiency, and cross-domain generalization. Crucially, our
lightweight approach substantially outperforms prevalent reasoning-steering
techniques, such as prompt design and decoding constraints, while preserving
the model's general instruction-following skills. These results highlight
reinforcing cognitive experts as a promising, practical, and interpretable
direction to enhance cognitive efficiency within advanced reasoning models.
Neurosymbolic Diffusion Models
Authors: name: Emile van Krieken, name: Pasquale Minervini, name: Edoardo Ponti, name: Antonio Vergari
Published: 2025-05-19 14:07:47

Neurosymbolic (NeSy) predictors combine neural perception with symbolic
reasoning to solve tasks like visual reasoning. However, standard NeSy
predictors assume conditional independence between the symbols they extract,
thus limiting their ability to model interactions and uncertainty - often
leading to overconfident predictions and poor out-of-distribution
generalisation. To overcome the limitations of the independence assumption, we
introduce neurosymbolic diffusion models (NeSyDMs), a new class of NeSy
predictors that use discrete diffusion to model dependencies between symbols.
Our approach reuses the independence assumption from NeSy predictors at each
step of the diffusion process, enabling scalable learning while capturing
symbol dependencies and uncertainty quantification. Across both synthetic and
real-world benchmarks - including high-dimensional visual path planning and
rule-based autonomous driving - NeSyDMs achieve state-of-the-art accuracy among
NeSy predictors and demonstrate strong calibration.
To Bias or Not to Bias: Detecting bias in News with bias-detector
Authors: name: Himel Ghosh, name: Ahmed Mosharafa, name: Georg Groh
Published: 2025-05-19 11:54:39

Media bias detection is a critical task in ensuring fair and balanced
information dissemination, yet it remains challenging due to the subjectivity
of bias and the scarcity of high-quality annotated data. In this work, we
perform sentence-level bias classification by fine-tuning a RoBERTa-based model
on the expert-annotated BABE dataset. Using McNemar's test and the 5x2
cross-validation paired t-test, we show statistically significant improvements
in performance when comparing our model to a domain-adaptively pre-trained
DA-RoBERTa baseline. Furthermore, attention-based analysis shows that our model
avoids common pitfalls like oversensitivity to politically charged terms and
instead attends more meaningfully to contextually relevant tokens. For a
comprehensive examination of media bias, we present a pipeline that combines
our model with an already-existing bias-type classifier. Our method exhibits
good generalization and interpretability, despite being constrained by
sentence-level analysis and dataset size because of a lack of larger and more
advanced bias corpora. We talk about context-aware modeling, bias
neutralization, and advanced bias type classification as potential future
directions. Our findings contribute to building more robust, explainable, and
socially responsible NLP systems for media bias detection.
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via
  Reinforcement Learning to Rank
Authors: name: Tianhe Wu, name: Jian Zou, name: Jie Liang, name: Lei Zhang, name: Kede Ma
Published: 2025-05-20 14:56:50

DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing
reasoning and generalization capabilities of large language models (LLMs)
through reinforcement learning. Nevertheless, the potential of
reasoning-induced computational modeling has not been thoroughly explored in
the context of image quality assessment (IQA), a task critically dependent on
visual reasoning. In this paper, we introduce VisualQuality-R1, a
reasoning-induced no-reference IQA (NR-IQA) model, and we train it with
reinforcement learning to rank, a learning algorithm tailored to the
intrinsically relative nature of visual quality. Specifically, for a pair of
images, we employ group relative policy optimization to generate multiple
quality scores for each image. These estimates are then used to compute
comparative probabilities of one image having higher quality than the other
under the Thurstone model. Rewards for each quality estimate are defined using
continuous fidelity measures rather than discretized binary labels. Extensive
experiments show that the proposed VisualQuality-R1 consistently outperforms
discriminative deep learning-based NR-IQA models as well as a recent
reasoning-induced quality regression method. Moreover, VisualQuality-R1 is
capable of generating contextually rich, human-aligned quality descriptions,
and supports multi-dataset training without requiring perceptual scale
realignment. These features make VisualQuality-R1 especially well-suited for
reliably measuring progress in a wide range of image processing tasks like
super-resolution and image generation.
Optimizing Anytime Reasoning via Budget Relative Policy Optimization
Authors: name: Penghui Qi, name: Zichen Liu, name: Tianyu Pang, name: Chao Du, name: Wee Sun Lee, name: Min Lin
Published: 2025-05-19 17:58:44

Scaling test-time compute is crucial for enhancing the reasoning capabilities
of large language models (LLMs). Existing approaches typically employ
reinforcement learning (RL) to maximize a verifiable reward obtained at the end
of reasoning traces. However, such methods optimize only the final performance
under a large and fixed token budget, which hinders efficiency in both training
and deployment. In this work, we present a novel framework, AnytimeReasoner, to
optimize anytime reasoning performance, which aims to improve token efficiency
and the flexibility of reasoning under varying token budget constraints. To
achieve this, we truncate the complete thinking process to fit within sampled
token budgets from a prior distribution, compelling the model to summarize the
optimal answer for each truncated thinking for verification. This introduces
verifiable dense rewards into the reasoning process, facilitating more
effective credit assignment in RL optimization. We then optimize the thinking
and summary policies in a decoupled manner to maximize the cumulative reward.
Additionally, we introduce a novel variance reduction technique, Budget
Relative Policy Optimization (BRPO), to enhance the robustness and efficiency
of the learning process when reinforcing the thinking policy. Empirical results
in mathematical reasoning tasks demonstrate that our method consistently
outperforms GRPO across all thinking budgets under various prior distributions,
enhancing both training and token efficiency.
The Aloe Family Recipe for Open and Specialized Healthcare LLMs
Authors: name: Dario Garcia-Gasulla, name: Jordi Bayarri-Planas, name: Ashwin Kumar Gururajan, name: Enrique Lopez-Cuena, name: Adrian Tormos, name: Daniel Hinjos, name: Pablo Bernabeu-Perez, name: Anna Arias-Duart, name: Pablo Agustin Martin-Torres, name: Marta Gonzalez-Mallo, name: Sergio Alvarez-Napagao, name: Eduard Ayguadé-Parra, name: Ulises Cortés
Published: 2025-05-07 13:13:14

Purpose: With advancements in Large Language Models (LLMs) for healthcare,
the need arises for competitive open-source models to protect the public
interest. This work contributes to the field of open medical LLMs by optimizing
key stages of data preprocessing and training, while showing how to improve
model safety (through DPO) and efficacy (through RAG). The evaluation
methodology used, which includes four different types of tests, defines a new
standard for the field. The resultant models, shown to be competitive with the
best private alternatives, are released with a permisive license.
  Methods: Building on top of strong base models like Llama 3.1 and Qwen 2.5,
Aloe Beta uses a custom dataset to enhance public data with synthetic Chain of
Thought examples. The models undergo alignment with Direct Preference
Optimization, emphasizing ethical and policy-aligned performance in the
presence of jailbreaking attacks. Evaluation includes close-ended, open-ended,
safety and human assessments, to maximize the reliability of results.
  Results: Recommendations are made across the entire pipeline, backed by the
solid performance of the Aloe Family. These models deliver competitive
performance across healthcare benchmarks and medical fields, and are often
preferred by healthcare professionals. On bias and toxicity, the Aloe Beta
models significantly improve safety, showing resilience to unseen jailbreaking
attacks. For a responsible release, a detailed risk assessment specific to
healthcare is attached to the Aloe Family models.
  Conclusion: The Aloe Beta models, and the recipe that leads to them, are a
significant contribution to the open-source medical LLM field, offering
top-of-the-line performance while maintaining high ethical requirements. This
work sets a new standard for developing and reporting aligned LLMs in
healthcare.
Fixing 7,400 Bugs for 1$: Cheap Crash-Site Program Repair
Authors: name: Han Zheng, name: Ilia Shumailov, name: Tianqi Fan, name: Aiden Hall, name: Mathias Payer
Published: 2025-05-19 13:32:51

The rapid advancement of bug-finding techniques has led to the discovery of
more vulnerabilities than developers can reasonably fix, creating an urgent
need for effective Automated Program Repair (APR) methods. However, the
complexity of modern bugs often makes precise root cause analysis difficult and
unreliable. To address this challenge, we propose crash-site repair to simplify
the repair task while still mitigating the risk of exploitation. In addition,
we introduce a template-guided patch generation approach that significantly
reduces the token cost of Large Language Models (LLMs) while maintaining both
efficiency and effectiveness.
  We implement our prototype system, WILLIAMT, and evaluate it against
state-of-the-art APR tools. Our results show that, when combined with the
top-performing agent CodeRover-S, WILLIAMT reduces token cost by 45.9% and
increases the bug-fixing rate to 73.5% (+29.6%) on ARVO, a ground-truth open
source software vulnerabilities benchmark. Furthermore, we demonstrate that
WILLIAMT can function effectively even without access to frontier LLMs: even a
local model running on a Mac M4 Mini achieves a reasonable repair rate. These
findings highlight the broad applicability and scalability of WILLIAMT.
Lessons from Defending Gemini Against Indirect Prompt Injections
Authors: name: Chongyang Shi, name: Sharon Lin, name: Shuang Song, name: Jamie Hayes, name: Ilia Shumailov, name: Itay Yona, name: Juliette Pluto, name: Aneesh Pappu, name: Christopher A. Choquette-Choo, name: Milad Nasr, name: Chawin Sitawarin, name: Gena Gibson, name: Andreas Terzis, name: John "Four" Flynn
Published: 2025-05-20 15:54:45

Gemini is increasingly used to perform tasks on behalf of users, where
function-calling and tool-use capabilities enable the model to access user
data. Some tools, however, require access to untrusted data introducing risk.
Adversaries can embed malicious instructions in untrusted data which cause the
model to deviate from the user's expectations and mishandle their data or
permissions. In this report, we set out Google DeepMind's approach to
evaluating the adversarial robustness of Gemini models and describe the main
lessons learned from the process. We test how Gemini performs against a
sophisticated adversary through an adversarial evaluation framework, which
deploys a suite of adaptive attack techniques to run continuously against past,
current, and future versions of Gemini. We describe how these ongoing
evaluations directly help make Gemini more resilient against manipulation.
Phare: A Safety Probe for Large Language Models
Authors: name: Pierre Le Jeune, name: Benoît Malézieux, name: Weixuan Xiao, name: Matteo Dora
Published: 2025-05-16 15:31:08

Ensuring the safety of large language models (LLMs) is critical for
responsible deployment, yet existing evaluations often prioritize performance
over identifying failure modes. We introduce Phare, a multilingual diagnostic
framework to probe and evaluate LLM behavior across three critical dimensions:
hallucination and reliability, social biases, and harmful content generation.
Our evaluation of 17 state-of-the-art LLMs reveals patterns of systematic
vulnerabilities across all safety dimensions, including sycophancy, prompt
sensitivity, and stereotype reproduction. By highlighting these specific
failure modes rather than simply ranking models, Phare provides researchers and
practitioners with actionable insights to build more robust, aligned, and
trustworthy language systems.
Visual Agentic Reinforcement Fine-Tuning
Authors: name: Ziyu Liu, name: Yuhang Zang, name: Yushan Zou, name: Zijian Liang, name: Xiaoyi Dong, name: Yuhang Cao, name: Haodong Duan, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-05-20 11:59:25

A key trend in Large Reasoning Models (e.g., OpenAI's o3) is the native
agentic ability to use external tools such as web browsers for searching and
writing/executing code for image manipulation to think with images. In the
open-source research community, while significant progress has been made in
language-only agentic abilities such as function calling and tool integration,
the development of multi-modal agentic capabilities that involve truly thinking
with images, and their corresponding benchmarks, are still less explored. This
work highlights the effectiveness of Visual Agentic Reinforcement Fine-Tuning
(Visual-ARFT) for enabling flexible and adaptive reasoning abilities for Large
Vision-Language Models (LVLMs). With Visual-ARFT, open-source LVLMs gain the
ability to browse websites for real-time information updates and write code to
manipulate and analyze input images through cropping, rotation, and other image
processing techniques. We also present a Multi-modal Agentic Tool Bench (MAT)
with two settings (MAT-Search and MAT-Coding) designed to evaluate LVLMs'
agentic search and coding abilities. Our experimental results demonstrate that
Visual-ARFT outperforms its baseline by +18.6% F1 / +13.0% EM on MAT-Coding and
+10.3% F1 / +8.7% EM on MAT-Search, ultimately surpassing GPT-4o. Visual-ARFT
also achieves +29.3 F1% / +25.9% EM gains on existing multi-hop QA benchmarks
such as 2Wiki and HotpotQA, demonstrating strong generalization capabilities.
Our findings suggest that Visual-ARFT offers a promising path toward building
robust and generalizable multimodal agents.
Towards eliciting latent knowledge from LLMs with mechanistic
  interpretability
Authors: name: Bartosz Cywiński, name: Emil Ryd, name: Senthooran Rajamanoharan, name: Neel Nanda
Published: 2025-05-20 13:36:37

As language models become more powerful and sophisticated, it is crucial that
they remain trustworthy and reliable. There is concerning preliminary evidence
that models may attempt to deceive or keep secrets from their operators. To
explore the ability of current techniques to elicit such hidden knowledge, we
train a Taboo model: a language model that describes a specific secret word
without explicitly stating it. Importantly, the secret word is not presented to
the model in its training data or prompt. We then investigate methods to
uncover this secret. First, we evaluate non-interpretability (black-box)
approaches. Subsequently, we develop largely automated strategies based on
mechanistic interpretability techniques, including logit lens and sparse
autoencoders. Evaluation shows that both approaches are effective in eliciting
the secret word in our proof-of-concept setting. Our findings highlight the
promise of these approaches for eliciting hidden knowledge and suggest several
promising avenues for future work, including testing and refining these methods
on more complex model organisms. This work aims to be a step towards addressing
the crucial problem of eliciting secret knowledge from language models, thereby
contributing to their safe and reliable deployment.
Visionary-R1: Mitigating Shortcuts in Visual Reasoning with
  Reinforcement Learning
Authors: name: Jiaer Xia, name: Yuhang Zang, name: Peng Gao, name: Yixuan Li, name: Kaiyang Zhou
Published: 2025-05-20 17:58:35

Learning general-purpose reasoning capabilities has long been a challenging
problem in AI. Recent research in large language models (LLMs), such as
DeepSeek-R1, has shown that reinforcement learning techniques like GRPO can
enable pre-trained LLMs to develop reasoning capabilities using simple
question-answer pairs. In this paper, we aim to train visual language models
(VLMs) to perform reasoning on image data through reinforcement learning and
visual question-answer pairs, without any explicit chain-of-thought (CoT)
supervision. Our findings indicate that simply applying reinforcement learning
to a VLM -- by prompting the model to produce a reasoning chain before
providing an answer -- can lead the model to develop shortcuts from easy
questions, thereby reducing its ability to generalize across unseen data
distributions. We argue that the key to mitigating shortcut learning is to
encourage the model to interpret images prior to reasoning. Therefore, we train
the model to adhere to a caption-reason-answer output format: initially
generating a detailed caption for an image, followed by constructing an
extensive reasoning chain. When trained on 273K CoT-free visual question-answer
pairs and using only reinforcement learning, our model, named Visionary-R1,
outperforms strong multimodal models, such as GPT-4o, Claude3.5-Sonnet, and
Gemini-1.5-Pro, on multiple visual reasoning benchmarks.