Orionnnn/hub-stats huggingface dataset:
Scaling External Knowledge Input Beyond Context Windows of LLMs via
  Multi-Agent Collaboration
Authors: name: Zijun Liu, name: Zhennan Wan, name: Peng Li, name: Ming Yan, name: Ji Zhang, name: Fei Huang, name: Yang Liu
Published: 2025-05-27 17:45:04

With the rapid advancement of post-training techniques for reasoning and
information seeking, large language models (LLMs) can incorporate a large
quantity of retrieved knowledge to solve complex tasks. However, the limited
context window of LLMs obstructs scaling the amount of external knowledge
input, prohibiting further improvement, especially for tasks requiring
significant amount of external knowledge. Existing context window extension
methods inevitably cause information loss. LLM-based multi-agent methods emerge
as a new paradigm to handle massive input in a distributional manner, where we
identify two core bottlenecks in existing knowledge synchronization and
reasoning processes. In this work, we develop a multi-agent framework,
ExtAgents, to overcome the bottlenecks and enable better scalability
in inference-time knowledge integration without longer-context training.
Benchmarked with our enhanced multi-hop question answering test,
$boldsymbol{inftyBench+}, and other public test sets including
long survey generation, ExtAgents significantly enhances the performance over
existing non-training methods with the same amount of external knowledge input,
regardless of whether it falls within or exceeds the context window$.
Moreover, the method maintains high efficiency due to high parallelism. Further
study in the coordination of LLM agents on increasing external knowledge input
could benefit real-world applications.
Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal
  Predefinition and Maximal Self-Evolution
Authors: name: Jiahao Qiu, name: Xuan Qi, name: Tongcheng Zhang, name: Xinzhe Juan, name: Jiacheng Guo, name: Yifu Lu, name: Yimin Wang, name: Zixin Yao, name: Qihan Ren, name: Xun Jiang, name: Xing Zhou, name: Dongrui Liu, name: Ling Yang, name: Yue Wu, name: Kaixuan Huang, name: Shilong Liu, name: Hongru Wang, name: Mengdi Wang
Published: 2025-05-26 17:58:53

Recent advances in large language models (LLMs) have enabled agents to
autonomously perform complex, open-ended tasks. However, many existing
frameworks depend heavily on manually predefined tools and workflows, which
hinder their adaptability, scalability, and generalization across domains. In
this work, we introduce Alita--a generalist agent designed with the principle
of "Simplicity is the ultimate sophistication," enabling scalable agentic
reasoning through minimal predefinition and maximal self-evolution. For minimal
predefinition, Alita is equipped with only one component for direct
problem-solving, making it much simpler and neater than previous approaches
that relied heavily on hand-crafted, elaborate tools and workflows. This clean
design enhances its potential to generalize to challenging questions, without
being limited by tools. For Maximal self-evolution, we enable the creativity of
Alita by providing a suite of general-purpose components to autonomously
construct, refine, and reuse external capabilities by generating task-related
model context protocols (MCPs) from open source, which contributes to scalable
agentic reasoning. Notably, Alita achieves 75.15% pass@1 and 87.27% pass@3
accuracy, which is top-ranking among general-purpose agents, on the GAIA
benchmark validation dataset, 74.00% and 52.00% pass@1, respectively, on
Mathvista and PathVQA, outperforming many agent systems with far greater
complexity. More details will be updated at
https://github.com/CharlesQ9/Alita{https://github.com/CharlesQ9/Alita}.
An Explainable Diagnostic Framework for Neurodegenerative Dementias via
  Reinforcement-Optimized LLM Reasoning
Authors: name: Andrew Zamai, name: Nathanael Fijalkow, name: Boris Mansencal, name: Laurent Simon, name: Eloi Navet, name: Pierrick Coupe
Published: 2025-05-26 13:18:32

The differential diagnosis of neurodegenerative dementias is a challenging
clinical task, mainly because of the overlap in symptom presentation and the
similarity of patterns observed in structural neuroimaging. To improve
diagnostic efficiency and accuracy, deep learning-based methods such as
Convolutional Neural Networks and Vision Transformers have been proposed for
the automatic classification of brain MRIs. However, despite their strong
predictive performance, these models find limited clinical utility due to their
opaque decision making. In this work, we propose a framework that integrates
two core components to enhance diagnostic transparency. First, we introduce a
modular pipeline for converting 3D T1-weighted brain MRIs into textual
radiology reports. Second, we explore the potential of modern Large Language
Models (LLMs) to assist clinicians in the differential diagnosis between
Frontotemporal dementia subtypes, Alzheimer's disease, and normal aging based
on the generated reports. To bridge the gap between predictive accuracy and
explainability, we employ reinforcement learning to incentivize diagnostic
reasoning in LLMs. Without requiring supervised reasoning traces or
distillation from larger models, our approach enables the emergence of
structured diagnostic rationales grounded in neuroimaging findings. Unlike
post-hoc explainability methods that retrospectively justify model decisions,
our framework generates diagnostic rationales as part of the inference
process-producing causally grounded explanations that inform and guide the
model's decision-making process. In doing so, our framework matches the
diagnostic performance of existing deep learning methods while offering
rationales that support its diagnostic conclusions.
Beyond Simple Concatenation: Fairly Assessing PLM Architectures for
  Multi-Chain Protein-Protein Interactions Prediction
Authors: name: Hazem Alsamkary, name: Mohamed Elshaffei, name: Mohamed Soudy, name: Sara Ossman, name: Abdallah Amr, name: Nehal Adel Abdelsalam, name: Mohamed Elkerdawy, name: Ahmed Elnaggar
Published: 2025-05-26 14:23:08

Protein-protein interactions (PPIs) are fundamental to numerous cellular
processes, and their characterization is vital for understanding disease
mechanisms and guiding drug discovery. While protein language models (PLMs)
have demonstrated remarkable success in predicting protein structure and
function, their application to sequence-based PPI binding affinity prediction
remains relatively underexplored. This gap is often attributed to the scarcity
of high-quality, rigorously refined datasets and the reliance on simple
strategies for concatenating protein representations. In this work, we address
these limitations. First, we introduce a meticulously curated version of the
PPB-Affinity dataset of a total of 8,207 unique protein-protein interaction
entries, by resolving annotation inconsistencies and duplicate entries for
multi-chain protein interactions. This dataset incorporates a stringent, less
than or equal to 30%, sequence identity threshold to ensure robust splitting
into training, validation, and test sets, minimizing data leakage. Second, we
propose and systematically evaluate four architectures for adapting PLMs to PPI
binding affinity prediction: embeddings concatenation (EC), sequences
concatenation (SC), hierarchical pooling (HP), and pooled attention addition
(PAD). These architectures were assessed using two training methods: full
fine-tuning and a lightweight approach employing ConvBERT heads over frozen PLM
features. Our comprehensive experiments across multiple leading PLMs (ProtT5,
ESM2, Ankh, Ankh2, and ESM3) demonstrated that the HP and PAD architectures
consistently outperform conventional concatenation methods, achieving up to 12%
increase in terms of Spearman correlation. These results highlight the
necessity of sophisticated architectural designs to fully exploit the
capabilities of PLMs for nuanced PPI binding affinity prediction.
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion
  Enhances Protein Representations
Authors: name: Hazem Alsamkary, name: Mohamed Elshaffei, name: Mohamed Elkerdawy, name: Ahmed Elnaggar
Published: 2025-05-26 14:41:10

Protein language models (PLMs) have emerged as powerful tools to detect
complex patterns of protein sequences. However, the capability of PLMs to fully
capture information on protein sequences might be limited by focusing on single
pre-training tasks. Although adding data modalities or supervised objectives
can improve the performance of PLMs, pre-training often remains focused on
denoising corrupted sequences. To push the boundaries of PLMs, our research
investigated a multi-task pre-training strategy. We developed Ankh3, a model
jointly optimized on two objectives: masked language modeling with multiple
masking probabilities and protein sequence completion relying only on protein
sequences as input. This multi-task pre-training demonstrated that PLMs can
learn richer and more generalizable representations solely from protein
sequences. The results demonstrated improved performance in downstream tasks,
such as secondary structure prediction, fluorescence, GB1 fitness, and contact
prediction. The integration of multiple tasks gave the model a more
comprehensive understanding of protein properties, leading to more robust and
accurate predictions.
Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with
  Minimalist Rule-Based RL
Authors: name: Che Liu, name: Haozhe Wang, name: Jiazhen Pan, name: Zhongwei Wan, name: Yong Dai, name: Fangzhen Lin, name: Wenjia Bai, name: Daniel Rueckert, name: Rossella Arcucci
Published: 2025-05-23 14:27:37

Improving performance on complex tasks and enabling interpretable decision
making in large language models (LLMs), especially for clinical applications,
requires effective reasoning. Yet this remains challenging without supervised
fine-tuning (SFT) on costly chain-of-thought (CoT) data distilled from
closed-source models (e.g., GPT-4o). In this work, we present AlphaMed, the
first medical LLM to show that reasoning capability can emerge purely through
reinforcement learning (RL), using minimalist rule-based rewards on public
multiple-choice QA datasets, without relying on SFT or distilled CoT data.
AlphaMed achieves state-of-the-art results on six medical QA benchmarks,
outperforming models trained with conventional SFT+RL pipelines. On challenging
benchmarks (e.g., MedXpert), AlphaMed even surpasses larger or closed-source
models such as DeepSeek-V3-671B and Claude-3.5-Sonnet. To understand the
factors behind this success, we conduct a comprehensive data-centric analysis
guided by three questions: (i) Can minimalist rule-based RL incentivize
reasoning without distilled CoT supervision? (ii) How do dataset quantity and
diversity impact reasoning? (iii) How does question difficulty shape the
emergence and generalization of reasoning? Our findings show that dataset
informativeness is a key driver of reasoning performance, and that minimalist
RL on informative, multiple-choice QA data is effective at inducing reasoning
without CoT supervision. We also observe divergent trends across benchmarks,
underscoring limitations in current evaluation and the need for more
challenging, reasoning-oriented medical QA benchmarks.
NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in
  Brain MRI
Authors: name: Cosmin I. Bercea, name: Jun Li, name: Philipp Raffler, name: Evamaria O. Riedel, name: Lena Schmitzer, name: Angela Kurz, name: Felix Bitzer, name: Paula Roßmüller, name: Julian Canisius, name: Mirjam L. Beyrle, name: Che Liu, name: Wenjia Bai, name: Bernhard Kainz, name: Julia A. Schnabel, name: Benedikt Wiestler
Published: 2025-05-20 08:10:57

In many real-world applications, deployed models encounter inputs that differ
from the data seen during training. Out-of-distribution detection identifies
whether an input stems from an unseen distribution, while open-world
recognition flags such inputs to ensure the system remains robust as
ever-emerging, previously unknown categories appear and must be addressed
without retraining. Foundation and vision-language models are pre-trained on
large and diverse datasets with the expectation of broad generalization across
domains, including medical imaging. However, benchmarking these models on test
sets with only a few common outlier types silently collapses the evaluation
back to a closed-set problem, masking failures on rare or truly novel
conditions encountered in clinical use.
  We therefore present NOVA, a challenging, real-life evaluation-only
benchmark of sim900 brain MRI scans that span 281 rare pathologies and
heterogeneous acquisition protocols. Each case includes rich clinical
narratives and double-blinded expert bounding-box annotations. Together, these
enable joint assessment of anomaly localisation, visual captioning, and
diagnostic reasoning. Because NOVA is never used for training, it serves as an
extreme stress-test of out-of-distribution generalisation: models must bridge
a distribution gap both in sample appearance and in semantic space. Baseline
results with leading vision-language models (GPT-4o, Gemini 2.0 Flash, and
Qwen2.5-VL-72B) reveal substantial performance drops across all tasks,
establishing NOVA as a rigorous testbed for advancing models that can detect,
localize, and reason about truly unknown anomalies.
MLLMs are Deeply Affected by Modality Bias
Authors: name: Xu Zheng, name: Chenfei Liao, name: Yuqian Fu, name: Kaiyu Lei, name: Yuanhuiyi Lyu, name: Lutao Jiang, name: Bin Ren, name: Jialei Chen, name: Jiawen Wang, name: Chengxin Li, name: Linfeng Zhang, name: Danda Pani Paudel, name: Xuanjing Huang, name: Yu-Gang Jiang, name: Nicu Sebe, name: Dacheng Tao, name: Luc Van Gool, name: Xuming Hu
Published: 2025-05-24 11:49:31

Recent advances in Multimodal Large Language Models (MLLMs) have shown
promising results in integrating diverse modalities such as texts and images.
MLLMs are heavily influenced by modality bias, often relying on language while
under-utilizing other modalities like visual inputs. This position paper argues
that MLLMs are deeply affected by modality bias. Firstly, we diagnose the
current state of modality bias, highlighting its manifestations across various
tasks. Secondly, we propose a systematic research road-map related to modality
bias in MLLMs. Thirdly, we identify key factors of modality bias in MLLMs and
offer actionable suggestions for future research to mitigate it. To
substantiate these findings, we conduct experiments that demonstrate the
influence of each factor: 1. Data Characteristics: Language data is compact and
abstract, while visual data is redundant and complex, creating an inherent
imbalance in learning dynamics. 2. Imbalanced Backbone Capabilities: The
dominance of pretrained language models in MLLMs leads to overreliance on
language and neglect of visual information. 3. Training Objectives: Current
objectives often fail to promote balanced cross-modal alignment, resulting in
shortcut learning biased toward language. These findings highlight the need for
balanced training strategies and model architectures to better integrate
multiple modalities in MLLMs. We call for interdisciplinary efforts to tackle
these challenges and drive innovation in MLLM research. Our work provides a
fresh perspective on modality bias in MLLMs and offers insights for developing
more robust and generalizable multimodal systems-advancing progress toward
Artificial General Intelligence.
AdInject: Real-World Black-Box Attacks on Web Agents via Advertising
  Delivery
Authors: name: Haowei Wang, name: Junjie Wang, name: Xiaojun Jia, name: Rupeng Zhang, name: Mingyang Li, name: Zhe Liu, name: Yang Liu, name: Qing Wang
Published: 2025-05-27 17:59:05

Vision-Language Model (VLM) based Web Agents represent a significant step
towards automating complex tasks by simulating human-like interaction with
websites. However, their deployment in uncontrolled web environments introduces
significant security vulnerabilities. Existing research on adversarial
environmental injection attacks often relies on unrealistic assumptions, such
as direct HTML manipulation, knowledge of user intent, or access to agent model
parameters, limiting their practical applicability. In this paper, we propose
AdInject, a novel and real-world black-box attack method that leverages the
internet advertising delivery to inject malicious content into the Web Agent's
environment. AdInject operates under a significantly more realistic threat
model than prior work, assuming a black-box agent, static malicious content
constraints, and no specific knowledge of user intent. AdInject includes
strategies for designing malicious ad content aimed at misleading agents into
clicking, and a VLM-based ad content optimization technique that infers
potential user intents from the target website's context and integrates these
intents into the ad content to make it appear more relevant or critical to the
agent's task, thus enhancing attack effectiveness. Experimental evaluations
demonstrate the effectiveness of AdInject, attack success rates exceeding 60%
in most scenarios and approaching 100% in certain cases. This strongly
demonstrates that prevalent advertising delivery constitutes a potent and
real-world vector for environment injection attacks against Web Agents. This
work highlights a critical vulnerability in Web Agent security arising from
real-world environment manipulation channels, underscoring the urgent need for
developing robust defense mechanisms against such threats. Our code is
available at https://github.com/NicerWang/AdInject.
Modality Curation: Building Universal Embeddings for Advanced Multimodal
  Information Retrieval
Authors: name: Fanheng Kong, name: Jingyuan Zhang, name: Yahui Liu, name: Hongzhi Zhang, name: Shi Feng, name: Xiaocui Yang, name: Daling Wang, name: Yu Tian, name: Victoria W., name: Fuzheng Zhang, name: Guorui Zhou
Published: 2025-05-26 08:09:44

Multimodal information retrieval (MIR) faces inherent challenges due to the
heterogeneity of data sources and the complexity of cross-modal alignment.
While previous studies have identified modal gaps in feature spaces, a
systematic approach to address these challenges remains unexplored. In this
work, we introduce UNITE, a universal framework that tackles these challenges
through two critical yet underexplored aspects: data curation and
modality-aware training configurations. Our work provides the first
comprehensive analysis of how modality-specific data properties influence
downstream task performance across diverse scenarios. Moreover, we propose
Modal-Aware Masked Contrastive Learning (MAMCL) to mitigate the competitive
relationships among the instances of different modalities. Our framework
achieves state-of-the-art results on multiple multimodal retrieval benchmarks,
outperforming existing methods by notable margins. Through extensive
experiments, we demonstrate that strategic modality curation and tailored
training protocols are pivotal for robust cross-modal representation learning.
This work not only advances MIR performance but also provides a foundational
blueprint for future research in multimodal systems. Our project is available
at https://friedrichor.github.io/projects/UNITE.
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial
  Algorithms
Authors: name: Baran Hashemi, name: Kurt Pasque, name: Chris Teska, name: Ruriko Yoshida
Published: 2025-05-22 18:01:25

Dynamic programming (DP) algorithms for combinatorial optimization problems
work with taking maximization, minimization, and classical addition in their
recursion algorithms. The associated value functions correspond to convex
polyhedra in the max plus semiring. Existing Neural Algorithmic Reasoning
models, however, rely on softmax-normalized dot-product attention where the
smooth exponential weighting blurs these sharp polyhedral structures and
collapses when evaluated on out-of-distribution (OOD) settings. We introduce
Tropical attention, a novel attention function that operates natively in the
max-plus semiring of tropical geometry. We prove that Tropical attention can
approximate tropical circuits of DP-type combinatorial algorithms. We then
propose that using Tropical transformers enhances empirical OOD performance in
both length generalization and value generalization, on algorithmic reasoning
tasks, surpassing softmax baselines while remaining stable under adversarial
attacks. We also present adversarial-attack generalization as a third axis for
Neural Algorithmic Reasoning benchmarking. Our results demonstrate that
Tropical attention restores the sharp, scale-invariant reasoning absent from
softmax.
SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning
  Logical Reasoning and Beyond
Authors: name: Junteng Liu, name: Yuanxiang Fan, name: Zhuo Jiang, name: Han Ding, name: Yongyi Hu, name: Chi Zhang, name: Yiqi Shi, name: Shitong Weng, name: Aili Chen, name: Shiqi Chen, name: Yunan Huang, name: Mozhi Zhang, name: Pengyu Zhao, name: Junjie Yan, name: Junxian He
Published: 2025-05-26 07:59:36

Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the
potential of Reinforcement Learning (RL) to enhance reasoning abilities in
Large Language Models (LLMs). While open-source replication efforts have
primarily focused on mathematical and coding domains, methods and resources for
developing general reasoning capabilities remain underexplored. This gap is
partly due to the challenge of collecting diverse and verifiable reasoning data
suitable for RL. We hypothesize that logical reasoning is critical for
developing general reasoning capabilities, as logic forms a fundamental
building block of reasoning. In this work, we present SynLogic, a data
synthesis framework and dataset that generates diverse logical reasoning data
at scale, encompassing 35 diverse logical reasoning tasks. The SynLogic
approach enables controlled synthesis of data with adjustable difficulty and
quantity. Importantly, all examples can be verified by simple rules, making
them ideally suited for RL with verifiable rewards. In our experiments, we
validate the effectiveness of RL training on the SynLogic dataset based on 7B
and 32B models. SynLogic leads to state-of-the-art logical reasoning
performance among open-source datasets, surpassing DeepSeek-R1-Distill-Qwen-32B
by 6 points on BBEH. Furthermore, mixing SynLogic data with mathematical and
coding tasks improves the training efficiency of these domains and
significantly enhances reasoning generalization. Notably, our mixed training
model outperforms DeepSeek-R1-Zero-Qwen-32B across multiple benchmarks. These
findings position SynLogic as a valuable resource for advancing the broader
reasoning capabilities of LLMs. We open-source both the data synthesis pipeline
and the SynLogic dataset at https://github.com/MiniMax-AI/SynLogic.
Do RAG Systems Suffer From Positional Bias?
Authors: name: Florin Cuconasu, name: Simone Filice, name: Guy Horowitz, name: Yoelle Maarek, name: Fabrizio Silvestri
Published: 2025-05-21 14:18:01

Retrieval Augmented Generation enhances LLM accuracy by adding passages
retrieved from an external corpus to the LLM prompt. This paper investigates
how positional bias - the tendency of LLMs to weight information differently
based on its position in the prompt - affects not only the LLM's capability to
capitalize on relevant passages, but also its susceptibility to distracting
passages. Through extensive experiments on three benchmarks, we show how
state-of-the-art retrieval pipelines, while attempting to retrieve relevant
passages, systematically bring highly distracting ones to the top ranks, with
over 60% of queries containing at least one highly distracting passage among
the top-10 retrieved passages. As a result, the impact of the LLM positional
bias, which in controlled settings is often reported as very prominent by
related works, is actually marginal in real scenarios since both relevant and
distracting passages are, in turn, penalized. Indeed, our findings reveal that
sophisticated strategies that attempt to rearrange the passages based on LLM
positional preferences do not perform better than random shuffling.