Radiology report generation requires precise alignment between medical imaging findings and clinically coherent textual descriptions. While current methods predominantly rely on either large vision-language models (LVLMs) for visual grounding or large language models (LLMs) for medical narrative generation, they often fail to effectively integrate multimodal clinical evidence with domain-specific knowledge. This paper proposes a novel multimodal dual-path framework that synergistically combines LVLMs and LLMs to address these limitations. Our approach establishes a dynamic fusion between LVLMs’ visual-semantic grounding capabilities and LLMs’ clinical knowledge reasoning. Specifically, we employ a structured prompting strategy that models the report generation task into three clinically meaningful sections and introduces fine-grained multi-label classification prompts to guide the models, enabling more accurate and comprehensive clinical report generation. Experiments on the public MIMIC-CXR benchmark demonstrate our framework’s superiority over state-of-the-art methods.
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Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning
PDF ↗Recent multimodal large language models (MLLMs) achieve strong performance on visual reasoning benchmarks, yet it remains unclear to what extent such performance reflects reasoning directly grounded in visual evidence. We introduce VisReason, a benchmark for vision-centric reasoning in everyday scenarios where perception and inference are tightly coupled. VisReason contains 1,505 questions across 10 categories spanning perceptual, structural, and conceptual reasoning. Our evaluation shows that VisReason poses a qualitatively different challenge from existing benchmarks, exposing substantial gaps between humans and current MLLMs and revealing limited benefits from test-time reasoning strategies. VisReason offers a focused diagnostic for evaluating vision-centric reasoning beyond language.
We present a Bengali mathematical reasoning model called GanitLLM (named after the Bangla word for mathematics, "Ganit"), together with a new difficulty-aware Bengali math corpus and a curriculum-based GRPO pipeline. Bengali is one of the world’s most widely spoken languages, yet existing LLMs either reason in English and then translate, or simply fail on multi-step Bengali math, in part because reinforcement learning recipes are tuned for high-resource languages and collapse under reward sparsity in low-resource settings. To address this, we construct Ganit, a rigorously filtered and decontaminated Bengali math dataset with automatic difficulty tags derived from the pass@k of a strong evaluator model. Building on this dataset, we propose Curriculum-GRPO, which combines multi-stage training (SFT + GRPO) with difficulty-aware sampling and verifiable rewards for format, numerical correctness, and Bengali reasoning. On Bn-MGSM and Bn-MSVAMP, GanitLLM-4B improves over its Qwen3-4B base by +8 and +7 accuracy points, respectively, while increasing the percentage of Bengali reasoning tokens from 14% to over 88% and reducing average solution length from 943 to 193 words.
Generalization or Memorization? Multi-Agent vs. Baseline LLMs and AutoML Models for Tabular Classification
PDF ↗Large Language Models (LLMs) are increasingly used for structured tabular data, yet it remains unclear whether their performance reflects genuine reasoning or memorization of pre-training corpora. We investigate this question through a rigorous, contamination-aware evaluation of a representative modular Multi-Agent LLM (MALLM) framework against state-of-the-art AutoML systems and established baselines (TABLET, TABLLM). We evaluate eleven binary classification tasks: five pre-cutoff benchmarks likely seen during LLM pre-training and six post-cutoff datasets released after the LLM knowledge cutoff. Results show a sharp performance dichotomy: MALLM achieves competitive or superior performance on pre-cutoff datasets but substantially underperforms AutoML on post-cutoff data, exhibiting poor calibration and high variance, especially on hard-to-classify instances. By contrast, AutoML models generalize consistently and align confidence more closely with instance hardness. These findings suggest that, despite agentic scaffolding, current LLMs cannot yet replace production-grade discriminative models for tabular classification, underscoring the need for contamination-free benchmarks to accurately assess tabular reasoning capabilities.
While large language models show promise in mental healthcare, evaluating their therapeutic competence remains challenging due to the unstructured and longitudinal nature of counseling. We argue that current evaluation paradigms suffer from an unanchored defect, leading to two forms of instability: process drift, where unsteered client simulation wanders away from specific counseling goals, and standard drift, where static pointwise scoring lacks the stability for reliable judgment. To address this, we introduce Ps, a unified framework that calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments. We first anchor the interaction trajectory in simulation, where clients precisely control the fluid consultation process to probe multifaceted capabilities. We then anchor the battle trajectory in judgments through an efficient Swiss-system tournament, utilizing dynamic pairwise battles to yield robust Elo ratings. Beyond ranking, we demonstrate that tournament trajectories can be transformed into credible reward signals, enabling on-policy reinforcement learning to enhance LLMs’ performance. Extensive experiments validate the effectiveness of PsychePass and its strong consistency with human expert judgments.
Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are both effective and biologically safe remains a major bottleneck. In practical screening, toxicity is a decision-level constraint: if a lipid is toxic, its efficiency prediction is clinically irrelevant. We propose LipoAgent , a safety-aware multi-agent LLM framework for lipid discovery. LipoAgent combines domain-specific fine-tuning with a conditional prediction objective that enforces toxicity as a prerequisite for efficiency prediction, and further improves reliability via multi-agent verification with lightweight human oversight when disagreement persists. Across multiple foundation models, LipoAgent achieves an average 32% relative improvement in mRNA transfection efficiency prediction compared with other reported models for lipid design. Wet-lab validation confirms that virtual screening rankings reliably translate to biological transfection outcomes. The code is publicly available at https://github.com/SAI-Lab-NYU/LipoAgent.git.
GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmark
PDF ↗Authorship verification (AV) is the task of determining whether two texts were written by the same author and has been studied extensively, predominantly for English data. In contrast, large-scale benchmarks and systematic evaluations for other languages remain scarce. We address this gap by introducing GerAV, a comprehensive benchmark for German AV comprising over 400k labeled text pairs. GerAV is built from Twitter and Reddit data, with the Reddit part further divided into in-domain and cross-domain message-based subsets, as well as a profile-based subset. This design enables controlled analysis of the effects of data source, topical domain, and text length. Using the provided training splits, we conduct a systematic evaluation of strong baselines and state-of-the-art models and find that our best approach, a fine-tuned large language model, outperforms recent baselines by up to 0.09 absolute F1 score and surpasses GPT-5 in a zero-shot setting by 0.08. We further observe a trade-off between specialization and generalization: models trained on specific data types perform best under matching conditions but generalize less well across data regimes, a limitation that can be mitigated by combining training sources. Overall, GerAV provides a challenging and versatile benchmark for advancing research on German and cross-domain AV.
Large language models (LLMs) are primarily accessed via commercial APIs, but this often requires users to expose their data to service providers. In this paper, we explore how users can stay in control of their data by using privacy profiles: simple natural language instructions that say what should and should not be revealed. We build a framework where a local model uses these instructions to rewrite queries, only hiding details deemed sensitive by the user, before sending them to an external model, thus balancing privacy with performance. To support this research, we introduce PEEP, a multilingual dataset of real user queries annotated to mark private content and paired with synthetic privacy profiles, alongside PROFIT, a training procedure that enables effective and efficient use of the pipeline. Experiments with lightweight local LLMs show that, after training, they not only achieve markedly better privacy preservation but also match or exceed the performance of much larger few-shot models.
In this work, we introduce SkillWeave, a modular improvement framework that enables large language models to specialize under fixed memory budgets. SkillWeave partitions full capabilities of a general-purpose model into domain-specific skillpacks—lightweight, domain-specific delta modules—that reorganize and refine the model’s internal knowledge. To ensure deployment efficiency, SkillWeave incorporates SkillZip, a compression component that transforms specialized parameters into lightweight, inference-ready skillpacks. Together, these components allow SkillWeave to achieve strong multi-domain performance and inference-efficient execution. On multi-task and agentic benchmarks, a 9B SkillWeave model outperforms task-specific baselines and even surpasses a 32B monolithic LLM, while achieving up to 4× speedup.
In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by the manual, repetitive chore of building project webpages to make their dense papers accessible. While automation has tackled static slides and posters, the dynamic, interactive nature of webpages has remained an unaddressed challenge. To bridge this gap, we reframe the problem, arguing that the solution lies not in a single command, but in a collaborative, hierarchical process. We introduce \textbf{AutoPage}, a novel multi-agent system that embodies this philosophy. AutoPage deconstructs paper-to-page creation into a coarse-to-fine pipeline from narrative planning to multimodal content generation and interactive rendering. To combat AI hallucination, dedicated "Checker" agents verify each step against the source paper, while optional human checkpoints ensure the final product aligns perfectly with the author’s vision, transforming the system from a mere tool into a powerful collaborative assistant. To rigorously validate our approach, we also construct \textbf{PageBench}, the first benchmark for this new task. Experiments show AutoPage not only generates high-quality, visually appealing pages but does so with remarkable efficiency in under 15 minutes for less than $0.1. Code and data will be released.
Large Language Models (LLMs) have demonstrated impressive results in natural language processing (NLP) tasks, however, their brittleness against subtle input perturbations continues to pose a significant challenge. Existing research on robustness has predominantly focused on standard text-based perturbations and the use of invisible characters and homoglyphs, while overlooking the impact of stylized characters increasingly prevalent on social media. To address this, we propose TinyAttack, a novel adversarial attack framework designed to exploit vulnerabilities in LLMs through Unicode-based stylistic transformations. TinyAttack utilises five Unicode variants to modify the visual rendering of text without altering its underlying semantic or syntactic structure. Our comprehensive evaluation on both open-source (Llama, Mistral, Gemma, Qwen) and closed-source LLMs (Gemini, GPT) demonstrates their susceptibility to these stylized inputs, with performance drops ranging from 29-92% and 6-88.5%, respectively, across all tasks.Our code is available at https://github.com/TRAI-group/TinyAttack.
Aligning Vision-Language Models (VLMs) with safety standards is essential to mitigate risks arising from their multimodal complexity, where integrating vision and language unveils subtle threats beyond the reach of conventional safeguards. Inspired by the insight that reasoning across modalities is key to preempting intricate vulnerabilities, we propose a novel direction for VLM safety: multimodal reasoning-driven prompt rewriting. To this end, we introduce VLMGuard-R1, a proactive framework that refines user inputs through a reasoning-guided rewriter, dynamically interpreting text-image interactions to deliver refined prompts that bolster safety across diverse VLM architectures without altering their core parameters. To achieve this, we devise a three-stage reasoning pipeline to synthesize a dataset that trains the rewriter to infer subtle threats, enabling tailored, actionable responses over generic refusals. Extensive experiments across five benchmarks with six VLMs reveal that VLMGuard-R1 outperforms four baselines. In particular, VLMGuard-R1 achieves a remarkable 43.59% increase in average safety across five models on the SIUO benchmark.
HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy
PDF ↗Cross-document relation extraction (RE) aims to identify relations between the head and tail entities located in different documents. Existing approaches typically adopt the paradigm of “Small Language Model (SLM) + Classifier”. However, the limited language understanding ability of SLMs hinders further improvement of their performance. In this paper, we conduct a preliminary study to explore the performance of Large Language Models (LLMs) in cross-document RE. Despite their extensive parameters, our findings indicate that LLMs do not consistently surpass existing SLMs. Further analysis suggests that the underperformance is largely attributed to the challenges posed by the numerous predefined relations. To overcome this issue, we propose an LLM-based Hierarchical Classification model for cross-document RE (HCRE), which consists of two core components: 1) an LLM for relation prediction and 2) a hierarchical relation tree derived from the predefined relation set. This tree enables the LLM to perform hierarchical classification, where the target relation is inferred level by level. Since the number of child nodes is much smaller than the size of entire predefined relation set, the hierarchical relation tree significantly reduces the number of relation options that LLM needs to consider during inference. However, hierarchical classification introduces the risk of error propagation across levels. To mitigate this, we propose a prediction-then-verification inference strategy that improves prediction reliability through multi-view verification at each level. Extensive experiments show that HCRE outperforms existing baselines, validating its effectiveness.
Knowledge about the visual world is not only constantly evolving but also inherently happening all over the world: breaking news in Tokyo, political events in São Paulo, and cultural phenomena in Cairo are first reported in Japanese, Portuguese, and Arabic, carrying regional context that English-centric resources cannot fully capture. Yet existing resources for visual knowledge remain confined to English, creating a "Worldwide Knowledge Gap" that hinders developing truly global assistants. To quantify this gap, we introduce LiveVQA-W(orldwide), the first dynamic-updating dataset for real-time, multilingual visual knowledge seeking and updating across ten major languages. Drawing from worldwide news outlets, YouTube videos, and academic platforms during August–December 2025, LiveVQA-W comprises 234K images, 873K questions, and 171K visual entities with hierarchical evaluation: Level 1 for visual entity recognition and Level 2 for multi-hop cross-lingual reasoning. Our comprehensive benchmarking of 15 state-of-the-art MLLMs reveals that models without search achieve near-random performance, while search-augmented models exhibit severe linguistic bias, with English accuracy nearly double that of other languages. Furthermore, we explore visual knowledge updating through large-scale training, finding that injected knowledge improves recall but remains fragile under prompt rephrasing and image perturbations such as rotation and flipping. We release the fully replicable data collection pipeline and raw dataset to support continuous community-driven expansion. The benchmark, code, and related resources are available at: https://worldwide-livevqa.github.io.
LLMs are increasingly being integrated into clinical workflows, yet they often lack clinical empathy, an essential aspect of effective doctor–patient communication. Existing NLP frameworks focus on reactively labeling empathy in doctors’ responses but offer limited support for anticipatory modeling of empathy needs, especially in general health queries. We introduce the Empathy Applicability Framework (EAF), a theory-driven approach that classifies patient queries in terms of the applicability of emotional reactions and interpretations, based on clinical, contextual, and linguistic cues. We release a benchmark of real patient queries, dual-annotated by human annotators and GPT-4o. In the subset with human consensus, we also observe substantial human–GPT alignment. To validate EAF, we train classifiers on human-labeled and GPT-only annotations to predict empathy applicability, achieving strong performance and outperforming the heuristic and zero-shot LLM baselines. Error analysis highlights persistent challenges: implicit distress, clinical-severity ambiguity, and contextual hardship, underscoring the need for multi-annotator modeling, clinician-in-the-loop calibration, and culturally diverse annotation. EAF provides a framework for identifying empathy needs before response generation, establishes a benchmark for anticipatory empathy modeling, and enables supporting empathetic communication in asynchronous healthcare.
Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: policies prematurely concentrate on a small set of dominant reasoning patterns, improving pass@1 while limiting rollout-level diversity and gains in pass@k. We argue that this failure stems from regularizing local token behavior rather than diversity over sets of solutions. To address this, we propose Uniqueness-Aware Reinforcement Learning, a rollout-level objective that explicitly rewards correct solutions that exhibit rare high-level strategies. Our method uses an LLM-based judge to cluster rollouts for the same problem according to their high-level solution strategies, ignoring superficial variations, and reweights policy advantages inversely with cluster size. As a result, correct but novel strategies receive higher rewards than redundant ones. Across mathematics, physics, and medical reasoning benchmarks, our approach consistently improves pass@k across large sampling budgets and increases the area under the pass@k curve (AUC@K) without sacrificing pass@1, while sustaining exploration and uncovering more diverse solution strategies at scale. Code is in Software part under submission page.
Large reasoning models (LRMs) already possess a latent capacity for long chain-of-thought reasoning. Prior work has shown that outcome-based reinforcement learning (RL) can incidentally elicit advanced reasoning behaviors such as self-correction, backtracking, and verification–phenomena often referred to as the model’s ”aha moment”. However, the timing and consistency of these emergent behaviors remain unpredictable and uncontrollable, limiting the scalability and reliability of LRMs’ reasoning capabilities. To address these limitations, we move beyond reliance on prompts and unpredictable ”aha moments”. Instead, we explicitly align models with three meta-abilities: deduction, induction, and abduction, using automatically generated, self-verifiable tasks. Our three-stage pipeline (individual alignment, parameter-space merging, domain-specific reinforcement learning) boosts performance by over 10% relative to instruction-tuned baselines. Furthermore, domain-specific RL from the aligned checkpoint yields an additional gain in performance ceiling for both 7B and 32B models across math, coding, and science benchmarks, showing that explicit meta-ability alignment offers a scalable and dependable foundation for reasoning. Code and data can be found in Software and Data part in submission page.
Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a controlled evaluation framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions. We execute a two-phase protocol: Engram (seeding private information into a target agent’s memory) and Resonance (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage using a two-stage recovery criterion that combines exact-match extraction with LLM-based inference over the attacker’s final output. We evaluate six canonical topologies (complete, circle, chain, tree, star, star-ring) across n\in\{4,5,6\}, attacker–target placements, and base models. Results are consistent: denser connectivity, shorter attacker–target distance, and higher target centrality increase leakage; most leakage occurs in early rounds and then plateaus; model choice shifts absolute rates but preserves broad structural trends; spatiotemporal/location attributes leak more readily than identity credentials or regulated identifiers. We distill practical guidance for system design: favor sparse or hierarchical connectivity, maximize attacker–target separation, and restrict hub/shortcut pathways via topology-aware access control. Our code is available at https://github.com/llll121/mama-eval.
Multimodal aspect-based sentiment classification (MABSC) requires aspect-level sentiment inference from textual-image data that jointly convey opinions. Yet most existing approaches primarily exploit discrete polarity patterns and generic visual embeddings, making them less effective when the affect is subtle, implicit, or expressed through imagery. In this work, we propose \textbf{\textit{VADE}}, a Valence–Arousal–Dominance~(\textbf{\textit{VAD}})-\textbf{\textit{E}}nhanced MABSC framework that brings continuous VAD signals into multimodal sentiment reasoning and learns emotion-sensitive image representations. Specifically, we design a VAD encoder to extract continuous affect cues from text for aspect-level sentiment reasoning. Furthermore, we fine-tune a CLIP-based image encoder on affect-enriched image–text pairs to obtain visual representations that are more sensitive to sentiment cues. To support the fine-tuning process, we construct an affect-enriched image–text dataset \textbf{\textit{Senti-COCO}} by rewriting MSCOCO captions with a multimodal large language model, which yields large-scale image-text pairs with richer affective expressions. Experiments on two mainstream datasets, Twitter-15 and Twitter-17, show that VADE achieves a new state-of-the-art performance, demonstrating the effectiveness of incorporating VAD signals for MABSC.
PseudoSeer is a novel search engine for academic pseudocode, enabling retrieval over 320,000 algorithm implementations extracted from the arXiv. Using the system’s caption-reference pairs, we study asymmetric retrieval, matching short queries with a median length of five words against long documents of roughly 300 words composed primarily of natural language with limited LaTeX notation. Our evaluation reveals scaling limitations in embedding models: a 149M parameter encoder outperforms 1.5B parameter alternatives, while BM25 remains competitive with pretrained models. Analyzing attention patterns over 33,000 caption document pairs, we identify two factors driving these results: attention efficiency and attention concentration. Models that significantly attend to sinks or non-discriminative tokens leave less attention for discriminative content, while models with overly diffuse attention fail to form discriminative representations. Guided by these findings, PseudoSeer’s embedding model, trained via contrastive learning with efficient attention patterns, outperforms the best pretrained model by 8.7 points. A hybrid approach combining learned embeddings with BM25 reaches 66.5% R@10. PseudoSeer is deployed at pseudoseer.ist.psu.edu as both a practical search system and a benchmark for retrieval evaluation.