Current large language models (LLMs), even those explicitly trained for reasoning, often struggle with ambiguous content moderation cases due to misleading "decision shortcuts" embedded in context. Inspired by cognitive psychology insights into expert moderation, we introduce CᴀʀO (Chain-of-Analogy Reasoning Optimization), a novel two-stage training framework to induce robust analogical reasoning in LLMs. First, CᴀʀO bootstraps analogical reasoning chains via retrieval-augmented generation (RAG) on moderation data and performs supervised fine-tuning (SFT). Second, we propose a customized direct preference optimization (DPO) approach to reinforce analogical reasoning behaviors explicitly. Unlike static retrieval methods, CᴀʀO dynamically generates tailored analogical references during inference, effectively mitigating harmful decision shortcuts. Extensive experiments demonstrate that CᴀʀO substantially outperforms state-of-the-art reasoning models (DeepSeek R1, QwQ), specialized moderation models (LLaMA Guard), and advanced fine-tuning and retrieval-augmented methods, achieving an average F1 score improvement of 24.9% on challenging ambiguous moderation benchmarks.
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Synthetic clinical data are essential for advancing AI in healthcare, given strict privacy constraints on electronic health records (EHRs), the scarcity of annotated data for rare or slowly progressing conditions, and demographic biases in observational cohorts. Large language models (LLMs) can generate fluent clinical text, but ensuring that such outputs are both clinically grounded and useful for downstream modeling remains challenging. We present DualAlign, a disease-agnostic framework for generating privacy-preserving, clinically faithful synthetic EHR narratives. DualAlign improves generation fidelity through two complementary alignment mechanisms: persona alignment, which conditions generation on patient demographics and risk factors, and symptom-trajectory alignment, which grounds narratives in empirically observed longitudinal symptom patterns. Using Alzheimer’s disease (AD) as a case study, DualAlign produces context-aware, symptom-rich sentences that more closely reflect real-world clinical documentation. Augmenting limited gold-standard data with DualAlign substantially improves AD symptom classification, outperforming both gold-only training and unconstrained synthetic baselines. Overall, DualAlign provides a generalizable approach for generating high-utility synthetic clinical text in chronic and progressive diseases, reducing annotation burden while enabling scalable and privacy-conscious clinical NLP research.
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
PDF ↗The accurate extraction of scientific measurements from literature is a critical yet challenging task in AI4Science, enabling large-scale analysis and integration of quantitative research findings. However, Large Language Models (LLMs) frequently exhibit severe hallucinations, which significantly undermine the reliability of automated scientific document understanding systems. To address this problem, we propose MeasHalu, a novel framework for mitigating scientific measurement hallucinations through enhanced reasoning and targeted optimization. We first present a fine-grained taxonomy of measurement-specific hallucinations, categorizing errors across quantities, units, modifiers, and relations. Our approach incorporates a two-stage reasoning-aware fine-tuning strategy using augmented scientific data and process-based supervision. Furthermore, we introduce a progressive reward curriculum designed to penalize specific hallucination types, significantly improving extraction faithfulness. Experimental results demonstrate that MeasHalu substantially reduces hallucination rates and improves overall accuracy on the MeasEval benchmark. This work provides a targeted solution to a key bottleneck in automated scientific knowledge extraction, facilitating more trustworthy and scalable machine-assisted scientific literature analysis.
Affectron: Emotional Speech Synthesis with Affective and Contextually Aligned Nonverbal Vocalizations
PDF ↗Nonverbal vocalizations (NVs), such as laughter and sighs, are central to the expression of affective cues in emotional speech synthesis. However, learning diverse and contextually aligned NVs remains challenging in open settings due to limited NV data and the lack of explicit supervision. Motivated by this challenge, we propose Affectron as a framework for affective and contextually aligned NV generation. Built on a small-scale open and decoupled corpus, Affectron introduces an NV-augmented training strategy that expands the distribution of NV types and insertion locations. We further incorporate NV structural masking into a speech backbone pre-trained on purely verbal speech to enable diverse and natural NV synthesis. Experimental results demonstrate that Affectron produces more expressive and diverse NVs than baseline systems while preserving the naturalness of the verbal speech stream.
Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking
PDF ↗Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-parallel datasets at scale. This work introduces HEALTHDIAL, a large-scale, multilingual, and multi-parallel dataset for developing and evaluating retrieval-augmented generation (RAG)–based spoken dialogue systems. The dataset comprises 6,000 information-seeking dialogues (1,500 per language) grounded in trusted content from the World Health Organization (WHO) and 163 hours of user speech recorded from native speakers of diverse dialects across four official WHO languages: Arabic, Chinese, English, and Spanish. Each speaker is annotated with demographic (e.g., gender, age) and sociolinguistic (e.g., primary language, region of origin) variables. We report benchmark results across key dialogue tasks, which reveal consistent performance disparities across languages, even among high-resource ones. To support future research, we release the dataset, a prototype system, and a toolkit for data collection and system evaluation.
Code optimization remains a core objective in software development, yet modern compilers struggle to navigate the enormous optimization spaces. While recent research has looked into employing large language models (LLMs) to optimize source code directly, these techniques can introduce semantic errors and miss fine-grained compiler-level optimization opportunities. We present HintPilot, which bridges LLM-based reasoning with traditional compiler infrastructures via synthesizing compiler hints—annotations that steer compiler behavior. HintPilot employs retrieval-augmented synthesis over compiler documentation and applies profiling-guided iterative refinement to synthesize semantics-preserving and effective hints. Upon PolyBench and HumanEval-CPP benchmarks, HintPilot achieves up to 6.88x geometric mean speedup over -Ofast while preserving program correctness.
Neural machine translation for extremely low-resource languages faces compounding challenges: scarce parallel data, orthographic inconsistency, and absence of quality metadata for principled training. We present Kumatigi, a quality-annotated French-Bambara corpus combining systematic curation with data augmentation strategies tailored to Bambara. We provide 67k quality-scored pairs that enable targeted data filtering and address pervasive orthographic normalization issues in existing resources. Our dual-dataset generation framework strategically exploits round-trip translation, producing synthetic pairs for fluency reinforcement alongside back-translated pairs that preserve authentic vocabulary for coverage expansion. We further introduce linguistically-motivated augmentation techniques addressing Bambara’s orthographic variability, improving model robustness for real-world text. Experiments with LoRA-based fine-tuning demonstrate consistent improvements across automatic metrics, with our full system achieving up to +3–4 BLEU over strong baselines. Data generation and augmentation strategies contribute +1-2 BLEU beyond high-quality parallel data alone. Human evaluation by native speakers confirms these automatic improvements align with substantial gains in translation adequacy and fluency, with our best model approaching human reference translation quality. Our methodology provides a reproducible framework applicable to other under-resourced languages facing similar data challenges.
Multimodal Named Entity Recognition relies on visual context to resolve textual ambiguities. To mitigate data scarcity, Data Augmentation (DA) has become a standard practice; however, existing methods predominantly adopt a one-size-fits-all and random perturbation paradigm, ignoring the internal state of the target model. In this paper, we first conduct a quantitative analysis, revealing that a significant portion of errors (over 30%) are model-specific, stemming from the unique biases of different architectures. To address this, we propose Memory-Guided Hard Data Augmentation, a framework designed to systematically repair these specific defects. First, we employ K-fold cross-validation to identify model-specific Hard Data. Second, we construct a Memory Tree and utilize Large Language Models (LLMs) with a clustering mechanism to induce macro-level error patterns from micro-level failures. This facilitates a paradigm shift from stateless instance-driven augmentation to a logical pattern-driven approach. Finally, we introduce an iterative augmentation mechanism that triggers recursive generation for stubborn instances that fail initial quality filters. Extensive experiments on Twitter-2015 and Twitter-2017 benchmarks demonstrate that our framework consistently yields significant performance gains across various MNER backbones.
While Large Language Models (LLMs) offer promising avenues for automated cognitive restructuring in mental health settings, current approaches predominantly focus on superficial positive reframing and lack the adaptability to balance conflicting therapeutic dimensions, such as empathy and rationality. To address these deficiencies, we propose CARE-CR, a context-aware framework that implements a decoupled optimization paradigm. We first train expert policies specialized for distinct therapeutic attributes rather than relying on a monolithic alignment strategy. To mitigate expert data scarcity, we introduce Dimension-Guided Hierarchical Monte Carlo Tree Search (DG-HMCTS) for data-efficient preference augmentation. At inference, a context-aware routing module dynamically predicts optimal preference weights to fuse expert outputs based on the user’s specific distress context. Extensive experiments demonstrate that CARE-CR achieves consistent improvements over strong baselines across multiple evaluation dimensions, including diagnostic accuracy, contextual appropriateness, task effectiveness, and overall helpfulness, while enabling controllable cognitive restructuring generation. The dataset and code are publicly available at https://github.com/HongzhiQ/CARE-CR.
CVRH: Cross-modal Variational Role Hypergraph Network via Semantic Enhancement for Multi-modal Event Argument Extraction
PDF ↗Multi-modal Event Argument Extraction task (MEAE) aims to extract all arguments related to a specific event from multiple modalities and identify their corresponding roles. Existing methods focus on weakly alignment of uni-modal representations and generatively data augmentation techniques. However, these methods ignore the potential impact of event role information on MEAE. To address this problem, we propose a Cross-modal Variational Role Hypergraph Network via Semantic Enhancement (CVRH). Unlike previous approaches, CVRH centers on event role information and designs a variational role hyperedge via semantic enhancement, which constructs a role hypergraph for event arguments within multi-modal documents. It explicitly modeling the high-order role correlations among cross-modal arguments in a document. Furthermore, CVRH introduces a modal shared encoder based on differential transformer, which effectively learns shared semantic representations across modalities and enhances the independence of argument representations. On the M2E2 benchmark, experimental results show that CVRH achieves a 6.9% improvement in F1-score on the MEAE compared to current state-of-the-art methods.
Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses. Effectively adapting RAG systems to domain-specific settings requires specialized, context-rich training data beyond general-purpose question-answering datasets. Here, we propose RAGen, a scalable and modular data-centric framework for generating domain-grounded question–answer–context (QAC) triples tailored to diverse RAG adaptation strategies. These QAC triples serve as training signals for multiple RAG adaptation approaches; in this work, we demonstrate their use for contrastive fine-tuning of embedding models and supervised fine-tuning of LLMs under retrieved contexts. RAGen generates QAC triples by identifying key concepts within documents, producing diverse questions guided by Bloom’s Taxonomy–inspired principles, and pairing them with precise answers extracted from relevant contexts. Its modular pipeline incorporates semantic chunking, hierarchical concept extraction, multi-chunk retrieval, and curated distractor contexts to encourage robust reasoning. Designed for scalability, RAGen efficiently handles large and evolving document corpora without redundant processing, making it particularly suitable for dynamic domains like enterprise knowledge bases.
Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation
PDF ↗The current RAG system requires uploading plaintext documents to the cloud, risking private data leakage. Parametric RAG (PRAG) encodes documents as LoRA parameters within LLMs, offering a possible way to reduce exposure of raw content. However, it still faces two issues: (1) PRAG demands synthesizing QA pairs and fine-tuning LLM for each individual document to create its corresponding LoRA, leading to unacceptable inference latency. (2) The performance of PRAG relies solely on synthetic QA data while lacking internal alignment with standard RAG, resulting in poor generalization on out-of-distribution (OOD) inputs. Therefore, achieving high-efficiency parameterization while maintaining RAG-level performance remains a critical challenge for privacy-preserving reasoning. In this paper, we propose DistilledPRAG, a generalizable knowledge-distilled parametric RAG model aligned with standard RAG in document structure and parameter activation. We first synthesize QA pairs from single and multi-documents to enhance cross-document reasoning. Then, we mask the plaintext documents with a special token and translate them to LoRA via a parameter generator, maintaining the standard RAG document structure. Finally, guided by synthetic QA data, we train the parameter generator to match standard RAG’s hidden states and output logits, enabling RAG-style reasoning without original documents. Experiments on four QA datasets show that DistilledPRAG outperforms baselines in accuracy and generalizes well on OOD data.
Task-oriented dialogue (TOD) systems are vital for facilitating complex, goal-directed interactions across sectors like customer support and online retail. However, they face persistent limitations: labor-intensive manual metadata tuning and sparse reinforcement learning (RL) rewards that fail to diagnose invocation errors. To address this, we propose ToolDNA, a dynamic adaptation framework enabling autonomous co-evolution of policy networks and tool metadata via RL, anchored by two synergistic loops. An RL loop optimizes policies by generating rollout trajectories (reasoning, actions, descriptive updates) from user inputs, with multi-dimensional rewards refining invocations. A tool metadata loop—coordinated by a dedicated Tool Manager—evolves metadata through policy-generated candidates during rollouts and Feedback LLM-derived refinements from historical data. These mutually reinforcing loops close traditional reward gaps, forming a closed-loop trial-error-reflection cycle for self-improvement. Extensive experiments on a real-world dataset of 3,100 customer service dialogues confirm ToolDNA’s superiority, with notable gains over baselines: it achieves +11% problem resolution and +54% accuracy over commercial LLMs with prompt engineering; +25%/+35% over supervised fine-tuning; and +15%/+15% over traditional RL baseline. Linguistic analysis corroborates evolved metadata retain semantic intent while enhancing parseability. Case studies in two typical contexts, i.e., car inventory search and loan calculation, further validates its ability to resolve critical ambiguities. ToolDNA pioneers scalable self-improvement for robust, deployable tool-augmented agents with minimal human oversight. We release our code to facilitate future research.
Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions
PDF ↗Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains labor-intensive. Existing automation methods mostly follow fixed template filling and cannot support dynamic updates for diverse, user-authored slide decks. We therefore define “Dynamic Slide Update via Natural Language Instructions on User-provided Templates” and introduce DynaSlide, a large-scale benchmark with 20,036 real-world instruction–execution triples (source slide, user instruction, target slide) grounded in a shared external database and built from business reporting slides under bring-your-own-template (BYO-template) conditions. To tackle this task, we propose SlideAgent, an agent-based framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions. SlideAgent updates content while preserving layout and style, providing a strong reference baseline on DynaSlide. We further design end-to-end and component-level evaluation protocols that reveal key challenges and opportunities for future research. The dataset and code are available at https://anonymous.4open.science/r/604E/.
Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) with external, up-to-date knowledge. Graph RAG has emerged as an advanced paradigm that leverages graph-based knowledge structures to provide more coherent and contextually rich answers. However, the move from plain document retrieval to structured graph traversal introduces new, under-explored privacy risks. This paper investigates the data extraction vulnerabilities of the Graph RAG systems. We design and execute tailored data extraction attacks to probe their susceptibility to leaking both raw text and structured data, such as entities and their relationships. Our findings reveal a critical trade-off: while Graph RAG systems may reduce raw text leakage, they are significantly more vulnerable to the extraction of structured entity and relationship information. We also explore potential defense mechanisms to mitigate these novel attack surfaces. This work provides a foundational analysis of the unique privacy challenges in Graph RAG and offers insights for building more secure systems.
While current LLM agents utilizing paradigms like ReAct or Plan-and-Solve have established a strong foundation for step-by-step reasoning, they remain brittle in open-ended environments due to two intrinsic limitations: (1) A closed action space: These frameworks are confined to static, pre-defined toolsets, rendering them unable to adapt when required tools are missing or obsolete. (2) Myopic error recovery: Existing agents often get trapped in repetitive local retries, failing to diagnose and rectify root causes within the high-level plan. To overcome these limitations, we introduce CAR (Create And Replan), a novel architecture that incorporates a meta-tool synthesizer to dynamically augment the action space and a reflective replanning mechanism to revise global strategies. To rigorously evaluate our approach, we release ToolHop-Pro, a diagnostic benchmark with systematically pruned toolsets to simulate tool scarcity. Experiments demonstrate that CAR significantly outperforms representative baselines, validating its superior robustness where static agents fail. Code and data are available at https://github.com/Zaiz-77/car.
HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation
PDF ↗Large Language Models excel at NLP tasks but remain prone to hallucinations, limiting trust in real-world applications. We present HalluGuard, a 4B-parameter Small Reasoning Model (SRM) designed as a guardrail for Retrieval-Augmented Generation (RAG) pipelines, which classify document-claim pairs as grounded or hallucinated in closed-book, document-grounded settings and produces evidence-grounded justifications. Our approach combines (i) a domain-agnostic synthetic dataset derived from FineWeb and refined through multi-stage curation and data reformation, (ii) synthetic grounded and hallucinated claims, and (iii) preference-based fine-tuning with Odds Ratio Preference Optimization (ORPO) to distill large-model reasoning into a smaller backbone. On the RAGTruth subset of the LLM-AggreFact benchmark, HalluGuard achieves 84.4% balanced accuracy (BAcc), surpassing specialized models, MiniCheck (7B; 84.0%) and Granite Guardian 3.3 (8B; 82.2%) while using roughly half their parameters. Across the benchmark, it reaches 77.1% BAcc, surpassing larger general-purpose LLMs such as GPT-4o (75.9%). HalluGuard and datasets will be released upon acceptance.
QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation
PDF ↗Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models (LLMs). However, existing methods rely on model-internal signals (e.g., logits, entropy), which are fundamentally unreliable because LLMs are typically ill-calibrated and often exhibit high confidence in erroneous outputs. We propose QuCo-RAG, which shifts from subjective confidence to objective statistics computed from pre-training data. Our method quantifies uncertainty through two stages: (1) before generation, we identify low-frequency entities indicating long-tail knowledge gaps; (2) during generation, we verify entity co-occurrence in the pre-training corpus, where zero co-occurrence often signals hallucination risk. Both stages leverage Infini-gram for millisecond-latency queries over 4 trillion tokens, triggering retrieval when uncertainty is high. Experiments on multi-hop QA benchmarks show QuCo-RAG achieves EM gains of 5–12 points over state-of-the-art baselines with OLMo-2 models, and transfers effectively to models with undisclosed pre-training data (Llama-3, Qwen2.5, GPT-4.1/5-chat), improving EM by up to 14 points. Generalization to long-form generation and biomedical QA further validates the robustness of our paradigm. These results establish corpus-grounded verification as a principled, practically model-agnostic paradigm for dynamic RAG.
Tree-Notebook: A Context-Aware Agent with Tree Search and Entropy-Aware Data Shadow for Interactive Data Science
PDF ↗While LLM-based agents have emerged as a focal point for automating data science tasks, they continue to grapple with inefficient context management, "silent failures" (where code executes correctly but fails the task objectives), and error propagation inherent in sequential generation. In this paper, we propose Tree-Notebook, an agentic framework designed to mimic the iterative cognitive process of human data scientists. At its core, Tree-Notebook conceptualizes Jupyter Notebook cells as nodes within a tree structure, facilitating organized and efficient context retrieval. We formalize the task-solving process as a Partially Observable Markov Decision Process (POMDP) over a dynamic tree, utilizing an entropy-based information gain function for path evaluation to enhance adaptability in real-world environments. Furthermore, we introduce the "Data Shadow" system, which resolves silent failures by performing real-time tracking of data distributions, provenance, and semantic constraints. Experimental results demonstrate that Tree-Notebook achieves state-of-the-art (SOTA) performance on both InfiAgent-DABench and DSBench. To further evaluate robustness, we introduce an augmented version of InfiAgent-DABench to simulate complex environments, where Tree-Notebook consistently maintains its SOTA standing. Code is available at: https://github.com/QJK-BUAA/Tree-Notebook
Designing proteins that satisfy natural language functional requirements is a central goal in protein engineering. A straightforward baseline is to fine-tune generic instruction-tuned LLMs as direct text-to-sequence generators, but this is data- and compute-hungry. With limited supervision, LLMs can produce coherent plans in text yet fail to reliably realize them as sequences. This plan–execute gap motivates ProtoCycle, an agentic framework for protein design that uses LLMs primarily to drive a multi-round, feedback-driven decision cycle. ProtoCycle couples an LLM planner with a lightweight tool environment designed to emulate the iterative workflow of human protein engineers and uses LLM-driven reflection on tool feedback to revise plans. Trained with supervised trajectories and online reinforcement learning, ProtoCycle achieves strong language alignment while maintaining competitive foldability, and ablations show that reflection substantially improves sequence quality.