arXiv subjects

Yanzeng Li

Publications and source records attributed to Yanzeng Li.

At least 19 recordsLinked to original sources

arXiv2026

LEAP: Efficient LLM Inference Scheduling with Generalizable Length Prediction

Length-aware scheduling reduces request latency in LLM serving by prioritizing shorter requests. However, despite their large model sizes, length predictors can suffer degraded ranking performance under domain and task shifts, and adapting them can require costly retraining. Their prediction overhead can further offset the benefits of scheduling. To improve the generalization and efficiency of length-aware scheduling, we propose \textbf{LEAP}, a length prediction system with fast online adaptation and asynchronous CPU execution. LEAP reuses the serving model's prefill hidden states, which contain information about the output length, and extracts this signal through a linear readout. To adapt to substantial domain and task shifts, it updates the readout at test time through recursive least squares, using completed requests as supervision without gradient computation or historical-data replay. Prediction and updates run asynchronously on CPU, while prediction and initial batch ordering overlap the remaining GPU prefill. Across the evaluated cross-domain transfers, LEAP retains strong ranking performance, and online adaptation improves Spearman correlation by 64.7\% under a larger domain shift. Serving experiments show that LEAP reduces average per-token latency by 15.9--40.8\% compared with the evaluated length predictors, while achieving 1.9\% higher throughput with $30{,}569\times$ fewer predictor parameters than the strongest evaluated baseline.

cs.AI↗

arXiv2026

Beyond Explicit Intent: Discovering and Shaping Artistic Intent through Human-AI Co-Creation

Generative AI is becoming an important medium for artistic creation, yet existing interactions often assume that creators can specify intentions in advance and accurately communicate them through text prompts. Artistic intentions, however, may be vague, latent, or recognizable only after seeing concrete visual possibilities. We propose a human-AI co-creation paradigm beyond explicit intent expression. By expanding model exploration, we expose artists to diverse visual possibilities, allowing them to identify previously unarticulated preferences through observation, comparison, and selection. We then integrate artists' curatorial judgments into reinforcement learning, assigning different learning strengths based on preferences over generation trajectories, so recognized directions are more strongly reinforced and the model gradually adapts to individual aesthetics. This creates a continuous "explore-identify-shape-re-explore" loop in which artistic intent is progressively discovered, externalized, and shaped. Through three user studies, we examine latent-preference discovery, personalized preference alignment, and the externalization of vague, difficult-to-express creative intentions.

cs.AI↗

arXiv2026

Are Benchmarks Reliable? Toward Structural Diagnosis via Item-Level Capability Boundaries

Evaluating large language models (LLMs) relies heavily on benchmark scores, yet aggregate metrics can obscure whether individual items serve their intended measurement purpose. BSDProbe provides an item-level framework for benchmark structural diagnosis that jointly characterizes correctness transitions and their supporting response evidence. Repeated responses along an empirically ordered model axis yield complementary correctness and normalized entropy trajectories, from which four-dimensional profiles of boundary position, boundary width, boundary-signal validity, and order consistency are constructed for benchmark-level aggregation. Experiments on six benchmarks identify varying proportions of potentially anomalous items. With Qwen3 as both the selection and reference axis, BSDProbe achieves a 17.9\% mean relative improvement in normalized discrimination ratio over the best baseline per benchmark and matches or exceeds all baselines in ranking agreement with the full benchmark.

cs.AI↗

arXiv2026

SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs

Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches often suffer from inaccuracies and prohibitive computational costs, particularly when processing intricate queries over large knowledge graphs. Specifically, large language models (LLMs) tend to generate syntactically invalid or semantically misaligned logical forms for complex multi-hop or aggregation queries, while conventional entity-relation linking methods face an exponentially growing candidate space. To address these limitations, we introduce SEAL, a novel two-stage semantic parsing framework grounded in self-evolving agentic learning. In the first stage, an LLM extracts a minimal S-expression core capturing the essential semantics, which is then refined by an agentic calibration module to correct syntactic inconsistencies and align entities and relations with the knowledge graph. The second stage employs template-based completion guided by question-type prediction to construct a fully executable S-expression. Crucially, SEAL incorporates a self-evolving mechanism integrating local and global memory with a reflection module, enabling continuous adaptation from dialog history and execution feedback without explicit retraining. Extensive experiments on the SPICE benchmark demonstrate that SEAL achieves state-of-the-art performance in multi-hop reasoning, comparison, and aggregation tasks, validating notable gains in both structural accuracy and computational efficiency.

cs.CL↗

arXiv2026

DySRec: Dynamic Context-Aware Psychometric Scale Recommendation via Multi-Agent Collaboration

Choosing suitable psychometric scales is an essential and difficult step in psychological consultation, which requires clinicians to integrate patient information, behaviors, and dynamic contextual information. Existing systems mainly use static pipelines to choose scale, or directly predict symptoms according to user inputs, limiting their ability to support dynamic assessment, risk management, and transparent decision-making. To address these limitations, we propose DySRec, a multi-agent conversational system for dynamic psychometric scale recommendation. DySRec operates as an interactive chatbot that engages users in multi-turn dialogue, models scale selection as a continuous conversational decision process, and coordinates specialized agents to maintain user context, recommend assessment scales, monitor psychological risk, and log decision trajectories. In this way, DySRec can integrate and capture heterogeneous signals, including semantic, interaction behaviors, assessment history, and content state, to dynamically update user representations and calculate scale-context compatibility score for recommending most matched scales. Moreover, DySRec incorporates a closed-loop refinement mechanism. Recommendation agent will feedback the missing or uncertain attributes and guide the conversation to elicit the targeted information. In this paper, we showcase the prototype design and architecture of DySRec, and this system has been verified in a real-world application.

cs.HC↗

arXiv2026

Pseudorotation and N-body Forces in an Optical Matter System

Isomerization in molecular systems almost invariably occurs through 3-dimensional motion due to the nature of chemical bonding. Pseudorotation is an unusual type of isomerization that occurs in some high symmetry systems that gives the appearance of rigid-body rotation yet only involves atom rearrangements. This paper demonstrates that pseudorotation occurs in 2-dimensions in an optical matter (OM) system of metal nanoparticle constituents. The difference in dimensionality of the dynamics arises from the electrodynamic field-interference nature of optical binding vs. quantum mechanical bonding in polyatomic molecules. The 8-nanoparticle OM "kite" structure we study in experiments and simulations has D2 (D2h) symmetry and a D4 symmetric transition state. The mechanism for pseudorotation involves correlated motion of all 8 nanoparticles with smooth (continuous) evolution of their interactions and without particles jumping in or out of the OM array. While the OM kite structure only occurs with 10% probability vs. other OM isomers, its rate of pseudorotation is rapid relative to transitions to other structural isomers (e.g., "teardrop"). The other isomers have structures that lie on a trigonal lattice with inter-particle separations at distances that enhance field interference and induced polarizations. Even though the kite isomer has inter-particle separations that would manifest destructive interference on a particle pair (i.e., 2-body) basis, the kite structure is the slowest to rearrange into any other isomer. We show that the unusual structure and dynamics of the kite optical matter system result from N-body interactions and forces demonstrating that N-body effects are important in this class of active matter and presumably more generally.

cond-mat.mes-hall↗

arXiv2026

Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph

The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist. Previous methods for detecting faithfulness hallucinations either neglect to capture the models' internal reasoning processes or handle those features coarsely, making it difficult for discriminators to learn. This paper proposes a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination. Specifically, we first extend the layer-wise relevance propagation algorithm from the token level to the semantic level, constructing an internal reasoning graph based on attribution vectors. This provides a more faithful semantic-level representation of dependency. Furthermore, we design a general framework based on a small pre-trained language model to utilize the dependencies in LLM's reasoning for training and hallucination detection, which can dynamically adjust the pass rate of correct samples through a threshold. Experimental results demonstrate that our method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k.

cs.CL↗

arXiv2025

GAP: Graph-Assisted Prompts for Dialogue-based Medication Recommendation

Medication recommendations have become an important task in the healthcare domain, especially in measuring the accuracy and safety of medical dialogue systems (MDS). Different from the recommendation task based on electronic health records (EHRs), dialogue-based medication recommendations require research on the interaction details between patients and doctors, which is crucial but may not exist in EHRs. Recent advancements in large language models (LLM) have extended the medical dialogue domain. These LLMs can interpret patients' intent and provide medical suggestions including medication recommendations, but some challenges are still worth attention. During a multi-turn dialogue, LLMs may ignore the fine-grained medical information or connections across the dialogue turns, which is vital for providing accurate suggestions. Besides, LLMs may generate non-factual responses when there is a lack of domain-specific knowledge, which is more risky in the medical domain. To address these challenges, we propose a \textbf{G}raph-\textbf{A}ssisted \textbf{P}rompts (\textbf{GAP}) framework for dialogue-based medication recommendation. It extracts medical concepts and corresponding states from dialogue to construct an explicitly patient-centric graph, which can describe the neglected but important information. Further, combined with external medical knowledge graphs, GAP can generate abundant queries and prompts, thus retrieving information from multiple sources to reduce the non-factual responses. We evaluate GAP on a dialogue-based medication recommendation dataset and further explore its potential in a more difficult scenario, dynamically diagnostic interviewing. Extensive experiments demonstrate its competitive performance when compared with strong baselines.

cs.CL↗

arXiv2025

A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reward Model (RM) has demonstrated impressive potential for enhancing Large Language Models (LLM), as RM can serve as a proxy for human preferences, providing signals to guide LLMs' behavior in various tasks. In this paper, we provide a comprehensive overview of relevant research, exploring RMs from the perspectives of preference collection, reward modeling, and usage. Next, we introduce the applications of RMs and discuss the benchmarks for evaluation. Furthermore, we conduct an in-depth analysis of the challenges existing in the field and dive into the potential research directions. This paper is dedicated to providing beginners with a comprehensive introduction to RMs and facilitating future studies. The resources are publicly available at github\footnote{https://github.com/JLZhong23/awesome-reward-models}.

cs.CL↗

arXiv2025

Exploiting Prefix-Tree in Structured Output Interfaces for Enhancing Jailbreak Attacking

The rise of Large Language Models (LLMs) has led to significant applications but also introduced serious security threats, particularly from jailbreak attacks that manipulate output generation. These attacks utilize prompt engineering and logit manipulation to steer models toward harmful content, prompting LLM providers to implement filtering and safety alignment strategies. We investigate LLMs' safety mechanisms and their recent applications, revealing a new threat model targeting structured output interfaces, which enable attackers to manipulate the inner logit during LLM generation, requiring only API access permissions. To demonstrate this threat model, we introduce a black-box attack framework called AttackPrefixTree (APT). APT exploits structured output interfaces to dynamically construct attack patterns. By leveraging prefixes of models' safety refusal response and latent harmful outputs, APT effectively bypasses safety measures. Experiments on benchmark datasets indicate that this approach achieves higher attack success rate than existing methods. This work highlights the urgent need for LLM providers to enhance security protocols to address vulnerabilities arising from the interaction between safety patterns and structured outputs.

cs.CR↗

arXiv2024

DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure

While speculative decoding has recently appeared as a promising direction for accelerating the inference of large language models (LLMs), the speedup and scalability are strongly bounded by the token acceptance rate. Prevalent methods usually organize predicted tokens as independent chains or fixed token trees, which fails to generalize to diverse query distributions. In this paper, we propose DySpec, a faster speculative decoding algorithm with a novel dynamic token tree structure. We begin by bridging the draft distribution and acceptance rate from intuitive and empirical clues, and successfully show that the two variables are strongly correlated. Based on this, we employ a greedy strategy to dynamically expand the token tree at run time. Theoretically, we show that our method can achieve optimal results under mild assumptions. Empirically, DySpec yields a higher acceptance rate and speedup than fixed trees. DySpec can drastically improve the throughput and reduce the latency of token generation across various data distribution and model sizes, which significantly outperforms strong competitors, including Specinfer and Sequoia. Under low temperature setting, DySpec can improve the throughput up to 9.1$\times$ and reduce the latency up to 9.4$\times$ on Llama2-70B. Under high temperature setting, DySpec can also improve the throughput up to 6.21$\times$, despite the increasing difficulty of speculating more than one token per step for draft model.

cs.LG↗

arXiv2024

MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient

Medical education relies heavily on Simulated Patients (SPs) to provide a safe environment for students to practice clinical skills, including medical image analysis. However, the high cost of recruiting qualified SPs and the lack of diverse medical imaging datasets have presented significant challenges. To address these issues, this paper introduces MedDiT, a novel knowledge-controlled conversational framework that can dynamically generate plausible medical images aligned with simulated patient symptoms, enabling diverse diagnostic skill training. Specifically, MedDiT integrates various patient Knowledge Graphs (KGs), which describe the attributes and symptoms of patients, to dynamically prompt Large Language Models' (LLMs) behavior and control the patient characteristics, mitigating hallucination during medical conversation. Additionally, a well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG. In this paper, we present the capabilities of MedDiT through a practical demonstration, showcasing its ability to act in diverse simulated patient cases and generate the corresponding medical images. This can provide an abundant and interactive learning experience for students, advancing medical education by offering an immersive simulation platform for future healthcare professionals. The work sheds light on the feasibility of incorporating advanced technologies like LLM, KG, and DiT in education applications, highlighting their potential to address the challenges faced in simulated patient-based medical education.

cs.AI↗

arXiv2024

0ptical trapping with optical magnetic field and photonic Hall effect forces

Optical trapping is having ever-increasing impact in science $-$ particularly biophysics, photonics and most recently in quantum optomechanics $-$ owing to its superior capability for manipulating nanoscale structures and materials. However, essentially all experimental optical trapping studies in the optical dipole regime have, to date, been dominated by the interaction between a material's electric polarizability, $α_{e}$, and the electric part of the incident electromagnetic field, and therefore described by electric field intensity gradient forces. Optical trapping based on optical magnetic light-matter interactions has not been experimentally addressed despite it's immediate extension of the boundaries of optical trapping research and applications. This paper addresses this long-standing deficiency through the realization of optical magnetic trapping of large index of refraction (i.e., Si) nanoparticles and also presents a formalism for quantitative understanding of the experimental findings. Our experimental optical trapping results require including optical magnetic polarizability, $α_{m}$, and electric-magnetic scattering forces associated with the Photonic Hall effect that are qualitatively and quantitatively validated by Maxwell stress tensor calculations. Our findings bring new opportunities for nanoparticle manipulation, potentially relax the limitations Ashkin claimed based on the optical Earnshaw's theorem, motivate optical matter formation by optical magnetic interactions, and suggest new N-body effects and symmetry breaking to drive dynamics of optical matter systems.

physics.optics↗

arXiv2024

MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph

Multi-modal knowledge graphs have emerged as a powerful approach for information representation, combining data from different modalities such as text, images, and videos. While several such graphs have been constructed and have played important roles in applications like visual question answering and recommendation systems, challenges persist in their development. These include the scarcity of high-quality Chinese knowledge graphs and limited domain coverage in existing multi-modal knowledge graphs. This paper introduces MMPKUBase, a robust and extensive Chinese multi-modal knowledge graph that covers diverse domains, including birds, mammals, ferns, and more, comprising over 50,000 entities and over 1 million filtered images. To ensure data quality, we employ Prototypical Contrastive Learning and the Isolation Forest algorithm to refine the image data. Additionally, we have developed a user-friendly platform to facilitate image attribute exploration.

cs.CL↗

arXiv2024

Leveraging Large Language Model as Simulated Patients for Clinical Education

Simulated Patients (SPs) play a crucial role in clinical medical education by providing realistic scenarios for student practice. However, the high cost of training and hiring qualified SPs, along with the heavy workload and potential risks they face in consistently portraying actual patients, limit students' access to this type of clinical training. Consequently, the integration of computer program-based simulated patients has emerged as a valuable educational tool in recent years. With the rapid development of Large Language Models (LLMs), their exceptional capabilities in conversational artificial intelligence and role-playing have been demonstrated, making them a feasible option for implementing Virtual Simulated Patient (VSP). In this paper, we present an integrated model-agnostic framework called CureFun that harnesses the potential of LLMs in clinical medical education. This framework facilitates natural conversations between students and simulated patients, evaluates their dialogue, and provides suggestions to enhance students' clinical inquiry skills. Through comprehensive evaluations, our approach demonstrates more authentic and professional SP-scenario dialogue flows compared to other LLM-based chatbots, thus proving its proficiency in simulating patients. Additionally, leveraging CureFun's evaluation ability, we assess several medical LLMs and discuss the possibilities and limitations of using LLMs as virtual doctors from the perspective of their diagnostic abilities.

cs.CL↗

arXiv2023

gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus

We design a user-friendly and scalable knowledge graph construction (KGC) system for extracting structured knowledge from the unstructured corpus. Different from existing KGC systems, gBuilder provides a flexible and user-defined pipeline to embrace the rapid development of IE models. More built-in template-based or heuristic operators and programmable operators are available for adapting to data from different domains. Furthermore, we also design a cloud-based self-adaptive task scheduling for gBuilder to ensure its scalability on large-scale knowledge graph construction. Experimental evaluation demonstrates the ability of gBuilder to organize multiple information extraction models for knowledge graph construction in a uniform platform, and confirms its high scalability on large-scale KGC tasks.

cs.CL↗

arXiv2023

LLMaAA: Making Large Language Models as Active Annotators

Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data. In practice, acquiring such data is a costly endeavor. Recently, the superior few-shot performance of large language models (LLMs) has propelled the development of dataset generation, where the training data are solely synthesized from LLMs. However, such an approach usually suffers from low-quality issues, and requires orders of magnitude more labeled data to achieve satisfactory performance. To fully exploit the potential of LLMs and make use of massive unlabeled data, we propose LLMaAA, which takes LLMs as annotators and puts them into an active learning loop to determine what to annotate efficiently. To learn robustly with pseudo labels, we optimize both the annotation and training processes: (1) we draw k-NN examples from a small demonstration pool as in-context examples, and (2) we adopt the example reweighting technique to assign training samples with learnable weights. Compared with previous approaches, LLMaAA features both efficiency and reliability. We conduct experiments and analysis on two classic NLP tasks, named entity recognition and relation extraction. With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples, which is much more cost-effective than other baselines.

cs.CL↗

arXiv2023

Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links

Incorporating multiple knowledge sources is proven to be beneficial for answering complex factoid questions. To utilize multiple knowledge bases (KB), previous works merge all KBs into a single graph via entity alignment and reduce the problem to question-answering (QA) over the fused KB. In reality, various link relations between KBs might be adopted in QA over multi-KBs. In addition to the identity between the alignable entities (i.e. full link), unalignable entities expressing the different aspects or types of an abstract concept may also be treated identical in a question (i.e. partial link). Hence, the KB fusion in prior works fails to represent all types of links, restricting their ability to comprehend multi-KBs for QA. In this work, we formulate the novel Multi-KB-QA task that leverages the full and partial links among multiple KBs to derive correct answers, a benchmark with diversified link and query types is also constructed to efficiently evaluate Multi-KB-QA performance. Finally, we propose a method for Multi-KB-QA that encodes all link relations in the KB embedding to score and rank candidate answers. Experiments show that our method markedly surpasses conventional KB-QA systems in Multi-KB-QA, justifying the necessity of devising this task.

cs.CL↗