Haolun Wu

Yuxia Wang

Ivan Koychev

Veselin Stoyanov

Mingzi Song

Yu Chen

Xue Liu … (see 1 more)

Preslav Nakov

FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task t… (see more)ests whether systems can select the correct answer to finance questions involving domain terminology, numerical interpretation, and conceptual financial reasoning across languages and scripts. The final-test set contains 800 questions, with 200 questions per language; gold answers were withheld during submission, and each language was ranked independently by accuracy. The final leaderboards contain 13 English, 11 Chinese, 11 Arabic, and 10 Hindi ranked submissions. Top accuracies range from 92.0% in Hindi to 97.5% in English and Arabic, with the same leading teams appearing near the top across all four languages. The documented systems used retrieval augmentation, direct answer-option scoring, language-specific prompting, selective self-consistency, confidence checks, and LLM-based review stages.

2026-07-21

arXiv (preprint)

Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering

Zhuohan Xie

Xueqing Peng

Georgi Georgiev

Dimitar Dimitrov

Yuyang Dai

Rania Elbadry

Vanshikaa Jani

Lingfei Qian

Fan Zhang

Jimin Huang

Jiahui Geng

Yankai Chen

Yuxia Wang

Ivan Koychev

Veselin Stoyanov

Mingzi Song

Yu Chen

Xue Liu … (see 1 more)

Preslav Nakov

FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence. Each final-test item pairs an English … (see more)question with financial statements and news in English, Chinese, Japanese, Spanish, and Greek. Participating systems submit one concise answer per item in JSONL format. The final-test set contains 256 items, split evenly between easy and expert tiers; each tier contains four question templates instantiated over 32 company-report groups. Gold answers were withheld during submission, and systems were ranked by macro-averaged item-level ROUGE-1 F1 against organizer-held reference answers. The final leaderboard includes 12 ranked submissions. The strongest systems are closely clustered, with the top four separated by less than one percentage point in ROUGE-1 F1. The submitted system papers document retrieval-augmented generation, cross-lingual evidence handling, structured prompting, answer compression, and validation strategies.

2026-07-21

arXiv (preprint)

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation

Weien Li

Rui Song

Zeyu Li

Haochen Liu

Xiangyu Kong

Zixuan Dong

Linfeng Du

Zipeng Sun

Weixu Zhang

Jiaxin Huang

Changjiang Han

Yonghan Yang

Zichen Zhao

Xiuyuan Hu

Yankai Chen

Fengran Mo

Jikun Kang

Bowei He … (see 2 more)

Philip S. Yu

Xue Liu

Discrete denoising diffusion models (DDMs) have recently emerged as a compelling alternative to autoregressive (AR) modeling for discrete da… (see more)ta, offering parallel generation and iterative global refinement capabilities. Unlike continuous diffusion, where the state space is fixed, DDMs are fundamentally shaped by how the discrete state space is constructed: the tokenization scheme, the vocabulary topology, and domain-specific structural alphabets. This work introduces a unified conceptual framework that views discrete diffusion models through the construction of the underlying discrete state space. Within this framework, existing formulations, including transition-matrix, masking/absorbing-state, and score/ratio-based approaches, emerge as different instantiations of a common design space. The framework further exposes common design trade-offs across training objectives, inference algorithms, scaling behavior, systems optimization, and evaluation protocols, suggesting several promising directions for future research.

2026-07-14

arXiv (preprint)

Retrieval-augmented generation for natural language processing: a survey

Shangyu Wu

Ying Xiong

Yufei Cui

Can Chen

Lianming Huang

Xue Liu

Tei-Wei Kuo

Nan Guan

Chun Jason Xue

Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters t… (see more)hat store knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update issues, and lacking domain-specific expertise. The appearance of retrieval-augmented generation (RAG), which leverages an external knowledge base to augment LLMs, mitigates these limitations. This paper presents a systematic review of RAG techniques for natural language processing (NLP), with a focus on retrievers and retrieval fusions. We introduce a novel taxonomy of retrieval fusions, such as query-based, logits-based, latent, and parametric fusion, and provide structured comparisons across accessibility, efficiency, and use cases. The paper further examines RAG applications across diverse NLP tasks, discusses evaluation methodologies and benchmark limitations, and analyzes training paradigms with and without knowledge base updates. Finally, we explore industrial deployment considerations and identify emerging challenges and future directions, including security, efficiency, and graph-based retrieval.

2026-05-31

Artificial Intelligence Review (published)

QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents

Rui Song

Weien Li

Zeyu Li

Haochen Liu

Xiangyu Kong

Changjiang Han

Yonghan Yang

Zichen Zhao

Zixuan Dong

Fuyuan Lyu

Bowei He

Jikun Kang

Xue Liu

Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language M… (see more)odel (LLM) agents. However, most environments are scored only by game outcomes such as win rates and largely remain to text-only interaction, making it difficult to tell whether an agent's language is actually grounded in what it perceived and did, or to identify the failure modes underlying its behavior. To address this gap, we introduce QUACK, an open-source environment and evaluation framework for auditing the grounding of agent language in multimodal social reasoning. QUACK evaluates agents at three levels: game outcomes, behavioral trajectories, and utterance-level consistency. Its core Statement Verification Pipeline reconstructs each agent's ground-truth trajectory from engine logs and checks every discussion claim against it, automatically flagging spatial hallucination, unsupported accusation, deception collapse, and language-action inconsistency. Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, we find that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and makes over half of its accusations without grounded evidence. We release the full engine, evaluation framework, toolkit, and logs at https://github.com/AAAAA-Academia-Attractions/QUACK.

2026-05-25

arXiv (preprint)

MINER: Mining Multimodal Internal Representation for Efficient Retrieval

Weien Li

Rui Song

Zeyu Li

Haochen Liu

Gonghao Zhang

Difan Jiao

Zhenwei Tang

Bowei He

Xue Liu

Visual document retrieval has become essential for accessing information in visually rich documents. Existing approaches fall into two camps… (see more). Late-interaction retrievers achieve strong quality through fine-grained token-level matching but store hundreds of vectors per page, incurring large index footprints and high serving costs. By contrast, dense single-vector retrievers retain storage and latency advantages but consistently lag in quality because they compress all information into a single final-layer embedding. In this work, we first conduct a layerwise diagnostic on single-vector retrievers, revealing that retrieval-relevant signal resides in internal representations. Motivated by these findings, we propose MINER (Mining Multimodal Internal RepreseNtation for Efficient Retrieval), a lightweight plug-in module that probes and fuses internal signals across transformer layers into a single compact embedding without modifying the backbone or sacrificing single-vector efficiency. The first Retrieval-Aligned Layer Probing stage attaches a lightweight probe at each layer, surfacing which dimensions carry retrieval-relevant information. The subsequent Adaptive Sparse Multi-Layer Fusion stage applies performance-adaptive neuron-level masking to the selected layers and fuses the surviving signals into the final dense vector. Across ViDoRe V1/V2/V3, MINER outperforms existing dense single-vector retrievers on the majority of benchmarks, with up to 4.5% nDCG@5 improvement over its corresponding backbone. Compared to strong late-interaction baselines, in some settings MINER substantially narrows the nDCG@

2026-05-06

arXiv (preprint)

Training Diffusion Language Models for Black-Box Optimization

Jiayao Gu

Xue Liu

We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem … (see more)common in robotics, DNA, and materials science with limited labeled samples. While recent work applies autoregressive LLMs to BBO by formatting tasks as natural-language prompts, their left-to-right design generation struggles to capture the strong bidirectional dependencies inherent in design problems. To address this, we propose adapting diffusion LLMs to offline BBO to leverage their bidirectional modeling capabilities. However, a domain gap exists between the natural text pre-training of diffusion LLMs and the heterogeneous signals in BBO (prompts, designs, and labels). To bridge this gap, we construct a unified prompt–response corpus and introduce delimiter tokens to explicitly mark field boundaries for *domain adaptation*. We further propose a two-stage *post-training* framework to align the diffusion LLM generation with high-label designs. The first stage performs supervised fine-tuning on the unified dataset via masked-response prediction, and the second stage adopts reinforcement learning with rewards defined by label improvements. Our method achieves state-of-the-art results on Design-Bench small-data settings. Code for our work is available here: https://anonymous.4open.science/r/Anonymous-dllm4bbo-D78A/README.md.

2026-04-29

International Conference on Machine Learning (spotlight)

openreview.net

Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding

Weixu Zhang

Fanghua Ye

Qiang Gao

Jian Li

Yuxing Tian

Sijing Duan

Nan Du

Xiaolong Li

Xue Liu

Large language models (LLMs) often produce content that contradicts or overlooks information provided in the input context, a phenomenon kno… (see more)wn as faithfulness hallucination. In this paper, we propose Context-Fidelity Boosting (CFB), a lightweight and general decoding-time framework that reduces such hallucinations by increasing the generation probability of source-supported tokens. Motivated by logit-shaping principles from watermarking techniques, CFB applies additive token-level logit adjustments based on a token's degree of support from the input context. Specifically, we develop three boosting strategies: static boosting, which applies a fixed bias to source-supported tokens; context-aware boosting, which scales this bias using the divergence between next-token distributions with and without context; and token-aware boosting, which further redistributes the adaptive bias according to local relevance estimated from source-position attention and source-scoped semantic similarity. CFB requires no retraining or architectural changes, making it compatible with a wide range of LLMs. Experiments on summarization and question answering tasks across multiple open-source LLMs show that CFB consistently improves faithfulness metrics with minimal generation overhead. Our implementation is fully open-sourced.

2026-04-23

arXiv (preprint)

Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization

Weixu Zhang

Changjiang Han

Yuxing Tian

Jikun Kang

Xue Liu

Large Language Models (LLMs) exhibit strong implicit personalization ability, yet most existing approaches treat this behavior as a black bo… (see more)x, relying on prompt engineering or fine tuning on user data. In this work, we adopt a mechanistic interpretability perspective and hypothesize the existence of a sparse set of Preference Heads, attention heads that encode user specific stylistic and topical preferences and exert a causal influence on generation. We introduce Differential Preference Steering (DPS), a training free framework that (1) identifies Preference Heads through causal masking analysis and (2) leverages them for controllable and interpretable personalization at inference time. DPS computes a Preference Contribution Score (PCS) for each attention head, directly measuring its causal impact on user aligned outputs. During decoding, we contrast model predictions with and without Preference Heads, amplifying the difference between personalized and generic logits to selectively strengthen preference aligned continuations. Experiments on widely used personalization benchmarks across multiple LLMs demonstrate consistent gains in personalization fidelity while preserving content coherence and low computational overhead. Beyond empirical improvements, DPS provides a mechanistic explanation of where and how personalization emerges within transformer architectures. Our implementation is publicly available.

2026-04-23

arXiv (preprint)

Give Users the Wheel: Towards Promptable Recommendation Paradigm

Fuyuan Lyu

Chenglin Luo

Qiyuan Zhang

Yupeng Hou

Xing Tang

Xue Liu

Jin L.C. Guo

xiuqiang He

Conventional sequential recommendation models have achieved remarkable success in mining implicit behavioral patterns. However, these archit… (see more)ectures remain structurally blind to explicit user intent: they struggle to adapt when a user's immediate goal (e.g., expressed via a natural language prompt) deviates from their historical habits. While Large Language Models (LLMs) offer the semantic reasoning to interpret such intent, existing integration paradigms force a dilemma: LLM-as-a-recommender paradigm sacrifices the efficiency and collaborative precision of ID-based retrieval, while Reranking methods are inherently bottlenecked by the recall capabilities of the underlying model. In this paper, we propose Decoupled Promptable Sequential Recommendation (DPR), a model-agnostic framework that empowers conventional sequential backbones to natively support Promptable Recommendation, the ability to dynamically steer the retrieval process using natural language without abandoning collaborative signals. DPR modulates the latent user representation directly within the retrieval space. To achieve this, we introduce a Fusion module to align the collaborative and semantic signals, a Mixture-of-Experts (MoE) architecture that disentangles the conflicting gradients from positive and negative steering, and a three-stage training strategy that progressively aligns the semantic space of prompts with the collaborative space. Extensive experiments on real-world datasets demonstrate that DPR significantly outperforms state-of-the-art baselines in prompt-guided tasks while maintaining competitive performance in standard sequential recommendation scenarios.

2026-02-20

arXiv (preprint)

Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization

Linfeng Du

Zichen Zhao

Fuyuan Lyu

Xiuying Chen

Jikun Kang

Xue Liu

Large Language Models (LLMs) excel at general-purpose tasks, yet adapting their responses to individual users remains challenging. Retrieval… (see more) augmentation provides a lightweight alternative to fine-tuning by conditioning LLMs on user history records, and existing approaches typically select these records based on semantic relevance. We argue that relevance serves as an unreliable proxy for utility: a record may be semantically similar to a query yet fail to improve generation quality or even degrade it due to redundancy or conflicting information. To bridge this gap, we propose PURPLE, a contextual bandit framework that oPtimizes UseR Profiles for Llm pErsonalization. In contrast to a greedy selection of the most relevant records, PURPLE treats profile construction as a set generation process and utilizes a Plackett-Luce ranking model to capture complex inter-record dependencies. By training with dense feedback provided by the likelihood of the reference response, our method aligns retrieval directly with generation quality. Extensive experiments on nine personalization tasks demonstrate that PURPLE consistently outperforms strong heuristic and retrieval-augmented baselines in both effectiveness and efficiency, establishing a principled and scalable solution for optimizing user profiles.

2025-12-31

arXiv (preprint)

Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization

Yonghan Yang

Bowei He

Can Chen

Xue Liu

Offline black-box optimization aims to discover novel designs with high property scores using only a static dataset, a task fundamentally ch… (see more)allenged by the out-of-distribution (OOD) extrapolation problem. Existing approaches typically bifurcate into inverse methods, which struggle with the ill-posed nature of mapping scores to designs, and forward methods, which often lack the distributional expressivity to quantify uncertainty effectively. In this work, we propose \textbf{SPADE} (\textbf{S}upport-\textbf{P}roximity \textbf{A}ugmented \textbf{D}iffusion \textbf{E}stimation), a novel framework that reimagines forward surrogate modeling through the lens of conditional generative modeling. SPADE models the forward likelihood

2025-12-31

International Conference on Machine Learning (Accept (regular))