论文检索

Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang

Retrieval-Augmented Generation is a powerful tool for NLP applications. Yet, it is challenging to encode large knowledge bases as compact offline structures while simultaneously achieving accurate, low-latency online retrieval. We propose **ZoomRAG**, a coarse-to-fine, hierarchical graph inference method to tackle the challenges. ZoomRAG formulates the retrieval task as random walks across multi-scale relational graphs. *At the coarse level*, it constructs a global relational graph and performs a query-initiated random walk to quickly locate a few relevant documents over the entire corpus. *At the finer level*, it “zooms into” the selected documents to capture fine-grained semantic and temporal relations, and conducts a second random walk to pinpoint salient evidence chunks for generation. This coarse-to-fine strategy substantially reduces offline indexing costs and accelerates online retrieval. Moreover, random-walk based topological reasoning over rich, multi-scale relational structures enables ZoomRAG to effectively aggregate multi-hop evidence while suppressing noise. Finally, we address the difficulty of handling concurrent RAG queries by **algorithm-parallel ZoomRAG**. Overall, ZoomRAG avoids building expensive knowledge graphs while achieving 2.2% – 4.9% absolute gains in accuracy over SOTA RAG models, with an average online retrieval latency per-query as low as 0.019 secs by processing hundreds of queries concurrently.

Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez, Mohammad Raza

Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large language models by aggregating multiple sampled outputs, but it comes at a high computational cost due to extensive sampling. We introduce a hybrid ensembling approach that leverages the complementary strengths of two distinct modes of reasoning: Chain-of-Thought (CoT) and Program-of-Thought (PoT). We describe a general framework for combining these two forms of reasoning in self-consistency, as well as particular strategies for both full sampling and early-stopping. We show that CoT-PoT ensembling not only improves overall accuracy, but also drastically reduces the number of samples required for SC by a factor of 9.3x. In particular, the majority of tasks (78.6%) can be addressed with only two samples, which has not been possible with any prior SC methods.

Kaleen Shrestha, Abhinav Gupta, Harish Dukkipati, Zhonghao Shi, Maja Mataric

Humans are able to predict each other’s actions by reasoning about the others’ underlying goals, preferences, and motives, such as greed and risk-aversion. Game theory provides a framework for studying human behaviors through incentivized games that simulate social situations. We utilized two validated games from the cognitive science literature—the Social Prediction Game (SPG) and the Inspection Game (IG)—to systematically study how well several recent open- and closed-source LLMs predict player actions and whether they can leverage and generalize the players’ motives learned from the iterated games. Our results indicate that state-of-the-art LLMs can achieve accuracy close to human levels in predicting players’ actions with underlying human motives in SPGs. However, unlike humans, who rely on reasoning about players’ motives to inform their predictions, LLMs failed to recognize statistical patterns in players’ actions. As a result, LLM prediction accuracy did not improve over multiple rounds. Our results in the IG further demonstrate that, unlike humans, LLMs were unable to recognize a player’s underlying motives and to generalize their understanding of the same player to a new context. This suggests that LLMs may lack reasoning capabilities. Our findings offer insights into differences in human and LLM reasoning mechanisms, suggesting that further research into human-AI alignment is needed before utilizing LLMs for human behavior modeling and simulation in this and related contexts.

Zhichao Sheng, Shilin Zhou, Chen Gong, Zhenghua Li

Large Audio Language Models (LALMs) employing the Chain-of-Thought paradigm have demonstrated remarkable reasoning capabilities. Though different problems naturally require varying depths of reasoning, existing methods often determine whether to perform reasoning, lacking fine-grained mechanisms to adapt reasoning length to problem complexity. As a result, LALMs often adopt a one-size-fits-all reasoning strategy, leading to redundant overthinking for simple tasks and insufficient reasoning for complex ones. In this paper, we conduct an in-depth analysis of LALM reasoning behavior and argue that effective and efficient reasoning should be adaptively aligned with task difficulty. To this end, we propose a difficulty-adaptive reasoning method for LALMs. Specifically, we introduce a reward function that dynamically links reasoning length to the model’s perceived problem difficulty, encouraging shorter reasoning for easy tasks and longer reasoning for more complex ones. Extensive experiments on three datasets demonstrate that our method consistently improves performance while reducing average reasoning length by at least 50%, achieving higher efficiency without sacrificing accuracy.

Sarmistha Das, Vaibhav Vishal, Syed Ibrahim Ahmad, Sriparna Saha, Manish Gupta

Financial decision-making in multilingual settings demands accurate numerical reasoning grounded in diverse modalities, yet existing benchmarks largely overlook this high-stakes, real-world challenge, especially for Indic languages. We introduce FinVQA, a benchmark for evaluating financial numerical and multimodal reasoning in multilingual Indic contexts. FinVQA spans English, Hindi, Bengali, Marathi, Gujarati, and Tamil, and comprises 18,900 samples across 14 financial domains. The dataset captures diverse reasoning paradigms under realistic constraints, and is structured across three difficulty levels (easy, moderate, hard) and four question formats: multiple choice, fill-in-the-blank, table matching, and true/false. To address these challenges, we propose FIND, a framework that combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning, robust multimodal grounding, and structured decision-making. Together, FinVQA and FIND establish a rigorous evaluation and modeling paradigm for high-stakes multilingual multimodal financial reasoning.

Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang 等

Speech quality assessment (SQA) is typically formulated as a score regression task based on subjective ratings, such as the Mean Opinion Score (MOS), which inherently suffer from inconsistent standards and limit cross-dataset training and evaluation. To address these limitations, we reformulate SQA as a preference-based comparison paradigm and construct MOS-Pref, a large-scale MOS-derived preference dataset. Building on MOS-Pref, we systematically implement and evaluate three reward modeling paradigms: scalar, semi-scalar, and generative reward models, alongside existing SQA approaches. Our experiments reveal three key findings: (1) scalar models achieve the strongest overall performance, consistently exceeding 74% accuracy; (2) score regression-based approaches generally underperform preference-based methods in both overall performance and generalization; and (3) all reward models struggle on pairs with very small MOS gap. Motivated by these observations, we propose a MOS-aware GRM design that incorporates MOS gap into the reward function during reinforcement learning. Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination. We hope this work fosters more rigorous and scalable research in SQA.

Hyeonseok Moon, Heuiseok Lim

Recent reports suggest that LLMs can handle increasingly long contexts. However, many existing benchmarks for context understanding embed substantial query-irrelevant content, which shifts evaluation toward retrieving relevant snippets rather than fully integrating all provided information. Under this setting, we view that current benchmarks can overestimate true context-understanding ability of LLMs. In particular, we demonstrate that when the context consists entirely of query-relevant text, even advanced models such as GPT-4o fail to reliably integrate inputs as short as 200 tokens. To evaluate this capability more rigorously, we introduce NeedleChain, a benchmark designed to test whether models can faithfully incorporate all given evidence. NeedleChain includes three variants that differ in the required order of comprehension, along with a parallel benchmark based on the needle-in-a-haystack(NIAH) paradigm. By comparing these variants, NeedleChain enables a more comprehensive assessment of context understanding. We further propose a training-free strategy that encourages models to reflect all available information, ROPE contraction, highlighting the importance of full-context integration and pointing to new directions for improving reliable reasoning over context.

Yueyang Ding, HaoPeng Zhang, Rui Dai, Yi Wang, Tianyu Zong, Kaikui Liu, Xiangxiang Chu

Comprehensive understanding of time series remains a significant challenge for Large Language Models (LLMs). Current research is hindered by fragmented task definitions and benchmarks with inherent ambiguities, precluding rigorous evaluation and the development of unified Time Series Reasoning Models (TSRMs). To bridge this gap, we formalize Time Series Reasoning (TSR) via a four-level taxonomy of increasing cognitive complexity. We introduce HiTSR, a **hi**erarchical **t**ime **s**eries **r**easoning dataset comprising 83k samples with diverse task combinations and verified Chain-of-Thought (CoT) trajectories. Leveraging HiTSR, we propose LLaTiSA, a strong TSRM that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models (VLMs). Through a multi-stage curriculum fine-tuning strategy, LLaTiSA achieves superior performance and exhibits robust out-of-distribution generalization across diverse TSR tasks and real-world scenarios. We will publicly release the code, dataset, and model weights.

Giulio Corallo, Paolo Papotti

Retrieval Augmented Generation faces a trade-off: concatenating documents in a long prompt enables multi-document reasoning but creates prefill bottlenecks, while encoding document KV caches separately offers speed but breaks cross-document interaction. We propose Parallel Context-of-Experts Decoding (PCED), a training-free framework that shifts evidence aggregation from the attention mechanism to the decoding. PCED treats retrieved documents as isolated "experts", synchronizing their predictions via a retrieval-aware extension of context-aware decoding. This approach recovers cross-document reasoning capabilities without constructing a shared attention across documents.

Sikai Bai, Haoxi Li, Jie Zhang, Yongjiang Liu, Song Guo

Despite significant advances in Large Reasoning Models (LRMs) driven by reinforcement learning with verifiable rewards (RLVR), this paradigm is fundamentally limited in specialized or novel domains where such supervision is prohibitively expensive or unavailable, posing a key challenge for test-time adaptation. While existing test-time methods offer a potential solution, they are constrained by learning from static query sets, risking overfitting to textual patterns. To address this gap, we introduce Test-Time Variational Synthesis (TTVS), a novel framework that enables LRMs to self-evolve by dynamically augmenting the training stream from unlabeled test queries. TTVS comprises two synergistic modules: (1) Online Variational Synthesis, which transforms static test queries into a dynamic stream of diverse, semantically-equivalent variations, enforcing the model to learn underlying problem logic rather than superficial patterns; (2) Test-time Hybrid Exploration, which balances accuracy-driven exploitation with consistency-driven exploration across synthetic variants. Extensive experiments show TTVS yields superior performance across eight model architectures. Notably, using only unlabeled test-time data, TTVS not only surpasses other test-time adaptation methods but also outperforms state-of-the-art supervised RL-based techniques trained on vast, high-quality labeled data.

Hao Zhang, Lyu Mengsi, Chenrui He, Yulong Ao, Yonghua Lin

Large Multimodal Models (LMMs) have achieved significant success across various tasks. These models usually encode visual inputs into dense token sequences, which are then concatenated with textual tokens and jointly processed by a language model. However, the increased token count substantially raises computational and memory costs during inference. Token pruning has emerged as a promising approach to address this issue. Existing token pruning methods often rely on costly calibration or suboptimal importance metrics, leading to redundant retained tokens. In this paper, we analyze the redundancy differences between visual and textual tokens and propose pruning exclusively on visual tokens. Based on this, we propose a visual token pruning strategy that explicitly preserves both cross-modal alignment and intra-modal informational diversity. We introduce a mutual information-based token pruning strategy that removes visual tokens semantically misaligned with textual tokens, effectively preserving the alignment between the visual and textual modalities. We further refine the retained tokens by maximizing their expected pairwise distances in the latent space to enhance representational quality and reduce redundancy. which is solved efficiently with a greedy algorithm. Extensive experiments demonstrate that our method maintains strong performance while reducing tokens by 88.9% on models such as LLaVA-1.5-7B and LLaVA-NEXT-7B, resulting in a 56.7% improvement in inference speed.

Maharaj Brahma, N J Karthika, Rajat Verma, Nagasai Saketh Naidu, Rohit Saluja, Maunendra Sankar Desarkar, Ganesh Ramakrishnan

Tokenization plays a pivotal role in NLP and is fundamental to training language models. However, existing tokenizers are often skewed towards high-resource languages, limiting their effectiveness for linguistically diverse and morphologically rich languages such as those in the Indian subcontinent. In this work, we present a comprehensive empirical study of multilingual tokenization across 17 Indic languages spanning 11 scripts and two language families. We systematically evaluate the effects of (i) widely used subword algorithms: BPE (CITATION) and Unigram LM (CITATION), (ii) script and orthography-aware normalization, (iii) vocabulary size, and (iv) multilingual vocabulary construction strategies. We use a combination of intrinsic and extrinsic evaluations to obtain the following observations: (i) script-specific normalization improves tokenization quality, (ii) Unigram LM better preserves morphological boundaries than BPE, (iii) cluster-based vocabulary construction shows improvement in downstream tasks compared to the joint method. Our findings highlight the importance of linguistically informed design choices in multilingual tokenization and offer practical guidance for building effective tokenizers for low-resource and morphologically complex languages.

Guy Rotman, Adi Kopilov, Danit Berger Zalmanson, Omri Allouche

In-context learning (ICL) is the standard method for low-resource classification, yet its efficacy in specialized domains remains largely unexplored. We address the challenge of classifying semantically complex, multi-party B2B conversations, where traditional ICL encounters significant limitations, especially as context length increases due to the concatenation of multiple few-shot examples. We introduce the Call Playbook dataset, featuring five classification tasks derived from real-world B2B conversations targeting core sales concepts. To bridge the gap between performance and practical utility, we propose novel knowledge extraction methods that distill verbose examples into compact, interpretable representations of structured classification criteria and precise task descriptions. Our approach achieves a 99% reduction in token usage and improves macro-averaged AUC by up to 7% over traditional ICL. Notably, it remains robust as context grows, unlike advanced token compression baselines which degrade by over 9 F1 points. Importantly, our framework enables direct refinement of classification logic, addressing critical needs for transparency, efficiency, and user interaction in real-world NLP applications.

Fabian Schmidt, Seyedehmoniba Ravan, Vladimir Vlassov

Accurate and interpretable predictions of depression severity are essential for clinical decision support, yet existing models often lack uncertainty estimates and temporal interpretability. We propose PTTSD, a Probabilistic framework for Depression Detection from clinical interview utterance sequences that predicts PHQ-8 scores while modeling calibrated uncertainty. PTTSD includes sequence-to-sequence and sequence-to-one variants, both combining LSTMs, self-attention, and residual connections with Gaussian or Student’s-t output heads trained via negative log-likelihood. The sequence-to-sequence variant enables temporal analysis of how predictive confidence evolves over an interview, despite the target being a single session-level score. Evaluated on E-DAIC and DAIC-WOZ, PTTSD achieves competitive performance among text-only systems (e.g., MAE = 3.85 on E-DAIC, 3.55 on DAIC) and produces well-calibrated prediction intervals. Ablations confirm the value of attention and probabilistic modeling, while a three-part calibration analysis and qualitative case studies highlight the clinical relevance of uncertainty-aware prediction.

Taehyeon Kim, Hojung Jung, Se-Young Yun

Speculative decoding (SD) accelerates large language model (LLM) inference by using a smaller model to draft future tokens, which are then verified by the target LLM. This preserves generation quality by accepting only aligned tokens. However, individual drafters, often trained for specific tasks or domains, exhibit limited effectiveness across diverse applications. To address this, we introduce MetaSD, a unified framework that integrates multiple drafters into the SD process. MetaSD dynamically allocates computational resources to heterogeneous drafters by leveraging alignment feedback and framing drafter selection as a multi-armed bandit problem. Extensive experiments show MetaSD consistently outperforms single-drafter approaches.

Pangjing Wu, Peter Q. Chen, Xiaodong Li, Wenqi Fan, Li Qing

Data marketplaces analyze strategic data exchanges among users, platforms, and buyers. However, most existing studies model static equilibria and complete information, which limits their realism. In this work, we study whether large language model (LLM)-driven agents can make equilibrium-consistent decisions in analytically tractable data marketplaces with evolving and incomplete-information. Specifically, we introduce EvoDM, an agent-based modeling framework that extends the static data marketplace to dynamic and incomplete-information settings while providing tractable equilibrium benchmarks for evaluating agent decisions. Building upon EvoDM, we propose Datamart-Agent, an LLM-driven game-theoretic agent that improves equilibrium-consistent decision execution through dynamic game tree memory and mechanism-guided reflection, without requiring parameter updates. Experiments demonstrate that Datamart-Agent closely matches equilibrium-consistent decision-making, achieving the lowest utility gap and over 20% higher Pass@\epsilon than strong baselines. After validating its effectiveness, we employ EvoDM with Datamart-Agent to analyze competition and regulation in assumption-relaxed settings where closed-form ground truth is unavailable, providing exploratory simulation-based insights into market dynamics and regulatory effects.

Gabriele Maraia, Fabio Massimo Zanzotto, Leonardo Ranaldi

Large Language Models (LLMs) have been shown to exhibit sycophancy, a tendency to align with user assertions even when they conflict with facts. We frame sycophancy as a sociolinguistic phenomenon, disentangling two distinct drivers of credibility: explicit authority (credentials) and implicit authority (linguistic register). We introduce the Sycophancy Matrix, an adversarial evaluation framework that isolates these variables. Using a controlled subset of TruthfulQA, we evaluate open-weight models across English, Spanish, and Portuguese variants. Our findings reveal that models often conflate high register with truthfulness: for some architectures, sophisticated tone triggers deference more effectively than explicit expertise. Furthermore, we observe statistically significant variability across cultural variants of Spanish and Portuguese, supporting the hypothesis that LLMs internalise language-specific sociolinguistic norms and that sycophancy is not a purely technical deficit but an emergent property of multilingual training and alignment. Finally, we identify stable sycophancy fingerprints–domain-specific vulnerability profiles that persist across languages–suggesting that alignment artefacts are intrinsic to model families rather than linguistic context.

Yuanjun Zhang, Mourad Oussalah

Humanitarian reports are long, noisy, and multi-topic, making it difficult to consolidate decision-relevant causal evidence. We present a ReliefWeb study (2000–2024) and a two-stage Large Language Model (LLM) pipeline that extracts structured intervention-outcome records with direction and strength attributes. Query-conditioned extraction restricts output to a specified intervention class, reducing retrieval-induced over-extraction, while snippet grounding links each relation to supporting text for auditability and classification. In an expert-annotated dataset of 100 reports, the best closed-source LLM achieved a weighted F1 score of 90.73% with strong cost-efficiency, while Llama-3.1-8B with supervised fine-tuning reached 94.15% weighted F1 score. We further propose context-preserving triangulation that aggregates strength-weighted evidence within disaster\timessource cells, applies Laplace smoothing and equally weights cells to quantify cross-context convergence via a Level-of-Evidence score. Applied to cash assistance, food-related outcomes show strong positive convergence (LoE=0.865) and stable long-horizon trajectories.

Shiji Yang, Min Cai, Hao Xiong, Congyao Mei, Haodong Zou, Shicheng Tan, Jie Chen, Fulan Qian, Shu Zhao

Model editing-based jailbreak backdoor attacks against LLMs have gained attention for being lightweight, enabling vulnerability discovery in LLMs. Existing methods are implemented by binding backdoors to predefined phrases as first few output tokens, inducing the LLM’s next-token prediction to produce continuous responses. However, their effectiveness is heavily dependent on the number of bound phrases, with attack costs rising as this number increases. In this work, we propose JEST, which achieves jailbreak backdoor attacks by hijacking LLM representations into a acceptance domain rather than binding to a few output tokens. Specifically, we propose a representation transition-guided model editing to inject jailbreak backdoors into LLMs. The activated backdoor transitions the LLM from rejection domain to acceptance domain, causing it to accept and generate jailbreak behavior. To clearly distinguish between rejection and acceptance domains within LLMs, we also design a domain modeling strategy for JEST that models these two opposing domains within the representation space. Additionally, JEST-hijacked LLMs exhibit greater vulnerability to direct prompt attacks. Experimental results show that JEST outperforms existing model editing methods, demonstrating stronger jailbreak capabilities across various LLMs and datasets. We also provide analysis to explore the safety boundary of LLM.

Soham Petkar, Hari Aakash K, Anirudh Vempati, Akshit Sinha, Ponnurangam Kumaraguru, Chirag Agarwal

Recent research has extensively explored the graph-reasoning capabilities of Large Language Models (LLMs) through textual descriptions. However, benchmarks specifically designed for Graph-Language Models (GLMs), which integrate Graph Neural Networks (GNNs) with LLMs, remain significantly underdeveloped. In this work, we first demonstrate that existing GLM evaluations, largely repurposed from unimodal node and edge level tasks, fail to assess true multimodal integration. Our analysis reveals that strong performance on these benchmarks is achievable using textual or structural features in isolation, bypassing the need for joint reasoning. To bridge this gap, we introduce CLEGR (Compositional Language-Graph Reasoning), a benchmark explicitly designed to evaluate multimodal reasoning over graph topology and textual semantics. Evaluation of representative GLMs on CLEGR shows that they exhibit significant performance degradation on CLEGR tasks and unimodal soft-prompted LLMs perform on par with complex multimodal GLMs. These findings collectively highlight limitations in the graph reasoning capabilities of existing GLMs and provide a foundation for advancing the community toward explicit multimodal reasoning involving graph structure and language.