This paper proposes a novel in-context learning approach to support low resource machine translation for the Coptic language, using prompts based on Universal Dependencies parses of input sentences. Building on existing work using bilingual dictionaries to support inference for vocabulary items, we add several representations of syntactic analyses to our inputs, specifically exploring the inclusion of raw parser outputs, verbalizations of parses in plain English, and explanations of specific difficult constructions identified in input subgraphs and how they can be translated. Our results show that while syntactic information alone is not as useful as dictionary-based glosses, combining retrieved dictionary items with syntactic information achieves significant gains across model sizes, achieving new state-of-the-art results for the language.
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Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions. However, existing neuron analyses generally focus on single tasks, limiting the comparability of neuron importance across tasks. Moreover, ranking strategies tend to score neurons in isolation, overlooking how task-dependent information pathways shape the write-in effects of feed-forward network (FFN) neurons. This oversight can exacerbate neuron polysemanticity in multi-task settings, introducing noise into the identification and intervention of task-critical neurons. In this study, we propose HONES (**H**ead-**O**riented **N**euron **E**xplanation **S**teering), a gradient-free framework for task-aware neuron attribution and steering in multi-task VLMs. HONES ranks FFN neurons by their causal write-in contributions conditioned on task-relevant attention heads, and further modulates salient neurons via lightweight scaling. Experiments on four diverse multimodal tasks and two popular VLMs show that HONES outperforms existing methods in identifying task-critical neurons and improves model performance after steering. Our source code is released at: https://github.com/petergit1/HONES.
We present **ViLegalLM**, comprising **ViLegalBERT** and **ViLegalQwen**, the first suite of Vietnamese pretrained language models for legal text understanding and generation. It includes one encoder-only model (ViLegalBERT, 135M parameters) and two decoder-only models (ViLegalQwen2.5-1.5B-Base and ViLegalQwen3-1.7B-Base), all continually pretrained on a newly curated 16GB Vietnamese legal corpus, significantly larger than previous work. To mitigate data scarcity, we construct three synthetic datasets using LLM-based generation and hard negative mining for True/False QA, Multiple Choice QA, and Natural Language Inference. We establish state-of-the-art results among open-source models on four main Vietnamese legal downstream tasks spanning ten benchmarks, demonstrating that continual pretraining from base models consistently outperforms instruction-tuned adaptation. Source codes, corpus, datasets, and model checkpoints are publicly available at https://github.com/ntphuc149/ViLegalLM.
We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single “best” explanation token-by-token, we train reward models to discriminate among multiple candidate explanations and learn their relative quality. Concretely, we construct per-instance candidate sets with graded quality levels and train listwise and pairwise ranking models (ListNet, LambdaRank, RankNet) to preserve ordinal structure and avoid score compression typical of pointwise regression or binary preference objectives. We observe three findings: First, ranking losses consistently outperform regression on score separation across all domains tested. Second, the optimal ranking loss depends on data characteristics: listwise objectives excel with well-separated quality tiers, while pairwise methods are more robust to noisy natural annotations. Third, when trained on carefully curated and well-structured data, small encoder models can match models that are orders of magnitude larger, suggesting that data quality matters more than model scale. Finally, when used as rewards in policy optimization, ranking-based scores enable stable convergence in settings where regression-based rewards fail entirely. Code and data are available at: https://github.com/Tankiit/PPO_Learning_to_rank
Do LLM Agents Really Mimic Humans? Diagnosing and Aligning Microeconomic Behaviors in Macro-ABMs
PDF ↗Large Language Models (LLMs) are increasingly adopted in macroeconomic agent-based modeling(ABM). However, existing research focuses on replicating macro-level stylized facts while often neglecting verification of micro-level decision-making. We investigate this gap by comparing LLM agents to human responses from the Survey of Consumer Expectations (SCE) dataset. Our empirical analysis identifies specific limitations: weak trend responsiveness, mode collapse, and a potential data leakage. We propose the Heterogeneous Shock-Response Causal Transmission Framework to tackle these issues. To ensure theoretical consistency, we use LLMs to build a literature-verified causal graph in which macroeconomic shocks influence decisions via generated mediator nodes, while agent profiles serve as edge moderators. Building on this, during inference, we perform a path search to retrieve relevant causal chains and inject them as an explicit Chain-of-Thought(CoT), prioritizing mechanistic logic over statistical pattern matching. To evaluate the effectiveness of our inference approach, we validate it via a two-stage process that combines micro-level dataset testing and macro-level simulation in the EconAgent system. Results from these experiments indicate that our framework improves alignment with human trends and effectively captures behavioral heterogeneity. Overall, this work contributes to the development of reliable and grounded economic simulations.
Large language models are increasingly used in inventive problem-solving, but effective support requires more than open-ended idea generation. Inventive problem-solving requires improving one aspect of a technical system without unintentionally worsening another. TRIZ (Theory of Inventive Problem Solving) provides a unique and structured framework for this setting by representing engineering trade-offs as contradictions and linking them to standardized inventive principles. However, prior TRIZ–LLM evaluations are typically small-scale, case studies in focused areas of technology, and rarely grounded in patent text, which makes it difficult to assess structured reasoning at scale. We introduce TRIZBench, a dataset and benchmark for TRIZ reasoning grounded in open technical sources and U.S. patents. TRIZBench evaluates the core TRIZ workflow through three tasks: contradiction prediction, inventive principle prediction, and grounded TRIZ reasoning. Experiments with multiple LLM baselines show that detecting contradictions is easier than recovering correct trade-off pairs, while principle prediction benefits from explicitly exploiting TRIZ structure. Our findings further underscore the importance of grounding. We show that semantic retrieval enables evidence-based justifications and helps explain why LLMs fail. Dataset and code are available at https://github.com/ellenzhuwang/trizbench.
Recently, watermarking has attracted growing attention as a practical technique for source attribution of machine-generated text. However, most prior work studies watermarking under benign prompts, while its behavior under jailbreaking prompts remains underexplored. This gap matters because jailbreaking can bypass safety policies and shift the generation regime, raising concerns that watermarking may interact with model alignment under attack. To address this gap, we evaluate six watermarking methods on four LLMs across two jailbreak benchmarks and three settings: Static, AutoDAN, and DSN. We find that watermarking can inflate judge-based attack success rate, denoted ASR, under jailbreaking, with the largest effects appearing in biased schemes that perturb logits. At the same time, these ASR increases often do not reflect higher harmful-goal compliance when measured by StrongREJECT or by human judgments. This suggests that ASR-only evaluations can be brittle to decoding perturbations and may overestimate harmful-goal compliance, motivating complementary goal-compliance metrics (e.g., StrongREJECT) and human evaluations.
Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World
PDF ↗Robustness evaluation in Question Answering (QA) has predominantly relied on synthetic perturbations that poorly capture natural text evolution in real-world settings, a limitation that becomes more pronounced with the widespread deployment of Large Language Models (LLMs) in dynamic, user-facing environments. In this work, we address this gap by proposing a framework for automatically evaluating QA models under naturally occurring textual perturbations, replacing context passages with revised counterparts from Wikipedia edit histories. Through extensive evaluation on SQUAD across diverse encoder architectures, we construct two challenging sets where human performance remains stable, yet state-of-the-art LLMs exhibit significant degradation, with performance drops of up to 28.28%. These robustness gaps further generalize to more complex QA scenarios, such as DROP and HOTPOTQA. To mitigate these errors, we show that robustness to natural perturbations can be improved via adversarial training for encoder-only models and in-context demonstrations of perturbed instances for LLMs, though a more generalizable and effective defense strategy remains an open challenge.
Multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. In practice, however, retrieval outcomes systematically reflect perspectival biases: deviations shaped by linguistic **prevalence** and **cultural** associations. We introduce the **Cross-Cultural, Cross-Modal, Cross-lingual Multimodal (3XCM)** benchmark to isolate these effects. Results from our studies indicate that, for image-to-text retrieval, models tend to favor entries from prevalent languages over those that are semantically faithful. For text-to-image retrieval, we observe a consistent "tugging effect” in the joint embedding space between semantic alignment and language-conditioned cultural association. When semantic representations are insufficiently resolved, particularly in low-resource languages, similarity is increasingly governed by culturally familiar visual patterns, leading to systematic association bias in retrieval. Our findings suggest that achieving equitable multimodal retrieval necessitates targeted strategies that explicitly decouple language from culture, rather than relying solely on broader data exposure. This work highlights the need to treat linguistic and cultural biases as distinct, measurable challenges in multimodal representation learning.
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption
PDF ↗Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, effectively mitigating their inherent knowledge limitations. However, RAG remains vulnerable to poisoning attacks that manipulate retrieved texts to mislead model outputs. Existing defense mechanisms often lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content. In this work, we propose PRA-RAG, a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts. PRA-RAG samples multiple combinations of retrieved texts and utilizes geometric structures in the embedding space to identify a robust subset, from which a stable aggregated representation is derived. We provide theoretical bounds on the maximum impact of poisoned retrieved content and establish a quantitative measure of RAG’s robustness. Experiments across multiple benchmarks and RAG architectures demonstrate that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art (SOTA) methods.
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine
PDF ↗Answering complex real-world questions in the medical domain often requires accurate retrieval from medical Textual Knowledge Graphs (medical TKGs), as the relational path information from TKGs could enhance the inference ability of Large Language Models (LLMs). However, the main bottlenecks lie in the scarcity of existing medical TKGs, the limited expressiveness of their topological structures, and the lack of comprehensive evaluations of current retrievers for medical TKGs. To address these challenges, we first develop a dataset for LLMs Complex Reasoning over medical Textual Knowledge Graphs (RiTeK), covering a broad range of topological structures. Specifically, we synthesize realistic user queries integrating diverse topological structures, relational information, and complex textual descriptions. We conduct a rigorous medical expert evaluation process to assess and validate the quality of our synthesized queries. RiTeK also serves as a comprehensive benchmark dataset for evaluating the capabilities of retrieval systems built upon LLMs. By assessing 11 representative retrievers on this benchmark, we observe that existing methods struggle to perform well, revealing notable limitations in current LLM-driven retrieval approaches. These findings highlight the pressing need for more effective retrieval systems tailored for semi-structured data in the medical domain.
Recent large vision–language models (LVLMs) have demonstrated strong potential for device control. However, existing research has primarily focused on point-and-click (PnC) interaction, while remote-control (RC) interaction commonly encountered in everyday TV usage remains largely underexplored. To fill this gap, we introduce TVWorld, an offline graph-based abstraction of real-world TV navigation that enables reproducible and deployment-free evaluation. On this basis, we derive two complementary benchmarks that comprehensively assess TV-use capabilities: TVWorld-N for topology-aware navigation and TVWorld-G for focus-aware grounding. These benchmarks expose a key limitation of existing agents: insufficient topology awareness for focus-based, long-horizon TV navigation. Motivated by this finding, we propose a Topology-Aware Training framework that injects topology awareness into LVLMs. Using this framework, we develop TVTheseus, a foundation model specialized for TV navigation. TVTheseus achieves a success rate of 68.3 on TVWorld-N, surpassing strong closed-source baselines such as Gemini 3 Flash and establishing state-of-the-art (SOTA) performance. Additional analyses further provide valuable insights into the development of effective TV-use agents.
Retrieval-Augmented Generation (RAG) remains unreliable in specialized domains due to semantic and lexical mismatch between lay queries and professional terminology, and existing generative expansion often introduces redundancy or hallucinations that cause semantic drift. We propose Generative Query Condensation (GQC), a query rewriting strategy that reframes rewriting as semantic condensation rather than expansion. To operationalize GQC, we introduce Query-to-Entity Inference (Q2EI), an entity-centric rewriting method that realizes semantic condensation through explicit inference of the underlying target entity. By moving semantic alignment from retrieval-time vector matching to the rewriting stage, Q2EI produces information-dense query representations. Experimental results on medical and legal benchmarks show that Q2EI consistently outperforms strong baselines across retrievers, improving retrieval effectiveness while substantially reducing rewriting token consumption compared to generative expansion methods. Further analysis confirms that these gains primarily arise from accurate entity inference, and that Q2EI’s semantic condensation design limits error amplification when inference is imperfect, leading to more stable and interpretable retrieval behavior.
EMPATH: An Ensemble Method for Automatic Fine-Grained Turn-Level Dialogue Empathy Evaluation with a Novel Emotional Distance Metric
PDF ↗Empathy is key to many professions. In recognition of this, the workshops on computational approaches to subjectivity, sentiment, and social media analysis (WASSA) hosted competitions to evaluate empathy in dialogue. While fine-tuning has proved successful in the competition, there are at least three shortcomings. First, novel metrics for empathy are absent. Second, classical dialogue evaluation metrics require further investigation. Third, the ensemble’s potential remained underdeveloped. To address these issues, we propose the EMPATH framework, which combines fine-tuned models, large language models, classical dialogue evaluation metrics, and a novel metric. The novel metric, ED, encourages the response’s emotional tone to be contextually appropriate. E.g., if the user expresses joy, a cheerful reaction should receive a higher ranking. Furthermore, we introduce a new robust and label-free ensemble strategy, HO, which integrates sub-metrics with the lowest correlation coefficient first. In addition to evaluating on the WASSA benchmark, we test EMPATH’s generalizability using the EmpatheticExchanges dataset (EX). Our experiment results demonstrate that EMPATH yields the best results on the competition dataset, and ablation studies validate our component selection. On EX, the Pearson correlation coefficient for the winner of WASSA 2024 is 0.4066, while EMPATH shows a statistically significant 8% improvement (i.e., 0.4860).
GAVEL: Evidence-Contract Debate with Mechanized Scrutiny for Provenance-Grounded Fact-Checking
PDF ↗Evidence-grounded fact-checking requires predicting claim veracity while returning faithful evidence at fine granularity, including exact sentences, table cells, and complete multi-document chains. Although large language models enable decomposition, planning, and multi-agent verification, they can still produce convincing rationales with weak provenance, especially under heterogeneous evidence and multi-hop requirements. We propose GAVEL, a multi-agent debate framework that enforces evidence grounding throughout inference. GAVEL introduces an Evidence Contract that requires debaters to state atomic subclaims and bind each to explicit evidence units, and a Mechanized Chain of Scrutiny in which a neutral Scrutinizer audits outputs and performs deterministic validation of cited identifiers and quoted spans. A Judge then selects a sufficient evidence set and produces the final decision. Experiments on FEVEROUS and HOVER in an open-book setting show that GAVEL improves provenance-aware metrics that jointly require correct labels and correct, complete evidence over strong recent baselines. Ablations confirm that both evidence binding and mechanized citation validation are key to the gains.
Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hallucinations and a lack of granular medical context. While Retrieval-Augmented Generation (RAG) can mitigate these issues, standard supervised pipelines require computationally intensive searches over massive external knowledge bases, leading to high latency that is impractical for time-sensitive care. To address this, we introduce Keys-to-Knowledge (K2K), a novel framework that replaces external retrieval with internal, key-based knowledge access. By encoding essential clinical information directly into the model’s parameter space, K2K enables rapid retrieval from internal key–value memory without inference-time overhead. We further enhance retrieval quality through activation-guided probe construction and cross-attention reranking. Experimental results demonstrate that K2K achieves state-of-the-art performance across four benchmark healthcare outcome prediction datasets.
LLM agents can reason and use tools, but they often break down on long-horizon tasks due to unbounded context growth and accumulated errors. Common remedies such as context compression or retrieval-augmented prompting introduce trade-offs between information fidelity and reasoning stability. We present InfiAgent, a general-purpose framework that keeps the agent’s reasoning context strictly bounded regardless of task duration by externalizing persistent state into a file-centric state abstraction. At each step, the agent reconstructs context from a workspace state snapshot plus a fixed window of recent actions. Experiments on DeepResearch and an 80-paper literature review task show that, without task-specific fine-tuning, InfiAgent with a 20B open-source model is competitive with larger proprietary systems and maintains substantially higher long-horizon coverage than context-centric baselines. These results support explicit state externalization as a practical foundation for stable long-horizon agents.
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area. Existing benchmarks often fall short by relying on synthetic vulnerabilities or evaluating functional correctness in isolation, failing to capture the complex interplay between functionality and security found in real-world software. To address this gap, we introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories. Our methodology employs a multi-stage pipeline that combines systematic SAST scanning with CodeQL, LLM-based false positive elimination, and rigorous human expert validation. The resulting benchmark contains 105 instances grounded in real-word repository contexts, spanning 19 Common Weakness Enumeration (CWE) types and exhibiting a wide diversity of data flow complexities, including vulnerabilities with up to 34-hop inter-procedural dependencies. Using RealSec-bench, we conduct an extensive empirical study on 5 popular LLMs. We introduce a novel composite metric, SecurePass@K, to assess both functional correctness and security simultaneously. We find that while Retrieval-Augmented Generation (RAG) techniques can improve functional correctness, they provide negligible benefits to security. Furthermore, explicitly prompting models with general security guidelines often leads to compilation failures, harming functional correctness without reliably preventing vulnerabilities. Our work highlights the gap between functional and secure code generation in current LLMs. Our code and data are available at https://github.com/DeepSoftwareAnalytics/Realsec-code-Bench.
Modern speech and multimodal generation systems, such as singing voice conversion and audio-driven lip synchronization, critically depend on temporally stable and semantically unambiguous vocal representations. In practical pipelines, such representations are typically derived from music source separation (MSS) applied to mixed musical recordings. However, standard MSS paradigms often aggregate lead vocals and backing harmonies into a single vocal stream. Although multi-stem separation has been explored, existing approaches remain primarily optimized for signal-level reconstruction, often overlooking the intricate structural disentanglement required by downstream generation tasks. From a generation-oriented perspective, this motivates revisiting vocal separation from a representation learning standpoint. To this end, we propose VocalRep, a structure-aware learning framework designed to disentangle lead vocals, harmonies, and accompaniment while enforcing role consistency across long-form audio. By integrating global vocal identity conditioning with ranking-based objectives, VocalRep extracts role-consistent lead vocal representations without relying on explicit pitch or symbolic annotations. Experimental results demonstrate that VocalRep significantly improves performance in downstream singing voice conversion and audio-driven lip synchronization.
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs
PDF ↗Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary models to construct preference datasets. We identify that this reliance introduces a distributional mismatch between the proprietary and target models that hinders efficient alignment. To address this, we propose Alignment via VErified Self-correction DPO (AVES-DPO), a framework that aligns LVLMs using in-distribution data derived from the model’s intrinsic knowledge. Our approach employs a consensus-based verification mechanism to diagnose diverse hallucinations and guides the model to self-correct, thereby generating preference pairs strictly compatible with its internal distribution. Extensive experiments demonstrate that AVES-DPO surpasses existing baselines in hallucination mitigation while requiring only 5.2k samples.