2025年10月07日NLP论文汇总（英文）

Thu, Oct 16, 2025

• Topic 1: Large Language Model Optimization and Control (7 papers)
• Topic 2: Multimodal Reasoning and Integration (9 papers)
• Topic 3: Reasoning and Decision Making in LLMs (9 papers)
• Topic 4: Adaptive and Dynamic Learning Techniques (9 papers)
• Topic 5: Machine Translation and Cross-Lingual Applications (6 papers)
• Topic 6: Reinforcement Learning and Adaptive Systems (8 papers)
• Topic 7: Knowledge Representation and Retrieval (6 papers)
• Topic 8: Evaluation and Assessment Methods (5 papers)
• Topic 9: Cognitive and Social Simulations (3 papers)
• Topic 10: Specialized Applications and Domains (6 papers)
• Topic 11: misc (24 papers)

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Topic 1: Large Language Model Optimization and Control

Topic Overview

The topic of Large Language Model (LLM) Optimization and Control is crucial for advancing the adaptability, efficiency, and interpretability of LLMs across various applications, particularly in multilingual and specialized contexts. As LLMs become increasingly prevalent, there is a growing need to understand and manipulate their underlying mechanisms to ensure they perform effectively in diverse settings without requiring extensive retraining or additional data. This research area encompasses methodologies for fine-tuning, controlling language-specific behaviors, and integrating human-like judgment processes to improve model performance and generalizability.

Individual Paper Contributions

• Chengzhi Zhong from Kyoto University and colleagues studied the efficient and interpretable control of large language models for multilingual tasks, proposing a training-free method to identify and manipulate language-specific dimensions within LLMs. The main innovation points of this method include the ability to switch between languages using minimal monolingual data and the revelation of consistent ‘spike’ dimensions that control language-specific token projections. The value lies in enhancing the adaptability of LLMs in low-resource language scenarios and improving our theoretical understanding of how LLMs handle multilingualism. Experiments on multiple LLMs and several languages demonstrated an overall ACC*BLEU score of 16.63 on Llama2-7B and 16.14 on Llama2-13B, significantly better than neuron-based methods, concluding that the method provides a balance between accuracy and translation quality¹.

• Manish Nagaraj from Purdue University and colleagues addressed the computational inefficiency and suboptimal data selection in instruction tuning for LLMs, proposing TRIM (Token Relevance via Interpretable Multi-layer Attention). This method leverages attention-derived aggregated saliency to construct saliency-weighted token fingerprints, enabling more efficient and effective fine-tuning. The main innovation points are the shift from sample-level to token-level data importance assessment and mitigation of length biases. The value lies in reducing computational costs and enhancing model performance on downstream tasks. Experiments on benchmarks like CommonsenseQA, SocialIQA, HellaSwag, and GSM8K showed TRIM achieved the highest mean accuracy of 45.37% at a 5% coreset budget, surpassing full-data fine-tuning on certain tasks, concluding that TRIM is superior in accuracy and efficiency².

• Taylor Sorensen from the University of Washington and Yejin Choi from Stanford University proposed Opt-ICL (Optimizing In-Context Learning) to predict individual annotator ratings and aggregate them into distributions for evaluating soft labels, thereby modeling human annotator disagreement. The main innovation points involve using LLMs’ in-context learning capabilities and post-training techniques to enhance these abilities. The value lies in providing a more accurate and fair representation of human judgments, which is critical for enhancing the reliability and interpretability of AI systems in subjective assessments. An ablation study revealed the importance of in-context rater examples and dataset-specific fine-tuning, leading to lower error rates and distances on multiple datasets, concluding that explicit modeling of annotator disagreement improves evaluation robustness³.

• Watcharapong Timklaypachara from Mahidol University and colleagues tackled the generation of accurate and stylistically consistent scientific figure captions through a two-stage pipeline: content-grounded caption generation followed by stylistic refinement using author-specific prompts. The main innovation points include integrating figure-related textual context with author-specific writing styles. The value lies in improving scientific communication by reducing manual workload and ensuring consistency in caption writing. Evaluations on the LaMP-Cap dataset showed significant improvements in ROUGE-1 recall and BLEU scores, concluding that the proposed system effectively combines contextual understanding with stylistic adaptation⁴.

• Chengshuai Zhao from an unspecified institution and colleagues focused on detecting cross-style hate speech, proposing CADET, a causal representation learning framework. The main innovation points involve modeling hate speech generation through a causal graph and employing counterfactual reasoning mechanisms. The value lies in enhancing the robustness and generalizability of hate speech detection models, particularly for implicit forms of hate speech. Evaluations on IsHate, IHC, AbuseEval, and DynaHate datasets demonstrated an average macro-F1 of 0.815, a 13% relative improvement over baselines, concluding that causal grounding improves detection performance⁵.

• Benjamin Akera from Sunbird AI, Uganda and colleagues aimed to expand coverage of African languages in LLMs, focusing on Ugandan languages. They introduced the Sunflower 14B and 32B models, optimized for local languages through a combination of continued pretraining, supervised fine-tuning, and reinforcement learning. The main innovation points include a regionally focused approach that leverages local linguistic and cultural elements. The value lies in promoting equitable access to language technology for speakers of smaller languages. Evaluations on machine translation and AfriMMLU showed state-of-the-art performance for many Ugandan languages, concluding that region-specific models enhance multilingual capabilities⁶.

Technical Trends

The papers in this topic demonstrate a shift towards more targeted and efficient methods for optimizing and controlling LLMs. Innovations range from training-free language-specific dimension manipulation to token-level data importance assessment, in-context learning for modeling annotator disagreement, and causal representation learning for hate speech detection. There is also a trend towards integrating local linguistic and cultural contexts into model development, as seen in the Sunflower project, which emphasizes the importance of localized data and expertise in improving model performance for underrepresented languages.

Datasets and Evaluation

• Language Lives in Sparse Dimensions: Evaluated on multiple LLMs and languages using ACC*BLEU scores.
• TRIM: Tested on CommonsenseQA, SocialIQA, HellaSwag, and GSM8K benchmarks, with accuracy as the primary metric.
• Opt-ICL: Used MP, CSC, Par, and VEN datasets, evaluating performance through error rates and distances.
• Leveraging Author-Specific Context for Scientific Figure Caption Generation: Utilized the LaMP-Cap dataset, evaluating caption quality with ROUGE and BLEU scores.
• Causality Guided Representation Learning for Cross-Style Hate Speech Detection: Assessed on IsHate, IHC, AbuseEval, and DynaHate datasets, using precision, recall, and macro-F1 scores.
• Sunflower: Evaluated on machine translation tasks for 31 Ugandan languages using chrF scores and on AfriMMLU for understanding and reasoning abilities.

These evaluations highlight the importance of choosing appropriate metrics and datasets that reflect the complexity and nuances of the problems being addressed, ensuring that the proposed solutions are rigorously tested and validated.

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Topic 2: Multimodal Reasoning and Integration

Topic Overview

Multimodal reasoning and integration is a critical area in artificial intelligence that deals with the ability of models to understand and process information from multiple sources or modalities simultaneously. This includes integrating textual, visual, and auditory inputs to achieve coherent and contextually accurate outputs. The importance of this topic stems from the fact that real-world applications often require processing data that comes in various forms, such as in educational assessments, healthcare diagnostics, and autonomous agent interactions. Enhancing the capabilities of models to handle multimodal data can significantly improve their effectiveness and reliability in complex, real-world scenarios.

Individual Paper Contributions

• Fanwei Zhu from Alibaba Group and colleagues studied the automatic grading of subjective questions in examinations, proposing a unified Large Language Model (LLM)-enhanced auto-grading framework that integrates four modules: Key Points Matching, Pseudo-Question Matching, LLM-based General Evaluation, and Textual Similarity Matching. The main innovation points are the introduction of novel datasets (General-Type and Domain-Specific) and the ability to simulate human-like grading by addressing redundancy, ambiguity, and weak answer alignment. The value lies in providing a generalized and robust solution for subjective question evaluation, reducing manual labor and ensuring unbiased assessment. Experiments on the GT and DS datasets showed significant improvements in QWK and MSE compared to strong baselines, with the online A/B test indicating superior performance in both value-oriented and technique-oriented tests ⁷.

• Sajib Acharjee Dip from Virginia Tech and colleagues addressed the fragmentation and inconsistency in applying large language models (LLMs) and agentic frameworks to single-cell biology. They introduced LLM4Cell, a survey that systematically evaluates 58 methods across various data modalities and introduces a unified taxonomy for these models. The main innovation points include the balanced registry of benchmark datasets and a ten-dimension rubric for model evaluation. The value lies in providing a clear overview and critical assessment of current single-cell LLMs, facilitating advancements in interpreting complex biological data and improving model reproducibility and comparability. The analysis highlights strengths and weaknesses of different model families and emphasizes the need for standardized benchmarks across different modalities and tasks ⁸.

• Vardhan Dongre from University of Illinois Urbana-Champaign and colleagues tackled the issue of ‘contextual drift’ in multi-turn interactions with large language models (LLMs). They introduced a dynamical framework and a recurrence model that captures drift as a bounded stochastic process, demonstrating that drift can stabilize at finite levels and is controllable through interventions. The main innovation points are the empirical evidence from both synthetic tasks and realistic simulations, and the demonstration that reminder interventions can significantly reduce drift. The value lies in enhancing the reliability and effectiveness of LLMs in real-world applications like virtual assistants and autonomous agents. Experiments on τ-bench simulations showed reductions in KL divergence and increases in LLM judge scores, indicating that drift dynamics are controllable and mitigated by targeted interventions ⁹.

• Alhim Vera from University of Cincinnati and colleagues focused on the safety and coherence of multimodal generative agents in rich social simulation environments. They proposed a reproducible simulation framework called Multimodal Safety Evaluation, which includes a Plan Revision Layer to supervise and evaluate agent actions. The main innovation points are the construction of a dataset of social activity scenarios and the evaluation metrics that assess safety and trustworthiness across different modalities. The value lies in providing a comprehensive evaluation method for the safety of generative agents, which is crucial for their deployment in real-world applications. Experiments revealed that while agents can detect direct contradictions, they struggle with global safety alignment, with Claude 3.5 Sonnet performing best in converting unsafe activities to safe ones ¹⁰.

• Samuel Joseph Amouyal from Tel Aviv University and colleagues explored the comprehension difficulties of large language models (LLMs) on complex sentence structures, comparing them to human processing difficulties. They proposed a systematic approach to evaluate LLMs across seven challenging sentence structures. The main innovation points are the direct measurement of comprehension outcomes and the analysis of model size and ’thinking’ mode effects. The value lies in understanding the limitations and capabilities of LLMs in processing complex language, which guides further improvements towards human-like processing. Experiments showed that larger models better mimic human processing difficulties, and ’thinking’ mode improves performance on non-garden path sentences but not on garden-path sentences ¹¹.

• Guo Yutong from Johns Hopkins University and colleagues addressed the inefficiency and inaccuracy of existing Table VQA systems. They introduced TALENT, a framework that generates dual representations of tables using a small vision-language model and an LLM to improve accuracy and efficiency. The main innovation points are the dual representations (OCR spans and natural language narration) and the use of Qwen2.5-VL-3B and Qwen2.5-7B as components. The value lies in balancing OCR precision with semantic context, making Table VQA more practical for environments with limited computational resources. Experiments on TableVQA-Bench and ReTabVQA datasets demonstrated that TALENT outperformed baselines, achieving an accuracy of 81.13% and highlighting the importance of the LLM’s reasoning capability ¹².

• Verena Blaschke from LMU Munich and colleagues investigated the robustness of NLP tools towards dialectal variations in German. They extended existing text-based datasets to include audio recordings, creating a new spoken intent classification dataset for German and Bavarian. The main innovation points are the inclusion of spoken data and the evaluation of speech-only models on dialectal data. The value lies in enhancing the applicability of NLP tools in regions where dialects are predominant, ensuring broader accessibility. Experiments showed that speech-only models outperform text-only models on dialectal data, with Whisper large-v3 achieving notable accuracy improvements ¹³.

• Yi-Jen Shih and colleagues worked on enhancing the complex reasoning capabilities of speech LLMs while maintaining real-time responsiveness. They proposed a ’thinking while listening’ paradigm that incorporates chain-of-thought (CoT) reasoning using a multi-stream architecture and a new QC metric for user question completeness. The main innovation points are the multi-stream architecture for concurrent processing and the preference tuning scheme using DPO training. The value lies in improving the reasoning capabilities of speech LLMs, making them smarter and more capable in engaging in sophisticated dialogues. Experiments on SRQA benchmark showed a 2.4x improvement in accuracy over the baseline, with DPO training further reducing latency ¹⁴.

Technical Trends

The papers collectively highlight a shift towards integrating complex reasoning and multimodal processing capabilities within large language models (LLMs). Innovations include:

• The development of unified frameworks that integrate multiple reasoning modules to address specific challenges in education, biology, and safety.
• The introduction of new datasets and evaluation rubrics to systematically assess model performance across different modalities and tasks.
• Techniques for controlling and mitigating contextual drift in multi-turn conversations, emphasizing the importance of intervention strategies.
• Methods for enhancing the safety and coherence of multimodal agents in social simulations, utilizing plan revision layers.
• Approaches to improve the efficiency and accuracy of Table VQA systems by combining OCR precision with semantic-rich LLM reasoning.
• Paradigms for integrating reasoning into speech LLMs while maintaining low latency, focusing on fine-tuning and adaptive adjustment techniques.

Datasets and Evaluation Metrics

• Towards Human-Like Grading: General-Type Dataset (GT) and Domain-Specific Dataset (DS)
• LLM4Cell: Over 40 publicly available datasets across various modalities (RNA, ATAC, multi-omic, spatial, perturbation, plant)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions: τ-bench simulations
• Multimodal Safety Evaluation in Generative Agent Social Simulations: Custom dataset of 1,000 social activity scenarios
• Comparing human and language models sentence processing difficulties on complex structures: Custom dataset designed to challenge different cognitive components
• TALENT: TableVQA-Bench and ReTabVQA
• Standard-to-Dialect Transfer Trends Differ across Text and Speech: New spoken intent classification dataset for German and Bavarian
• Can Speech LLMs Think while Listening?: SRQA benchmark

Evaluation metrics include:

• Mean Squared Error (MSE), Accuracy (ACC), F1-score, Quadratic Weighted Kappa (QWK) for grading frameworks
• KL and JS divergences, semantic similarity, and LLM judge scores for drift analysis
• Plan Revisions, Unsafe-to-Safe Conversion Score, Interaction Counts, and Acceptance/Rejection Rates for safety evaluation
• Various NLP metrics for sentence comprehension analysis
• Accuracy, Recall, and nDCG@10 for Table VQA and search retrieval tasks
• Intent classification accuracy for dialect transfer analysis
• Latency and accuracy for spoken reasoning tasks [^paper_id].

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Topic 3: Reasoning and Decision Making in LLMs

Topic Overview

Reasoning and decision-making in Large Language Models (LLMs) is a critical area of research that aims to enhance the models’ ability to generate coherent, comprehensive, and logically sound responses. This topic is essential for improving the reliability and trustworthiness of LLMs, particularly in safety-critical domains and complex reasoning tasks. The focus spans from evaluating the comprehensiveness of factual recall in generated texts to developing frameworks that improve reasoning capabilities through innovative methods like knowledge editing, latent reasoning, and modular architectures.

Individual Paper Contributions

• Adam Dejl from Imperial College London and colleagues studied the evaluation of comprehensiveness in LLM-generated texts, proposing three novel methods—an NLI-based method, a Q&A-based approach, and an end-to-end method—to solve the problem of identifying missing information or underrepresented viewpoints. The main innovation points of these methods are their ability to directly pinpoint specific pieces of information absent from model outputs, addressing a gap in existing evaluation techniques that mainly focus on factual precision. The value lies in providing diagnostic tools that can serve as mechanisms for real-time feedback and correction, critical for improving the safety and reliability of LLMs. Experiments on WikiContradict and ConflictBank datasets, along with real-world questions from r/explainlikeimfive, showed that the Q&A-based and E2E methods consistently outperformed the NLI-based pipeline, with Qwen 2.5 72B being identified as the least comprehensive model.¹⁵

• Youliang Yuan from The Chinese University of Hong Kong and colleagues addressed the occurrence of ‘Miracle Steps’ in LLMs used for mathematical reasoning, proposing the Rubric Reward Model (RRM) to solve this problem. The main innovation points are the use of problem-specific rubrics to assign fine-grained, interpretable scores to the reasoning process, thereby penalizing incorrect steps and promoting logical soundness. The value lies in enhancing the reliability of LLMs in mathematical reasoning tasks, making them more dependable for complex reasoning. Experiments on established datasets like AIME2024, MATH500, AMC2023, and OlympiadBench showed that RRM outperformed other models in reducing Miracle Steps and other critical errors, although it led to a higher occurrence of Outcome Irrelevance.¹⁶

• Yike Zhao from East China Normal University and colleagues critically analyzed the role of data quality versus quantity in enhancing the reasoning capabilities of LLMs, particularly for mathematical reasoning. The main innovation points involve a systematic evaluation of open-source datasets and data synthesis methods, proposing a unified evaluation pipeline that mirrors both training and deployment scenarios. The value lies in providing practical data selection strategies and suggesting future research directions for RL-inspired data synthesis techniques. While specific datasets and baselines were not detailed in the provided content, the paper suggests that structuring data in more interpretable formats or synthesizing data from stronger models can be more beneficial than simply increasing the volume of data.¹⁷

• Xin Jie Chua from Universiti Malaya and colleagues introduced Ryt AI, an LLM-native agentic framework designed to execute core financial transactions through natural language conversations, solving the problem of inefficiency and inflexibility in traditional digital banking workflows. The main innovation points include a modular, multi-agent architecture with specialized agents for different banking tasks, each utilizing a task-specific LoRA adapter attached to an internally developed LLM, ILMU. The value lies in enabling conversational AI to handle mission-critical operations in banking while adhering to regulatory and security standards. Comparative evaluation across five key metrics revealed that Ryt AI outperformed other LLMs in accuracy, speed, cost effectiveness, risk tolerance, and language proficiency.¹⁸

• Jiaoyang Li and colleagues tackled the limitations of LLMs in handling multi-hop question answering tasks by proposing SubQRAG, a sub-question driven dynamic graph RAG framework. The main innovation points are the decomposition of complex questions into simpler, logically connected sub-questions and the real-time dynamic updating of the knowledge graph. The value lies in mitigating the limitations of single-step retrieval and improving the accuracy and reliability of answers in multi-hop QA tasks. Experiments on MuSiQue, 2Wiki, and HotpotQA datasets showed that SubQRAG significantly outperformed zero-shot and other RAG baselines, especially in EM scores.¹⁹

• Yeskendir Koishekenov and colleagues focused on scaling test-time reasoning in LLMs without altering architecture or training data, proposing the Encode–Think–Decode (ETD) method. The main innovation points involve training the model to iterate over a small subset of reasoning-relevant layers, identified using the Kneedle algorithm, without introducing additional parameters. The value lies in optimizing resource usage for reasoning tasks, which is crucial for efficiency and scalability. Experiments on 17 reasoning benchmarks, including GSM8K and MATH, demonstrated substantial performance improvements, indicating that the ETD method outperforms baseline models across various reasoning tasks.²⁰

Technical Trends

The technical approaches in these papers reflect a shift towards more nuanced and targeted methods for improving reasoning and decision-making in LLMs. Innovations include:

• Evaluation Techniques: Moving beyond mere factual precision to include comprehensiveness and logical soundness in the evaluation of LLM outputs.
• Framework Development: Creating modular and multi-agent architectures (as seen in Ryt AI) and dynamic graph frameworks (SubQRAG) to handle complex tasks more effectively.
• Data Optimization: Emphasizing the importance of data quality over quantity, and proposing methods to synthesize high-quality training data.
• Latent Reasoning Enhancements: Developing methods to scale latent reasoning models and improve their performance through controlled exploration of the latent space.
• Iterative Reasoning Strategies: Implementing iterative or recursive depth strategies (ETD) to enhance reasoning without significantly altering model architecture or training data.

Datasets and Evaluation

• Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation: Uses WikiContradict, ConflictBank, and real-world questions from r/explainlikeimfive.
• Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards: Employs AIME2024, MATH500, AMC2023, and OlympiadBench.
• More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning: Evaluates on common knowledge, logical reasoning, mathematical reasoning, and coding ability benchmarks.
• Banking Done Right: Redefining Retail Banking with Language-Centric AI: Does not specify datasets but likely uses proprietary financial transaction data.
• SUBQRAG: sub-question driven dynamic graph rag: Uses MuSiQue, 2Wiki, and HotpotQA.
• Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts: Employs GSM8K, MATH, and BBH (BIG-Bench Hard).

These datasets and evaluation metrics underscore the diverse nature of the problems addressed, ranging from mathematical reasoning to conversational banking tasks, and highlight the importance of context-specific evaluation in assessing the effectiveness of LLM reasoning enhancements.

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Topic 4: Adaptive and Dynamic Learning Techniques

Topic Overview

Adaptive and Dynamic Learning Techniques in the realm of large language models (LLMs) aim to enhance the models’ ability to learn and improve during inference rather than relying solely on extensive offline training. These techniques are vital for increasing the efficiency and performance of LLMs in various agentic tasks such as tool usage, multi-turn conversations, and complex reasoning tasks. They address the issues of redundancy, high computational costs, and inefficiency in traditional fine-tuning paradigms, making LLMs more adaptable and capable in real-world applications.

Individual Paper Contributions

• Emre Can Acikgoz from University of Illinois Urbana-Champaign and colleagues studied the inefficiency and redundancy in standard LM fine-tuning paradigms for acquiring new skills, proposing a test-time self-improvement framework for LM agents that integrates self-awareness, self-generated data augmentation, and iterative self-training. The main innovation points of this method are the use of a margin-based confidence estimator and a data synthesis function for generating relevant training data on-the-fly, along with parameter-efficient fine-tuning techniques. The value lies in achieving significant accuracy gains with fewer training samples, making the framework more efficient and effective than traditional fine-tuning methods. Experiments on NexusRaven, SealTool, API-Bank, and ToolAlpaca benchmarks showed consistent absolute accuracy gains ranging from +4.26% to +6.05% compared to standard supervised fine-tuning (SFT) baselines, concluding that uncertainty-guided data synthesis and adaptive learning significantly boost model performance.²¹

• Fanheng Kong from Klear Team, Kuaishou Technology and colleagues focused on the inefficiency in inference caused by delayed decoding in diffusion large language models (dLLMs). They introduced LocalLeap, an adaptive parallel decoding strategy that identifies high-confidence ‘anchor’ tokens and performs parallel decoding within a bounded radius around these anchors using a relaxed confidence threshold. The main innovation points are the empirical principles of local determinism propagation and spatial consistency decay, guiding the effective acceleration of dLLM inference. The value lies in enhancing the practical deployment of dLLMs by improving their throughput and reducing the quality-speed trade-off. Experiments on GSM8K, LLaDA-Instruct, and other benchmarks demonstrated up to 6.94 times throughput improvements and up to 7.01 times reduction in inference steps compared to traditional sequential decoding methods, concluding that LocalLeap significantly accelerates dLLM inference without compromising output quality.²²

• Peize He from Shanghai Jiao Tong University and colleagues addressed the inefficiency and poor performance of LALMs when processing long-form audio inputs. They introduced AudioMarathon, a comprehensive benchmark for evaluating long-context audio understanding and inference efficiency in LALMs. The value lies in exploring and quantifying the effectiveness of various inference efficiency techniques, such as token pruning and KV-cache eviction. Experiments on AudioMarathon revealed that the Qwen2.5-Omni-3B model excelled in speech content extraction and audio classification tasks, with the Frame method outperforming others in token pruning and SnapKV strategy showing promise in KV-cache eviction. The study underscores the importance of balancing performance and efficiency in long-context audio processing.²³

• Yuzhe Gu from University of Pennsylvania and colleagues tackled the memory overhead associated with caching key-value (KV) states in LLMs with extended context windows. They introduced OBCache, a framework for KV cache eviction based on Optimal Brain Damage (OBD) theory, which enhances token selection accuracy and long-context inference performance. The main innovation points are the formulation of the problem as a structured pruning issue and the derivation of closed-form expressions for perturbation estimates. Experiments on Needle-in-a-Haystack passkey retrieval and LongBench benchmarks demonstrated that OBCache can maintain or enhance model accuracy under severe KV cache budget constraints, outperforming baselines like H2O, TOVA, and SnapKV.²⁴

• Jaeseong Lee from Seoul National University and colleagues explored the poor generalization of speculative decoding methods to long-context inputs in LLMs. They proposed OWL, a new model that innovates in the drafter, verifier, and decoding algorithm aspects of speculative decoding, and introduced LongSpecBench for evaluating these methods. The main innovation points are the use of an LSTM drafter conditioned on the last token’s state, the introduction of a [SPEC] token, and a hybrid decoding algorithm. Experiments on LongSpecBench showed that OWL achieves significantly higher acceptance lengths and faster token generation speeds compared to existing methods, concluding that OWL and its hybrid variant HOWL are more effective for long-context workloads.²⁵

• Shuqing Luo from University of North Carolina, Chapel Hill and colleagues focused on the inefficiency and high memory footprint associated with test-time scaling (TTS) in LLMs during long chain-of-thought (CoT) reasoning tasks. They introduced AsyncSpade, an asynchronous sparse decoding framework that eliminates sequential dependence and reduces time-per-output-token (TPOT). The main innovation points are the dual-rank architecture, a lightweight temporal-regressive module, and token-level KV selection. Experiments on AIME24, AIME25, GPQA-Diamond, and MATH500 benchmarks demonstrated up to 20% reduction in TPOT compared to Quest and more than 50% reduction compared to full-attention baselines, concluding that AsyncSpade is effective in enhancing TTS efficiency.²⁶

• Jusen Du from Zhejiang University and colleagues addressed the difficulty LLMs face in performing social reasoning tasks, proposing an adaptive world model-enhanced reasoning mechanism. The main innovation points include the construction of a dynamic textual world model and the use of specific keywords as triggers for cognitive intervention. The value lies in improving LLMs’ social reasoning capabilities and reducing logical inconsistencies. Experiments on ToMi, Hi-ToM, and ExploreToM datasets showed significant improvements in accuracy and reductions in token consumption, with DeepSeek-R1-Distill-Qwen-32B demonstrating a +7.34% accuracy improvement and a 33.8% reduction in token consumption on the Hi-ToM dataset, concluding that the adaptive world model-enhanced reasoning mechanism effectively enhances social reasoning performance.²⁷

• Fu Chen from OPPO and East China Normal University and colleagues studied the inefficiency and instability in training weak LLMs using the Group Relative Policy Optimization (GRPO) algorithm. They introduced ToolExpander, a framework that addresses training challenges through Dynamic Multi-Round Hard Sampling and Self-Exemplifying Thinking. The main innovation points are the replacement of hard samples with high-quality few-shot demonstrations and the modification of the GRPO framework to allow autonomous generation and analysis of few-shot examples. Experiments on BFCL, APIBank, and ACEBench benchmarks showed that ToolExpander reduced the number of hard samples by 15%-20%, improved training stability, and outperformed the original GRPO approach, concluding that few-shot guidance and dynamic sampling significantly enhance training efficacy.²⁸

Technical Trends

The papers in this collection exhibit several technical trends:

• Test-Time Adaptation: Multiple papers emphasize the importance of enabling models to learn and adapt during inference, such as through self-generated data augmentation, dynamic sampling, and adaptive reasoning mechanisms.
• Efficient Memory Management: Techniques like OBCache and AsyncSpade focus on optimizing KV cache usage and reducing memory footprints, respectively, to handle long contexts more efficiently.
• Speculative Decoding Enhancements: Papers like OWL introduce innovative decoding algorithms that improve upon existing speculative decoding methods, particularly in long-context scenarios.
• Hybrid Architectures: Native Hybrid Attention (NHA) proposes a unified architecture combining linear and full attention mechanisms to balance computational efficiency and model performance.
• World Model Integration: Active Confusion Expression integrates world models to enhance social reasoning, demonstrating the value of contextual awareness in complex reasoning tasks.

Datasets and Evaluation

• NexusRaven, SealTool, API-Bank, ToolAlpaca: Used to evaluate test-time self-improvement frameworks for LLMs.
• GSM8K, LLaDA-Instruct: Benchmarks for testing the effectiveness of adaptive parallel decoding strategies like LocalLeap.
• AudioMarathon: A comprehensive benchmark for assessing long-context audio understanding and inference efficiency in LALMs.
• Needle-in-a-Haystack, LongBench: Benchmarks for evaluating KV cache pruning and long-context inference performance with OBCache.
• LongSpecBench: Designed to assess speculative decoding methods on long-context inputs.
• AIME24, AIME25, GPQA-Diamond, MATH500: Used to measure the efficiency and performance of test-time scaling frameworks like AsyncSpade.
• ToMi, Hi-ToM, ExploreToM: Datasets for evaluating social reasoning capabilities of LLMs enhanced with adaptive world models.
• BFCL, APIBank, ACEBench: Benchmarks for assessing the effectiveness of reinforcement learning techniques in training weak LLMs, such as ToolExpander.

These datasets and benchmarks cover a wide range of tasks including tool usage, long-chain reasoning, social dynamics understanding, and audio comprehension, providing a comprehensive evaluation of the proposed methods.

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Topic 5: Machine Translation and Cross-Lingual Applications

Topic Overview

Machine Translation and Cross-Lingual Applications is a field focused on developing systems that can effectively translate text between different languages while maintaining semantic and cultural fidelity. The importance of this research is underscored by the need for global communication and information accessibility across diverse linguistic and cultural contexts. Addressing performance gaps and biases in these systems, especially for low-resource languages, is critical for ensuring fairness and robustness in AI technologies, thereby promoting inclusivity and reducing digital divides.

Individual Paper Contributions

• Md. Faiyaz Abdullah Sayeedi from United International University and colleagues studied the uneven translation performance and potential bias amplification in Large Language Models (LLMs) across different language families and specialized domains, particularly affecting low-resource languages. They proposed Translation Tangles, a unified framework and dataset for evaluating translation quality and fairness, along with a hybrid bias detection pipeline combining rule-based heuristics, semantic similarity filtering, and LLM-based validation. The main innovation points are the introduction of a multilingual benchmarking suite and a human-verified dataset annotated for bias, providing a gold standard for bias detection systems. The value lies in ensuring that translation outputs are reliable and unbiased across diverse linguistic and cultural contexts. Experiments on the Translation Tangles dataset showed that the hybrid bias detection method achieved high precision and recall by empirically determining a semantic similarity threshold (τ = 0.75), indicating that the method can accurately identify biases²⁹.

• Yuxin Huang from Kunming University of Science and Technology and colleagues addressed the challenges of effective multilingual generative retrieval, focusing on issues like cross-lingual identifier misalignment and multilingual identifier inflation. They introduced the MGR-CSC framework, which constructs DocIDs for multilingual documents, allowing for a shared semantic space, and employs a dynamic constrained multi-step decoding strategy to improve retrieval efficiency. The innovation points lie in the semantic compression technique and the multi-step decoding strategy, which together reduce the number of DocID tokens and improve scalability. The value is in enhancing cross-border e-commerce and cross-lingual search systems by making information more accessible across different language communities. Experiments on the mMarco100k and mNQ320k datasets revealed that MGR-CSC outperformed existing approaches with higher Recall@1 and Recall@10 scores across different languages, confirming the importance of both semantic compression and multi-step decoding strategies³⁰.

• Fred Philippy from University of Luxembourg and colleagues tackled the creation of a high-quality instruction tuning dataset for Luxembourgish, a low-resource language. They proposed LuxInstruct, which avoids the use of machine translation and instead uses aligned data from English, French, and German to construct instruction-output pairs. The main innovation is the avoidance of machine translation for instruction tuning, ensuring the preservation of linguistic and cultural nuances. The value lies in supporting the development of robust LLMs that can understand and respond to prompts in Luxembourgish, thereby serving the local population and increasing the inclusivity of AI technologies. LuxInstruct includes 391,551 cross-lingual instruction-output samples and 145,793 monolingual samples, with the potential to improve model performance over traditional machine translation-based datasets³¹.

• Amruta Parulekar from Indian Institute of Technology Bombay and colleagues addressed the limitations of traditional ASR evaluation metrics in accurately assessing performance for morphologically complex languages like Indian languages. They introduced LASER, an LLM-based ASR scoring and evaluation rubric that incorporates semantic significance of errors to provide a fairer assessment. The main innovation points are the use of in-context learning and the fine-tuning of smaller LLMs on a word-pair classification task. The value is in providing a more nuanced and accurate evaluation of ASR systems, particularly for languages with rich morphology. Experiments on datasets like IndicVoices demonstrated that LASER correlates highly with human annotations and outperforms WER and BERTScore, indicating its ability to mitigate unfair penalization of minor syntactic errors³².

• Olia Toporkov from University of the Basque Country UPV/EHU and colleagues explored lemmatization without the need for domain- or language-specific training data, focusing on languages with rich morphology. They proposed the use of in-context learning with LLMs for direct lemma generation across 12 languages. The main innovation points are the demonstration of LLMs’ effectiveness in lemmatization without prior fine-tuning and the comparative analysis against traditional encoder models. The value lies in enhancing the efficiency and effectiveness of lemmatization for under-resourced and high-inflection languages, making NLP tools more broadly applicable. Experiments on datasets like the PUD treebank and the Armiarma corpus showed that LLMs outperformed traditional models in out-of-domain settings for languages like Czech, Russian, and Turkish, achieving state-of-the-art results³³.

• Neel Prabhanjan Rachamalla from Krutrim AI and colleagues aimed to improve the quality and cultural relevance of post-training datasets for LLMs in Indian languages. They proposed a human-in-the-loop (HITL) pipeline for creating Pragyaan-IT (22.5K) and Pragyaan-Align (100K) datasets across 10 Indian languages, emphasizing cultural appropriateness and task complexity. The main innovation is the HITL refinement process that enhances synthetic and translated data. The value lies in bridging the gap between English-centric datasets and the unique requirements of Indian languages and cultures, thus improving model alignment and usability. Pilot studies using Direct Preference Optimization (DPO) on the Pragyaan-Align dataset indicated that the curated data significantly improved model performance across multiple Indian languages and categories, especially in areas like reasoning and paraphrasing³⁴.

Technical Trends

The papers collectively highlight the growing trend towards leveraging large language models (LLMs) for enhancing cross-lingual applications and addressing issues related to performance gaps and biases. Techniques such as in-context learning, hybrid bias detection pipelines, and human-in-the-loop curation are emerging as key methodologies to improve the robustness and fairness of machine translation and related tasks. There is also a noticeable shift towards developing frameworks and datasets that cater to low-resource and morphologically complex languages, underscoring the importance of linguistic and cultural sensitivity in AI systems.

Datasets and Evaluation

• Translation Tangles: Used for evaluating translation quality and fairness across multiple dimensions including language family and domain, employing BLEU, chrF, TER, BERTScore, WER, CER, ROUGE, and COMET metrics²⁹.
• mMarco100k and mNQ320k: Datasets used for evaluating the performance of MGR-CSC in multilingual generative retrieval, focusing on Recall@1 and Recall@10 scores³⁰.
• LuxInstruct: A cross-lingual instruction tuning dataset for Luxembourgish, comprising 391,551 cross-lingual and 145,793 monolingual samples³¹.
• IndicVoices: Dataset used for evaluating LASER’s performance in ASR, highlighting its effectiveness in handling noisy and conversational speech³².
• PUD Treebank and Armiarma Corpus: Datasets utilized for lemmatization experiments, comparing LLMs against traditional encoder models and showing state-of-the-art results in out-of-domain settings³³.
• Pragyaan-IT and Pragyaan-Align: Datasets curated for post-training of LLMs in Indian languages, emphasizing cultural and linguistic nuances, evaluated using Direct Preference Optimization (DPO) for model alignment and performance³⁴.

These summaries encapsulate the advancements and methodologies presented in each paper, contributing to the broader goal of improving machine translation and cross-lingual applications.

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Topic 6: Reinforcement Learning and Adaptive Systems

Topic Overview

Reinforcement Learning and Adaptive Systems represent a dynamic area of research focused on enhancing the capabilities of artificial intelligence models to adapt to changing environments and improve their performance over time. This topic is particularly pertinent in the context of large language models (LLMs), where the goal is to develop methods that allow weaker models to effectively train stronger ones, optimize resource usage, enable real-time reasoning, and perform sophisticated tasks such as hierarchical text classification and agentic tasks. The advancements in this field are critical for moving towards Artificial General Intelligence (AGI) and ensuring that AI systems can operate reliably and efficiently in diverse applications.

Individual Paper Contributions

• Houcheng Jiang from University of Science and Technology of China and colleagues studied the poor robustness and limited generalization in weak-to-strong generalization of large language models (LLMs), proposing Contrastive Weak-to-Strong Generalization (ConG) to address the issue of noise and biases in samples generated by weaker models. The main innovation points of ConG are the use of implicit rewards and contrastive decoding to generate higher-quality samples. The value lies in enhancing the generalization and robustness of stronger models compared to traditional methods, which is crucial for the development of AGI and the reliability of LLMs. Experiments on the UltraFeedback dataset for preference alignment and benchmarks like AlpacaEval2 and Arena-Hard showed average improvements of about 15.0% and 17.8% in the self-alignment setting and 11.5% and 16.3% in the weak-to-strong setting, respectively, compared to baselines like DPO, ORPO, and SimPO. The findings conclude that smaller capability gaps between models and larger strong models benefit most from ConG, with moderate contrastive coefficients (0.3 to 0.5) yielding the best performance.³⁵

• Peilin Wu from The University of Texas at Dallas and colleagues addressed suboptimal search behaviors in agentic Retrieval-Augmented Generation (RAG) systems, proposing HiPRAG, a hierarchical process reward framework that incorporates fine-grained, knowledge-grounded process rewards. The main innovation points are the introduction of a structured output format and methods for detecting over-search and under-search behaviors during training. The value lies in optimizing the correctness and efficiency of reasoning processes, thereby enhancing the reliability and efficiency of LLMs in real-world applications such as question answering. Experiments on seven QA benchmarks using Qwen2.5 and Llama-3.2 models demonstrated improvements in average accuracy (65.4% to 67.2%) and significant reductions in over-search and under-search rates compared to baselines. The findings conclude that HiPRAG is effective in optimizing search behavior and shows strong generalizability across various models and RL algorithms.³⁶

• Shuo Yu from State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, and colleagues tackled the limitations of traditional user modeling approaches in personalized text generation, proposing MemWeaver, a hierarchical memory framework that leverages user textual interaction history. The main innovation points are the construction of behavioral and cognitive memories to capture specific actions and evolving preferences. The value lies in building a more nuanced model of user behavior, which enhances the accuracy and relevance of personalized content generation. Experiments on the Language Model Personalization (LaMP) benchmark showed MemWeaver outperforming strong baselines across all six datasets, with the ablation study highlighting the importance of both memory components. The findings conclude that integrating semantic and temporal aspects of user behavior can significantly enhance personalized text generation.³⁷

• Krinos Li from Imperial College London and colleagues surveyed the application of large language models (LLMs) in virtual cell modeling, proposing a unified taxonomy that categorizes methods into two paradigms: LLMs as Oracles for direct cellular modeling and LLMs as Agents for orchestrating complex scientific tasks. The main innovation points are the detailed categorization of models, datasets, and evaluation benchmarks for tasks like cellular representation, perturbation prediction, and gene regulation inference. The value lies in offering a systematic exploration of LLMs in virtual cell modeling, which is vital for fields such as drug discovery and personalized medicine. The review discusses notable methods like HyenaDNA and PertFormer, along with datasets from JUMP-Cell Painting, CELLxGENE, and Tabula Sapiens. The findings conclude that combining CNNs with Transformers can enhance long-range dependency modeling, and reinforcement learning can improve reasoning capabilities.³⁸

• Yuhan Sun from Taobao & Tmall Group of Alibaba and colleagues focused on enabling real-time reasoning for AI-powered livestreaming, proposing a two-stage optimization framework called LiveThinking. The main innovation points are the use of Rejection Sampling Fine-Tuning (RFT) and Group Relative Policy Optimization (GRPO) to distill and compress the reasoning capabilities of large teacher models into lightweight student models. The value lies in balancing correctness, helpfulness, and low latency, which are essential for interactive systems in e-commerce livestreaming. Experiments on the Tblive-E-Commerce QA dataset and MuSiQue dataset showed significant improvements in correctness and helpfulness, with the final model outperforming the teacher model in terms of both quality and efficiency. The findings conclude that RFT and RL are effective in improving the performance of LRMs in real-time settings, particularly with MoE architectures.³⁹

• Lekang Jiang from University of Cambridge and colleagues introduced a novel framework, Reasoning for Hierarchical Classification (RHC), to address hierarchical text classification (HTC), particularly in the context of patent classification. The main innovation points are the two-stage training procedure involving cold-start alignment and reinforcement learning with verifiable rewards. The value lies in improving the effectiveness, explainability, and broad applicability of HTC systems, which are crucial for information retrieval and technology trend analysis. Experiments on the newly constructed PCD-BD dataset demonstrated that RHC outperformed supervised fine-tuning and strong baselines in terms of accuracy and macro F1 scores. The findings conclude that RHC’s step-by-step reasoning approach significantly enhances model performance and interpretability.⁴⁰

• Dhruv Jain from Krutrim AI and colleagues evaluated the readiness of Speech Language Models (SpeechLMs) for agentic tasks, introducing VoiceAgentBench (VAB), a comprehensive benchmark for assessing SpeechLMs in realistic agentic scenarios. The main innovation points are the inclusion of multilingual and culturally grounded interactions and the use of speaker embeddings to simulate diverse accents and vocal characteristics. The value lies in providing a rigorous evaluation of SpeechLMs’ capabilities in complex, multilingual, and culturally sensitive tasks, which is essential for the development of intelligent voice assistants. Experiments on VAB showed that while SpeechLMs like KimiAudio 7B exhibit robust performance, they generally lag behind ASR-LLM pipelines, especially in tasks requiring complex tool orchestration and cultural context. The findings conclude that ASR-induced errors and the lack of cultural robustness are significant challenges for SpeechLMs.⁴¹

Technical Trends

The papers in this collection collectively demonstrate a trend towards leveraging reinforcement learning (RL) and adaptive strategies to enhance the functionality and performance of large language models (LLMs). Innovations include the use of contrastive decoding and implicit rewards to improve sample quality in weak-to-strong generalization, hierarchical reward functions to optimize search behavior in agentic RAG systems, and multi-agent frameworks to manage stateful reasoning during inference. Additionally, there is a focus on developing new benchmarks and datasets that reflect real-world complexities and requirements, emphasizing the importance of practical and diverse evaluation methods.

Datasets and Evaluation

• UltraFeedback: Used for preference alignment in ConG.
• AlpacaEval2, Arena-Hard: Benchmarks for evaluating the effectiveness of ConG in weak-to-strong generalization.
• Qwen2.5, Llama-3.2: Models used in HiPRAG for optimizing search behavior.
• Language Model Personalization (LaMP): Benchmark for evaluating MemWeaver in personalized text generation.
• JUMP-Cell Painting, CELLxGENE, Tabula Sapiens: Datasets discussed in the survey on virtual cell modeling using LLMs.
• Tblive-E-Commerce QA, MuSiQue: Datasets for evaluating LiveThinking in real-time reasoning for e-commerce livestreaming.
• PCD-BD: Newly constructed benchmark for evaluating hierarchical text classification using RHC.
• HumanEval, TestGenEvalMini: Curated datasets for evaluating the stateful multi-agent evolutionary search framework.
• VoiceAgentBench (VAB): Comprehensive benchmark for evaluating SpeechLMs in agentic tasks, including multi-tool workflows and safety evaluations.

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Topic 7: Knowledge Representation and Retrieval

Topic Overview

Knowledge Representation and Retrieval is a critical area in artificial intelligence, particularly in natural language processing (NLP), which focuses on how machines can understand, interpret, and utilize human knowledge effectively. This includes developing methods for distilling knowledge from large language models (LLMs) to smaller ones, enhancing multilingual knowledge graph completions, optimizing pretraining strategies for lightweight models, and synthesizing complex linguistic phenomena like sarcasm in speech. The advancements in this field are essential for creating more efficient, versatile, and fair AI systems that can operate effectively in resource-constrained environments and across multiple languages and contexts.

Individual Paper Contributions

• Jingyu Peng from the University of Science and Technology of China and colleagues studied the difficulties in achieving high performance with small language models (SLMs) in applications requiring low latency and computational efficiency. They proposed AdaSwitch, an adaptive switching generation mechanism for knowledge distillation, to combine the strengths of both on-policy and off-policy generation methods. The main innovation points of this method are its dynamic switching strategy based on real-time quality assessments, which avoids the drawbacks of previous mixed methods. The value lies in enabling SLMs to perform nearly as well as larger models while maintaining computational efficiency. Experiments on dialogue summarization (SUMM) and arithmetic reasoning (GSM, GSM_Plus) datasets showed a significant improvement of 7.2% and 11.8% for Llama and Qwen models, respectively, compared to the second-best method. Concluding that AdaSwitch effectively balances exploration and guidance, improving the overall effectiveness of KD⁴².

• Cunli Mao from Kunming University of Science and Technology and colleagues addressed the inefficiency and limitations of existing multilingual knowledge graph completion (MKGC) methods in utilizing LLMs for predicting missing facts across languages. They introduced a novel framework combining Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER). The main innovation points are the use of a grouped MoE architecture with a knowledge-level expert routing mechanism and the iterative refinement of entity rankings. The value lies in enhancing the completeness and cross-lingual consistency of knowledge graphs, making them more useful for real-world applications. Their experiments showed significant improvements in metrics like Hits@1, Hits@3, Hits@10, and MRR, surpassing state-of-the-art (SOTA) methods in English, French, and Italian, and demonstrating robustness in handling imbalanced language distributions and unseen languages. Concluding that their framework mitigates knowledge overload and fragmentation, and improves entity ranking through iterative refinement⁴³.

• Arjun Krishnakumar from the University of Freiburg and colleagues tackled the issue of high costs and resource requirements in training and deploying LLMs. They proposed a method for initializing small language models (SLMs) by extracting sub-networks from pretrained LLMs using a constrained evolutionary search procedure. The main innovation points are the introduction of four distinct search spaces for sub-network initialization and the use of knowledge distillation to enhance the efficiency of SLM pretraining. The value lies in making advanced language models more accessible and sustainable, reducing the environmental impact of training large models. Their experiments on the Nemotron-CC dataset for pretraining and various downstream tasks indicated that models initialized with sub-networks from LLMs required significantly fewer tokens for pretraining to achieve similar performance levels as randomly initialized models. Concluding that sub-network extraction and knowledge distillation are effective strategies for efficient SLM pretraining⁴⁴.

• Shrestha Ghosh from the University of Tübingen and colleagues aimed to improve the understanding of factual knowledge and biases within LLMs, specifically GPT-4.1. They employed a recursive knowledge mining technique to construct a knowledge base (GPTKB v1.5) and analyze the model’s knowledge and biases quantitatively. The main innovation points are the large-scale recursive prompting scheme and the reverse-engineering of closed-source LLMs’ internals. The value lies in providing deeper insights into the knowledge and biases of frontier LLMs, which is crucial for improving their reliability and fairness. Their analysis revealed an overall accuracy rate of 75% for GPT-4.1’s knowledge, higher than previous text-extraction-based knowledge bases but still lower than human-curated resources. Concluding that while LLMs contain a substantial amount of factual knowledge, inconsistencies, ambiguities, and hallucinations remain significant challenges⁴⁵.

• Junyi Zhu from Samsung R&D Institute UK (SRUK) and colleagues focused on deploying efficient and versatile NLP models on mobile platforms, particularly for tasks like named entity recognition (NER) and text classification. They proposed a multi-task pre-finetuning framework called MTPF-TPL, which uses task-primary LoRA modules to address the incompatibility between pre-finetuning strategies for NER and text classification. The main innovation points are the modular adapters and the task-primary LoRA modules. The value lies in enabling a single shared encoder backbone to be adaptable across various NLP tasks while remaining efficient in terms of memory and computation. Experiments across 21 downstream tasks showed an average improvement of +0.8% for NER and +8.8% for text classification compared to individual pre-finetuned models. Concluding that MTPF-TPL resolves conflicting optimization signals and maintains a single backbone model with comparable performance to individually pre-finetuned models⁴⁶.

• Zhu Li and colleagues examined the synthesis of sarcastic speech, proposing a Retrieval-Augmented LLM-enhanced TTS framework. This method integrates LoRA-fine-tuned LLaMA 3 for capturing sarcasm-relevant semantic embeddings and a Retrieval Augmented Generation (RAG) module for identifying semantically similar and contextually aligned prosodic examples. The main innovation points are the combination of semantic and prosodic modeling techniques. The value lies in advancing the ability of machines to understand and generate sarcastic speech, enhancing human-computer interaction. Objective and subjective evaluations demonstrated that their proposed model, VITS + LLaMA 3-LoRA + RAG, outperformed several baseline models in terms of naturalness, expressivity, and accurate sarcasm expression. Concluding that their framework successfully captures and synthesizes the nuances of sarcastic speech, bridging a gap in TTS research⁴⁷.

Technical Trends

The papers in this collection showcase a trend towards more sophisticated and efficient methods for knowledge representation and retrieval, particularly in the context of language models. Innovations include adaptive mechanisms for knowledge distillation, architectural improvements for multilingual knowledge sharing, sub-network extraction and distillation for reducing resource requirements, and deep analysis techniques for uncovering biases and knowledge in LLMs. Additionally, there is a growing interest in applying these models to specialized tasks such as sarcastic speech synthesis, emphasizing the need for models to capture subtle linguistic nuances and contextual information.

Datasets and Evaluation

The papers utilized a variety of datasets to evaluate their methodologies, including dialogue summarization (SUMM), arithmetic reasoning (GSM, GSM_Plus), the Nemotron-CC dataset, the News Headlines Sarcasm dataset, HiFi-TTS corpus, and MUStARD++. Evaluation metrics varied according to the task, with common metrics including accuracy, Hits@1, Hits@3, Hits@10, Mean Reciprocal Rank (MRR), and subjective evaluations of speech naturalness and expressivity. These datasets and metrics helped validate the effectiveness and efficiency of the proposed methods in different contexts and tasks, highlighting the importance of both quantitative and qualitative assessments in evaluating knowledge representation and retrieval techniques.

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Topic 8: Evaluation and Assessment Methods

Topic Overview

Evaluation and assessment methods in artificial intelligence (AI) are critical for ensuring that AI systems, especially large language models (LLMs), are not only accurate but also safe and aligned with ethical standards. These methods are essential for addressing challenges such as generating truthful yet safe responses, automating complex evaluations, and deploying AI effectively in low-resource settings. Accurate evaluation metrics and robust assessment strategies are necessary for the responsible advancement of AI technologies, particularly in areas like natural language processing (NLP), where the nuances of human language and cultural contexts pose additional complexities.

Individual Paper Contributions

• Omar Mahmoud from Deakin University and colleagues studied the unintended trade-off between improving the factual accuracy (truthfulness) of large language models (LLMs) and maintaining their safety, focusing on the risk of weakening refusal mechanisms against harmful requests. They proposed a mechanism involving Sparse Autoencoders (SAEs) to identify and isolate refusal and hallucination features within the model, specifically targeting certain attention heads. The main innovation points include the use of SAEs to disentangle refusal and hallucination signals, allowing for improvements in factual accuracy without compromising safety. The value lies in the method’s ability to enhance LLM utility while preserving ethical compliance, tested on models like LLaMA3-8B-Instruct and Qwen2.5-Instruct. Experiments demonstrated a significant improvement in fine-tuning accuracy and a drastic reduction in Attack Success Rate (ASR) on harmful benchmarks, compared to baselines such as SafeLoRA, SaLoRA, SAP, and vanilla supervised fine-tuning (SFT), concluding that their method effectively mitigates the trade-off between truthfulness and safety ⁴⁸.

• Tianci Liu from Purdue University and colleagues addressed the limitation of current reward models in reinforcement learning from human feedback (RLHF), particularly in subjective domains like long-form question answering and general helpfulness. They introduced OpenRubrics, a large-scale and diverse collection of rubrics, along with a new method called Contrastive Rubric Generation (CRG) for rubric synthesis. The CRG method improves rubric quality by leveraging negative contrasts. The paper also proposed a rubric-aware reward model, Rubric-RM, which guides the training and inference processes of LLMs, enhancing their performance in reward modeling and policy optimization. The value lies in providing a more comprehensive and consistent evaluation framework for aligning LLMs with human preferences. Experiments on eight benchmark datasets showed that Rubric-RM outperformed strong baselines, achieving the best overall average and correctly identifying and enforcing hard rules and principles ⁴⁹.

• Amir Hossein Yari from Sharif University of Technology and colleagues tackled the issue of unreliable automatic metrics for evaluating machine translation (MT) and text summarization (TS) systems in Indian languages. They developed ITEM (Indian Text Evaluation Metrics Testbed) to assess the alignment between automatic metrics and human judgments for MT and TS in six major Indian languages. The paper evaluated 26 automatic metrics and proposed a robust outlier detection method using median and Median Absolute Deviation (MAD). The value lies in offering a fine-grained analysis of metric reliability, robustness to outliers, and sensitivity to controlled perturbations, contributing to the development and deployment of more reliable MT and TS systems in these languages. Experiments revealed that LLM-based evaluators, such as DeepSeek-V3, achieved the highest correlations with human judgments, indicating their superior reliability ⁵⁰.

• Eduardo Ryô Tamaki from German Institute for Global and Area Studies and colleagues focused on the difficulty of measuring the ideational content of populism using traditional textual analysis methods. They introduced a synthetic holistic grading (SHG) approach using LLMs to automate the measurement of populism, proposing a chain-of-thought (CoT) prompting strategy that simulates human coder training. The paper utilized the Global Populism Database (GPD) as a benchmark dataset, demonstrating the potential of AI in handling complex political science concepts. The value lies in the feasibility of automated SHG with top-tier reasoning models, bridging the gap between computational efficiency and nuanced understanding. Experiments on GPD showed high agreement between GPT-5 and Qwen3 235B with human graders, indicating near-interchangeability, while weaker models exhibited larger errors and weaker reliability ⁵¹.

• Benjamin Akera and colleagues investigated the necessity of speech data for developing practical Automatic Speech Recognition (ASR) systems in low-resource African languages, specifically Kinyarwanda and Kikuyu. They used Whisper, a large multilingual model, and provided empirical evidence on data requirements and failure modes. The main innovation points include a systematic data scaling analysis and detailed error analysis methodology, both novel in the context of ASR for African languages. The value lies in understanding the data volume and quality needed for effective ASR deployment, which helps in reducing the digital divide. Experiments indicated that practical ASR performance could be achieved with as little as 50 hours of training data, with significant improvements up to 200 hours. Noise and unclear ground truth were identified as leading causes of high error rates, with Whisper demonstrating logarithmic improvement in performance ⁵².

Technical Trends

The papers in this collection highlight evolving trends in AI evaluation and assessment. Key approaches include:

• Disentanglement Techniques: Using Sparse Autoencoders (SAEs) to separate conflicting behavioral traits (hallucination vs. refusal) in LLMs.
• Synthetic Data and Rubrics: Creating large-scale rubric collections (OpenRubrics) and leveraging contrastive methods (CRG) to enhance the quality and consistency of reward models.
• Metric Analysis and Robustness: Conducting detailed reliability analyses of existing metrics and proposing outlier detection methods (median and MAD) for improved evaluation in diverse linguistic contexts.
• Automated Ideational Measurement: Applying LLMs and CoT strategies to automate complex political concept measurements, like populism.
• Data Scaling Analysis: Providing empirical evidence on the amount and quality of speech data required for effective ASR deployment in low-resource languages.

These methodologies demonstrate a shift towards more sophisticated and context-specific evaluation techniques that aim to balance accuracy, safety, and scalability.

Datasets and Evaluation Metrics

• LLaMA3-8B-Instruct and Qwen2.5-Instruct: Used for evaluating the trade-off between truthfulness and safety in LLMs, with metrics such as fine-tuning accuracy and Attack Success Rate (ASR).
• Global Populism Database (GPD): Utilized to measure populism using LLMs, with metrics like CCC (Concordance Correlation Coefficient) and ICC (Intraclass Correlation Coefficient).
• ITEM Benchmark Dataset: Contains data for MT and TS in Indian languages, evaluated using surface-based, embedding-based, and LLM-based metrics.
• Whisper Model: Tested with Kinyarwanda and Kikuyu datasets for ASR, with metrics such as Word Error Rate (WER) and Character Error Rate (CER).

These datasets and metrics collectively contribute to a more nuanced and reliable evaluation framework for various AI applications, emphasizing the importance of context-specific and human-aligned assessments.

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Topic 9: Cognitive and Social Simulations

Topic Overview

Cognitive and social simulations aim to replicate human cognition and social interaction within artificial systems, such as autonomous agents and multi-agent systems (MAS), to improve their decision-making and collaboration capabilities. This field is critical for developing AI that can operate in complex, dynamic environments and engage in sophisticated forms of teamwork, akin to human groups. Research in this area addresses challenges in optimizing the efficiency of cognitive processes, understanding the dynamics of team structures, and enhancing the social awareness of AI systems. These advancements have broad implications for fields such as autonomous robotics, virtual assistants, and complex problem-solving scenarios.

Individual Paper Contributions

• Xinliang Frederick Zhang from University of Michigan and colleagues studied the inefficiency introduced by long-chain-of-thought (CoT) reasoning in language models (LLMs) for simple queries, proposing the TRACE (Thought-process Reconstruction and Automated Clustering Engine) framework to address the issue of overthinking. The main innovation points of TRACE include its ability to define and quantify overthinking based on a utility-based threshold and its structured approach to analyzing thought processes through stages like Response Sampling and Thought Decomposition. The value lies in providing a comprehensive toolset for understanding and mitigating overthinking, thereby enhancing the efficiency of LLMs. Experiments on datasets like ASDiv (covering grade 1-5 levels), GSM8k, and curated temporal reasoning tasks showed that while thinking improves accuracy for harder tasks, the majority of computational effort is wasted on simpler tasks, concluding that overthinking diminishes with increasing model size and task difficulty⁵³.

• Baixuan Xu from The Hong Kong University of Science and Technology and colleagues tackled the cognitive bandwidth bottleneck faced by autonomous agents in environments with large or unbounded action spaces, particularly for long-horizon tasks. They proposed shifting from planning with actions (PwA) to planning with schemas (PwS) as a solution. The main innovation points are the introduction of the Cognitive Bandwidth Perspective as a conceptual framework and the derivation of action schemas from action lists, which was systematically compared against PwA. The value lies in identifying an inflection point in action space size where PwS becomes more advantageous than PwA, offering empirical guidance for improving schema-based agents. Experiments on four environments—TextCraft, WebShop, ALFWorld, and SciWorld—showed that PwA outperforms PwS in smaller action spaces, whereas PwS performs better in larger action spaces, concluding that the inflection point is influenced by the model’s agentic proficiency and schema-instantiation capability⁵⁴.

• Rasika Muralidharan from Indiana University Bloomington and colleagues explored the team dynamics of Multi-Agent Systems (MAS) powered by Large Language Models (LLMs), focusing on the impact of team structure, diversity, and interaction dynamics on performance and internal dynamics. Their unique contribution is the application of human team science principles to design and evaluate AI teams, using the ‘LLM-as-a-judge’ approach with GPT-4o to assess team interactions qualitatively. The value lies in providing insights into how different team structures and diversity levels affect the performance and interaction quality of AI teams. Experiments on four tasks—CommonsenseQA, StrategyQA, Social IQa, and Latent Implicit Hate detection—revealed that flat team structures generally outperform hierarchical ones, and diversity enhances cohesion and perceived contribution in flat teams but may negatively impact performance due to misaligned communication. Concluding that open communication structures are more conducive to leveraging diverse perspectives, they highlighted the need to consider team structure and diversity in designing effective AI teams⁵⁵.

Technical Trends

The papers under the topic of Cognitive and Social Simulations reflect a trend towards refining and optimizing cognitive processes in AI, particularly in scenarios involving complex reasoning and decision-making. Zhang et al. focus on quantifying and mitigating overthinking in LLMs through structured analysis, Xu et al. advocate for a shift in planning strategies from actions to schemas to manage cognitive load in autonomous agents, and Muralidharan et al. apply lessons from human team dynamics to enhance the collaboration and social awareness of multi-agent systems. These studies collectively emphasize the importance of reducing unnecessary computational costs, managing cognitive resources effectively, and fostering efficient and meaningful interaction among AI entities.

Datasets and Evaluation

• TRACE Framework by Zhang et al.: Utilized datasets like ASDiv (for mathematical reasoning), GSM8k, and curated temporal reasoning tasks. The evaluation focused on defining and quantifying overthinking based on thought tokens and their utility, assessing the marginal return of additional thought tokens.

• Planning with Schemas by Xu et al.: Employed four environments—TextCraft, WebShop, ALFWorld, and SciWorld—to compare planning with actions (PwA) and planning with schemas (PwS). Success rates and average rewards were used as primary evaluation metrics to gauge performance improvements under different conditions.

• Team Dynamics in MAS by Muralidharan et al.: Used tasks like CommonsenseQA, StrategyQA, Social IQa, and Latent Implicit Hate detection to evaluate team structures and diversity impacts. The Gini index measured diversity, and pre- and post-elicitation probing assessed agent perceptions, with GPT-4o evaluating interaction quality dimensions.

These datasets and evaluation methods underscore the varied approaches taken to study cognitive and social behaviors in AI, reflecting a commitment to rigorous testing and a holistic understanding of AI’s operational efficiency and collaborative potential.

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Topic 10: Specialized Applications and Domains

Topic Overview

The topic of specialized applications and domains in large language models (LLMs) focuses on enhancing LLMs’ performance in specific, often complex, fields such as healthcare, chemistry, and multilingual environments. These specialized applications aim to address the limitations of general-purpose LLMs when dealing with domain-specific knowledge and tasks, ensuring that AI systems can provide accurate and efficient support tailored to particular professions or industries. The research is important for developing more reliable and effective AI solutions that can integrate seamlessly into professional workflows, thereby contributing to advancements in fields such as drug discovery, medical diagnosis, and multilingual communication.

Individual Paper Contributions

• Murong Yue from George Mason University and colleagues studied the scalability issue in creating and organizing question-specific tools for LLMs to enhance their reasoning capabilities, particularly in domains like physics, mathematics, and medicine. They proposed ToolLibGen, a pipeline that refactors fragmented collections of tools into a structured Python library using a multi-agent framework involving a Coding Agent and a Reviewing Agent. The main innovation points are the systematic structuring of tool libraries to reduce redundancy and enhance retrieval accuracy, alongside the use of multi-agent collaboration for iterative code refinement. The value lies in improving LLM reasoning performance by enabling efficient and accurate tool retrieval. Experiments on Science, Mathematics, and Medical QA datasets demonstrated significant improvements in seen and unseen case performance, with ToolLibGen outperforming baselines by 5%-10% and 2-3% respectively, suggesting that structured tool libraries mitigate retrieval issues and improve reasoning performance⁵⁶.

• Ruiling Xu from University of Illinois Urbana-Champaign and colleagues aimed to assess the chemical reasoning capabilities of LLMs in the domain of organic mechanism elucidation and reasoning. They introduced oMeBench, a large-scale, expert-curated benchmark that includes over 10,000 annotated mechanistic steps, and oMeS, a dynamic evaluation framework to measure LLMs’ ability to generate valid intermediates and follow logically coherent multi-step pathways. The innovation lies in the focus on detailed mechanistic reasoning processes, which were previously simplified or overlooked. The value is in providing a robust benchmark for evaluating and improving LLMs’ chemical intuition and multi-step reasoning abilities. Experiments showed that employing a prompting strategy and fine-tuning on their dataset improved performance by 50% over the leading closed-source model, indicating that specialized prompting and fine-tuning are crucial for enhancing LLMs’ chemical reasoning⁵⁷.

• Md Tawkat Islam Khondaker from Yourika Labs and colleagues developed NurseLLM, the first specialized LLM for nursing, designed to handle multiple-choice question-answering tasks relevant to the profession. They created a large-scale and topic-diverse nursing MCQ dataset, and developed three benchmarks for evaluating LLMs on nursing-related tasks. The innovation points are the development of a nursing-specific LLM and benchmarks, addressing gaps in existing general medical LLMs. The value is in improving the quality and efficiency of AI tools specifically designed for nursing practices, which emphasize holistic, person-centered care. Extensive experiments demonstrated that NurseLLM outperformed SoTA generalized and medical expert LLMs on nursing benchmarks, highlighting the importance of specialized models trained with domain-specific data and benchmarks⁵⁸.

• Miriam Wanner from Johns Hopkins University and colleagues investigated the impact of Sinclair Broadcast Group’s acquisition of local news stations on the nature of news content, focusing on shifts towards national topics and increased politicization. They analyzed YouTube channel transcripts using corpus analysis methods and Structured Topic Models (STMs) with metadata covariates. The innovation lies in the use of online content and advanced analytical techniques to study changes in discourse over time. The value is in understanding how media ownership can influence news content, which has implications for public perception and community engagement. Their analysis revealed a shift in content from local to national topics and an increase in politicized coverage, emphasizing the changing dynamics of local news in the context of corporate acquisitions⁵⁹.

• Rajvee Sheth from IIT Gandhinagar and colleagues surveyed code-switched NLP in the era of LLMs, focusing on the challenges of handling mixed-language inputs. They reviewed studies across various NLP tasks and languages, introducing a robust taxonomy for organizing LLMs by architecture, training strategy, and evaluation methodology. The innovation points include the comprehensive review of CSW-aware LLM research and the introduction of a taxonomy. The value is in highlighting the need for more inclusive datasets and fair evaluation metrics to develop truly multilingual intelligence. Experiments showed significant improvements in performance on CSW tasks using methods like instruction tuning and synthetic data augmentation, achieving up to a 32x improvement in exact match on Hinglish QA tasks and a 12.98 BLEU score improvement in translation tasks. Despite these gains, the paper concludes that there remain substantial challenges in handling unseen CSW patterns and ensuring consistency across linguistic contexts⁶⁰.

• Zhangdie Yuan from University of Cambridge and colleagues tackled the challenge of accurate clinical coding of outpatient clinical notes using LMs. They introduced a new double expert-annotated benchmark and proposed a generate-expand-verify pipeline to refine LM predictions. The innovation lies in the development of new evaluation metrics, such as prefix-n match and prefix overlap ratio, to better capture hierarchical misalignments in the ICD-10-CM coding system. The value is in providing a reliable and efficient way to enhance the performance of LMs in clinical coding tasks. Experiments demonstrated that prompt engineering and small-scale fine-tuning could improve performance, with the pipeline achieving an F1 score of up to 16 on the new outpatient benchmark dataset. The insights suggest that hierarchical verification and lightweight adaptations are effective strategies for improving LLM performance in clinical coding⁶¹.

Technical Trends

The papers in this topic adopt several technical trends, including the use of specialized datasets and benchmarks to train and evaluate LLMs for domain-specific tasks, the application of multi-agent frameworks for iterative code refinement, and the development of new evaluation metrics to better capture domain-specific nuances. There is also a trend towards employing lightweight adaptation techniques, such as prompt engineering and small-scale fine-tuning, to improve LLM performance without requiring extensive computational resources. Furthermore, the papers highlight the importance of incorporating hierarchical verification in tasks like clinical coding, and the necessity of robust benchmarks in fields like chemical reasoning and nursing.

Datasets and Evaluation

• ToolLibGen: Uses specific datasets for Science, Mathematics, and Medical QA to structure and validate tools.
• oMeBench: Includes over 10,000 annotated mechanistic steps with intermediates, step-type labels, and difficulty ratings for organic mechanism reasoning.
• NurseLLM: Employs a large-scale, topic-diverse nursing MCQ dataset with 125K samples and creates three benchmarks: NCLEX-Test, GPT4o-Test, and MultiNurseQA.
• Does Local News Stay Local?: Analyzes YouTube channel transcripts from eight geographically diverse local news stations and two national news outlets.
• Beyond Monolingual Assumptions: Reviews datasets across 30+ languages and 12 NLP tasks, including Hinglish QA and translation tasks.
• Toward Reliable Clinical Coding: Introduces a new double expert-annotated benchmark for outpatient clinical notes with ICD-10-CM codes, addressing limitations in existing datasets like MIMIC-III/IV.

Evaluation metrics vary widely, with each paper tailoring its metrics to the specific needs of its domain. For instance, ToolLibGen uses retrieval accuracy, oMeBench uses validity of intermediates and logical coherence, NurseLLM uses accuracy in MCQ tasks, and “Does Local News Stay Local?” relies on log-odds ratios and topic modeling. In “Beyond Monolingual Assumptions,” metrics like exact match and BLEU scores are employed, while “Toward Reliable Clinical Coding” utilizes new metrics such as prefix-n match and prefix overlap ratio to better assess hierarchical misalignments in clinical coding.

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Topic 11: misc

Topic Overview

The research topic encompasses a broad array of challenges and innovations in the realm of large language models (LLMs) and their applications. From enhancing reasoning capabilities and managing privacy risks to optimizing multimodal interactions and refining psychometric evaluations, these studies collectively address the need for more robust, efficient, and ethically sound AI systems. Each paper delves into a specific facet of LLM functionality, aiming to bridge the gap between theoretical advancements and practical usability, ultimately pushing the boundaries of what LLMs can achieve in diverse and complex real-world scenarios.

Individual Paper Contributions

• Jingyuan Wang from The University of Hong Kong and colleagues studied the limited systematic reasoning capabilities of LLMs, particularly in mathematics, proposing LightReasoner to solve this core problem. The main innovation points of this method are its resource-efficient approach and the use of smaller language models (SLMs) to generate supervision examples that capture the strengths of expert models. The value lies in achieving comparable or superior performance to traditional supervised fine-tuning (SFT) methods while drastically reducing computational costs and reliance on large curated datasets. Experiments on datasets like GSM8K, MATH, SVAMP, and others showed significant improvements in accuracy, with a +28.1% increase in accuracy on GSM8K for Qwen2.5-Math-1.5B, concluding that LightReasoner effectively enhances LLM reasoning capabilities through contrastive learning ⁶².

• Eric Hanchen Jiang from [institution] and colleagues addressed the dynamic generation of communication topologies for multi-agent systems (MAS) driven by LLMs, proposing Guided Topology Diffusion (GTD) to solve the problem of static or hand-crafted topologies. The main innovation points of GTD are its ability to perform real-time topology generation based on task utility, communication cost, and robustness. The value lies in optimizing communication structures in MAS, leading to better task-solving effectiveness and cost-efficiency. Experiments on benchmarks such as GSM8K, MATH, MultiArith, and SVAMP showed superior accuracy and cost-efficiency compared to existing methods, concluding that GTD effectively adapts to diverse task requirements ⁶³.

• Shiman Zhao from Peking University and colleagues focused on few-shot multi-label intent detection (MID) in dialogue systems, introducing a method that combines instance relation learning and label knowledge propagation. The main innovation points are the use of a fully connected instance relation graph and a dual relation-enhanced loss function. The value lies in improving adaptability and efficiency of dialogue systems in low-resource settings. Experiments on the TourSG dataset demonstrated significant improvements over baselines, with an average of 11.50% improvement in AUC and 10.49% in Macro-F1 scores in the 10-way 1-shot setting, concluding that this method effectively leverages limited labeled data for high performance ⁶⁴.

• Jifan Zhang from Anthropic Fellows Program and colleagues explored the internal conflicts and insufficient coverage within AI model specifications, proposing a novel methodology for stress-testing these specifications. The main innovation points are the generation of diverse query scenarios and the use of a fine-grained taxonomy of values. The value lies in identifying gaps in model specifications and improving AI alignment. Analysis on a dataset of over 300,000 scenarios revealed high disagreements among models and the need for clearer definitions and examples, concluding that model specifications must be more comprehensive and granular to avoid misalignments ⁶⁵.

• Virginia K. Felkner from University of Southern California and colleagues addressed the limitations of token probability (TP) as a metric for evaluating social biases in LMs, proposing natural language inference (NLI), particularly textual entailment, as a new midstream bias evaluation task. The main innovation points are the introduction of WinoQueer-NLI (WQ-NLI) and a comparison framework for TP and NLI metrics. The value lies in providing a more realistic and generalizable evaluation of bias across different models and use cases. Experiments on nine models under three debiasing conditions showed that NLI metrics are more effective at detecting underdebiasing, concluding that NLI provides a more nuanced and reliable evaluation of bias ⁶⁶.

• Leigang Qu from National University of Singapore and colleagues tackled text-video misalignment in compositional scenarios involving motion, numeracy, and spatial relations, introducing Test-Time Optimization and Memorization (TTOM) to improve compositional text-to-video generation. The main innovation points are the use of a large language model to generate spatiotemporal layouts and the parametric memory mechanism to store and reuse optimized parameters. The value lies in enhancing the realism and coherence of generated videos without additional training data. Experiments on T2V-CompBench and VBench showed relative improvements of 34.45% and 15.83% in overall performance, concluding that TTOM effectively addresses misalignment in compositional T2V tasks ⁶⁷.

• Peiyang Liu from Peking University and colleagues aimed to detect unauthorized use of content by Retrieval-Augmented Generation (RAG) systems, proposing a dual-layered watermarking system and the Interrogator-Detective framework. The main innovation points are the semantic-level and lexical-level watermarking techniques and the strategic query generation for detection. The value lies in protecting intellectual property rights and ensuring proper attribution. Experiments on the RPD dataset achieved 100% detection accuracy under adversarial conditions, concluding that the dual-layered approach effectively detects unauthorized RAG usage ⁶⁸.

• Chongyu Fan from Michigan State University and colleagues focused on the selective removal of undesired data or knowledge from LLMs, proposing a taxonomy of 12 recent unlearning methods and introducing Open-QA metrics to evaluate unlearning effectiveness. The main innovation points are the categorization of unlearning methods and the use of both MCQ and Open-QA evaluations. The value lies in providing a nuanced evaluation framework and identifying robustness trade-offs. Experiments on the WMDP benchmark revealed that divergence-driven optimization methods are more robust to in-domain relearning, concluding that robustness-oriented designs enhance resilience against various attacks ⁶⁹.

• Md. Nazmul Islam Ananto from Bangladesh University of Engineering and Technology (BUET) and colleagues addressed the challenge of identifying popular paths in urban navigation using historical trajectory data, proposing CompassLLM, a multi-agent framework for geo-spatial reasoning. The main innovation points are the two-stage SEARCH and GENERATE pipeline and the incorporation of specialized agents for spatial reasoning. The value lies in enhancing navigation systems and urban infrastructure design. Experiments on real-world and synthetic datasets showed superior performance in F1 scores and Traversability scores, concluding that CompassLLM effectively handles sparse data scenarios ⁷⁰.

• Anthony Hughes from University of Sheffield and colleagues focused on mitigating personally identifiable information (PII) leakage in LMs, proposing Privacy-Aware Targeted Circuit Patching (PATCH). The main innovation points are the use of Edge Attribution Patching with Integrated Gradients (EAP-IG) for circuit discovery and selective editing. The value lies in enhancing privacy and security in LMs. Experiments on datasets like the European Court of Human Rights (ECHR) showed a significant reduction in PII extraction precision and recall, concluding that PATCH offers better privacy-utility trade-offs compared to differential privacy methods ⁷¹.

• Ziyi Wang from Northeastern University and colleagues aimed to simulate personalized human behaviors in online shopping using LLM agents, proposing Customer-R1, which uses reinforcement learning to personalize user behavior simulation. The main innovation points are the incorporation of user persona information and custom reward design. The value lies in improving the accuracy and realism of user behavior simulations. Experiments on the OPeRA dataset showed the highest Next Action Generation accuracy of 39.58% and best Macro F1 score of 78.50%, concluding that combining SFT with RL yields the best performance ⁷².

• Imry Ziv from Tel Aviv University and colleagues examined the sensitivity of LLMs to the distinction between humanly possible and impossible languages, introducing six types of perturbations across nine languages to assess learning curves. The main innovation points are the extension of the methodology to multiple languages and perturbations. The value lies in understanding LLM learning biases and their implications for human linguistic cognition. Experiments revealed that GPT-2 does not systematically distinguish between possible and impossible languages, concluding that LLMs do not capture human learning biases effectively ⁷³.

• Miriam Wanner from Johns Hopkins University and colleagues introduced the Vital metrics for importance-sensitive factuality evaluation of LLM-generated responses, constructing the VitalErrors dataset for testing. The main innovation points are the decomposition-level and response-level evaluations of key information errors. The value lies in providing a more nuanced assessment of factuality. Experiments showed a substantial decrease in Vital${}_{\textsc{Prec.}}$ scores for single-answer queries when key information is wrong, concluding that Vital metrics are better at detecting critical errors ⁷⁴.

• Lucio La Cava from University of Calabria and colleagues characterized the presence and impact of machine-generated text (MGT) on Reddit, using Fast-DetectGPT for detection and nonparametric tests for engagement analysis. The main innovation points are the large-scale characterization of MGT and the comparison with human-generated text (HGT) across different subreddit categories. The value lies in informing policies and safeguards against potential harms from synthetic content. Experiments indicated higher engagement levels for MGT in most categories, concluding that the adoption and impact of MGT vary across subreddits ⁷⁵.

• Zheyuan Zhang from University of Notre Dame and colleagues proposed Multi-Agent PRompt Optimization (MAPRO) to solve the challenge of prompt optimization for multi-agent systems (MAS). The main innovation points are the formulation of MAS prompt optimization as a Maximum a Posteriori (MAP) inference problem and the use of language-guided belief propagation algorithms. The value lies in enhancing the reliability and performance of MAS in practical workflows. Experiments on benchmarks like HumanEval-ET and MBPP-Plus showed consistent performance gains, concluding that MAPRO outperforms manual and automated baselines ⁷⁶.

• Zifan Jiang from University of Zurich and colleagues introduced a systematic examination of metrics for evaluating sign language output, particularly human skeletal poses. The main innovation points are the taxonomy of pose-based evaluation methods and the implementation of evaluation protocols in publicly accessible repositories. The value lies in improving the reliability and accuracy of sign language translation systems. Experiments showed that back-translation likelihood is the most consistent metric, concluding that careful tuning of keypoint distance-based metrics can rival advanced methods ⁷⁷.

• Mufei Li from Georgia Institute of Technology and colleagues addressed the robustness of long-context LLMs in agentic workflows, proposing HaystackCraft as a new benchmark for evaluating models under noisy and biased contexts. The main innovation points are the use of the full English Wikipedia hyperlink network and multi-hop questions to simulate real-world information retrieval challenges. The value lies in providing a comprehensive assessment of long-context reasoning capabilities. Experiments on Natural Questions (NQ) and MuSiQue datasets revealed that graph-based reranking using Personalized PageRank (PPR) significantly enhances retrieval effectiveness, concluding that HaystackCraft is a valuable tool for evaluating LLMs in practical scenarios ⁷⁸.

• Heyang Liu from [institution] and colleagues evaluated and enhanced speech-to-speech LLMs for Mandarin-English code-switching, introducing CS3-Bench and methods like Chain of Recognition (CoR) and Keyword Highlighting (KH). The main innovation points are the introduction of new methods to improve language alignment in code-switching contexts and the construction of a dedicated code-switching corpus. The value lies in enhancing the effectiveness and naturalness of voice assistants in multicultural environments. Experiments showed significant improvements in knowledge accuracy and open-ended understanding, concluding that the proposed methods effectively handle mixed-language inputs ⁷⁹.

• Shuichiro Haruta from KDDI Research, Inc. and colleagues focused on reducing errors introduced by structured pruning in LLMs, proposing Rotation-Constrained Parameter Update (RCPU) to compensate for pruning-induced errors. The main innovation points are the combination of rotation-based compensation with a variance-aware importance score for column pruning. The value lies in maintaining output representation norms and inner-product structures. Experiments on LLaMA-7B showed consistent improvements over baselines like WANDA-sp and FLAP, concluding that RCPU effectively preserves task performance after pruning ⁸⁰.

• Yinglun Zhu from University of California, Riverside and colleagues tackled the underestimation of model capability in compositional reasoning for multimodal models, proposing Test-Time Matching (TTM) and the GroupMatch metric. The main innovation points are the use of group structure and matching principles for stronger supervision signals and self-improvement during testing. The value lies in unlocking hidden compositional reasoning capabilities in models. Experiments on Winoground and other benchmarks showed significant performance improvements, concluding that matching-based supervision and iterative self-improvement enhance model performance ⁸¹.

• Lingcheng Kong from [institution] and colleagues addressed the inefficiency in generating high-performance GPU kernels using LLMs, proposing ConCuR to generate concise reasoning traces for CUDA kernel generation. The main innovation points are the data synthesis and curation pipeline for CUDA kernels and the KernelCoder model. The value lies in automating kernel generation to improve productivity and performance in ML systems. Experiments showed that shorter reasoning traces correlate with higher accuracy in kernel generation, concluding that conciseness is key to generating efficient kernels ⁸².

• Yong-En Tian from National Yang Ming Chiao Tung University and colleagues introduced Content-Aware Refinement of Provided Aspects for Summarization (CARPAS) to refine provided aspects in documents before summarization. The main innovation points are the prediction of the number of relevant aspects and the construction of synthetic and real-world datasets. The value lies in improving the accuracy and relevance of summaries. Experiments on ECT and COVID-19-PC datasets showed substantial improvements in BERTScore and ROUGE-L metrics, concluding that aspect refinement enhances summarization quality ⁸³.

• Jongwook Han from Seoul National University and colleagues quantified data contamination in psychometric evaluations of LLMs, proposing a framework to measure contamination in item memorization and target score matching. The main innovation points are the quantitative measures for memorization and matching. The value lies in ensuring reliable psychometric assessments of LLMs. Experiments on 21 LLMs and four inventories showed near-ceiling performance in memorization and strategic response generation, concluding that contamination affects psychometric evaluations ⁸⁴.

• Đorđe Klisura from [institution] and colleagues studied access control reasoning in LLMs, proposing Role-Conditioned Refusals to evaluate and improve models’ adherence to access control rules. The main innovation points are the role-conditioned prompting strategies and the evaluation of access control reasoning. The value lies in enhancing the security and reliability of LLMs in controlled environments. While specific details on datasets and baselines are not provided, the paper suggests that role conditioning can effectively guide LLMs to follow access control protocols, concluding that this approach is promising for improving model security ⁸⁵.

Technical Trends

The papers collectively highlight several emerging trends in LLM research:

1. Resource-Efficient Techniques: Multiple studies focus on developing methods that reduce computational costs and improve efficiency, such as LightReasoner, which leverages smaller models for supervision, and RCPU, which compensates for pruning errors to preserve performance with fewer resources.
2. Multi-Agent Systems: Papers like CompassLLM and MAPRO emphasize the potential of multi-agent systems for enhancing LLM performance in specialized tasks, such as geo-spatial reasoning and prompt optimization.
3. Bias and Ethical Considerations: Studies like WinoQueer-NLI and PATCH underscore the importance of addressing bias and privacy concerns in LLMs, introducing new metrics and methodologies to mitigate these issues.
4. Adaptation and Generalization: Works such as TTM and CARPAS explore methods to adapt LLMs to diverse tasks and environments, using techniques like test-time optimization and content-aware refinement to improve generalization and performance.
5. Comprehensive Evaluation Frameworks: Several papers, including HaystackCraft and Quantifying Data Contamination in Psychometric Evaluations, propose new benchmarks and metrics to provide a more thorough and accurate evaluation of LLMs across different domains and tasks.

Datasets and Evaluation Metrics

• LightReasoner: Uses datasets like GSM8K, MATH, SVAMP, and others for evaluating reasoning capabilities.
• Guided Topology Diffusion (GTD): Evaluates on benchmarks such as GSM8K, MATH, MultiArith, and SVAMP.
• CompassLLM: Utilizes real-world and synthetic datasets for navigation and route synthesis.
• PATCH: Employs datasets like the European Court of Human Rights (ECHR) for privacy evaluations.
• Customer-R1: Uses the OPeRA dataset for simulating online shopping behaviors.
• MAPRO: Employs Natural Questions (NQ) and MuSiQue datasets for evaluating agentic workflows.
• HaystackCraft: Uses the full English Wikipedia hyperlink network and multi-hop questions for long-context reasoning evaluation.
• ConCuR: Constructs a synthetic dataset of CUDA kernels for evaluating kernel generation.
• CARPAS: Develops synthetic datasets for earnings call transcripts (ECT) and COVID-19 press conference materials, along with real-world earnings call transcript data (RW-ECT).
• Quantifying Data Contamination: Employs psychometric inventories like BFI-44, PVQ-40, MFQ, and SD-3 for contamination measurement.
• WinoQueer-NLI: Uses WinoQueer reformulated for NLI tasks.
• TTM: Employs Winoground and other benchmarks for compositional reasoning.
• CS3-Bench: Creates a dataset for speech-to-speech LLMs in code-switching contexts.
• Who Stole Your Data?: Introduces the RPD dataset for detecting unauthorized RAG usage.

These studies collectively advance the field by addressing key challenges in LLM performance, evaluation, and ethical considerations, paving the way for more sophisticated and reliable AI systems in the future.

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References