Computation and Language
Data-efficient Targeted Token-level Preference Optimization for LLM-based Text-to-Speech
Rikuto Kotoge and Yuichi Sasaki
Aligning text-to-
speech (TTS) system outputs with human feedback through preference optimization has been shown to effectively improve the robustness and naturalness of
language model-based TTS models. Current approaches primarily require paired desirable and undesirable samples at the utterance level. However, such pairs are often limited in TTS output data, and utterance-level formulation prevents fine-grained
token-level optimization needed for accurate pronunciation
alignment. In this study, we propose TKTO that eliminates the need for paired data, enabling a more data-efficient training paradigm, and directly targets
token-level units, automatically providing fine-grained
alignment signals without
token-level annotations. TKTO improves the challenging Japanese TTS accuracy by 39% and reduces CER by 54%, automatically assigning 12.8 times stronger reward to targeted
tokens.
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Computation and Language
Mixture of Neuron Experts
Runxi Cheng, Yuchen Guan, Yucheng Ding et al. (+6 more)
In this work, we first explore whether the parameters activated by the MoE layer remain highly sparse at inference. We perform a sparsification study on several representative MoE models. For each expert, we rank parameters by the magnitude of their activations from the gate projection and progressively prune the activated subset.
Pruning up to 60% of parameters within that subset causes only negligible task-performance degradation; substantial drops occur only after more than 90% are removed. We further decompose experts into
neuron-granular MoE and visualize their activation values, finding that most
neuron activations are near zero. This observation motivates us to select only high-activation
neuron experts during pretraining. Based on this insight, we propose Mixture of
Neuron Experts (MoNE). MoNE achieves
neuron-granular expert selection by only applying a simple top-k selection within each expert, incurs negligible latency, and requires no additional routing parameters or inter-expert communication. Extensive experiments demonstrate that MoNE matches traditional MoE performance while activating only 50% of the MoE-layer parameters, and it consistently outperforms traditional MoE when compared at equal numbers of activated parameters. These results suggest that MoNE is a practical approach to improving parameter utilization and inference efficiency in MoE-like models.
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All authors Runxi Cheng, Yuchen Guan, Yucheng Ding, Qingguo Hu, Yongxian Wei, Chun Yuan, Yelong Shen, Weizhu Chen, Yeyun Gong
Computation and Language
InforME: Improving Informativeness of Abstractive Text Summarization With Informative Attention Guided by Named Entity Salience
Jianbin Shen, Christy Jie Liang and Junyu Xuan
Abstractive text summarization is integral to the Big Data era, which demands advanced methods to turn voluminous and often long text data into concise but coherent and informative summaries for efficient human consumption. Despite significant progress, there is still room for improvement in various aspects. One such aspect is to improve informativeness. Hence, this paper proposes a novel learning approach consisting of two methods: an optimal transport-based informative
attention method to improve learning focal information in reference summaries and an accumulative joint
entropy reduction method on named entities to enhance informative salience. Experiment results show that our approach achieves better ROUGE scores compared to prior work on CNN/Daily Mail while having competitive results on XSum. Human evaluation of informativeness also demonstrates the better performance of our approach over a strong baseline. Further analysis gives insight into the plausible reasons underlying the evaluation results.
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Computation and Language
Diversity Is All You Need for Contrastive Learning: Spectral Bounds on Gradient Magnitudes
Peter Ochieng
We derive non-asymptotic spectral bands that bound the squared InfoNCE gradient norm via
alignment, temperature, and batch spectrum, recovering the \(1/\tau^{2}\) law and closely tracking batch-mean gradients on synthetic data and ImageNet. Using effective rank \(R_{\mathrm{eff}}\) as an anisotropy proxy, we design spectrum-aware batch selection, including a fast greedy builder. On ImageNet-100, Greedy-64 cuts time-to-67.5% top-1 by 15% vs.\ random (24% vs.\ Pool–P3) at equal accuracy; CIFAR-10 shows similar gains. In-batch whitening promotes isotropy and reduces 50-step gradient variance by \(1.37\times\), matching our theoretical upper bound.
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Computation and Language · Cross-listed from cs.AI
Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis
Sedat Dogan, Nina Dethlefs and Debarati Chakraborty
Memes are a central part of online culture, yet their virality remains difficult to predict, especially in cross-lingual settings. We present a large-scale, time-series
dataset of 46,578 Reddit memes collected from 25 meme-centric subreddits across eight
language groups, with more than one million engagement tracking points. We propose a data-driven definition of virality based on a Hybrid Score that normalises engagement by community size and integrates dynamic features such as velocity and acceleration. This approach directly addresses the field's reliance on static, simple volume-based thresholds with arbitrary cut-offs. Building on this target, we construct a
multimodal feature set that combines Visual, Textual, Contextual, Network, and Temporal signals, including structured annotations from a
multimodal LLM to scale cross-lingual content labelling in a consistent way. We
benchmark interpretable baselines (XGBoost, MLP) against end-to-end deep models (BERT, InceptionV3, CLIP) across early observation windows from 30 to 420 minutes. Our best model, a
multimodal XGBoost classifier, achieves a PR AUC of 0.43 at 30 minutes and 0.80 at 420 minutes, indicating that early prediction of meme virality is feasible even under strong class imbalance. The results reveal a clear Content Ceiling, where content-only and deep
multimodal baselines plateau at low PR AUC, while structural Network and Temporal features are necessary to surpass this limit. A SHAP-based temporal analysis further uncovers an evidentiary transition, where early predictions are dominated by network priors (author and community context), and later predictions increasingly rely on temporal dynamics (velocity, acceleration) as engagement accumulates. Overall, we reframe meme virality as a dynamic, path-dependent process governed by exposure and early interaction patterns rather than by intrinsic content alone.
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Computation and Language · Cross-listed from cs.AI
ARM: Discovering Agentic Reasoning Modules for Generalizable Multi-Agent Systems
Bohan Yao, Shiva Krishna Reddy Malay and Vikas Yadav
Large
Language Model (LLM)-powered Multi-
agent systems (MAS) have achieved state-of-the-art results on various complex
reasoning tasks. Recent works have proposed techniques to automate the design of MASes, eliminating the need for manual engineering. However, these techniques perform poorly, often achieving similar or inferior performance to simple baselines. Furthermore, they require computationally expensive re-discovery of architectures for each new task domain and expensive data annotation on domains without existing labeled validation sets. A critical insight is that simple Chain of Thought (CoT)
reasoning often performs competitively with these complex systems, suggesting that the fundamental
reasoning unit of MASes, CoT, warrants further investigation. To this end, we present a new paradigm for automatic MAS design that pivots the focus to optimizing CoT
reasoning. We introduce the Agentic
Reasoning Module (ARM), an agentic generalization of CoT where each granular
reasoning step is executed by a specialized
reasoning module. This module is discovered through a tree search over the code space, starting from a simple CoT module and evolved using mutations informed by reflection on execution traces. The resulting ARM acts as a versatile
reasoning building block which can be utilized as a direct recursive loop or as a subroutine in a learned meta-orchestrator. Our approach significantly outperforms both manually designed MASes and state-of-the-art automatic MAS design methods. Crucially, MASes built with ARM exhibit superb generalization, maintaining high performance across different foundation models and task domains without further optimization.
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Computation and Language
Adaptive and Multi-Source Entity Matching for Name Standardization of Astronomical Observation Facilities
Liza Fretel, Baptiste Cecconi and Laura Debisschop
This ongoing work focuses on the development of a methodology for generating a multi-source mapping of astronomical observation facilities. To compare two entities, we compute scores with adaptable criteria and Natural
Language Processing (NLP) techniques (Bag-of-Words approaches, sequential approaches, and surface approaches) to map entities extracted from eight semantic artifacts, including Wikidata and astronomy-oriented resources. We utilize every property available, such as labels, definitions, descriptions, external identifiers, and more domain-specific properties, such as the observation wavebands, spacecraft launch dates, funding agencies, etc. Finally, we use a Large
Language Model (LLM) to accept or reject a mapping suggestion and provide a justification, ensuring the plausibility and
FAIRness of the validated synonym pairs. The resulting mapping is composed of multi-source synonym sets providing only one standardized label per entity. Those mappings will be used to feed our Name Resolver API and will be integrated into the International Virtual Observatory Alliance (IVOA) Vocabularies and the OntoPortal-Astro platform.
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Computation and Language · Cross-listed from cs.LG
Improving Discrete Diffusion Unmasking Policies Beyond Explicit Reference Policies
Chunsan Hong, Seonho An, Min-Soo Kim et al. (+1 more)
Masked
diffusion models (MDMs) have recently emerged as a novel framework for
language modeling. MDMs generate sentences by iteratively denoising masked sequences, filling in [MASK]
tokens step by step. Although MDMs support any-order sampling, performance is highly sensitive to the choice of which position to unmask next. Prior work typically relies on rule-based schedules (e.g., max-confidence, max-margin), which provide ad hoc improvements. In contrast, we replace these heuristics with a learned scheduler. Specifically, we cast denoising as a KL-regularized Markov decision process (MDP) with an explicit reference policy and optimize a regularized objective that admits policy improvement and convergence guarantees under standard assumptions. We prove that the optimized policy under this framework generates samples that more closely match the data distribution than heuristic schedules. Empirically, across four
benchmarks, our learned policy consistently outperforms max-confidence: for example, on SUDOKU, where unmasking order is critical, it yields a 20.1% gain over random and a 11.2% gain over max-confidence. Code is available at https://github.com/chunsanHong/UPO.
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All authors Chunsan Hong, Seonho An, Min-Soo Kim, Jong Chul Ye
Computation and Language · Cross-listed from cs.CR
Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering
Mary Llewellyn, Isobel Thornton, James Bishop et al. (+1 more)
LLM benchmarking metrics often misstate performance and uncertainty as they rely on two assumptions that frequently do not hold in practice: (i) a sufficient number of evaluations are available for classical inference, and (ii) test
prompts are independent. We propose a corrective
Bayesian hierarchical model with
embedding-space clustering that provides robust performance metrics in limited-data settings while correcting for
prompt dependence. We apply the approach to
adversarial robustness
benchmarks, showing consistent recovery of clustering structure, resulting in more reliable performance metrics, with 4-73% improvements to mean absolute errors and 40-450 unit improvements to expected log posterior densities.
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All authors Mary Llewellyn, Isobel Thornton, James Bishop, Annie Gray
Computation and Language
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision
Yongqi Leng, Yikun Lei, Xikai Liu et al. (+7 more)
Agentic
Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic
retrieval and adaptive workflows. Recent advances (e.g., Search-R1) have shown that outcome-supervised reinforcement learning demonstrate strong performance. However, this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback. To address these challenges, we propose DecEx-RAG, which models RAG as a Markov Decision Process (MDP) incorporating decision-making and execution, while introducing an efficient
pruning strategy to optimize data expansion. Through comprehensive process-level policy optimization, DecEx-RAG significantly enhances the autonomous task decomposition, dynamic
retrieval, and high-quality answer generation capabilities of large
language models (LLMs). Experiments show that DecEx-RAG achieves an average absolute performance improvement of $6.2\%$ across six
datasets, significantly outperforming existing baselines. Moreover, the
pruning strategy improves data construction efficiency by nearly $6 \times$, providing an efficient solution for process-supervised RAG training. The code is available at https://github.com/sdsxdxl/DecEx-RAG.
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All authors Yongqi Leng, Yikun Lei, Xikai Liu, Meizhi Zhong, Bojian Xiong, Yurong Zhang, Yan Gao, Yi Wu, Yao Hu, Deyi Xiong
Computation and Language
Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs
Haneul Yoo, Jiho Jin, Kyunghyun Cho et al. (+1 more)
While large
language models (LLMs) have achieved notable progress in
multilingual settings, their performance remains uneven across
languages as LLMs often rely on English-centric latent representations. In this work, we introduce code-switching in-context learning (CSICL), an inference-time mechanism for cross-lingual representational
alignment. Rather than relying on an abrupt translation pivot, CSICL explicitly scaffolds the
reasoning trajectory by gradually transitioning from a target
language to English, aligning non-English inputs with an English-centric
reasoning space. Across 4 LLMs, 6
datasets, and 10
languages, CSICL consistently outperforms cross-lingual in-context learning baselines, yielding average gains of 6.0pp and 4.8pp in target and unseen
languages, respectively. The improvements generalize across
language families and are even more pronounced in low-resource settings, with gains of 14.7pp in target and 5.3pp in unseen
languages. These findings establish code-switching as a robust and effective approach for reducing cross-lingual misalignment during inference, moving LLMs towards more equitable and effective
multilingual systems.
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All authors Haneul Yoo, Jiho Jin, Kyunghyun Cho, Alice Oh
Computation and Language
The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP
Sheriff Issaka, Keyi Wang, Yinka Ajibola et al. (+17 more)
Despite representing nearly one-third of the world's
languages, African
languages remain critically underserved by modern NLP technologies, with 88% classified as severely underrepresented or completely ignored in computational linguistics. We present the African
Languages Lab (All Lab), a comprehensive research initiative that addresses this technological gap through systematic data collection, model development, and capacity building. Our contributions include: (1) a quality-controlled data collection pipeline, yielding the largest validated African multi-modal
speech and text
dataset spanning 40
languages with 19 billion
tokens of monolingual text and 12,628 hours of aligned
speech data; (2) extensive experimental validation demonstrating that our
dataset, combined with fine-tuning, achieves substantial improvements over baseline models, averaging +23.69 ChrF++, +0.33 COMET, and +15.34 BLEU points across 31 evaluated
languages; and (3) a structured research program that has successfully mentored fifteen early-career researchers, establishing sustainable local capacity. Our comparative evaluation against Google Translate reveals competitive performance in several
languages while identifying areas that require continued development.
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All authors Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim, Lieqi Liu, Erick Rosas Gonzalez, Sylvester Kpei, Jemimah Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, Saadia Gabriel
Computation and Language · Cross-listed from cs.NI
Generative AI-Driven Hierarchical Multi-Agent Framework for Zero-Touch Optical Networks
Yao Zhang, Yuchen Song, Shengnan Li et al. (+5 more)
The rapid development of Generative Artificial Intelligence (GenAI) has catalyzed a transformative technological revolution across all walks of life. As the backbone of wideband communication, optical networks are expecting high-level autonomous operation and zero-touch management to accommodate their expanding network scales and escalating transmission bandwidth. The integration of GenAI is deemed as the pivotal solution for realizing zero-touch optical networks. However, the lifecycle management of optical networks involves a multitude of tasks and necessitates seamless collaboration across multiple layers, which poses significant challenges to the existing single-
agent GenAI systems. In this paper, we propose a GenAI-driven hierarchical multi-
agent framework designed to streamline multi-task autonomous execution for zero-touch optical networks. We present the architecture, implementation, and applications of this framework. A field-deployed mesh network is utilized to demonstrate three typical scenarios throughout the lifecycle of optical network: quality of transmission estimation in the planning stage, dynamic channel adding/dropping in the operation stage, and system capacity increase in the upgrade stage. The case studies, illustrate the capabilities of multi-
agent framework in multi-task allocation, coordination, execution, evaluation, and summarization. This work provides a promising approach for the future development of intelligent, efficient, and collaborative network management solutions, paving the way for more specialized and adaptive zero-touch optical networks.
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All authors Yao Zhang, Yuchen Song, Shengnan Li, Yan Shi, Shikui Shen, Xiongyan Tang, Min Zhang, Danshi Wang
Computation and Language
MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction
Wei-Chieh Huang and Cornelia Caragea
Implicit Attribute Value Extraction (AVE) is essential for accurately representing products in e-commerce, as it infers latent attributes from
multimodal data. Despite advances in
multimodal large
language models (MLLMs), implicit AVE remains challenging due to the complexity of multidimensional data and gaps in
vision-text understanding. In this work, we introduce MADIAVE, a multi-
agent debate framework that employs multiple MLLM
agents to iteratively refine inferences. Through a series of debate rounds,
agents verify and update each other's responses, thereby improving inference performance and robustness. Experiments on the ImplicitAVE
dataset demonstrate that even a few rounds of debate significantly boost accuracy, especially for attributes with initially low performance. We systematically evaluate various debate configurations, including identical or different MLLM
agents, and analyze how debate rounds affect convergence dynamics. Our findings highlight the potential of multi-
agent debate strategies to address the limitations of single-
agent approaches and offer a scalable solution for implicit AVE in
multimodal e-commerce.
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Computation and Language
A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks
Shuzheng Si, Haozhe Zhao, Kangyang Luo et al. (+5 more)
Agents based on large
language models (LLMs) struggle with brainless trial-and-error and generating hallucinatory actions due to a lack of global planning in long-horizon tasks. In this paper, we introduce a plan-and-execute framework and propose EAGLET, an efficient and effective planner training method to enhance the executor
agent's planning abilities without human effort. Specifically, we train a plug-and-play global planner through a two-step process: we first synthesize high-quality plans from an advanced LLM using our proposed homologous consensus filtering strategy, and apply fine-tuning as a cold start. Moreover, we further improve the planner with a rule-based reinforcement learning stage using a novel executor capability gain reward, ensuring it can handle task instructions of varying difficulty. Experiments on three long-horizon
agent tasks show that executor
agents equipped with our planner outperform existing methods, achieving new state-of-the-art performance. Meanwhile, EAGLET reduces training costs by 8x compared to RL-based baselines, and it does not require manual effort or extra training data, offering an efficient and effective solution.
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All authors Shuzheng Si, Haozhe Zhao, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun
Computation and Language · Cross-listed from cs.CV
Improving Chain-of-Thought Efficiency for Autoregressive Image Generation
Zeqi Gu, Markos Georgopoulos, Xiaoliang Dai et al. (+10 more)
Autoregressive
multimodal large
language models have recently gained popularity for image generation, driven by advances in foundation models. To enhance
alignment and detail, newer approaches employ chain-of-thought (CoT)
reasoning, expanding user inputs into elaborated
prompts prior to image synthesis. However, this strategy can introduce unnecessary redundancy – a phenomenon we call visual overthinking – which increases computational costs and can introduce details that contradict the original
prompt. In this work, we explore how to generate more concise CoT sequences for more efficient image generation. We introduce ShortCoTI, a lightweight optimization framework that encourages more concise CoT while preserving output image quality. ShortCoTI rewards more concise
prompts with an adaptive function that scales according to an estimated difficulty for each task. Incorporating this reward into a reinforcement learning paradigm reduces
prompt reasoning length by 54% while maintaining or slightly improving quality metrics across multiple
benchmarks (T2I-CompBench, GenEval). Qualitative analysis shows that our method eliminates verbose explanations and repetitive refinements, producing
reasoning prompts that are both concise and semantically rich. As a result, ShortCoTI improves computational efficiency without compromising the fidelity or visual appeal of generated images.
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All authors Zeqi Gu, Markos Georgopoulos, Xiaoliang Dai, Marjan Ghazvininejad, Chu Wang, Felix Juefei-Xu, Kunpeng Li, Yujun Shi, Zecheng He, Zijian He, Jiawei Zhou, Abe Davis, Jialiang Wang
Computation and Language · Cross-listed from cs.AI
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
Zhuofeng Li, Haoxiang Zhang, Seungju Han et al. (+6 more)
Outcome-driven reinforcement learning has advanced
reasoning in large
language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios. Agentic systems offer a promising alternative by decomposing work across specialized modules, yet most remain training-free or rely on offline training decoupled from the live dynamics of multi-turn interaction. We introduce AgentFlow, a trainable, in-the-flow agentic framework that coordinates four modules (planner, executor, verifier, generator) through an evolving
memory and directly optimizes its planner inside the multi-turn loop. To train on-policy in live environments, we propose Flow-based Group Refined Policy Optimization (Flow-GRPO), which tackles long-horizon, sparse-reward credit assignment by converting multi-turn optimization into a sequence of tractable single-turn policy updates. It broadcasts a single, verifiable trajectory-level outcome to every turn to align local planner decisions with global success and stabilizes learning with group-normalized advantages. Across ten
benchmarks, AgentFlow with a 7B-scale backbone outperforms top-performing baselines with average accuracy gains of 14.9% on search, 14.0% on agentic, 14.5% on mathematical, and 4.1% on scientific tasks, even surpassing larger proprietary models like GPT-4o. Further analyses confirm the benefits of in-the-flow optimization, showing improved planning, enhanced tool-calling reliability, and positive scaling with model size and
reasoning turns.
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All authors Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu
Computation and Language
Mission Impossible: Feedback-Guided Dynamic Interactive Planning for Improving Reasoning on LLMs
Dong Yan, Gaochen Wu and Bowen Zhou
Recent advancements in
language agents have led to significant improvements in multi-hop
reasoning tasks. However, existing approaches often struggle with handling open-domain problems, which require massive information
retrieval due to their reliance on a fixed sequence of actions. To address this, we propose Feedback-Guided Dynamic Interactive Planning (FGDIP), a novel framework tailored to enhance
reasoning in LLMs by utilizing dynamic and adaptive strategies for information exploration in open-domain multi-hop
reasoning tasks. Our approach begins by identifying key entities relevant to the problem, which serve as the initial nodes in the
reasoning process. From these initial nodes, we then generate
reasoning child nodes with the process being refined through a combination of historical error analysis and real-time feedback, which allows the framework to dynamically adjust and optimize its
reasoning strategies. By integrating depth-first search with an innovative node generation technique, our framework adapts based on both prior error paths and concurrently generated nodes at the same hierarchical level. This dynamic strategy effectively expands the search space while ensuring the
reasoning process systematically converges toward accurate solutions. Experimental results show that FGDIP achieved up to 54.47% F1 score on the HotpotQA
dataset and 70.05% on the StrategyQA
dataset, surpassing the best baseline by 5.03% and 7.25% respectively, highlighting its versatility and potential to enhance
language agents in multi-hop
reasoning tasks.
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Computation and Language
Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for Academic Presentations
Chengzhi Liu, Yuzhe Yang, Kaiwen Zhou et al. (+5 more)
The promotion of academic papers has become an important means of enhancing research visibility. However, existing automated methods struggle limited storytelling, insufficient aesthetic quality, and constrained self-adjustment, making it difficult to achieve efficient and engaging dissemination. At the heart of those challenges is a simple principle: there is no way to improve it when you cannot evaluate it right. To address this, we introduce EvoPresent, a self-improvement
agent framework that unifies coherent narratives, aesthetic-aware designs, and realistic presentation delivery via virtual characters. Central to EvoPresent is PresAesth, a multi-task reinforcement learning (RL) aesthetic model that provides reliable aesthetic scoring, defect adjustment, and comparative feedback, enabling iterative self-improvement even under limited aesthetic training data. To systematically evaluate the methods, we introduce EvoPresent
Benchmark, a comprehensive
benchmark comprising: Presentation Generation Quality, built on 650 top-tier AI conference papers with
multimodal resources (slides,
videos and scripts) to assess both content and design; and Aesthetic Awareness, consisting of 2,000 slide pairs with varying aesthetic levels, supporting joint training and evaluation on scoring, defect adjustment, and comparison. Our findings highlight that (i) High-quality feedback is essential for
agent self-improvement, while initial capability alone does not guarantee effective self-correction. (ii) Automated generation pipelines exhibit a trade-off between visual design and content construction. (iii) Multi-task RL training shows stronger generalization in aesthetic awareness tasks.
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All authors Chengzhi Liu, Yuzhe Yang, Kaiwen Zhou, Zhen Zhang, Yue Fan, Yanan Xie, Peng Qi, Xin Eric Wang
Computation and Language · Cross-listed from stat.ML
Domain-Shift-Aware Conformal Prediction for Large Language Models
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna et al. (+2 more)
Large
language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as
hallucinations, poses risks in real-world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under domain shift, often leading to under-coverage and unreliable prediction sets. We propose a new framework called Domain-Shift-Aware Conformal Prediction (DS-CP). Our framework adapts conformal prediction to large
language models under domain shift, by systematically reweighting calibration samples based on their proximity to the test
prompt, thereby preserving validity while enhancing adaptivity. Our theoretical analysis and experiments on the MMLU
benchmark demonstrate that the proposed method delivers more reliable coverage than standard conformal prediction, especially under substantial distribution shifts, while maintaining efficiency. This provides a practical step toward trustworthy uncertainty quantification for large
language models in real-world deployment.
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All authors Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz
Computation and Language
Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM
Ryan Solgi, Parsa Madinei, Jiayi Tian et al. (+4 more)
Large
language models (LLM) and
vision-
language models (VLM) have achieved state-of-the-art performance, but they impose significant
memory and computing challenges in deployment. We present a novel low-rank compression framework to address this challenge. First, we upper bound the change of network loss via layer-wise activation-based compression errors, filling a theoretical gap in the literature. We then formulate low-rank model compression as a bi-objective optimization and prove that a single uniform tolerance yields surrogate Pareto-optimal heterogeneous ranks. Based on our theoretical insights, we propose Pareto-Guided Singular Value Decomposition (PGSVD), a zero-shot pipeline that improves activation-aware compression via Pareto-guided rank selection and alternating least-squares implementation. We apply PGSVD to both LLM and VLM, showing better accuracy at the same compression levels and inference speedup.
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All authors Ryan Solgi, Parsa Madinei, Jiayi Tian, Rupak Swaminathan, Jing Liu, Nathan Susanj, Zheng Zhang
Computation and Language · Cross-listed from cs.SD
Sci-Phi: A Large Language Model Spatial Audio Descriptor
Xilin Jiang, Hannes Gamper and Sebastian Braun
Acoustic scene perception involves describing the type of sounds, their timing, their direction and distance, as well as their loudness and reverberation. While audio
language models excel in sound recognition, single-channel input fundamentally limits spatial understanding. This work presents Sci-Phi, a spatial audio large
language model with dual spatial and spectral encoders that estimates a complete parameter set for all sound sources and the surrounding environment. Learning from over 4,000 hours of synthetic first-order Ambisonics recordings including metadata, Sci-Phi enumerates and describes up to four directional sound sources in one pass, alongside non-directional background sounds and room characteristics. We evaluate the model with a permutation-invariant protocol and 15 metrics covering content, location, timing, loudness, and reverberation, and analyze its robustness across source counts, signal-to-noise ratios, reverberation levels, and challenging mixtures of acoustically, spatially, or temporally similar sources. Notably, Sci-Phi generalizes to real room impulse responses with only minor performance degradation. Overall, this work establishes the first audio LLM capable of full spatial-scene description, with strong potential for real-world deployment. Demo: https://sci-phi-audio.github.io/demo
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Computation and Language
Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty
Yao Xiao, Jung-jae Kim, Roy Ka-wei Lee et al. (+1 more)
Self-play preference optimization has emerged as a prominent paradigm for aligning large
language models (LLMs). It typically involves a
language model to generate on-policy responses for
prompts and a reward model (RM) to guide the selection of chosen and rejected responses, which can be further trained with direct preference optimization (DPO). However, the role of
prompts remains underexplored, despite being a core component in this pipeline. In this work, we investigate how
prompts of varying difficulty influence self-play preference optimization. We use the mean reward of sampled responses of a
prompt as a proxy for its difficulty. We first find that difficult
prompts exhibit substantially inferior self-play optimization performance compared to easy
prompts for
language models. Moreover, incorporating difficult
prompts into training fails to enhance overall performance and, in fact, leads to slight degradation compared to training on easy
prompts alone. Third, there is a clear upward trend in optimization performance as
prompt difficulty decreases. We also observe that the performance gap between difficult and easy
prompts tends to close as the model capacity increases, suggesting that
prompt difficulty interacts with the model capacity. Building on these findings, we explore strategies to mitigate the adversary effect of difficult
prompts on final performance. We demonstrate that only training on a small portion (30%) of the easiest
prompts improves overall self-play performance on AlpacaEval~2 and Arena-Hard. We also report failed attempts and lessons learned.
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All authors Yao Xiao, Jung-jae Kim, Roy Ka-wei Lee, Lidong Bing
Computation and Language
H1B-KV: Hybrid One-Bit Caches for Memory-Efficient Large Language Model Inference
Harshil Vejendla
Autoregressive decoding in large
language models (LLMs) requires caching a growing list of past key-value (KV) pairs, making long-context inference a
memory-bound problem. While recent methods have explored quantizing the cache, evicting
tokens, or using binary sketches for keys (e.g., Loki), these approaches often provide an incomplete solution by leaving one component (like values) uncompressed or by discarding context information. This paper introduces the Hybrid One-Bit KV Cache (H1B-KV), a comprehensive compression scheme that radically reduces
memory usage without sacrificing context. H1B-KV represents each key vector using a 1-bit binary sketch, enabling hardware-friendly bitwise
attention, and further compresses value vectors using 4-bit quantization. This holistic, hybrid approach allows a 7-billion parameter LLM to handle an 8k-
token context with under 60 MB of cache
memory - a 70x reduction. We demonstrate that after a lightweight finetuning, H1B-KV matches full-precision performance not only on perplexity
benchmarks but also on complex downstream tasks like mathematical
reasoning (GSM8K), multi-task understanding (MMLU), and code generation (HumanEval). Our results show H1B-KV significantly outperforms leading quantization (KIVI),
token eviction (SparseLLM), and key-only sketching (Loki) methods in quality-per-byte, establishing it as a robust solution for deploying LLMs in
memory-constrained environments.
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Computation and Language
KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance
Kuangshi Ai, Jonathan A. Karr Jr, Meng Jiang et al. (+2 more)
We present Knowledge Extraction on OMIn (KEO), a domain-specific knowledge extraction and
reasoning framework with large
language models (LLMs) in safety-critical contexts. Using the Operations and Maintenance Intelligence (OMIn)
dataset, we construct a QA
benchmark spanning global sensemaking and actionable maintenance tasks. KEO builds a structured Knowledge Graph (KG) and integrates it into a
retrieval-augmented generation (RAG) pipeline, enabling more coherent,
dataset-wide
reasoning than traditional text-chunk RAG. We evaluate locally deployable LLMs (Gemma-3, Phi-4, Mistral-Nemo) and employ stronger models (GPT-4o, Llama-3.3) as judges. Experiments show that KEO markedly improves global sensemaking by revealing patterns and system-level insights, while text-chunk RAG remains effective for fine-grained procedural tasks requiring localized
retrieval. These findings underscore the promise of KG-augmented LLMs for secure, domain-specific QA and their potential in high-stakes
reasoning. The code is available at https://github.com/JonathanKarr33/keo.
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All authors Kuangshi Ai, Jonathan A. Karr Jr, Meng Jiang, Nitesh V. Chawla, Chaoli Wang
Computation and Language
CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension
Rui Li, Zeyu Zhang, Xiaohe Bo et al. (+5 more)
Current Large
Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive
memory module, which can elevate vanilla LLMs into autonomous reading
agents. Despite the emergence of some heuristic approaches, a systematic design principle remains absent. To fill this void, we draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic
memory – structured schemata, flexible assimilation, and dynamic accommodation. This blueprint forges a clear path toward a more robust and efficient
memory system for LLM-based reading comprehension. To this end, we develop CAM, a prototype implementation of Constructivist Agentic
Memory that simultaneously embodies the structurality, flexibility, and dynamicity. At its core, CAM is endowed with an incremental overlapping clustering
algorithm for structured
memory development, supporting both coherent hierarchical summarization and online batch integration. During inference, CAM adaptively explores the
memory structure to activate query-relevant information for contextual response, akin to the human associative process. Compared to existing approaches, our design demonstrates dual advantages in both performance and efficiency across diverse long-text reading comprehension tasks, including question answering, query-based summarization, and claim verification.
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All authors Rui Li, Zeyu Zhang, Xiaohe Bo, Zihang Tian, Xu Chen, Quanyu Dai, Zhenhua Dong, Ruiming Tang
Computation and Language
Prototype-Based Dynamic Steering for Large Language Models
Ceyhun Efe Kayan and Li Zhang
Despite impressive breadth, LLMs still rely on explicit
reasoning instructions or static, one-fits-all steering methods, leaving a gap for adaptive, instruction-free
reasoning amplification. We present Prototype-Based Dynamic Steering (PDS), a test-time method that amplifies large
language model (LLM)
reasoning without adding or altering instructions. We introduce "
reasoning prototypes" by clustering activation differences between Chain-of-Thought (CoT) and neutral
prompts. At inference, an input's hidden state is projected onto these prototypes to form an instance-specific steering vector. Evaluated on GSM8K, AQuA-RAT, and BIG-Bench tasks, PDS consistently improves accuracy without fine-tuning or
prompt engineering. Notably, the gains persist even when CoT is explicitly suppressed to improve cost-efficiency, indicating that the intervention strengthens latent
reasoning processes rather than inducing a superficial behavioral shift. These results position dynamic, prototype-guided steering as a lightweight alternative to training-time approaches for enhancing LLM
reasoning.
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Computation and Language · Cross-listed from cs.LG
NorMuon: Making Muon more efficient and scalable
Zichong Li, Liming Liu, Chen Liang et al. (+2 more)
The choice of optimizer significantly impacts the training efficiency and computational costs of large
language models (LLMs). Recently, the
Muon optimizer has demonstrated promising results by orthogonalizing parameter updates, improving optimization geometry through better conditioning. Despite
Muon's emergence as a candidate successor to Adam, the potential for jointly leveraging their strengths has not been systematically explored. In this work, we bridge this gap by proposing NorMuon (
Neuron-wise Normalized
Muon), an optimizer that synergistically combines orthogonalization with
neuron-level adaptive learning rates. Our analysis reveals that while
Muon effectively reduces condition numbers, the resulting updates exhibit highly non-uniform
neuron norms, causing certain
neurons to dominate the optimization process. NorMuon addresses this imbalance by maintaining second-order momentum statistics for each
neuron and applying row-wise normalization after orthogonalization, ensuring balanced parameter utilization while preserving
Muon's conditioning benefits. To enable practical deployment at scale, we develop an efficient distributed implementation under the FSDP2 framework that strategically distributes orthogonalization computations across devices. Experiments across multiple model scales demonstrate that NorMuon consistently outperforms both Adam and
Muon, achieving 21.74% better training efficiency than Adam and 11.31% improvement over
Muon on 1.1 B pretraining setting, while maintaining a comparable
memory footprint to
Muon. Our findings suggest that orthogonalization and adaptive learning rates are complementary rather than competing approaches, opening new avenues for optimizer design in large-scale deep learning.
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All authors Zichong Li, Liming Liu, Chen Liang, Weizhu Chen, Tuo Zhao
Computation and Language
LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation
Zhoutong Fu, Yihan Cao, Yi-Lin Chen et al. (+16 more)
Large
language models (LLMs) have achieved strong performance across a wide range of natural
language processing tasks. However, deploying LLMs at scale for domain specific applications, such as job-person fit and explanation in job seeking platforms, introduces distinct challenges. At LinkedIn, the job person fit task requires analyzing a candidate's public profile against job requirements to produce both a fit assessment and a detailed explanation. Directly applying open source or finetuned LLMs to this task often fails to yield high quality, actionable feedback due to the complexity of the domain and the need for structured outputs. Moreover, the large size of these models leads to high inference latency and limits scalability, making them unsuitable for online use. To address these challenges, we introduce LANTERN, a novel LLM knowledge
distillation framework tailored specifically for job person fit tasks. LANTERN involves modeling over multiple objectives, an encoder model for classification purpose, and a decoder model for explanation purpose. To better distill the knowledge from a strong black box teacher model to multiple downstream models, LANTERN incorporates multi level knowledge
distillation that integrates both data and logit level insights. In addition to introducing the knowledge
distillation framework, we share our insights on post training techniques and
prompt engineering, both of which are crucial for successfully adapting LLMs to domain specific downstream tasks. Extensive experimental results demonstrate that LANTERN significantly improves task specific metrics for both job person fit and explanation. Online evaluations further confirm its effectiveness, showing measurable gains in job seeker engagement, including a 0.24% increase in apply rate and a 0.28% increase in qualified applications.
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All authors Zhoutong Fu, Yihan Cao, Yi-Lin Chen, Aman Lunia, Liming Dong, Neha Saraf, Ruijie Jiang, Yun Dai, Qingquan Song, Tan Wang, Guoyao Li, Derek Koh, Haichao Wei, Zhipeng Wang, Aman Gupta, Chengming Jiang, Jianqiang Shen, Liangjie Hong, Wenjing Zhang
Computation and Language
Language Model as Planner and Formalizer under Constraints
Cassie Huang, Stuti Mohan, Ziyi Yang et al. (+2 more)
LLMs have been widely used in planning, either as planners to generate action sequences end-to-end, or as formalizers to represent the planning domain and problem in a formal
language that can derive plans deterministically. However, both lines of work rely on standard
benchmarks that include only generic and simplistic environmental specifications, leading to potential overestimation of the planning ability of LLMs and safety concerns in downstream tasks. We bridge this gap by augmenting widely used planning
benchmarks with manually annotated, fine-grained, and rich natural
language constraints spanning four formally defined categories. Over 4 state-of-the-art
reasoning LLMs, 4 formal
languages, and 4
datasets, we show that the introduction of one-sentence constraints consistently halves performance, indicating current LLMs' lack of robustness and an avenue for future research.
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All authors Cassie Huang, Stuti Mohan, Ziyi Yang, Stefanie Tellex, Li Zhang