- Replacing Multi-Step Assembly of Data Preparation Pipelines with One-Step LLM Pipeline Generation for Table QA
Fengyu Li, Junhao Zhu, Kaishi Song, Lu Chen, Zhongming Yao · Feb 26, 2026
Automatic Metrics General
Experiments on two benchmark datasets show that, with the same LLM backbone, Operation-R1 achieves average absolute accuracy gains of 9.55 and 6.08 percentage points over multi-step preparation baselines, with 79\% table compression and a 2
- Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue
Ning Gao, Wei Zhang, Yuqin Dai, Ling Shi, Ziyin Wang · Feb 26, 2026
Automatic Metrics General
The rapid evolution of Large Language Models (LLMs) has accelerated the transition from conversational chatbots to general agents.
- Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization
Qianben Chen, Tianrui Qin, King Zhu, Qiexiang Wang, Chengjun Yu · Feb 26, 2026
Automatic Metrics General
Recent deep research agents primarily improve performance by scaling reasoning depth, but this leads to high inference cost and latency in search-intensive scenarios.
- When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models
Satyam Kumar Navneet, Joydeep Chandra, Yong Zhang · Feb 25, 2026
Automatic Metrics General
Large Language Models (LLMs) are increasingly used to ``professionalize'' workplace communication, often at the cost of linguistic identity.
- Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference
Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026
Automatic Metrics General
Experiments on the Massive Multitask Language Understanding (MMLU) benchmark show that our approach achieves accuracy comparable to the largest model while reducing computational costs by 20\% to 40\%.
- JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang · Feb 25, 2026
Automatic Metrics General
Extensive evaluations on MNIST and CIFAR-10 demonstrate that JSAM achieves up to 15% improvement in test accuracy compared to existing unbiased selection mechanisms while maintaining cost efficiency across varying data heterogeneity levels.
- From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
Zhihao Li, Yu Feng, Zhilu Lai, Wei Wang · Feb 25, 2026
Automatic Metrics General
On standard PDE benchmarks and real datasets, our method attains state-of-the-art competitive accuracy while providing intrinsic interpretability.
- Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG
Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy, Chiara Picardi, Amit Giloni · Feb 24, 2026
Automatic Metrics General
Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation components.
- The Headless Firm: How AI Reshapes Enterprise Boundaries
Tassilo Klein, Sebastian Wieczorek · Feb 24, 2026
Automatic Metrics General
We argue that agentic AI induces a structural change in how coordination costs scale: in prior modular systems, integration cost grew with interaction topology (O(n^2) in the number of components); in protocol-mediated agentic systems, inte
- Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
Zhangjie Xia, Yu Yang, Pan Xu · Feb 24, 2026
Simulation Env General
Off-dynamics offline reinforcement learning (RL) aims to learn a policy for a target domain using limited target data and abundant source data collected under different transition dynamics.
- Motivation is Something You Need
Mehdi Acheli, Walid Gaaloul · Feb 24, 2026
Automatic Metrics General
Inspired by the interplay of emotions and cognition in the human brain and more specifically the SEEKING motivational state, we design a dual-model framework where a smaller base model is trained continuously, while a larger motivated model
- CAMEL: Confidence-Gated Reflection for Reward Modeling
Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar · Feb 24, 2026
Automatic Metrics General
Reward models play a fundamental role in aligning large language models with human preferences.
- Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
Anna Hart, Chi Han, Jeonghwan Kim, Huimin Zhao, Heng Ji · Feb 24, 2026
Automatic Metrics General
Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties.
- Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content
Simon Münker, Nils Schwager, Kai Kugler, Michael Heseltine, Achim Rettinger · Feb 22, 2026
Automatic Metrics General
The increasing use of Large Language Models (LLMs) as proxies for human participants in social science research presents a promising, yet methodologically risky, paradigm shift.
- Predicting Contextual Informativeness for Vocabulary Learning using Deep Learning
Tao Wu, Adam Kapelner · Feb 20, 2026
Automatic Metrics General
In summary, we demonstrate that a modern embedding model on neural network architecture, when guided by human supervision, results in a low-cost large supply of near-perfect contexts for teaching vocabulary for a variety of target words.
- Information-Theoretic Storage Cost in Sentence Comprehension
Kohei Kajikawa, Shinnosuke Isono, Ethan Gotlieb Wilcox · Feb 20, 2026
Automatic Metrics General
Real-time sentence comprehension imposes a significant load on working memory, as comprehenders must maintain contextual information to anticipate future input.
- Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning
Jyotin Goel, Souvik Maji, Pratik Mazumder · Feb 19, 2026
Automatic Metrics General
Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and worsen under adversarial updates.
- Sign Lock-In: Randomly Initialized Weight Signs Persist and Bottleneck Sub-Bit Model Compression
Akira Sakai, Yuma Ichikawa · Feb 19, 2026
Automatic Metrics General
Sub-bit model compression seeks storage below one bit per weight; as magnitudes are aggressively compressed, the sign bit becomes a fixed-cost bottleneck.
- TabAgent: A Framework for Replacing Agentic Generative Components with Tabular-Textual Classifiers
Ido Levy, Eilam Shapira, Yinon Goldshtein, Avi Yaeli, Nir Mashkif · Feb 18, 2026
Automatic Metrics General
Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large language model (LLM) calls for closed-set decision tasks such as routing, shortlisting, gating,
- *-PLUIE: Personalisable metric with Llm Used for Improved Evaluation
Quentin Lemesle, Léane Jourdan, Daisy Munson, Pierre Alain, Jonathan Chevelu · Feb 17, 2026
Automatic Metrics General
Evaluating the quality of automatically generated text often relies on LLM-as-a-judge (LLM-judge) methods.
- Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework
Mengze Hong, Chen Jason Zhang, Zichang Guo, Hanlin Gu, Di Jiang · Feb 17, 2026
Automatic Metrics General
Existing approaches either rely on modular system designs with extensive agent orchestration or employ over-simplified instruction schemas, providing limited guidance and poor generalizability.
- Protecting Language Models Against Unauthorized Distillation through Trace Rewriting
Xinhang Ma, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik · Feb 16, 2026
Automatic Metrics General
Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models.
- Buy versus Build an LLM: A Decision Framework for Governments
Jiahao Lu, Ziwei Xu, William Tjhi, Junnan Li, Antoine Bosselut · Feb 13, 2026
Automatic Metrics General
This paper provides a strategic framework for making this decision by evaluating these options across dimensions including sovereignty, safety, cost, resource capability, cultural fit, and sustainability.
- The Single-Multi Evolution Loop for Self-Improving Model Collaboration Systems
Shangbin Feng, Kishan Panaganti, Yulia Tsvetkov, Wenhao Yu · Feb 5, 2026
Simulation Env General
Model collaboration -- systems where multiple language models (LMs) collaborate -- combines the strengths of diverse models with cost in loading multiple LMs.
- Large Language Models as Automatic Annotators and Annotation Adjudicators for Fine-Grained Opinion Analysis
Gaurav Negi, MA Waskow, John McCrae, Paul Buitelaar · Jan 23, 2026
Human Eval General
Although this level of detail is sound, it requires considerable human effort and substantial cost to annotate opinions in datasets for training models, especially across diverse domains and real-world applications.
- Fast-weight Product Key Memory
Tianyu Zhao, Llion Jones · Jan 2, 2026
Automatic Metrics General
Notably, in Needle-in-a-Haystack evaluations, FwPKM generalizes to 128K-token contexts despite being trained on only 4K-token sequences.
- Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
Ang Lv, Jin Ma, Yiyuan Ma, Siyuan Qiao · Dec 29, 2025
Automatic Metrics General
Mixture-of-Experts (MoE) models lack explicit constraints to ensure the router's decisions align well with the experts' capabilities, which ultimately limits model performance.
- DIAL: Direct Iterative Adversarial Learning for Realistic Multi-Turn Dialogue Simulation
Ziyi Zhu, Olivier Tieleman, Caitlin A. Stamatis, Luka Smyth, Thomas D. Hull · Dec 23, 2025
Automatic MetricsSimulation Env General
Realistic user simulation is crucial for training and evaluating multi-turn dialogue systems, yet creating simulators that accurately replicate human behavior remains a significant challenge.
- Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL
Khushboo Thaker, Yony Bresler · Dec 18, 2025
Automatic Metrics General
Deploying accurate Text-to-SQL systems at the enterprise level faces a difficult trilemma involving cost, security and performance.
- Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning
Huilin Xu, Zhuoyang Liu, Yixiang Luomei, Feng Xu · Dec 9, 2025
Simulation Env General
Extensive experiments on the AerialVLN and OpenFly benchmark validate the effectiveness of our method.
- PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark
Robert Belanec, Branislav Pecher, Ivan Srba, Maria Bielikova · Nov 26, 2025
Simulation Env General
Despite the advances in PEFT methods, current evaluations remain limited (in terms of evaluated models and datasets) and difficult to reproduce.
- Batch Prompting Suppresses Overthinking Reasoning Under Constraint: How Batch Prompting Suppresses Overthinking in Reasoning Models
Saurabh Srivastava, Janit Bidhan, Hao Yan, Abhishek Dey, Tanu Kansal · Nov 6, 2025
Automatic Metrics General
Across 13 diverse benchmarks with DeepSeek-R1 and OpenAI-o1, batch prompting {reduces reasoning tokens by 76\% (2{,}950$\mapsto$710), on average, while preserving or improving accuracy}.
- Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization
Hee-Jin Lee, Zhen Guo, Luchao Jin, Morteza Moazami Goudarzi · Nov 4, 2025
Automatic Metrics General
We introduce an Analyze-Revise-Finetune (ARF) pipeline that enables smaller open-source language models (LLMs) to surpass substantially larger proprietary models in customer service summarization tasks.
- FML-bench: Benchmarking Machine Learning Agents for Scientific Research
Qiran Zou, Hou Hei Lam, Wenhao Zhao, Yiming Tang, Tingting Chen · Oct 12, 2025
Automatic Metrics General
Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments.
- EconCausal: A Context-Aware Causal Reasoning Benchmark for Large Language Models in Social Science
Donggyu Lee, Hyeok Yun, Meeyoung Cha, Sungwon Park, Sangyoon Park · Oct 8, 2025
Automatic MetricsSimulation Env General
To address this, we introduce EconCausal, a large-scale benchmark comprising 10,490 context-annotated causal triplets extracted from 2,595 high-quality empirical studies published in top-tier economics and finance journals.
- BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals
Chenqi Li, Yu Liu, Timothy Denison, Tingting Zhu · Oct 2, 2025
Automatic Metrics General
Biosignals offer valuable insights into the physiological states of the human body.
- mini-vec2vec: Scaling Universal Geometry Alignment with Linear Transformations
Guy Dar · Sep 27, 2025
Automatic Metrics General
We build upon vec2vec, a procedure designed to align text embedding spaces without parallel data.
- Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents
Yaorui Shi, Yuxin Chen, Siyuan Wang, Sihang Li, Hengxing Cai · Sep 27, 2025
Automatic Metrics General
To tackle these challenges, we present ReMemR1, which integrates the mechanism of memory retrieval into the memory update process, enabling the agent to selectively callback historical memories for non-linear reasoning.
- EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Mohammad Hossein Samaei, Faryad Darabi Sahneh, Lee W. Cohnstaedt, Caterina Scoglio · Sep 24, 2025
Llm As JudgeSimulation Env General
We introduce EpidemIQs, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modeling, mechanistic modeling, stochastic simulations, data visualization and
- Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
Zhicheng Yang, Zhijiang Guo, Yinya Huang, Yongxin Wang, Dongchun Xie · Aug 19, 2025
Automatic Metrics General
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models, yet its full potential is hindered by two under-explored dimensions: Depth-the hardest pr
- DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging
Neha Verma, Kenton Murray, Kevin Duh · Jul 6, 2025
Automatic Metrics General
Structured pruning methods designed for Large Language Models (LLMs) generally focus on identifying and removing the least important components to optimize model size.
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev · Jun 26, 2025
Automatic Metrics General
General-purpose Large Language Models (LLMs) are frequently fine-tuned through supervised fine-tuning (SFT) to enhance performance in specific domains.
- Cost-of-Pass: An Economic Framework for Evaluating Language Models
Mehmet Hamza Erol, Batu El, Mirac Suzgun, Mert Yuksekgonul, James Zou · Apr 17, 2025
Automatic Metrics General
We then define the frontier cost-of-pass: the minimum cost-of-pass achievable across available models or the human-expert(s), using the approx.
- vCache: Verified Semantic Prompt Caching
Luis Gaspar Schroeder, Aditya Desai, Alejandro Cuadron, Kyle Chu, Shu Liu · Feb 6, 2025
Automatic Metrics General
We release the vCache implementation and four benchmarks to support future research.
- GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression
Kainan Liu, Yong Zhang, Ning Cheng, Zhitao Li, Shaojun Wang · Dec 31, 2024
Automatic Metrics General
Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inference cost.
