Papers

Guan-Ju Peng

Highly coherent learned dictionaries can assign different physical meanings to the same sparse representation; you need to account for dictionary uncertainty and check which physical interpretations survive across all calibration-compatible alternatives.

When learning dictionaries from data to represent signals sparsely, the selected atoms may seem physically meaningful but could be arbitrary artifacts of the calibration data—especially when many different dictionaries fit equally well.

Zachary Speck, Asa Shepard

Individual training examples can be learned and then completely forgotten during pre-training, leaving virtually no measurable impact on final model behavior or internal structure, suggesting that what matters for final performance is the aggregate signal, not individual data points.

Researchers trained 32 GPT-2 models from scratch and injected a single training example at peak learning rate to measure its actual impact. The example was learned immediately but completely forgotten by the end of training, leaving no detectable trace in the final model's weights, geometry, or performance—despite moving the model within its loss basin during training.

Steven Morse, Daniel Runfola, Trenton W. Ford

Political and social topics show measurable semantic drift in online discourse over time, detectable through embedding-space analysis—a technique that could help track how language and meaning evolve around contentious issues.

This paper analyzes how topics shift and evolve in Reddit discussions over 16 years by tracking semantic embeddings of 12.7 billion comments. Using pretrained language models and unsupervised clustering, the researchers show that politically charged topics drift significantly in meaning over time, while stable domains like sports remain consistent.

Jiaming Li

When you have a fast but imperfect analytical model and limited expensive simulation data, teaching a neural network to correct the analytical model's errors—or pre-training it on the analytical model first—can cut your data requirements dramatically.

This paper shows how to make machine learning models more data-efficient by combining cheap analytical equations with expensive high-fidelity simulations. Using Helmholtz resonators as a test case, the authors demonstrate two approaches: learning to correct analytical predictions, or distilling analytical knowledge into a neural network before fine-tuning with limited simulation data.

Girish G N, Ashutosh Sahoo, Akshay SP et al.

On-chain lending needs to separate a wallet's total token holdings from its liquid, spendable balance—two wallets with identical net worth can have very different repayment capacity depending on which assets they actually hold.

zLend is a framework that assesses borrower creditworthiness in decentralized lending by analyzing on-chain transaction history. It reconstructs daily wallet balances from token transfers in two ways—using only stablecoins and using all tokens—to distinguish between total wealth and spendable cash.

Samarasimha Reddy Chittamuru, Ayhan Akinturk, Allison Kennedy et al.

When validating ML models on time-series data like operational sensor readings, use time-aware cross-validation instead of random splits—random splits create unrealistic performance estimates that won't hold in real deployment.

This paper reveals a critical flaw in how machine learning models for ship fuel consumption are validated: most studies use random train-test splits that leak temporal information and give overly optimistic results.

Junjie Chu, Ye Leng, Mingjie Li et al.

Generative search engines are vulnerable to content optimization tactics that inflate the visibility of low-authority or false information; systematic detection methods are now possible but require careful design to avoid relying on author-based shortcuts.

This paper introduces GEO-Flag, a system for detecting web pages optimized for generative search engines (like Google's AI Overviews).

Hao Yan, Lisa Pilgram, Dan Liu et al.

You can now generate realistic synthetic tabular health data from multiple heterogeneous sources by standardizing them statistically first, then using diffusion transformers to learn and reproduce their patterns.

This paper presents a method for generating synthetic health data from multiple different database tables with varying structures. It works in two stages: first converting diverse tables into a standardized statistical format, then using a diffusion transformer to learn patterns and generate new synthetic tables.

Fanfei Li, Jana Zeller, Manuel Prada-Corral et al.

Training on a carefully curated, grade-level-appropriate curriculum creates a sandbox for studying knowledge acquisition with clear boundaries—useful for understanding how models learn and what happens when you try to teach them new concepts.

Researchers created LittleLeaner, a 5B-parameter language model trained on an 88B-token curriculum limited to U.S. Grade 5 material, to study how models acquire knowledge under controlled conditions. Unlike models trained on messy web data, LittleLeaner has clear, interpretable knowledge boundaries, making it easier to understand what the model knows and how it learns new information.

Peter Schneider-Kamp, Jacob Nielsen, Gianluca Barmina et al.

You can build competitive frontier-level language models at 1B parameters using only openly licensed data, making it feasible for researchers and organizations to develop ethical AI without relying on scraped or restricted datasets.

Mimir v1 is a 1-billion-parameter language model trained entirely on permissible (legally and ethically sourced) data that achieves competitive performance with much larger models. It uses a Hierarchical Reasoning Model architecture and excels at English, math, code, and Danish tasks—showing that high-quality open-source models don't require massive proprietary datasets.

Yuto Nishida, Hirokazu Kiyomaru, Yusuke Oda et al.

You can measure training data importance during pretraining by tracking parameter trajectories, revealing that different data types matter at different training stages—literature early, STEM later—without needing task-specific validation sets.

This paper introduces a new method to measure how much training data influences language model development without needing to pick specific downstream tasks. Instead of testing on particular benchmarks, the researchers measure influence by tracking how each piece of training data pushes the model toward its final parameters.

David Chushig-Muzo, María Ángeles Rodríguez de Cara, Eva Milara et al.

Self-organizing maps can encode tabular data more effectively than simpler dimensionality reduction by capturing feature relationships alongside values, improving both predictive performance and model interpretability.

TabSOM converts tabular data into images using self-organizing maps, preserving both feature values and relationships between features. Unlike existing methods that only encode individual feature values, TabSOM captures feature interactions as spatial patterns, enabling vision models to achieve better performance while remaining interpretable.

Sabin Roman, Ljupco Todorovski, Saso Dzeroski

SORT shifts equation discovery from brittle library selection to basis design: by learning sparse coefficients in well-chosen orthogonal bases, it provides a more stable intermediate representation that gracefully degrades under noise and sampling sparsity.

SORT is a machine learning technique that learns compact mathematical representations of dynamical systems from noisy, irregularly sampled data by fitting sparse coefficients in orthogonal basis expansions.

Serli Kopar, Sam Gijsen, Abner Hernandez et al.

Speech-based disease detection models may not be learning genuine disease characteristics but rather dataset-specific patterns, raising serious concerns about their reliability for real-world clinical use.

This paper investigates whether speech models trained to detect Parkinson's disease actually learn disease-specific patterns or just exploit dataset quirks.

Yikai Xu, Zhao Chen, Jian Huang

You can robustly learn from contaminated data by selecting the subset of samples that maximizes Wasserstein distance from the full dataset—this works as a model-agnostic preprocessing tool before training any model.

This paper introduces Wasserstein Filtering, a method to clean contaminated datasets by selecting samples whose distribution is most different from the full dataset. The approach uses optimal transport theory to identify and remove outliers, with theoretical guarantees and practical algorithms that work as a preprocessing step for any downstream task.

Avijit Roy, Proma Roy

AI systems for underrepresented languages fail at the infrastructure level—in data collection, tokenization, and deployment design—long before model training begins. Fixing this requires treating offline-first design and linguistic diversity as core infrastructure priorities, not afterthoughts.

This paper reveals how AI infrastructure systematically disadvantages speakers of underrepresented languages like Bengali before models are even trained.

Bryan Torres, Daniel Riofrío, José Vega-Sánchez et al.

Lightweight, domain-specific NLP models can effectively detect fraud signals in real-world government data without expensive infrastructure—useful for building practical oversight tools in resource-constrained settings.

This paper develops a hybrid NLP pipeline to detect accusatory language in public procurement comments from Ecuador's procurement system.

Chen Lyu, Xingwei Tan, Simon Cullen et al.

This work demonstrates how to generate sensitive, realistic training data for abuse detection by modeling VAWG as temporally unfolding multi-turn conversations rather than isolated toxic sentences, enabling better downstream safety systems.

ConVAWG is a framework for generating synthetic multi-turn dialogues depicting Violence Against Women and Girls scenarios. Using retrieval-grounded methods, persona seeds, crime definitions, and real case reviews, it creates realistic abuse scenarios with controlled toxicity while respecting privacy constraints that prevent releasing real conversation data.

Changhao Xiang, Shangyu Xing, Zhen Wu et al.

By interleaving visual objects directly into text during pretraining, you can teach multimodal models object-level grounding 12x more efficiently than traditional image-text pair training.

This paper introduces MultiModal Code-Switching (MMCS), a new way to train vision-language models by replacing words in text with their corresponding visual objects. Instead of just pairing whole images with descriptions, MMCS explicitly shows the model which objects match which words, making training much more efficient—achieving the same performance with 12x less data.

Donna Hooshmand, Shubham Shahi, Cameron Barrie et al.

Automating semantic schema construction lets non-technical users query databases without expert help, and TYTAN's hybrid symbolic-LLM approach achieves production-ready accuracy by knowing when to ask humans for clarification.

TYTAN automatically builds semantic schemas for relational databases by combining symbolic analysis with LLM inference to identify entities, relationships, and data roles. It asks targeted questions when ambiguous, achieving 100% coverage and correctness on real-world databases—eliminating the manual work that currently bottlenecks data analysis tools.

Omid Bazgir, Md Nasir, Jacob Hoffman et al.

Synthetic clinical benchmarks need explicit realism optimization separate from utility validation—passing utility checks alone doesn't guarantee a benchmark reflects real-world data patterns, which matters for training reliable healthcare AI agents.

This paper addresses a critical gap in synthetic clinical benchmarks: they can pass utility checks while remaining structurally unrealistic. The authors develop methods to improve benchmark realism (measured by data missingness patterns, actionability, and population alignment) while maintaining the utility thresholds required for downstream AI systems.

Robin Trombetta, Carole Lartizien

Using optimal transport to blend medical images creates more realistic and varied synthetic lesions than traditional mixing strategies, leading to better segmentation model performance.

This paper presents OTLesMix, a data augmentation method that uses optimal transport and Wasserstein barycenters to generate synthetic medical images with diverse lesion shapes and locations. Tested on brain lesion segmentation, it improves model performance by 2.9-6.6 Dice points compared to baseline and outperforms existing mixing-based augmentation methods.

Yiting Zheng, Cheng Fang, Anthony Donofrio et al.

Using a unified graph representation of reactions instead of separate reactant/product graphs enables better learning of chemical transformations, leading to more accurate yield predictions and a foundation model that could generalize across diverse reaction types.

RxnCLF is a self-supervised learning framework that represents chemical reactions as unified graphs (condensed reaction graphs) to better capture how molecules transform.

Damien Sileo, Valentin Lacombe, Dimitri Kachler

Procedural datasets for training reasoning models need careful design beyond correctness: difficulty calibration, compact targets, and rigorous auditing (combining model review, human judgment, and testing) significantly impact training utility.

This paper introduces Reasoning Core, a collection of 50 procedural generators that create verifiable reasoning problems across diverse domains (math, logic, planning, code, etc.). The authors compare their dataset with three alternatives using completion-supervised fine-tuning on 3B models, showing Reasoning Core achieves better performance on reasoning benchmarks.

Indraneil Paul, Falko Helm, Goran Glavaš et al.

Training language models on code contexts that span multiple related files—rather than isolated snippets—dramatically improves their ability to handle long contexts and understand complex codebases, even when this data makes up a small fraction of total training.

OctoLong is a pipeline that creates long, dependency-rich code contexts by automatically retrieving related code files across repositories.

Junhao Chen, Mingjin Chen, Jingjia Mao et al.

Music tokenization design is more important than model size for text-to-music generation—a small model with the right token representation outperforms massive models with poor representations, challenging the field's scaling assumptions.

This paper investigates how music tokenization—the way music is converted into discrete symbols for language models—affects text-to-music generation quality. By fixing model size, data, and training approach while swapping only the tokenization scheme, researchers found that representation choice matters far more than model scale.

Seyed Kahaki, Shijie Li, Weijie Chen et al.

When generating synthetic medical images, use domain-specific evaluation metrics rather than generic ones—and prioritize generating diverse data over pixel-perfect realism for downstream medical AI tasks.

This paper evaluates how well synthetic histopathology images generated by diffusion models work for medical AI tasks. The authors show that standard image quality metrics (FID, IS) designed for natural images fail for medical images, and propose using pathology-specific metrics instead.

Yimin Chen, Brian Fricke, Bo Shen et al.

FDD-ON solves interoperability problems in HVAC fault diagnosis by creating a shared vocabulary and logical structure that lets different diagnostic tools, datasets, and applications understand each other.

This paper introduces FDD-ON, a structured knowledge framework (ontology) for understanding and diagnosing faults in HVAC systems. It standardizes how different systems describe equipment problems, symptoms, and impacts, enabling AI tools and building management systems to share diagnostic information reliably.

Bing Yan, Gregory Wolfe, Stefano Martiniani et al.

Instead of searching papers, search claims: AskChem lets you find specific findings with built-in source verification, reducing manual work for literature synthesis and improving AI agent reliability by 12% on citation accuracy.

AskChem is a search infrastructure that converts chemistry papers into atomic, provenance-tracked claims rather than returning full documents. Scientists and AI agents can search across 2.4M claims from 147K papers, with each claim linked to its source DOI and exact evidence location, enabling faster synthesis of findings across multiple papers.

Haomin Qi, Xingliang Wang, Xuanqi Gao et al.

By mining repository history and reconstructing code states, you can generate verified, executable coding tasks at scale—reducing the manual effort of creating training data for code-generation agents while maintaining realistic development scenarios.

Change2Task automatically converts pull requests from repository history into executable coding tasks for training AI agents.

Manyi Wang, Junjielong Xu, Pinjia He

When using SWE-bench benchmarks to evaluate LLM coding abilities, be aware that over 1 in 10 test cases have misaligned problem statements and solutions—PAIChecker can automatically identify these problematic cases to improve benchmark reliability.

This paper identifies a critical quality issue in SWE-bench-like benchmarks used to evaluate AI coding abilities: 13.6% of PR-Issue pairs are misaligned, meaning the issue description doesn't actually match the code changes.

Mengfei Ran, Yifeng Shen, Ruijie Guan

When analyzing longitudinal medical data with irregular measurements, you can use neural encoders to create summaries that maintain the statistical properties needed for valid causal inference—representation error behaves like ordinary estimation error under explicit conditions.

This paper addresses causal inference from messy medical data with irregular measurements (lab values, vital signs) taken at different times. The authors propose DR-FRL, a method that converts these fragmented histories into meaningful summaries while preserving statistical guarantees.

Ruman Wang, Hangting Ye

LLMs can transfer medical knowledge into auditable, locally-executable feature programs instead of making direct predictions—improving data efficiency, privacy, and reproducibility in medical image classification.

ScaFE uses an LLM to generate executable programs that measure clinical scar features from photos, rather than asking the model to diagnose directly. These programs run locally, protecting patient data, and feed structured features into a simple Random Forest classifier.

Kingsley Ugwuanyi

AI systems aren't neutral—they embed and amplify existing power structures around which English dialects are considered 'legitimate,' with real consequences for speakers of non-standard varieties who are increasingly mistaken for AI.

This paper examines how AI language models reflect and reinforce biases toward 'standard' English while marginalizing non-dominant varieties spoken globally. Using examples from training data, model design, and public discourse, it shows how AI systems reproduce language ideologies that privilege English from wealthy countries while treating other Englishes as suspect or AI-like.

Daohan Zhu, Sitong Ge, Ruofei Wang et al.

Financial sentiment analysis misses critical predictive signals; extracting structured semantic dimensions (event type, impact scope, temporal horizon, confidence) alongside sentiment improves stock prediction accuracy and reveals that sentiment-return relationships are highly nonlinear.

This paper shows that financial news contains multiple independent information dimensions beyond sentiment—like event type, impact scope, and time horizon—that together predict stock movements better than sentiment alone.

Andreu Casamayor-Segarra, Vicent Ahuir, Antonio Molina-Marco et al.

Domain-specific Spanish models combined with ICE's automatic relabeling can detect mental health risks earlier and more reliably than general approaches, addressing a critical gap in non-English mental health AI tools.

This paper tackles mental health screening in Spanish by creating specialized language models and a new method called Incremental Context Expansion (ICE) that automatically identifies when enough social media messages accumulate to signal a mental health disorder. The approach reduces the time needed to detect problems while maintaining accuracy, with all models made publicly available.

Antonio Delgado-Rosa, David Muñoz-Valero, Enrique Adrian Villarrubia-Martin et al.

On-device inference on resource-constrained satellites combined with synthetic data generation can enable autonomous, real-time aircraft detection without overwhelming downlink capacity—shifting satellites from passive data collectors to active decision-makers.

This paper tackles satellite-based aircraft detection by running AI inference directly on small satellites (CubeSats) instead of sending raw images to Earth, and uses AI-generated synthetic images to train better detection models. The approach balances limited satellite bandwidth with scarce training data, enabling real-time autonomous surveillance from orbit.

Jia Yu, Yan Zhu, Yili He et al.

Routine clinical reports contain rich supervision signals—you can extract finding-to-frame correspondence at scale to train medical vision-language models without expensive frame-level annotation, turning existing documentation into training data.

EndoCLIP is a vision-language model trained on 280,000 colonoscopy reports to link clinical findings with individual video frames.

Shady E. Ahmed, Panos Stinis

You can solve regression problems by storing local patterns and using similarity matching instead of training large global models—this is faster, uses less memory, and works well for scientific data that repeats in certain regions.

This paper proposes a novel regression method inspired by how fruitflies sense their environment. Instead of building complex global models, the approach stores a library of representative local patterns and makes predictions by finding similar patterns to a query and combining their responses.

Zhen Huang, Yikun Wang, Shijie Xia et al.

Instead of applying uniform data processing rules, adapting the cleaning strategy per example—deciding what operation each piece of data needs—improves LLM pretraining efficiency and downstream performance.

DataOrchestra is a framework that customizes data processing for each example in pretraining, rather than applying one fixed strategy to all data. An orchestrator decides whether to drop, keep, or clean each data chunk, and if cleaning is needed, selects specific operations like editing or rewriting.

Nethmi Muthugala, Supryadi, Surangika Ranathunga et al.

LLMs trained primarily on Western data misalign with non-Western cultural values; this work provides a replicable framework for embedding country-specific values in low-resource languages through survey-grounded datasets and targeted fine-tuning.

This paper introduces LKValues, a resource suite for aligning large language models with Sri Lankan cultural values. The authors surveyed 205 Sri Lankans to identify 40 key societal values, created a 150k-instance instruction dataset in Sinhala and English, and built an evaluation benchmark.

Pouria Mahdi, Haq Nawaz Malik

Synthetic data generation can solve the annotation bottleneck for OCR in complex, low-resource scripts—Persian Pixel demonstrates this by creating a scalable, cost-effective alternative to manual labeling that works with modern transformer-based OCR models.

Persian Pixel is a large-scale synthetic dataset of 343,000+ image-text pairs designed to train OCR systems for Persian script.

Andrea Napoli

ARROW enables variance reduction for domain adaptation on streaming data by adaptively reweighting minibatches, making it practical for online and distributed learning scenarios where offline algorithms fail.

This paper introduces ARROW, an online algorithm for reducing variance in domain adaptation tasks using streaming data. Unlike existing offline methods, ARROW works with data arriving continuously by maintaining moving averages of alignment statistics and reweighting incoming batches to match reference statistics, enabling efficient learning in real-time settings.

Tuhin Chakrabarty, Xinyue Liu, Jane C. Ginsburg et al.

AI-generated books are reshaping creative markets through sheer volume and cost advantage, not quality—they're winning shelf space and sales share from human authors despite being lower-quality, raising serious questions about fair use and market harm in copyright law.

This paper analyzes 14,419 self-published fiction books on Amazon from 2023-2026 to measure the impact of AI-generated content. Using AI detection, researchers found that while AI-heavy books represent a small share of sales, they're growing rapidly and displacing human-written books—especially in genres with high AI adoption.

Hae Min Kim, Stacy Stanislaw

Academic libraries need to optimize their metadata and ensure open access to resources, as generative AI is now a primary discovery mechanism that directs substantial user traffic to institutional repositories.

This study tracked how generative AI tools like ChatGPT and Perplexity drive traffic to academic library resources. Using website analytics from 2023-2025, researchers found that AI significantly increases discovery of library materials, especially theses and dissertations with open access and stable links. The findings show AI is becoming a major pathway for users to find academic resources.

Lance Kennedy, Hossein Amiri, Yueyang Liu et al.

Staypoint detection from noisy GPS data is harder than previously thought; existing algorithms fail in realistic conditions, but unsupervised and supervised learning methods can substantially improve accuracy.

This paper creates the first large-scale benchmark for staypoint detection—converting raw GPS trajectories into meaningful locations like homes or workplaces.

Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru et al.

By diagnosing model weaknesses at the granular level of individual rubric criteria rather than whole prompts, you can generate more targeted fine-tuning data that produces measurably better models across multiple domains.

CRAFT is a diagnostic method that analyzes rubric-based evaluations to identify specific capability gaps in language models, then uses those insights to generate targeted fine-tuning data.

Ingo Ziegler, Martin Krebs, Desmond Elliott

Token, byte, and pixel encodings each have different strengths depending on your task and capacity constraints—there's no universally best choice, only task-specific tradeoffs.

This paper compares three ways to encode text for language models—tokens, bytes, and pixels—by controlling both the linguistic content and model capacity. Using parallel sentences across 13 languages, the researchers measure how well each encoding preserves information under compression.

Victoria Graf, Hannaneh Hajishirzi, Noah A. Smith et al.

Pretraining data poisoning is practical at web scale through public discussion interfaces, and detecting whether poison survives data curation pipelines is critical for understanding real-world LM security risks.

This paper shows that language models can be poisoned during pretraining by injecting malicious content into public discussion forums and other web-scale sources.

Qiwei Li, Jorge Ortiz

LLMs can bridge the gap between natural-language questions and rigorous causal analysis by mapping user queries to structured causal models, making complex statistical reasoning accessible to domain experts without deep technical expertise.

teLLMe is a system that helps traffic researchers answer causal questions about urban driving using dashcam video data. It combines causal structure learning with large language models to translate natural-language questions into structured causal analyses, returning explainable results that show estimated effects, assumptions, and uncertainty.

Yazhi Zhang, Fuqiang Niu, Bowen Zhang

This dataset enables training multimodal models to detect political stance in short-form video discussions while accounting for conversational context—a capability previously limited by lack of labeled data combining video, audio, and nested comment structures.

TikStance is a dataset of 13,876 comments on 161 TikTok videos about three 2024 U.S. political figures, labeled for stance (Favor/Against/None) with both video and comment-level annotations. It preserves audio, video, and hierarchical comment threads to enable research on how people express political opinions across short-video platforms.

Aniruddhan Ganesaraman, VIdyadhar Kulkarni

You can learn optimal maintenance schedules from operational failure data using multi-armed bandit algorithms, achieving regret bounds that match theoretical limits and requiring only a small number of suboptimal decisions.

This paper develops data-driven algorithms to learn the optimal maintenance schedule for machines under block replacement policies, where machines are replaced individually upon failure and all machines are jointly replaced at fixed intervals.

Hoang-Loc Cao, Van Pham, Truong Thanh Hung Nguyen et al.

For mental health AI systems, structured annotation with expert-in-the-loop feedback and traceable clinical reasoning produces more reliable, interpretable datasets than traditional labeling—critical for building trustworthy diagnostic support tools.

This paper presents a framework for creating high-quality, explainable depression datasets by combining LLM-assisted annotation with expert feedback. The system produces DSM-5-aligned labels with clinical evidence and reasoning traces, using a dual-memory architecture that learns from expert corrections to improve future annotations without retraining.

Daniel Grillmeyer, Marius Hadry, Michael Stenger et al.

Wrapper-based feature selection methods outperform simpler approaches for renewable energy prediction, and the proposed CSFS method achieves comparable accuracy to existing wrappers with significantly lower computational overhead.

This paper addresses feature selection for renewable energy prediction by proposing CSFS, a clustering-based wrapper method that automatically identifies the most important variables for wind and solar power forecasting. The approach matches the performance of traditional methods while reducing computational costs by 21%, making it practical for real-world energy systems.

Ruoran Xu, Wending Gao, Qiufeng Wang

By using formal languages as an intermediate representation between language models and geometric rendering, this framework achieves high-precision diagram generation for math problems without human annotation—a scalable approach for creating training data in underexplored domains.

FormalAnalyticGeo is a framework that automatically generates multimodal analytic geometry problems by combining formal languages with neural models.

Nathan Pruyne, Benjamin Stoler, William Chen et al.

Automatic music transcription models still struggle with real-world pop music—the best model only achieves 38% Onset F1—suggesting this dataset will be valuable for developing better transcription systems.

MulTTiPop is a dataset of 572 pop music segments (3.5 hours) paired with multitrack MIDI transcriptions, spanning from the 1930s to 2000s. The authors created it by matching audio from existing datasets, manually aligning beats, and using tempo warping. They benchmark state-of-the-art transcription models and show significant room for improvement.

Duen Horng Chau, Donghao Ren, Fred Hohman et al.

The kNN graph UMAP builds internally preserves high-dimensional structure better than the 2D visualization; applying network analysis to it gives you cleaner insights into data organization than the embedding alone.

UMAP is popular for visualizing high-dimensional data, but researchers typically ignore its internal k-nearest-neighbor graph.

Manuel Pita

High agreement between LLMs and human annotators doesn't guarantee the model understands the construct being measured—you need to test whether the model follows the theory's logic or just correlates with surface features.

This paper tests whether Portugal's AMALIA language model can reliably annotate moral concepts by comparing its agreement with human coders against its actual understanding of the underlying construct.

Xinlong Zhao, Dongsheng Liu, Hengyu Zhao et al.

As data quantity plateaus, improving LLM performance now requires smarter data quality refinement—UltraX shows you can train a specialized editing model that's both efficient and reliable enough to process massive datasets and improve downstream model quality.

UltraX is a framework that automatically improves training data quality at scale by learning to edit text through insertion, deletion, and modification. Instead of relying on fixed rules or expensive LLM calls, it trains a model to make targeted edits by learning from examples of how an expert LLM would refine raw text, achieving better model performance with less data.

Yair Feldman, Linxi Zhao, Nathan Godey et al.

Externalizing factual knowledge to a continuous vector-indexed database lets smaller models achieve better factual accuracy and knowledge control than larger models, while keeping retrieved facts attributable and editable.

This paper introduces CO-LMLM, a language model that stores factual knowledge in an external database with continuous vector keys instead of memorizing facts in its weights. During generation, the model queries this knowledge base flexibly and retrieves human-readable information to cite.

Tianming Sha, Yue Zhao, Lichao Sun et al.

For building reliable autonomous agents, having a large, source-grounded skill library with quality verification is critical—SkillCenter provides 216K+ skills where every claim traces back to its original source.

SkillCenter is a massive open library of 216,938 structured skills for AI agents, with over 114,000 skills verified against peer-reviewed sources and technical documentation. The system uses an LLM-based quality filter and source-grounding to ensure each skill's claims are traceable to exact quotations, helping agents execute tasks that are not just runnable but correct, secure, and maintainable.

Hyunjae Kim, Dain Kim, Pan Xiao et al.

High-fidelity data curation from scientific literature can create large-scale medical multimodal datasets that rival or exceed models trained on much larger datasets, enabling better medical AI without requiring new data collection.

MedPMC is a framework that automatically extracts and curates 11 million high-quality medical image-text pairs from 6.1 million PubMed Central articles. The resulting dataset trains multimodal models that significantly outperform existing biomedical baselines on medical imaging tasks, with 95.3% of extracted images validated as medically relevant by human reviewers.

Aparna Madva, Sharath Srivatsa, Srinath Srinivasa et al.

Building AI for Indian languages requires moving beyond generic NLP approaches to address unique linguistic structures and cultural contexts; the paper introduces 'Culture Sensing' as a framework for culturally-aware, equitable AI development.

This paper examines how AI and NLP technologies impact Indian languages and cultural heritage, tracing the historical development of Indic NLP while highlighting unique linguistic challenges like complex scripts, rich morphology, and diglossia.

Ramon Ferrer-i-Cancho, Catherine Hobaiter, Thore Bergman et al.

Dependency parsing of animal communication is paradoxically easier to evaluate than human language because primate sequences have mathematical properties that constrain parser accuracy, enabling validation without labeled data.

This paper explores whether dependency parsing—finding tree structures in sequences—can work for animal communication without labeled training data. Using network science, the authors show that primate vocalizations and gestures have mathematical properties that force parsers to be accurate, making evaluation possible without gold standards.

Xiaolei Guo, Chenyu Zhou, Jianghao Lin et al.

For building MILP solvers and learned optimization policies, GraphBU generates synthetic instances that preserve the graph structure and feasibility of real problems better than existing methods, improving downstream model training by ~8%.

GraphBU is a method for generating realistic MILP instances by treating local subproblems and their connections as fundamental units. Unlike existing generators that use templates or statistics, it explicitly preserves how different parts of an optimization problem couple together, maintaining structural properties that solvers and learned policies depend on.

Kevin Xu, Alexander Quispe

You can now map GitHub repositories to standardized industry sectors with high accuracy, enabling research on how different industries contribute to open-source software and how technologies spread across economic sectors.

This paper introduces NAICS-GH, a dataset of 6,588 GitHub repositories labeled with industry sectors using the North American Industry Classification System. The authors use a pipeline combining embeddings, retrieval, and GPT-4 scoring to automatically classify repositories, achieving 97% precision on human-validated samples and releasing the dataset with code and trained models.

Qian Sun, Yong-Ming Tian, Jia-Wei Huang et al.

Real-world multivariate time series data substantially improves foundation model generalization compared to synthetic data, suggesting that practitioners should prioritize real-world datasets when pretraining time series models.

This paper introduces RMISC, a large-scale collection of 200 real-world multivariate time series datasets with 142 billion data points across diverse domains.

Xuanyu Chen, Nan Yang, Shuai Wang et al.

When training on decentralized, non-uniform data, use Masked Image Modeling instead of Contrastive Learning—it's theoretically more robust. Better network connectivity always improves robustness, so federated learning is a viable alternative to fully decentralized systems.

This paper analyzes how distributed self-supervised learning systems handle non-uniform data across devices. The researchers prove that Masked Image Modeling is more robust to data heterogeneity than Contrastive Learning, and that federated learning performs as well as fully decentralized approaches. They introduce MAR loss, a practical improvement that aligns local and global representations.

M. Doris, S. Guo, S. M. Koh et al.

This package makes it practical for physics researchers to apply modern ML techniques (classification, object detection) to quantum gas experiments without building infrastructure from scratch.

Q-GAIN is a Python package that combines machine learning with physics-informed analysis for cold-atom experiments. It provides pre-built tools for classifying images, detecting objects, and analyzing quantum gas systems like Bose-Einstein condensates, with a modular workflow that connects data loading, ML-based feature detection, and physics analysis.

Benjamin Nichols, Michael Schlichtkrull, Nedjma Ousidhoum

MEDIAREF enables reproducible, cost-effective evaluation of how well LLMs can assess source credibility for fact-checking, addressing a gap where existing systems assume all retrieved evidence is equally reliable.

This paper introduces MEDIAREF, a public database of web documents from 200 media sources designed to help AI systems verify information credibility. Instead of relying on expensive search APIs, researchers can now use MEDIAREF to test how well language models assess whether news sources are trustworthy—a key step in fact-checking systems that cite their sources.

Nina Begus

Literary disciplines offer practical tools for making AI systems more culturally literate and pluralistic, moving beyond the monolingual, automated cultural encounters that current LLMs create.

This essay argues that literary analysis methods—comparative reading, narratology, critical theory, and world literature approaches—are essential for building culturally aware AI systems.

Chih-Han Yang, Dai-Jie Wu, Yun-Ping Huang et al.

Language-based feedback preserves more information than scalar signals when learning from imperfect data, enabling policies to understand not just what went wrong but why and how to fix it.

This paper proposes using natural language critiques as structured supervision signals for learning from suboptimal demonstrations. Instead of compressing feedback into scalar scores, the method generates language labels describing task progress, failures, and corrections, then trains policies directly on these rich signals.

Yuqing Yang, Qi Zhu, Zhen Han et al.

Data referencing errors are a widespread problem in LLM table reasoning that goes beyond final-answer accuracy; using a lightweight critic model to catch these errors during inference significantly improves reliability.

LLMs make data referencing errors when reading tables—citing wrong values or missing data despite understanding table structure. This paper systematically measures these errors across models and shows that using a critic model to detect and filter bad outputs improves accuracy by up to 12%, even with a small 4B-parameter critic.

Kaisen Yang, Zheng Jiang, Yuzhao Peng et al.

Browser agents can scale more efficiently by learning from the implicit skills already present in human web interactions rather than from manually designed tasks, using skill distillation to convert trajectories into reusable, composable natural-language instructions.

This paper proposes a scalable approach for training browser agents by distilling human web browsing interactions into reusable natural-language skills. Rather than training agents from scratch on individual tasks, the method converts user interaction traces into compact skill descriptions that agents can retrieve and compose, organized in a skill graph to enable efficient learning and reuse.

Ekaterina Alimaskina, Denis Shveykin, Gleb Molodtsov et al.

Synthetic QA generation for model training has hidden failure modes: biased coverage of documents and susceptibility to instruction injection. Simple fixes like anchoring questions to specific targets and filtering instruction-like text can substantially reduce these problems.

This paper reveals that using synthetic question-answer pairs to train language models is riskier than assumed. Models generating QA pairs don't uniformly cover documents—they focus on salient regions and can be hijacked by artifacts like markup.

Yen-Jen Wang, Jiaman Li, Sirui Chen et al.

Synthetic data from reconstructed 3D scenes can effectively train perception-based humanoid robots for real-world loco-manipulation, eliminating the need for expensive human-annotated robot trajectories.

This paper solves a key bottleneck in training humanoid robots: the lack of paired data combining egocentric camera views, language instructions, and robot motion. The authors generate 48,000 synthetic training examples by reconstructing real indoor scenes with 3D Gaussian Splatting, simulating robot trajectories, and rendering first-person views.

Serhii Hamotskyi, Akash Kumar Gautam, Christian Hänig

LLMs can replace rigid rule-based systems for document compliance verification, handling messy real-world text better than traditional NER while requiring no task-specific training data.

This paper applies large language models to automatically verify whether securities meet eligibility criteria for use as collateral at the German Central Bank. Instead of manually reading through complex, bilingual prospectuses, the system uses LLMs to extract, normalize, and interpret financial and legal information, achieving 91% precision while avoiding false acceptances.

Lang Huang, Jinglue Xu, Luke Darlow

Before building bigger models, optimize your data preprocessing: context length, normalization strategy, and regularization can close most of the accuracy gap at a fraction of the computational cost.

This paper shows that simple linear models (Ridge regression) can match or beat complex deep learning architectures for time-series forecasting by carefully tuning preprocessing—context length, normalization, and regularization—rather than scaling model size.

Maria Levchenko

Historical text imposes a consistent encoding penalty on LLMs, but models retain semantic understanding—making them safe for retrieval tasks if generative applications are adapted with temporal context.

This paper diagnoses why language models struggle with historical text by breaking down the problem into four dimensions: tokenization cost, predictive uncertainty, semantic robustness, and context sensitivity.

MSVPJ Sathvik, Parmitha Vangapadu, Nishit Rane et al.

The dataset enables building explainable AI systems to automatically detect manipulative betting ads on social media, with practical applications for user protection and regulatory monitoring.

This paper introduces BetXplain, a dataset of betting advertisements from Instagram and Reddit annotated for manipulative tactics and deceptive practices. Each ad includes human explanations of why it's manipulative, enabling research into detecting misleading betting promotions that could harm users' mental health and financial well-being.

Parmitha Vangapandu, Sai Ganesh Mokkapati, Sathwik Narkedimilli et al.

Mental health NLP models perform better when trained on data that includes relational context and interpersonal triggers, not just isolated symptoms—this shift from individual-centric to context-aware modeling improves both accuracy and clinical relevance.

This paper introduces RSPC, a dataset of 1,799 Reddit posts about long-distance relationships annotated by psychiatrists for mental health conditions, relationship stressors, and relationship phases.

John Sweeney

FisherSketch enables practical source selection for LLM families by measuring task similarity through Fisher alignment signatures (16 KB per task) instead of expensive full Fisher matrices, revealing whether tasks differ in activations, errors, or their interaction.

This paper solves the problem of selecting training data sources for language models that share vocabularies but differ in tasks (like SMILES vs protein sequences).