Papers

Yucheng Jiang, Zora Zhiruo Wang, Ruishi Chen et al.

By converting raw computer activity into structured task models with goal hierarchies and control flow, TMI enables AI agents to learn realistic work procedures and organizations to audit and reuse task knowledge from employee activity traces.

This paper presents Task Model Induction (TMI), a method that automatically discovers and structures how people actually work on computers by analyzing screenshots and input logs.

Jun Ni Du, Lukas Adamek, Maxim Kryukov et al.

This work shows how to build interpretable clinical prediction models from structured EHR data by combining effective lab value encoding with attribution methods that clinicians can understand and trust.

BERT-LER is a machine learning model designed to predict health outcomes from patient medical records while explaining its decisions. It represents lab test results as discrete tokens with percentile-based information and uses a technique called Integrated Gradients to show which medical events influenced each prediction.

Thales Bertaglia, Catalina Goanta, Gerasimos Spanakis et al.

This benchmark helps detect undisclosed advertising in child-facing content by combining video transcripts, metadata, and linked sales pages—revealing widespread non-compliance with YouTube's disclosure requirements.

ChildSafeAds is a shared task that identifies commercial content in YouTube videos targeting children. Using 3,360 videos with sponsor segments from SponsorBlock, systems classify what products are promoted, categorize them, and flag legal risks. The dataset reveals that 45.5% of videos fail to properly disclose paid promotions, highlighting gaps in platform compliance.

Anran Wang, Wen Shi, Yong Luo et al.

AI climate predictions become more trustworthy when you can explain which physical mechanisms drive the forecast; here, interpretability methods confirmed the model's drought prediction is based on real atmospheric physics, not spurious patterns.

Researchers used a deep learning model combined with interpretability techniques to predict a summer 2026 drought in central China.

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.

Kejia Zhang, Youran Sun, Xinyu Ren et al.

Symbolic regression works better when you preserve the scientific reasoning behind each equation candidate—not just the formula itself—allowing AI agents to learn from failed attempts and build credible explanations for why one equation is better than others.

AutoSR is an automated system that discovers scientific equations from data by treating symbolic regression as a research investigation rather than just equation fitting.

Shuai Huang, Zhe Qu, Zhaowei Hua et al.

Quantile-based survival prediction reveals how treatment effects vary across the survival distribution—some patients benefit early, others late—information hidden by standard hazard ratios and now achievable without crossing quantile curves.

This paper introduces a method for predicting survival times that captures how different patients respond differently at early vs. late stages of disease. Unlike traditional survival models that give a single risk number, this approach estimates the full distribution of survival times while ensuring predictions stay logically consistent (quantiles don't cross).

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.

Minh-Ha Nguyen, Cathy Shyr

You can improve a frozen language model's performance on specialized tasks by treating policy refinement as a human-in-the-loop process: have an AI critic identify recurring failures, propose natural-language policy changes, and let domain experts decide what gets deployed.

This paper presents Policy Iteration with Human Feedback (PIHF), a method that improves a fixed language model's performance on rare-disease diagnosis by iteratively refining its decision-making policy through human expert review.

Yi-Chung Chen, Philip Jacobson, Tom Lampo et al.

Using physical trajectory data as training supervision helps video embedding models better understand motion-centric driving events, improving retrieval accuracy by 5-10% while keeping inference simple and efficient.

This paper tackles retrieving relevant driving video clips from large datasets by improving multimodal embedding models. The key innovation is TraVEL, which fine-tunes video embeddings using trajectory (vehicle motion) as training supervision, helping the model understand motion-centric events like turning or accelerating.

Saisha Shetty, Satvik Tripathi, Austin Lin et al.

For clinical AI, breaking monolithic prompts into traceable multi-agent workflows improves interpretability and failure diagnosis compared to single-LLM approaches, while remaining accessible to non-programmers.

MARC is an open-source framework that replaces single-prompt LLM calls with coordinated teams of specialized agents for clinical reasoning. Each agent handles a specific task (extracting data, reasoning, generating answers, evaluating results), with clear handoffs between stages so failures can be traced.

Mohammed Ayman Habib, Rylan Hart, Morteza Fayazi

LLMs can automate the full analog circuit design process—not just sizing or topology separately—by using multiple agents that debate and refine designs, achieving 40x faster results than manual approaches while discovering novel circuit topologies.

AaLLM is an end-to-end framework that uses multiple AI agents working together to automatically design analog circuits from scratch. It takes design requirements as input and outputs complete circuit designs (both the circuit structure and component values), reducing the time-consuming manual work that circuit engineers typically do.

Van Khoa Nguyen, Alexandros Kalousis

By reversing the physics concept of spontaneous symmetry breaking, this diffusion model generates complete crystal structures with proper global symmetries—a significant improvement over methods that only generate partial specifications.

This paper proposes a new method for generating crystal structures by using a diffusion-based model that starts from low-symmetry configurations and gradually breaks symmetries to create complete crystal specifications.

Juan Irving Vasquez, Juan Terven, Laura-Ivoone Garay-Jimenez

Instead of using a single LLM to judge AI readiness, using multiple specialized LLMs with a conservative aggregation rule produces more reliable and honest assessments of project maturity.

This paper creates AIRL, a unified 9-level AI readiness scale combining three existing frameworks, and RAIL, an automated classifier using multiple specialized LLMs to assess AI project maturity. The system evaluates projects across six dimensions (data, specifications, expertise, algorithms) and prevents overestimation through a conservative review process.

Mirko Tritella, Riccardo Pozzi, Matteo Palmonari

When building RAG systems over sensitive texts like parliamentary records, weighting sources by topic-dependent expertise rather than frequency prevents political bias and ensures accurate attribution.

ParliamentRAG is a retrieval system for Italian parliamentary transcripts that addresses bias in AI-generated summaries by modeling speaker authority based on expertise, profession, and topic relevance.

Paul Savala

Predicting course enrollment alongside grades significantly improves academic performance forecasting—jointly modeling what students take and how they'll perform is more accurate than predicting grades alone.

This paper presents TRACE, a transformer-based model that jointly predicts which courses students will take and their grades in those courses for upcoming semesters. Unlike traditional approaches that treat student history as a simple sequence, TRACE captures how courses taken concurrently within a semester affect performance, reducing prediction error by nearly 50% compared to grade-only models.

Saman Marandi, Yu-Shu Hu, Mohammad Modarres

LLMs with retrieval-augmented generation can automatically build executable functional models from technical docs, enabling scalable diagnostic reasoning for complex systems that traditionally required manual expert work.

This paper automates the construction of Dynamic Master Logic models—hierarchical diagrams showing how system failures propagate—by converting technical documentation into knowledge graphs using AI. The approach enables faster analysis of complex systems like nuclear reactors for safety and diagnostics without requiring manual expert interpretation.

Aman Tyagi, Hemanth Boinpally, Jonathan Chen et al.

Instead of manually tweaking prompts and settings repeatedly, you can use an AI agent to systematically optimize video generation—first refining what you ask for, then finding the best random parameters—making video synthesis more predictable and reliable.

This paper presents an agentic framework that optimizes image-to-video generation by automatically refining prompts and hyperparameters rather than relying on trial-and-error. It uses a multimodal language model to improve prompts based on semantic checks, then applies Bayesian optimization to find the best random seeds and settings, achieving 69% preference over baseline methods.

Alireza Kargarzadeh, Nariman Khaledian, Navid Parvini et al.

LLM-extracted sentiment matters for trading, but how you separate firm-specific signals from macro-driven ones, and which allocation method you choose, matters just as much as which sentiment model you use.

This paper shows how large language models can improve small-cap stock trading by extracting sentiment from financial news and combining it with macro indicators and technical signals. Rather than treating portfolio risk as fixed, the authors feed model-predicted risk (split into two types of uncertainty) directly into portfolio construction.

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.

Pedro Sousa, Will Tebbutt, Sadiq Jaffer et al.

Foundation models trained on satellite imagery can capture persistent surface properties (terrain, vegetation, water) that explain local weather variations better than hand-crafted features, enabling more accurate probabilistic weather predictions at arbitrary locations.

This paper shows that Earth observation embeddings from satellite data can improve weather downscaling—predicting local weather at specific locations from coarse global weather models.

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.

Weihao Bo, Shan Zhang, Yanpeng Sun et al.

Current multimodal AI models excel at understanding diagrams visually but struggle significantly with converting them to executable code, suggesting this is a critical gap to address for scientific writing tools.

Diagram-MMU is a benchmark with 3.7k scientific diagrams and 18.3k questions that tests how well AI models can understand diagrams and convert them to code. The benchmark evaluates 12 models on three tasks: turning diagrams into LaTeX code, editing diagram code, and answering questions about diagrams—revealing that code generation is much harder for models than visual understanding.

Junyi Ye, Ivy Gateri Wanjiku

For financial forecasting models deployed at 4-bit precision, activation calibration method (how you estimate value ranges from past data) is critical—percentile calibration substantially outperforms standard approaches, but 8-bit activations remain the safest choice when accuracy matters most.

This paper studies how to properly calibrate neural networks for low-precision inference in financial forecasting. When deploying models with 4-bit quantization, the choice of how to estimate activation ranges from historical data significantly impacts prediction accuracy—more so than at 8 bits.

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.

Alan Li, Rahul Saha, Anton Xue et al.

AI systems can make genuine research contributions in mathematics by discovering non-obvious insights, but their effectiveness depends heavily on how they're integrated into human workflows and problem formulation.

Researchers used an AI system to improve mathematical bounds on the Grothendieck constant, a fundamental problem in computational complexity. The AI discovered novel insights that human experts recognized as significant, demonstrating how AI agents can contribute meaningfully to pure mathematics research when given the right conditions.

Shiqi Huang, Jiani He, Dingyan Shang et al.

For real supply-chain problems, choosing the best intervention requires balancing model complexity against domain knowledge—adaptive ranking improves outcomes by 5-16% in some cases, but simpler policies often work just as well or better, especially under uncertainty.

This paper introduces a benchmark for choosing interventions in supply-chain disruptions based on maximizing recoverable value, not just identifying what went wrong.

Kaan Buyukkalayci, Kyle Pak, Merve Karakas et al.

A recommendation-system framework can efficiently select the best subset of sensors for tracking by learning from acoustic features, outperforming simpler signal-strength methods while maintaining real-time performance.

This paper tackles sensor selection for tracking systems by using a recommendation-system approach. Instead of relying on simple signal strength measurements that fail under acoustic interference, the method uses frequency-based audio features and a two-tower neural network to intelligently choose which expensive sensors (like cameras) to activate.

Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy et al.

Neural networks can solve critical power grid problems 30-85x faster than traditional solvers while staying physically consistent—opening the door to AI-powered grid analysis at scale.

GENCO is a neural network solver that handles power flow, optimal power flow, and state estimation for electrical grids in a single unified model. It matches or beats classical solvers like Newton-Raphson and IPOPT in speed and accuracy while maintaining physical consistency, and comes with an open-source framework and large datasets for reproducible research.

Xu Ouyang, Moontae Lee

Language models can be trained to output financial predictions and portfolio weights as constrained tokens, matching or beating traditional regression-based approaches while maintaining a unified model architecture.

This paper shows that language models can directly generate financial forecasts and investment decisions as tokens, rather than using separate prediction heads. FinATOM uses constrained token generation to predict stock returns and allocate across ETFs, achieving better risk-adjusted returns than traditional approaches when trained with ranking supervision and reinforcement learning.

Brian Llinas, Nikos Chrisochoides

LLM preprocessing can make quantum NLP systems practical for real financial text by simplifying sentences before processing, though the relationship between training data size and performance is counterintuitive.

This paper explores using LLMs to rewrite financial sentences to make them compatible with DisCoCat, a quantum natural language processing framework. The researchers found that LLM-assisted rewriting can reduce computational complexity by over 70% while maintaining sentiment meaning, achieving modest accuracy improvements on financial sentiment analysis tasks.

Ishan Patel, Sahil Sen, Elias Lumer et al.

Programmatic tool calling—letting models write code to invoke tools—is a simpler, more flexible alternative to JSON-based tool calling that performs as well or better across model generations, particularly for complex multi-tool scenarios.

This paper compares two ways for language models to use tools: programmatic tool calling (where models write Python code to invoke tools) versus native JSON tool calling (rigid structured calls).

Soorya Ram Shimgekar, Michelle Hu, Dorisa Shehi et al.

Automated feature engineering for clinical data is feasible when grounded in clinical guidelines and evidence trails, but requires careful validation and auditing to ensure reliability in real-world healthcare settings.

Researchers built an automated system (nMAS) to extract and engineer features from fragmented heart-failure patient records in electronic health records. The system combines multi-agent AI with clinical guidelines to generate interpretable features, reducing manual work that typically consumes 39-45% of data scientists' time.

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.

Tao Wang, Qihao Yang, Rongjiao Liang et al.

LLMs struggle with rule-intensive document review tasks that require checking consistency across long, structured documents—a critical gap for professional applications like standards compliance where accuracy is non-negotiable.

This paper introduces GB/T-Bench, a benchmark for evaluating how well large language models can review national standard documents (like China's GB/T standards) for quality issues.

Sagar Tamang, Ayush Vyas, Tabarakul Hazarika

For structured documents with tables and inherited metadata, agentic search using deterministic operations outperforms embedding-based retrieval by 43 percentage points—the gain comes from the interface design, not from iteration or agent reasoning.

This paper challenges the standard retrieval approach for document-heavy domains like financial reports. Instead of embedding chunks and finding top-k matches, the authors propose READ: an agent that searches documents through deterministic operations (lexical search, structural navigation, span reads) exposed as tools.

Varun Ursekar, Apaar Shanker, Yash Maurya et al.

LLM capability depends heavily on the surrounding harness (prompts, tools, orchestration), and frontier models show measurable but inconsistent ability to optimize these components—a skill that will become increasingly important as AI systems become more agentic.

This paper introduces HarnessOpt-Bench, a benchmark for measuring how well large language models can automatically improve AI agent systems by editing their prompts, tools, and control flow.

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.

Dohyun Ku, Min Gu Kwak, Francisco J. Pasquel et al.

Domain-specialized language models can organize scattered biochemical knowledge into interpretable, predictive representations—showing that fine-tuning LLMs on specific scientific domains significantly improves both accuracy and explainability for real-world medical applications.

MetaboLLM is a specialized AI model trained on metabolomics data that learns to understand biochemical knowledge and convert it into predictive graphs for medical predictions. The model outperforms standard AI systems at tasks like predicting stress hyperglycemia and hormone regimen classification, while producing biologically meaningful insights.

Saad Ahmed, Md Khalid Syfullaha

You can build accurate, deployable sign language recognition systems from scratch without pretrained models—this lightweight attention-based approach matches heavy ImageNet models while using 8.5-68x fewer parameters and fitting in 0.48 MB on phones.

This paper introduces RSBdSL38, an expert-validated dataset of 10,874 images covering all 38 Bangla Sign Language hand signs, and a lightweight neural network (298K parameters) that achieves 96.37% accuracy while running on smartphones.

Elad Yoshai, Natan T. Shaked

Per-feature gating based on distribution distance enables better control in unpaired image translation, letting you preserve important structures while achieving realistic style changes without retraining.

PRISM is a new method for changing images from one style to another (like turning day photos into night) without paired training examples. Instead of using a single global control value, it learns a per-feature gate based on how far each image feature is from the target style, allowing precise control over what changes and what stays the same.

Yuanjing Xu, Xinyan Liu, Weidong Chen et al.

Adding depth information significantly improves object counting in crowded scenes—the method reduces counting errors by 62% compared to RGB-only approaches, showing that 3D spatial cues are crucial for handling occlusions.

This paper tackles counting objects in crowded, occluded video scenes by combining RGB images with depth information. The method uses a depth-guided detector with cross-attention between color and depth data, plus occlusion prediction, to better identify individual objects even when they overlap. The authors also release a new RGB-D dataset for this task.

Alperen Kenan, Paul Bremner, Manuel Giuliani

By incorporating force, timing, and multi-segment trajectory handling into standard learning-from-demonstration techniques, robots can generate handwriting that humans perceive as significantly more natural and human-like.

This paper presents a framework for teaching robots to write letters by learning from human handwriting demonstrations. The approach combines force and timing data with trajectory learning to generate robot motions that feel natural to humans. A user study confirmed the generated handwriting was perceived as 71.5% human-like, with the dataset released openly for future research.

Ayoub Kirouane, Christos Petrocheilos

Domain-specific fine-tuning dramatically improves retrieval and generation for underrepresented languages: a modest 1B embedder outperforms multilingual models when trained on just 65K in-domain Greek examples, showing that language adaptation is more important than model size for specialized tasks.

This paper adapts NVIDIA's Nemotron retrieval system for Modern Greek across legal, energy, financial, and medical domains. The authors mine Greek corpora, train specialized retrieval models, and create HERA—the first large-scale Greek RAG benchmark.

Yuezhang Peng, Yuxin Liu, Changfeng Gao et al.

Framing spoken language understanding as structured function calling—rather than slot-filling—lets audio language models generalize to new tasks without retraining, similar to how code models handle function calls.

This paper introduces Spoken Function Calling (SFC), a new way to understand spoken language that treats semantic extraction like function calls with structured definitions.

Roberto Aliaga Medina, Paulina Quintanilla, Antonio del Rio Chanona

LLMs can significantly accelerate scientific model discovery by injecting domain expertise into symbolic regression, cutting experimental iterations roughly in half while maintaining prediction accuracy.

This paper combines symbolic regression with large language models to automatically discover kinetic models in chemistry and bioprocessing. The LLM critiques candidate models and proposes new ones based on chemical knowledge, reducing the number of iterations needed to find correct models by 41-79% compared to standard symbolic regression alone.

Paritosh Parmar, Landy Lan, Hong Yang et al.

Motion-based reasoning (tracking how bodies move) is more efficient and generalizable than pose-based reasoning for incident detection, and knowledge distillation can compress this understanding into models small enough for real-world deployment.

This paper tackles classroom safety monitoring using privacy-preserving computer vision. The authors create a hybrid benchmark mixing synthetic and real classroom data, then propose a lightweight motion-reasoning model that captures how incidents differ in movement patterns (speed, direction, acceleration) rather than just body poses.

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.

Mirac Suzgun, James Zou, Stuart M. Shieber et al.

This tool makes string algorithms transparent and inspectable: you can see not just the score but the exact reasoning (alignment paths, metric matches) behind it, all running fast in your browser without sending data anywhere.

string2string Studio is an interactive web-based tool for analyzing string-to-string algorithms across NLP, biology, and humanities. It runs locally in your browser (no data upload needed), offers six analysis modules, and shows you exactly how algorithms work by displaying their evidence—alignments, edit paths, and search results—making it easy to debug and compare methods.

Ilya Mikhelson

Conversational AI can transform assessment from deficit-based grading to mastery-focused evaluation by dynamically adapting difficulty and providing diagnostic feedback in real-time, making testing fairer and more informative.

This paper proposes the 'Socratic Test,' an automated conversational assessment system that replaces traditional static exams with dynamic, multimodal dialogues. By combining Dynamic Assessment, Bloom's Taxonomy, and the SOLO Taxonomy, it measures student learning through graduated scaffolding rather than penalizing mistakes, while reducing anxiety and bias from traditional oral exams.

Jonathan J. Heckman, Shani Meynet, Alessandro Mininno et al.

Neural networks can learn to recognize complex mathematical dualities faster than deterministic algorithms, suggesting ML has practical value for computational theoretical physics problems beyond typical data-driven domains.

This paper applies machine learning to detect Seiberg dualities—mathematical equivalences between different supersymmetric gauge theories. The researchers train neural networks (transformers and MLPs) to recognize when two quiver diagrams represent dual systems, a task analogous to unknotting problems.

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.

Shentong Mo, Yatao Bian

Physical consistency constraints alone can guide better 3D structure prediction than supervised coordinate matching, enabling practical structure modeling when ground-truth labels are unavailable.

APO is an unsupervised method for predicting 3D atomic structures (crystals, proteins) without needing labeled reference structures. It uses a dual-reward system—one that identifies physically realistic configurations through statistical analysis, another that enforces thermodynamic stability—allowing models to self-correct during training.

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.

Ali Rayat, Yunhao Fan, Gia-Wei Chern

GNNs can replace expensive electronic calculations for simulating spin dynamics in magnets by learning effective magnetic force fields, similar to how machine-learned potentials work for atomic systems.

Researchers developed a graph neural network framework that learns to predict magnetic forces in metallic magnets directly from electronic calculations. This approach eliminates expensive repeated electronic simulations during time evolution, enabling fast and accurate predictions of spin dynamics across different magnetic structures.

Kingsley Ugwuanyi, Christian Mair, Sender Dovchin et al.

GenAI tools in academic publishing can either reinforce linguistic hierarchies favoring standard English or become tools for resistance—the outcome depends on how they're designed and governed by scholarly communities.

This article examines how generative AI tools affect academic writing and publishing, particularly for non-native English speakers and speakers of World Englishes. Five sociolinguists discuss whether GenAI democratizes writing or reinforces dominant English norms, highlighting concerns about marginalizing linguistic diversity and the need for inclusive AI design.

Yuto Suzuki, Farnoush Banaei-Kashani

Learning to generate problem-specific abstractions from candidate papers significantly improves finding relevant scientific inspirations, especially for distant domains that share transferable principles rather than surface-level topics.

This paper tackles scientific inspiration retrieval by learning to extract transferable abstract principles from papers that are specifically tailored to a target research problem.

Srikanth Avasarala, Varun Gupta, Shahin Jabbari et al.

Recourse systems that ignore causal relationships between features and outcomes enable strategic gaming and distribution shift; incorporating causal structure into recourse design creates stable equilibria where recommended changes genuinely improve qualifications rather than just flip predictions.

This paper addresses a critical flaw in algorithmic recourse systems: they often recommend changes that help people game classifiers rather than genuinely improve their qualifications.

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.

Ngoc Thai Le, Thanh Ma, Umberto Straccia

Neuro-symbolic systems can improve both accuracy and interpretability in infrastructure assessment by separating perception (what defects are present) from reasoning (how defects determine severity), making decisions traceable to specific visual evidence.

This paper presents a neuro-symbolic system for assessing sewer pipe damage severity by combining a vision model (Swin Transformer) that detects visual defects with fuzzy logic rules derived from decision trees.

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.

Amol Khanna, Manu Nandan, Cristian Viorel Popa et al.

Chain-of-thought reasoning improves security alert triage accuracy, but requires a separate confidence calibrator trained on reasoning traces—a finetuned 30B model with reasoning outperforms larger general-purpose models without it.

This paper tackles alert fatigue in security operations by training language models to reason through cybersecurity detections step-by-step before classifying them as threats or benign.

Julien Benchek, Austin Bennett, Jasmin Kern et al.

Even frontier AI models struggle significantly with real-world accounting tasks—the best model only achieves 56% on the main metric—suggesting accounting work requires capabilities beyond current language models or needs better prompting strategies.

APEX-Accounting is a benchmark testing whether AI models can perform real accounting work like reconciling accounts, accruing expenses, and producing reports. Built by Mercor and Ramp with expert-authored tasks and grading rubrics, it evaluates frontier models on 160 private tasks across 10 accounting systems.

Ben Glickenhaus, Katherine Thai, Jenna Russell et al.

If you're building content moderation or authenticity verification systems, Pangram 4 demonstrates that modern classifiers can reliably detect AI-generated text across diverse domains while handling real-world challenges like mixed authorship and adversarial manipulation.

Pangram 4 is an AI-text detection model that identifies whether text was written by AI or humans. It achieves 99.16% accuracy with very low error rates, and can detect mixed human-AI writing and subtle edits better than previous versions. The model also handles out-of-distribution data and adversarial attacks more robustly.

Ji Xin, Xiao Xiao, Ishan Bhatt et al.

By generating implicit user intents to expand search results, e-commerce platforms can surface complementary and substitute products while maintaining relevance, improving both user satisfaction and visibility for long-tail inventory.

This paper presents a system that improves product discovery in e-commerce by generating related search intents beyond exact query matches. Using large language models for popular queries and fine-tuned smaller models for niche queries, the system expands what products users see while keeping results relevant—increasing discovery coverage from 60% to 80% at lower computational cost.

Yihao Chen, Shi Chang, Feng Lin et al.

Breaking program synthesis into explicit specification-first and code-synthesis phases—inspired by requirements engineering—dramatically improves LLM agents' ability to build programs from scratch by preventing early misinterpretations and maintaining behavioral intent.

SpecFirst separates program synthesis into two stages: first, an agent explores a binary and documentation to write a detailed behavioral specification, then a second agent uses that spec to write code. This two-stage approach significantly outperforms single-pass synthesis on from-scratch programming tasks, improving test pass rates by 6.9%-21.3% on ProgramBench.

Jingbo Zhou, Yusai Zhao, Qi Bao et al.

LLM agents are faster and cheaper than humans on office tasks, but they don't yet match human quality—this benchmark lets you measure the cost-quality tradeoff for your use case.

OmegaUse-OfficeVal is a benchmark for testing LLM agents on realistic office tasks (like document editing, spreadsheet work) that take ~2.3 hours of human labor each. It uniquely pairs tasks with economic data—human labor costs and LLM inference costs—so you can directly compare whether AI is cheaper and faster than hiring someone.

Roshan Kenia, Stephanie L McNamara, William Lotter

You can efficiently adapt pre-trained medical imaging models to understand anatomy-level details by decomposing scans into regions, learning how they relate to each other, and aligning them with medical text—without retraining from scratch.

This paper presents Anatomy Contextualized Adaptation (ACA), a lightweight method that improves CT scan foundation models by aligning fine-grained anatomical regions with text descriptions from radiology reports while preserving whole-scan context.

Francesco Pinto, Luca Lanzilao, Paco Lopez Dekker et al.

Satellite scatterometer data can be used directly for short-term wind forecasting via deep learning, achieving 23% better accuracy than traditional weather models at 1-hour lead times—opening a new approach to renewable energy forecasting.

WindCastNet is a machine learning model that forecasts offshore wind speed and direction using satellite scatterometer data instead of traditional weather models. It handles irregular satellite observations from multiple sources and predicts wind fields 1-2 hours ahead, outperforming standard numerical weather prediction models for short-term forecasts critical to offshore wind farm operations.

Syed Mhamudul Hasan, Anas AlSobeh, Hussein Zangoti et al.

Combining multimodal inputs (images + symptoms) with structured workflow management and safety checks significantly improves disease screening accuracy compared to image-only predictions, and this pattern applies beyond veterinary use.

VetClaw is an edge-cloud system that helps veterinarians screen animals for diseases by combining camera images with symptom descriptions. It uses a vision-language model on a server and intelligent workflow management to collect evidence, validate inputs, apply safety rules, and alert users when cases need escalation—turning a simple image classifier into a practical diagnostic tool.

Abhishek Pillai, Samir Kumar Nayak, Yuan Chen

Current desktop AI agents struggle to verify that their actions caused expected GUI changes—a crucial capability for error recovery and reliable task execution.

This paper introduces Desktop-Delta Bench, a benchmark testing whether computer-use AI agents can understand how desktop GUI changes after actions.

Yuan Yin, Elias Ramzi, Marc Lafon et al.

Training driving policies directly from egocentric camera images at scale is now practical—eliminating the need for privileged observations during training while maintaining competitive performance and improving real-world transfer.

Pictura is a GPU-accelerated driving simulator that renders camera views for each agent in real-time, enabling self-play training of driving policies directly from images rather than privileged vectorized data.

Farooq Shaikh

When LLMs generate infrastructure security fixes, providing live cluster topology (service call graphs and account bindings) is critical—it prevents patches that comply with security rules but break dependent services, raising correctness from 11% to 78% for topology-sensitive issues.

This paper shows that giving LLMs information about a Kubernetes cluster's live service dependencies significantly improves their ability to generate correct security patches. The authors built KuTIE, a system that feeds LLMs real-time cluster topology data alongside security findings, and tested it on a healthcare cluster.

Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler et al.

Frontier LLMs can automatically generate optimized, production-ready quantum compiler code by iteratively refining specifications, eliminating months of manual engineering work and achieving better performance than expert-written versions.

Researchers used Claude Opus and Claude Fable LLMs to automatically generate Python code for shuttling compilers—software that optimizes how quantum bits move within trapped-ion quantum computers.

Ali Ansari, Yasmin Mohammadi, Farnoush Nili et al.

Vision-language models can extract simple database schema elements from diagrams but fail on advanced ER constructs—this benchmark reveals critical gaps in multimodal understanding of structured technical diagrams that matter for AI-assisted database engineering.

ERUnderstand is a benchmark dataset of 2,960 Entity-Relationship diagrams that tests how well AI vision-language models can understand database schemas from images. The researchers found that while models handle basic diagram elements well, they struggle significantly with complex features like weak entities and N-ary relationships, even when augmented with reasoning capabilities.

Zeyu Zhang, Xue Li, Iacer Calixto et al.

When adapting language models for entity matching, the model's pretraining variant (embedding-focused vs. general) matters more than raw size, and architectural advantages depend heavily on whether you're dealing with distribution shift or cross-dataset scenarios.

This paper systematically compares how language models perform at entity matching (identifying records referring to the same real-world entity) across different architectures, model variants, and sizes.

Vedant Shah, Onkar Susladkar, Tushar Prakash et al.

Using structured graphs to represent object relationships provides a more scalable and precise way to control video generation than trajectory drawing or text prompts, especially for complex multi-object scenes.

GraphVid enables precise control over multi-object interactions in video generation by using structured interaction graphs instead of text or pixel-level motion inputs. The method outperforms existing motion-control approaches while using less training data, and the authors release GraphVid-Bench, a new dataset with relational annotations for interaction-aware video generation.

Korota Arsène Coulibaly, Mohamed Hamlich, Khalid Hmali et al.

Synthetic data generation can effectively replace scarce real-world defect images for training industrial quality control systems, achieving strong performance on real data while eliminating costly manual annotation.

This paper presents a synthetic data generation framework that automatically creates realistic images of printing defects (creases, streaks, misregistration) with annotations for training object detection models. The framework solves a critical problem in rotogravure printing: the extreme scarcity of real defect images needed to train deep learning models.

Qian Wu, Xinrong Zhou, Zizhan Ma et al.

LLMs can structure medical education as decision-centered narratives rather than isolated Q&A, making learning more engaging and clinically realistic through interactive storytelling.

MedGame transforms static medical cases into interactive storytelling games using LLMs. A Medical Narrative Designer creates branching clinical storylines with decision points, while a Story Director orchestrates them into multimodal interactive experiences.

Xiao Yu, Baolin Peng, Ruize Xu et al.

You can now train agentic AI systems end-to-end with production harnesses and real environments using standard RL tools—no need to build custom training infrastructure for each harness type.

OpenForgeRL is an open-source framework that enables training AI agents end-to-end using complex inference systems (harnesses) like Claude Code and OpenClaw. It works by running a lightweight proxy that intercepts the harness's operations while feeding them into standard RL training systems, and uses Kubernetes to scale training across remote containers.

Fares Fourati, Hinrich Schütze, Eyke Hüllermeier et al.

Automation has conceptual limits: in activities where goals emerge through human-AI interaction (like creative work, learning, or complex decision-making), human participation is constitutive of the outcome itself, not just a workaround for imperfect AI.

This paper argues that human participation in AI systems isn't just a temporary limitation—it's fundamentally necessary in many domains.

Nicolas Kosanovic, Jordan Dowdy, Jean Chagas Vaz

Miniature humanoids can perform complex tele-loco-manipulation (remote control of both movement and arm tasks) by combining VR teleoperation for upper-body control with RL-based locomotion—making advanced robot control accessible beyond expensive full-sized platforms.

This paper develops a control system for miniature humanoid robots that combines virtual reality teleoperation for arm control with reinforcement learning for walking and balance. The system was tested on a small ROBOTIS OP3 robot, demonstrating it could walk while manipulating objects and relocate cubes under human remote control.

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.

Eva McCord, Ernest Pedapati, Zag ElSayed

Building AI-augmented clinical systems requires explicit state machines, privacy-first architecture (self-hosted, encrypted), and deterministic fallbacks—not just bolting AI onto existing workflows.

FMRP-LEAN is a HIPAA-compliant laboratory management system that automates clinical biomarker workflows using AI and structured state tracking. It replaces error-prone spreadsheets with a secure, self-hosted system that tracks samples through multi-day assays, ensures data privacy, and improves communication between lab and clinical teams.

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.

Roger Sala Sisó, Tiago Silvério, Jakob Sand et al.

Deploying VLA robots in the real world is primarily a systems integration problem—thoughtful data curation, control alignment, and targeted fine-tuning on a single GPU can turn a failing policy into a working one.

This paper presents DEED, a practical framework for deploying vision-language-action (VLA) robots in real retail environments.

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.

Rahul Sajnani, Yulia Gryaditskaya, Radomír Měch et al.

Appearance pointers provide a modality-agnostic way to add regional control to existing Diffusion Transformers, letting you specify exactly where text descriptions or image references should apply in the generated output without expensive retraining.

This paper introduces appearance pointers, a new technique for controlling where and how text and image inputs influence image generation in Diffusion Transformers.

Grace Hui Yang, Pranav N. Venkit, Hooman Sedghamiz et al.

Deploying agentic systems requires more than algorithmic innovation—you need robust verification, fallback mechanisms, and human-in-the-loop safeguards to handle real-world failure modes at scale.

This tutorial bridges the gap between agentic AI research and real-world deployment, covering how LLM-based systems that reason, plan, and use tools are moving from labs to production. It shares practical lessons from pharmaceutical and financial deployments, including design patterns, failure modes, and safety strategies like verification pipelines and human oversight.