Planning road maintenance at scale requires repeatedly simulating how traffic redistributes when capacity is reduced --- a process that's normally too computationally expensive to embed directly into scheduling optimization. This paper builds data-driven surrogate models that predict equilibrium traffic flows directly from origin-destination demand, bypassing expensive equilibrium solvers, and validates the approach using real traffic data from Newark, NJ. This offers city planners and transportation departments a scalable tool for smarter maintenance scheduling that minimizes disruption, with potential extension to broader infrastructure and network planning problems.

Authors: Charitha Nandepu, Lohitha Kalepu, Gabriele Ciavarella, SangWoo Park

Paper: https://arxiv.org/abs/2608.14491v1

Healthcare researchers often need synthetic data spanning multiple related but heterogeneous tables, yet most generative models only handle single tables. This paper's two-stage framework first standardizes diverse tables into common statistical summaries capturing distributions and correlations, then uses a diffusion transformer to generate new synthetic statistical tables, which are reconstructed back into realistic raw data. This enables privacy-preserving generation of unlimited realistic multi-table datasets. Applications include healthcare data sharing without exposing patient records, benchmark creation for medical AI research, and synthetic data generation for any domain with complex relational tables.

Authors: Hao Yan, Lisa Pilgram, Dan Liu, Linglong Kong, Fida Dankar, Khaled El Emam

Paper: https://arxiv.org/abs/2608.14496v1

Discovering new materials computationally usually relies on ground-state energy calculations, ignoring temperature and pressure effects that determine real-world phase stability. This paper introduces TIP, which upgrades an interatomic potential model into a full thermodynamically consistent free energy model using automatic differentiation, letting a single calculation reveal phase transitions and equations of state. Built on the universal UMA potential and fine-tunable to alloys, TIP makes finite-temperature behavior as computationally accessible as static energy. This has major implications for high-throughput materials discovery, alloy design, and computational chemistry pipelines.

Authors: Juno Nam, Bowen Deng, Xiaochen Du, Luis Barroso-Luque, Benjamin Kurt Miller, Rafael Gómez-Bombarelli

Paper: https://arxiv.org/abs/2608.14502v1

Many real-world processes --- chemical synthesis, drug formulation, manufacturing --- are naturally represented as ordered, structured steps rather than flat tables, yet most machine learning models flatten this structure and lose crucial dependencies. RecipeNet addresses this with a hierarchical Transformer that separately models relationships within each step and dependencies across the full sequence. By outperforming standard tabular learning methods, it offers a general-purpose architecture for procedural or "recipe-like" data. Potential applications include materials science R&D, pharmaceutical formulation optimization, and industrial process modeling wherever step-ordered structured data needs to be learned.

Authors: Pin-Yen Huang, Sachin Chhabra, Prasanth Sai Gouripeddi, Abhinav Kumar, Baoxin Li

Paper: https://arxiv.org/abs/2608.14505v1

When LLMs are asked to synthesize conclusions from many sources, cramming everything into one prompt conflates two different jobs: understanding each piece of evidence and combining conclusions fairly. This paper identifies a subtle failure mode, "count-scale drift," where naive vote-counting shifts as more sources are added, and proposes separating interpretation from aggregation using a structured evidence format and calibrated probability pooling. Validated on a real medical/survival dataset, this approach could improve any multi-source reasoning system, from diagnostic AI panels to fraud detection to automated research synthesis tools.

Authors: Zhelun Wu

Paper: https://arxiv.org/abs/2608.14509v1

Modern wireless systems need to decode signals sent simultaneously from many antennas, a computationally brutal search problem especially at high signal orders. This paper reframes MIMO detection as a learned sequential decision process, using a Transformer to guide symbol-by-symbol search while producing reliable confidence scores for downstream error correction. A clever "hard-to-soft" training trick transfers a well-trained detector into a full iterative decoding pipeline. Applications span 5G/6G wireless infrastructure, massive MIMO systems, and any communications hardware needing fast, accurate, learnable alternatives to traditional signal detection algorithms.

Authors: Yubo Zhang, Yiyao Liu, Xiaodong Wang

Paper: https://arxiv.org/abs/2608.14511v1

Democratic AI alignment approaches poll people on moral dilemmas to train policies --- but this study shows the "neutral" aggregation process is shaped by hidden developer choices. Across studies involving kidney allocation, AI worker simulation, and deepfakes of the deceased, the researchers show that which features get voted on, who votes, and how questions are worded all systematically shift outcomes, sometimes along political lines. This has major implications for AI governance and alignment research, arguing that voting-based systems need transparency audits at every design stage, not just at the final vote tally.

Authors: Taenyun Kim, Edyta Bogucka, Daniele Quercia

Paper: https://arxiv.org/abs/2608.14522v1

When an LLM conversation exceeds context limits, restarts, or gets handed to another agent, someone must decide what information survives the transition. This paper formalizes that handover problem mathematically, distinguishing exact recovery from statistically sufficient preservation, and proposes a three-part memory record for decisions, summarized evidence, and irreplaceable raw observations. With theoretical bounds from Gaussian regression and nonparametric settings, it quantifies how much memory a task-continuation actually requires. This is directly applicable to multi-agent LLM systems, long-running assistants, and any application needing principled memory compression across session boundaries.

Authors: Masahiro Kato, Taka Kato

Paper: https://arxiv.org/abs/2608.14528v1

Game world models that generate video directly often lose track of physical consistency over long sequences --- characters clip through walls or drift apart. Marionette fixes this by separating concerns: a neural model predicts explicit 3D skeletal states, a zero-parameter renderer handles geometry exactly, and video diffusion paints the final appearance. This structure lets developers impose simple rules (like collision limits) directly on the state to fix long-horizon errors, without retraining the visual model. Applications include interactive game engines, character animation systems, and any simulation needing controllable, physically coherent long-horizon generation.

Authors: Zian Meng, Zhen Li, Chuanhao Li, Qiang Li, Kaipeng Zhang

Paper: https://arxiv.org/abs/2608.14530v1

Archaeologists have long puzzled over who made Upper Paleolithic hand stencils, since no ground truth exists and prehistoric hands differ from modern ones. This paper tackles that uncertainty head-on with a deep learning pipeline that generates multiple silhouette interpretations per stencil, then runs them through two neural network ensembles trained on thousands of contemporary hand images. By combining this with manifold mapping and explainable AI, the system doesn't just guess sex --- it flags how confident that guess is. Applications include rock art archaeology, anthropology, and any forensic-style problem where degraded imagery demands uncertainty-aware classification rather than false certainty.

Authors: Karel Becerra, Boris Mederos, Dean Snow, Ramón A. Mollineda

Paper: https://arxiv.org/abs/2608.14539v1