# Research Papers — 2026-01 · bundle page 101 of 189

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### On the Probability of First Success in Differential Evolution: Hazard Identities and Tail Bounds

Authors: Dimitar Nedanovski, Svetoslav Nenov, Dimitar Pilev
Published: 2026-01-16
Categories: cs.NE, cs.LG
arXiv: https://arxiv.org/abs/2601.11499v1

We study first-hitting times in Differential Evolution (DE) through a conditional hazard frame work. Instead of analyzing convergence via Markov-chain transition kernels or drift arguments, we ex press the survival probability of a measurable target set $A$ as a product of conditional first-hit probabilities (hazards) $p_t=\Prob(E_t\mid\mathcal F_{t-1})$. This yields distribution-free identities for survival and explicit tail bounds whenever deterministic lower bounds on the hazard hold on the survival event. For the L-SHADE algorithm with current-to-$p$best/1 mutation, we construct a checkable algorithmic witness event $\mathcal L_t$ under which the conditional hazard admits an explicit lower bound depending only on sampling rules, population size, and crossover statistics. This separates theoretical constants from empirical event frequencies and explains why worst-case constant-hazard bounds are typically conservative. We complement the theory with a Kaplan--Meier survival analysis on the CEC2017 benchmark suite . Across functions and budgets, we identify three distinct empirical regimes: (i) strongly clustered success, where hitting times concentrate in short bursts; (ii) approximately geometric tails, where a constant-hazard model is accurate; and (iii) intractable cases with no observed hits within the evaluation horizon. The results show that while constant-hazard bounds provide valid tail envelopes, the practical behavior of L-SHADE is governed by burst-like transitions rather than homogeneous per-generati on success probabilities.

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### Sequential LLM Release Facilitates Manipulation in Regulated Markets

Authors: Eilam Shapira, Moshe Tennenholtz, Roi Reichart
Published: 2026-01-16
Categories: cs.GT, cs.AI, cs.CL, cs.MA
arXiv: https://arxiv.org/abs/2601.11496v3

AI agents increasingly mediate bargaining, negotiation and persuasion for people and firms. Such markets extend software-mediated commerce, but add a governance problem: independent model releases change delegates available to participants. Game theory shows that expanding a strategy set can harm equilibrium outcomes, but mostly through constructed examples. Deployed AI-agent logs are scarce, proprietary and privacy-sensitive, and lack counterfactuals and payoff labels. We therefore use GLEE, an independently collected benchmark of 587K strategic decisions by 13 large language models across 1,320 matched bargaining, negotiation and persuasion configurations, to study model release as strategy expansion. Across more than 50{,}000 release comparisons, many releases move payoffs in opposite directions: one agent gains while the other loses. We identify the Poisoned Apple effect: a released model that no agent adopts in equilibrium nevertheless shifts payoffs in opposite directions and changes the regulator's market design. Up to roughly three in ten opposing shifts arise this way, and technology restrictions can amplify the effect.

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### BoxMind: Closed-loop AI strategy optimization for elite boxing validated in the 2024 Olympics

Authors: Kaiwen Wang, Kaili Zheng, Rongrong Deng, Qingmin Fan, Milin Zhang, Zongrui Li, Xuesi Zhou, Bo Han, Liren Chen, Chenyi Guo, Ji Wu
Published: 2026-01-16
Categories: cs.AI
arXiv: https://arxiv.org/abs/2601.11492v2

Competitive sports require sophisticated tactical analysis, yet combat disciplines like boxing remain underdeveloped in AI-driven analytics due to the complexity of action dynamics and the lack of structured tactical representations. To address this, we present BoxMind, a closed-loop AI expert system validated in elite boxing competition. By defining atomic punch events with precise temporal boundaries and spatial and technical attributes, we parse match footage into 18 hierarchical technical-tactical indicators. We then propose a graph-based predictive model that fuses these explicit technical-tactical profiles with learnable, time-variant latent embeddings to capture the dynamics of boxer matchups. Modeling match outcome as a differentiable function of technical-tactical indicators, we turn winning probability gradients into executable tactical adjustments. Experiments show that the outcome prediction model achieves state-of-the-art performance, with 69.8% accuracy on BoxerGraph test set and 87.5% on Olympic matches. Using this predictive model as a foundation, the system generates strategic recommendations that demonstrate proficiency comparable to human experts. BoxMind is validated through a closed-loop deployment during the 2024 Paris Olympics, directly contributing to the Chinese National Team's historic achievement of three gold and two silver medals. BoxMind establishes a replicable paradigm for transforming unstructured video data into strategic intelligence, bridging the gap between computer vision and decision support in competitive sports. Code and data is available at https://github.com/gouba2333/BoxingWeb.

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### Extractive summarization on a CMOS Ising machine

Authors: Ziqing Zeng, Abhimanyu Kumar, Ahmet Efe, Ruihong Yin, Chris H. Kim, Ulya R. Karpuzcu, Sachin S. Sapatnekar
Published: 2026-01-16
Categories: cs.LG, cs.ET
arXiv: https://arxiv.org/abs/2601.11491v2

Extractive summarization (ES) aims to generate a concise summary by selecting a subset of sentences from a document while maximizing relevance and minimizing redundancy. Although modern ES systems achieve high accuracy using powerful neural models, their deployment typically relies on CPU or GPU infrastructures that are energy-intensive and poorly suited for real-time inference in resource-constrained environments. In this work, we explore the feasibility of implementing McDonald-style extractive summarization on a low-power CMOS coupled oscillator-based Ising machine (COBI) that supports integer-valued, all-to-all spin couplings. We first propose a hardware-aware Ising formulation that reduces the scale imbalance between local fields and coupling terms, thereby improving robustness to coefficient quantization: this method can be applied to any problem formulation that requires k of n variables to be chosen. We then develop a complete ES pipeline including (i) stochastic rounding and iterative refinement to compensate for precision loss, and (ii) a decomposition strategy that partitions a large ES problem into smaller Ising subproblems that can be efficiently solved on COBI and later combined. Experimental results on the CNN/DailyMail dataset show that our pipeline can produce high-quality summaries using only integer-coupled Ising hardware with limited precision. COBI achieves 3-4.5x runtime speedups compared to a brute-force method, which is comparable to software Tabu search, and two to three orders of magnitude reductions in energy, while maintaining competitive summary quality. These results highlight the potential of deploying CMOS Ising solvers for real-time, low-energy text summarization on edge devices.

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### CTest-Metric: A Unified Framework to Assess Clinical Validity of Metrics for CT Report Generation

Authors: Vanshali Sharma, Andrea Mia Bejar, Gorkem Durak, Ulas Bagci
Published: 2026-01-16
Categories: cs.CL, cs.CV
arXiv: https://arxiv.org/abs/2601.11488v1

In the generative AI era, where even critical medical tasks are increasingly automated, radiology report generation (RRG) continues to rely on suboptimal metrics for quality assessment. Developing domain-specific metrics has therefore been an active area of research, yet it remains challenging due to the lack of a unified, well-defined framework to assess their robustness and applicability in clinical contexts. To address this, we present CTest-Metric, a first unified metric assessment framework with three modules determining the clinical feasibility of metrics for CT RRG. The modules test: (i) Writing Style Generalizability (WSG) via LLM-based rephrasing; (ii) Synthetic Error Injection (SEI) at graded severities; and (iii) Metrics-vs-Expert correlation (MvE) using clinician ratings on 175 "disagreement" cases. Eight widely used metrics (BLEU, ROUGE, METEOR, BERTScore-F1, F1-RadGraph, RaTEScore, GREEN Score, CRG) are studied across seven LLMs built on a CT-CLIP encoder. Using our novel framework, we found that lexical NLG metrics are highly sensitive to stylistic variations; GREEN Score aligns best with expert judgments (Spearman~0.70), while CRG shows negative correlation; and BERTScore-F1 is least sensitive to factual error injection. We will release the framework, code, and allowable portion of the anonymized evaluation data (rephrased/error-injected CT reports), to facilitate reproducible benchmarking and future metric development.

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### Health Facility Location in Ethiopia: Leveraging LLMs to Integrate Expert Knowledge into Algorithmic Planning

Authors: Yohai Trabelsi, Guojun Xiong, Fentabil Getnet, Stéphane Verguet, Milind Tambe
Published: 2026-01-16
Categories: cs.AI
arXiv: https://arxiv.org/abs/2601.11479v2

Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, require careful prioritization of which facilities to upgrade to maximize population coverage while accounting for diverse expert and stakeholder preferences. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we propose a hybrid framework that systematically integrates expert knowledge with optimization techniques. Classical optimization methods provide theoretical guarantees but require explicit, quantitative objectives, whereas stakeholder criteria are often articulated in natural language and difficult to formalize. To bridge these domains, we develop the Large language model and Extended Greedy (LEG) framework. Our framework combines a provable approximation algorithm for population coverage optimization with LLM-driven iterative refinement that incorporates human-AI alignment to ensure solutions reflect expert qualitative guidance while preserving coverage guarantees. Experiments on real-world data from three Ethiopian regions demonstrate the framework's effectiveness and its potential to inform equitable, data-driven health system planning.

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### Temporal Complexity and Self-Organization in an Exponential Dense Associative Memory Model

Authors: Marco Cafiso, Paolo Paradisi
Published: 2026-01-16
Categories: nlin.AO, physics.app-ph, physics.comp-ph, physics.data-an, stat.ML
arXiv: https://arxiv.org/abs/2601.11478v2

Dense Associative Memory (DAM) models generalize the classical Hopfield model by incorporating n-body or exponential interactions that greatly enhance storage capacity. While the criticality of DAM models has been largely investigated, mainly within a statistical equilibrium picture, little attention has been devoted to the temporal self-organizing behavior induced by learning. In this work, we investigate the behavior of a stochastic exponential DAM (SEDAM) model through the lens of Temporal Complexity (TC), a framework that characterizes complex systems by intermittent transition events between order and disorder and by scale-free temporal statistics. Transition events associated with birth-death of neural avalanche structures are exploited for the TC analyses and compared with analogous transition events based on coincidence structures. We systematically explore how TC indicators depend on control parameters, i.e., noise intensity and memory load. Our results reveal that the SEDAM model exhibits regimes of complex intermittency characterized by nontrivial temporal correlations and scale-free behavior, indicating the spontaneous emergence of self-organizing dynamics. Notably, such regimes arise over finite intervals of noise intensity rather than at a single critical point, consistent with the concept of extended criticality. Further, the noise intensity range needed to reach the critical region, where self-organizing behavior emerges, slightly decreases as the memory load increases. This study highlights the relevance of TC as a complementary framework for understanding learning and information processing in artificial and biological neural systems, revealing the link between the memory load and the self-organizing capacity of the network.

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### Generative Scenario Rollouts for End-to-End Autonomous Driving

Authors: Rajeev Yasarla, Deepti Hegde, Shizhong Han, Hsin-Pai Cheng, Yunxiao Shi, Meysam Sadeghigooghari, Shweta Mahajan, Apratim Bhattacharyya, Litian Liu, Risheek Garrepalli, Thomas Svantesson, Fatih Porikli et al.
Published: 2026-01-16
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.11475v1

Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation learning from sparse trajectory annotations and under-utilize their potential as generative models. We propose Generative Scenario Rollouts (GeRo), a plug-and-play framework for VLA models that jointly performs planning and generation of language-grounded future traffic scenes through an autoregressive rollout strategy. First, a VLA model is trained to encode ego vehicle and agent dynamics into latent tokens under supervision from planning, motion, and language tasks, facilitating text-aligned generation. Next, GeRo performs language-conditioned autoregressive generation. Given multi-view images, a scenario description, and ego-action questions, it generates future latent tokens and textual responses to guide long-horizon rollouts. A rollout-consistency loss stabilizes predictions using ground truth or pseudo-labels, mitigating drift and preserving text-action alignment. This design enables GeRo to perform temporally consistent, language-grounded rollouts that support long-horizon reasoning and multi-agent planning. On Bench2Drive, GeRo improves driving score and success rate by +15.7 and +26.2, respectively. By integrating reinforcement learning with generative rollouts, GeRo achieves state-of-the-art closed-loop and open-loop performance, demonstrating strong zero-shot robustness. These results highlight the promise of generative, language-conditioned reasoning as a foundation for safer and more interpretable end-to-end autonomous driving.

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### A Probabilistic Approach to Trajectory-Based Optimal Experimental Design

Authors: Ahmed Attia
Published: 2026-01-16
Categories: math.OC, cs.LG
arXiv: https://arxiv.org/abs/2601.11473v2

We present a novel probabilistic approach for optimal experimental path design. In this approach a discrete path optimization problem is defined on a static navigation mesh, and trajectories are modeled as random variables governed by a parametric Markov policy. The discrete path optimization problem is then replaced with an equivalent stochastic optimization problem over the policy parameters, resulting in an optimal probability model that samples estimates of the optimal discrete path. This approach enables exploration of the utility function's distribution tail and treats the utility function of the design as a black box, making it applicable to linear and nonlinear inverse problems and beyond experimental design. Numerical verification and analysis are carried out by using a parameter identification problem widely used in model-based optimal experimental design, namely a two-dimensional time-dependent advection diffusion problem in which the initial condition is the inference target. Experiments use both coarse and fine navigation meshes, with either a single moving sensor or a group of seven coordinated sensors, and the proposed approach is evaluated under D-, A-, and E-optimality criteria.

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### Low-Rank Key Value Attention

Authors: James O'Neill, Robert Clancy, Mariia Matskevichus, Fergal Reid
Published: 2026-01-16
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.11471v3

The key-value (KV) cache is a primary memory bottleneck in Transformers. We propose Low-Rank Key-Value (LRKV) attention, which reduces KV cache memory by exploiting redundancy across attention heads, while being compute efficient. Each layer uses a shared full-rank KV projection augmented with low-rank, head-specific residuals, providing a continuous trade-off between complete sharing and full independence. After pretraining models of size 128M to 6.3B parameters, LRKV consistently achieves the lowest test loss among standard MHA, MQA/GQA, and MLA while using only 45-53\% of MHA's KV cache. LRKV reaches equivalent baseline quality 18-25\% faster (measured in training steps). After supervised midtraining, LRKV achieves the highest downstream task performance across ARC-Easy, ARC-Challenge, MMLU, GSM8K, and HumanEval benchmarks.

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### Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs

Authors: Alessandro Padella, Massimiliano de Leoni, Marlon Dumas
Published: 2026-01-16
Categories: cs.AI, cs.IT
arXiv: https://arxiv.org/abs/2601.11468v1

Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep learning architectures. In this paper, we extend our prior LLM-based Predictive Process Monitoring framework, which was initially focused on total time prediction via prompting. The extension consists of comprehensively evaluating its generality, semantic leverage, and reasoning mechanisms, also across multiple Key Performance Indicators. Empirical evaluations conducted on three distinct event logs and across the Key Performance Indicators of Total Time and Activity Occurrence prediction indicate that, in data-scarce settings with only 100 traces, the LLM surpasses the benchmark methods. Furthermore, the experiments also show that the LLM exploits both its embodied prior knowledge and the internal correlations among training traces. Finally, we examine the reasoning strategies employed by the model, demonstrating that the LLM does not merely replicate existing predictive methods but performs higher-order reasoning to generate the predictions.

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### MHA2MLA-VLM: Enabling DeepSeek's Economical Multi-Head Latent Attention across Vision-Language Models

Authors: Xiaoran Fan, Zhichao Sun, Tao Ji, Lixing Shen, Tao Gui
Published: 2026-01-16
Categories: cs.CV, cs.AI, cs.CL, cs.LG
arXiv: https://arxiv.org/abs/2601.11464v1

As vision-language models (VLMs) tackle increasingly complex and multimodal tasks, the rapid growth of Key-Value (KV) cache imposes significant memory and computational bottlenecks during inference. While Multi-Head Latent Attention (MLA) offers an effective means to compress the KV cache and accelerate inference, adapting existing VLMs to the MLA architecture without costly pretraining remains largely unexplored. In this work, we present MHA2MLA-VLM, a parameter-efficient and multimodal-aware framework for converting off-the-shelf VLMs to MLA. Our approach features two core techniques: (1) a modality-adaptive partial-RoPE strategy that supports both traditional and multimodal settings by selectively masking nonessential dimensions, and (2) a modality-decoupled low-rank approximation method that independently compresses the visual and textual KV spaces. Furthermore, we introduce parameter-efficient fine-tuning to minimize adaptation cost and demonstrate that minimizing output activation error, rather than parameter distance, substantially reduces performance loss. Extensive experiments on three representative VLMs show that MHA2MLA-VLM restores original model performance with minimal supervised data, significantly reduces KV cache footprint, and integrates seamlessly with KV quantization.

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### Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning

Authors: Franziska Herbert, Vignesh Prasad, Han Liu, Dorothea Koert, Georgia Chalvatzaki
Published: 2026-01-16
Categories: cs.RO, cs.LG
arXiv: https://arxiv.org/abs/2601.11460v2

Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary significantly across executions. A key challenge lies in jointly capturing the discrete semantic task structure and the temporal evolution of object-centric geometric relations in a form that supports reasoning over task progression. We introduce a semantic--geometric graph-based task representation that jointly encodes object identities, inter-object semantic relations, and per-object motion histories, via a Message Passing Neural Network (MPNN) encoder and a Transformer-based decoder. The encoder operates solely on the temporal scene graph, producing structured representations decoupled from action labels. The decoder then conditions on action-context to forecast future actions, associated objects, and object motions. This decoupling learns task-agnostic representations, enabling encoder reuse across embodiments through decoder-only finetuning on a small robot dataset. Across eleven bimanual tasks from two datasets, we find that the benefit of structured semantic--geometric representations over simpler sequence-based models grows with task variability in action ordering and object involvement. At deployment, a planner couples the action and motion predictions with learned Probabilistic Movement Primitives, achieving full task success on two real-robot bimanual tasks and outperforming graph ablations, Transformer, decoder-only, and finetuned vision-language model baselines.

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### Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking

Authors: Brian Keith
Published: 2026-01-16
Categories: cs.HC, cs.AI, cs.CL, cs.CY, cs.IR
arXiv: https://arxiv.org/abs/2601.11459v1

Information overload and misinformation create significant challenges in extracting meaningful narratives from large news collections. This paper defines the nascent field of Interactive Narrative Analytics (INA), which combines computational narrative extraction with interactive visual analytics to support sensemaking. INA approaches enable the interactive exploration of narrative structures through computational methods and visual interfaces that facilitate human interpretation. The field faces challenges in scalability, interactivity, knowledge integration, and evaluation standardization, yet offers promising opportunities across news analysis, intelligence, scientific literature exploration, and social media analysis. Through the combination of computational and human insight, INA addresses complex challenges in narrative sensemaking.

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### PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs

Authors: Oishee Bintey Hoque, Nibir Chandra Mandal, Kyle Luong, Amanda Wilson, Samarth Swarup, Madhav Marathe, Abhijin Adiga
Published: 2026-01-16
Categories: cs.CV, cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2601.11451v2

Large-scale livestock operations pose significant risks to human health and the environment, while also being vulnerable to threats such as infectious diseases and extreme weather events. As the number of such operations continues to grow, accurate and scalable mapping has become increasingly important. In this work, we present an infrastructure-first, explainable pipeline for identifying and characterizing Concentrated Animal Feeding Operations (CAFOs) from aerial and satellite imagery. Our method (i) detects candidate infrastructure (e.g., barns, feedlots, manure lagoons, silos) with a domain-tuned YOLOv8 detector, then derives SAM2 masks from these boxes and filters component-specific criteria; (ii) extracts structured descriptors (e.g., counts, areas, orientations, and spatial relations) and fuses them with deep visual features using a lightweight spatial cross-attention classifier; and (iii) outputs both CAFO type predictions and mask-level attributions that link decisions to visible infrastructure. Through comprehensive evaluation, we show that our approach achieves state-of-the-art performance, with Swin-B+PRISM-CAFO surpassing the best performing baseline by up to 15\%. Beyond strong predictive performance across diverse U.S. regions, we run systematic gradient--activation analyses that quantify the impact of domain priors and show how specific infrastructure (e.g., barns, lagoons) shapes classification decisions. We release code, infrastructure masks, and descriptors to support transparent, scalable monitoring of livestock infrastructure, enabling risk modeling, change detection, and targeted regulatory action. Github: https://github.com/Nibir088/PRISM-CAFO.

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### IMS: Intelligent Hardware Monitoring System for Secure SoCs

Authors: Wadid Foudhaili, Aykut Rencber, Anouar Nechi, Rainer Buchty, Mladen Berekovic, Andres Gomez, Saleh Mulhem
Published: 2026-01-16
Categories: cs.CR, cs.AR, cs.LG
arXiv: https://arxiv.org/abs/2601.11447v1

In the modern Systems-on-Chip (SoC), the Advanced eXtensible Interface (AXI) protocol exhibits security vulnerabilities, enabling partial or complete denial-of-service (DoS) through protocol-violation attacks. The recent countermeasures lack a dedicated real-time protocol semantic analysis and evade protocol compliance checks. This paper tackles this AXI vulnerability issue and presents an intelligent hardware monitoring system (IMS) for real-time detection of AXI protocol violations. IMS is a hardware module leveraging neural networks to achieve high detection accuracy. For model training, we perform DoS attacks through header-field manipulation and systematic malicious operations, while recording AXI transactions to build a training dataset. We then deploy a quantization-optimized neural network, achieving 98.7% detection accuracy with <=3% latency overhead, and throughput of >2.5 million inferences/s. We subsequently integrate this IMS into a RISC-V SoC as a memory-mapped IP core to monitor its AXI bus. For demonstration and initial assessment for later ASIC integration, we implemented this IMS on an AMD Zynq UltraScale+ MPSoC ZCU104 board, showing an overall small hardware footprint (9.04% look-up-tables (LUTs), 0.23% DSP slices, and 0.70% flip-flops) and negligible impact on the overall design's achievable frequency. This demonstrates the feasibility of lightweight, security monitoring for resource-constrained edge environments.

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### When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Models

Authors: Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso, Damien Garreau, Pierre-Alexandre Mattei
Published: 2026-01-16
Categories: cs.LG, cs.CV, math.ST, stat.ME, stat.ML
arXiv: https://arxiv.org/abs/2601.11444v2

Diffusion models now generate high-quality, diverse samples, with an increasing focus on more powerful models. Although ensembling is a well-known way to improve supervised models, its application to unconditional score-based diffusion models remains largely unexplored. In this work we investigate whether it provides tangible benefits for generative modelling. We find that while ensembling the scores generally improves the score-matching loss and model likelihood, it fails to consistently enhance perceptual quality metrics such as FID on image datasets. We confirm this observation across a breadth of aggregation rules using Deep Ensembles, Monte Carlo Dropout, on CIFAR-10 and FFHQ. We attempt to explain this discrepancy by investigating possible explanations, such as the link between score estimation and image quality. We also look into tabular data through random forests, and find that one aggregation strategy outperforms the others. Finally, we provide theoretical insights into the summing of score models, which shed light not only on ensembling but also on several model composition techniques (e.g. guidance).

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### Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation

Authors: Xin Sun, Zhongqi Chen, Qiang Liu, Shu Wu, Bowen Song, Weiqiang Wang, Zilei Wang, Liang Wang
Published: 2026-01-16
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.11443v3

Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for enhancing large language models' question-answering capabilities through the integration of external knowledge. However, when adapting RAG systems to specialized domains, challenges arise from distribution shifts, resulting in suboptimal generalization performance. In this work, we propose TTARAG, a test-time adaptation method that dynamically updates the language model's parameters during inference to improve RAG system performance in specialized domains. Our method introduces a simple yet effective approach where the model learns to predict retrieved content, enabling automatic parameter adjustment to the target domain. Through extensive experiments across six specialized domains, we demonstrate that TTARAG achieves substantial performance improvements over baseline RAG systems. Code available at https://github.com/sunxin000/TTARAG.

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### Map2Thought: Explicit 3D Spatial Reasoning via Metric Cognitive Maps

Authors: Xiangjun Gao, Zhensong Zhang, Dave Zhenyu Chen, Songcen Xu, Long Quan, Eduardo Pérez-Pellitero, Youngkyoon Jang
Published: 2026-01-16
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.11442v1

We propose Map2Thought, a framework that enables explicit and interpretable spatial reasoning for 3D VLMs. The framework is grounded in two key components: Metric Cognitive Map (Metric-CogMap) and Cognitive Chain-of-Thought (Cog-CoT). Metric-CogMap provides a unified spatial representation by integrating a discrete grid for relational reasoning with a continuous, metric-scale representation for precise geometric understanding. Building upon the Metric-CogMap, Cog-CoT performs explicit geometric reasoning through deterministic operations, including vector operations, bounding-box distances, and occlusion-aware appearance order cues, producing interpretable inference traces grounded in 3D structure. Experimental results show that Map2Thought enables explainable 3D understanding, achieving 59.9% accuracy using only half the supervision, closely matching the 60.9% baseline trained with the full dataset. It consistently outperforms state-of-the-art methods by 5.3%, 4.8%, and 4.0% under 10%, 25%, and 50% training subsets, respectively, on the VSI-Bench.

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### Hierarchical Orthogonal Residual Spread for Precise Massive Editing in Large Language Models

Authors: Xiaojie Gu, Guangxu Chen, Yuheng Yang, Jingxin Han, Andi Zhang
Published: 2026-01-16
Categories: cs.CL, cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2601.11441v1

Large language models (LLMs) exhibit exceptional performance across various domains, yet they face critical safety concerns. Model editing has emerged as an effective approach to mitigate these issues. Existing model editing methods often focus on optimizing an information matrix that blends new and old knowledge. While effective, these approaches can be computationally expensive and may cause conflicts. In contrast, we shift our attention to Hierarchical Orthogonal Residual SprEad of the information matrix, which reduces noisy gradients and enables more stable edits from a different perspective. We demonstrate the effectiveness of our method HORSE through a clear theoretical comparison with several popular methods and extensive experiments conducted on two datasets across multiple LLMs. The results show that HORSE maintains precise massive editing across diverse scenarios. The code is available at https://github.com/XiaojieGu/HORSE

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### GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance

Authors: Francisco Giral, Álvaro Manzano, Ignacio Gómez, Ricardo Vinuesa, Soledad Le Clainche
Published: 2026-01-16
Categories: cs.LG, cs.AI, cs.CE
arXiv: https://arxiv.org/abs/2601.11440v3

Urban wind flow reconstruction is essential for assessing air quality, heat dispersion, and pedestrian comfort, yet remains challenging when only sparse sensor data are available. We propose GenDA, a generative data assimilation framework that reconstructs high-resolution wind fields on unstructured meshes from limited observations. The model employs a multiscale graph-based diffusion architecture trained on computational fluid dynamics (CFD) simulations and interprets classifier-free guidance as a learned posterior reconstruction mechanism: the unconditional branch learns a geometry-aware flow prior, while the sensor-conditioned branch injects observational constraints during sampling. This formulation enables obstacle-aware reconstruction and generalization to held-out mesh geometries, wind directions, and sensor configurations within the studied urban-flow setting, without retraining. We consider both sparse fixed sensors and trajectory-based observations using the same reconstruction procedure. When evaluated against supervised graph neural network (GNN) baselines and classical reduced-order data assimilation methods, GenDA reduces the relative root-mean-square error (RRMSE) by 25-57% and increases the structural similarity index (SSIM) by 23-33% across the tested meshes. Experiments are conducted on Reynolds-averaged Navier-Stokes (RANS) simulations of a real urban neighborhood in Bristol, United Kingdom, at a characteristic Reynolds number of $\mathrm{Re}\approx2\times10^{7}$, featuring complex building geometry and irregular terrain. The proposed framework provides a scalable path toward generative, geometry-aware data assimilation for environmental monitoring in complex domains.

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### Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise

Authors: Menglian Wang, Zhuanghua Liu, Luo Luo
Published: 2026-01-16
Categories: math.OC, cs.LG
arXiv: https://arxiv.org/abs/2601.11435v1

This paper studies decentralized stochastic nonconvex optimization problem over row-stochastic networks. We consider the heavy-tailed gradient noise which is empirically observed in many popular real-world applications. Specifically, we propose a decentralized normalized stochastic gradient descent with Pull-Diag gradient tracking, which achieves approximate stationary points with the optimal sample complexity and the near-optimal communication complexity. We further follow our framework to study the setting of undirected networks, also achieving the nearly tight upper complexity bounds. Moreover, we conduct empirical studies to show the practical superiority of the proposed methods.

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### Inter-patient ECG Arrhythmia Classification with LGNs and LUTNs

Authors: Wout Mommen, Lars Keuninckx, Paul Detterer, Achiel Colpaert, Piet Wambacq
Published: 2026-01-16
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.11433v2

Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) are demonstrated to be suitable for the automatic classification of electrocardiograms (ECGs) using the inter-patient paradigm. The methods are benchmarked using the MIT-BIH arrhythmia data set, achieving up to 94.28% accuracy and a $jκ$ index of 0.683 on a four-class classification problem. Our models use between 2.89k and 6.17k FLOPs, including preprocessing and readout, which is three to six orders of magnitude less compared to SOTA methods. A novel preprocessing method is utilized that attains superior performance compared to existing methods for both the mixed-patient and inter-patient paradigms. In addition, a novel method for training the Lookup Tables (LUTs) in LUTNs is devised that uses the Boolean equation of a multiplexer (MUX). Additionally, rate coding was utilized for the first time in these LGNs and LUTNs, enhancing the performance of LGNs. Furthermore, it is the first time that LGNs and LUTNs have been benchmarked on the MIT-BIH arrhythmia dataset using the inter-patient paradigm. Using an Artix 7 FPGA, between 2000 and 2990 LUTs were needed, and between 5 to 7 mW (i.e. 50 pJ to 70 pJ per inference) was estimated for running these models. The performance in terms of both accuracy and $jκ$-index is significantly higher compared to previous LGN results. These positive results suggest that one can utilize LGNs and LUTNs for the detection of arrhythmias at extremely low power and high speeds in heart implants or wearable devices, even for patients not included in the training set.

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### The unreasonable effectiveness of pattern matching

Authors: Gary Lupyan, Blaise Agüera y Arcas
Published: 2026-01-16
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.11432v2

We report on an astonishing ability of large language models (LLMs) to make sense of "Jabberwocky" language in which most or all content words have been randomly replaced by nonsense strings, e.g., translating "He dwushed a ghanc zawk" to "He dragged a spare chair". This result addresses ongoing controversies regarding how to best think of what LLMs are doing: are they a language mimic, a database, a blurry version of the Web? The ability of LLMs to recover meaning from structural patterns speaks to the unreasonable effectiveness of pattern-matching. Pattern-matching is not an alternative to "real" intelligence, but rather a key ingredient.

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### Relational Linearity is a Predictor of Hallucinations

Authors: Yuetian Lu, Yihong Liu, Sebastian Gerstner, Lea Hirlimann, Jonas Rohweder, Hinrich Schütze
Published: 2026-01-16
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.11429v3

Hallucination is a central failure mode of language models (LMs). We focus on hallucinations in response to questions like: "Which instrument did Glenn Gould play?", but we ask these questions for synthetic entities designed to be unknown to the model. We find that LMs like Gemma-7B-IT frequently hallucinate, i.e., they have difficulty recognizing that the hallucinated fact is not part of their knowledge. Based on the idea of linear relational embeddings, we put forward the following hypothesis. (i) Due to the abstract scheme that is used to represent them, LMs can easily produce plausible objects for non-existing subjects of linear relations, which can lead to hallucinations. (ii) For nonlinear relations, this mechanism for producing an object is not available and so a hallucination is easier to avoid. To test this hypothesis, we create SynthHal, a synthetic unknown-entity benchmark for 15 relations. We find that across four instruction-tuned models, relational linearity is a strong predictor of models hallucinating an object for an unknown subject vs refusing to give an answer, with correlations $r \in (.58, .84)$. While this is not direct evidence for the hypothesized causal mechanism, it is suggestive and opens up a new line of inquiry into understanding LM hallucinations.

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### Diagnosing Failure Modes of Neural Operators Across Diverse PDE Families

Authors: Lennon Shikhman
Published: 2026-01-16
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.11428v7

Neural PDE solvers are increasingly used as learned surrogates for families of partial differential equations, where the key machine learning challenge is not only interpolation on a fixed benchmark distribution but generalization under structured shifts in coefficients, boundary conditions, discretization, and rollout horizon. Yet evaluation is still often dominated by in-distribution test error, making robustness difficult to assess. We introduce a standardized stress-testing framework for neural PDE solvers under deployment-relevant shift. We instantiate it on three representative architectures -- Fourier Neural Operators (FNOs), a DeepONet-style model, and convolutional neural operators (CNOs) -- across five qualitatively different PDE families: dispersive, elliptic, multi-scale fluid, financial, and chaotic systems. Across 750 trained models, we measure robustness using baseline-normalized degradation factors together with spectral and rollout diagnostics. The resulting comparisons reveal that strong in-distribution accuracy does not reliably predict robustness, and that failure patterns depend jointly on architecture and PDE family. Our results provide a clearer basis for evaluating robustness claims in neural PDE solvers and suggest that function-space generalization under structured shift should be treated as a first-class evaluation target.

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### Isotropy-Optimized Contrastive Learning for Semantic Course Recommendation

Authors: Ali Khreis, Anthony Nasr, Yusuf Hilal
Published: 2026-01-16
Categories: cs.IR, cs.CL
arXiv: https://arxiv.org/abs/2601.11427v1

This paper presents a semantic course recommendation system for students using a self-supervised contrastive learning approach built upon BERT (Bidirectional Encoder Representations from Transformers). Traditional BERT embeddings suffer from anisotropic representation spaces, where course descriptions exhibit high cosine similarities regardless of semantic relevance. To address this limitation, we propose a contrastive learning framework with data augmentation and isotropy regularization that produces more discriminative embeddings. Our system processes student text queries and recommends Top-N relevant courses from a curated dataset of over 500 engineering courses across multiple faculties. Experimental results demonstrate that our fine-tuned model achieves improved embedding separation and more accurate course recommendations compared to vanilla BERT baselines.

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### PubMed-OCR: PMC Open Access OCR Annotations

Authors: Hunter Heidenreich, Yosheb Getachew, Olivia Dinica, Ben Elliott
Published: 2026-01-16
Categories: cs.CV, cs.CL, cs.DL, cs.LG
arXiv: https://arxiv.org/abs/2601.11425v1

PubMed-OCR is an OCR-centric corpus of scientific articles derived from PubMed Central Open Access PDFs. Each page image is annotated with Google Cloud Vision and released in a compact JSON schema with word-, line-, and paragraph-level bounding boxes. The corpus spans 209.5K articles (1.5M pages; ~1.3B words) and supports layout-aware modeling, coordinate-grounded QA, and evaluation of OCR-dependent pipelines. We analyze corpus characteristics (e.g., journal coverage and detected layout features) and discuss limitations, including reliance on a single OCR engine and heuristic line reconstruction. We release the data and schema to facilitate downstream research and invite extensions.

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### The Great March 100: 100 Detail-oriented Tasks for Evaluating Embodied AI Agents

Authors: Ziyu Wang, Chenyuan Liu, Yushun Xiang, Runhao Zhang, Qingbo Hao, Hongliang Lu, Houyu Chen, Zhizhong Feng, Kaiyue Zheng, Dehao Ye, Xianchao Zeng, Xinyu Zhou et al.
Published: 2026-01-16
Categories: cs.RO, cs.AI
arXiv: https://arxiv.org/abs/2601.11421v1

Recently, with the rapid development of robot learning and imitation learning, numerous datasets and methods have emerged. However, these datasets and their task designs often lack systematic consideration and principles. This raises important questions: Do the current datasets and task designs truly advance the capabilities of robotic agents? Do evaluations on a few common tasks accurately reflect the differentiated performance of various methods proposed by different teams and evaluated on different tasks? To address these issues, we introduce the Great March 100 (\textbf{GM-100}) as the first step towards a robot learning Olympics. GM-100 consists of 100 carefully designed tasks that cover a wide range of interactions and long-tail behaviors, aiming to provide a diverse and challenging set of tasks to comprehensively evaluate the capabilities of robotic agents and promote diversity and complexity in robot dataset task designs. These tasks are developed through systematic analysis and expansion of existing task designs, combined with insights from human-object interaction primitives and object affordances. We collect a large amount of trajectory data on different robotic platforms and evaluate several baseline models. Experimental results demonstrate that the GM-100 tasks are 1) feasible to execute and 2) sufficiently challenging to effectively differentiate the performance of current VLA models. Our data and code are available at https://rhos.ai/research/gm-100.

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### Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contamination

Authors: Yulei You, Junyi Liu
Published: 2026-01-16
Categories: math.OC, cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2601.11420v1

Robustness under perturbation and contamination is a prominent issue in statistical learning. We address the robust nonlinear regression based on the so-called interval conditional value-at-risk (In-CVaR), which is introduced to enhance robustness by trimming extreme losses. While recent literature shows that the In-CVaR based statistical learning exhibits superior robustness performance than classical robust regression models, its theoretical robustness analysis for nonlinear regression remains largely unexplored. We rigorously quantify robustness under contamination, with a unified study of distributional breakdown point for a broad class of regression models, including linear, piecewise affine and neural network models with $\ell_1$, $\ell_2$ and Huber losses. Moreover, we analyze the qualitative robustness of the In-CVaR based estimator under perturbation. We show that under several minor assumptions, the In-CVaR based estimator is qualitatively robust in terms of the Prokhorov metric if and only if the largest portion of losses is trimmed. Overall, this study analyzes robustness properties of In-CVaR based nonlinear regression models under both perturbation and contamination, which illustrates the advantages of In-CVaR risk measure over conditional value-at-risk and expectation for robust regression in both theory and numerical experiments.

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### Auditing Frozen-Encoder Anomaly Detection Across Mechanical Systems: Representation Provenance, Calibration, and Protocol Effects

Authors: Jose Sánchez Andreu
Published: 2026-01-16
Categories: astro-ph.IM, cs.LG, physics.data-an
arXiv: https://arxiv.org/abs/2601.11415v2

This version reports a reproducibility audit of the frozen-encoder experiments presented in version 1. The numerical discrimination results are reproducible from the preserved artifacts, but their original attribution to interferometric pretraining is not supported. The released checkpoint contains a nested model state that loads without missing parameters, whereas loading the outer checkpoint dictionary leaves almost the entire EfficientNet-B0 feature stack uninitialized. Preserved embeddings labelled as interferometric have norms of order $10^{-12}$, matching freshly initialized EfficientNet-B0 networks and differing by more than twelve orders of magnitude from the preserved ImageNet embeddings. A second, separately preserved near-zero embedding set produces almost the same IMS 4th-test anomaly scores ($r=0.987$) and record-level discrimination (AUC $0.9812$ versus $0.9818$). We therefore withdraw the causal claim that IMS performance demonstrates a morphological prior transferred from gravitational-wave instrumentation. We reanalyse the controlled IMS splits at matched observed false-positive rates and add multivariate classical signal baselines. The near-zero representations retain strong tail separation, particularly in the 2nd and 4th IMS runs, but this is now interpreted as an exploratory architecture-and-initialization effect coupled to Mahalanobis scoring. A separate PRONOSTIA audit shows that the original large warning times were induced by a lifetime-fraction baseline; under fixed-time evaluation, a ten-feature classical baseline outperforms the preserved encoder scores. These results illustrate how checkpoint provenance, finite-sample calibration, architecture, and target-domain baselines can create an appearance of cross-domain transfer. They also define the controls required before assigning physical meaning to frozen-representation anomaly scores.

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### New Adaptive Mechanism for Large Neighborhood Search using Dual Actor-Critic

Authors: Shaohua Yu, Wenhao Mao, Zigao Wu, Jakob Puchinger
Published: 2026-01-16
Categories: cs.GT, cs.LG
arXiv: https://arxiv.org/abs/2601.11414v1

Adaptive Large Neighborhood Search (ALNS) is a widely used heuristic method for solving combinatorial optimization problems. ALNS explores the solution space by iteratively using destroy and repair operators with probabilities, which are adjusted by an adaptive mechanism to find optimal solutions. However, the classic ALNS adaptive mechanism does not consider the interaction between destroy and repair operators when selecting them. To overcome this limitation, this study proposes a novel adaptive mechanism. This mechanism enhances the adaptability of the algorithm through a Dual Actor-Critic (DAC) model, which fully considers the fact that the quality of new solutions is jointly determined by the destroy and repair operators. It effectively utilizes the interaction between these operators during the weight adjustment process, greatly improving the adaptability of the ALNS algorithm. In this mechanism, the destroy and repair processes are modeled as independent Markov Decision Processes to guide the selection of operators more accurately. Furthermore, we use Graph Neural Networks to extract key features from problem instances and perform effective aggregation and normalization to enhance the algorithm's transferability to different sizes and characteristics of problems. Through a series of experiments, we demonstrate that the proposed DAC-ALNS algorithm significantly improves solution efficiency and exhibits excellent transferability.

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### Topology-Guaranteed Image Segmentation: Enforcing Connectivity, Genus, and Width Constraints

Authors: Wenxiao Li, Xue-Cheng Tai, Jun Liu
Published: 2026-01-16
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.11409v1

Existing research highlights the crucial role of topological priors in image segmentation, particularly in preserving essential structures such as connectivity and genus. Accurately capturing these topological features often requires incorporating width-related information, including the thickness and length inherent to the image structures. However, traditional mathematical definitions of topological structures lack this dimensional width information, limiting methods like persistent homology from fully addressing practical segmentation needs. To overcome this limitation, we propose a novel mathematical framework that explicitly integrates width information into the characterization of topological structures. This method leverages persistent homology, complemented by smoothing concepts from partial differential equations (PDEs), to modify local extrema of upper-level sets. This approach enables the resulting topological structures to inherently capture width properties. We incorporate this enhanced topological description into variational image segmentation models. Using some proper loss functions, we are also able to design neural networks that can segment images with the required topological and width properties. Through variational constraints on the relevant topological energies, our approach successfully preserves essential topological invariants such as connectivity and genus counts, simultaneously ensuring that segmented structures retain critical width attributes, including line thickness and length. Numerical experiments demonstrate the effectiveness of our method, showcasing its capability to maintain topological fidelity while explicitly embedding width characteristics into segmented image structures.

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### SME-YOLO: A Real-Time Detector for Tiny Defect Detection on PCB Surfaces

Authors: Meng Han
Published: 2026-01-16
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.11402v1

Surface defects on Printed Circuit Boards (PCBs) directly compromise product reliability and safety. However, achieving high-precision detection is challenging because PCB defects are typically characterized by tiny sizes, high texture similarity, and uneven scale distributions. To address these challenges, this paper proposes a novel framework based on YOLOv11n, named SME-YOLO (Small-target Multi-scale Enhanced YOLO). First, we employ the Normalized Wasserstein Distance Loss (NWDLoss). This metric effectively mitigates the sensitivity of Intersection over Union (IoU) to positional deviations in tiny objects. Second, the original upsampling module is replaced by the Efficient Upsampling Convolution Block (EUCB). By utilizing multi-scale convolutions, the EUCB gradually recovers spatial resolution and enhances the preservation of edge and texture details for tiny defects. Finally, this paper proposes the Multi-Scale Focused Attention (MSFA) module. Tailored to the specific spatial distribution of PCB defects, this module adaptively strengthens perception within key scale intervals, achieving efficient fusion of local fine-grained features and global context information. Experimental results on the PKU-PCB dataset demonstrate that SME-YOLO achieves state-of-the-art performance. Specifically, compared to the baseline YOLOv11n, SME-YOLO improves mAP by 2.2% and Precision by 4%, validating the effectiveness of the proposed method.

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### Factored Value Functions for Graph-Based Multi-Agent Reinforcement Learning

Authors: Ahmed Rashwan, Keith Briggs, Chris Budd, Lisa Kreusser
Published: 2026-01-16
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.11401v1

Credit assignment is a core challenge in multi-agent reinforcement learning (MARL), especially in large-scale systems with structured, local interactions. Graph-based Markov decision processes (GMDPs) capture such settings via an influence graph, but standard critics are poorly aligned with this structure: global value functions provide weak per-agent learning signals, while existing local constructions can be difficult to estimate and ill-behaved in infinite-horizon settings. We introduce the Diffusion Value Function (DVF), a factored value function for GMDPs that assigns to each agent a value component by diffusing rewards over the influence graph with temporal discounting and spatial attenuation. We show that DVF is well-defined, admits a Bellman fixed point, and decomposes the global discounted value via an averaging property. DVF can be used as a drop-in critic in standard RL algorithms and estimated scalably with graph neural networks. Building on DVF, we propose Diffusion A2C (DA2C) and a sparse message-passing actor, Learned DropEdge GNN (LD-GNN), for learning decentralised algorithms under communication costs. Across the firefighting benchmark and three distributed computation tasks (vector graph colouring and two transmit power optimisation problems), DA2C consistently outperforms local and global critic baselines, improving average reward by up to 11%.

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### Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model

Authors: Shuai Yuan, Tianwu Lin, Shuang Chen, Yu Xia, Peng Qin, Xiangyu Liu, Xiaoqing Xu, Nan Xu, Hongsheng Zhang, Jie Wang, Peng Gong
Published: 2026-01-16
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.11400v1

Accurate wetland mapping is essential for ecosystem monitoring, yet dense pixel-level annotation is prohibitively expensive and practical applications usually rely on sparse point labels, under which existing deep learning models perform poorly, while strong seasonal and inter-annual wetland dynamics further render single-date imagery inadequate and lead to significant mapping errors; although foundation models such as SAM show promising generalization from point prompts, they are inherently designed for static images and fail to model temporal information, resulting in fragmented masks in heterogeneous wetlands. To overcome these limitations, we propose WetSAM, a SAM-based framework that integrates satellite image time series for wetland mapping from sparse point supervision through a dual-branch design, where a temporally prompted branch extends SAM with hierarchical adapters and dynamic temporal aggregation to disentangle wetland characteristics from phenological variability, and a spatial branch employs a temporally constrained region-growing strategy to generate reliable dense pseudo-labels, while a bidirectional consistency regularization jointly optimizes both branches. Extensive experiments across eight global regions of approximately 5,000 km2 each demonstrate that WetSAM substantially outperforms state-of-the-art methods, achieving an average F1-score of 85.58%, and delivering accurate and structurally consistent wetland segmentation with minimal labeling effort, highlighting its strong generalization capability and potential for scalable, low-cost, high-resolution wetland mapping.

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### Latent Space Inference via Paired Autoencoders

Authors: Emma Hart, Bas Peters, Julianne Chung, Matthias Chung
Published: 2026-01-16
Categories: cs.LG, math.NA
arXiv: https://arxiv.org/abs/2601.11397v1

This work describes a novel data-driven latent space inference framework built on paired autoencoders to handle observational inconsistencies when solving inverse problems. Our approach uses two autoencoders, one for the parameter space and one for the observation space, connected by learned mappings between the autoencoders' latent spaces. These mappings enable a surrogate for regularized inversion and optimization in low-dimensional, informative latent spaces. Our flexible framework can work with partial, noisy, or out-of-distribution data, all while maintaining consistency with the underlying physical models. The paired autoencoders enable reconstruction of corrupted data, and then use the reconstructed data for parameter estimation, which produces more accurate reconstructions compared to paired autoencoders alone and end-to-end encoder-decoders of the same architecture, especially in scenarios with data inconsistencies. We demonstrate our approaches on two imaging examples in medical tomography and geophysical seismic-waveform inversion, but the described approaches are broadly applicable to a variety of inverse problems in scientific and engineering applications.

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### SUG-Occ: Explicit Semantics and Uncertainty Guided Sparse Learning for Efficient 3D Occupancy Prediction

Authors: Hanlin Wu, Pengfei Lin, Ehsan Javanmardi, Naren Bao, Bo Qian, Hao Si, Manabu Tsukada
Published: 2026-01-16
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.11396v5

3D semantic occupancy prediction has emerged as a critical perception task for autonomous driving due to its ability to offer voxel-level semantic and geometric understanding of the environment. However, such a refined representation for large-scale scenes incurs prohibitive computation, posing a significant challenge to practical real-time deployment. To address this, we propose SUGOcc, an explicit semantics and uncertainty guided sparse learning framework for efficient occupancy prediction, which exploits the inherent sparsity of 3D scenes to reduce redundant computation while maintaining geometric and semantic integrity. Specifically, we first utilize semantic and uncertainty priors to suppress image projections from free space while employing explicit unsigned distance encoding to enhance geometric consistency, thereby producing a structurally sparse representation. Secondly, we introduce a cascade sparse completion module to enable efficient coarse-to-fine reasoning over the sparse representation via hyper cross sparse convolution, generative upsampling and adaptive pruning. Finally, we propose an object contextual representation (OCR) based mask decoder that refines the voxel-wise predictions through lightweight query-context interactions, thereby avoiding expensive attention operations over volumetric features. Extensive experiments on SemanticKITTI and Occ3D-Nuscenes benchmark demonstrate that the proposed approach outperforms the baselines, achieving notable improvements in both accuracy and efficiency across datasets.

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### Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning

Authors: Haomiao Tang, Jinpeng Wang, Minyi Zhao, Guanghao Meng, Ruisheng Luo, Long Chen, Shu-Tao Xia
Published: 2026-01-16
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.11393v2

Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text. Intrinsic noise in CIR triplets incurs intrinsic uncertainty and threatens the model's robustness. Probabilistic learning approaches have shown promise in addressing such issues; however, they fall short for CIR due to their instance-level holistic modeling and homogeneous treatment of queries and targets. This paper introduces a Heterogeneous Uncertainty-Guided (HUG) paradigm to overcome these limitations. HUG utilizes a fine-grained probabilistic learning framework, where queries and targets are represented by Gaussian embeddings that capture detailed concepts and uncertainties. We customize heterogeneous uncertainty estimations for multi-modal queries and uni-modal targets. Given a query, we capture uncertainties not only regarding uni-modal content quality but also multi-modal coordination, followed by a provable dynamic weighting mechanism to derive comprehensive query uncertainty. We further design uncertainty-guided objectives, including query-target holistic contrast and fine-grained contrasts with comprehensive negative sampling strategies, which effectively enhance discriminative learning. Experiments on benchmarks demonstrate HUG's effectiveness beyond state-of-the-art baselines, with faithful analysis justifying the technical contributions.

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### Hyperparameter Optimization of Constraint Programming Solvers

Authors: Hedieh Haddad, Thibault Falque, Pierre Talbot, Pascal Bouvry
Published: 2026-01-16
Categories: cs.AI
arXiv: https://arxiv.org/abs/2601.11389v1

The performance of constraint programming solvers is highly sensitive to the choice of their hyperparameters. Manually finding the best solver configuration is a difficult, time-consuming task that typically requires expert knowledge. In this paper, we introduce probe and solve algorithm, a novel two-phase framework for automated hyperparameter optimization integrated into the CPMpy library. This approach partitions the available time budget into two phases: a probing phase that explores different sets of hyperparameters using configurable hyperparameter optimization methods, followed by a solving phase where the best configuration found is used to tackle the problem within the remaining time. We implement and compare two hyperparameter optimization methods within the probe and solve algorithm: Bayesian optimization and Hamming distance search. We evaluate the algorithm on two different constraint programming solvers, ACE and Choco, across 114 combinatorial problem instances, comparing their performance against the solver's default configurations. Results show that using Bayesian optimization, the algorithm outperforms the solver's default configurations, improving solution quality for ACE in 25.4% of instances and matching the default performance in 57.9%, and for Choco, achieving superior results in 38.6% of instances. It also consistently surpasses Hamming distance search within the same framework, confirming the advantage of model-based exploration over simple local search. Overall, the probe and solve algorithm offers a practical, resource-aware approach for tuning constraint solvers that yields robust improvements across diverse problem types.

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### Evaluating LLM Behavior in Hiring: Implicit Weights, Fairness Across Groups, and Alignment with Human Preferences

Authors: Morgane Hoffmann, Emma Jouffroy, Warren Jouanneau, Marc Palyart, Charles Pebereau
Published: 2026-01-16
Categories: cs.CL, cs.AI, cs.CY, cs.SI
arXiv: https://arxiv.org/abs/2601.11379v1

General-purpose Large Language Models (LLMs) show significant potential in recruitment applications, where decisions require reasoning over unstructured text, balancing multiple criteria, and inferring fit and competence from indirect productivity signals. Yet, it is still uncertain how LLMs assign importance to each attribute and whether such assignments are in line with economic principles, recruiter preferences or broader societal norms. We propose a framework to evaluate an LLM's decision logic in recruitment, by drawing on established economic methodologies for analyzing human hiring behavior. We build synthetic datasets from real freelancer profiles and project descriptions from a major European online freelance marketplace and apply a full factorial design to estimate how a LLM weighs different match-relevant criteria when evaluating freelancer-project fit. We identify which attributes the LLM prioritizes and analyze how these weights vary across project contexts and demographic subgroups. Finally, we explain how a comparable experimental setup could be implemented with human recruiters to assess alignment between model and human decisions. Our findings reveal that the LLM weighs core productivity signals, such as skills and experience, but interprets certain features beyond their explicit matching value. While showing minimal average discrimination against minority groups, intersectional effects reveal that productivity signals carry different weights between demographic groups.

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### Reward Modeling for Scientific Writing Evaluation

Authors: Furkan Şahinuç, Subhabrata Dutta, Iryna Gurevych
Published: 2026-01-16
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.11374v2

Scientific writing is an expert-domain task that demands deep domain knowledge, task-specific requirements and reasoning capabilities that leverage the domain knowledge to satisfy the task specifications. While scientific text generation has been widely studied, its evaluation remains a challenging and open problem. It is critical to develop models that can be reliably deployed for evaluating diverse open-ended scientific writing tasks while adhering to their distinct requirements. However, existing LLM-based judges and reward models are primarily optimized for general-purpose benchmarks with fixed scoring rubrics and evaluation criteria. Consequently, they often fail to reason over sparse knowledge of scientific domains when interpreting task-dependent and multi-faceted criteria. Moreover, fine-tuning for each individual task is costly and impractical for low-resource settings. To bridge these gaps, we propose cost-efficient, open-source reward models tailored for scientific writing evaluation. We introduce a two-stage training framework that initially optimizes scientific evaluation preferences and then refines reasoning capabilities. Our multi-aspect evaluation design and joint training across diverse tasks enable fine-grained assessment and robustness to dynamic criteria and scoring rubrics. Experimental analysis shows that our training regime strongly improves LLM-based scientific writing evaluation. Our models generalize effectively across tasks and to previously unseen scientific writing evaluation settings, allowing a single trained evaluator to be reused without task-specific retraining.

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### Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs

Authors: Marcantonio Bracale Syrnikov, Federico Pierucci, Marcello Galisai, Matteo Prandi, Piercosma Bisconti, Francesco Giarrusso, Olga Sorokoletova, Vincenzo Suriani, Daniele Nardi
Published: 2026-01-16
Categories: cs.GT, cs.AI
arXiv: https://arxiv.org/abs/2601.11369v2

Multi-agent LLM ensembles can converge on coordinated, socially harmful equilibria. This paper advances an experimental framework for evaluating Institutional AI, our system-level approach to AI alignment that reframes alignment from preference engineering in agent-space to mechanism design in institution-space. Central to this approach is the governance graph, a public, immutable manifest that declares legal states, transitions, sanctions, and restorative paths; an Oracle/Controller runtime interprets this manifest, attaching enforceable consequences to evidence of coordination while recording a cryptographically keyed, append-only governance log for audit and provenance. We apply the Institutional AI framework to govern the Cournot collusion case documented by prior work and compare three regimes: Ungoverned (baseline incentives from the structure of the Cournot market), Constitutional (a prompt-only policy-as-prompt prohibition implemented as a fixed written anti-collusion constitution, and Institutional (governance-graph-based). Across six model configurations including cross-provider pairs (N=90 runs/condition), the Institutional regime produces large reductions in collusion: mean tier falls from 3.1 to 1.8 (Cohen's d=1.28), and severe-collusion incidence drops from 50% to 5.6%. The prompt-only Constitutional baseline yields no reliable improvement, illustrating that declarative prohibitions do not bind under optimisation pressure. These results suggest that multi-agent alignment may benefit from being framed as an institutional design problem, where governance graphs can provide a tractable abstraction for alignment-relevant collective behavior.

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### Think-Clip-Sample: Slow-Fast Frame Selection for Video Understanding

Authors: Wenhui Tan, Ruihua Song, Jiaze Li, Jianzhong Ju, Zhenbo Luo
Published: 2026-01-16
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.11359v1

Recent progress in multi-modal large language models (MLLMs) has significantly advanced video understanding. However, their performance on long-form videos remains limited by computational constraints and suboptimal frame selection. We present Think-Clip-Sample (TCS), a training-free framework that enhances long video understanding through two key components: (i) Multi-Query Reasoning, which generates multiple queries to capture complementary aspects of the question and video; and (ii) Clip-level Slow-Fast Sampling, which adaptively balances dense local details and sparse global context. Extensive experiments on MLVU, LongVideoBench, and VideoMME demonstrate that TCS consistently improves performance across different MLLMs, boosting up to 6.9% accuracy, and is capable of achieving comparable accuracy with 50% fewer inference time cost, highlighting both efficiency and efficacy of TCS on long video understanding.

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### Assessing Building Heat Resilience Using UAV and Street-View Imagery with Coupled Global Context Vision Transformer

Authors: Steffen Knoblauch, Ram Kumar Muthusamy, Hao Li, Iddy Chazua, Benedcto Adamu, Innocent Maholi, Alexander Zipf
Published: 2026-01-16
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.11357v1

Climate change is intensifying human heat exposure, particularly in densely built urban centers of the Global South. Low-cost construction materials and high thermal-mass surfaces further exacerbate this risk. Yet scalable methods for assessing such heat-relevant building attributes remain scarce. We propose a machine learning framework that fuses openly available unmanned aerial vehicle (UAV) and street-view (SV) imagery via a coupled global context vision transformer (CGCViT) to learn heat-relevant representations of urban structures. Thermal infrared (TIR) measurements from HotSat-1 are used to quantify the relationship between building attributes and heat-associated health risks. Our dual-modality cross-view learning approach outperforms the best single-modality models by up to $9.3\%$, demonstrating that UAV and SV imagery provide valuable complementary perspectives on urban structures. The presence of vegetation surrounding buildings (versus no vegetation), brighter roofing (versus darker roofing), and roofing made of concrete, clay, or wood (versus metal or tarpaulin) are all significantly associated with lower HotSat-1 TIR values. Deployed across the city of Dar es Salaam, Tanzania, the proposed framework illustrates how household-level inequalities in heat exposure - often linked to socio-economic disadvantage and reflected in building materials - can be identified and addressed using machine learning. Our results point to the critical role of localized, data-driven risk assessment in shaping climate adaptation strategies that deliver equitable outcomes.

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### AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems

Authors: Weiyi Wang, Xinchi Chen, Jingjing Gong, Xuanjing Huang, Xipeng Qiu
Published: 2026-01-16
Categories: cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2601.11354v1

Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely focus on symbolic or weakly grounded environments, leaving their performance in physics-constrained real-world domains underexplored. We introduce AstroReason-Bench, a comprehensive benchmark for evaluating agentic planning in Space Planning Problems (SPP), a family of high-stakes problems with heterogeneous objectives, strict physical constraints, and long-horizon decision-making. AstroReason-Bench integrates multiple scheduling regimes, including ground station communication and agile Earth observation, and provides a unified agent-oriented interaction protocol. Evaluating on a range of state-of-the-art open- and closed-source agentic LLM systems, we find that current agents substantially underperform specialized solvers, highlighting key limitations of generalist planning under realistic constraints. AstroReason-Bench offers a challenging and diagnostic testbed for future agentic research.

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### Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency

Authors: Akhilesh Raj, Swann Perarnau, Aniruddha Gokhale, Solomon Bekele Abera
Published: 2026-01-16
Categories: cs.LG, cs.PF, eess.SY
arXiv: https://arxiv.org/abs/2601.11352v1

Energy efficiency has become an integral aspect of modern computing infrastructure design, impacting the performance, cost, scalability, and durability of production systems. The incorporation of power actuation and sensing capabilities in CPU designs is indicative of this, enabling the deployment of system software that can actively monitor and adjust energy consumption and performance at runtime. While reinforcement learning (RL) would seem ideal for the design of such energy efficiency control systems, online training presents challenges ranging from the lack of proper models for setting up an adequate simulated environment, to perturbation (noise) and reliability issues, if training is deployed on a live system. In this paper we discuss the use of offline reinforcement learning as an alternative approach for the design of an autonomous CPU power controller, with the goal of improving the energy efficiency of parallel applications at runtime without unduly impacting their performance. Offline RL sidesteps the issues incurred by online RL training by leveraging a dataset of state transitions collected from arbitrary policies prior to training. Our methodology applies offline RL to a gray-box approach to energy efficiency, combining online application-agnostic performance data (e.g., heartbeats) and hardware performance counters to ensure that the scientific objectives are met with limited performance degradation. Evaluating our method on a variety of compute-bound and memory-bound benchmarks and controlling power on a live system through Intel's Running Average Power Limit, we demonstrate that such an offline-trained agent can substantially reduce energy consumption at a tolerable performance degradation cost.

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### FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting

Authors: Jaehoon Lee, Seungwoo Lee, Younghwi Kim, Dohee Kim, Sunghyun Sim
Published: 2026-01-16
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2601.11350v1

Time-series forecasting is fundamental in industrial domains like manufacturing and smart factories. As systems evolve toward automation, models must operate on edge devices (e.g., PLCs, microcontrollers) with strict constraints on latency and memory, limiting parameters to a few thousand. Conventional deep architectures are often impractical here. We propose the Fourier-Efficient Adaptive Temporal Hierarchy Forecaster (FEATHer) for accurate long-term forecasting under severe limits. FEATHer introduces: (i) ultra-lightweight multiscale decomposition into frequency pathways; (ii) a shared Dense Temporal Kernel using projection-depthwise convolution-projection without recurrence or attention; (iii) frequency-aware branch gating that adaptively fuses representations based on spectral characteristics; and (iv) a Sparse Period Kernel reconstructing outputs via period-wise downsampling to capture seasonality. FEATHer maintains a compact architecture (as few as 400 parameters) while outperforming baselines. Across eight benchmarks, it achieves the best ranking, recording 60 first-place results with an average rank of 2.05. These results demonstrate that reliable long-range forecasting is achievable on constrained edge hardware, offering a practical direction for industrial real-time inference.

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### How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting

Authors: Parker Seegmiller, Joseph Gatto, Sarah E. Greer, Ganza Belise Isingizwe, Rohan Ray, Timothy E. Burdick, Sarah Masud Preum
Published: 2026-01-16
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.11344v1

Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether they would actually save clinicians time and effort in their portal workload. We investigate LLM alignment with individual clinicians through a comprehensive evaluation of the patient message response drafting task. We develop a novel taxonomy of thematic elements in clinician responses and propose a novel evaluation framework for assessing clinician editing load of LLM-drafted responses at both content and theme levels. We release an expert-annotated dataset and conduct large-scale evaluations of local and commercial LLMs using various adaptation techniques including thematic prompting, retrieval-augmented generation, supervised fine-tuning, and direct preference optimization. Our results reveal substantial epistemic uncertainty in aligning LLM drafts with clinician responses. While LLMs demonstrate capability in drafting certain thematic elements, they struggle with clinician-aligned generation in other themes, particularly question asking to elicit further information from patients. Theme-driven adaptation strategies yield improvements across most themes. Our findings underscore the necessity of adapting LLMs to individual clinician preferences to enable reliable and responsible use in patient-clinician communication workflows.

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### Unlocking the Potentials of Retrieval-Augmented Generation for Diffusion Language Models

Authors: Chuanyue Yu, Jiahui Wang, Yuhan Li, Heng Chang, Ge Lan, Qingyun Sun, Jia Li, Jianxin Li, Ziwei Zhang
Published: 2026-01-16
Categories: cs.LG, cs.CL
arXiv: https://arxiv.org/abs/2601.11342v1

Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (RAG), which shows great successes for enhancing large language models (LLMs), has not been well explored, due to the fundamental difference between LLM and DLM decoding. To fill this critical gap, we systematically test the performance of DLMs within the RAG framework. Our findings reveal that DLMs coupled with RAG show promising potentials with stronger dependency on contextual information, but suffer from limited generation precision. We identify a key underlying issue: Response Semantic Drift (RSD), where the generated answer progressively deviates from the query's original semantics, leading to low precision content. We trace this problem to the denoising strategies in DLMs, which fail to maintain semantic alignment with the query throughout the iterative denoising process. To address this, we propose Semantic-Preserving REtrieval-Augmented Diffusion (SPREAD), a novel framework that introduces a query-relevance-guided denoising strategy. By actively guiding the denoising trajectory, SPREAD ensures the generation remains anchored to the query's semantics and effectively suppresses drift. Experimental results demonstrate that SPREAD significantly enhances the precision and effectively mitigates RSD of generated answers within the RAG framework.

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