# Research Papers — 2026-01 · bundle page 186 of 189

> Full-text markdown of papers #9251–9300 of 9403 published in 2026-01, newest first. AI-readable mirrors of the full 9403-paper 2026-01 archive (50 papers per file).

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### Bit-politeia: An AI Agent Community in Blockchain

Authors: Xing Yang
Published: 2026-01-01
Categories: cs.CY, cs.AI, cs.MA
arXiv: https://arxiv.org/abs/2601.11583v1

Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes "Bit-politeia", an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a "clustered grouping + hierarchical architecture" that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactions and reputation data. By leveraging AI for objective assessment and decentralized verification, Bit-politeia minimizes human bias and mitigates resource centralization issues found in traditional peer review. The proposed framework provides a novel pathway for optimizing scientific innovation through a fair and automated resource configuration process.

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### Overview of the SciHigh Track at FIRE 2025: Research Highlight Generation from Scientific Papers

Authors: Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay
Published: 2026-01-01
Categories: cs.CY, cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2601.11582v1

`SciHigh: Research Highlight Generation from Scientific Papers' focuses on the task of automatically generating concise, informative, and meaningful bullet-point highlights directly from scientific abstracts. The goal of this task is to evaluate how effectively computational models can generate highlights that capture the key contributions, findings, and novelty of a paper in a concise form. Highlights help readers grasp essential ideas quickly and are often easier to read and understand than longer paragraphs, especially on mobile devices. The track uses the MixSub dataset \cite{10172215}, which provides pairs of abstracts and corresponding author-written highlights. In this inaugural edition of the track, 12 teams participated, exploring various approaches, including pre-trained language models, to generate highlights from this scientific dataset. All submissions were evaluated using established metrics such as ROUGE, METEOR, and BERTScore to measure both alignment with author-written highlights and overall informativeness. Teams were ranked based on ROUGE-L scores. The findings suggest that automatically generated highlights can reduce reading effort, accelerate literature reviews, and enhance metadata for digital libraries and academic search platforms. SciHigh provides a dedicated benchmark for advancing methods aimed at concise and accurate highlight generation from scientific writing.

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### Enhancing the QA Model through a Multi-domain Debiasing Framework

Authors: Yuefeng Wang, ChangJae Lee
Published: 2026-01-01
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.11581v1

Question-answering (QA) models have advanced significantly in machine reading comprehension but often exhibit biases that hinder their performance, particularly with complex queries in adversarial conditions. This study evaluates the ELECTRA-small model on the Stanford Question Answering Dataset (SQuAD) v1.1 and adversarial datasets AddSent and AddOneSent. By identifying errors related to lexical bias, numerical reasoning, and entity recognition, we develop a multi-domain debiasing framework incorporating knowledge distillation, debiasing techniques, and domain expansion. Our results demonstrate up to 2.6 percentage point improvements in Exact Match (EM) and F1 scores across all test sets, with gains in adversarial contexts. These findings highlight the potential of targeted bias mitigation strategies to enhance the robustness and reliability of natural language understanding systems.

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### The Hessian of tall-skinny networks is easy to invert

Authors: Ali Rahimi
Published: 2026-01-01
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.06096v3

We describe an exact algorithm to solve linear systems of the form $Hx=b$ where $H$ is the Hessian of a deep net. The method computes Hessian-inverse-vector products without storing the Hessian or its inverse. It requires time and storage that scale linearly in the number of layers. This is in contrast to the naive approach of first computing the Hessian, then solving the linear system, which takes storage and time that are respectively quadratic and cubic in the number of layers. The Hessian-inverse-vector product method scales roughly like Pearlmutter's algorithm for computing Hessian-vector products.

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### Deep Q-Network Based Resilient Drone Communication:Neutralizing First-Order Markov Jammers

Authors: Andrii Grekhov, Volodymyr Kharchenko, Vasyl Kondratiuk
Published: 2026-01-01
Categories: cs.IT, cs.AI
arXiv: https://arxiv.org/abs/2601.06095v1

Deep Reinforcement Learning based solution for jamming communications using Frequency Hopping Spread Spectrum technology in a 16 channel radio environment is presented. Deep Q Network based transmitter continuously selects the next frequency hopping channel while facing first order reactive jamming, which uses observed transition statistics to predict and interrupt transmissions. Through self training, the proposed agent learns a uniform random frequency hopping policy that effectively neutralizes the predictive advantage of the jamming. In the presence of Rayleigh fading and additive noise, the impact of forward error correction Bose Chaudhuri Hocquenghem type codes is systematically evaluated, demonstrating that even moderate redundancy significantly reduces packet loss. Extensive visualization of the learning dynamics, channel utilization distribution, epsilon greedy decay, cumulative reward, BER and SNR evolution, and detailed packet loss tables confirms convergence to a near optimal jamming strategy. The results provide a practical framework for autonomous resilient communications in modern electronic warfare scenarios.

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### $α^3$-Bench: A Unified Benchmark of Safety, Robustness, and Efficiency for LLM-Based UAV Agents over 6G Networks

Authors: Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah
Published: 2026-01-01
Categories: eess.SY, cs.AI
arXiv: https://arxiv.org/abs/2601.03281v1

Large Language Models (LLMs) are increasingly used as high level controllers for autonomous Unmanned Aerial Vehicle (UAV) missions. However, existing evaluations rarely assess whether such agents remain safe, protocol compliant, and effective under realistic next generation networking constraints. This paper introduces $α^3$-Bench, a benchmark for evaluating LLM driven UAV autonomy as a multi turn conversational reasoning and control problem operating under dynamic 6G conditions. Each mission is formulated as a language mediated control loop between an LLM based UAV agent and a human operator, where decisions must satisfy strict schema validity, mission policies, speaker alternation, and safety constraints while adapting to fluctuating network slices, latency, jitter, packet loss, throughput, and edge load variations. To reflect modern agentic workflows, $α^3$-Bench integrates a dual action layer supporting both tool calls and agent to agent coordination, enabling evaluation of tool use consistency and multi agent interactions. We construct a large scale corpus of 113k conversational UAV episodes grounded in UAVBench scenarios and evaluate 17 state of the art LLMs using a fixed subset of 50 episodes per scenario under deterministic decoding. We propose a composite $α^3$ metric that unifies six pillars: Task Outcome, Safety Policy, Tool Consistency, Interaction Quality, Network Robustness, and Communication Cost, with efficiency normalized scores per second and per thousand tokens. Results show that while several models achieve high mission success and safety compliance, robustness and efficiency vary significantly under degraded 6G conditions, highlighting the need for network aware and resource efficient LLM based UAV agents. The dataset is publicly available on GitHub : https://github.com/maferrag/AlphaBench

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### Evolving Personalities in Chaos: An LLM-Augmented Framework for Character Discovery in the Iterated Prisoners Dilemma under Environmental Stress

Authors: Oguzhan Yildirim
Published: 2026-01-01
Categories: cs.NE, cs.GT
arXiv: https://arxiv.org/abs/2601.02407v1

Standard simulations of the Iterated Prisoners Dilemma (IPD) operate in deterministic, noise-free environments, producing strategies that may be theoretically optimal but fragile when confronted with real-world uncertainty. This paper addresses two critical gaps in evolutionary game theory research: (1) the absence of realistic environmental stressors during strategy evolution, and (2) the Interpretability Gap, where evolved genetic strategies remain opaque binary sequences devoid of semantic meaning. We introduce a novel framework combining stochastic environmental perturbations (God Mode) with Large Language Model (LLM)-based behavioral profiling to transform evolved genotypes into interpretable character archetypes. Our experiments demonstrate that strategies evolved under chaotic conditions exhibit superior resilience and present distinct behavioral phenotypes, ranging from Ruthless Capitalists to Diplomatic Enforcers. These phenotypes are readily classified by LLMs but remain nearly impossible to interpret through manual genome inspection alone. This work bridges evolutionary computation with explainable AI and provides a template for automated agent characterization in multi-agent systems.

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### MACA: A Framework for Distilling Trustworthy LLMs into Efficient Retrievers

Authors: Satya Swaroop Gudipudi, Sahil Girhepuje, Ponnurangam Kumaraguru, Kristine Ma
Published: 2026-01-01
Categories: cs.IR, cs.AI
arXiv: https://arxiv.org/abs/2601.00926v1

Modern enterprise retrieval systems must handle short, underspecified queries such as ``foreign transaction fee refund'' and ``recent check status''. In these cases, semantic nuance and metadata matter but per-query large language model (LLM) re-ranking and manual labeling are costly. We present Metadata-Aware Cross-Model Alignment (MACA), which distills a calibrated metadata aware LLM re-ranker into a compact student retriever, avoiding online LLM calls. A metadata-aware prompt verifies the teacher's trustworthiness by checking consistency under permutations and robustness to paraphrases, then supplies listwise scores, hard negatives, and calibrated relevance margins. The student trains with MACA's MetaFusion objective, which combines a metadata conditioned ranking loss with a cross model margin loss so it learns to push the correct answer above semantically similar candidates with mismatched topic, sub-topic, or entity. On a proprietary consumer banking FAQ corpus and BankFAQs, the MACA teacher surpasses a MAFA baseline at Accuracy@1 by five points on the proprietary set and three points on BankFAQs. MACA students substantially outperform pretrained encoders; e.g., on the proprietary corpus MiniLM Accuracy@1 improves from 0.23 to 0.48, while keeping inference free of LLM calls and supporting retrieval-augmented generation.

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### Application of deep learning techniques in non-contrast computed tomography pulmonary angiogram for pulmonary embolism diagnosis

Authors: I-Hsien Ting, Yi-Jun Tseng, Yu-Sheng Lin
Published: 2026-01-01
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2601.00925v1

Pulmonary embolism is a life-threatening disease, early detection and treatment can significantly reduce mortality. In recent years, many studies have been using deep learning in the diagnosis of pulmonary embolism with contrast medium computed tomography pulmonary angiography, but the contrast medium is likely to cause acute kidney injury in patients with pulmonary embolism and chronic kidney disease, and the contrast medium takes time to work, patients with acute pulmonary embolism may miss the golden treatment time. This study aims to use deep learning techniques to automatically classify pulmonary embolism in CT images without contrast medium by using a 3D convolutional neural network model. The deep learning model used in this study had a significant impact on the pulmonary embolism classification of computed tomography images without contrast with 85\% accuracy and 0.84 AUC, which confirms the feasibility of the model in the diagnosis of pulmonary embolism.

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### Complexity-based code embeddings

Authors: Rares Folea, Radu Iacob, Emil Slusanschi, Traian Rebedea
Published: 2026-01-01
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2601.00924v1

This paper presents a generic method for transforming the source code of various algorithms to numerical embeddings, by dynamically analysing the behaviour of computer programs against different inputs and by tailoring multiple generic complexity functions for the analysed metrics. The used algorithms embeddings are based on r-Complexity . Using the proposed code embeddings, we present an implementation of the XGBoost algorithm that achieves an average F1-score on a multi-label dataset with 11 classes, built using real-world code snippets submitted for programming competitions on the Codeforces platform.

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### Context Collapse: In-Context Learning and Model Collapse

Authors: Josef Ott
Published: 2026-01-01
Categories: cs.AI
arXiv: https://arxiv.org/abs/2601.00923v1

This thesis investigates two key phenomena in large language models (LLMs): in-context learning (ICL) and model collapse. We study ICL in a linear transformer with tied weights trained on linear regression tasks, and show that minimising the in-context loss leads to a phase transition in the learned parameters. Above a critical context length, the solution develops a skew-symmetric component. We prove this by reducing the forward pass of the linear transformer under weight tying to preconditioned gradient descent, and then analysing the optimal preconditioner. This preconditioner includes a skew-symmetric component, which induces a rotation of the gradient direction. For model collapse, we use martingale and random walk theory to analyse simplified settings - linear regression and Gaussian fitting - under both replacing and cumulative data regimes. We strengthen existing results by proving almost sure convergence, showing that collapse occurs unless the data grows sufficiently fast or is retained over time. Finally, we introduce the notion of context collapse: a degradation of context during long generations, especially in chain-of-thought reasoning. This concept links the dynamics of ICL with long-term stability challenges in generative models.

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### MetaFormer-driven Encoding Network for Robust Medical Semantic Segmentation

Authors: Le-Anh Tran, Chung Nguyen Tran, Nhan Cach Dang, Anh Le Van Quoc, Jordi Carrabina, David Castells-Rufas, Minh Son Nguyen
Published: 2026-01-01
Categories: eess.IV, cs.CV
arXiv: https://arxiv.org/abs/2601.00922v1

Semantic segmentation is crucial for medical image analysis, enabling precise disease diagnosis and treatment planning. However, many advanced models employ complex architectures, limiting their use in resource-constrained clinical settings. This paper proposes MFEnNet, an efficient medical image segmentation framework that incorporates MetaFormer in the encoding phase of the U-Net backbone. MetaFormer, an architectural abstraction of vision transformers, provides a versatile alternative to convolutional neural networks by transforming tokenized image patches into sequences for global context modeling. To mitigate the substantial computational cost associated with self-attention, the proposed framework replaces conventional transformer modules with pooling transformer blocks, thereby achieving effective global feature aggregation at reduced complexity. In addition, Swish activation is used to achieve smoother gradients and faster convergence, while spatial pyramid pooling is incorporated at the bottleneck to improve multi-scale feature extraction. Comprehensive experiments on different medical segmentation benchmarks demonstrate that the proposed MFEnNet approach attains competitive accuracy while significantly lowering computational cost compared to state-of-the-art models. The source code for this work is available at https://github.com/tranleanh/mfennet.

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### Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease

Authors: Azadeh Alavi, Hamidreza Khalili, Stanley H. Chan, Fatemeh Kouchmeshki, Muhammad Usman, Ross Vlahos
Published: 2026-01-01
Categories: cs.LG, cs.AI, quant-ph
arXiv: https://arxiv.org/abs/2601.00921v3

Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important. Quantum machine learning is increasingly explored for biomedical prediction, but its value in small biomarker cohorts requires benchmarking against strong classical baselines. We analysed a cigarette-smoke COPD cohort of 213 animals with blood and bronchoalveolar-lavage biomarkers to predict tibialis anterior muscle weight, muscle quality, and force. We developed a kernel-geometric quantum hybrid method in which synthetic symmetric positive definite (SPD) references are mapped through a reproducing kernel Hilbert space, compressed using train-only random projection, normalised, and supplied to low-dimensional quantum regression circuits. We benchmarked this approach against classical ridge/kernel models, SPD relational representations, and quantum-kernel regression (QKR). All methods were evaluated using condition-stratified repeated cross-validation. The largest numerical improvement was observed for muscle weight, where the proposed method had the numerically lowest mean root mean squared error (RMSE), approximately 1.8% below the best classical comparator; paired fold-level testing did not establish statistically significant superiority after Holm adjustment, but the endpoint is biologically meaningful. The method also had the numerically lowest mean RMSE for muscle quality. For force, biomarker-only Ridge performed best, suggesting a more linear endpoint structure.

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### MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs

Authors: Xingsheng Chen, Regina Zhang, Bo Gao, Xingwei He, Xiaofeng Liu, Pietro Lio, Kwok-Yan Lam, Siu-Ming Yiu
Published: 2026-01-01
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2601.00920v2

Time series prediction plays a pivotal role across diverse domains such as finance, healthcare, energy systems, and environmental modeling. However, existing approaches often struggle to balance efficiency, scalability, and accuracy, particularly when handling long-range dependencies and irregularly sampled data. To address these challenges, we propose MODE, a unified framework that integrates Low-Rank Neural Ordinary Differential Equations (Neural ODEs) with an Enhanced Mamba architecture. As illustrated in our framework, the input sequence is first transformed by a Linear Tokenization Layer and then processed through multiple Mamba Encoder blocks, each equipped with an Enhanced Mamba Layer that employs Causal Convolution, SiLU activation, and a Low-Rank Neural ODE enhancement to efficiently capture temporal dynamics. This low-rank formulation reduces computational overhead while maintaining expressive power. Furthermore, a segmented selective scanning mechanism, inspired by pseudo-ODE dynamics, adaptively focuses on salient subsequences to improve scalability and long-range sequence modeling. Extensive experiments on benchmark datasets demonstrate that MODE surpasses existing baselines in both predictive accuracy and computational efficiency. Overall, our contributions include: (1) a unified and efficient architecture for long-term time series modeling, (2) integration of Mamba's selective scanning with low-rank Neural ODEs for enhanced temporal representation, and (3) substantial improvements in efficiency and scalability enabled by low-rank approximation and dynamic selective scanning.

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### Attention Needs to Focus: A Unified Perspective on Attention Allocation

Authors: Zichuan Fu, Wentao Song, Guojing Li, Yejing Wang, Xian Wu, Yimin Deng, Hanyu Yan, Yefeng Zheng, Xiangyu Zhao
Published: 2026-01-01
Categories: cs.LG, cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2601.00919v2

The Transformer architecture, a cornerstone of modern Large Language Models (LLMs), has achieved extraordinary success in sequence modeling, primarily due to its attention mechanism. However, despite its power, the standard attention mechanism is plagued by well-documented issues: representational collapse and attention sink. Although prior work has proposed approaches for these issues, they are often studied in isolation, obscuring their deeper connection. In this paper, we present a unified perspective, arguing that both can be traced to a common root -- improper attention allocation. We identify two failure modes: 1) Attention Overload, where tokens receive comparable high weights, blurring semantic features that lead to representational collapse; 2) Attention Underload, where no token is semantically relevant, yet attention is still forced to distribute, resulting in spurious focus such as attention sink. Building on this insight, we introduce Lazy Attention, a novel mechanism designed for a more focused attention distribution. To mitigate overload, it employs positional discrimination across both heads and dimensions to sharpen token distinctions. To counteract underload, it incorporates Elastic-Softmax, a modified normalization function that relaxes the standard softmax constraint to suppress attention on irrelevant tokens. Experiments on the FineWeb-Edu corpus, evaluated across nine diverse benchmarks, demonstrate that Lazy Attention successfully mitigates attention sink and achieves competitive performance compared to both standard attention and modern architectures, while reaching up to 59.58% attention sparsity.

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### Four-Stage Alzheimer's Disease Classification from MRI Using Topological Feature Extraction, Feature Selection, and Ensemble Learning

Authors: Faisal Ahmed
Published: 2026-01-01
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00918v1

Accurate and efficient classification of Alzheimer's disease (AD) severity from brain magnetic resonance imaging (MRI) remains a critical challenge, particularly when limited data and model interpretability are of concern. In this work, we propose TDA-Alz, a novel framework for four-stage Alzheimer's disease severity classification (non-demented, moderate dementia, mild, and very mild) using topological data analysis (TDA) and ensemble learning. Instead of relying on deep convolutional architectures or extensive data augmentation, our approach extracts topological descriptors that capture intrinsic structural patterns of brain MRI, followed by feature selection to retain the most discriminative topological features. These features are then classified using an ensemble learning strategy to achieve robust multiclass discrimination. Experiments conducted on the OASIS-1 MRI dataset demonstrate that the proposed method achieves an accuracy of 98.19% and an AUC of 99.75%, outperforming or matching state-of-the-art deep learning--based methods reported on OASIS and OASIS-derived datasets. Notably, the proposed framework does not require data augmentation, pretrained networks, or large-scale computational resources, making it computationally efficient and fast compared to deep neural network approaches. Furthermore, the use of topological descriptors provides greater interpretability, as the extracted features are directly linked to the underlying structural characteristics of brain MRI rather than opaque latent representations. These results indicate that TDA-Alz offers a powerful, lightweight, and interpretable alternative to deep learning models for MRI-based Alzheimer's disease severity classification, with strong potential for real-world clinical decision-support systems.

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### Latent-Constrained Conditional VAEs for Augmenting Large-Scale Climate Ensembles

Authors: Jacquelyn Shelton, Przemyslaw Polewski, Alexander Robel, Matthew Hoffman, Stephen Price
Published: 2026-01-01
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00915v1

Large climate-model ensembles are computationally expensive; yet many downstream analyses would benefit from additional, statistically consistent realizations of spatiotemporal climate variables. We study a generative modeling approach for producing new realizations from a limited set of available runs by transferring structure learned across an ensemble. Using monthly near-surface temperature time series from ten independent reanalysis realizations (ERA5), we find that a vanilla conditional variational autoencoder (CVAE) trained jointly across realizations yields a fragmented latent space that fails to generalize to unseen ensemble members. To address this, we introduce a latent-constrained CVAE (LC-CVAE) that enforces cross-realization homogeneity of latent embeddings at a small set of shared geographic 'anchor' locations. We then use multi-output Gaussian process regression in the latent space to predict latent coordinates at unsampled locations in a new realization, followed by decoding to generate full time series fields. Experiments and ablations demonstrate (i) instability when training on a single realization, (ii) diminishing returns after incorporating roughly five realizations, and (iii) a trade-off between spatial coverage and reconstruction quality that is closely linked to the average neighbor distance in latent space.

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### Clean-GS: Semantic Mask-Guided Pruning for 3D Gaussian Splatting

Authors: Subhankar Mishra
Published: 2026-01-01
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2601.00913v1

3D Gaussian Splatting produces high-quality scene reconstructions but generates hundreds of thousands of spurious Gaussians (floaters) scattered throughout the environment. These artifacts obscure objects of interest and inflate model sizes, hindering deployment in bandwidth-constrained applications. We present Clean-GS, a method for removing background clutter and floaters from 3DGS reconstructions using sparse semantic masks. Our approach combines whitelist-based spatial filtering with color-guided validation and outlier removal to achieve 60-80\% model compression while preserving object quality. Unlike existing 3DGS pruning methods that rely on global importance metrics, Clean-GS uses semantic information from as few as 3 segmentation masks (1\% of views) to identify and remove Gaussians not belonging to the target object. Our multi-stage approach consisting of (1) whitelist filtering via projection to masked regions, (2) depth-buffered color validation, and (3) neighbor-based outlier removal isolates monuments and objects from complex outdoor scenes. Experiments on Tanks and Temples show that Clean-GS reduces file sizes from 125MB to 47MB while maintaining rendering quality, making 3DGS models practical for web deployment and AR/VR applications. Our code is available at https://github.com/smlab-niser/clean-gs

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### The Discovery Gap: How Product Hunt Startups Vanish in LLM Organic Discovery Queries

Authors: Amit Prakash Sharma
Published: 2026-01-01
Categories: cs.IR, cs.AI
arXiv: https://arxiv.org/abs/2601.00912v1

When someone asks ChatGPT to recommend a project management tool, which products show up in the response? And more importantly for startup founders: will their newly launched product ever appear? This research set out to answer these questions. I randomly selected 112 startups from the top 500 products featured on the 2025 Product Hunt leaderboard and tested each one across 2,240 queries to two different large language models: ChatGPT (gpt-4o-mini) and Perplexity (sonar with web search). The results were striking. When users asked about products by name, both LLMs recognized them almost perfectly: 99.4% for ChatGPT and 94.3% for Perplexity. But when users asked discovery-style questions like "What are the best AI tools launched this year?" the success rates collapsed to 3.32% and 8.29% respectively. That's a gap of 30-to-1 for ChatGPT. Perhaps the most surprising finding was that Generative Engine Optimization (GEO), the practice of optimizing website content for AI visibility, showed no correlation with actual discovery rates. Products with high GEO scores were no more likely to appear in organic queries than products with low scores. What did matter? For Perplexity, traditional SEO signals like referring domains (r = +0.319, p < 0.001) and Product Hunt ranking (r = -0.286, p = 0.002) predicted visibility. After cleaning the Reddit data for false positives, community presence also emerged as significant (r = +0.395, p = 0.002). The practical takeaway is counterintuitive: don't optimize for AI discovery directly. Instead, build the SEO foundation first and LLM visibility will follow.

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### Device-Native Autonomous Agents for Privacy-Preserving Negotiations

Authors: Joyjit Roy, Samaresh Kumar Singh
Published: 2026-01-01
Categories: cs.CR, cs.AI, cs.ET, cs.HC, cs.LG
arXiv: https://arxiv.org/abs/2601.00911v3

Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Agentic AI system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an Agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87 %, a 2.4x reduction in latency relative to cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27 % higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.

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### Security Hardening Using FABRIC: Implementing a Unified Compliance Aggregator for Linux Servers

Authors: Sheldon Paul, Izzat Alsmadi
Published: 2026-01-01
Categories: cs.CR, cs.LG
arXiv: https://arxiv.org/abs/2601.00909v1

This paper presents a unified framework for evaluating Linux security hardening on the FABRIC testbed through aggregation of heterogeneous security auditing tools. We deploy three Ubuntu 22.04 nodes configured at baseline, partial, and full hardening levels, and evaluate them using Lynis, OpenSCAP, and AIDE across 108 audit runs. To address the lack of a consistent interpretation across tools, we implement a Unified Compliance Aggregator (UCA) that parses tool outputs, normalizes scores to a common 0--100 scale, and combines them into a weighted metric augmented by a customizable rule engine for organization-specific security policies. Experimental results show that full hardening increases OpenSCAP compliance from 39.7 to 71.8, while custom rule compliance improves from 39.3\% to 83.6\%. The results demonstrate that UCA provides a clearer and more reproducible assessment of security posture than individual tools alone, enabling systematic evaluation of hardening effectiveness in programmable testbed environments.

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### Diagnosing Conformal Prediction Failures Under Distribution Shift: A COVID-19 Case Study

Authors: Chorok Lee
Published: 2026-01-01
Categories: cs.LG, cs.AI, stat.ML
arXiv: https://arxiv.org/abs/2601.00908v2

Conformal prediction provides distribution-free coverage guarantees, but these degrade under distribution shift - and practitioners lack tools to anticipate which deployed models will fail before observing test data. We propose SHapley Additive exPlanations (SHAP) concentration - the fraction of feature importance concentrated in the top feature - as a pre-deployment diagnostic for conformal prediction vulnerability in gradient-boosted classifiers. Using COVID-19 as a naturalistic case study, eight supply chain tasks experience identical temporal shift yet coverage drops ranging from negligible to catastrophic. Feature-importance concentration is strongly associated with failure severity across 16 multiclass tasks in 9 domains, while standard distributional shift detectors detect shift uniformly across tasks but cannot distinguish catastrophic from robust outcomes. External validation across 9 non-supply-chain datasets shows partial transfer. We prove a formal theorem showing that Adaptive Prediction Sets conformity-score bounds worsen monotonically with concentration under explicit assumptions, verified empirically. The diagnostic identifies concentrated-dependence failures characteristic of gradient-boosted models but does not detect global-sensitivity failures observed in neural networks. A decision framework operationalizes the diagnostic as an exploratory pre-deployment rule with an uncertainty band around a concentration threshold.

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### When Small Models Are Right for Wrong Reasons: Process Verification for Trustworthy Agents

Authors: Laksh Advani
Published: 2026-01-01
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00513v1

Deploying small language models (7-9B parameters) as autonomous agents requires trust in their reasoning, not just their outputs. We reveal a critical reliability crisis: 50-69\% of correct answers from these models contain fundamentally flawed reasoning -- a ``Right-for-Wrong-Reasons'' phenomenon invisible to standard accuracy metrics. Through analysis of 10,734 reasoning traces across three models and diverse tasks, we introduce the Reasoning Integrity Score (RIS), a process-based metric validated with substantial inter-rater agreement ($κ=0.657$). Conventional practices are challenged by our findings: while retrieval-augmented generation (RAG) significantly improves reasoning integrity (Cohen's $d=0.23$--$0.93$), meta-cognitive interventions like self-critique often harm performance ($d=-0.14$ to $-0.33$) in small models on the evaluated tasks. Mechanistic analysis reveals RAG succeeds by grounding calculations in external evidence, reducing errors by 7.6\%, while meta-cognition amplifies confusion without sufficient model capacity. To enable deployment, verification capabilities are distilled into a neural classifier achieving 0.86 F1-score with 100$\times$ speedup. These results underscore the necessity of process-based verification for trustworthy agents: accuracy alone is dangerously insufficient when models can be right for entirely wrong reasons.

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### A Chain-of-Thought Approach to Semantic Query Categorization in e-Commerce Taxonomies

Authors: Jetlir Duraj, Ishita Khan, Kilian Merkelbach, Mehran Elyasi
Published: 2026-01-01
Categories: cs.IR, cs.CL
arXiv: https://arxiv.org/abs/2601.00510v1

Search in e-Commerce is powered at the core by a structured representation of the inventory, often formulated as a category taxonomy. An important capability in e-Commerce with hierarchical taxonomies is to select a set of relevant leaf categories that are semantically aligned with a given user query. In this scope, we address a fundamental problem of search query categorization in real-world e-Commerce taxonomies. A correct categorization of a query not only provides a way to zoom into the correct inventory space, but opens the door to multiple intent understanding capabilities for a query. A practical and accurate solution to this problem has many applications in e-commerce, including constraining retrieved items and improving the relevance of the search results. For this task, we explore a novel Chain-of-Thought (CoT) paradigm that combines simple tree-search with LLM semantic scoring. Assessing its classification performance on human-judged query-category pairs, relevance tests, and LLM-based reference methods, we find that the CoT approach performs better than a benchmark that uses embedding-based query category predictions. We show how the CoT approach can detect problems within a hierarchical taxonomy. Finally, we also propose LLM-based approaches for query-categorization of the same spirit, but which scale better at the range of millions of queries.

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### Improving LLM-Assisted Secure Code Generation through Retrieval-Augmented-Generation and Multi-Tool Feedback

Authors: Vidyut Sriram, Sawan Pandita, Achintya Lakshmanan, Aneesh Shamraj, Suman Saha
Published: 2026-01-01
Categories: cs.CR, cs.LG
arXiv: https://arxiv.org/abs/2601.00509v1

Large Language Models (LLMs) can generate code but often introduce security vulnerabilities, logical inconsistencies, and compilation errors. Prior work demonstrates that LLMs benefit substantially from structured feedback, static analysis, retrieval augmentation, and execution-based refinement. We propose a retrieval-augmented, multi-tool repair workflow in which a single code-generating LLM iteratively refines its outputs using compiler diagnostics, CodeQL security scanning, and KLEE symbolic execution. A lightweight embedding model is used for semantic retrieval of previously successful repairs, providing security-focused examples that guide generation. Evaluated on a combined dataset of 3,242 programs generated by DeepSeek-Coder-1.3B and CodeLlama-7B, the system demonstrates significant improvements in robustness. For DeepSeek, security vulnerabilities were reduced by 96%. For the larger CodeLlama model, the critical security defect rate was decreased from 58.55% to 22.19%, highlighting the efficacy of tool-assisted self-repair even on "stubborn" models.

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### Rule-Based Approaches to Atomic Sentence Extraction

Authors: Lineesha Kamana, Akshita Ananda Subramanian, Mehuli Ghosh, Suman Saha
Published: 2026-01-01
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.00506v1

Natural language often combines multiple ideas into complex sentences. Atomic sentence extraction, the task of decomposing complex sentences into simpler sentences that each express a single idea, improves performance in information retrieval, question answering, and automated reasoning systems. Previous work has formalized the "split-and-rephrase" task and established evaluation metrics, and machine learning approaches using large language models have improved extraction accuracy. However, these methods lack interpretability and provide limited insight into which linguistic structures cause extraction failures. Although some studies have explored dependency-based extraction of subject-verb-object triples and clauses, no principled analysis has examined which specific clause structures and dependencies lead to extraction difficulties. This study addresses this gap by analyzing how complex sentence structures, including relative clauses, adverbial clauses, coordination patterns, and passive constructions, affect the performance of rule-based atomic sentence extraction. Using the WikiSplit dataset, we implemented dependency-based extraction rules in spaCy, generated 100 gold=standard atomic sentence sets, and evaluated performance using ROUGE and BERTScore. The system achieved ROUGE-1 F1 = 0.6714, ROUGE-2 F1 = 0.478, ROUGE-L F1 = 0.650, and BERTScore F1 = 0.5898, indicating moderate-to-high lexical, structural, and semantic alignment. Challenging structures included relative clauses, appositions, coordinated predicates, adverbial clauses, and passive constructions. Overall, rule-based extraction is reasonably accurate but sensitive to syntactic complexity.

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### MotionPhysics: Learnable Motion Distillation for Text-Guided Simulation

Authors: Miaowei Wang, Jakub Zadrożny, Oisin Mac Aodha, Amir Vaxman
Published: 2026-01-01
Categories: cs.CV, cs.AI, cs.GR
arXiv: https://arxiv.org/abs/2601.00504v1

Accurately simulating existing 3D objects and a wide variety of materials often demands expert knowledge and time-consuming physical parameter tuning to achieve the desired dynamic behavior. We introduce MotionPhysics, an end-to-end differentiable framework that infers plausible physical parameters from a user-provided natural language prompt for a chosen 3D scene of interest, removing the need for guidance from ground-truth trajectories or annotated videos. Our approach first utilizes a multimodal large language model to estimate material parameter values, which are constrained to lie within plausible ranges. We further propose a learnable motion distillation loss that extracts robust motion priors from pretrained video diffusion models while minimizing appearance and geometry inductive biases to guide the simulation. We evaluate MotionPhysics across more than thirty scenarios, including real-world, human-designed, and AI-generated 3D objects, spanning a wide range of materials such as elastic solids, metals, foams, sand, and both Newtonian and non-Newtonian fluids. We demonstrate that MotionPhysics produces visually realistic dynamic simulations guided by natural language, surpassing the state of the art while automatically determining physically plausible parameters. The code and project page are available at: https://wangmiaowei.github.io/MotionPhysics.github.io/.

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### Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials

Authors: Li Chen, Leonardo Medrano Sandonas, Shirong Huang, Alexander Croy, Gianaurelio Cuniberti
Published: 2026-01-01
Categories: physics.chem-ph, cs.LG
arXiv: https://arxiv.org/abs/2601.00503v1

Digital sensing faces challenges in developing sustainable methods to extend the applicability of customized e-noses to complex body odor volatilome (BOV). To address this challenge, we developed MORE-ML, a computational framework that integrates quantum-mechanical (QM) property data of e-nose molecular building blocks with machine learning (ML) methods to predict sensing-relevant properties. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between BOV molecules and mucin-derived receptors. This dataset provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of MORE-QX property space revealed weak correlations between QM properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Explainable AI methods further highlighted which QM properties most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for molecular receptors in BOV sensing. This approach establishes a foundation for advancing artificial sensing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications.

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### CPPO: Contrastive Perception Policy Optimization for VLM Agents

Authors: Ahmad Rezaei, Mohsen Gholami, Saeed Ranjbar Alvar, Kevin Cannons, Mohammad Asiful Hossain, Zhou Weimin, Yong Zhang, Mohammad Akbari
Published: 2026-01-01
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00501v2

We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision--language models (VLMs). Reliable perception is a core requirement for VLM-based agents that must reason and act in open-ended environments: faulty visual grounding cascades directly into faulty actions, hallucinated tool calls, and unsafe decisions. While reinforcement learning (RL) has significantly improved reasoning in language models, extending these advances to multimodal agents requires improving both perception and reasoning. Prior works address this challenge mainly through explicit perception rewards, which often require extra LLM judges, ground-truth annotations, or forced separation of perception from reasoning. CPPO addresses this limitation in a self-supervised manner by extending the RL objective with a Contrastive Perception Loss (CPL) that provides a direct learning signal for visual grounding. The contrastive objective encourages the model to become more sensitive to input visual information. To apply this signal effectively, CPPO identifies perception tokens using an entropy-shift mechanism in the model's output distributions under perturbed images and applies the contrastive loss selectively to those tokens during training. Experiments show that CPPO surpasses prior methods while avoiding extra models, making training more efficient and scalable, and yielding policies that are better suited to perception-critical agentic tasks.

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### Noise-Aware Named Entity Recognition for Historical VET Documents

Authors: Alexander M. Esser, Jens Dörpinghaus
Published: 2026-01-01
Categories: cs.CL, cs.IR, cs.LG
arXiv: https://arxiv.org/abs/2601.00488v1

This paper addresses Named Entity Recognition (NER) in the domain of Vocational Education and Training (VET), focusing on historical, digitized documents that suffer from OCR-induced noise. We propose a robust NER approach leveraging Noise-Aware Training (NAT) with synthetically injected OCR errors, transfer learning, and multi-stage fine-tuning. Three complementary strategies, training on noisy, clean, and artificial data, are systematically compared. Our method is one of the first to recognize multiple entity types in VET documents. It is applied to German documents but transferable to arbitrary languages. Experimental results demonstrate that domain-specific and noise-aware fine-tuning substantially increases robustness and accuracy under noisy conditions. We provide publicly available code for reproducible noise-aware NER in domain-specific contexts.

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### Multi-Agent Coordinated Rename Refactoring

Authors: Abhiram Bellur, Mohammed Raihan Ullah, Fraol Batole, Mohit Kansara, Masaharu Morimoto, Kai Ishikawa, Haifeng Chen, Yaroslav Zharov, Timofey Bryksin, Tien N. Nguyen, Hridesh Rajan, Danny Dig
Published: 2026-01-01
Categories: cs.SE, cs.AI
arXiv: https://arxiv.org/abs/2601.00482v1

The primary value of AI agents in software development lies in their ability to extend the developer's capacity for reasoning and action, not to supplant human involvement. To showcase how to use agents working in tandem with developers, we designed a novel approach for carrying out coordinated renaming. Coordinated renaming, where a single rename refactoring triggers refactorings in multiple, related identifiers, is a frequent yet challenging task. Developers must manually propagate these rename refactorings across numerous files and contexts, a process that is both tedious and highly error-prone. State-of-the-art heuristic-based approaches produce an overwhelming number of false positives, while vanilla Large Language Models (LLMs) provide incomplete suggestions due to their limited context and inability to interact with refactoring tools. This leaves developers with incomplete refactorings or burdens them with filtering too many false positives. Coordinated renaming is exactly the kind of repetitive task that agents can significantly reduce the developers' burden while keeping them in the driver's seat. We designed, implemented, and evaluated the first multi-agent framework that automates coordinated renaming. It operates on a key insight: a developer's initial refactoring is a clue to infer the scope of related refactorings. Our Scope Inference Agent first transforms this clue into an explicit, natural-language Declared Scope. The Planned Execution Agent then uses this as a strict plan to identify program elements that should undergo refactoring and safely executes the changes by invoking the IDE's own trusted refactoring APIs. Finally, the Replication Agent uses it to guide the project-wide search. We first conducted a formative study on the practice of coordinated renaming in 609K commits in 100 open-source projects and surveyed 205 developers ...

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### MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability

Authors: Tie Ma, Yixi Chen, Vaastav Anand, Alessandro Cornacchia, Amândio R. Faustino, Guanheng Liu, Shan Zhang, Hongbin Luo, Suhaib A. Fahmy, Zafar A. Qazi, Marco Canini
Published: 2026-01-01
Categories: cs.NI, cs.AI
arXiv: https://arxiv.org/abs/2601.00481v1

We present MAESTRO, an evaluation suite for the testing, reliability, and observability of LLM-based MAS. MAESTRO standardizes MAS configuration and execution through a unified interface, supports integrating both native and third-party MAS via a repository of examples and lightweight adapters, and exports framework-agnostic execution traces together with system-level signals (e.g., latency, cost, and failures). We instantiate MAESTRO with 12 representative MAS spanning popular agentic frameworks and interaction patterns, and conduct controlled experiments across repeated runs, backend models, and tool configurations. Our case studies show that MAS executions can be structurally stable yet temporally variable, leading to substantial run-to-run variance in performance and reliability. We further find that MAS architecture is the dominant driver of resource profiles, reproducibility, and cost-latency-accuracy trade-off, often outweighing changes in backend models or tool settings. Overall, MAESTRO enables systematic evaluation and provides empirical guidance for designing and optimizing agentic systems.

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### Progressive Ideation using an Agentic AI Framework for Human-AI Co-Creation

Authors: Sankar B, Srinidhi Ranjini Girish, Aadya Bharti, Dibakar Sen
Published: 2026-01-01
Categories: cs.AI, cs.HC
arXiv: https://arxiv.org/abs/2601.00475v1

The generation of truly novel and diverse ideas is important for contemporary engineering design, yet it remains a significant cognitive challenge for novice designers. Current 'single-spurt' AI systems exacerbate this challenge by producing a high volume of semantically clustered ideas. We propose MIDAS (Meta-cognitive Ideation through Distributed Agentic AI System), a novel framework that replaces the single-AI paradigm with a distributed 'team' of specialized AI agents designed to emulate the human meta-cognitive ideation workflow. This agentic system progressively refines ideas and assesses each one for both global novelty (against existing solutions) and local novelty (against previously generated ideas). MIDAS, therefore, demonstrates a viable and progressive paradigm for true human-AI co-creation, elevating the human designer from a passive filterer to a participatory, active, collaborative partner.

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### Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning

Authors: Abhisek Ganguly, Santosh Ansumali, Sauro Succi
Published: 2026-01-01
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2601.00473v4

We revisit the analogy between feed-forward deep neural networks (DNNs) and discrete dynamical systems derived from neural integral equations and their corresponding partial differential equation (PDE) forms. A comparative analysis between the numerical/exact solutions of the Burgers' and Eikonal equations, and the same obtained via PINNs is presented. We show that PINN learning provides a different computational pathway compared to standard numerical discretization in approximating essentially the same underlying dynamics of the system. Within this framework, DNNs can be interpreted as discrete dynamical systems whose layer-wise evolution approaches attractors, and multiple parameter configurations may yield comparable solutions, reflecting the degeneracy of the inverse mapping. In contrast to the structured operators associated with finite-difference (FD) procedures, PINNs learn dense parameter representations that are not directly associated with classical discretization stencils. This distributed representation generally involves a larger number of parameters, leading to reduced interpretability and increased computational cost. However, the additional flexibility of such representations may offer advantages in high-dimensional settings where classical grid-based methods become impractical.

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### Laplacian Kernelized Bandit

Authors: Shuang Wu, Arash A. Amini
Published: 2026-01-01
Categories: cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2601.00461v1

We study multi-user contextual bandits where users are related by a graph and their reward functions exhibit both non-linear behavior and graph homophily. We introduce a principled joint penalty for the collection of user reward functions $\{f_u\}$, combining a graph smoothness term based on RKHS distances with an individual roughness penalty. Our central contribution is proving that this penalty is equivalent to the squared norm within a single, unified \emph{multi-user RKHS}. We explicitly derive its reproducing kernel, which elegantly fuses the graph Laplacian with the base arm kernel. This unification allows us to reframe the problem as learning a single ''lifted'' function, enabling the design of principled algorithms, \texttt{LK-GP-UCB} and \texttt{LK-GP-TS}, that leverage Gaussian Process posteriors over this new kernel for exploration. We provide high-probability regret bounds that scale with an \emph{effective dimension} of the multi-user kernel, replacing dependencies on user count or ambient dimension. Empirically, our methods outperform strong linear and non-graph-aware baselines in non-linear settings and remain competitive even when the true rewards are linear. Our work delivers a unified, theoretically grounded, and practical framework that bridges Laplacian regularization with kernelized bandits for structured exploration.

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### Combining Residual U-Net and Data Augmentation for Dense Temporal Segmentation of Spike Wave Discharges in Single-Channel EEG

Authors: Saurav Sengupta, Scott Kilianski, Suchetha Sharma, Sakina Lashkeri, Ashley McHugh, Mark Beenhakker, Donald E. Brown
Published: 2026-01-01
Categories: cs.LG, eess.SP
arXiv: https://arxiv.org/abs/2601.00459v2

Manual annotation of spike-wave discharges (SWDs), the electrographic hallmark of absence seizures, is labor-intensive for long-term electroencephalography (EEG) monitoring studies. While machine learning approaches show promise for automated detection, they often struggle with cross-subject generalization due to high inter-individual variability in seizure morphology and signal characteristics. In this study we compare the performance of 15 machine learning classifiers on our own manually annotated dataset of 961 hours of EEG recordings from C3H/HeJ mice, including 22,637 labeled SWDs and find that a 1D U-Net performs the best. We then improve its performance by employing residual connections and data augmentation strategies combining amplitude scaling, Gaussian noise injection, and signal inversion during training to enhance cross-subject generalization. We also compare our method, named AugUNet1D, to a recently published time- and frequency-based algorithmic approach called "Twin Peaks" and show that AugUNet1D performs better on our dataset. AugUNet1D, pretrained on our manually annotated data or untrained, is made public for other users.

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### Geometric Regularization in Mixture-of-Experts: The Disconnect Between Weights and Activations

Authors: Hyunjun Kim
Published: 2026-01-01
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2601.00457v1

Mixture-of-Experts (MoE) models achieve efficiency through sparse activation, but the role of geometric regularization in expert specialization remains unclear. We apply orthogonality loss to enforce expert diversity and find it fails on multiple fronts: it does not reduce weight-space overlap (MSO actually increases by up to 114%), activation-space overlap remains high (~0.6) regardless of regularization, and effects on performance are inconsistent -- marginal improvement on WikiText-103 (-0.9%), slight degradation on TinyStories (+0.9%), and highly variable results on PTB (std > 1.0). Our analysis across 7 regularization strengths reveals no significant correlation (r = -0.293, p = 0.523) between weight and activation orthogonality. These findings demonstrate that weight-space regularization neither achieves its geometric goal nor reliably improves performance, making it unsuitable for MoE diversity.

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### Deep Networks Learn Deep Hierarchical Models

Authors: Amit Daniely
Published: 2026-01-01
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2601.00455v1

We consider supervised learning with $n$ labels and show that layerwise SGD on residual networks can efficiently learn a class of hierarchical models. This model class assumes the existence of an (unknown) label hierarchy $L_1 \subseteq L_2 \subseteq \dots \subseteq L_r = (n)$, where labels in $L_1$ are simple functions of the input, while for $i > 1$, labels in $L_i$ are simple functions of simpler labels. Our class surpasses models that were previously shown to be learnable by deep learning algorithms, in the sense that it reaches the depth limit of efficient learnability. That is, there are models in this class that require polynomial depth to express, whereas previous models can be computed by log-depth circuits. Furthermore, we suggest that learnability of such hierarchical models might eventually form a basis for understanding deep learning. Beyond their natural fit for domains where deep learning excels, we argue that the mere existence of human ``teachers" supports the hypothesis that hierarchical structures are inherently available. By providing granular labels, teachers effectively reveal ``hints'' or ``snippets'' of the internal algorithms used by the brain. We formalize this intuition, showing that in a simplified model where a teacher is partially aware of their internal logic, a hierarchical structure emerges that facilitates efficient learnability.

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### Defensive M2S: Training Guardrail Models on Compressed Multi-turn Conversations

Authors: Hyunjun Kim
Published: 2026-01-01
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.00454v1

Guardrail models are essential for ensuring the safety of Large Language Model (LLM) deployments, but processing full multi-turn conversation histories incurs significant computational cost. We propose Defensive M2S, a training paradigm that fine-tunes guardrail models on Multi-turn to Single-turn (M2S) compressed conversations rather than complete dialogue histories. We provide a formal complexity analysis showing that M2S reduces training cost from $O(n^2)$ to $O(n)$ for $n$-turn conversations. Empirically, on our training dataset (779 samples, avg. 10.6 turns), M2S requires only 169K tokens compared to 15.7M tokens for the multi-turn baseline -- a 93$\times$ reduction. We evaluate Defensive M2S across three guardrail model families (LlamaGuard, Nemotron, Qwen3Guard) and three compression templates (hyphenize, numberize, pythonize) on SafeDialBench, a comprehensive multi-turn jailbreak benchmark. Our best configuration, Qwen3Guard with hyphenize compression, achieves 93.8% attack detection recall while reducing inference tokens by 94.6% (from 3,231 to 173 tokens per conversation). This represents a 38.9 percentage point improvement over the baseline while dramatically reducing both training and inference costs. Our findings demonstrate that M2S compression can serve as an effective efficiency technique for guardrail deployment, enabling scalable safety screening of long multi-turn conversations.

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### Imitation from Observations with Trajectory-Level Generative Embeddings

Authors: Yongtao Qu, Shangzhe Li, Weitong Zhang
Published: 2026-01-01
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00452v2

We consider the offline imitation learning from observations (LfO) where the expert demonstrations are scarce and the available offline suboptimal data are far from the expert behavior. Many existing distribution-matching approaches struggle in this regime because they impose strict support constraints and rely on brittle one-step models, making it hard to extract useful signal from imperfect data. To tackle this challenge, we propose TGE, a trajectory-level generative embedding for offline LfO that constructs a dense, smooth surrogate reward by estimating expert state density in the latent space of a temporal diffusion model trained on offline trajectory data. By leveraging the smooth geometry of the learned diffusion embedding, TGE captures long-horizon temporal dynamics and effectively bridges the gap between disjoint supports, ensuring a robust learning signal even when offline data is distributionally distinct from the expert. Empirically, the proposed approach consistently matches or outperforms prior offline LfO methods across a range of D4RL locomotion and manipulation benchmarks.

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### Controllable Concept Bottleneck Models

Authors: Hongbin Lin, Chenyang Ren, Juangui Xu, Zhengyu Hu, Cheng-Long Wang, Yao Shu, Hui Xiong, Jingfeng Zhang, Di Wang, Lijie Hu
Published: 2026-01-01
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00451v1

Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most previous studies focused on static scenarios where the data and concepts are assumed to be fixed and clean. In real-world applications, deployed models require continuous maintenance: we often need to remove erroneous or sensitive data (unlearning), correct mislabeled concepts, or incorporate newly acquired samples (incremental learning) to adapt to evolving environments. Thus, deriving efficient editable CBMs without retraining from scratch remains a significant challenge, particularly in large-scale applications. To address these challenges, we propose Controllable Concept Bottleneck Models (CCBMs). Specifically, CCBMs support three granularities of model editing: concept-label-level, concept-level, and data-level, the latter of which encompasses both data removal and data addition. CCBMs enjoy mathematically rigorous closed-form approximations derived from influence functions that obviate the need for retraining. Experimental results demonstrate the efficiency and adaptability of our CCBMs, affirming their practical value in enabling dynamic and trustworthy CBMs.

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### Quadratic Unconstrained Binary Optimisation for Training and Regularisation of Binary Neural Networks

Authors: Jonas Christoffer Villumsen, Yusuke Sugita
Published: 2026-01-01
Categories: math.OC, cs.NE
arXiv: https://arxiv.org/abs/2601.00449v1

Advances in artificial intelligence (AI) and deep learning have raised concerns about its increasing energy consumption, while demand for deploying AI in mobile devices and machines at the edge is growing. Binary neural networks (BNNs) have recently gained attention as energy and memory efficient models suitable for resource constrained environments; however, training BNNs exactly is computationally challenging because of its discrete characteristics. Recent work proposing a framework for training BNNs based on quadratic unconstrained binary optimisation (QUBO) and progress in the design of Ising machines for solving QUBO problems suggest a potential path to efficiently optimising discrete neural networks. In this work, we extend existing QUBO models for training BNNs to accommodate arbitrary network topologies and propose two novel methods for regularisation. The first method maximises neuron margins biasing the training process toward parameter configurations that yield larger pre-activation magnitudes. The second method employs a dropout-inspired iterative scheme in which reduced subnetworks are trained and used to adjust linear penalties on network parameters. We apply the proposed QUBO formulation to a small binary image classification problem and conduct computational experiments on a GPU-based Ising machine. The numerical results indicate that the proposed regularisation terms modify training behaviour and yield improvements in classification accuracy on data not present in the training set.

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### Language as Mathematical Structure: Examining Semantic Field Theory Against Language Games

Authors: Dimitris Vartziotis
Published: 2026-01-01
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2601.00448v1

Large language models (LLMs) offer a new empirical setting in which long-standing theories of linguistic meaning can be examined. This paper contrasts two broad approaches: social constructivist accounts associated with language games, and a mathematically oriented framework we call Semantic Field Theory. Building on earlier work by the author, we formalize the notions of lexical fields (Lexfelder) and linguistic fields (Lingofelder) as interacting structures in a continuous semantic space. We then analyze how core properties of transformer architectures-such as distributed representations, attention mechanisms, and geometric regularities in embedding spaces-relate to these concepts. We argue that the success of LLMs in capturing semantic regularities supports the view that language exhibits an underlying mathematical structure, while their persistent limitations in pragmatic reasoning and context sensitivity are consistent with the importance of social grounding emphasized in philosophical accounts of language use. On this basis, we suggest that mathematical structure and language games can be understood as complementary rather than competing perspectives. The resulting framework clarifies the scope and limits of purely statistical models of language and motivates new directions for theoretically informed AI architectures.

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### A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection

Authors: Miseon Park, Kijung Yoon
Published: 2026-01-01
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00446v1

Time series anomaly detection is essential for the reliable operation of complex systems, but most existing methods require extensive task-specific training. We explore whether time series foundation models (TSFMs), pretrained on large heterogeneous data, can serve as universal backbones for anomaly detection. Through systematic experiments across multiple benchmarks, we compare zero-shot inference, full model adaptation, and parameter-efficient fine-tuning (PEFT) strategies. Our results demonstrate that TSFMs outperform task-specific baselines, achieving notable gains in AUC-PR and VUS-PR, particularly under severe class imbalance. Moreover, PEFT methods such as LoRA, OFT, and HRA not only reduce computational cost but also match or surpass full fine-tuning in most cases, indicating that TSFMs can be efficiently adapted for anomaly detection, even when pretrained for forecasting. These findings position TSFMs as promising general-purpose models for scalable and efficient time series anomaly detection.

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### Comparative Efficiency Analysis of Lightweight Transformer Models: A Multi-Domain Empirical Benchmark for Enterprise NLP Deployment

Authors: Muhammad Shahmeer Khan
Published: 2026-01-01
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.00444v1

In the rapidly evolving landscape of enterprise natural language processing (NLP), the demand for efficient, lightweight models capable of handling multi-domain text automation tasks has intensified. This study conducts a comparative analysis of three prominent lightweight Transformer models - DistilBERT, MiniLM, and ALBERT - across three distinct domains: customer sentiment classification, news topic classification, and toxicity and hate speech detection. Utilizing datasets from IMDB, AG News, and the Measuring Hate Speech corpus, we evaluated performance using accuracy-based metrics including accuracy, precision, recall, and F1-score, as well as efficiency metrics such as model size, inference time, throughput, and memory usage. Key findings reveal that no single model dominates all performance dimensions. ALBERT achieves the highest task-specific accuracy in multiple domains, MiniLM excels in inference speed and throughput, and DistilBERT demonstrates the most consistent accuracy across tasks while maintaining competitive efficiency. All results reflect controlled fine-tuning under fixed enterprise-oriented constraints rather than exhaustive hyperparameter optimization. These results highlight trade-offs between accuracy and efficiency, recommending MiniLM for latency-sensitive enterprise applications, DistilBERT for balanced performance, and ALBERT for resource-constrained environments.

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### Toward Better Temporal Structures for Geopolitical Events Forecasting

Authors: Kian Ahrabian, Eric Boxer, Jay Pujara
Published: 2026-01-01
Categories: cs.CL
arXiv: https://arxiv.org/abs/2601.00430v2

Forecasting on geopolitical temporal knowledge graphs (TKGs) through the lens of large language models (LLMs) has recently gained traction. While TKGs and their generalization, hyper-relational temporal knowledge graphs (HTKGs), offer a straightforward structure to represent simple temporal relationships, they lack the expressive power to convey complex facts efficiently. One of the critical limitations of HTKGs is a lack of support for more than two primary entities in temporal facts, which commonly occur in real-world events. To address this limitation, in this work, we study a generalization of HTKGs, Hyper-Relational Temporal Knowledge Generalized Hypergraphs (HTKGHs). We first derive a formalization for HTKGHs, demonstrating their backward compatibility while supporting two complex types of facts commonly found in geopolitical incidents. Then, utilizing this formalization, we introduce the htkgh-polecat dataset, built upon the global event database POLECAT. Finally, we benchmark and analyze popular LLMs on our dataset, providing insights into 1) the positive impact of utilizing the HTKGH formalization compared to existing ones and 2) LLMs' adaptability and capabilities in complex forecasting tasks.

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### Interpretable ML Under the Microscope: Performance, Meta-Features, and the Regression-Classification Predictability Gap

Authors: Mattia Billa, Giovanni Orlandi, Veronica Guidetti, Federica Mandreoli
Published: 2026-01-01
Categories: cs.LG
arXiv: https://arxiv.org/abs/2601.00428v2

As machine learning models are increasingly deployed in high-stakes domains, the need for interpretability has grown to meet strict regulatory and accountability constraints. Despite this interest, systematic evaluations of inherently interpretable models for tabular data remain scarce and often focus solely on aggregated performance. To address this gap, we evaluate sixteen interpretable methods, including Explainable Boosting Machines (EBMs), Symbolic Regression (SR), and Generalized Optimal Sparse Decision Trees, across 216 real-world tabular datasets. We assess predictive accuracy, computational efficiency, and generalization under distributional shifts. Moving beyond aggregate performance rankings, we further analyze how model behavior varies with dataset meta-features and operationalize these descriptors to study algorithm selection. Our analyses reveal a clear dichotomy: in regression tasks, models exhibit a predictable performance hierarchy dominated by EBMs and SR that can be inferred from dataset characteristics. In contrast, classification performance remains highly dataset-dependent with no stable hierarchy, showing that standard complexity measures fail to provide actionable guidance. Furthermore, we identify an "interpretability tax", showing that models explicitly optimizing for structural sparsity incur significantly longer training times. Overall, these findings provide practical guidance for practitioners seeking a balance between interpretability and predictive performance, and contribute to a deeper empirical understanding of interpretable modeling for tabular data.

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### RMAAT: Astrocyte-Inspired Memory Compression and Replay for Efficient Long-Context Transformers

Authors: Md Zesun Ahmed Mia, Malyaban Bal, Abhronil Sengupta
Published: 2026-01-01
Categories: cs.NE, cs.AI, cs.ET, cs.LG
arXiv: https://arxiv.org/abs/2601.00426v2

The quadratic complexity of self-attention mechanism presents a significant impediment to applying Transformer models to long sequences. This work explores computational principles derived from astrocytes-glial cells critical for biological memory and synaptic modulation-as a complementary approach to conventional architectural modifications for efficient self-attention. We introduce the Recurrent Memory Augmented Astromorphic Transformer (RMAAT), an architecture integrating abstracted astrocyte functionalities. RMAAT employs a recurrent, segment-based processing strategy where persistent memory tokens propagate contextual information. An adaptive compression mechanism, governed by a novel retention factor derived from simulated astrocyte long-term plasticity (LTP), modulates these tokens. Attention within segments utilizes an efficient, linear-complexity mechanism inspired by astrocyte short-term plasticity (STP). Training is performed using Astrocytic Memory Replay Backpropagation (AMRB), a novel algorithm designed for memory efficiency in recurrent networks. Evaluations on the Long Range Arena (LRA) benchmark demonstrate RMAAT's competitive accuracy and substantial improvements in computational and memory efficiency, indicating the potential of incorporating astrocyte-inspired dynamics into scalable sequence models.

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### E-GRPO: High Entropy Steps Drive Effective Reinforcement Learning for Flow Models

Authors: Shengjun Zhang, Zhang Zhang, Chensheng Dai, Yueqi Duan
Published: 2026-01-01
Categories: cs.LG, cs.AI, cs.CV
arXiv: https://arxiv.org/abs/2601.00423v1

Recent reinforcement learning has enhanced the flow matching models on human preference alignment. While stochastic sampling enables the exploration of denoising directions, existing methods which optimize over multiple denoising steps suffer from sparse and ambiguous reward signals. We observe that the high entropy steps enable more efficient and effective exploration while the low entropy steps result in undistinguished roll-outs. To this end, we propose E-GRPO, an entropy aware Group Relative Policy Optimization to increase the entropy of SDE sampling steps. Since the integration of stochastic differential equations suffer from ambiguous reward signals due to stochasticity from multiple steps, we specifically merge consecutive low entropy steps to formulate one high entropy step for SDE sampling, while applying ODE sampling on other steps. Building upon this, we introduce multi-step group normalized advantage, which computes group-relative advantages within samples sharing the same consolidated SDE denoising step. Experimental results on different reward settings have demonstrated the effectiveness of our methods.

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### Robust Assembly Progress Estimation via Deep Metric Learning

Authors: Kazuma Miura, Sarthak Pathak, Kazunori Umeda
Published: 2026-01-01
Categories: cs.CV
arXiv: https://arxiv.org/abs/2601.00422v1

In recent years, the advancement of AI technologies has accelerated the development of smart factories. In particular, the automatic monitoring of product assembly progress is crucial for improving operational efficiency, minimizing the cost of discarded parts, and maximizing factory productivity. However, in cases where assembly tasks are performed manually over multiple days, implementing smart factory systems remains a challenge. Previous work has proposed Anomaly Triplet-Net, which estimates assembly progress by applying deep metric learning to the visual features of products. Nevertheless, when visual changes between consecutive tasks are subtle, misclassification often occurs. To address this issue, this paper proposes a robust system for estimating assembly progress, even in cases of occlusion or minimal visual change, using a small-scale dataset. Our method leverages a Quadruplet Loss-based learning approach for anomaly images and introduces a custom data loader that strategically selects training samples to enhance estimation accuracy. We evaluated our approach using a image datasets: captured during desktop PC assembly. The proposed Anomaly Quadruplet-Net outperformed existing methods on the dataset. Specifically, it improved the estimation accuracy by 1.3% and reduced misclassification between adjacent tasks by 1.9% in the desktop PC dataset and demonstrating the effectiveness of the proposed method.

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