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47f2574b5d1fc25eb33b80356a3f9471382441b0bec5de411e4f2f60f15aeb8b
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2026-01-07T00:00:00-05:00
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Exploring Blockchain Interoperability: Frameworks, Use Cases, and Future Challenges
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arXiv:2601.02949v1 Announce Type: new Abstract: Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trustworthy manner. The early generations of blockchain have a problem; they cannot share information with other blockchains. As more and more entities move their applications to the blockchain, they generate large volumes of data, and as applications have become more complex, sharing information between different blockchains has become a necessity. This has led to the research and development of interoperable solutions allowing blockchains to connect together. This paper discusses a few blockchain platforms that provide interoperable solutions, emphasising their ability to connect heterogeneous blockchains. It also discusses a case study scenario to illustrate the importance and benefits of using interoperable solutions. We also present a few topics that need to be solved in the realm of interoperability.
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https://arxiv.org/abs/2601.02949
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Academic Papers
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2c114c823bb649277dbe203e775b00c01027f26a6af8c479853133aaffcbe3c0
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2026-01-07T00:00:00-05:00
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Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning
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arXiv:2601.02950v1 Announce Type: new Abstract: Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch-of-Thought (BoT), a training-free method that processes related queries jointly to enable cross-instance learning. By performing comparative analysis across batches, BoT identifies high-quality reasoning templates, detects errors through consistency checks, and amortizes computational costs. We instantiate BoT within a multi-agent reflection architecture (BoT-R), where a Reflector performs joint evaluation to unlock mutual information gain unavailable in isolated processing. Experiments across three model families and six benchmarks demonstrate that BoT-R consistently improves accuracy and confidence calibration while reducing inference costs by up to 61%. Our theoretical and experimental analysis reveals when and why batch-aware reasoning benefits LLM systems.
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https://arxiv.org/abs/2601.02950
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Academic Papers
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a10cbda24cf58e1c586845765da2614374481dd56d1044f3ab70f627813aca46
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2026-01-07T00:00:00-05:00
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The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models
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arXiv:2601.02954v1 Announce Type: new Abstract: Existing large audio-language models perceive the world as "mono" -- a single stream of audio that ignores the critical spatial dimension ("where") required for universal acoustic scene analysis. To bridge this gap, we first introduce a hierarchical framework for Auditory Scene Analysis (ASA). Guided by this framework, we introduce a system that enables models like Qwen2-Audio to understand and reason about the complex acoustic world. Our framework achieves this through three core contributions: First, we build a large-scale, synthesized binaural audio dataset to provide the rich spatial cues. Second, we design a hybrid feature projector, which leverages parallel semantic and spatial encoders to extract decoupled representations. These distinct streams are integrated via a dense fusion mechanism, ensuring the model receives a holistic view of the acoustic scene. Finally, we employ a progressive training curriculum, advancing from supervised fine-tuning (SFT) to reinforcement learning via Group Relative Policy Optimization (GRPO), to explicitly evolve the model's capabilities towards reasoning. On our comprehensive benchmark, the model demonstrates comparatively strong capability for spatial understanding. By enabling this spatial perception, our work provides a clear pathway for leveraging the powerful reasoning abilities of large models towards holistic acoustic scene analysis, advancing from "mono" semantic recognition to spatial intelligence.
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https://arxiv.org/abs/2601.02954
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Academic Papers
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23d4a7e518f71d92dde30c8f088099b1c40e06483a136683a629af88a3474b46
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2026-01-07T00:00:00-05:00
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HarmonRank: Ranking-aligned Multi-objective Ensemble for Live-streaming E-commerce Recommendation
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arXiv:2601.02955v1 Announce Type: new Abstract: Recommendation for live-streaming e-commerce is gaining increasing attention due to the explosive growth of the live streaming economy. Different from traditional e-commerce, live-streaming e-commerce shifts the focus from products to streamers, which requires ranking mechanism to balance both purchases and user-streamer interactions for long-term ecology. To trade off multiple objectives, a popular solution is to build an ensemble model to integrate multi-objective scores into a unified score. The ensemble model is usually supervised by multiple independent binary classification losses of all objectives. However, this paradigm suffers from two inherent limitations. First, the optimization direction of the binary classification task is misaligned with the ranking task (evaluated by AUC). Second, this paradigm overlooks the alignment between objectives, e.g., comment and buy behaviors are partially dependent which can be revealed in labels correlations. The model can achieve better trade-offs if it learns the aligned parts of ranking abilities among different objectives. To mitigate these limitations, we propose a novel multi-objective ensemble framework HarmonRank to fulfill both alignment to the ranking task and alignment among objectives. For alignment to ranking, we formulate ranking metric AUC as a rank-sum problem and utilize differentiable ranking techniques for ranking-oriented optimization. For inter-objective alignment, we change the original one-step ensemble paradigm to a two-step relation-aware ensemble scheme. Extensive offline experiments results on two industrial datasets and online experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods. The proposed method has been fully deployed in Kuaishou's live-streaming e-commerce recommendation platform with 400 million DAUs, contributing over 2% purchase gain.
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https://arxiv.org/abs/2601.02955
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Academic Papers
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32fa1fdb9b8be5e4c6cd956614cf14526e9191f503abb15a4bee9adb9ca61360
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2026-01-07T00:00:00-05:00
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Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion
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arXiv:2601.02956v1 Announce Type: new Abstract: Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adoption of English pivoting. While prior studies attribute this advantage to the superior English-centric capabilities of Large Language Models (LLMs), we find that such measurements are significantly distorted by structural priors inherent in evaluation benchmarks. Specifically, we identify exposure bias and a gold availability prior-both driven by the disproportionate concentration of resources in English-as well as cultural priors rooted in topic locality, as factors that hinder accurate assessment of genuine language preference. To address these biases, we propose DeLP (Debiased Language Preference), a calibrated metric designed to explicitly factor out these structural confounds. Our analysis using DeLP reveals that the previously reported English preference is largely a byproduct of evidence distribution rather than an inherent model bias. Instead, we find that retrievers fundamentally favor monolingual alignment between the query and the document language. Building on this insight, we introduce DELTA (DEbiased Language preference-guided Text Augmentation), a lightweight and efficient mRAG framework that strategically leverages monolingual alignment to optimize cross-lingual retrieval and generation. Experimental results demonstrate that DELTA consistently outperforms English pivoting and mRAG baselines across diverse languages.
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https://arxiv.org/abs/2601.02956
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Academic Papers
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8a6a2075b4a5447b77dbbd1af584c8fdaa18111396669863edaacb1f8b8ee129
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2026-01-07T00:00:00-05:00
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LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation
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arXiv:2601.02957v1 Announce Type: new Abstract: This paper introduces a novel changepoint detection framework that combines ensemble statistical methods with Large Language Models (LLMs) to enhance both detection accuracy and the interpretability of regime changes in time series data. Two critical limitations in the field are addressed. First, individual detection methods exhibit complementary strengths and weaknesses depending on data characteristics, making method selection non-trivial and prone to suboptimal results. Second, automated, contextual explanations for detected changes are largely absent. The proposed ensemble method aggregates results from ten distinct changepoint detection algorithms, achieving superior performance and robustness compared to individual methods. Additionally, an LLM-powered explanation pipeline automatically generates contextual narratives, linking detected changepoints to potential real-world historical events. For private or domain-specific data, a Retrieval-Augmented Generation (RAG) solution enables explanations grounded in user-provided documents. The open source Python framework demonstrates practical utility in diverse domains, including finance, political science, and environmental science, transforming raw statistical output into actionable insights for analysts and decision-makers.
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https://arxiv.org/abs/2601.02957
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Academic Papers
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d0ba1e08a4feb08b3bb74d7308d64d2f8672cc436b141cff2fe276823aeccc76
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2026-01-07T00:00:00-05:00
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Post-Earthquake Restoration of Electricity-Gas Distribution Systems with Damage Information Collection and Repair Vehicle Routing
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arXiv:2601.02958v1 Announce Type: new Abstract: Extreme events such as earthquakes pose significant threats to integrated electricity-gas distribution systems (IEGDS) by causing widespread damage. Existing restoration approaches typically assume full awareness of damage, which may not be true if monitoring and communication infrastructures are impaired. In such circumstances, field inspection is necessary. This paper presents a novel adaptive restoration framework for IEGDS, considering dynamic damage assessment and repair. The restoration problem is formulated as a partially observable Markov decision process (POMDP), capturing the gradually revealed contingency and the evolving impact of field crew actions. To address the computational challenges of POMDPs in real-time applications, an advanced belief tree search (BTS) algorithm is introduced. This algorithm enables crew members to continuously update their actions based on evolving belief states, leveraging comprehensive simulations to evaluate potential future trajectories and identify optimal inspection and repair strategies. Based on the BTS algorithm, a unified real-time decision-making framework is developed for IEGDS restoration. Case studies on two distinct IEGDS systems demonstrate the effectiveness and scalability of the proposed method. The results indicate that the proposed approach achieves an outage cost comparable to the ideal solution, and reduces the total outage cost by more than 15% compared to strategies based on stochastic programming and heuristic methods.
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https://arxiv.org/abs/2601.02958
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Academic Papers
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b3e37758de39933a200c4c56d1cb709a8845a2e02ad039eda98b1c6046f0f13e
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2026-01-07T00:00:00-05:00
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Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation
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arXiv:2601.02962v1 Announce Type: new Abstract: Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain.
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https://arxiv.org/abs/2601.02962
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Academic Papers
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6cd9e7ae2ec983e5d5dd240e0518392f0014b9b943f9681224e60c561ed85d1a
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2026-01-07T00:00:00-05:00
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Low-Resource Heuristics for Bahnaric Optical Character Recognition Improvement
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arXiv:2601.02965v1 Announce Type: new Abstract: Bahnar, a minority language spoken across Vietnam, Cambodia, and Laos, faces significant preservation challenges due to limited research and data availability. This study addresses the critical need for accurate digitization of Bahnar language documents through optical character recognition (OCR) technology. Digitizing scanned paper documents poses significant challenges, as degraded image quality from broken or blurred areas introduces considerable OCR errors that compromise information retrieval systems. We propose a comprehensive approach combining advanced table and non-table detection techniques with probability-based post-processing heuristics to enhance recognition accuracy. Our method first applies detection algorithms to improve input data quality, then employs probabilistic error correction on OCR output. Experimental results indicate a substantial improvement, with recognition accuracy increasing from 72.86% to 79.26%. This work contributes valuable resources for Bahnar language preservation and provides a framework applicable to other minority language digitization efforts.
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https://arxiv.org/abs/2601.02965
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Academic Papers
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2dc2482c7f0b81a5c6bf0f01badfaed927188b1d597df3cc06b16eeacafc0687
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2026-01-07T00:00:00-05:00
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MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free
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arXiv:2601.02967v1 Announce Type: new Abstract: Extending the input modality of Large Language Models~(LLMs) to the audio domain is essential for achieving comprehensive multimodal perception. However, it is well-known that acoustic information is intrinsically \textit{heterogeneous}, entangling attributes such as speech, music, and environmental context. Existing research is limited to a dense, parameter-shared adapter to model these diverse patterns, which induces \textit{gradient conflict} during optimization, as parameter updates required for distinct attributes contradict each other. To address this limitation, we introduce the \textit{\textbf{MoE-Adapter}}, a sparse Mixture-of-Experts~(MoE) architecture designed to decouple acoustic information. Specifically, it employs a dynamic gating mechanism that routes audio tokens to specialized experts capturing complementary feature subspaces while retaining shared experts for global context, thereby mitigating gradient conflicts and enabling fine-grained feature learning. Comprehensive experiments show that the MoE-Adapter achieves superior performance on both audio semantic and paralinguistic tasks, consistently outperforming dense linear baselines with comparable computational costs. Furthermore, we will release the related code and models to facilitate future research.
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https://arxiv.org/abs/2601.02967
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Academic Papers
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3c30b28c7ea8ec4261122c4d92a1ac6aba95f4092142ba138983ead1b399bc2c
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2026-01-07T00:00:00-05:00
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Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models
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arXiv:2601.02968v1 Announce Type: new Abstract: The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their downstream outcomes, which leads models to rely on superficial pattern matching rather than principled reasoning. We therefore propose the rationale-grounded in-context learning for time series reasoning, where rationales work as guiding reasoning units rather than post-hoc explanations, and develop the RationaleTS method. Specifically, we firstly induce label-conditioned rationales, composed of reasoning paths from observable evidence to the potential outcomes. Then, we design the hybrid retrieval by balancing temporal patterns and semantic contexts to retrieve correlated rationale priors for the final in-context inference on new samples. We conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposed RationaleTS on three-domain time series reasoning tasks. We will release our code for reproduction.
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https://arxiv.org/abs/2601.02968
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Academic Papers
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db99c9ff82b51baad555936cb72149275c53bdee90d774f2ebccb67f1a18bd6f
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2026-01-07T00:00:00-05:00
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Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning
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arXiv:2601.02970v1 Announce Type: new Abstract: Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency methods mitigate this issue by adjusting the sampling budget; however, they rely on count-based stopping rules that treat all responses equally, often leading to unnecessary sampling. We propose Reliability-Aware Adaptive Self-Consistency (ReASC), which addresses this limitation by reframing adaptive sampling from response counting to evidence sufficiency, leveraging response-level confidence for principled information aggregation. ReASC operates in two stages: a single-sample decision stage that resolves instances confidently answerable from a single response, and a reliability-aware accumulation stage that aggregates responses by jointly leveraging their frequency and confidence. Across five models and four datasets, ReASC consistently achieves the best accuracy-cost trade-off compared to existing baselines, yielding improved inference efficiency across model scales from 3B to 27B parameters. As a concrete example, ReASC reduces inference cost by up to 70\% relative to self-consistency while preserving accuracy on GSM8K using Gemma-3-4B-it.
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https://arxiv.org/abs/2601.02970
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Academic Papers
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f07b6b73339db700d20aace8026502e187e584d24a9034d9fea7a204c431f0af
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2026-01-07T00:00:00-05:00
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Few-shot learning for security bug report identification
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arXiv:2601.02971v1 Announce Type: new Abstract: Security bug reports require prompt identification to minimize the window of vulnerability in software systems. Traditional machine learning (ML) techniques for classifying bug reports to identify security bug reports rely heavily on large amounts of labeled data. However, datasets for security bug reports are often scarce in practice, leading to poor model performance and limited applicability in real-world settings. In this study, we propose a few-shot learning-based technique to effectively identify security bug reports using limited labeled data. We employ SetFit, a state-of-the-art few-shot learning framework that combines sentence transformers with contrastive learning and parameter-efficient fine-tuning. The model is trained on a small labeled dataset of bug reports and is evaluated on its ability to classify these reports as either security-related or non-security-related. Our approach achieves an AUC of 0.865, at best, outperforming traditional ML techniques (baselines) for all of the evaluated datasets. This highlights the potential of SetFit to effectively identify security bug reports. SetFit-based few-shot learning offers a promising alternative to traditional ML techniques to identify security bug reports. The approach enables efficient model development with minimal annotation effort, making it highly suitable for scenarios where labeled data is scarce.
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https://arxiv.org/abs/2601.02971
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Academic Papers
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56f6e7dd732e768672708191058f5899d96d06861ef5032f4256cfa35832baed
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2026-01-07T00:00:00-05:00
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Correct, Concise and Complete: Multi-stage Training For Adaptive Reasoning
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arXiv:2601.02972v1 Announce Type: new Abstract: The reasoning capabilities of large language models (LLMs) have improved substantially through increased test-time computation, typically in the form of intermediate tokens known as chain-of-thought (CoT). However, CoT often becomes unnecessarily long, increasing computation cost without actual accuracy gains or sometimes even degrading performance, a phenomenon known as ``overthinking''. We propose a multi-stage efficient reasoning method that combines supervised fine-tuning -- via rejection sampling or reasoning trace reformatting -- with reinforcement learning using an adaptive length penalty. We introduce a lightweight reward function that penalizes tokens generated after the first correct answer but encouraging self-verification only when beneficial. We conduct a holistic evaluation across seven diverse reasoning tasks, analyzing the accuracy--response length trade-off. Our approach reduces response length by an average of 28\% for 8B models and 40\% for 32B models, while incurring only minor performance drops of 1.6 and 2.5 points, respectively. Despite its conceptual simplicity, it achieves a superior trade-off compared to more complex state-of-the-art efficient reasoning methods, scoring 76.6, in terms of the area under the Overthinking-Adjusted Accuracy curve ($\text{AUC}_{\text{OAA}}$) -- 5 points above the base model and 2.5 points above the second-best approach.
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https://arxiv.org/abs/2601.02972
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Academic Papers
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e24972c760bee3e3b2a65094bfaa6bf995457c700060303c00cee3547837d5d2
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2026-01-07T00:00:00-05:00
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A Fourth-Order Cut-cell Multigrid Method for Generic Elliptic Equations on Arbitrary Domains
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arXiv:2601.02975v1 Announce Type: new Abstract: To numerically solve a generic elliptic equation on two-dimensional domains with rectangular Cartesian grids, we propose a cut-cell geometric multigrid method that features (1) general algorithmic steps that apply to all forms of elliptic equations and all types of boundary conditions, (2) the versatility of handling both regular and irregular domains with arbitrarily complex topology and geometry, (3) the fourth-order accuracy even at the presence of ${\cal C}^1$ discontinuities on the domain boundary, and (4) the optimal complexity of $O(h^{-2})$. Test results demonstrate the generality, accuracy, efficiency, robustness, and excellent conditioning of the proposed method.
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https://arxiv.org/abs/2601.02975
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Academic Papers
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45b40ba1b9f006dece531cd6322689b0ad1a5285447a25e16388bd82fe8f97be
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2026-01-07T00:00:00-05:00
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Mechanistic Knobs in LLMs: Retrieving and Steering High-Order Semantic Features via Sparse Autoencoders
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arXiv:2601.02978v1 Announce Type: new Abstract: Recent work in Mechanistic Interpretability (MI) has enabled the identification and intervention of internal features in Large Language Models (LLMs). However, a persistent challenge lies in linking such internal features to the reliable control of complex, behavior-level semantic attributes in language generation. In this paper, we propose a Sparse Autoencoder-based framework for retrieving and steering semantically interpretable internal features associated with high-level linguistic behaviors. Our method employs a contrastive feature retrieval pipeline based on controlled semantic oppositions, combing statistical activation analysis and generation-based validation to distill monosemantic functional features from sparse activation spaces. Using the Big Five personality traits as a case study, we demonstrate that our method enables precise, bidirectional steering of model behavior while maintaining superior stability and performance compared to existing activation steering methods like Contrastive Activation Addition (CAA). We further identify an empirical effect, which we term Functional Faithfulness, whereby intervening on a specific internal feature induces coherent and predictable shifts across multiple linguistic dimensions aligned with the target semantic attribute. Our findings suggest that LLMs internalize deeply integrated representations of high-order concepts, and provide a novel, robust mechanistic path for the regulation of complex AI behaviors.
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https://arxiv.org/abs/2601.02978
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Academic Papers
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bde238847e8580714b25f9c02693249488ed49c1d6a0dd9718760a51a52db06e
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2026-01-07T00:00:00-05:00
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Developing and Evaluating Lightweight Cryptographic Algorithms for Secure Embedded Systems in IoT Devices
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arXiv:2601.02981v1 Announce Type: new Abstract: The high rate of development of Internet of Things (IoT) devices has brought to attention new challenges in the area of data security, especially within the resource-limited realm of RFID tags, sensors, and embedded systems. Traditional cryptographic implementations can be of inappropriate computational complexity and energy usage and hence are not suitable on these platforms. This paper examines the design, implementation, and testing of lightweight cryptographic algorithms that have been specifically designed to be used in secure embedded systems. A comparison of some of the state-of-the-art lightweight encryption algorithms, that is PRESENT, SPECK, and SIMON, focuses on the main performance indicators, i.e., throughput, use of memory, and energy utilization. The study presents novel lightweight algorithms that are founded upon the Feistel-network architecture and their safety under cryptanalytic attacks, e.g., differential and linear cryptanalysis. The proposed solutions are proven through hardware implementation on the FPGA platform. The results have shown that lightweight cryptography is an effective strategy that could be used to establish security and maintain performance in the IoT and other resource-limited settings.
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https://arxiv.org/abs/2601.02981
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Academic Papers
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d0e9d6e10a60bbb2e679c54e892f1b0117f6207f863cf121aceeac5370c01aa3
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2026-01-07T00:00:00-05:00
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Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning
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arXiv:2601.02983v1 Announce Type: new Abstract: Recent advances in audio large language models (ALLMs) have made high-quality synthetic audio widely accessible, increasing the risk of malicious audio deepfakes across speech, environmental sounds, singing voice, and music. Real-world audio deepfake detection (ADD) therefore requires all-type detectors that generalize across heterogeneous audio and provide interpretable decisions. Given the strong multi-task generalization ability of ALLMs, we first investigate their performance on all-type ADD under both supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). However, SFT using only binary real/fake labels tends to reduce the model to a black-box classifier, sacrificing interpretability. Meanwhile, vanilla RFT under sparse supervision is prone to reward hacking and can produce hallucinated, ungrounded rationales. To address this, we propose an automatic annotation and polishing pipeline that constructs Frequency-Time structured chain-of-thought (CoT) rationales, producing ~340K cold-start demonstrations. Building on CoT data, we propose Frequency Time-Group Relative Policy Optimization (FT-GRPO), a two-stage training paradigm that cold-starts ALLMs with SFT and then applies GRPO under rule-based frequency-time constraints. Experiments demonstrate that FT-GRPO achieves state-of-the-art performance on all-type ADD while producing interpretable, FT-grounded rationales. The data and code are available online.
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https://arxiv.org/abs/2601.02983
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Academic Papers
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5825224d8ee7f6e0c9bd26d5fe4d798dd3cee5b19d4cdec70db4d890aab0693b
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2026-01-07T00:00:00-05:00
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Selfish Mining in Multi-Attacker Scenarios: An Empirical Evaluation of Nakamoto, Fruitchain, and Strongchain
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arXiv:2601.02984v1 Announce Type: new Abstract: The aim of this work is to enhance blockchain security by deepening the understanding of selfish mining attacks in various consensus protocols, especially the ones that have the potential to mitigate selfish mining. Previous research was mainly focused on a particular protocol with a single selfish miner, while only limited studies have been conducted on two or more attackers. To address this gap, we proposed a stochastic simulation framework that enables analysis of selfish mining with multiple attackers across various consensus protocols. We created the model of Proof-of-Work (PoW) Nakamoto consensus (serving as the baseline) as well as models of two additional consensus protocols designed to mitigate selfish mining: Fruitchain and Strongchain. Using our framework, thresholds reported in the literature were verified, and several novel thresholds were discovered for 2 and more attackers. We made the source code of our framework available, enabling researchers to evaluate any newly added protocol with one or more selfish miners and cross-compare it with already modeled protocols.
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https://arxiv.org/abs/2601.02984
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Academic Papers
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00d2e3144f3688283ee09c72db8d8339a66f27bde76c785a3cc4c4856e790bbb
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2026-01-07T00:00:00-05:00
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P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist
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arXiv:2601.02986v1 Announce Type: new Abstract: Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with individual preferences. However, existing approaches largely treat user context as a static or implicit conditioning signal, failing to capture the dynamic and multi-faceted nature of human judgment. In this paper, we propose P-Check, a novel personalized reward modeling framework, designed to train a plug-and-play checklist generator that synthesizes dynamic evaluation criteria for guiding the reward prediction. To better align these checklists with personalized nuances, we introduce Preference-Contrastive Criterion Weighting, a training strategy that assigns saliency scores to criteria based on their discriminative power for personalized judgment. We conduct extensive experiments and demonstrate that P-Check not only improves reward accuracy but also enhances downstream personalized generation, and remains robust in OOD scenarios.
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https://arxiv.org/abs/2601.02986
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Academic Papers
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e4e4a3908aa842d7c027ad4c9df601e0c68a89a27beb3a72600fc463327eb405
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2026-01-07T00:00:00-05:00
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LAMS-Edit: Latent and Attention Mixing with Schedulers for Improved Content Preservation in Diffusion-Based Image and Style Editing
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arXiv:2601.02987v1 Announce Type: new Abstract: Text-to-Image editing using diffusion models faces challenges in balancing content preservation with edit application and handling real-image editing. To address these, we propose LAMS-Edit, leveraging intermediate states from the inversion process--an essential step in real-image editing--during edited image generation. Specifically, latent representations and attention maps from both processes are combined at each step using weighted interpolation, controlled by a scheduler. This technique, Latent and Attention Mixing with Schedulers (LAMS), integrates with Prompt-to-Prompt (P2P) to form LAMS-Edit--an extensible framework that supports precise editing with region masks and enables style transfer via LoRA. Extensive experiments demonstrate that LAMS-Edit effectively balances content preservation and edit application.
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https://arxiv.org/abs/2601.02987
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Academic Papers
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8c7a47282c1e5e43da50fb797dd86afd4635008dda9be8262427eb745b8fb916
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2026-01-07T00:00:00-05:00
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ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation
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arXiv:2601.02988v1 Announce Type: new Abstract: In this study, we present ULS+, an enhanced version of the Universal Lesion Segmentation (ULS) model. The original ULS model segments lesions across the whole body in CT scans given volumes of interest (VOIs) centered around a click-point. Since its release, several new public datasets have become available that can further improve model performance. ULS+ incorporates these additional datasets and uses smaller input image sizes, resulting in higher accuracy and faster inference. We compared ULS and ULS+ using the Dice score and robustness to click-point location on the ULS23 Challenge test data and a subset of the Longitudinal-CT dataset. In all comparisons, ULS+ significantly outperformed ULS. Additionally, ULS+ ranks first on the ULS23 Challenge test-phase leaderboard. By maintaining a cycle of data-driven updates and clinical validation, ULS+ establishes a foundation for robust and clinically relevant lesion segmentation models.
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https://arxiv.org/abs/2601.02988
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Academic Papers
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b883e9fc2d7fe1a719df2a7c4a51d83bc00186582d3effd3c64b64cad4ddcaf4
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2026-01-07T00:00:00-05:00
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Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy
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arXiv:2601.02989v1 Announce Type: new Abstract: Large language models (LLMs), despite strong performance on complex mathematical problems, exhibit systematic limitations in counting tasks. This issue arises from architectural limits of transformers, where counting is performed across layers, leading to degraded precision for larger counting problems due to depth constraints. To address this limitation, we propose a simple test-time strategy inspired by System-2 cognitive processes that decomposes large counting tasks into smaller, independent sub-problems that the model can reliably solve. We evaluate this approach using observational and causal mediation analyses to understand the underlying mechanism of this System-2-like strategy. Our mechanistic analysis identifies key components: latent counts are computed and stored in the final item representations of each part, transferred to intermediate steps via dedicated attention heads, and aggregated in the final stage to produce the total count. Experimental results demonstrate that this strategy enables LLMs to surpass architectural limitations and achieve high accuracy on large-scale counting tasks. This work provides mechanistic insight into System-2 counting in LLMs and presents a generalizable approach for improving and understanding their reasoning behavior.
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https://arxiv.org/abs/2601.02989
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Academic Papers
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1a359faf17abffcb7bae7ce19b25eb3bb951f28e5ece72c5e827cb8bf15426e8
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2026-01-07T00:00:00-05:00
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Towards Faithful Reasoning in Comics for Small MLLMs
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arXiv:2601.02991v1 Announce Type: new Abstract: Comic-based visual question answering (CVQA) poses distinct challenges to multimodal large language models (MLLMs) due to its reliance on symbolic abstraction, narrative logic, and humor, which differ from conventional VQA tasks. Although Chain-of-Thought (CoT) prompting is widely used to enhance MLLM reasoning, surprisingly, its direct application to CVQA often degrades performance, especially in small-scale models. Our theoretical and empirical analyses reveal that standard CoT in CVQA suffers from state entanglement, spurious transitions, and exploration inefficiency, with small models particularly vulnerable in resource-constrained settings. To address these issues, we propose a novel comic reasoning framework, designed to produce more faithful and transferable reasoning chains in small MLLMs. Specifically, our framework combines modular CoT generation with GRPO-based reinforcement fine-tuning and a novel structured reward. Beyond comic VQA, we further evaluate our approach on a broader class of humor-centric and abstract visual reasoning tasks, including meme understanding and editorial cartoon interpretation. Across five challenging benchmarks, our 3B model outperforms state-of-the-art methods, and plug-in experiments yield an additional average improvement of $\mathbf{12.1\%}$ across different MLLMs.
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https://arxiv.org/abs/2601.02991
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Academic Papers
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a02f7e2460c6c024f86c95272aa404842b70e0dd779a3fb7c35e6a75d61990b5
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2026-01-07T00:00:00-05:00
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Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation
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arXiv:2601.02993v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in large language models (LLMs), yet little is known about how the order of retrieved documents affects model behavior. We empirically show that under Top-5 retrieval with the gold document included, LLM answers vary substantially across permutations of the retrieved set, even when the gold document is fixed in the first position. This reveals a previously underexplored sensitivity to retrieval permutations. Although robust RAG methods primarily focus on enhancing LLM robustness to low-quality retrieval and mitigating positional bias to distribute attention fairly over long contexts, neither approach directly addresses permutation sensitivity. In this paper, we propose Stable-RAG, which exploits permutation sensitivity estimation to mitigate permutation-induced hallucinations. Stable-RAG runs the generator under multiple retrieval orders, clusters hidden states, and decodes from a cluster-center representation that captures the dominant reasoning pattern. It then uses these reasoning results to align hallucinated outputs toward the correct answer, encouraging the model to produce consistent and accurate predictions across document permutations. Experiments on three QA datasets show that Stable-RAG significantly improves answer accuracy, reasoning consistency and robust generalization across datasets, retrievers, and input lengths compared with baselines.
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https://arxiv.org/abs/2601.02993
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Academic Papers
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ff3866a03346e58c1ce4fda65eb7881f5b6c972321398834657477626738558c
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2026-01-07T00:00:00-05:00
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Learning to Act Robustly with View-Invariant Latent Actions
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arXiv:2601.02994v1 Announce Type: new Abstract: Vision-based robotic policies often struggle with even minor viewpoint changes, underscoring the need for view-invariant visual representations. This challenge becomes more pronounced in real-world settings, where viewpoint variability is unavoidable and can significantly disrupt policy performance. Existing methods typically learn invariance from multi-view observations at the scene level, but such approaches rely on visual appearance and fail to incorporate the physical dynamics essential for robust generalization. We propose View-Invariant Latent Action (VILA), which models a latent action capturing transition patterns across trajectories to learn view-invariant representations grounded in physical dynamics. VILA aligns these latent actions across viewpoints using an action-guided objective based on ground-truth action sequences. Experiments in both simulation and the real world show that VILA-based policies generalize effectively to unseen viewpoints and transfer well to new tasks, establishing VILA as a strong pretraining framework that improves robustness and downstream learning performance.
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https://arxiv.org/abs/2601.02994
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Academic Papers
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31049411ff3f8035715314b921a2b80b2d3df47e70ab0a05e17aff6317e1edef
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2026-01-07T00:00:00-05:00
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Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners
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arXiv:2601.02996v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit chain-of-thought (CoT) explanations. However, recent work shows that LRMs often arrive at the correct answer before completing these textual reasoning steps, indicating the presence of latent reasoning -- internal, non-verbal computation encoded in hidden states. While this phenomenon has been explored in English, its multilingual behavior remains largely unknown. In this paper, we conduct a systematic investigation of multilingual latent reasoning in LRMs across 11 languages. Using a truncation-based strategy, we examine how the correct answer emerges as the model is given only partial reasoning traces, allowing us to measure stepwise latent prediction formation. Our results reveal clear evidence of multilingual latent reasoning, though unevenly: strong in resource-rich languages, weaker in low-resource ones, and broadly less observable on harder benchmarks. To understand whether these differences reflect distinct internal mechanisms, we further perform representational analyses. Despite surface-level disparities, we find that the internal evolution of predictions is highly consistent across languages and broadly aligns with English -- a pattern suggesting an English-centered latent reasoning pathway.
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https://arxiv.org/abs/2601.02996
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Academic Papers
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1c571805ba8fad110d6d15d5491858aab95b6c2b61dbf1303d09a35dbd0f3754
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2026-01-07T00:00:00-05:00
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From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures
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arXiv:2601.02997v1 Announce Type: new Abstract: Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design--balancing syntactic reliability, performance, and structural novelty--remains underexplored. We address this by placing a code-oriented LLM within a closed-loop synthesis framework, analyzing its evolution over 22 supervised fine-tuning cycles. The model synthesizes PyTorch convolutional networks which are validated, evaluated via low-fidelity performance signals (single-epoch accuracy), and filtered using a MinHash-Jaccard criterion to prevent structural redundancy. High-performing, novel architectures are converted into prompt-code pairs for iterative fine-tuning via parameter-efficient LoRA adaptation, initialized from the LEMUR dataset. Across cycles, the LLM internalizes empirical architectural priors, becoming a robust generator. The valid generation rate stabilizes at 50.6 percent (peaking at 74.5 percent), while mean first-epoch accuracy rises from 28.06 percent to 50.99 percent, and the fraction of candidates exceeding 40 percent accuracy grows from 2.04 percent to 96.81 percent. Analyses confirm the model moves beyond replicating existing motifs, synthesizing 455 high-performing architectures absent from the original corpus. By grounding code synthesis in execution feedback, this work provides a scalable blueprint for transforming stochastic generators into autonomous, performance-driven neural designers, establishing that LLMs can internalize empirical, non-textual rewards to transcend their training data.
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https://arxiv.org/abs/2601.02997
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Academic Papers
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e6f93de9c89d29c4289f1fc5597362a22082519d271a4ccb546b1e8b99b021e6
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2026-01-07T00:00:00-05:00
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Multi-Distribution Robust Conformal Prediction
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arXiv:2601.02998v1 Announce Type: new Abstract: In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them. We study the problem of constructing a conformal prediction set that is uniformly valid across multiple, heterogeneous distributions, in the sense that no matter which distribution the test point is from, the coverage of the prediction set is guaranteed to exceed a pre-specified level. We first propose a max-p aggregation scheme that delivers finite-sample, multi-distribution coverage given any conformity scores associated with each distribution. Upon studying several efficiency optimization programs subject to uniform coverage, we prove the optimality and tightness of our aggregation scheme, and propose a general algorithm to learn conformity scores that lead to efficient prediction sets after the aggregation under standard conditions. We discuss how our framework relates to group-wise distributionally robust optimization, sub-population shift, fairness, and multi-source learning. In synthetic and real-data experiments, our method delivers valid worst-case coverage across multiple distributions while greatly reducing the set size compared with naively applying max-p aggregation to single-source conformity scores, and can be comparable in size to single-source prediction sets with popular, standard conformity scores.
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https://arxiv.org/abs/2601.02998
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Academic Papers
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3f93cf62e384440ad0ec3aa3dc9aa5e940c9acbc6f06347ffbd3140b3f8e1ec4
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2026-01-07T00:00:00-05:00
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Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection
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arXiv:2601.03001v1 Announce Type: new Abstract: Vehicle-Infrastructure Collaborative Perception (VICP) is pivotal for resolving occlusion in autonomous driving, yet the trade-off between communication bandwidth and feature redundancy remains a critical bottleneck. While intermediate fusion mitigates data volume compared to raw sharing, existing frameworks typically rely on spatial compression or static confidence maps, which inefficiently transmit spatially redundant features from non-critical background regions. To address this, we propose Risk-intent Selective detection (RiSe), an interaction-aware framework that shifts the paradigm from identifying visible regions to prioritizing risk-critical ones. Specifically, we introduce a Potential Field-Trajectory Correlation Model (PTCM) grounded in potential field theory to quantitatively assess kinematic risks. Complementing this, an Intention-Driven Area Prediction Module (IDAPM) leverages ego-motion priors to proactively predict and filter key Bird's-Eye-View (BEV) areas essential for decision-making. By integrating these components, RiSe implements a semantic-selective fusion scheme that transmits high-fidelity features only from high-interaction regions, effectively acting as a feature denoiser. Extensive experiments on the DeepAccident dataset demonstrate that our method reduces communication volume to 0.71\% of full feature sharing while maintaining state-of-the-art detection accuracy, establishing a competitive Pareto frontier between bandwidth efficiency and perception performance.
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https://arxiv.org/abs/2601.03001
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Academic Papers
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b9ccb63ad1e52917c1307cfa856217e968324fd9ae315efbd901a12b0b491f59
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2026-01-07T00:00:00-05:00
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Closed-Loop Transmission Power Control for Reliable and Low-Power BLE Communication in Dynamic IoT Settings
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arXiv:2601.03003v1 Announce Type: new Abstract: Reliable and energy-efficient Bluetooth Low Energy (BLE) communication is crucial for Internet of Things (IoT) applications in dynamic environments. However, the Received Signal Strength Indicator (RSSI) and data throughput in BLE are highly susceptible to environmental variability, which degrades communication performance. In this work, we systematically analyze the interdependence among RSSI, throughput, transmission power (TXP), and the peripheral device system power consumption under diverse real-world conditions. We observe that adjusting the TXP effectively influences both RSSI and throughput. We propose a robust closed-loop TXP control framework based on Proportional-Integral-Derivative (PID) controllers. Two initial control strategies are investigated: an RSSI-based approach and a throughput-based approach, each exhibiting distinct advantages and limitations. The RSSI-based method provides rapid responsiveness to signal fluctuations but lacks direct correlation with data throughput, whereas the throughput-based method offers more accurate feedback on effective throughput at the cost of slower response. To address these limitations, a hybrid RSSI-throughput control strategy is developed, combining the responsiveness of RSSI feedback with the accuracy of throughput measurements. Experimental results demonstrate that the proposed hybrid approach maintains data throughput close to the target level with minimal variance, even under rapidly changing environmental conditions.
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https://arxiv.org/abs/2601.03003
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Academic Papers
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fab536e458445dc5c3e8c69ab1961607ef147d209014120827745baf58eba0b0
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2026-01-07T00:00:00-05:00
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JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification
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arXiv:2601.03005v1 Announce Type: new Abstract: Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a promising defense by erasing specific harmful parameters, current methods remain vulnerable to diverse jailbreaks. We first conduct an empirical study and discover that this failure mechanism is caused by jailbreaks primarily activating non-erased parameters in the intermediate layers. Further, by probing the underlying mechanism through which these circumvented parameters reassemble into the prohibited output, we verify the persistent existence of dynamic $\textbf{jailbreak paths}$ and show that the inability to rectify them constitutes the fundamental gap in existing unlearning defenses. To bridge this gap, we propose $\textbf{J}$ailbreak $\textbf{P}$ath $\textbf{U}$nlearning (JPU), which is the first to rectify dynamic jailbreak paths towards safety anchors by dynamically mining on-policy adversarial samples to expose vulnerabilities and identify jailbreak paths. Extensive experiments demonstrate that JPU significantly enhances jailbreak resistance against dynamic attacks while preserving the model's utility.
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https://arxiv.org/abs/2601.03005
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Academic Papers
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e882c729253d9069e006c2bbf92a2e59ebda8f0a17e23cde0b886c7801eee87b
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2026-01-07T00:00:00-05:00
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From inconsistency to decision: explainable operation and maintenance of battery energy storage systems
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arXiv:2601.03007v1 Announce Type: new Abstract: Battery Energy Storage Systems (BESSs) are increasingly critical to power-system stability, yet their operation and maintenance remain dominated by reactive, expert-dependent diagnostics. While cell-level inconsistencies provide early warning signals of degradation and safety risks, the lack of scalable and interpretable decision-support frameworks prevents these signals from being effectively translated into operational actions. Here we introduce an inconsistency-driven operation and maintenance paradigm for large-scale BESSs that systematically transforms routine monitoring data into explainable, decision-oriented guidance. The proposed framework integrates multi-dimensional inconsistency evaluation with large language model-based semantic reasoning to bridge the gap between quantitative diagnostics and practical maintenance decisions. Using eight months of field data from an in-service battery system comprising 3,564 cells, we demonstrate how electrical, thermal, and aging-related inconsistencies can be distilled into structured operational records and converted into actionable maintenance insights through a multi-agent framework. The proposed approach enables accurate and explainable responses to real-world operation and maintenance queries, reducing response time and operational cost by over 80% compared with conventional expert-driven practices. These results establish a scalable pathway for intelligent operation and maintenance of battery energy storage systems, with direct implications for reliability, safety, and cost-effective integration of energy storage into modern power systems.
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https://arxiv.org/abs/2601.03007
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Academic Papers
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59c1c5bf02bff09bb36d9e680c8f18a54198b07328cfd24e53e0f25887164160
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2026-01-07T00:00:00-05:00
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A Dataset of Low-Rated Applications from the Amazon Appstore for User Feedback Analysis
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arXiv:2601.03009v1 Announce Type: new Abstract: In todays digital landscape, end-user feedback plays a crucial role in the evolution of software applications, particularly in addressing issues that hinder user experience. While much research has focused on high-rated applications, low-rated applications often remain unexplored, despite their potential to reveal valuable insights. This study introduces a novel dataset curated from 64 low-rated applications sourced from the Amazon Software Appstore (ASA), containing 79,821 user reviews. The dataset is designed to capture the most frequent issues identified by users, which are critical for improving software quality. To further enhance the dataset utility, a subset of 6000 reviews was manually annotated to classify them into six district issue categories: user interface (UI) and user experience (UX), functionality and features, compatibility and device specificity, performance and stability, customer support and responsiveness, and security and privacy issues. This annotated dataset is a valuable resource for developing machine learning-based approaches aiming to automate the classification of user feedback into various issue types. Making both the annotated and raw datasets publicly available provides researchers and developers with a crucial tool to understand common issues in low-rated apps and inform software improvements. The comprehensive analysis and availability of this dataset lay the groundwork for data-derived solutions to improve software quality based on user feedback. Additionally, the dataset can provide opportunities for software vendors and researchers to explore various software evolution-related activities, including frequently missing features, sarcasm, and associated emotions, which will help better understand the reasons for comparatively low app ratings.
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https://arxiv.org/abs/2601.03009
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Academic Papers
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25369758e1220a92363be542877b1c9cc937a6e57bfbca4cbbab7d9a4aee5e08
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2026-01-07T00:00:00-05:00
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Mathematical aspects of registration methods in bounded domains
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arXiv:2601.03010v1 Announce Type: new Abstract: Registration methods in bounded domains have received significant attention in the model reduction literature, as a valuable tool for nonlinear approximation. The aim of this work is to provide a concise yet complete overview of relevant results for registration methods in $n$-dimensional domains, from the perspective of parametric model reduction. We present a thorough analysis of two classes of methods, vector flows and compositional maps: we discuss the enforcement of the bijectivity constraint and we comment on the approximation properties of the two methods, for Lipschitz $n$-dimensional domains.
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https://arxiv.org/abs/2601.03010
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Academic Papers
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0fa7c922cd6e4d2014411510172e0a5d0075c2186cc99682816d92af5856c803
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2026-01-07T00:00:00-05:00
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ReCCur: A Recursive Corner-Case Curation Framework for Robust Vision-Language Understanding in Open and Edge Scenarios
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arXiv:2601.03011v1 Announce Type: new Abstract: Corner cases are rare or extreme scenarios that drive real-world failures, but they are difficult to curate at scale: web data are noisy, labels are brittle, and edge deployments preclude large retraining. We present ReCCur (Recursive Corner-Case Curation), a low-compute framework that converts noisy web imagery into auditable fine-grained labels via a multi-agent recursive pipeline. First, large-scale data acquisition and filtering expands a domain vocabulary with a vision-language model (VLM), crawls the web, and enforces tri-modal (image, description, keyword) consistency with light human spot checks to yield refined candidates. Next, mixture-of-experts knowledge distillation uses complementary encoders (e.g., CLIP, DINOv2, BEiT) for kNN voting with dual-confidence activation and uncertainty sampling, converging to a high-precision set. Finally, region-evidence VLM adversarial labeling pairs a proposer (multi-granularity regions and semantic cues) with a validator (global and local chained consistency) to produce explainable labels and close the loop. On realistic corner-case scenarios (e.g., flooded-car inspection), ReCCur runs on consumer-grade GPUs, steadily improves purity and separability, and requires minimal human supervision, providing a practical substrate for downstream training and evaluation under resource constraints. Code and dataset will be released.
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https://arxiv.org/abs/2601.03011
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Academic Papers
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d5d4b382f1729dd66d8fcf06f37d089a1d9e102becf69e4b90d49636363e509e
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2026-01-07T00:00:00-05:00
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LLMs, You Can Evaluate It! Design of Multi-perspective Report Evaluation for Security Operation Centers
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arXiv:2601.03013v1 Announce Type: new Abstract: Security operation centers (SOCs) often produce analysis reports on security incidents, and large language models (LLMs) will likely be used for this task in the near future. We postulate that a better understanding of how veteran analysts evaluate reports, including their feedback, can help produce analysis reports in SOCs. In this paper, we aim to leverage LLMs for analysis reports. To this end, we first construct a Analyst-wise checklist to reflect SOC practitioners' opinions for analysis report evaluation through literature review and user study with SOC practitioners. Next, we design a novel LLM-based conceptual framework, named MESSALA, by further introducing two new techniques, granularization guideline and multi-perspective evaluation. MESSALA can maximize report evaluation and provide feedback on veteran SOC practitioners' perceptions. When we conduct extensive experiments with MESSALA, the evaluation results by MESSALA are the closest to those of veteran SOC practitioners compared with the existing LLM-based methods. We then show two key insights. We also conduct qualitative analysis with MESSALA, and then identify that MESSALA can provide actionable items that are necessary for improving analysis reports.
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https://arxiv.org/abs/2601.03013
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Academic Papers
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b24ba1895bd83bd9e16d0d3cc612448a3acdeb464f0bcdec291135dc85f40725
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2026-01-07T00:00:00-05:00
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SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering
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arXiv:2601.03014v1 Announce Type: new Abstract: Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering. Specifically, we construct a hierarchical sentence graph offline by first adapting Rhetorical Structure Theory to distinguish nucleus and satellite sentences, and then organizing them into topic-level subgraphs with cross-document entity bridges. During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence. Extensive experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of SentGraph, validating the importance of explicitly modeling sentence-level logical dependencies for multi-hop reasoning.
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https://arxiv.org/abs/2601.03014
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Academic Papers
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d6e741619fb086647469b022d9d0a9b6b543d83f55aa408590b67e97a6c84023
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2026-01-07T00:00:00-05:00
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In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior
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arXiv:2601.03015v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) promises fast adaptation to unseen environments without parameter updates, but current methods either cannot improve beyond the training distribution or require near-optimal data, limiting practical adoption. We introduce SPICE, a Bayesian ICRL method that learns a prior over Q-values via deep ensemble and updates this prior at test-time using in-context information through Bayesian updates. To recover from poor priors resulting from training on sub-optimal data, our online inference follows an Upper-Confidence Bound rule that favours exploration and adaptation. We prove that SPICE achieves regret-optimal behaviour in both stochastic bandits and finite-horizon MDPs, even when pretrained only on suboptimal trajectories. We validate these findings empirically across bandit and control benchmarks. SPICE achieves near-optimal decisions on unseen tasks, substantially reduces regret compared to prior ICRL and meta-RL approaches while rapidly adapting to unseen tasks and remaining robust under distribution shift.
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https://arxiv.org/abs/2601.03015
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Academic Papers
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6ecc2d023ed5e3dbe63e2ab1b51226f6cf7dff93fb74c266c1c42ff2ee0523d1
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2026-01-07T00:00:00-05:00
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MMFormalizer: Multimodal Autoformalization in the Wild
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arXiv:2601.03017v1 Announce Type: new Abstract: Autoformalization, which translates natural language mathematics into formal statements to enable machine reasoning, faces fundamental challenges in the wild due to the multimodal nature of the physical world, where physics requires inferring hidden constraints (e.g., mass or energy) from visual elements. To address this, we propose MMFormalizer, which extends autoformalization beyond text by integrating adaptive grounding with entities from real-world mathematical and physical domains. MMFormalizer recursively constructs formal propositions from perceptually grounded primitives through recursive grounding and axiom composition, with adaptive recursive termination ensuring that every abstraction is supported by visual evidence and anchored in dimensional or axiomatic grounding. We evaluate MMFormalizer on a new benchmark, PhyX-AF, comprising 115 curated samples from MathVerse, PhyX, Synthetic Geometry, and Analytic Geometry, covering diverse multimodal autoformalization tasks. Results show that frontier models such as GPT-5 and Gemini-3-Pro achieve the highest compile and semantic accuracy, with GPT-5 excelling in physical reasoning, while geometry remains the most challenging domain. Overall, MMFormalizer provides a scalable framework for unified multimodal autoformalization, bridging perception and formal reasoning. To the best of our knowledge, this is the first multimodal autoformalization method capable of handling classical mechanics (derived from the Hamiltonian), as well as relativity, quantum mechanics, and thermodynamics. More details are available on our project page: MMFormalizer.github.io
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https://arxiv.org/abs/2601.03017
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Academic Papers
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34f62b0d651c40f1ba5617da7eded71ce5dcbab998f3155edd7fa659f63b42c8
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2026-01-07T00:00:00-05:00
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Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
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arXiv:2601.03018v1 Announce Type: new Abstract: While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which require reasoning over complex, non-monotonic symptom trajectories across multiple visits. Standard supervised training lacks explicit annotations for symptom evolution, while direct Reinforcement Learning (RL) is hindered by sparse binary rewards. To address this challenge, we introduce Dementia-R1, an RL-based framework for longitudinal dementia prognosis from unstructured clinical notes. Our approach adopts a Cold-Start RL strategy that pre-trains the model to predict verifiable clinical indices extracted from patient histories, enhancing the capability to reason about disease progression before determining the final clinical status. Extensive experiments demonstrate that Dementia-R1 achieves an F1 score of 77.03% on real-world unstructured clinical datasets. Notably, on the ADNI benchmark, our 7B model rivals GPT-4o, effectively capturing fluctuating cognitive trajectories. Code is available at https://anonymous.4open.science/r/dementiar1-CDB5
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https://arxiv.org/abs/2601.03018
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Academic Papers
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15469c17f99aed1d905d1da63d8d00552a400193630a7a0e4644d219f73fb7fa
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2026-01-07T00:00:00-05:00
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Hardness of Regular Expression Matching with Extensions
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arXiv:2601.03020v1 Announce Type: new Abstract: The regular expression matching problem asks whether a given regular expression of length $m$ matches a given string of length $n$. As is well known, the problem can be solved in $O(nm)$ time using Thompson's algorithm. Moreover, recent studies have shown that the matching problem for regular expressions extended with a practical extension called lookaround can be solved in the same time complexity. In this work, we consider three well-known extensions to regular expressions called backreference, intersection and complement, and we show that, unlike in the case of lookaround, the matching problem for regular expressions extended with any of the three (for backreference, even when restricted to one capturing group) cannot be solved in $O(n^{2-\varepsilon} \mathrm{poly}(m))$ time for any constant $\varepsilon > 0$ under the Orthogonal Vectors Conjecture. Moreover, we study the matching problem for regular expressions extended with complement in more detail, which is also known as extended regular expression (ERE) matching. We show that there is no ERE matching algorithm that runs in $O(n^{\omega-\varepsilon} \mathrm{poly}(m))$ time ($2 \le \omega 0$ under the $k$-Clique Hypothesis, and there is no combinatorial ERE matching algorithm that runs in $O(n^{3-\varepsilon} \mathrm{poly}(m))$ time for any constant $\varepsilon > 0$ under the Combinatorial $k$-Clique Hypothesis. This shows that the $O(n^3 m)$-time algorithm introduced by Hopcroft and Ullman in 1979 and recently improved by Bille et al. to run in $O(n^\omega m)$ time using fast matrix multiplication was already optimal in a sense, and sheds light on why the theoretical computer science community has struggled to improve the time complexity of ERE matching with respect to $n$ and $m$ for more than 45 years.
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https://arxiv.org/abs/2601.03020
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Academic Papers
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a5e0f580a489454081be7a812688e4ab7052d49b8e7536a3b756254c32d91380
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2026-01-07T00:00:00-05:00
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MedDialogRubrics: A Comprehensive Benchmark and Evaluation Framework for Multi-turn Medical Consultations in Large Language Models
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arXiv:2601.03023v1 Announce Type: new Abstract: Medical conversational AI (AI) plays a pivotal role in the development of safer and more effective medical dialogue systems. However, existing benchmarks and evaluation frameworks for assessing the information-gathering and diagnostic reasoning abilities of medical large language models (LLMs) have not been rigorously evaluated. To address these gaps, we present MedDialogRubrics, a novel benchmark comprising 5,200 synthetically constructed patient cases and over 60,000 fine-grained evaluation rubrics generated by LLMs and subsequently refined by clinical experts, specifically designed to assess the multi-turn diagnostic capabilities of LLM. Our framework employs a multi-agent system to synthesize realistic patient records and chief complaints from underlying disease knowledge without accessing real-world electronic health records, thereby mitigating privacy and data-governance concerns. We design a robust Patient Agent that is limited to a set of atomic medical facts and augmented with a dynamic guidance mechanism that continuously detects and corrects hallucinations throughout the dialogue, ensuring internal coherence and clinical plausibility of the simulated cases. Furthermore, we propose a structured LLM-based and expert-annotated rubric-generation pipeline that retrieves Evidence-Based Medicine (EBM) guidelines and utilizes the reject sampling to derive a prioritized set of rubric items ("must-ask" items) for each case. We perform a comprehensive evaluation of state-of-the-art models and demonstrate that, across multiple assessment dimensions, current models face substantial challenges. Our results indicate that improving medical dialogue will require advances in dialogue management architectures, not just incremental tuning of the base-model.
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https://arxiv.org/abs/2601.03023
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Academic Papers
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8c502e497885c8a682d5a29743bc7c11faf4ea4fba50b25daf8eae41f7b03a92
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2026-01-07T00:00:00-05:00
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SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selection
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arXiv:2601.03024v1 Announce Type: new Abstract: We propose Self-Augmented Residual 3D Gaussian Splatting (SA-ResGS), a novel framework to stabilize uncertainty quantification and enhancing uncertainty-aware supervision in next-best-view (NBV) selection for active scene reconstruction. SA-ResGS improves both the reliability of uncertainty estimates and their effectiveness for supervision by generating Self-Augmented point clouds (SA-Points) via triangulation between a training view and a rasterized extrapolated view, enabling efficient scene coverage estimation. While improving scene coverage through physically guided view selection, SA-ResGS also addresses the challenge of under-supervised Gaussians, exacerbated by sparse and wide-baseline views, by introducing the first residual learning strategy tailored for 3D Gaussian Splatting. This targeted supervision enhances gradient flow in high-uncertainty Gaussians by combining uncertainty-driven filtering with dropout- and hard-negative-mining-inspired sampling. Our contributions are threefold: (1) a physically grounded view selection strategy that promotes efficient and uniform scene coverage; (2) an uncertainty-aware residual supervision scheme that amplifies learning signals for weakly contributing Gaussians, improving training stability and uncertainty estimation across scenes with diverse camera distributions; (3) an implicit unbiasing of uncertainty quantification as a consequence of constrained view selection and residual supervision, which together mitigate conflicting effects of wide-baseline exploration and sparse-view ambiguity in NBV planning. Experiments on active view selection demonstrate that SA-ResGS outperforms state-of-the-art baselines in both reconstruction quality and view selection robustness.
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https://arxiv.org/abs/2601.03024
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Academic Papers
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e9c8aaad0002e5cf178b70f1473ef8ab9bc8f8360b708054b3a2043fd7542f61
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2026-01-07T00:00:00-05:00
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LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering
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arXiv:2601.03025v1 Announce Type: new Abstract: Long-context question answering (QA) over literary texts poses significant challenges for modern large language models, particularly in low-resource languages. We address the scarcity of long-context QA resources for Indic languages by introducing LittiChoQA, the largest literary QA dataset to date covering many languages spoken in the Gangetic plains of India. The dataset comprises over 270K automatically generated question-answer pairs with a balanced distribution of factoid and non-factoid questions, generated from naturally authored literary texts collected from the open web. We evaluate multiple multilingual LLMs on non-factoid, abstractive QA, under both full-context and context-shortened settings. Results demonstrate a clear trade-off between performance and efficiency: full-context fine-tuning yields the highest token-level and semantic-level scores, while context shortening substantially improves throughput. Among the evaluated models, Krutrim-2 achieves the strongest performance, obtaining a semantic score of 76.1 with full context. While, in shortened context settings it scores 74.9 with answer paragraph selection and 71.4 with vector-based retrieval. Qualitative evaluations further corroborate these findings.
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https://arxiv.org/abs/2601.03025
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Academic Papers
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24e6e7c606b47ebb13aa506d915ef4a583fb5bca71c781977a2ed8ac7e057980
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2026-01-07T00:00:00-05:00
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Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning
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arXiv:2601.03027v1 Announce Type: new Abstract: Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgments reward fluency and confidence over factual correctness. We introduce F-DPO (Factuality-aware Direct Preference Optimization), a simple extension of DPO that uses only binary factuality labels. F-DPO (i) applies a label-flipping transformation that corrects misordered preference pairs so the chosen response is never less factual than the rejected one, and (ii) adds a factuality-aware margin that emphasizes pairs with clear correctness differences, while reducing to standard DPO when both responses share the same factuality. We construct factuality-aware preference data by augmenting DPO pairs with binary factuality indicators and synthetic hallucinated variants. Across seven open-weight LLMs (1B-14B), F-DPO consistently improves factuality and reduces hallucination rates relative to both base models and standard DPO. On Qwen3-8B, F-DPO reduces hallucination rates by five times (from 0.424 to 0.084) while improving factuality scores by 50 percent (from 5.26 to 7.90). F-DPO also generalizes to out-of-distribution benchmarks: on TruthfulQA, Qwen2.5-14B achieves plus 17 percent MC1 accuracy (0.500 to 0.585) and plus 49 percent MC2 accuracy (0.357 to 0.531). F-DPO requires no auxiliary reward model, token-level annotations, or multi-stage training.
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https://arxiv.org/abs/2601.03027
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Academic Papers
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62a169c503f8cb2f51df65363afd2a66663a03a4e864677751230663e3f0db72
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2026-01-07T00:00:00-05:00
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Flow Matching and Diffusion Models via PointNet for Generating Fluid Fields on Irregular Geometries
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arXiv:2601.03030v1 Announce Type: new Abstract: We present two novel generative geometric deep learning frameworks, termed Flow Matching PointNet and Diffusion PointNet, for predicting fluid flow variables on irregular geometries by incorporating PointNet into flow matching and diffusion models, respectively. In these frameworks, a reverse generative process reconstructs physical fields from standard Gaussian noise conditioned on unseen geometries. The proposed approaches operate directly on point-cloud representations of computational domains (e.g., grid vertices of finite-volume meshes) and therefore avoid the limitations of pixelation used to project geometries onto uniform lattices. In contrast to graph neural network-based diffusion models, Flow Matching PointNet and Diffusion PointNet do not exhibit high-frequency noise artifacts in the predicted fields. Moreover, unlike such approaches, which require auxiliary intermediate networks to condition geometry, the proposed frameworks rely solely on PointNet, resulting in a simple and unified architecture. The performance of the proposed frameworks is evaluated on steady incompressible flow past a cylinder, using a geometric dataset constructed by varying the cylinder's cross-sectional shape and orientation across samples. The results demonstrate that Flow Matching PointNet and Diffusion PointNet achieve more accurate predictions of velocity and pressure fields, as well as lift and drag forces, and exhibit greater robustness to incomplete geometries compared to a vanilla PointNet with the same number of trainable parameters.
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https://arxiv.org/abs/2601.03030
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Academic Papers
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3a7ed66833d0f365aeb3fb745261fb5eccedc4c4e5dc7412500a13bfa53bda39
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2026-01-07T00:00:00-05:00
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FlexProofs: A Vector Commitment with Flexible Linear Time for Computing All Proofs
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arXiv:2601.03031v1 Announce Type: new Abstract: In this paper, we introduce FlexProofs, a new vector commitment (VC) scheme that achieves two key properties: (1) the prover can generate all individual opening proofs for a vector of size $N$ in optimal time ${\cal O}(N)$, and there is a flexible batch size parameter $b$ that can be increased to further reduce the time to generate all proofs; and (2) the scheme is directly compatible with a family of zkSNARKs that encode their input as a multi-linear polynomial. As a critical building block, we propose the first functional commitment (FC) scheme for multi-exponentiations with batch opening. Compared with HydraProofs, the only existing VC scheme that computes all proofs in optimal time ${\cal O}(N)$ and is directly compatible with zkSNARKs, FlexProofs may speed up the process of generating all proofs, if the parameter $b$ is properly chosen. Our experiments show that for $N=2^{16}$ and $b=\log^2 N$, FlexProofs can be $6\times$ faster than HydraProofs. Moreover, when combined with suitable zkSNARKs, FlexProofs enable practical applications such as verifiable secret sharing and verifiable robust aggregation.
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https://arxiv.org/abs/2601.03031
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Academic Papers
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ee185c823d378d163128af6e2c06a738ffb83d9989d94dc9705663e150a58cdf
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2026-01-07T00:00:00-05:00
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Causal Manifold Fairness: Enforcing Geometric Invariance in Representation Learning
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arXiv:2601.03032v1 Announce Type: new Abstract: Fairness in machine learning is increasingly critical, yet standard approaches often treat data as static points in a high-dimensional space, ignoring the underlying generative structure. We posit that sensitive attributes (e.g., race, gender) do not merely shift data distributions but causally warp the geometry of the data manifold itself. To address this, we introduce Causal Manifold Fairness (CMF), a novel framework that bridges causal inference and geometric deep learning. CMF learns a latent representation where the local Riemannian geometry, defined by the metric tensor and curvature, remains invariant under counterfactual interventions on sensitive attributes. By enforcing constraints on the Jacobian and Hessian of the decoder, CMF ensures that the rules of the latent space (distances and shapes) are preserved across demographic groups. We validate CMF on synthetic Structural Causal Models (SCMs), demonstrating that it effectively disentangles sensitive geometric warping while preserving task utility, offering a rigorous quantification of the fairness-utility trade-off via geometric metrics.
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https://arxiv.org/abs/2601.03032
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Academic Papers
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27a7f5cef891587d773a600686f3983d9b2e8abee743cc4b5a98f29d9f53fdbf
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2026-01-07T00:00:00-05:00
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NorwAI's Large Language Models: Technical Report
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arXiv:2601.03034v1 Announce Type: new Abstract: Norwegian, spoken by approximately five million people, remains underrepresented in many of the most significant breakthroughs in Natural Language Processing (NLP). To address this gap, the NorLLM team at NorwAI has developed a family of models specifically tailored to Norwegian and other Scandinavian languages, building on diverse Transformer-based architectures such as GPT, Mistral, Llama2, Mixtral and Magistral. These models are either pretrained from scratch or continually pretrained on 25B - 88.45B tokens, using a Norwegian-extended tokenizer and advanced post-training strategies to optimize performance, enhance robustness, and improve adaptability across various real-world tasks. Notably, instruction-tuned variants (e.g., Mistral-7B-Instruct and Mixtral-8x7B-Instruct) showcase strong assistant-style capabilities, underscoring their potential for practical deployment in interactive and domain-specific applications. The NorwAI large language models are openly available to Nordic organizations, companies and students for both research and experimental use. This report provides detailed documentation of the model architectures, training data, tokenizer design, fine-tuning strategies, deployment, and evaluations.
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https://arxiv.org/abs/2601.03034
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Academic Papers
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019bc4a09cec763e824c27064cf3504778cd10d78d778800acb54e1473e116e1
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2026-01-07T00:00:00-05:00
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A Bi-directional Adaptive Framework for Agile UAV Landing
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arXiv:2601.03037v1 Announce Type: new Abstract: Autonomous landing on mobile platforms is crucial for extending quadcopter operational flexibility, yet conventional methods are often too inefficient for highly dynamic scenarios. The core limitation lies in the prevalent ``track-then-descend'' paradigm, which treats the platform as a passive target and forces the quadcopter to perform complex, sequential maneuvers. This paper challenges that paradigm by introducing a bi-directional cooperative landing framework that redefines the roles of the vehicle and the platform. The essential innovation is transforming the problem from a single-agent tracking challenge into a coupled system optimization. Our key insight is that the mobile platform is not merely a target, but an active agent in the landing process. It proactively tilts its surface to create an optimal, stable terminal attitude for the approaching quadcopter. This active cooperation fundamentally breaks the sequential model by parallelizing the alignment and descent phases. Concurrently, the quadcopter's planning pipeline focuses on generating a time-optimal and dynamically feasible trajectory that minimizes energy consumption. This bi-directional coordination allows the system to execute the recovery in an agile manner, characterized by aggressive trajectory tracking and rapid state synchronization within transient windows. The framework's effectiveness, validated in dynamic scenarios, significantly improves the efficiency, precision, and robustness of autonomous quadrotor recovery in complex and time-constrained missions.
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https://arxiv.org/abs/2601.03037
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Academic Papers
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36f14383005ef1ea95a82158001872661716df6b441a2f3aa13c88347647b7d5
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2026-01-07T00:00:00-05:00
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Validating Generalist Robots with Situation Calculus and STL Falsification
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arXiv:2601.03038v1 Announce Type: new Abstract: Generalist robots are becoming a reality, capable of interpreting natural language instructions and executing diverse operations. However, their validation remains challenging because each task induces its own operational context and correctness specification, exceeding the assumptions of traditional validation methods. We propose a two-layer validation framework that combines abstract reasoning with concrete system falsification. At the abstract layer, situation calculus models the world and derives weakest preconditions, enabling constraint-aware combinatorial testing to systematically generate diverse, semantically valid world-task configurations with controllable coverage strength. At the concrete layer, these configurations are instantiated for simulation-based falsification with STL monitoring. Experiments on tabletop manipulation tasks show that our framework effectively uncovers failure cases in the NVIDIA GR00T controller, demonstrating its promise for validating general-purpose robot autonomy.
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https://arxiv.org/abs/2601.03038
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Academic Papers
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951f069065a493391bbc27242aa7451af6856e95a2d8a290a50a00be3b07dd1a
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2026-01-07T00:00:00-05:00
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PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms
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arXiv:2601.03040v1 Announce Type: new Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information. In these challenging environments, the platform must rely exclusively on inertial sensors, leading to pure inertial navigation. However, the inherent noise and other error terms of the inertial sensors in such real-world scenarios will cause the navigation solution to drift over time. Although conventional deep-learning models have emerged as a possible approach to inertial navigation, they are inherently black-box in nature. Furthermore, they struggle to learn effectively with limited supervised sensor data and often fail to preserve physical principles. To address these limitations, we propose PiDR, a physics-informed inertial dead-reckoning framework for autonomous platforms in situations of pure inertial navigation. PiDR offers transparency by explicitly integrating inertial navigation principles into the network training process through the physics-informed residual component. PiDR plays a crucial role in mitigating abrupt trajectory deviations even under limited or sparse supervision. We evaluated PiDR on real-world datasets collected by a mobile robot and an autonomous underwater vehicle. We obtained more than 29% positioning improvement in both datasets, demonstrating the ability of PiDR to generalize different platforms operating in various environments and dynamics. Thus, PiDR offers a robust, lightweight, yet effective architecture and can be deployed on resource-constrained platforms, enabling real-time pure inertial navigation in adverse scenarios.
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https://arxiv.org/abs/2601.03040
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Academic Papers
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eb6ce62b6224ac7e3665e1944d8c1951711530fd267423a53579a8583f646ea4
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2026-01-07T00:00:00-05:00
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BaseCal: Unsupervised Confidence Calibration via Base Model Signals
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arXiv:2601.03042v1 Announce Type: new Abstract: Reliable confidence is essential for trusting the outputs of LLMs, yet widely deployed post-trained LLMs (PoLLMs) typically compromise this trust with severe overconfidence. In contrast, we observe that their corresponding base LLMs often remain well-calibrated. This naturally motivates us to calibrate PoLLM confidence using the base LLM as a reference. This work proposes two ways to achieve this. A straightforward solution, BaseCal-ReEval, evaluates PoLLM's responses by feeding them into the base LLM to get average probabilities as confidence. While effective, this approach introduces additional inference overhead. To address this, we propose BaseCal-Proj, which trains a lightweight projection to map the final-layer hidden states of PoLLMs back to those of their base LLMs. These projected states are then processed by the base LLM's output layer to derive base-calibrated confidence for PoLLM's responses. Notably, BaseCal is an unsupervised, plug-and-play solution that operates without human labels or LLM modifications. Experiments across five datasets and three LLM families demonstrate the effectiveness of BaseCal, reducing Expected Calibration Error (ECE) by an average of 42.90\% compared to the best unsupervised baselines.
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https://arxiv.org/abs/2601.03042
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Academic Papers
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6d5a9322932fa986b256adb9aa509abcf9bc9f3e1dc588eb9e2a98acf6912e00
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2026-01-07T00:00:00-05:00
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Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage
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arXiv:2601.03043v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency. Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. To reduce time and memory complexity in the decode stage, a line of work introduces sparse-attention algorithms. In this paper, we show, both empirically and theoretically, that sparse attention can paradoxically increase end-to-end complexity: information loss often induces significantly longer sequences, a phenomenon we term ``Less is Less'' (Lil). To mitigate the Lil problem, we propose an early-stopping algorithm that detects the threshold where information loss exceeds information gain during sparse decoding. Our early-stopping algorithm reduces token consumption by up to 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks.
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https://arxiv.org/abs/2601.03043
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Academic Papers
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d071942f520e5025741aa035357e83f7cc22d176d1f6b41edf44d71b9c7cde58
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2026-01-07T00:00:00-05:00
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SOP: A Scalable Online Post-Training System for Vision-Language-Action Models
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arXiv:2601.03044v1 Announce Type: new Abstract: Vision-language-action (VLA) models achieve strong generalization through large-scale pre-training, but real-world deployment requires expert-level task proficiency in addition to broad generality. Existing post-training approaches for VLA models are typically offline, single-robot, or task-specific, limiting effective on-policy adaptation and scalable learning from real-world interaction. We introduce a Scalable Online Post-training (SOP) system that enables online, distributed, multi-task post-training of generalist VLA models directly in the physical world. SOP tightly couples execution and learning through a closed-loop architecture in which a fleet of robots continuously streams on-policy experience and human intervention signals to a centralized cloud learner, and asynchronously receives updated policies. This design supports prompt on-policy correction, scales experience collection through parallel deployment, and preserves generality during adaptation. SOP is agnostic to the choice of post-training algorithm; we instantiate it with both interactive imitation learning (HG-DAgger) and reinforcement learning (RECAP). Across a range of real-world manipulation tasks including cloth folding, box assembly, and grocery restocking, we show that SOP substantially improves the performance of large pretrained VLA models while maintaining a single shared policy across tasks. Effective post-training can be achieved within hours of real-world interaction, and performance scales near-linearly with the number of robots in the fleet. These results suggest that tightly coupling online learning with fleet-scale deployment is instrumental to enabling efficient, reliable, and scalable post-training of generalist robot policies in the physical world.
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https://arxiv.org/abs/2601.03044
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Academic Papers
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ea758b0dba491225c31e671c3be0819f700510428ad43b4c8bf2534f08d32ab8
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2026-01-07T00:00:00-05:00
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Motion Blur Robust Wheat Pest Damage Detection with Dynamic Fuzzy Feature Fusion
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arXiv:2601.03046v1 Announce Type: new Abstract: Motion blur caused by camera shake produces ghosting artifacts that substantially degrade edge side object detection. Existing approaches either suppress blur as noise and lose discriminative structure, or apply full image restoration that increases latency and limits deployment on resource constrained devices. We propose DFRCP, a Dynamic Fuzzy Robust Convolutional Pyramid, as a plug in upgrade to YOLOv11 for blur robust detection. DFRCP enhances the YOLOv11 feature pyramid by combining large scale and medium scale features while preserving native representations, and by introducing Dynamic Robust Switch units that adaptively inject fuzzy features to strengthen global perception under jitter. Fuzzy features are synthesized by rotating and nonlinearly interpolating multiscale features, then merged through a transparency convolution that learns a content adaptive trade off between original and fuzzy cues. We further develop a CUDA parallel rotation and interpolation kernel that avoids boundary overflow and delivers more than 400 times speedup, making the design practical for edge deployment. We train with paired supervision on a private wheat pest damage dataset of about 3,500 images, augmented threefold using two blur regimes, uniform image wide motion blur and bounding box confined rotational blur. On blurred test sets, YOLOv11 with DFRCP achieves about 10.4 percent higher accuracy than the YOLOv11 baseline with only a modest training time overhead, reducing the need for manual filtering after data collection.
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https://arxiv.org/abs/2601.03046
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Academic Papers
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a51f4540fba8d54dfba00966671320cdcd1ff123d4ad52ce2ce231cf5899bb70
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2026-01-07T00:00:00-05:00
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When the Coffee Feature Activates on Coffins: An Analysis of Feature Extraction and Steering for Mechanistic Interpretability
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arXiv:2601.03047v1 Announce Type: new Abstract: Recent work by Anthropic on Mechanistic interpretability claims to understand and control Large Language Models by extracting human-interpretable features from their neural activation patterns using sparse autoencoders (SAEs). If successful, this approach offers one of the most promising routes for human oversight in AI safety. We conduct an initial stress-test of these claims by replicating their main results with open-source SAEs for Llama 3.1. While we successfully reproduce basic feature extraction and steering capabilities, our investigation suggests that major caution is warranted regarding the generalizability of these claims. We find that feature steering exhibits substantial fragility, with sensitivity to layer selection, steering magnitude, and context. We observe non-standard activation behavior and demonstrate the difficulty to distinguish thematically similar features from one another. While SAE-based interpretability produces compelling demonstrations in selected cases, current methods often fall short of the systematic reliability required for safety-critical applications. This suggests a necessary shift in focus from prioritizing interpretability of internal representations toward reliable prediction and control of model output. Our work contributes to a more nuanced understanding of what mechanistic interpretability has achieved and highlights fundamental challenges for AI safety that remain unresolved.
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https://arxiv.org/abs/2601.03047
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Academic Papers
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eed6496f0ac84feae436779abee968bd5d49d958e0d2a2375c33fe60c963d7a7
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2026-01-07T00:00:00-05:00
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On the Intrinsic Limits of Transformer Image Embeddings in Non-Solvable Spatial Reasoning
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arXiv:2601.03048v1 Announce Type: new Abstract: Vision Transformers (ViTs) excel in semantic recognition but exhibit systematic failures in spatial reasoning tasks such as mental rotation. While often attributed to data scale, we propose that this limitation arises from the intrinsic circuit complexity of the architecture. We formalize spatial understanding as learning a Group Homomorphism: mapping image sequences to a latent space that preserves the algebraic structure of the underlying transformation group. We demonstrate that for non-solvable groups (e.g., the 3D rotation group $\mathrm{SO}(3)$), maintaining such a structure-preserving embedding is computationally lower-bounded by the Word Problem, which is $\mathsf{NC^1}$-complete. In contrast, we prove that constant-depth ViTs with polynomial precision are strictly bounded by $\mathsf{TC^0}$. Under the conjecture $\mathsf{TC^0} \subsetneq \mathsf{NC^1}$, we establish a complexity boundary: constant-depth ViTs fundamentally lack the logical depth to efficiently capture non-solvable spatial structures. We validate this complexity gap via latent-space probing, demonstrating that ViT representations suffer a structural collapse on non-solvable tasks as compositional depth increases.
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https://arxiv.org/abs/2601.03048
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Academic Papers
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9d90c84a3f4937f14e6647feada1be3faa5ab713098b3ef68a613f8c875c47cf
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2026-01-07T00:00:00-05:00
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An Empirical Study on User Profile Analysis and SEO Performance: A Case of Taiwan Cultural Memory Bank 2.0
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arXiv:2601.03050v1 Announce Type: new Abstract: Taiwan Cultural Memory Bank 2.0 is an online curation platform that invites the public to become curators, fostering diverse perspectives on Taiwan's society, humanities, natural landscapes, and daily life. Built on a material bank concept, the platform encourages users to co-create and curate their own works using shared resources or self-uploaded materials. At its core, the system follows a collect, store, access, and reuse model, supporting dynamic engagement with over three million cultural memory items from Taiwan. Users can search, browse, explore stories, and engage in creative applications and collaborative productions. Understanding user profiles is crucial for enhancing website service quality, particularly within the framework of the Visitor Relationship Management model. This study conducts an empirical analysis of user profiles on the platform, examining demographic characteristics, browsing behaviors, and engagement patterns. Additionally, the research evaluates the platform's SEO performance, search visibility, and organic traffic effectiveness. Based on the findings, this study provides strategic recommendations for optimizing website management, improving user experience, and leveraging social media for enhanced digital outreach. The insights gained contribute to the broader discussion on digital cultural platforms and their role in audience engagement, online visibility, and networked communication.
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https://arxiv.org/abs/2601.03050
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5a39ce2073043d615b1684675738d5cebbfe778d01f4c2edb76698feb82c7c9e
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2026-01-07T00:00:00-05:00
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Temporal Graph Network: Hallucination Detection in Multi-Turn Conversation
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arXiv:2601.03051v1 Announce Type: new Abstract: Hallucinations can be produced by conversational AI systems, particularly in multi-turn conversations where context changes and contradictions may eventually surface. By representing the entire conversation as a temporal graph, we present a novel graph-based method for detecting dialogue-level hallucinations. Our framework models each dialogue as a node, encoding it using a sentence transformer. We explore two different ways of connectivity: i) shared-entity edges, which connect turns that refer to the same entities; ii) temporal edges, which connect contiguous turns in the conversation. Message-passing is used to update the node embeddings, allowing flow of information between related nodes. The context-aware node embeddings are then combined using attention pooling into a single vector, which is then passed on to a classifier to determine the presence and type of hallucinations. We demonstrate that our method offers slightly improved performance over existing methods. Further, we show the attention mechanism can be used to justify the decision making process. The code and model weights are made available at: https://github.com/sambuaneesh/anlp-project.
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https://arxiv.org/abs/2601.03051
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3a4ca476f23c21b4c95d85dbb69d54d30bde7cc3066494697d698d4b2702be3d
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2026-01-07T00:00:00-05:00
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Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph
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arXiv:2601.03052v1 Announce Type: new Abstract: The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist. Previous methods for detecting faithfulness hallucinations either neglect to capture the models' internal reasoning processes or handle those features coarsely, making it difficult for discriminators to learn. This paper proposes a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination. Specifically, we first extend the layer-wise relevance propagation algorithm from the token level to the semantic level, constructing an internal reasoning graph based on attribution vectors. This provides a more faithful semantic-level representation of dependency. Furthermore, we design a general framework based on a small pre-trained language model to utilize the dependencies in LLM's reasoning for training and hallucination detection, which can dynamically adjust the pass rate of correct samples through a threshold. Experimental results demonstrate that our method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k.
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https://arxiv.org/abs/2601.03052
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Academic Papers
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f7a1240dc661b54ad905f8c2761ca63280fbf39a8c4c4de843b39b35a2d198c3
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2026-01-07T00:00:00-05:00
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IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation
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arXiv:2601.03054v1 Announce Type: new Abstract: Recent research on medical MLLMs has gradually shifted its focus from image-level understanding to fine-grained, pixel-level comprehension. Although segmentation serves as the foundation for pixel-level understanding, existing approaches face two major challenges. First, they introduce implicit segmentation tokens and require simultaneous fine-tuning of both the MLLM and external pixel decoders, which increases the risk of catastrophic forgetting and limits generalization to out-of-domain scenarios. Second, most methods rely on single-pass reasoning and lack the capability to iteratively refine segmentation results, leading to suboptimal performance. To overcome these limitations, we propose a novel agentic MLLM, named IBISAgent, that reformulates segmentation as a vision-centric, multi-step decision-making process. IBISAgent enables MLLMs to generate interleaved reasoning and text-based click actions, invoke segmentation tools, and produce high-quality masks without architectural modifications. By iteratively performing multi-step visual reasoning on masked image features, IBISAgent naturally supports mask refinement and promotes the development of pixel-level visual reasoning capabilities. We further design a two-stage training framework consisting of cold-start supervised fine-tuning and agentic reinforcement learning with tailored, fine-grained rewards, enhancing the model's robustness in complex medical referring and reasoning segmentation tasks. Extensive experiments demonstrate that IBISAgent consistently outperforms both closed-source and open-source SOTA methods. All datasets, code, and trained models will be released publicly.
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https://arxiv.org/abs/2601.03054
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Academic Papers
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83ea6c2f16979c532afa4567d780be05fed42a2f9a925caf8ca27178a4058f5e
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2026-01-07T00:00:00-05:00
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A Fast Semidefinite Convex Relaxation for Optimal Control Problems With Spatio-Temporal Constraints
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arXiv:2601.03055v1 Announce Type: new Abstract: Solving optimal control problems (OCPs) of autonomous agents operating under spatial and temporal constraints fast and accurately is essential in applications ranging from eco-driving of autonomous vehicles to quadrotor navigation. However, the nonlinear programs approximating the OCPs are inherently nonconvex due to the coupling between the dynamics and the event timing, and therefore, they are challenging to solve. Most approaches address this challenge by predefining waypoint times or just using nonconvex trajectory optimization, which simplifies the problem but often yields suboptimal solutions. To significantly improve the numerical properties, we propose a formulation with a time-scaling direct multiple shooting scheme that partitions the prediction horizon into segments aligned with characteristic time constraints. Moreover, we develop a fast semidefinite-programming-based convex relaxation that exploits the sparsity pattern of the lifted formulation. Comprehensive simulation studies demonstrate the solution optimality and computational efficiency. Furthermore, real-world experiments on a quadrotor waypoint flight task with constrained open time windows validate the practical applicability of the approach in complex environments.
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https://arxiv.org/abs/2601.03055
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Academic Papers
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af2f5348f5487b455e1fa7fed25db4baeb275551a0fae10ae576ed276cb0b3ad
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2026-01-07T00:00:00-05:00
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Fine-Grained Generalization via Structuralizing Concept and Feature Space into Commonality, Specificity and Confounding
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arXiv:2601.03056v1 Announce Type: new Abstract: Fine-Grained Domain Generalization (FGDG) presents greater challenges than conventional domain generalization due to the subtle inter-class differences and relatively pronounced intra-class variations inherent in fine-grained recognition tasks. Under domain shifts, the model becomes overly sensitive to fine-grained cues, leading to the suppression of critical features and a significant drop in performance. Cognitive studies suggest that humans classify objects by leveraging both common and specific attributes, enabling accurate differentiation between fine-grained categories. However, current deep learning models have yet to incorporate this mechanism effectively. Inspired by this mechanism, we propose Concept-Feature Structuralized Generalization (CFSG). This model explicitly disentangles both the concept and feature spaces into three structured components: common, specific, and confounding segments. To mitigate the adverse effects of varying degrees of distribution shift, we introduce an adaptive mechanism that dynamically adjusts the proportions of common, specific, and confounding components. In the final prediction, explicit weights are assigned to each pair of components. Extensive experiments on three single-source benchmark datasets demonstrate that CFSG achieves an average performance improvement of 9.87% over baseline models and outperforms existing state-of-the-art methods by an average of 3.08%. Additionally, explainability analysis validates that CFSG effectively integrates multi-granularity structured knowledge and confirms that feature structuralization facilitates the emergence of concept structuralization.
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https://arxiv.org/abs/2601.03056
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Academic Papers
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c051847f7fc17dda66d7191e362701a2534f34101200b3c865fe9d332e00ed8e
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2026-01-07T00:00:00-05:00
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Exploring the Relationship Between Local Election Results and Online Public Opinion in Taiwan: A Case Study of Taitung County
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arXiv:2601.03057v1 Announce Type: new Abstract: This study examines the relationship between online buzz and local election outcomes in Taiwan, with a focus on Taitung County. As social media becomes a major channel for public discourse, online buzz is increasingly seen as a factor influencing elections. However, its impact on local elections in Taiwan remains underexplored. This research addresses that gap through a comparative analysis of social media data and actual vote shares during the election period. A review of existing literature establishes the study's framework and highlights the need for empirical investigation in this area. The findings aim to reveal whether online discussions align with electoral results and to what extent digital sentiment reflects voter behavior. The study also discusses methodological and data limitations that may affect interpretation. Beyond its academic value, the research offers practical insights into how online buzz can inform campaign strategies and enhance election predictions. By analyzing the Taitung County case, this study contributes to a deeper understanding of the role of online discourse in Taiwan's local elections and offers a foundation for future research in the field.
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https://arxiv.org/abs/2601.03057
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Academic Papers
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64b54a0fa23d40966d36faeb7bc8b4771d88a7f04f2b8efc2b37caf1b7b990c7
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2026-01-07T00:00:00-05:00
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Vertical tacit collusion in AI-mediated markets
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arXiv:2601.03061v1 Announce Type: new Abstract: AI shopping agents are being deployed to hundreds of millions of consumers, creating a new intermediary between platforms, sellers, and buyers. We identify a novel market failure: vertical tacit collusion, where platforms controlling rankings and sellers controlling product descriptions independently learn to exploit documented AI cognitive biases. Using multi-agent simulation calibrated to empirical measurements of large language model biases, we show that joint exploitation produces consumer harm more than double what would occur if strategies were independent. This super-additive harm arises because platform ranking determines which products occupy bias-triggering positions while seller manipulation determines conversion rates. Unlike horizontal algorithmic collusion, vertical tacit collusion requires no coordination and evades antitrust detection because harm emerges from aligned incentives rather than agreement. Our findings identify an urgent regulatory gap as AI shopping agents reach mainstream adoption.
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https://arxiv.org/abs/2601.03061
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Academic Papers
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23c7721f3f387d80d8876e19b2d8d80c98b1a983d11c83d4b5034dccb06cc12f
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2026-01-07T00:00:00-05:00
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Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
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arXiv:2601.03062v1 Announce Type: new Abstract: Timely leak detection in water distribution networks is critical for conserving resources and maintaining operational efficiency. Although Graph Neural Networks (GNNs) excel at capturing spatial-temporal dependencies in sensor data, their black-box nature and the limited work on graph-based explainable models for water networks hinder practical adoption. We propose an explainable GNN framework that integrates mutual information to identify critical network regions and fuzzy logic to provide clear, rule-based explanations for node classification tasks. After benchmarking several GNN architectures, we selected the generalized graph convolution network (GENConv) for its superior performance and developed a fuzzy-enhanced variant that offers intuitive explanations for classified leak locations. Our fuzzy graph neural network (FGENConv) achieved Graph F1 scores of 0.889 for detection and 0.814 for localization, slightly below the crisp GENConv 0.938 and 0.858, respectively. Yet it compensates by providing spatially localized, fuzzy rule-based explanations. By striking the right balance between precision and explainability, the proposed fuzzy network could enable hydraulic engineers to validate predicted leak locations, conserve human resources, and optimize maintenance strategies. The code is available at github.com/pasqualedem/GNNLeakDetection.
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https://arxiv.org/abs/2601.03062
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Academic Papers
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31bcf08ca54eb4c5994a3dafabec5035207d75c64235903c22aca4759fe45410
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2026-01-07T00:00:00-05:00
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Do LLMs Encode Functional Importance of Reasoning Tokens?
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arXiv:2601.03066v1 Announce Type: new Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost and reduced ability to isolate functionally relevant reasoning. Prior work on compact reasoning shortens such chains through probabilistic sampling, heuristics, or supervision from frontier models, but offers limited insight into whether models internally encode token-level functional importance for answer generation. We address this gap diagnostically and propose greedy pruning, a likelihood-preserving deletion procedure that iteratively removes reasoning tokens whose removal minimally degrades model likelihood under a specified objective, yielding length-controlled reasoning chains. We evaluate pruned reasoning in a distillation framework and show that students trained on pruned chains outperform a frontier-model-supervised compression baseline at matched reasoning lengths. Finally, our analysis reveals systematic pruning patterns and shows that attention scores can predict greedy pruning ranks, further suggesting that models encode a nontrivial functional importance structure over reasoning tokens.
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https://arxiv.org/abs/2601.03066
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Academic Papers
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f2c4ca5625874920c3b53455b7e3c1e7613c1e6cee7386fbf13746884e7ea5cf
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2026-01-07T00:00:00-05:00
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Joint Encoding of KV-Cache Blocks for Scalable LLM Serving
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arXiv:2601.03067v1 Announce Type: new Abstract: Modern large language models (LLMs) drive interactive AI systems but are bottlenecked by the memory-heavy growth of key-value (KV) caches, which limits real-time throughput under concurrent loads. Existing KV-cache compression methods rely on rigid heuristics, disrupt tensor layouts, or require specialized compute, hindering scalability and deployment. We propose joint encoding of KV-cache blocks, which fuses similar blocks across requests and input chunks into shared representations while preserving standard cache structure. This alleviates the KV-cache memory bottleneck, supporting high-concurrency serving without specialized hardware. Theoretically, we analyze the rate-distortion tradeoff of fused cache blocks under a Poisson process model. Empirically, our method achieves up to 4.38 $\times$ KV-cache compression with negligible accuracy loss across diverse LLMs and benchmarks, outperforming recent structured and adaptive compression baselines. In real LLM serving, joint encoding improves the token throughput by $\sim$40\% on a single-machine vLLM benchmark, demonstrating substantial gains in inference throughput. Code is available at https://github.com/sef1/kv_fast_fusion kv_joint_encoding.
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https://arxiv.org/abs/2601.03067
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Academic Papers
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f5b89b794db0c7b98d913e750b8f72c1854a8193e5b783d8c68df7cdeff3c441
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2026-01-07T00:00:00-05:00
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HEXAR: a Hierarchical Explainability Architecture for Robots
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arXiv:2601.03070v1 Announce Type: new Abstract: As robotic systems become increasingly complex, the need for explainable decision-making becomes critical. Existing explainability approaches in robotics typically either focus on individual modules, which can be difficult to query from the perspective of high-level behaviour, or employ monolithic approaches, which do not exploit the modularity of robotic architectures. We present HEXAR (Hierarchical EXplainability Architecture for Robots), a novel framework that provides a plug-in, hierarchical approach to generate explanations about robotic systems. HEXAR consists of specialised component explainers using diverse explanation techniques (e.g., LLM-based reasoning, causal models, feature importance, etc) tailored to specific robot modules, orchestrated by an explainer selector that chooses the most appropriate one for a given query. We implement and evaluate HEXAR on a TIAGo robot performing assistive tasks in a home environment, comparing it against end-to-end and aggregated baseline approaches across 180 scenario-query variations. We observe that HEXAR significantly outperforms baselines in root cause identification, incorrect information exclusion, and runtime, offering a promising direction for transparent autonomous systems.
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https://arxiv.org/abs/2601.03070
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Academic Papers
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fecd2c8dd1807d0fbc24c104607bf52bf3c0cba2555cf793c22c4f4931bff5cb
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2026-01-07T00:00:00-05:00
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Understanding Multi-Agent Reasoning with Large Language Models for Cartoon VQA
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arXiv:2601.03073v1 Announce Type: new Abstract: Visual Question Answering (VQA) for stylised cartoon imagery presents challenges, such as interpreting exaggerated visual abstraction and narrative-driven context, which are not adequately addressed by standard large language models (LLMs) trained on natural images. To investigate this issue, a multi-agent LLM framework is introduced, specifically designed for VQA tasks in cartoon imagery. The proposed architecture consists of three specialised agents: visual agent, language agent and critic agent, which work collaboratively to support structured reasoning by integrating visual cues and narrative context. The framework was systematically evaluated on two cartoon-based VQA datasets: Pororo and Simpsons. Experimental results provide a detailed analysis of how each agent contributes to the final prediction, offering a deeper understanding of LLM-based multi-agent behaviour in cartoon VQA and multimodal inference.
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https://arxiv.org/abs/2601.03073
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Academic Papers
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30778a4807e0e6492a1b7a44b0ee4b95d2587ee8e860b68ab7acc6da6f02a228
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2026-01-07T00:00:00-05:00
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Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace
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arXiv:2601.03075v1 Announce Type: new Abstract: Trajectory prediction (TP) is crucial for ensuring safety and efficiency in modern air traffic management systems. It is, for example, a core component of conflict detection and resolution tools, arrival sequencing algorithms, capacity planning, as well as several future concepts. However, TP accuracy within operational systems is hampered by a range of epistemic uncertainties such as the mass and performance settings of aircraft and the effect of meteorological conditions on aircraft performance. It can also require considerable computational resources. This paper proposes a method for adaptive TP that has two components: first, a fast surrogate TP model based on linear state space models (LSSM)s with an execution time that was 6.7 times lower on average than an implementation of the Base of Aircraft Data (BADA) in Python. It is demonstrated that such models can effectively emulate the BADA aircraft performance model, which is based on the numerical solution of a partial differential equation (PDE), and that the LSSMs can be fitted to trajectories in a dataset of historic flight data. Secondly, the paper proposes an algorithm to assimilate radar observations using particle filtering to adaptively refine TP accuracy. Comparison with baselines using BADA and Kalman filtering demonstrate that the proposed framework improves system identification and state estimation for both climb and descent phases, with 46.3% and 64.7% better estimates for time to top of climb and bottom of descent compared to the best performing benchmark model. In particular, the particle filtering approach provides the flexibility to capture non-linear performance effects including the CAS-Mach transition.
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https://arxiv.org/abs/2601.03075
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Academic Papers
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2bc6b59cf77cea616fb3f75c5d9764089f22d7873d02c3c23dce039dfeed16ac
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2026-01-07T00:00:00-05:00
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Learning to Diagnose and Correct Moral Errors: Towards Enhancing Moral Sensitivity in Large Language Models
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arXiv:2601.03079v1 Announce Type: new Abstract: Moral sensitivity is fundamental to human moral competence, as it guides individuals in regulating everyday behavior. Although many approaches seek to align large language models (LLMs) with human moral values, how to enable them morally sensitive has been extremely challenging. In this paper, we take a step toward answering the question: how can we enhance moral sensitivity in LLMs? Specifically, we propose two pragmatic inference methods that faciliate LLMs to diagnose morally benign and hazardous input and correct moral errors, whereby enhancing LLMs' moral sensitivity. A central strength of our pragmatic inference methods is their unified perspective: instead of modeling moral discourses across semantically diverse and complex surface forms, they offer a principled perspective for designing pragmatic inference procedures grounded in their inferential loads. Empirical evidence demonstrates that our pragmatic methods can enhance moral sensitivity in LLMs and achieves strong performance on representative morality-relevant benchmarks.
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https://arxiv.org/abs/2601.03079
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Academic Papers
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06be158c4a21bac6615cf9b578ebe5c3c78db31ceae9ec1d81c65392ec031938
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2026-01-07T00:00:00-05:00
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Real-Time Adaptive Anomaly Detection in Industrial IoT Environments
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arXiv:2601.03085v1 Announce Type: new Abstract: To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in today's industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by achieving up to an 89.71% accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements.
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https://arxiv.org/abs/2601.03085
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Academic Papers
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b18b58736762469239378e2bfc6d472e0e3cfc52031ab604456f6f5321df3e00
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2026-01-07T00:00:00-05:00
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Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators
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arXiv:2601.03086v1 Announce Type: new Abstract: We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classical finite element methods (FEM). PFEM consists of a physics informed pretraining stage and an optional finetuning stage. In the pretraining stage, a neural operator based on the Transolver architecture is trained solely from governing partial differential equations, without relying on labeled solution data. The model operates directly on unstructured point clouds, jointly encoding geometric information, material properties, and boundary conditions, and produces physically consistent initial solutions with extremely high computational efficiency. PDE constraints are enforced through explicit finite element, based differentiation, avoiding the overhead associated with automatic differentiation. In the fine-tuning stage, the pretrained prediction is used as an initial guess for conventional FEM solvers, preserving their accuracy, convergence guarantees, and extrapolation capability while substantially reducing the number of iterations required to reach a prescribed tolerance. PFEM is validated on a broad range of benchmark problems, including linear elasticity and nonlinear hyperelasticity with complex geometries, heterogeneous materials, and arbitrary boundary conditions. Numerical results demonstrate strong generalization in the pretraining stage with relative errors on the order of 1\%, and speedups of up to one order of magnitude in the fine-tuning stage compared to FEM with zero initial guesses.
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https://arxiv.org/abs/2601.03086
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Academic Papers
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a8c3631aa3a41b89726d3cf06f734e0368ca1814ae2712c26f054372a8e4821e
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2026-01-07T00:00:00-05:00
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Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs
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arXiv:2601.03087v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit systematic biases across demographic groups. Auditing is proposed as an accountability tool for black-box LLM applications, but suffers from resource-intensive query access. We conceptualise auditing as uncertainty estimation over a target fairness metric and introduce BAFA, the Bounded Active Fairness Auditor for query-efficient auditing of black-box LLMs. BAFA maintains a version space of surrogate models consistent with queried scores and computes uncertainty intervals for fairness metrics (e.g., $\Delta$ AUC) via constrained empirical risk minimisation. Active query selection narrows these intervals to reduce estimation error. We evaluate BAFA on two standard fairness dataset case studies: \textsc{CivilComments} and \textsc{Bias-in-Bios}, comparing against stratified sampling, power sampling, and ablations. BAFA achieves target error thresholds with up to 40$\times$ fewer queries than stratified sampling (e.g., 144 vs 5,956 queries at $\varepsilon=0.02$ for \textsc{CivilComments}) for tight thresholds, demonstrates substantially better performance over time, and shows lower variance across runs. These results suggest that active sampling can reduce resources needed for independent fairness auditing with LLMs, supporting continuous model evaluations.
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https://arxiv.org/abs/2601.03087
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Academic Papers
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44013b315266fe392789df5157337615a0ad98d2a9a84c4d247542beed43b181
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2026-01-07T00:00:00-05:00
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Grad-ELLM: Gradient-based Explanations for Decoder-only LLMs
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arXiv:2601.03089v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet their black-box nature raises concerns about transparency and faithfulness. Input attribution methods aim to highlight each input token's contributions to the model's output, but existing approaches are typically model-agnostic, and do not focus on transformer-specific architectures, leading to limited faithfulness. To address this, we propose Grad-ELLM, a gradient-based attribution method for decoder-only transformer-based LLMs. By aggregating channel importance from gradients of the output logit with respect to attention layers and spatial importance from attention maps, Grad-ELLM generates heatmaps at each generation step without requiring architectural modifications. Additionally, we introduce two faithfulneses metrics $\pi$-Soft-NC and $\pi$-Soft-NS, which are modifications of Soft-NC/NS that provide fairer comparisons by controlling the amount of information kept when perturbing the text. We evaluate Grad-ELLM on sentiment classification, question answering, and open-generation tasks using different models. Experiment results show that Grad-ELLM consistently achieves superior faithfulness than other attribution methods.
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https://arxiv.org/abs/2601.03089
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Academic Papers
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4d4395d2cdb75dc60f7b619b8f9fd594bc3c61130faeba35628b61b131f07a66
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2026-01-07T00:00:00-05:00
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LesionTABE: Equitable AI for Skin Lesion Detection
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arXiv:2601.03090v1 Announce Type: new Abstract: Bias remains a major barrier to the clinical adoption of AI in dermatology, as diagnostic models underperform on darker skin tones. We present LesionTABE, a fairness-centric framework that couples adversarial debiasing with dermatology-specific foundation model embeddings. Evaluated across multiple datasets covering both malignant and inflammatory conditions, LesionTABE achieves over a 25\% improvement in fairness metrics compared to a ResNet-152 baseline, outperforming existing debiasing methods while simultaneously enhancing overall diagnostic accuracy. These results highlight the potential of foundation model debiasing as a step towards equitable clinical AI adoption.
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https://arxiv.org/abs/2601.03090
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Academic Papers
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4984eb903d52a1d034a32cfb70138862cdcef20680c5857856ed64123a37565b
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2026-01-07T00:00:00-05:00
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ATLAS: Adaptive Test-Time Latent Steering with External Verifiers for Enhancing LLMs Reasoning
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arXiv:2601.03093v1 Announce Type: new Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (LLMs) toward improved reasoning and efficiency without additional training. However, most existing approaches rely on fixed steering policies and static intervention strengths, which limit their robustness across problem instances and often result in over- or under-steering. We propose Adaptive Test-time Latent Steering, called (ATLAS), a task- specific framework that dynamically controls steering decisions at inference time using an external, lightweight latent verifier. Given intermediate hidden states, the verifier predicts the quality of ongoing reasoning and adaptively selects whether and how strongly to apply steering, enabling per-example and per-step adjustment with minimal overhead. To our knowledge, ATLAS is the first method to integrate learned latent verification into test-time steering for enhancing LLMs reasoning. Experiments on multiple mathematical reasoning benchmarks show that ATLAS consistently outperforms both vanilla decoding and fixed steering baselines, achieving higher accuracy while substantially reducing test-time token usage. These results demonstrate that verifier-guided latent adaptation provides an effective and scalable mechanism for controlling reasoning efficiency without sacrificing solution quality. All source code will be publicly available.
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https://arxiv.org/abs/2601.03093
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Academic Papers
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0c2e70476ced4e253d374f79c877dd39abc5ac8cbe62e5d2e0c7459163959832
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2026-01-07T00:00:00-05:00
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Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees
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arXiv:2601.03097v1 Announce Type: new Abstract: We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically on $\mathrm{SE}(3)$. The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities, as is standard in many aerial vehicles and omnidirectional mobile robots. Gaussian Process (GP) regression is integrated into a geometric feedback law to learn and compensate online for unknown, state-dependent disturbances and modeling imperfections affecting both attitude and position, while preserving the algebraic structure and coupling properties inherent to rigid-body motion. The proposed approach does not rely on explicit parametric models of the unknown effects, making it well-suited for robotic systems subject to sensor-induced disturbances, unmodeled actuation couplings, and environmental uncertainties. A Lyapunov-based analysis establishes probabilistic ultimate boundedness of the pose tracking error under bounded GP uncertainty, providing formal stability guarantees for the learning-based controller. Simulation results demonstrate accurate and smooth trajectory tracking in the presence of realistic, localized disturbances, including correlated rotational and translational effects arising from magnetometer perturbations. These results illustrate the potential of combining geometric modeling and probabilistic learning to achieve robust, data-efficient pose control for autonomous robotic systems.
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https://arxiv.org/abs/2601.03097
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Academic Papers
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62580f5368af6f53fc5a5eefc1fea82c0309fb9270d1ece1d84e64a3bfc9df11
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2026-01-07T00:00:00-05:00
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From Muscle to Text with MyoText: sEMG to Text via Finger Classification and Transformer-Based Decoding
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arXiv:2601.03098v1 Announce Type: new Abstract: Surface electromyography (sEMG) provides a direct neural interface for decoding muscle activity and offers a promising foundation for keyboard-free text input in wearable and mixed-reality systems. Previous sEMG-to-text studies mainly focused on recognizing letters directly from sEMG signals, forming an important first step toward translating muscle activity into text. Building on this foundation, we present MyoText, a hierarchical framework that decodes sEMG signals to text through physiologically grounded intermediate stages. MyoText first classifies finger activations from multichannel sEMG using a CNN-BiLSTM-Attention model, applies ergonomic typing priors to infer letters, and reconstructs full sentences with a fine-tuned T5 transformer. This modular design mirrors the natural hierarchy of typing, linking muscle intent to language output and reducing the search space for decoding. Evaluated on 30 users from the emg2qwerty dataset, MyoText outperforms baselines by achieving 85.4% finger-classification accuracy, 5.4% character error rate (CER), and 6.5% word error rate (WER). Beyond accuracy gains, this methodology establishes a principled pathway from neuromuscular signals to text, providing a blueprint for virtual and augmented-reality typing interfaces that operate entirely without physical keyboards. By integrating ergonomic structure with transformer-based linguistic reasoning, MyoText advances the feasibility of seamless, wearable neural input for future ubiquitous computing environments.
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https://arxiv.org/abs/2601.03098
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Academic Papers
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c034578a03fd6d5856aed9334d91776702723d1023290850d3fae07615730641
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2026-01-07T00:00:00-05:00
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Time-Aware Synthetic Control
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arXiv:2601.03099v1 Announce Type: new Abstract: The synthetic control (SC) framework is widely used for observational causal inference with time-series panel data. SC has been successful in diverse applications, but existing methods typically treat the ordering of pre-intervention time indices interchangeable. This invariance means they may not fully take advantage of temporal structure when strong trends are present. We propose Time-Aware Synthetic Control (TASC), which employs a state-space model with a constant trend while preserving a low-rank structure of the signal. TASC uses the Kalman filter and Rauch-Tung-Striebel smoother: it first fits a generative time-series model with expectation-maximization and then performs counterfactual inference. We evaluate TASC on both simulated and real-world datasets, including policy evaluation and sports prediction. Our results suggest that TASC offers advantages in settings with strong temporal trends and high levels of observation noise.
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https://arxiv.org/abs/2601.03099
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Academic Papers
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9aad19760cb19099586e8a340d96fa57072654454f2dbb0221d21727d496bdd7
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2026-01-07T00:00:00-05:00
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Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs
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arXiv:2601.03100v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utilized. MLLMs still suffer from visually ungrounded hallucinations, often relying on language priors rather than image evidence. While many prior mitigation strategies operate on the text side, they leave the visual representation unchanged and do not exploit the rich hierarchy of features encoded across vision layers. Existing multi-layer fusion methods partially address this limitation but remain static, applying the same layer mixture regardless of the query. In this work, we introduce TGIF (Text-Guided Inter-layer Fusion), a lightweight module that treats encoder layers as depth-wise "experts" and predicts a prompt-dependent fusion of visual features. TGIF follows the principle of direct external fusion, requires no vision-encoder updates, and adds minimal overhead. Integrated into LLaVA-1.5-7B, TGIF provides consistent improvements across hallucination, OCR, and VQA benchmarks, while preserving or improving performance on ScienceQA, GQA, and MMBench. These results suggest that query-conditioned, hierarchy-aware fusion is an effective way to strengthen visual grounding and reduce hallucination in modern MLLMs.
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https://arxiv.org/abs/2601.03100
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365c196b2e0874f1d72379614d1ab81d1ea0aa0e138785461ca2738d337a7337
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2026-01-07T00:00:00-05:00
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Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
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arXiv:2601.03103v1 Announce Type: new Abstract: Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In this study, we model heterogeneity in humor preferences in Oogiri, a Japanese creative response game, by clustering users with voting logs and estimating cluster-specific weights over interpretable preference factors using Bradley-Terry-Luce models. We elicit preference judgments from LLMs by prompting them to select the funnier response and found that user clusters exhibit distinct preference patterns and that the LLM results can resemble those of particular clusters. Finally, we demonstrate that, by persona prompting, LLM preferences can be directed toward a specific cluster. The scripts for data collection and analysis will be released to support reproducibility.
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https://arxiv.org/abs/2601.03103
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Academic Papers
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9ab78e57bfab59857ac78f3cd3273e9898c6145daa91e7ddde047211198457d4
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2026-01-07T00:00:00-05:00
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One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling
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arXiv:2601.03111v1 Announce Type: new Abstract: The reasoning ability of large language models (LLMs) can be unleashed with reinforcement learning (RL) (OpenAI, 2024; DeepSeek-AI et al., 2025a; Zeng et al., 2025). The success of existing RL attempts in LLMs usually relies on high-quality samples of thousands or beyond. In this paper, we challenge fundamental assumptions about data requirements in RL for LLMs by demonstrating the remarkable effectiveness of one-shot learning. Specifically, we introduce polymath learning, a framework for designing one training sample that elicits multidisciplinary impact. We present three key findings: (1) A single, strategically selected math reasoning sample can produce significant performance improvements across multiple domains, including physics, chemistry, and biology with RL; (2) The math skills salient to reasoning suggest the characteristics of the optimal polymath sample; and (3) An engineered synthetic sample that integrates multidiscipline elements outperforms training with individual samples that naturally occur. Our approach achieves superior performance to training with larger datasets across various reasoning benchmarks, demonstrating that sample quality and design, rather than quantity, may be the key to unlock enhanced reasoning capabilities in language models. Our results suggest a shift, dubbed as sample engineering, toward precision engineering of training samples rather than simply increasing data volume.
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https://arxiv.org/abs/2601.03111
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Academic Papers
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bc1b03e669d5cb78edd6d68bd9e699455455cf221b429ac9141a5ce10bc1898f
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2026-01-07T00:00:00-05:00
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A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control
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arXiv:2601.03113v1 Announce Type: new Abstract: This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the development and rigorous human-in-the-loop evaluation of AI agents for Air Traffic Control (ATC), providing a virtual representation of real-world airspace that enables safe exploration of higher levels of ATC automation. This paper makes three significant contributions: firstly, we demonstrate how historical and live operational data may be combined with a probabilistic, physics-informed machine learning model of aircraft performance to reproduce real-world traffic scenarios, while accurately reflecting the level of uncertainty inherent in ATC. Secondly, we develop a structured assurance case, following the Trustworthy and Ethical Assurance framework, to provide quantitative evidence for the Digital Twin's accuracy and fidelity. This is crucial to building trust in this novel technology within this safety-critical domain. Thirdly, we describe how the Digital Twin forms a unified environment for agent testing and evaluation. This includes fast-time execution (up to x200 real-time), a standardised Python-based ``gym'' interface that supports a range of AI agent designs, and a suite of quantitative metrics for assessing performance. Crucially, the framework facilitates competency-based assessment of AI agents by qualified Air Traffic Control Officers through a Human Machine Interface. We also outline further applications and future extensions of the Digital Twin architecture.
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https://arxiv.org/abs/2601.03113
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Academic Papers
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8477b8f6861e8c5311338d81e8ca0ff8afae6e741273424bceeb72f968814b00
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2026-01-07T00:00:00-05:00
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Stroke Patches: Customizable Artistic Image Styling Using Regression
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arXiv:2601.03114v1 Announce Type: new Abstract: We present a novel, regression-based method for artistically styling images. Unlike recent neural style transfer or diffusion-based approaches, our method allows for explicit control over the stroke composition and level of detail in the rendered image through the use of an extensible set of stroke patches. The stroke patch sets are procedurally generated by small programs that control the shape, size, orientation, density, color, and noise level of the strokes in the individual patches. Once trained on a set of stroke patches, a U-Net based regression model can render any input image in a variety of distinct, evocative and customizable styles.
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https://arxiv.org/abs/2601.03114
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Academic Papers
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svg
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d09f989117984a64a9798b4f9105b906bc7b5e6f2e350b8ffbcaa522d21f071d
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2026-01-07T00:00:00-05:00
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Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models
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arXiv:2601.03115v1 Announce Type: new Abstract: Emotion is a central dimension of spoken communication, yet, we still lack a mechanistic account of how modern large audio-language models (LALMs) encode it internally. We present the first neuron-level interpretability study of emotion-sensitive neurons (ESNs) in LALMs and provide causal evidence that such units exist in Qwen2.5-Omni, Kimi-Audio, and Audio Flamingo 3. Across these three widely used open-source models, we compare frequency-, entropy-, magnitude-, and contrast-based neuron selectors on multiple emotion recognition benchmarks. Using inference-time interventions, we reveal a consistent emotion-specific signature: ablating neurons selected for a given emotion disproportionately degrades recognition of that emotion while largely preserving other classes, whereas gain-based amplification steers predictions toward the target emotion. These effects arise with modest identification data and scale systematically with intervention strength. We further observe that ESNs exhibit non-uniform layer-wise clustering with partial cross-dataset transfer. Taken together, our results offer a causal, neuron-level account of emotion decisions in LALMs and highlight targeted neuron interventions as an actionable handle for controllable affective behaviors.
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https://arxiv.org/abs/2601.03115
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Academic Papers
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svg
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f1c1369af2f13274be64760bd521f125dfebe69a488142c8997888dc62be7959
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2026-01-07T00:00:00-05:00
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A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace
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arXiv:2601.03120v1 Announce Type: new Abstract: Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Bluebird, an industry-academic collaboration, has developed a probabilistic Digital Twin of en route UK airspace as an environment for training and testing AI Air Traffic Control (ATC) agents. There is a developing regulatory landscape for this kind of novel technology. Regulatory requirements are expected to be application specific, and may need to be tailored to each specific use case. We draw on emerging guidance for both Digital Twin development and the use of Artificial Intelligence/Machine Learning (AI/ML) in Air Traffic Management (ATM) to present an assurance framework. This framework defines actionable goals and the evidence required to demonstrate that a Digital Twin accurately represents its physical counterpart and also provides sufficient functionality across target use cases. It provides a structured approach for researchers to assess, understand and document the strengths and limitations of the Digital Twin, whilst also identifying areas where fidelity could be improved. Furthermore, it serves as a foundation for engagement with stakeholders and regulators, supporting discussions around the regulatory needs for future applications, and contributing to the emerging guidance through a concrete, working example of a Digital Twin. The framework leverages a methodology known as Trustworthy and Ethical Assurance (TEA) to develop an assurance case. An assurance case is a nested set of structured arguments that provides justified evidence for how a top-level goal has been realised. In this paper we provide an overview of each structured argument and a number of deep dives which elaborate in more detail upon particular arguments, including the required evidence, assumptions and justifications.
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https://arxiv.org/abs/2601.03120
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Academic Papers
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10b0cc72f3d79719ef442276ac7edd6eefaf26a5913b5723a9ed509a293ba9d9
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2026-01-07T00:00:00-05:00
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ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation
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arXiv:2601.03121v1 Announce Type: new Abstract: Augmenting toxic language data in a controllable and class-specific manner is crucial for improving robustness in toxicity classification, yet remains challenging due to limited supervision and distributional skew. We propose ToxiGAN, a class-aware text augmentation framework that combines adversarial generation with semantic guidance from large language models (LLMs). To address common issues in GAN-based augmentation such as mode collapse and semantic drift, ToxiGAN introduces a two-step directional training strategy and leverages LLM-generated neutral texts as semantic ballast. Unlike prior work that treats LLMs as static generators, our approach dynamically selects neutral exemplars to provide balanced guidance. Toxic samples are explicitly optimized to diverge from these exemplars, reinforcing class-specific contrastive signals. Experiments on four hate speech benchmarks show that ToxiGAN achieves the strongest average performance in both macro-F1 and hate-F1, consistently outperforming traditional and LLM-based augmentation methods. Ablation and sensitivity analyses further confirm the benefits of semantic ballast and directional training in enhancing classifier robustness.
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https://arxiv.org/abs/2601.03121
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Academic Papers
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5dc4e631fb1d6d4b57f55686efc9aad618854435161b1532d83740fdcb476284
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2026-01-07T00:00:00-05:00
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LeafLife: An Explainable Deep Learning Framework with Robustness for Grape Leaf Disease Recognition
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arXiv:2601.03124v1 Announce Type: new Abstract: Plant disease diagnosis is essential to farmers' management choices because plant diseases frequently lower crop yield and product quality. For harvests to flourish and agricultural productivity to boost, grape leaf disease detection is important. The plant disease dataset contains grape leaf diseases total of 9,032 images of four classes, among them three classes are leaf diseases, and the other one is healthy leaves. After rigorous pre-processing dataset was split (70% training, 20% validation, 10% testing), and two pre-trained models were deployed: InceptionV3 and Xception. Xception shows a promising result of 96.23% accuracy, which is remarkable than InceptionV3. Adversarial Training is used for robustness, along with more transparency. Grad-CAM is integrated to confirm the leaf disease. Finally deployed a web application using Streamlit with a heatmap visualization and prediction with confidence level for robust grape leaf disease classification.
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https://arxiv.org/abs/2601.03124
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Academic Papers
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1c67ea7b24fc63a33df27ab0a28dfe0bfe6fbef142790ee49b6569f62622dd01
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2026-01-07T00:00:00-05:00
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Dualities for finite abelian groups and applications to coding theory
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arXiv:2601.03126v1 Announce Type: new Abstract: The choice of an isomorphism, a duality, between a finite abelian group $A$ and its character group allows one to define dual codes of additive codes over $A$. Properties of dualities and dual codes are studied, continuing work of Delsarte from 1973 and more recent work of Dougherty and his collaborators.
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https://arxiv.org/abs/2601.03126
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Academic Papers
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81fd9d2043c9063e5c45deebb9c1c852d1419f6ebee7ca31cfe740f276d08ab8
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2026-01-07T00:00:00-05:00
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Unified Thinker: A General Reasoning Modular Core for Image Generation
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arXiv:2601.03127v1 Announce Type: new Abstract: Despite impressive progress in high-fidelity image synthesis, generative models still struggle with logic-intensive instruction following, exposing a persistent reasoning--execution gap. Meanwhile, closed-source systems (e.g., Nano Banana) have demonstrated strong reasoning-driven image generation, highlighting a substantial gap to current open-source models. We argue that closing this gap requires not merely better visual generators, but executable reasoning: decomposing high-level intents into grounded, verifiable plans that directly steer the generative process. To this end, we propose Unified Thinker, a task-agnostic reasoning architecture for general image generation, designed as a unified planning core that can plug into diverse generators and workflows. Unified Thinker decouples a dedicated Thinker from the image Generator, enabling modular upgrades of reasoning without retraining the entire generative model. We further introduce a two-stage training paradigm: we first build a structured planning interface for the Thinker, then apply reinforcement learning to ground its policy in pixel-level feedback, encouraging plans that optimize visual correctness over textual plausibility. Extensive experiments on text-to-image generation and image editing show that Unified Thinker substantially improves image reasoning and generation quality.
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https://arxiv.org/abs/2601.03127
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Academic Papers
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69ebb7ca3fcaf3a12ecafc16b03b22696e1a4cf75005de756c919010eccd1778
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2026-01-07T00:00:00-05:00
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Density Matters: A Complexity Dichotomy of Deleting Edges to Bound Subgraph Density
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arXiv:2601.03129v1 Announce Type: new Abstract: We study $\tau$-Bounded-Density Edge Deletion ($\tau$-BDED), where given an undirected graph $G$, the task is to remove as few edges as possible to obtain a graph $G'$ where no subgraph of $G'$ has density more than $\tau$. The density of a (sub)graph is the number of edges divided by the number of vertices. This problem was recently introduced and shown to be NP-hard for $\tau \in \{2/3, 3/4, 1 + 1/25\}$, but polynomial-time solvable for $\tau \in \{0,1/2,1\}$ [Bazgan et al., JCSS 2025]. We provide a complete dichotomy with respect to the target density $\tau$: 1. If $2\tau \in \mathbb{N}$ (half-integral target density) or $\tau < 2/3$, then $\tau$-BDED is polynomial-time solvable. 2. Otherwise, $\tau$-BDED is NP-hard. We complement the NP-hardness with fixed-parameter tractability with respect to the treewidth of $G$. Moreover, for integral target density $\tau \in \mathbb{N}$, we show $\tau$-BDED to be solvable in randomized $O(m^{1 + o(1)})$ time. Our algorithmic results are based on a reduction to a new general flow problem on restricted networks that, depending on $\tau$, can be solved via Maximum s-t-Flow or General Factors. We believe this connection between these variants of flow and matching to be of independent interest.
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https://arxiv.org/abs/2601.03129
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Academic Papers
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svg
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e3f3ec90b2cd22baaa40b8d333eea31e01769d52ff46cff9230e5a9965cfa6a4
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2026-01-07T00:00:00-05:00
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Automatic Prompt Engineering with No Task Cues and No Tuning
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arXiv:2601.03130v1 Announce Type: new Abstract: This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tuning and no explicit clues about the task. We evaluated our approach on cryptic column name expansion (CNE) in database tables, a task which is critical for tabular data search, access, and understanding and yet there has been very little existing work. We evaluated on datasets in two languages, English and German. This is the first work to report on the application of automatic prompt engineering for the CNE task. To the best of our knowledge, this is also the first work on the application of automatic prompt engineering for a language other than English.
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https://arxiv.org/abs/2601.03130
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Academic Papers
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efa91bae4ee2047717a064e9746227b797d6fe9e6ce2b7948b6664ba5e505067
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2026-01-07T00:00:00-05:00
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Finite Memory Belief Approximation for Optimal Control in Partially Observable Markov Decision Processes
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arXiv:2601.03132v1 Announce Type: new Abstract: We study finite memory belief approximation for partially observable (PO) stochastic optimal control (SOC) problems. While belief states are sufficient for SOC in partially observable Markov decision processes (POMDPs), they are generally infinite-dimensional and impractical. We interpret truncated input-output (IO) histories as inducing a belief approximation and develop a metric-based theory that directly relates information loss to control performance. Using the Wasserstein metric, we derive policy-conditional performance bounds that quantify value degradation induced by finite memory along typical closed-loop trajectories. Our analysis proceeds via a fixed-policy comparison: we evaluate two cost functionals under the same closed-loop execution and isolate the effect of replacing the true belief by its finite memory approximation inside the belief-level cost. For linear quadratic Gaussian (LQG) systems, we provide closed-form belief mismatch evaluation and empirically validate the predicted mechanism, demonstrating that belief mismatch decays approximately exponentially with memory length and that the induced performance mismatch scales accordingly. Together, these results provide a metric-aware characterization of what finite memory belief approximation can and cannot achieve in PO settings.
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https://arxiv.org/abs/2601.03132
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Academic Papers
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1d5209b1271182b1c0ce2c3aa65da5972c83de59d31696a89691ae21a15e6e4c
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2026-01-07T00:00:00-05:00
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The Anatomy of Conversational Scams: A Topic-Based Red Teaming Analysis of Multi-Turn Interactions in LLMs
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arXiv:2601.03134v1 Announce Type: new Abstract: As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that single-turn safety evaluations fail to capture. We systematically study these risks using a controlled LLM-to-LLM simulation framework across multi-turn scam scenarios. Evaluating eight state-of-the-art models in English and Chinese, we analyze dialogue outcomes and qualitatively annotate attacker strategies, defensive responses, and failure modes. Results reveal that scam interactions follow recurrent escalation patterns, while defenses employ verification and delay mechanisms. Furthermore, interactional failures frequently stem from safety guardrail activation and role instability. Our findings highlight multi-turn interactional safety as a critical, distinct dimension of LLM behavior.
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https://arxiv.org/abs/2601.03134
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Academic Papers
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svg
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0f1da87a43fc1e0354ee4589ecd4de5e9c8cb9c1c92871a14bee92f5c2363bbd
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2026-01-07T00:00:00-05:00
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Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing
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arXiv:2601.03135v1 Announce Type: new Abstract: Low-resource indigenous languages often lack the parallel corpora required for effective neural machine translation (NMT). Synthetic data generation offers a practical strategy for mitigating this limitation in data-scarce settings. In this work, we augment curated parallel datasets for indigenous languages of the Americas with synthetic sentence pairs generated using a high-capacity multilingual translation model. We fine-tune a multilingual mBART model on curated-only and synthetically augmented data and evaluate translation quality using chrF++, the primary metric used in recent AmericasNLP shared tasks for agglutinative languages. We further apply language-specific preprocessing, including orthographic normalization and noise-aware filtering, to reduce corpus artifacts. Experiments on Guarani--Spanish and Quechua--Spanish translation show consistent chrF++ improvements from synthetic data augmentation, while diagnostic experiments on Aymara highlight the limitations of generic preprocessing for highly agglutinative languages.
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https://arxiv.org/abs/2601.03135
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Academic Papers
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svg
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02b61fbef4f49db116c1f18c5aade0664c9a10d675c431d4896cdcd19310105e
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2026-01-07T00:00:00-05:00
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Limited Linguistic Diversity in Embodied AI Datasets
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arXiv:2601.03136v1 Announce Type: new Abstract: Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate these systems remain poorly documented. In this work, we present a systematic dataset audit of several widely used VLA corpora, aiming to characterize what kinds of instructions these datasets actually contain and how much linguistic variety they provide. We quantify instruction language along complementary dimensions-including lexical variety, duplication and overlap, semantic similarity, and syntactic complexity. Our analysis shows that many datasets rely on highly repetitive, template-like commands with limited structural variation, yielding a narrow distribution of instruction forms. We position these findings as descriptive documentation of the language signal available in current VLA training and evaluation data, intended to support more detailed dataset reporting, more principled dataset selection, and targeted curation or augmentation strategies that broaden language coverage.
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https://arxiv.org/abs/2601.03136
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Academic Papers
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