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arXiv:2601.20886v1 Announce Type: new Abstract: IDE-Bench is a comprehensive framework for evaluating AI IDE agents on real-world software engineering tasks through an IDE-native tool interface....
arXiv:2601.20890v1 Announce Type: new Abstract: Single-word Automatic Speech Recognition (ASR) is a challenging task due to the lack of linguistic context and sensitivity to noise,...
arXiv:2601.20892v1 Announce Type: new Abstract: Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases,...
arXiv:2601.20893v1 Announce Type: new Abstract: Background: Systematic literature reviews (SLRs) have become prevalent in software engineering research. Several researchers may conduct SLRs on similar topics...
arXiv:2601.20894v1 Announce Type: new Abstract: Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each...
arXiv:2601.20895v1 Announce Type: new Abstract: Research aimed at scaling up neuroscience inspired learning algorithms for neural networks is accelerating. Recently, a key research area has...
arXiv:2601.20896v1 Announce Type: new Abstract: Self-supervised learning (SSL) has transformed speech processing, yet its reliance on massive pre-training datasets remains a bottleneck. While robustness is...
arXiv:2601.20900v1 Announce Type: new Abstract: Adapting automatic speech recognition (ASR) systems based on large language models (LLMs) to new domains using text-only data is a...
arXiv:2601.20901v1 Announce Type: new Abstract: With LLMs increasingly deployed in corporate data management, it is crucial to ensure that these models do not leak sensitive...
arXiv:2601.20903v1 Announce Type: new Abstract: Multi-turn jailbreak attacks have emerged as a critical threat to Large Language Models (LLMs), bypassing safety mechanisms by progressively constructing...
arXiv:2601.20906v1 Announce Type: new Abstract: Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We...
arXiv:2601.20909v1 Announce Type: new Abstract: Domain-Driven Design (DDD) is a key framework for developing customer-oriented software, focusing on the precise modeling of an application's domain....
arXiv:2601.20910v1 Announce Type: new Abstract: In the context of large population symmetric games, approximate Nash equilibria are introduced through equilibrium solutions of the corresponding mean...
arXiv:2601.20911v1 Announce Type: new Abstract: Conversational image generation requires a model to follow user instructions across multiple rounds of interaction, grounded in interleaved text and...
arXiv:2601.20913v1 Announce Type: new Abstract: Reliable certification of Large Language Models (LLMs)-verifying that failure rates are below a safety threshold-is critical yet challenging. While "LLM-as-a-Judge"...
arXiv:2601.20915v1 Announce Type: new Abstract: Federated Learning (FL) facilitates collaborative model training among distributed clients while ensuring that raw data remains on local devices.Despite this...
arXiv:2601.20916v1 Announce Type: new Abstract: Objective: Accurate noninvasive estimation of intracranial pressure (ICP) remains a major challenge in critical care. We developed a bespoke machine...
arXiv:2601.20917v1 Announce Type: new Abstract: We present masked Lagrange reconstruction, a technique that enables threshold ML-DSA (FIPS 204) with arbitrary thresholds $T$ while producing standard...
arXiv:2601.20918v1 Announce Type: new Abstract: Context: Blockchain and AI are increasingly explored to enhance trustworthiness in software engineering (SE), particularly in supporting software evolution tasks....
arXiv:2601.20920v1 Announce Type: new Abstract: There are increasing indications that LLMs are not only used for producing scientific papers, but also as part of the...
arXiv:2601.20921v1 Announce Type: new Abstract: Random access remains a central bottleneck in DNA-based data storage. Existing systems typically retrieve records by PCR enrichment or other...
arXiv:2601.20961v1 Announce Type: new Abstract: We provide a complete theory of optimal universal rates for binary classification in the agnostic setting. This extends the realizable-case...
arXiv:2601.20968v1 Announce Type: new Abstract: In this paper we tackle the problem of adjusting roadmap graphs for robot motion planning to non-static environments. We introduce...
arXiv:2601.20969v1 Announce Type: new Abstract: Epistemic planning extends (multi-agent) automated planning by making agents' knowledge and beliefs first-class aspects of the planning formalism. One of...