arXiv:2511.03328v1 Announce Type: cross Abstract: A recent advancement in Multimodal Large Language Models (MLLMs) research is the emergence of “reasoning MLLMs” that offer explicit control over their internal thinking processes (normally referred as the “thinking mode”) alongside the standard “non-thinking mode”. This capability allows these models to engage in a step-by-step process of internal deliberation […]
Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement
arXiv:2511.03421v1 Announce Type: cross Abstract: Elicited performance requirements need to be quantified for compliance in different engineering tasks, e.g., configuration tuning and performance testing. Much existing work has relied on manual quantification, which is expensive and error-prone due to the imprecision. In this paper, we present LQPR, a highly efficient automatic approach for performance requirements […]
RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring
arXiv:2511.03153v1 Announce Type: cross Abstract: Large Language Models (LLMs) have substantially influenced various software engineering tasks. Indeed, in the case of software refactoring, traditional LLMs have shown the ability to reduce development time and enhance code quality. However, these LLMs often rely on static, detailed instructions for specific tasks. In contrast, LLM-based agents can dynamically […]
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
arXiv:2511.03217v1 Announce Type: cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers deliver precise and interpretable evidence, yet suffer from limited coverage or latency. By integrating LLMs with knowledge graphs and real-time search agents, we introduce a hybrid fact-checking […]
Evolution under Stochastic Transmission: Mutation-Rate Modifiers
arXiv:2511.03073v1 Announce Type: new Abstract: In evolutionary models of large populations, it is common to analyze the effects of cyclic or random variation in the parameters that describe selection. It is less common, however, to study how stochasticity in the genetic transmission process itself affects evolutionary outcomes. Suppose that a gene locus has alleles $A$ […]
Image-Intrinsic Priors for Integrated Circuit Defect Detection and Novel Class Discovery via Self-Supervised Learning
arXiv:2511.03120v1 Announce Type: cross Abstract: Integrated circuit manufacturing is highly complex, comprising hundreds of process steps. Defects can arise at any stage, causing yield loss and ultimately degrading product reliability. Supervised methods require extensive human annotation and struggle with emergent categories and rare, data scarce defects. Clustering-based unsupervised methods often exhibit unstable performance due to […]
Response function as a quantitative measure of consciousness in brain dynamics
arXiv:2509.00730v2 Announce Type: replace Abstract: Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we investigate the relationship between consciousness and neural responsiveness by analyzing intracranial ECoG recordings from non-human primates across three distinct states: wakefulness, anesthesia, and recovery. Using a nonequilibrium recurrent neural network (RNN) model, we fit […]
Kosmos: An AI Scientist for Autonomous Discovery
arXiv:2511.02824v2 Announce Type: replace Abstract: Data-driven scientific discovery requires iterative cycles of literature search, hypothesis generation, and data analysis. Substantial progress has been made towards AI agents that can automate scientific research, but all such agents remain limited in the number of actions they can take before losing coherence, thus limiting the depth of their […]
Traversal Verification for Speculative Tree Decoding
arXiv:2505.12398v2 Announce Type: replace-cross Abstract: Speculative decoding is a promising approach for accelerating large language models. The primary idea is to use a lightweight draft model to speculate the output of the target model for multiple subsequent timesteps, and then verify them in parallel to determine whether the drafted tokens should be accepted or rejected. […]
Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
arXiv:2410.20749v3 Announce Type: replace-cross Abstract: Despite the impressive generative abilities of black-box large language models (LLMs), their inherent opacity hinders further advancements in capabilities such as reasoning, planning, and personalization. Existing works aim to enhance LLM capabilities via domain-specific adaptation, which require additional training on accessible model parameters, an infeasible option for black-box LLMs. To […]