CacheMind: From Miss Rates to Why -- Natural-Language, Trace-Grounded Reasoning for Cache Replacement
arXiv:2602.12422v1 Announce Type: cross Abstract: Cache replacement remains a challenging problem in CPU microarchitecture, often addressed using hand-crafted heuristics, limiting cache performance. Cache data analysis requires parsing millions of trace entries with manual filtering, making the process slow and non-interactive....
Beyond Normalization: Rethinking the Partition Function as a Difficulty Scheduler for RLVR
arXiv:2602.12642v1 Announce Type: new Abstract: Reward-maximizing RL methods enhance the reasoning performance of LLMs, but often reduce the diversity among outputs. Recent works address this issue by adopting GFlowNets, training LLMs to match a target distribution while jointly learning its...
Constraint-Rectified Training for Efficient Chain-of-Thought
arXiv:2602.12526v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), especially when combined with reinforcement learning (RL) based post-training methods. While longer reasoning traces can improve answer quality and unlock abilities such...
Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
arXiv:2602.12613v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas many practical applications require continuous predictions, that predictions are issued frequently...
Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
arXiv:2602.12624v1 Announce Type: new Abstract: Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by high sampling costs. While prior work focuses on training objectives or individual solvers, the holistic design of...
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing - ACL Anthology
ASML-Mistral AI: It's the Geopolitics, Stupid - AI Now Institute
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The Sky's the Limit? SkyKick v Sky and Speculative Trade Mark Registration
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The Quantization Trap: Breaking Linear Scaling Laws in Multi-Hop Reasoning
arXiv:2602.13595v1 Announce Type: new Abstract: Neural scaling laws provide a predictable recipe for AI advancement: reducing numerical precision should linearly improve computational efficiency and energy profile (E proportional to bits). In this paper, we demonstrate that this scaling law breaks...
Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation
arXiv:2602.13263v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often degrade on accented speech because acoustic-phonetic and prosodic shifts induce a mismatch to training data, making labeled accent adaptation costly. However, common pseudo-label selection heuristics are largely text-centric (e.g.,...
Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
arXiv:2602.15067v1 Announce Type: new Abstract: Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based...
SOMtime the World Ain$'$t Fair: Violating Fairness Using Self-Organizing Maps
arXiv:2602.18201v1 Announce Type: new Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. We show that this assumption is false. Using SOMtime, a topology-preserving representation method based on...
ScaleBITS: Scalable Bitwidth Search for Hardware-Aligned Mixed-Precision LLMs
arXiv:2602.17698v1 Announce Type: cross Abstract: Post-training weight quantization is crucial for reducing the memory and inference cost of large language models (LLMs), yet pushing the average precision below 4 bits remains challenging due to highly non-uniform weight sensitivity and the...
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
arXiv:2602.17750v1 Announce Type: cross Abstract: A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain and stress. Machine learning has lead to considerable advances in this field lately....
Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
arXiv:2602.17751v1 Announce Type: cross Abstract: Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to their popularity and the ease of identifying them through their...
Symbolic computation of conservation laws of nonlinear partial differential equations in multi‐dimensions
Abstract A direct method for the computation of polynomial conservation laws of polynomial systems of nonlinear partial differential equations (PDEs) in multi‐dimensions is presented. The method avoids advanced differential‐geometric tools. Instead, it is solely based on calculus, variational calculus, and...
Modularity is the Bedrock of Natural and Artificial Intelligence
arXiv:2602.18960v1 Announce Type: new Abstract: The remarkable performance of modern AI systems has been driven by unprecedented scales of data, computation, and energy -- far exceeding the resources required by human intelligence. This disparity highlights the need for new guiding...
InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing
arXiv:2602.18985v1 Announce Type: new Abstract: Infrared radiation computing underpins advances in climate science, remote sensing and spectroscopy but remains constrained by manual workflows. We introduce InfEngine, an autonomous intelligent computational engine designed to drive a paradigm shift from human-led orchestration...
Characterizing MARL for Energy Control: A Multi-KPI Benchmark on the CityLearn Environment
arXiv:2602.19223v1 Announce Type: new Abstract: The optimization of urban energy systems is crucial for the advancement of sustainable and resilient smart cities, which are becoming increasingly complex with multiple decision-making units. To address scalability and coordination concerns, Multi-Agent Reinforcement Learning...
Hiding in Plain Text: Detecting Concealed Jailbreaks via Activation Disentanglement
arXiv:2602.19396v1 Announce Type: new Abstract: Large language models (LLMs) remain vulnerable to jailbreak prompts that are fluent and semantically coherent, and therefore difficult to detect with standard heuristics. A particularly challenging failure mode occurs when an attacker tries to hide...
PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agents
arXiv:2602.23668v1 Announce Type: new Abstract: Large language model (LLM) agents typically rely on reactive decision-making paradigms such as ReAct, selecting actions conditioned on growing execution histories. While effective for short tasks, these approaches often lead to redundant tool usage, unstable...