MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
arXiv:2602.17088v1 Announce Type: new Abstract: The growing concern over training data privacy has elevated the "Right to be Forgotten" into a critical requirement, thereby raising the demand for effective Machine Unlearning. However, existing unlearning approaches commonly suffer from a fundamental...
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
arXiv:2602.17095v1 Announce Type: new Abstract: Parameter-efficient fine-tuning techniques such as low-rank adaptation (LoRA) enable large language models (LLMs) to adapt to downstream tasks efficiently. Federated learning (FL) further facilitates this process by enabling collaborative fine-tuning across distributed clients without sharing...
Input out, output in: towards positive-sum solutions to AI-copyright tensions
Abstract This article addresses the legal tensions between artificial intelligence (AI) development and copyright law, exploring policymaking on the use of copyrighted data for AI training at the input level and the generation of AI content at the output level....
Justices to consider constitutionality of tax foreclosure sales
The argument next week in Pung v Isabella County asks the court to consider the constitutionality of the longstanding practice of tax foreclosures sales. This is one of those cases […]The postJustices to consider constitutionality of tax foreclosure salesappeared first...
Gated Tree Cross-attention for Checkpoint-Compatible Syntax Injection in Decoder-Only LLMs
arXiv:2602.15846v1 Announce Type: new Abstract: Decoder-only large language models achieve strong broad performance but are brittle to minor grammatical perturbations, undermining reliability for downstream reasoning. However, directly injecting explicit syntactic structure into an existing checkpoint can interfere with its pretrained...
Multi-source Heterogeneous Public Opinion Analysis via Collaborative Reasoning and Adaptive Fusion: A Systematically Integrated Approach
arXiv:2602.15857v1 Announce Type: new Abstract: The analysis of public opinion from multiple heterogeneous sources presents significant challenges due to structural differences, semantic variations, and platform-specific biases. This paper introduces a novel Collaborative Reasoning and Adaptive Fusion (CRAF) framework that systematically...
From Transcripts to AI Agents: Knowledge Extraction, RAG Integration, and Robust Evaluation of Conversational AI Assistants
arXiv:2602.15859v1 Announce Type: new Abstract: Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented knowledge, and the requirement for accurate human hand-off - particularly in domains that depend heavily on real-time information. This...
Understanding LLM Failures: A Multi-Tape Turing Machine Analysis of Systematic Errors in Language Model Reasoning
arXiv:2602.15868v1 Announce Type: new Abstract: Large language models (LLMs) exhibit failure modes on seemingly trivial tasks. We propose a formalisation of LLM interaction using a deterministic multi-tape Turing machine, where each tape represents a distinct component: input characters, tokens, vocabulary,...
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing...
Surgical Activation Steering via Generative Causal Mediation
arXiv:2602.16080v1 Announce Type: new Abstract: Where should we intervene in a language model (LM) to control behaviors that are diffused across many tokens of a long-form response? We introduce Generative Causal Mediation (GCM), a procedure for selecting model components, e.g.,...
Beyond Learning: A Training-Free Alternative to Model Adaptation
arXiv:2602.16189v1 Announce Type: new Abstract: Despite the continuous research and evolution of language models, they sometimes underperform previous versions. Existing approaches to overcome these challenges are resource-intensive, highlighting the need for alternatives that enable immediate action. We assume that each...
Long-Tail Knowledge in Large Language Models: Taxonomy, Mechanisms, Interventions and Implications
arXiv:2602.16201v1 Announce Type: new Abstract: Large language models (LLMs) are trained on web-scale corpora that exhibit steep power-law distributions, in which the distribution of knowledge is highly long-tailed, with most appearing infrequently. While scaling has improved average-case performance, persistent failures...
A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation
arXiv:2602.15834v1 Announce Type: new Abstract: We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an...
MoE-Spec: Expert Budgeting for Efficient Speculative Decoding
arXiv:2602.16052v1 Announce Type: new Abstract: Speculative decoding accelerates Large Language Model (LLM) inference by verifying multiple drafted tokens in parallel. However, for Mixture-of-Experts (MoE) models, this parallelism introduces a severe bottleneck: large draft trees activate many unique experts, significantly increasing...
Multi-Objective Alignment of Language Models for Personalized Psychotherapy
arXiv:2602.16053v1 Announce Type: new Abstract: Mental health disorders affect over 1 billion people worldwide, yet access to care remains limited by workforce shortages and cost constraints. While AI systems show therapeutic promise, current alignment approaches optimize objectives independently, failing to...
Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods
arXiv:2602.16057v1 Announce Type: new Abstract: Railway crossings present complex safety challenges where driver behavior varies by location, time, and conditions. Traditional approaches analyze crossings individually, limiting the ability to identify shared behavioral patterns across locations. We propose a multi-view tensor...
Why Any-Order Autoregressive Models Need Two-Stream Attention: A Structural-Semantic Tradeoff
arXiv:2602.16092v1 Announce Type: new Abstract: Any-order autoregressive models (AO-ARMs) offer a promising path toward efficient masked diffusion by enabling native key-value caching, but competitive performance has so far required two-stream attention, typically motivated as a means of decoupling token content...
Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations
arXiv:2602.16145v1 Announce Type: new Abstract: There are a number of existing studies analysing the convergence behaviour of graph neural networks on large random graphs. Unfortunately, the majority of these studies do not model correlations between node features, which would naturally...
ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
arXiv:2602.16147v1 Announce Type: new Abstract: Cross-subject generalization in EEG-based brain-computer interfaces (BCIs) remains challenging due to individual variability in neural signals. We investigate whether spectral representations offer more stable features for cross-subject transfer than temporal waveforms. Through correlation analyses across...
Differentially Private Non-convex Distributionally Robust Optimization
arXiv:2602.16155v1 Announce Type: new Abstract: Real-world deployments routinely face distribution shifts, group imbalances, and adversarial perturbations, under which the traditional Empirical Risk Minimization (ERM) framework can degrade severely. Distributionally Robust Optimization (DRO) addresses this issue by optimizing the worst-case expected...
Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce...
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
arXiv:2602.16197v1 Announce Type: new Abstract: Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably...
Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
arXiv:2602.16209v1 Announce Type: new Abstract: Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. Existing neural operators, however, often suffer from instability in multi-layer iteration and...
Bayesian Quadrature: Gaussian Processes for Integration
arXiv:2602.16218v1 Announce Type: new Abstract: Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Although Bayesian quadrature was popularised already in the 1980s, no systematic and comprehensive treatment has been published. The...
Nvidia deepens early-stage push into India’s AI startup ecosystem
Nvidia is working with investors, nonprofits, and venture firms to build earlier ties with India's fast-growing AI founder ecosystem.
Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos
arXiv:2602.15757v1 Announce Type: new Abstract: Online sexism appears in various forms, which makes its detection challenging. Although automated tools can enhance the identification of sexist content, they are often restricted to binary classification. Consequently, more subtle manifestations of sexism may...
Avey-B
arXiv:2602.15814v1 Announce Type: new Abstract: Compact pretrained bidirectional encoders remain the backbone of industrial NLP under tight compute and memory budgets. Their effectiveness stems from self-attention's ability to deliver high-quality bidirectional contextualization with sequence-level parallelism, as popularized by BERT-style architectures....
Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
arXiv:2602.15198v1 Announce Type: cross Abstract: Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a unique safety problem when individual agents form a coalition and \emph{collude} to pursue secondary goals...
The Information Geometry of Softmax: Probing and Steering
arXiv:2602.15293v1 Announce Type: cross Abstract: This paper concerns the question of how AI systems encode semantic structure into the geometric structure of their representation spaces. The motivating observation of this paper is that the natural geometry of these representation spaces...