Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization
arXiv:2604.02666v1 Announce Type: new Abstract: Optimization is as much about modeling the right problem as solving it. Identifying the right objectives, constraints, and trade-offs demands extensive interaction between researchers and stakeholders. Large language models can empower decision-makers with optimization capabilities...
A Survey on AI for 6G: Challenges and Opportunities
arXiv:2604.02370v1 Announce Type: cross Abstract: As wireless communication evolves, each generation of networks brings new technologies that change how we connect and interact. Artificial Intelligence (AI) is becoming crucial in shaping the future of sixth-generation (6G) networks. By combining AI...
GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics
arXiv:2604.02830v1 Announce Type: new Abstract: Detecting whether a model's internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying responsible LLMs. In addition to verbalising the confidence by LLM self-report, more recent methods explore...
OntoKG: Ontology-Oriented Knowledge Graph Construction with Intrinsic-Relational Routing
arXiv:2604.02618v1 Announce Type: new Abstract: Organizing a large-scale knowledge graph into a typed property graph requires structural decisions -- which entities become nodes, which properties become edges, and what schema governs these choices. Existing approaches embed these decisions in pipeline...
Analytic Drift Resister for Non-Exemplar Continual Graph Learning
arXiv:2604.02633v1 Announce Type: new Abstract: Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably...
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
arXiv:2604.03174v1 Announce Type: new Abstract: Large language models (LLMs) encode vast world knowledge in their parameters, yet they remain fundamentally limited by static knowledge, finite context windows, and weakly structured causal reasoning. This survey provides a unified account of augmentation...
Internalized Reasoning for Long-Context Visual Document Understanding
arXiv:2604.02371v1 Announce Type: cross Abstract: Visual long-document understanding is critical for enterprise, legal, and scientific applications, yet the best performing open recipes have not explored reasoning, a capability which has driven leaps in math and code performance. We introduce a...
Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
arXiv:2604.02644v1 Announce Type: new Abstract: We present Conditional Wasserstein Autoencoders (CWAEs), a framework for conditional simulation that exploits low-dimensional structure in both the conditioned and the conditioning variables. The key idea is to modify a Wasserstein autoencoder to use a...
Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models
arXiv:2604.02485v1 Announce Type: new Abstract: Confirmation bias, the tendency to seek evidence that supports rather than challenges one's belief, hinders one's reasoning ability. We examine whether large language models (LLMs) exhibit confirmation bias by adapting the rule-discovery study from human...
Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
arXiv:2604.02659v1 Announce Type: new Abstract: The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular value decomposition (SVD) provides a principled approach for model reduction, but its exact computation is...
From Broad Exploration to Stable Synthesis: Entropy-Guided Optimization for Autoregressive Image Generation
arXiv:2604.02355v1 Announce Type: new Abstract: Combining Chain-of-Thought (CoT) with Reinforcement Learning (RL) improves text-to-image (T2I) generation, yet the underlying interaction between CoT's exploration and RL's optimization remains unclear. We present a systematic entropy-based analysis that yields three key insights: (1)...
Haiku to Opus in Just 10 bits: LLMs Unlock Massive Compression Gains
arXiv:2604.02343v1 Announce Type: cross Abstract: We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is possible at the cost of more compute. For lossless compression, domain-adapted LoRA adapters can improve...
Communication-Efficient Distributed Learning with Differential Privacy
arXiv:2604.02558v1 Announce Type: new Abstract: We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that guarantees the privacy of the agents' data. The first goal...
AXELRAM: Quantize Once, Never Dequantize
arXiv:2604.02638v1 Announce Type: new Abstract: We propose AXELRAM, a smart SRAM macro architecture that computes attention scores directly from quantized KV cache indices without dequantization. The key enabler is a design-time fixed codebook: orthogonal-transform-based quantization concentrates each coordinate's distribution to...
TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning
arXiv:2604.02361v1 Announce Type: cross Abstract: Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data,...
Multiple-Debias: A Full-process Debiasing Method for Multilingual Pre-trained Language Models
arXiv:2604.02772v1 Announce Type: new Abstract: Multilingual Pre-trained Language Models (MPLMs) have become essential tools for natural language processing. However, they often exhibit biases related to sensitive attributes such as gender, race, and religion. In this paper, we introduce a comprehensive...
Principled and Scalable Diversity-Aware Retrieval via Cardinality-Constrained Binary Quadratic Programming
arXiv:2604.02554v1 Announce Type: new Abstract: Diversity-aware retrieval is essential for Retrieval-Augmented Generation (RAG), yet existing methods lack theoretical guarantees and face scalability issues as the number of retrieved passages $k$ increases. We propose a principled formulation of diversity retrieval as...
Council Mode: Mitigating Hallucination and Bias in LLMs via Multi-Agent Consensus
arXiv:2604.02923v1 Announce Type: new Abstract: Large Language Models (LLMs), particularly those employing Mixture-of-Experts (MoE) architectures, have achieved remarkable capabilities across diverse natural language processing tasks. However, these models frequently suffer from hallucinations -- generating plausible but factually incorrect content --...
LLM Reasoning with Process Rewards for Outcome-Guided Steps
arXiv:2604.02341v1 Announce Type: cross Abstract: Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final answers can be checked automatically and converted into reliable training signals. Most such pipelines optimize outcome correctness only,...
Reliability Gated Multi-Teacher Distillation for Low Resource Abstractive Summarization
arXiv:2604.03192v1 Announce Type: new Abstract: We study multiteacher knowledge distillation for low resource abstractive summarization from a reliability aware perspective. We introduce EWAD (Entropy Weighted Agreement Aware Distillation), a token level mechanism that routes supervision between teacher distillation and gold...
Self-Directed Task Identification
arXiv:2604.02430v1 Announce Type: new Abstract: In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI...
VoxelCodeBench: Benchmarking 3D World Modeling Through Code Generation
arXiv:2604.02580v1 Announce Type: new Abstract: Evaluating code generation models for 3D spatial reasoning requires executing generated code in realistic environments and assessing outputs beyond surface-level correctness. We introduce a platform VoxelCode, for analyzing code generation capabilities for 3D understanding and...
Dynamical structure of vanishing gradient and overfitting in multi-layer perceptrons
arXiv:2604.02393v1 Announce Type: new Abstract: Vanishing gradient and overfitting are two of the most extensively studied problems in the literature about machine learning. However, they are frequently considered in some asymptotic setting, which obscure the underlying dynamical mechanisms responsible for...
Analysis of Optimality of Large Language Models on Planning Problems
arXiv:2604.02910v1 Announce Type: new Abstract: Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks on success rates rather than plan efficiency. We examine the degree to which frontier models...
Student-in-the-Loop Chain-of-Thought Distillation via Generation-Time Selection
arXiv:2604.02819v1 Announce Type: new Abstract: Large reasoning models achieve strong performance on complex tasks through long chain-of-thought (CoT) trajectories, but directly transferring such reasoning processes to smaller models remains challenging. A key difficulty is that not all teacher-generated reasoning trajectories...
SIEVE: Sample-Efficient Parametric Learning from Natural Language
arXiv:2604.02339v1 Announce Type: new Abstract: Natural language context-such as instructions, knowledge, or feedback-contains rich signal for adapting language models. While in-context learning provides adaptation via the prompt, parametric learning persists into model weights and can improve performance further, though is...
Do Audio-Visual Large Language Models Really See and Hear?
arXiv:2604.02605v1 Announce Type: new Abstract: Audio-Visual Large Language Models (AVLLMs) are emerging as unified interfaces to multimodal perception. We present the first mechanistic interpretability study of AVLLMs, analyzing how audio and visual features evolve and fuse through different layers of...
Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network
arXiv:2604.02670v1 Announce Type: new Abstract: Muscle fatigue detection plays an important role in physical rehabilitation. Previous researches have demonstrated that sEMG offers superior sensitivity in detecting muscle fatigue compared to other biological signals. However, features extracted from sEMG may vary...
Domain-Adapted Retrieval for In-Context Annotation of Pedagogical Dialogue Acts
arXiv:2604.03127v1 Announce Type: new Abstract: Automated annotation of pedagogical dialogue is a high-stakes task where LLMs often fail without sufficient domain grounding. We present a domain-adapted RAG pipeline for tutoring move annotation. Rather than fine-tuning the generative model, we adapt...
Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks
arXiv:2604.02358v1 Announce Type: cross Abstract: Vehicular Ad Hoc Networks (VANETs) play a crucial role in realizing vehicle-road collaboration and intelligent transportation. However, urban VANETs often face challenges such as frequent link disconnections and subnet fragmentation, which hinder reliable connectivity. To...