arXiv:2604.04037v2 Announce Type: replace-cross Abstract: Knowledge distillation compresses large teachers into smaller students, but performance saturates at a loss floor that persists across training methods and objectives. We argue this floor is geometric: neural networks represent far more features than dimensions through superposition, and a student of width $d_S$ can encode at most $d_S cdot […]
Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
arXiv:2604.05520v1 Announce Type: cross Abstract: Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate Radio Environment Map (REM) estimation is therefore increasingly important for effective network planning and operation. Existing methods, from ray-tracing simulators to deep learning generative […]
Controllable Singing Style Conversion with Boundary-Aware Information Bottleneck
arXiv:2604.05526v1 Announce Type: cross Abstract: This paper presents the submission of the S4 team to the Singing Voice Conversion Challenge 2025 (SVCC2025)-a novel singing style conversion system that advances fine-grained style conversion and control within in-domain settings. To address the critical challenges of style leakage, dynamic rendering, and high-fidelity generation with limited data, we introduce […]
Turbulence-like 5/3 spectral scaling in contextual representations of language as a complex system
arXiv:2604.05536v1 Announce Type: cross Abstract: Natural language is a complex system that exhibits robust statistical regularities. Here, we represent text as a trajectory in a high-dimensional embedding space generated by transformer-based language models, and quantify scale-dependent fluctuations along the token sequence using an embedding-step signal. Across multiple languages and corpora, the resulting power spectrum exhibits […]
Vero: An Open RL Recipe for General Visual Reasoning
arXiv:2604.04917v2 Announce Type: replace-cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) show such broad visual reasoning is within reach, but the recipe behind them remains unclear, locked behind proprietary reinforcement learning (RL) pipelines with non-public data. We […]
Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya
arXiv:2604.04937v1 Announce Type: new Abstract: Large language models produce fluent text but struggle with systematic reasoning, often hallucinating confident but unfounded claims. When Apple researchers added irrelevant context to mathematical problems, LLM performance degraded by 65% Apple Machine Learning Research, exposing brittle pattern-matching beneath apparent reasoning. This epistemic gap, the inability to ground claims in […]
On the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors
arXiv:2604.05743v1 Announce Type: cross Abstract: Modern image compression methods are typically optimized for the rate–distortion–perception trade-off, whereas their robustness to bit-level corruption is rarely examined. We show that diffusion-based compressors built on the Reverse Channel Coding (RCC) paradigm are substantially more robust to bit flips than classical and learned codecs. We further introduce a more […]
Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
arXiv:2604.02623v2 Announce Type: replace-cross Abstract: Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack surface that spans websites and sessions. While existing security research on memory assumes attackers can directly inject into memory storage or exploit shared memory across users, we […]
Does Pass Rate Tell the Whole Story? Evaluating Design Constraint Compliance in LLM-based Issue Resolution
arXiv:2604.05955v1 Announce Type: cross Abstract: Repository-level issue resolution benchmarks have become a standard testbed for evaluating LLM-based agents, yet success is still predominantly measured by test pass rates. In practice, however, acceptable patches must also comply with project-specific design constraints, such as architectural conventions, error-handling policies, and maintainability requirements, which are rarely encoded in tests […]
Unifying VLM-Guided Flow Matching and Spectral Anomaly Detection for Interpretable Veterinary Diagnosis
arXiv:2604.05482v1 Announce Type: cross Abstract: Automatic diagnosis of canine pneumothorax is challenged by data scarcity and the need for trustworthy models. To address this, we first introduce a public, pixel-level annotated dataset to facilitate research. We then propose a novel diagnostic paradigm that reframes the task as a synergistic process of signal localization and spectral […]
Multiplayer Nash Preference Optimization
arXiv:2509.23102v3 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) has emerged as the standard paradigm for aligning large language models with human preferences. However, reward-based methods grounded in the Bradley-Terry assumption struggle to capture the nontransitivity and heterogeneity of real-world preferences. To address this, recent studies have reframed alignment as a two-player Nash […]
Safety, Security, and Cognitive Risks in World Models
arXiv:2604.01346v2 Announce Type: replace-cross Abstract: World models – learned internal simulators of environment dynamics – are rapidly becoming foundational to autonomous decision-making in robotics, autonomous vehicles, and agentic AI. By predicting future states in compressed latent spaces, they enable sample-efficient planning and long-horizon imagination without direct environment interaction. Yet this predictive power introduces a distinctive […]