arXiv AI
Jul 15
Mathematics of Data Science
arXiv:2607. 11938v1 Announce Type: cross Abstract: This book is about the mathematical foundations of data science.
Inference efficiency
Quantization, distillation, pruning and serving work aimed at the same accuracy for less memory, latency and money.
arXiv Machine Learning
Jul 15
RAFP: Identifying LLM Lineages via Rare-Region Fingerprints
arXiv:2505. 12682v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly released under restricted licenses, creating a growing need for robust model ownership verification.
arXiv Machine Learning
Jul 15
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs
arXiv:2602. 19938v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently, delivering strong accuracy under fixed compute budgets.
arXiv AI
Jul 15
Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
arXiv:2603. 28583v2 Announce Type: replace-cross Abstract: Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations.
arXiv AI
Jul 15
Do You Remember? Toward Memory-Centric Multimodal AI
arXiv:2607. 11919v1 Announce Type: cross Abstract: Human memory is reconstructive, not a faithful recording.
arXiv Machine Learning
Jul 15
Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models
arXiv:2602. 02244v3 Announce Type: replace Abstract: The standard post-training recipe for large reasoning models, supervised fine-tuning followed by reinforcement learning (SFT-then-RL), may limit the benefits of the RL stage: while SFT imitates expert demonstrations, it often causes overconfidence and reduces generation diversity, leaving RL with a narrowed solution space to explore.
arXiv Machine Learning
Jul 15
Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg
arXiv:2512. 23043v2 Announce Type: replace Abstract: Federated Averaging (FedAvg) often degrades under non-IID client data, but it remains unclear whether this degradation reflects the loss of client-learned representations or a failure to use representations that are still present.
arXiv Machine Learning
Jul 15
AVQ-Attention: Adaptive Vector-Quantized Attention
arXiv:2607. 12789v1 Announce Type: new Abstract: The $\mathcal{O}(N^2)$ complexity of attention over $N$ tokens remains a computational bottleneck in transformer models.
arXiv AI
Jul 15
Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?
arXiv:2607. 12787v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc.
arXiv Machine Learning
Jul 15
Efficient Sequential Calibration with $O(T^{2/3-\epsilon})$ Error Bound
arXiv:2607. 12928v1 Announce Type: new Abstract: We study the online binary sequential calibration problem.
Hugging Face Trending Papers
Jul 15
Demystifying On-Policy Distillation: Roles, Pathologies, and Regulations
On-policy distillation (OPD) has become a key paradigm in LLM post-training, yet its training dynamics remain poorly understood. We present a systematic study examining the role, pathologies, and regulations of OPD.
Hugging Face Trending Papers
Jul 14
Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?
Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc. However, these performance improvements are often accompanied by an increase in model parameter size (e.
Hugging Face Trending Papers
Jul 14
Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices
Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity.
Hugging Face Trending Papers
Jul 14
Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences
How can an agent build a structured map of its world from nothing but an ongoing sequence of raw sensory input and its own movements, especially when natural variation means exact sensory patterns rarely repeat? The Clone-Structured Causal Graph algorithm (CSCG), a normative hippocampus model, shows how an interpretable map can be learned from aliased observations.
arXiv AI
Jul 14
What Does It Mean to Break a Distillation Defense?
arXiv:2606. 25059v2 Announce Type: replace-cross Abstract: Black-box LLMs (accessible only via API) are vulnerable to distillation attacks, in which an attacker queries the model and trains a student on its outputs.
arXiv Machine Learning
Jul 14
Reliability Scaling Laws for Quantized Large Language Models
arXiv:2607. 10855v1 Announce Type: new Abstract: Quantization is a powerful strategy to build capable and resource-efficient large language models (LLMs) by reducing the bitwidth of the parameters.
arXiv AI
Jul 14
The Verifier is the Curriculum: Execution-Gated Self-Distillation for Cross-Family Game Generation
arXiv:2607. 09709v1 Announce Type: new Abstract: Post-training a code generator against a learned judge can optimize proxy features that raise the score without improving the artifact.
arXiv Machine Learning
Jul 14
RDQ: Residual Distribution Quantization for Large Language Models
arXiv:2607. 10137v1 Announce Type: new Abstract: Post-training quantization (PTQ) of large language models degrades sharply below 4-bit precision.
arXiv Machine Learning
Jul 14
FlashTrie: A GPU-Accelerated Constrained Beam Search for Generative Retrieval
arXiv:2607. 10044v1 Announce Type: new Abstract: Constrained decoding is essential in generative retrieval, where document identifiers generated directly from a query must exactly match a predefined library of valid IDs.
arXiv AI
Jul 14
Annotation-Free Furniture Codes: What They Encode, and How Far They Transfer
arXiv:2607. 10461v1 Announce Type: cross Abstract: Layout-based 3D scene synthesizers place each object using two human-annotated channels: a categorical class label and a canonical-pose convention.