arXiv AI
Jul 21
Trace-Based On-Policy Distillation for Masked Diffusion Language Models
arXiv:2607. 16872v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are a promising alternative to autoregressive generation.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv AI
Jul 21
LookME: Lookup-Based Multimodal Embeddings for Layer Injection in Vision-Language Models
arXiv:2607. 16305v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved strong progress in multimodal understanding.
arXiv AI
Jul 21
Rethinking Heterogeneous LLM Merging: A Weighted Model Averaging Perspective
arXiv:2607. 18026v1 Announce Type: new Abstract: Can large language models with substantially different parameter spaces be merged by direct weighted averaging, without training or semantic alignment?
arXiv Machine Learning
Jul 21
Patch Policy: Efficient Embodied Control via Dense Visual Representations
arXiv:2607. 18236v1 Announce Type: cross Abstract: Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning.
arXiv Machine Learning
Jul 21
Online learning of neural state-space models
arXiv:2607. 17614v1 Announce Type: cross Abstract: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) models that achieve state-of-the-art performance in offline settings by estimating initial model states from past input-output data.
arXiv AI
Jul 21
A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing
arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.
arXiv AI
Jul 21
Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints
arXiv:2607. 18144v1 Announce Type: cross Abstract: Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules.
arXiv Machine Learning
Jul 21
DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation
arXiv:2607. 17467v1 Announce Type: cross Abstract: Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples.
arXiv AI
Jul 21
Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition
arXiv:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.
arXiv AI
Jul 21
ProEvent: An Event-centric Benchmark for Proactive Agents
arXiv:2607. 17701v1 Announce Type: new Abstract: Proactive agents are expected to anticipate user needs and provide autonomous assistance by perceiving environmental context without explicit instructions.
arXiv Machine Learning
Jul 21
A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization
arXiv:2406. 06629v2 Announce Type: replace Abstract: This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-objective continuous black-box optimization.
arXiv Machine Learning
Jul 21
Uncertainty Decomposition for Bayes-Filtered Transformers via Bayesian Predictive Inference
arXiv:2602. 04596v2 Announce Type: replace-cross Abstract: Bayes-filtered transformers are transformers meta-learned on sequences from a prior predictive distribution to approximate the corresponding posterior predictive distribution.
arXiv Machine Learning
Jul 21
The Geometry of Semantic Space: A Continuous Geometric Framework for the Transformer Architecture
arXiv:2607. 17146v1 Announce Type: cross Abstract: We present a continuous geometric framework that models the discrete algebraic operations of the Transformer architecture as an integro-differential equation (IDE) on a semantic fiber bundle $\calE = \calM \times \R^d$.
arXiv AI
Jul 21
Topological Signatures of Context-Level Reliability in TabPFN
arXiv:2607. 17962v1 Announce Type: cross Abstract: TabPFN is a transformer-based foundation model for tabular prediction that performs inference without task-specific training by conditioning on a support set and query inputs.
arXiv Machine Learning
Jul 21
Exploiting Low-Rank Objective Structure in Discrete Quadratic Optimization
arXiv:2602. 20376v3 Announce Type: replace-cross Abstract: We study the problem of maximizing a complex-valued quadratic form over the $K^{\text{th}}$ roots of unity.
arXiv AI
Jul 21
FlowBlock: Wavefront-Parallel Decoding for Self-Correcting Diffusion Language Models
arXiv:2607. 17652v1 Announce Type: new Abstract: Block-wise diffusion large language models (dLLMs) decode sequentially at the block level, enabling effective KV-cache reuse across blocks but making inter-block decoding strictly serial.
arXiv AI
Jul 21
Self-Modifying Lean Proof Agents with Verifier-Grounded Benchmark Coevolution
arXiv:2607. 17352v1 Announce Type: new Abstract: Designing effective Lean proof agents is a central challenge in formal mathematical reasoning.
arXiv AI
Jul 21
Thermodynamic Limits of Physical Intelligence
arXiv:2602. 05463v2 Announce Type: replace-cross Abstract: Modern AI systems achieve remarkable capabilities at the cost of substantial energy consumption.
arXiv AI
Jul 21
L2GTX: From Local to Global Time Series Explanations
arXiv:2603. 13065v2 Announce Type: replace-cross Abstract: Deep learning models achieve high accuracy in time series classification, yet understanding their class-level decision behaviour remains challenging.
arXiv AI
Jul 21
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
arXiv:2607. 17461v1 Announce Type: cross Abstract: The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.