arXiv AI
Jun 30
Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv AI
Jun 30
Selective Memory Retention for Long-Horizon LLM Agents
arXiv:2606. 29178v1 Announce Type: new Abstract: When does retention matter for memory-augmented LLM agents?
arXiv Machine Learning
Jun 30
Beyond Trajectory Matching: Reflow with Marginal Distribution Alignment
arXiv:2606. 29287v1 Announce Type: new Abstract: Diffusion and continuous-flow generative models achieve high-quality generation, and their deterministic sampling can be formulated as solving learned ODE dynamics.
arXiv AI
Jun 30
HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
arXiv:2606. 29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity.
arXiv AI
Jun 30
Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation
arXiv:2606. 29464v1 Announce Type: cross Abstract: Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs that can efficiently train contrastive vision-language models under strict data and compute budgets.
arXiv AI
Jun 30
Reported Confidence in LLMs Tracks Commitment More Than Correctness
arXiv:2606. 29490v1 Announce Type: cross Abstract: Confidence is an estimate of the probability that a chosen answer is correct.
arXiv AI
Jun 30
Code Reasoning for Software Engineering Tasks: A Survey and A Call to Action
arXiv:2506. 13932v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks.
arXiv Machine Learning
Jun 30
How Far Do On-Prem Open LLMs Get on Text-to-SQL? A Cross-Family Size x Technique Frontier on BIRD
arXiv:2606. 29733v1 Announce Type: cross Abstract: Organizations that cannot send data to a cloud API increasingly ask: how good is Text-to-SQL if the model must run on-premises on open weights, and which popular accuracy "recipes" are worth their compute?
arXiv AI
Jun 30
One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models
arXiv:2606. 29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid surfaces.
arXiv Machine Learning
Jun 30
Diffusion Fine-tuning with Rewarded Moment Matching Distillation
arXiv:2606. 30414v1 Announce Type: new Abstract: Distillation and Reinforcement Learning (RL) fine-tuning are the primary pillars of diffusion post-training.
arXiv Machine Learning
Jun 30
Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion
arXiv:2606. 28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures.
arXiv Machine Learning
Jun 30
BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
arXiv:2606. 29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident predictions and miscalibrated uncertainty, especially in low-data regimes.
arXiv AI
Jun 30
HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression
arXiv:2606. 28831v1 Announce Type: cross Abstract: Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.
arXiv AI
Jun 30
SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows
arXiv:2606. 29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making.
arXiv AI
Jun 30
Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
arXiv:2606. 28446v1 Announce Type: cross Abstract: Light curves describe temporal variations in the brightness of celestial objects.
arXiv AI
Jun 30
Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving
arXiv:2606. 30537v1 Announce Type: cross Abstract: Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations.
arXiv AI
Jun 30
Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning
arXiv:2508. 09883v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving.
arXiv Machine Learning
Jun 30
Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
arXiv:2510. 08762v2 Announce Type: replace Abstract: Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions.
arXiv AI
Jun 30
Exploiting Local Flatness for Efficient Out-of-Distribution Detection
arXiv:2606. 29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment.
arXiv AI
Jun 30
Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model
arXiv:2606. 30104v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings.