arXiv Machine Learning
Jun 10
TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification
arXiv:2606. 09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv Machine Learning
Jun 10
Representation-Aware Advantage Estimation: Your Reward Model Provides More Than A Scalar Output
arXiv:2606. 10528v1 Announce Type: new Abstract: Current reinforcement learning from human feedback (RLHF) methods primarily rely on scalar rewards from a trained reward model (RM).
arXiv AI
Jun 10
Building Change Detection in Earthquake: A Multi-Scale Interaction Network and A Change Detection Dataset
arXiv:2606. 10329v1 Announce Type: cross Abstract: As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses.
arXiv AI
Jun 10
Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing
arXiv:2606. 10223v1 Announce Type: cross Abstract: Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produce overconfident predictions.
arXiv AI
Jun 10
PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks
arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.
arXiv AI
Jun 10
SPACE: Source-free Proxy Anchor Concept Erasure for MLLMs
arXiv:2606. 09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial solution for removing sensitive data while preserving model performance.
arXiv AI
Jun 10
NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices
arXiv:2504. 03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles.
arXiv Machine Learning
Jun 10
Decision-Making under Combinatorial Risk
arXiv:2606. 10092v1 Announce Type: new Abstract: Decision-making under risk is typically studied through single-shot lottery choices.
arXiv Machine Learning
Jun 10
It\^o maps for any-step SDEs
arXiv:2606. 11156v1 Announce Type: cross Abstract: Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics.
arXiv AI
Jun 10
Piper: A Programmable Distributed Training System
arXiv:2606. 11169v1 Announce Type: cross Abstract: Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together with memory-saving optimizations like ZeRO.
arXiv Machine Learning
Jun 10
Time series forecasting from partial observations via Non-negative Matrix Factorization
arXiv:2102. 05314v2 Announce Type: replace Abstract: In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values.
arXiv Machine Learning
Jun 10
Gradient-Guided Reward Optimization for Inference-time Alignment
arXiv:2606. 09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation.
arXiv AI
Jun 10
AuRA: Internalizing Audio Understanding into LLMs as LoRA
arXiv:2606. 11033v1 Announce Type: cross Abstract: Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models, or bridge/distillation-based adaptation.
arXiv Machine Learning
Jun 10
Validation-Stage Combinatorial Fusion Analysis for Imbalanced Credit-Card Fraud Detection
arXiv:2606. 10393v1 Announce Type: new Abstract: Credit-card fraud detection is difficult because fraudulent transactions are rare, costly, and unevenly distributed.
arXiv AI
Jun 10
Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
arXiv:2606. 10457v1 Announce Type: new Abstract: Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis.
arXiv AI
Jun 10
Whisfusion: Parallel ASR Decoding with Masked Diffusion
arXiv:2508. 07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency scale with transcript length.
arXiv Machine Learning
Jun 10
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.
arXiv Machine Learning
Jun 10
When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms
arXiv:2606. 10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds of steps, small per-step errors compound into phase drift that pointwise metrics fail to detect.
arXiv Machine Learning
Jun 10
OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
arXiv:2606. 11144v1 Announce Type: new Abstract: Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories.
arXiv Machine Learning
Jun 10
On the Controllability-Fidelity Frontier in Diffusion Editing
arXiv:2606. 09901v1 Announce Type: cross Abstract: Diffusion-based generative models enable powerful image editing capabilities, but achieving precise control while maintaining fidelity and safety remains challenging.