7/2/2026
QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling
Quasi-Monte Carlo test-time scaling method for language models reducing redundancy in parallel sampling while improving inference efficiency.
7/2/2026
Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations
RLVR framework combining verifiable rewards and human demonstrations for LM training, addressing diversity collapse from objective-only optimization.
7/2/2026
Neural Certificate Pricing for Combinatorial Optimization Problems
Neural Certificate Pricing applies unsupervised learning to combinatorial optimization by leveraging asymmetry between search and verification complexity.
7/2/2026
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
Empirical comparison of quantum machine learning models versus classical machine learning approaches across benchmarks.
7/2/2026
TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
TiRex-2 extends univariate time series foundation model to multivariate forecasting using recurrent xLSTM with streaming capability.
7/2/2026
Language-Critique Imitation Learning from Suboptimal Demonstrations
Language-critique framework for imitation learning from suboptimal demonstrations using natural language feedback instead of scalar signals.
7/2/2026
Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training
Study showing single transformer layer training matches full-parameter RL fine-tuning for LLMs, revealing unequal layer-wise contribution during RL post-training.
7/2/2026
Why Advanced Encoders Lag on Sparse Retrieval? The Answer and an Approach to Bridging Vocabulary Gaps
Identifies vocabulary gap in modern encoders for sparse retrieval and proposes approach to bridge gap between dense and sparse retrieval.
7/2/2026
Harnessing the Latent Space: From Steering Vectors to Model Calibrators for Control and Trust
Framework using steering vectors and latent space analysis to control and calibrate language model behavior for trustworthy deployment.
7/2/2026
Steal the Patch Size: Adversarially Manipulate Vision-Language Models
Black-box attack recovering private vision-tokenizer configurations of vision-language models through side-channel analysis.
7/2/2026
SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing
SLIM-RL proposes risk-budgeted random-masking reinforcement learning for diffusion LLMs, eliminating trajectory slicing overhead from prior TraceRL method.
7/2/2026
Sample Complexities of Estimating Gumbel--Max Watermark Proportions with and without Reduction to Pivotal Statistics
Analysis of sample complexity for estimating watermark proportions in documents under Gumbel-max LLM watermarking mechanism.
7/2/2026
Computer vision-based neural networks for radioisotope identification in urban environments
Computer vision neural networks for radioisotope identification from gamma-ray spectrograms in urban environments.
7/2/2026
Rosetta: Composable Native Multimodal Pretraining
Rosetta: Composable multimodal pretraining approach addressing gradient conflicts when integrating new modalities without catastrophic forgetting.
7/2/2026
DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
DiscoLoop: Method for internalizing multi-hop reasoning in LLMs within single forward pass using discrete embeddings and continuous states.
7/2/2026
SoK: Attack and Defense Landscape of Mobile On-device AI Systems
Systematization of knowledge on attack and defense landscape for mobile on-device AI systems.
7/2/2026
The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models
Structured evaluation showing text-to-image diffusion safety alignment methods create illusion of high utility through coarse metrics.
7/2/2026
A Mechanistic View of Authority Hierarchy in LLM Sycophancy
Mechanistic investigation of authority bias in LLMs showing how models prioritize source credibility over factual consistency.
7/2/2026
Information-Regularized Attention for Visual-Centric Reasoning
Information-regularized attention mechanism to improve visual grounding and reduce hallucination in vision-language models.
7/2/2026
StochasT: Learning with Stochastic Turn Depth for Visual Instruction Tuning
StochasT: Visual instruction tuning method addressing visual attention decay in multi-turn vision-language model conversations.
7/2/2026
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Deep neural networks and ensemble methods to predict mortality outcomes and identify biomarkers in acute myocardial infarction.
7/2/2026
Auditing Forgetting in Limited Memory Language Models
Causal auditing framework to detect whether deleted facts persist in limited memory language models through parametric memory or retrieval artifacts.
7/2/2026
Coachable agents for interactive gameplay
RL framework enabling interactive real-time control of agent behavior during gameplay through coachability mechanisms instead of learning single optimal policy.
7/2/2026
LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter
Lightweight backbone-agnostic segmentation benchmark adapter enabling fair comparison of transformer backbones independent of decoder and pretraining.
7/2/2026
Soft Mixture-of-Recursions: Going Deeper with Recursive Vision Transformers
Recursive Vision Transformer approach using soft mixture-of-recursions to build deeper models with better parameter efficiency and performance.
7/2/2026
Shapley in Context: Explaining Financial Language with Domain Expertise
Method for explaining financial LLM decisions using Shapley values combined with domain expertise, addressing regulatory explainability requirements.
7/2/2026
Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Graph-native RL system for materials discovery generating scientifically valid hypotheses through multi-step reasoning with traceable intermediate steps.
7/2/2026
Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity
Multitask learning framework handling mixed-type outcomes with shared sparsity by unifying task-specific losses through transformation.
7/2/2026
Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads
Method to identify attention heads in LLMs that synthesize answers from context meaning rather than literal copying, improving long-context model interpretability.
7/2/2026
Message Passing Enables Efficient Reasoning
Research on message passing between LLM threads for efficient parallel reasoning, reducing computational cost of long chains-of-thought.
7/2/2026
Group-invariant Coresets for Data-efficient Active Learning
GRINCO uses group-invariant coresets for active learning that respect data symmetries and transformation groups.
7/2/2026
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
FAR enables robots to learn from failures at test time, adapt behavior, and improve policy without human intervention.
7/2/2026
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
Cartridge distillation method exposes hidden biases in LLMs that favor specific entities or viewpoints.
7/2/2026
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
Compares PPO and SAC reinforcement learning algorithms for fault tolerance in autonomous machines.
7/2/2026
Invariance Pair Guidance: Robustness to Spurious Correlations via Corrective Gradients
Invariance Pair Guidance improves robustness to spurious correlations through corrective gradients without dense labels.
7/2/2026
Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning
Studies inherent many-to-many multiplicity in multimodal learning relationships beyond deterministic alignment.
7/2/2026
FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Data
FusionFactory fuses capabilities of multiple LLMs using multi-LLM log data for improved performance.
7/2/2026
FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learning
FLAT reveals hidden backdoor failures in federated learning through latent-conditioned reliability stress testing.
7/2/2026
FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning
FedIA improves federated graph learning through importance-aware aggregation on distributed social media networks.
7/2/2026
Predicting LLM Reasoning Performance with Small Proxy Model
rBridge predicts reasoning performance of large LLMs using small proxy models under 1B parameters.
7/2/2026
K-Merge: Online Continual Merging of Adapters for On-device Large Language Models
K-Merge enables online merging of LoRA adapters for efficient on-device LLM deployment with limited storage.
7/2/2026
Recursively Enumerably Representable Classes and Computable Versions of the Fundamental Theorem of Statistical Learning
Studies computable PAC learning and derives analogs of the Fundamental Theorem of Statistical Learning in the computable setting.
7/2/2026
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
FlowPath: invertible flow-based method for learning manifolds from irregularly-sampled time series, improving neural controlled differential equations robustness.
7/2/2026
When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models
Analysis showing 8-bit quantization unexpectedly improves continual learning in LLMs compared to FP16, reducing catastrophic forgetting with replay buffers.
7/2/2026
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
Study using AlphaEarth foundation model embeddings from satellite imagery to improve hydrological river flow prediction in data-sparse regions.
7/2/2026
OpFML: Pipeline for ML-based Operational Inference
OpFML pipeline for operationalizing ML-based climate and Earth science models with data acquisition, preprocessing, and failure handling infrastructure.
7/2/2026
KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning
KAGE-Bench: JAX-native benchmark for systematically evaluating RL agent visual generalization by independently controlling observation distribution shifts.
7/2/2026
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
PaAno: lightweight patch-based representation learning for time-series anomaly detection, outperforming large transformer models on constrained hardware.
7/2/2026
Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
Attentive kernel smoothing approach for efficient Neural Controlled Differential Equations, reducing function evaluations via smoother path construction.
7/2/2026
Geometry-Preserving Neural Architectures on Manifolds with Boundary
Unified framework for geometry-preserving neural architectures on manifolds with boundary, organizing constraint enforcement strategies.