4/7/2026
Autorubric: Unifying Rubric-based LLM Evaluation
Open-source framework unifying rubric-based LLM evaluation techniques including ensemble judging, bias mitigation, and few-shot calibration with consistent implementation.
4/7/2026
"When to Hand Off, When to Work Together": Expanding Human-Agent Co-Creative Collaboration through Concurrent Interaction
Research on human-AI agent collaboration exploring how agents can maintain workspace awareness and interpret concurrent user actions on shared artifacts during co-creative tasks.
4/7/2026
NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
Research on multi-agent reinforcement learning algorithm (NePPO) addressing training stability and convergence in general-sum games with heterogeneous agents.
4/7/2026
PlayWorld: Learning Robot World Models from Autonomous Play
PlayWorld pipeline training action-conditioned video models on autonomous robot play data for improved world model physics prediction.
4/7/2026
Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination Reduction
Five prompt engineering strategies to reduce LLM hallucinations and improve consistency in industrial applications like design and IoT.
4/7/2026
Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings
Hindsight-anchored policy optimization method addressing advantage collapse in sparse-reward RL for reasoning model post-training.
4/7/2026
UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization
UtilityMax Prompting framework using formal mathematical language to specify multi-objective LLM tasks with influence diagrams.
4/7/2026
Truth as a Compression Artifact in Language Model Training
Controlled experiments showing LMs prefer correct answers because error compressibility structure guides learning, not inherent truth preference.
4/7/2026
Security Considerations for Artificial Intelligence Agents
Perplexity's recommendations on security considerations for frontier AI agents based on operating agentic systems at scale.
4/7/2026
Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies
Quality diversity optimization for red-teaming vision-language-action robot models to improve robustness against prompt variations.
4/7/2026
Brittlebench: Quantifying LLM robustness via prompt sensitivity
Brittlebench framework quantifying LLM robustness through prompt sensitivity evaluation beyond static benchmarks.
4/7/2026
Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving
Proposes parallel framework combining imitation and reinforcement learning for end-to-end autonomous driving instead of sequential fine-tuning.
4/7/2026
Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
Studies causal discovery in chain-reaction dynamical systems using interventional data with identifiability guarantees.
4/7/2026
Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning
Framework using LLMs to automatically synthesize reward programs for cooperative multi-agent reinforcement learning systems.
4/7/2026
Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering
Introduces StackRepoQA, a repository-level QA benchmark for evaluating LLMs on multi-file program comprehension tasks beyond isolated code snippets.
4/7/2026
Toward Evaluation Frameworks for Multi-Agent Scientific AI Systems
Analyzes challenges in benchmarking multi-agent scientific AI systems, including reasoning vs retrieval, data contamination, ground truth, tool use, and reproducibility in evolving knowledge bases.
4/7/2026
Towards Context-Aware Image Anonymization with Multi-Agent Reasoning
CAIAMAR framework uses multi-agent reasoning for context-aware anonymization of personally identifiable information in street-level imagery.
4/7/2026
HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention
Hierarchical indexing method HISA optimizes sparse attention mechanisms in LLMs by reducing indexer bottlenecks in token selection.
4/7/2026
OptiMer: Optimal Distribution Vector Merging Is Better than Data Mixing for Continual Pre-Training
OptiMer method for continual pre-training of LLMs. Decouples data mixture ratio selection from training by optimizing distribution vectors.
4/7/2026
On the Mirage of Long-Range Dependency, with an Application to Integer Multiplication
Analysis of long-range dependency in neural networks for integer multiplication. Argues dependency is a computational artifact, not an intrinsic problem.
4/7/2026
Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
Backdoor attacks on federated learning with realistic semantic triggers. Proposes SABLE method using in-distribution patterns instead of synthetic corner patches.
4/7/2026
Security in LLM-as-a-Judge: A Comprehensive SoK
Systematization of knowledge on security and reliability risks in LLM-as-a-Judge paradigm. Documents vulnerabilities where judges become targets of adversarial manipulation.
4/7/2026
Task-Centric Personalized Federated Fine-Tuning of Language Models
Personalized federated learning approach for fine-tuning language models on heterogeneous tasks. Improves performance on diverse client tasks while maintaining privacy.
4/7/2026
RAGShield: Detecting Numerical Claim Manipulation in Government RAG Systems
Security evaluation of RAG systems in government applications. Demonstrates embedding-based defenses fail to detect subtle numerical claim manipulation in tax/benefits systems.
4/7/2026
Spectral Compact Training: Pre-Training Large Language Models via Permanent Truncated SVD and Stiefel QR Retraction
Spectral Compact Training (SCT) for LLMs on consumer hardware. Uses permanent truncated SVD factors to avoid materializing dense weight matrices during training.
4/7/2026
Screening Is Enough
Multiscreen attention mechanism for language models. Introduces absolute query-key relevance to reject irrelevant keys, addressing softmax attention limitations.
4/7/2026
Adaptive Stopping for Multi-Turn LLM Reasoning
Adaptive stopping mechanism for multi-turn LLM reasoning. Determines optimal stopping points for agents using retrieval-augmented generation and ReAct-style interactions.
4/7/2026
GPA: Learning GUI Process Automation from Demonstrations
Vision-based robotic process automation (RPA) using sequential Monte Carlo localization. Enables stable GUI automation from single demonstrations with improved robustness.
4/7/2026
Beyond Message Passing: A Semantic View of Agent Communication Protocols
Framework for analyzing agent communication protocols across three layers: communication, syntactic, and semantic. Systematically studies 18 representative protocols for LLM systems.
4/7/2026
Scaling DPPs for RAG: Density Meets Diversity
Method scaling determinantal point processes for RAG systems to improve diversity of retrieved context while maintaining relevance.
4/7/2026
DRAFT: Task Decoupled Latent Reasoning for Agent Safety
Framework for monitoring safety of tool-using LLM agents through latent reasoning that decouples safety judgment into trainable stages.
4/7/2026
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
Novel neural network architecture for solving PDEs addressing limitations of physics-informed neural networks.
4/7/2026
NativeTernary: A Self-Delimiting Binary Encoding with Unary Run-Length Hierarchy Markers for Ternary Neural Network Weights, Structured Data, and General Computing Infrastructure
Binary encoding scheme for ternary neural network weights enabling efficient storage and computation for compressed LLMs.
4/7/2026
Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
AI framework combining spatio-temporal and graph learning for electricity theft detection in smart grids.
4/7/2026
Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks
Analysis of computational efficiency for Kolmogorov-Arnold Networks on hardware-constrained deployment scenarios.
4/7/2026
From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
Multi-stage pipeline combining experimental design and machine learning surrogates to explore agent-based models efficiently.
4/7/2026
The limits of bio-molecular modeling with large language models : a cross-scale evaluation
Cross-scale evaluation of LLMs on biomolecular modeling tasks revealing performance gaps compared to mechanistic understanding.
4/7/2026
Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters
Bayesian fine-tuning method for LLMs using low-rank adapters to improve uncertainty quantification in safety-critical applications.
4/7/2026
Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization
Using LLMs and vision models trained on human preferences to improve network visualization aesthetics beyond traditional heuristic metrics.
4/7/2026
Adversarial Robustness of Deep State Space Models for Forecasting
Control-theoretic analysis of state-space model robustness under adversarial perturbations, examining Spacetime SSM forecasters and Kalman filter representations.
4/7/2026
MetaSAEs: Joint Training with a Decomposability Penalty Produces More Atomic Sparse Autoencoder Latents
MetaSAEs: sparse autoencoder training with decomposability penalty producing more atomic, single-concept latents for safety-relevant LLM applications.
4/7/2026
Olmo Hybrid: From Theory to Practice and Back
OLMo Hybrid: theoretical and empirical analysis of hybrid models combining linear RNNs and attention as alternatives to pure transformers with scaling benefits.
4/7/2026
Neural Operators for Multi-Task Control and Adaptation
Neural operator methods for multi-task optimal control problems, mapping task descriptions to control policies using permutation-invariant architectures.
4/7/2026
Earth Embeddings Reveal Diverse Urban Signals from Space
Benchmark of Earth embedding models (AlphaEarth, Prithvi, Clay) for neighborhood-scale urban monitoring from satellite imagery.
4/7/2026
Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction
Analysis of trajectory prediction models revealing that surrounding agents often degrade accuracy due to learned confounders, using Shapley attribution.
4/7/2026
Investigating Data Interventions for Subgroup Fairness: An ICU Case Study
Study of data intervention techniques for improving fairness across demographic subgroups in ICU prediction models using real healthcare data.
4/7/2026
Improving Feasibility via Fast Autoencoder-Based Projections
Data-driven approach using trained autoencoders as fast projectors to enforce complex nonconvex operational constraints in learning and control systems.
4/7/2026
Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks
Empirical survey comparing regularization frameworks (Ridge, Lasso, ElasticNet, Post-Lasso) across 134,400 simulations with historical development context.
4/7/2026
BlazeFL: Fast and Deterministic Federated Learning Simulation
BlazeFL: lightweight federated learning simulation framework enabling fast, deterministic training of hundreds or thousands virtual clients on single node.
4/7/2026
Neural Global Optimization via Iterative Refinement from Noisy Samples
Neural approach for black-box global optimization from noisy samples using iterative refinement to avoid local minima in multi-modal functions.