Search: “model-releases”

PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments

arXiv:2603.23231v2 Announce Type: replace Abstract: Empowering large language models with long-term memory is crucial for building agents that adapt to users' evolving needs. Existing evaluations of t

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PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting

DGX agent

arXiv:2605.16449v1 Announce Type: cross Abstract: Deep forecasting models often suffer from attenuated periodic perception and entangled trend-noise representations as network depth increases. Moreove

model-releasesarxiv-cs-ai

19 May 2026

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media

arXiv:2605.17187v1 Announce Type: cross Abstract: Social media are shifting towards pluralism -- community-governed platforms where groups define their own norms. What violates rules in one community

Position: AI Evaluations Should be Grounded on a Theory of Capability

arXiv:2509.19590v2 Announce Type: replace Abstract: Evaluations of generative models are now ubiquitous, and their outcomes critically shape public and scientific expectations of AI's capabilities. Ye

POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection

arXiv:2605.18128v1 Announce Type: new Abstract: Existing Multivariate Time Series Anomaly Detection (MTSAD) frameworks increasingly rely on integrating Graph Neural Networks (GNNs) with sequence model

Post-Trained MoE Can Skip Half Experts via Self-Distillation

arXiv:2605.18643v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its dynamic variant further reduces computation by a

Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency

arXiv:2605.18732v1 Announce Type: cross Abstract: While scaling laws govern aggregate large language model performance, no scaling law has linked factual recall to both model size and training-data co

PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong

arXiv:2604.14215v2 Announce Type: replace-cross Abstract: To address the unsustainable rise in public health expenditures, the Hong Kong SAR Government is shifting its strategic focus to primary healt

PRIME: Physically-consistent Robotic Inertial and Motion Estimation for Legged and Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2605.17681v1 Announce Type: new Abstract: Humanoid and legged robots interact with the environment through intermittent contacts, making accurate motion estimation fundamentally dependent on rea

PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation

arXiv:2605.16612v1 Announce Type: new Abstract: Rapid identification of candidate materials with target properties has become a key task in materials science. Machine learning has emerged as an altern

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation

arXiv:2605.17034v1 Announce Type: cross Abstract: Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individual

Privacy Preserving Reinforcement Learning with One-Sided Feedback

arXiv:2605.18246v1 Announce Type: cross Abstract: We study reinforcement learning (RL) in multi-dimensional continuous state and action spaces with one-sided feedback, where the agent receives partial

Probing for Representation Manifolds in Superposition

arXiv:2605.18537v1 Announce Type: cross Abstract: This paper introduces the Manifold Probe, a supervised method for discovering representation manifolds in superposition. The method generalizes linear

Probing SMEFT Operators through tar{t}tar{t} Production with Hyper-Graph Neural Networks at the LHC

arXiv:2605.18382v1 Announce Type: cross Abstract: We present a phenomenological study of tar{t}tar{t} production in proton-proton collisions at sqrt{s} = 13~TeV, using a Hyper-Graph Neural Network (H-

ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge

arXiv:2510.18941v2 Announce Type: replace-cross Abstract: Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to task

Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA

arXiv:2605.17932v1 Announce Type: cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus primarily on autoregressive archite

Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation

arXiv:2605.18474v1 Announce Type: cross Abstract: The widespread deployment and redistribution of large language models (LLMs) have made model provenance tracking a critical challenge. While existing

Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control

arXiv:2605.18414v1 Announce Type: cross Abstract: Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when u

Protection Is (Nearly) All You Need: Structural Protection Dominates Scoring in Globally Capped KV Eviction

arXiv:2605.18053v1 Announce Type: cross Abstract: We study KV cache eviction under a shared globally capped decode-time harness. Seven policies (LRU, H2O, SnapKV, StreamingLLM, Ada-KV, QUEST, Random)

Protein Fold Classification at Scale: Benchmarking and Pretraining

arXiv:2605.18552v1 Announce Type: new Abstract: Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoi

ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification

arXiv:2510.12534v4 Announce Type: replace Abstract: The rapid growth of user-generated text across digital platforms has intensified the need for interpretable models capable of fine-grained text clas

Provable Knowledge Acquisition and Extraction in One-Layer Transformers

arXiv:2508.00901v4 Announce Type: replace-cross Abstract: Large language models may encounter factual knowledge during pre-training yet fail to reliably use that knowledge after fine-tuning. Despite g

Provably Shorter Scratchpads in Hybrid DeltaNet-Attention Decoders

arXiv:2605.16640v1 Announce Type: new Abstract: We investigate the expressive power of hybrid recurrent-attention decoders, a class of architectures used in recent open-source language models such as

ProxyKV: Cross-Model Proxy Pruning for Efficient Long-Context LLM Inference

arXiv:2605.16360v1 Announce Type: cross Abstract: Efficient long-context inference in Large Language Models (LLMs) is severely constrained by the Key-Value (KV) cache memory wall, yet existing pruning

QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting

arXiv:2605.18333v1 Announce Type: cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in mult

QSTRBench: a New Benchmark to Evaluate the Ability of Language Models to Reason with Qualitative Spatial and Temporal Calculi

arXiv:2605.18380v1 Announce Type: new Abstract: We introduce an extensive qualitative spatial and temporal reasoning (QSTR) benchmark for evaluating large language models (LLMs). We pose questions con

QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation

arXiv:2512.19134v2 Announce Type: replace Abstract: Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models

Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models

arXiv:2501.17549v2 Announce Type: replace Abstract: Graph-structured data plays a vital role in numerous domains, such as social networks, citation networks, commonsense reasoning graphs and knowledge

Queue Length Regret Bounds for Contextual Queueing Bandits

arXiv:2601.19300v2 Announce Type: replace Abstract: We introduce contextual queueing bandits, a new context-aware framework for scheduling while simultaneously learning unknown service rates. Individu

Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning

model-releasesarxiv-cs-cv

arXiv:2605.17429v1 Announce Type: cross Abstract: Noisy-label methods often estimate sample reliability from forward-space signals such as loss, confidence, or entropy. These signals indicate whether

RAP: Runtime Adaptive Pruning for LLM Inference

arXiv:2505.17138v5 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at language understanding and generation, but their enormous computational and memory requirements hinder d

ReBaR: Reference-Based Reasoning for Robust Pose Estimation from Monocular Images

model-releasesarxiv-cs-cv

arXiv:2303.11675v3 Announce Type: replace Abstract: R}easoning for Robust Human Pose and Shape Estimation), designed to estimate human body shape and pose from single-view images. ReBaR effectively ad

REBAR: Reference Ethical Benchmark for Autonomy Readiness

model-releasesarxiv-cs-ro

arXiv:2605.18423v1 Announce Type: new Abstract: As autonomous systems grow more advanced, objective metrics to evaluate their ethical and legal compliance are critical for informing end users of their

Red-Bandit: Test-Time Adaptation for LLM Red-Teaming via Bandit-Guided LoRA Experts

arXiv:2510.07239v2 Announce Type: replace Abstract: Automated red-teaming has emerged as a scalable approach for auditing Large Language Models (LLMs) prior to deployment, yet existing approaches lack

Residual Semantic Decomposition of Word Embeddings

arXiv:2605.17482v1 Announce Type: new Abstract: We introduce Residual Semantic Decomposition (RSD), a neural additive decomposition of word embeddings that balances embedding reconstruction with relat

Responsible Agentic AI Requires Explicit Provenance

arXiv:2605.17169v1 Announce Type: new Abstract: Agentic AI is rapidly proliferating across diverse real-world domains such as software engineering, yet public trust has not kept pace. The central reas

Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution

arXiv:2601.04855v2 Announce Type: replace-cross Abstract: Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sens

Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free

arXiv:2605.16767v1 Announce Type: new Abstract: Multi-label legal annotation requires assigning multiple labels from large, evolving taxonomies to long, fact-intensive documents, often under limited s

Reverse-Engineering Model Editing on Language Models

arXiv:2602.10134v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive informatio

Revisiting the Adam-SGD Gap in LLM Pre-Training: The Role of Large Effective Learning Rates

arXiv:2605.17787v1 Announce Type: new Abstract: It is widely believed that stochastic gradient descent (SGD) performs significantly worse than adaptive optimizers such as Adam in pre-training Large La

RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits

arXiv:2603.11276v2 Announce Type: replace-cross Abstract: Real-world contextual bandit problems with complex reward models are often tackled with iteratively trained models, such as boosting trees. Ho

Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method

arXiv:2605.18174v1 Announce Type: new Abstract: Muon has recently emerged as a strong alternative to AdamW for training neural networks, with encouraging large-scale pretraining results and growing ev

RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards

arXiv:2509.21319v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) are the main RL paradigms used in

RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies

arXiv:2603.04639v2 Announce Type: replace-cross Abstract: Memory is critical for long-horizon and history-dependent robotic manipulation. Such tasks often involve counting repeated actions or manipula

ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2508.13977v3 Announce Type: replace Abstract: Depth estimation is a fundamental component of spatial perception for autonomous driving and other unmanned systems operating in open urban environm

RTI-Bench: A Structured Dataset for Indian Right-to-Information Decision Analysis

arXiv:2605.16843v1 Announce Type: new Abstract: India's Right to Information Act, 2005 gives every citizen the right to demand information from public authorities, yet in practice most people cannot m

SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering

arXiv:2605.17526v1 Announce Type: cross Abstract: As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end

SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening

arXiv:2605.17610v1 Announce Type: cross Abstract: The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-world deplo