Search: “models”

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

arXiv:2605.23082v1 Announce Type: cross Abstract: Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as

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Model Releases

OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents

DGX agent

arXiv:2605.23657v1 Announce Type: new Abstract: Skills, i.e., structured workflow instructions distilled for large language models (LLMs), are becoming an increasingly important mechanism for improvin

model-releasesarxiv-cs-cl

25 May 2026

PGT: Procedurally Generated Tasks for improving visual grounding in MLLMs

arXiv:2605.23883v1 Announce Type: cross Abstract: Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this wo

PoisonForge: Task-Level Targeted Poisoning Benchmark for Instruction-Tuned LLMs

arXiv:2605.23168v1 Announce Type: cross Abstract: When practitioners fine-tune LLMs on unvetted datasets, an adversary can exploit the data supply chain through task-level poisoning: inserting a small

Research

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation

arXiv:2412.19098v4 Announce Type: replace Abstract: Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding ta

researcharxiv-cs-lg

Integrable Elasticity via Neural Demand Potentials

arXiv:2605.22820v1 Announce Type: new Abstract: We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-dema

23 May 2026

On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs

arXiv:2602.12506v3 Announce Type: replace Abstract: Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivatin

23 May 2026

Hardware

WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving

arXiv:2512.09472v2 Announce Type: replace-cross Abstract: Deploying multiple models within shared GPU clusters is a key strategy to improve resource efficiency in large language model (LLM) serving. E

hardwarearxiv-cs-lg

23 May 2026

Code Researcher: Deep Research Agent for Large Systems Code and Commit History

arXiv:2506.11060v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code rema

IdioLink: Retrieving Meaning Beyond Words Across Idiomatic and Literal Expressions

arXiv:2605.22247v1 Announce Type: new Abstract: Idioms pose a fundamental challenge for language models, as their meaning cannot be inferred from surface form alone. Understanding such expressions, th

Safety

Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook

arXiv:2605.20442v1 Announce Type: cross Abstract: Generative AI systems are increasingly deployed as interactive agents in online environments, such as a social network called Moltbook. In Moltbook, l

safetyarxiv-cs-ai

OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind

arXiv:2605.20423v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. E

SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

arXiv:2511.07820v3 Announce Type: replace-cross Abstract: Despite the rise of billion-parameter foundation models trained across thousands of GPUs, similar scaling gains have not been shown for humano

Token-Level LLM Collaboration via FusionRoute

arXiv:2601.05106v4 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a singl

DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions

arXiv:2605.21273v1 Announce Type: new Abstract: Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning,

Findings of the Counter Turing Test: AI-Generated Text Detection

arXiv:2605.20761v1 Announce Type: new Abstract: The rapid proliferation of AI-generated text has introduced significant challenges in maintaining the integrity of digital content. Advanced generative

Spectral Unforgetting: Post-Hoc Recovery of Damaged Capabilities Without Retraining

arXiv:2605.20296v1 Announce Type: new Abstract: Fine-tuning a language model for a target task routinely degrades capabilities the training data never explicitly threatened. We study this phenomenon,

Safety

Task-conditioned probing of instruction-tuned multimodal LLMs: Region-specific brain alignment patterns under naturalistic stimuli

arXiv:2506.08277v3 Announce Type: replace-cross Abstract: Recent voxel-wise multimodal brain encoding studies have shown that multimodal large language models (MLLMs) exhibit a higher degree of brain

safetyarxiv-cs-cl

Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs

arXiv:2505.19075v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable general capabilities, but enhancing skills such as reasoning often demands substanti

VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence

arXiv:2605.20676v1 Announce Type: new Abstract: Establishing a clear link between model predictions and the visual evidence that supports them is critical for transparency and reliability in multimoda

Auditing Reasoning-Trace Memorization Claims after Unlearning with Head-Conditioned Canaries

arXiv:2605.18891v1 Announce Type: cross Abstract: Evaluations of unlearning on reasoning models sometimes show a bypass pattern. The answer side looks unlearned, but the model's own thinking trace kee

Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations

arXiv:2603.17305v2 Announce Type: replace Abstract: We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness aga

Cross-View Attention Fusion Net: A Prior-Guided Dual-View Representation Learning for Cardiac Output Estimation from Short-Term PPG Signals

arXiv:2605.19666v1 Announce Type: cross Abstract: Accurate cardiac output (CO) estimation from photoplethysmography (PPG) is promising for unobtrusive hemodynamic monitoring, but remains difficult sin

EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design

arXiv:2605.19743v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address mul

Local Ai

iDiff: Interpretable Difference-aware Framework for Pairwise Image Quality Assessment

arXiv:2605.19522v1 Announce Type: new Abstract: Pairwise image quality assessment (IQA) in professional photography requires a model not only to identify the preferred image between two candidates, bu

local-aiarxiv-cs-cv

Introducing: Cohere Command A+ We’ve created our most powerful LLM yet, optimized it to run on as little hardware as possible, and released …

Cohere announced Command A+, their most advanced large language model to date, designed with optimization for efficient hardware requirements to enable broader deployment and accessibility. The model

model-releasescohere--x

Applications

LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight

arXiv:2601.03645v2 Announce Type: replace Abstract: Emotional coordination is a core property of human interaction that shapes how relational meaning is constructed in real time. While text-based affe

applicationsarxiv-cs-cl

MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification

arXiv:2605.19359v1 Announce Type: new Abstract: Deep learning methods have demonstrated promising results in predicting BI-RADS scores from mammography images. However, the interpretation of these ima

POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents

arXiv:2605.19127v1 Announce Type: new Abstract: LLM agents increasingly have access to private user data and act on the user's behalf when interacting with third-party systems. The user defines what m

TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards

arXiv:2605.19320v1 Announce Type: new Abstract: Faithful text rendering remains a persistent weakness of large text-to-image generative models, as it requires both semantic instruction following and f

The Evaluation Game: Beyond Static LLM Benchmarking

arXiv:2605.19377v1 Announce Type: cross Abstract: As jailbreaks, adversarially crafted inputs that bypass safety constraints, continue to be discovered in Large Language Models, practitioners increasi

To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents

arXiv:2605.18882v1 Announce Type: cross Abstract: LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models

Hardware

UCCI: Calibrated Uncertainty for Cost-Optimal LLM Cascade Routing

arXiv:2605.18796v1 Announce Type: cross Abstract: LLM cascades and model routing promise lower inference cost by sending easy queries to a small model and escalating hard ones to a large model, but mo

hardwarearxiv-cs-cl

Agents

VL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving

arXiv:2605.20082v1 Announce Type: cross Abstract: The rapid growth of autonomous driving datasets has enabled the scaling of powerful motion forecasting models. While large-scale pretraining provides

agentsarxiv-cs-ai

Safety

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

arXiv:2602.07008v2 Announce Type: replace Abstract: Reliable models should not only predict correctly, but also justify decisions with acceptable evidence. Yet conventional supervised learning typical

safetyarxiv-cs-cv

Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation

arXiv:2602.16990v2 Announce Type: replace Abstract: Most recommendation benchmarks evaluate how well a model imitates user behavior. In financial advisory, however, observed actions can be noisy or sh

Detecting Verbatim LLM Copy-Paste in Homework

arXiv:2605.16336v1 Announce Type: cross Abstract: Large language models (LLMs) have made fluent essay writing, code drafting, and quiz answering instantly available to students at every level, from se

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

arXiv:2603.12676v2 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harde

EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control

arXiv:2605.16692v1 Announce Type: cross Abstract: We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorit

Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents

arXiv:2605.17625v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into persistent scientific collaborators, context window saturation has emerged as a critical bottleneck. Scienti

GLT-PEFT: Gated Lie-Tucker Parameter-Efficient Fine-Tuning for Alzheimer's Disease Diagnosis with Hippocampal Segmentation Pretraining

arXiv:2605.16769v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a promising paradigm for adapting pretrained models under limited data conditions. However, most e

Learning-Zone Energy: Online Data Selection for Efficient RL Post-Training

arXiv:2605.17003v1 Announce Type: cross Abstract: Reinforcement Learning (RL) post-training has emerged as the dominant paradigm for eliciting mathematical reasoning in Large Language Models (LLMs), y

MiniGPT: Rebuilding GPT from First Principles

arXiv:2605.17398v1 Announce Type: new Abstract: This paper presents MiniGPT, a compact from-scratch implementation of GPT-style autoregressive language modeling in PyTorch. The aim is to rebuild the c

Research

Monocular Depth Perception Enhancement Based on Joint Shading/Contrast Model and Motion Parallax (JSM)

arXiv:2605.17252v1 Announce Type: new Abstract: Stereoscopic 3D displays adopt a binocular depth cue to provide depth perception. However, users should be equipped with expensive special devices to ap

researcharxiv-cs-cv

Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction

arXiv:2605.17360v1 Announce Type: new Abstract: Real-time duplex interaction is essential for multimodal AI systems operating in real-world scenarios, where models must continuously process streaming

OmniPro: A Comprehensive Benchmark for Omni-Proactive Streaming Video Understanding

arXiv:2605.18577v1 Announce Type: new Abstract: Omni-proactive streaming video understanding, i.e., autonomously deciding when to speak and what to say from continuous audio-visual streams, is an emer

OpenJarvis: Personal AI, On Personal Devices

arXiv:2605.17172v1 Announce Type: cross Abstract: Personal AI stacks, like OpenClaw and Hermes Agent, are becoming central to daily work, yet they route nearly every query (often over sensitive local

PARALLAX: Separating Genuine Hallucination Detection from Benchmark Construction Artifacts

arXiv:2605.17028v1 Announce Type: cross Abstract: Large language models (LLMs) hallucinate with confidence: their outputs can be fluent, authoritative, and simply wrong. In medical, legal, and scienti