Search: “models”

Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration

arXiv:2606.07316v1 Announce Type: cross Abstract: Byzantine collaboration among large-language-model agents requires a finality-control primitive: given delivered stochastic, structured natural-langua

Model Releases

Hierarchical Semantic-Constrained Heterogeneous Graph for Audio-Visual Event Localization

DGX agent

arXiv:2606.07033v1 Announce Type: new Abstract: Open-vocabulary audio-visual event localization (OV-AVEL) jointly models audio-visual cues to recognize and temporally localize events, including catego

model-releasesarxiv-cs-ai

8 Jun 2026

Improving Cross-Lingual Factual Recall via Consistency-Driven Reinforcement Learning

arXiv:2606.06586v1 Announce Type: new Abstract: Large language models (LLMs) trained predominantly on English data encode substantial world knowledge, yet often fail to express it reliably in other la

It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents

arXiv:2512.23128v2 Announce Type: replace-cross Abstract: Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their r

MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism

arXiv:2606.07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and

Safety

Online Pandora's Box for Contextual LLM Cascading

arXiv:2606.07392v1 Announce Type: new Abstract: Motivated by Large Language Model (LLM) cascading, we propose an online contextual Pandora's Box model for adaptively querying and selecting LLM APIs. I

safetyarxiv-cs-ai

PromptPrint: Behavioral Biometrics Through Natural Language Prompting in LLMs

arXiv:2606.06755v1 Announce Type: new Abstract: Authorship attribution research has traditionally focused on long-form, expressive texts; however, interactions with large language models (LLMs) are ty

Research

Real-Time AttentionBender: Granular Interactive Network Bending of Video Diffusion Transformers

arXiv:2606.06497v1 Announce Type: cross Abstract: Generative video models have achieved remarkable visual fidelity, yet their prompt-only interface offers thin creative agency and obscures the model's

researcharxiv-cs-cv

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606.07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owne

RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking

arXiv:2606.07181v1 Announce Type: cross Abstract: Single-step retrosynthesis needs both accurate first-ranked suggestions and candidate lists that are rich enough for downstream selection. We study th

Hardware

Scalable Joint Resource Allocation for SLO-Constrained LLM Inference in Heterogeneous GPU Clouds

arXiv:2604.07472v2 Announce Type: replace Abstract: Serving large language model (LLM) inference in cloud environments requires jointly optimizing model selection, GPU provisioning, parallelism config

hardwarearxiv-cs-lg

Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs

arXiv:2606.07175v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have raised new privacy challenges. On the data side, user-provided inputs often include unpredictable sensitiv

SigmaScale: LLM Compression with SVD-based Low-Rank Decomposition and Learned Scaling Matrices

arXiv:2606.07098v1 Announce Type: new Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices S to aid truncated Singular Value Decomposition (SVD) based Large Language Model

STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation

arXiv:2606.07036v1 Announce Type: cross Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including patient privacy and the growing need for

TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment

arXiv:2606.07451v1 Announce Type: cross Abstract: Vision-language models such as CLIP are highly useful for diverse tasks due to their shared image-text embedding space. Despite this, the image and te

VeriDrive: Verifiable Counterfactual Supervision for Cost-Efficient Vision-Language Planning

arXiv:2606.07338v1 Announce Type: new Abstract: Vision-language driving models increasingly use reasoning supervision to bridge perception, prediction, and planning, but existing driving rationales ar

When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing

arXiv:2606.07171v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) enable flexible instruction-driven image editing, but privacy risks arise when user images expose diverse and u

Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillatio

arXiv:2606.05682v1 Announce Type: new Abstract: Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost c

Evaluating Agentic Configuration Repair for Computer Networks

arXiv:2606.06212v1 Announce Type: new Abstract: Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automa

Geographic Bias and Diversity in AI Evaluation

arXiv:2606.05187v1 Announce Type: cross Abstract: Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its v

GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection

arXiv:2606.05566v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB) attack

Multilingual Fine-Tuning via Localized Gradient Conflict Resolution

arXiv:2606.05613v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems. However, fine-tun

Research

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

arXiv:2606.05555v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong

researcharxiv-cs-ai

SentinelBench: A Benchmark for Long-Running Monitoring Agents

arXiv:2606.05342v1 Announce Type: new Abstract: AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: i

Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability

arXiv:2606.06333v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) are widely used for mechanistic interpretability in large language models, yet their formulation assigns each latent featur

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

arXiv:2606.06337v1 Announce Type: new Abstract: Large language model (LLM) deployments for long-horizon tasks face a fundamental constraint: context windows are finite while productive work sessions a

ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents

arXiv:2606.06284v1 Announce Type: new Abstract: Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool ca

Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents

arXiv:2606.06453v1 Announce Type: new Abstract: Sparse attention is becoming increasingly important for serving large language models (LLMs) as generation lengths continue to grow. However, deploying

When Should Memory Stay Silent: Measuring Memory-Use Boundaries in Memory-Augmented Conversational Agents

arXiv:2606.06055v1 Announce Type: new Abstract: Long-term memory enables language model agents to support personalized interactions, but it remains unclear when available memories warrant integration

Arena AI Agentic User Benchmark Ranking

Arena AI's agentic benchmark ranks AI models on how well they orchestrate tools for real-world agentic tasks, based on signals like tool reliability, task completion, and steerability. The leaderboard

model-releasesr-chatgpt

At least until (if?) rapid improvement stops, it seems less likely someone is going to catch the Big Three AI Labs. Microsoft and Meta relea…

model-releasesethan-mollick--x

At least until (if?) rapid improvement stops, it seems less likely someone is going to catch the Big Three AI Labs. Microsoft and Meta released their models, which were fine, but not frontier. SpaceX

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.06219v1 Announce Type: new Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion mod

CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement

arXiv:2606.05793v1 Announce Type: new Abstract: While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conver

DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections

arXiv:2508.15851v2 Announce Type: replace Abstract: Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the core challenge of real-world scientific information

Tutorials

Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning

arXiv:2606.05471v1 Announce Type: new Abstract: Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this

tutorialsarxiv-cs-cv

From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation

arXiv:2606.06266v1 Announce Type: new Abstract: Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough a

Generic Triple-Latent Compression with Gated Associative Retrieval

arXiv:2606.05175v1 Announce Type: new Abstract: We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token inte

Local Ai

https://ollama.com/library/gemma4/tags

Gemma4 is a language model available through Ollama's model library with multiple tagged versions for different use cases and configurations. The Ollama platform enables users to run open-source large

local-aiollama--x

IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval

arXiv:2606.06044v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has shown strong effectiveness in grounding Large Language Models (LLMs) with external knowledge. However, existing

Local Ai

Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO

arXiv:2606.05174v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong promise in healthcare applications. Yet deploying general-purpose models in real-world settings remains d

local-aiarxiv-cs-cl

Research

Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning

arXiv:2606.06178v1 Announce Type: cross Abstract: Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense. LLM routing aims to m

researcharxiv-cs-cl

LoRi: Low-Rank Distillation for Implicit Reasoning

arXiv:2606.05315v1 Announce Type: new Abstract: Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empiri

Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

arXiv:2606.05173v1 Announce Type: new Abstract: Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are st

ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL

arXiv:2606.05836v1 Announce Type: new Abstract: Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world d

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606.05901v1 Announce Type: new Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based sys

Self-supervised User Profile Generation for Personalization

arXiv:2606.05336v1 Announce Type: new Abstract: Personalizing large language models (LLMs) has become a central challenge as LLMs are deployed across recommendation, search, dialogue, and content gene

TextWand: A Unified Framework for Scene Text Editing

arXiv:2606.05730v1 Announce Type: new Abstract: We propose TextWand, a general-purpose framework that unifies scene text removal, generation, and replacement into a single model. By decomposing comple

The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?

arXiv:2504.10020v4 Announce Type: replace Abstract: Contrastive decoding strategies are widely used to reduce object hallucinations in multimodal large language models (MLLMs). These methods work by c