Search: “models”

Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models

arXiv:2605.29607v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) enable parallel decoding by predicting all masked positions at each denoising step, yet existing training-free

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Agents

CompilerDream: Learning a Compiler World Model for General Code Optimization

DGX agent

arXiv:2404.16077v4 Announce Type: replace-cross Abstract: Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depend

agentsarxiv-cs-lg

29 May 2026

Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models

arXiv:2605.28866v1 Announce Type: cross Abstract: Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior s

Different models need different prompts, sometimes tools “Harness profiles” are how we do that in deepagents

applicationsharrison-chase--x

Different models need different prompts, sometimes tools “Harness profiles” are how we do that in deepagents Deep Agents v0.6 makes harness profiles a first-class abstraction. Now, you can get product

Local Ai

DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration

arXiv:2605.29511v1 Announce Type: cross Abstract: Tackling complex reasoning tasks typically relies on massive monolithic LLMs, which suffer from severe computational redundancy. While task decomposit

local-aiarxiv-cs-cl

Local Ai

Finding DoRI: Discovery of Retained Images in Diffusion Models

arXiv:2507.16880v3 Announce Type: replace-cross Abstract: Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectu

local-aiarxiv-cs-ai

Grounded 3D-Aware Spatial Vision-Language Modeling

arXiv:2605.30307v1 Announce Type: new Abstract: We present GR3D, a spatial vision language model equipped with three complementary grounding capabilities--explicit 2D grounding, implicit 2D grounding,

How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency

arXiv:2605.30096v1 Announce Type: cross Abstract: Large language models (LLMs) can autonomously conduct multi-stage cyber attacks, but the consistency of their offensive behavior under repeated trials

Local Ai

I developed a brutalist GUI to interact with Ollama models and PI. It is open-sourced and available for arm64 and intel Macs. Repository in comments

A developer created an open-source GUI application with a brutalist design for interacting with Ollama models and the Personal Iris (PI) platform. The application is compatible with both ARM64 and Int

local-air-ollama

Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content

arXiv:2605.29245v1 Announce Type: cross Abstract: This paper presents a survey and taxonomy of LLM fingerprinting and watermarking for identity, ownership verification, provenance, and generated-conte

researcharxiv-cs-cl

Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

arXiv:2410.15236v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling app

LsrIF: Enhancing Logic-Structured Instruction Following of Large Language Models

arXiv:2601.06431v3 Announce Type: replace Abstract: Instruction following is critical for large language models, yet real-world instructions often involve multiple constraints with logical structures,

applicationsarxiv-cs-ai

Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning

arXiv:2605.29577v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising framework that unifies perception, reasoning, and control for robot manipulation by adap

OISD: On-Policy Internal Self-Distillation of Language Models

arXiv:2605.29089v1 Announce Type: cross Abstract: Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, while large

RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains

arXiv:2605.29156v1 Announce Type: cross Abstract: Pointwise reward modeling offers critical signals for LLM post-training, yet struggles with absolute scoring in subjective, non-verifiable settings. R

safetyarxiv-cs-cl

Spectral Guidance for Flexible and Efficient Control of Diffusion Models

arXiv:2605.28900v1 Announce Type: new Abstract: We introduce Spectral Guidance, a framework for controlling diffusion models by leveraging the intrinsic geometry of the generative process. As data is

Thinking Before Constraining: A Unified Decoding Framework for Large Language Models

arXiv:2601.07525v2 Announce Type: replace-cross Abstract: Natural generation allows Large Language Models (LLMs) to produce free-form responses with rich reasoning, yet the lack of structure makes out

When Do Graph Foundation Models Transfer? A Data-Centric Theory

arXiv:2605.29828v1 Announce Type: new Abstract: Graph foundation models (GFMs) aim to reuse a single backbone across diverse graph domains, yet their transfer is often uneven and can exhibit negative

Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models

arXiv:2605.28051v1 Announce Type: new Abstract: Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. Existing methods typically rel

researcharxiv-cs-cv

Bilinear Coordinate Alignment for Training-Free Task-Vector Transfer

model-releasesarxiv-cs-lg

arXiv:2605.28444v1 Announce Type: new Abstract: Fine-tuning large-scale pre-trained models is a recent prevalent paradigm for adapting general representations to specialized tasks. However, when a new

BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving

arXiv:2605.27480v1 Announce Type: cross Abstract: Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related path

Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models

arXiv:2602.12586v2 Announce Type: replace Abstract: While plan-and-infill decoding in Masked Diffusion Models (MDMs) shows promise for mathematical and code reasoning, performance remains highly sensi

ChildEval: When large language models meet children's personalities

arXiv:2605.27805v1 Announce Type: cross Abstract: While LLMs enable personalized chatbots, their effectiveness in child-centered personalization remains unclear, as systematic evaluation of child-spec

CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models

arXiv:2605.28292v1 Announce Type: new Abstract: Implicit Chain-of-Thought (CoT) reduces the inference cost of large language models by internalizing the explicit rationales. However, existing approach

safetyarxiv-cs-cl

Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections

arXiv:2605.27418v1 Announce Type: cross Abstract: The imminent integration of autonomous vehicles and mobile robots in urban settings presents a critical safety challenge for future intelligent transp

safetyarxiv-cs-ro

EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection

arXiv:2505.17654v4 Announce Type: replace-cross Abstract: E-commerce platforms increasingly rely on Large Language Models (LLMs) and Vision Language Models (VLMs) to detect illicit or misleading produ

Explicit Critic Guidance for Aligning Diffusion Models

arXiv:2605.27736v1 Announce Type: cross Abstract: Online reinforcement learning is becoming increasingly important for aligning diffusion models with non-differentiable objectives. However, existing m

researcharxiv-cs-cv

From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons

arXiv:2605.27387v1 Announce Type: cross Abstract: Diffusion models promise efficient parallel text generation but rely on bidirectional attention, creating a structural mismatch with pre-trained Autor

Hardware

Heterogeneous Parallelism for Multimodal Large Language Model Training

arXiv:2605.27678v1 Announce Type: new Abstract: Foundation model training is becoming multimodal, from post-training pipelines to large-scale pretraining. As modality coverage broadens, context window

hardwarearxiv-cs-lg

HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling

arXiv:2512.23959v3 Announce Type: replace-cross Abstract: Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that

History-aware adaptive reduced-order models via incremental singular value decomposition

arXiv:2605.28684v1 Announce Type: new Abstract: Reduced-order models (ROMs) can accelerate high-dimensional dynamical simulations, but their accuracy often deteriorates when online dynamics leave the

Human-like in-group bias in instruction-tuned language model agents

arXiv:2605.28114v1 Announce Type: new Abstract: As autonomous AI agents are deployed in persistent, interacting networks -- coordinating tasks, routing resources, and accumulating reputational histori

JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models

arXiv:2605.28609v1 Announce Type: new Abstract: Forensic vision-language models (VLMs) have recently been developed to detect image tampering and provide natural-language explanations. However, their

Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communities

arXiv:2605.27388v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized as proxies for computational social analysis; yet, their ability to faithfully represent the 't

Next-Scale Autoregressive Models for Text-to-Motion Generation

arXiv:2604.03799v2 Announce Type: replace Abstract: Autoregressive (AR) models offer stable and efficient training, but standard next-token prediction is not well aligned with the temporal structure r

researcharxiv-cs-cv

Not All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation

arXiv:2605.27616v1 Announce Type: cross Abstract: Real-time anomaly segmentation demands both high recall and efficient low-precision inference. We study the three-way interaction of model architectur

Object-Centric Vision Token Pruning for Vision Language Models

arXiv:2511.20439v2 Announce Type: replace-cross Abstract: In Vision Language Models (VLMs), vision tokens are quantity-heavy yet information-dispersed compared with language tokens, thus consume too m

OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents

arXiv:2605.28158v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used to assist with operations research (OR) modeling, yet existing OR-oriented benchmarks often redu

Agents

Segment to Focus: Guiding Latent Action Models in the Presence of Distractors

arXiv:2602.02259v2 Announce Type: replace-cross Abstract: Latent action models (LAMs) offer a promising path to pre-training embodied agents on large amounts of action-free video. They infer latent ac

agentsarxiv-cs-cv

Surrogate modeling for Bayesian optimization beyond a single Gaussian process

arXiv:2205.14090v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions eme

applicationsarxiv-cs-lg

Tell Me a Story! Narrative-Driven XAI with Large Language Models

arXiv:2309.17057v3 Announce Type: replace Abstract: In many AI applications today, the predominance of black-box machine learning models, due to their typically higher accuracy, amplifies the need for

Text-Only Data Synthesis for Vision Language Model Training

arXiv:2503.22655v2 Announce Type: replace Abstract: Training vision-language models (VLMs) typically requires large-scale, high-quality image-text pairs, but collecting or synthesizing such data is co

Hardware

Today, we're releasing LFM2.5-8B-A1B, a device-optimized model designed to power real-life applications on phones, laptops, PCs, robots, and…

Today, we're releasing LFM2.5-8B-A1B, a device-optimized model designed to power real-life applications on phones, laptops, PCs, robots, and fast & lightweight server-side use-cases. > 8B MoE, 1.5B ac

hardwareclem-delangue--x

Towards Unified Vision-Language Models with Incomplete Multi-Modal Inputs

arXiv:2605.27894v1 Announce Type: new Abstract: Video-Language Models (VLMs) have demonstrated impressive multi-modal reasoning capabilities across diverse computer vision applications. However, these

Training Azerbaijani language models on Amazon SageMaker AI

Azercell Telecom LLC, Azerbaijan's leading telecommunications provider, wanted to build an Azerbaijani large language model (LLM) on Amazon SageMaker AI for telecom use cases and a customer-facing cha

applicationsaws-ml-blog

Training Stratigraphy: Persistent Behavioral Artifacts in Large Language Models Observed Through Longitudinal AI-Human Interaction

arXiv:2605.28102v1 Announce Type: new Abstract: Large language models trained with Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI exhibit persistent behavioral patterns that s

Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models

arXiv:2605.27376v1 Announce Type: cross Abstract: While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control

When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models

arXiv:2605.28181v1 Announce Type: new Abstract: Diffusion language models decode text by iteratively denoising masked token sequences, making the choice of which positions to decode a central inferenc

researcharxiv-cs-cl