All entries

Less is More: Towards Simple Graph Contrastive Learning

arXiv:2509.25742v4 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) has shown strong promise for unsupervised graph representation learning, yet its effectiveness on heterophilic grap

researcharxiv-cs-lg

Less Redundancy: Boosting Practicality of Vision Language Model in Walking Assistants

DGX agent

arXiv:2508.16070v3 Announce Type: replace Abstract: Approximately 283 million people worldwide live with visual impairments, motivating increasing research into leveraging Visual Language Models (VLMs

researcharxiv-cs-cl

12 May 2026

Let the Target Select for Itself: Data Selection via Target-Aligned Paths

arXiv:2605.09404v1 Announce Type: cross Abstract: Targeted data selection aims to identify training samples from a large candidate pool that improve performance on a specific downstream task. Many rec

safetyarxiv-cs-cl

Hardware

Leveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes

arXiv:2505.02184v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for generating parallel scientific codes, with a primary focus on generating functionally correct

hardwarearxiv-cs-ai

LEVI: Stronger Search Architectures Can Substitute for Larger LLMs in Evolutionary Search

arXiv:2605.09764v1 Announce Type: cross Abstract: LLM-guided evolutionary methods such as AlphaEvolve have proven effective in domains like math, systems research, and algorithmic discovery, but their

LightAVSeg: Lightweight Audio-Visual Segmentation

arXiv:2605.08805v1 Announce Type: new Abstract: Audio-Visual Segmentation (AVS) targets pixel level localization of sounding emitting objects in videos. However, existing models rely on dense cross-mo

Likelihood scoring for continuations of mathematical text: a self-supervised benchmark with tests for shortcut vulnerabilities

model-releasesarxiv-cs-lg

arXiv:2605.10810v1 Announce Type: new Abstract: We introduce an automatically generated benchmark for predicting hidden text in technical papers. A paper supplies visible context X and a hidden contin

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training

arXiv:2509.20786v3 Announce Type: replace Abstract: Training deep neural networks with noise and data heterogeneity is a major challenge. We introduce Lightweight Learnable Adaptive Weighting (LiLAW),

safetyarxiv-cs-lg

LILO: Bayesian Optimization with Natural Language Feedback

arXiv:2510.17671v2 Announce Type: replace-cross Abstract: Many real-world optimization problems are guided by complex, subjective preferences that are difficult to express as explicit closed-form obje

LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency

arXiv:2605.10319v1 Announce Type: new Abstract: Layered image assets are widely used in real-world creative workflows, enabling non-destructive iteration and flexible re-composition. Recent advances i

Liouville PDE-based sliced-Wasserstein flow

arXiv:2505.17204v3 Announce Type: replace-cross Abstract: The sliced Wasserstein flow (SWF), a nonparametric and implicit generative gradient flow, is transformed into a Liouville partial differential

safetyarxiv-cs-lg

LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering

arXiv:2605.09384v1 Announce Type: cross Abstract: The reasoning gap between large and compact vision-language models (VLMs) limits the deployment of medical AI on portable clinical devices. Compact VL

LiteParse is the best open-source, model-free document parser for AI agents. Run it over over 50+ document types, and it will parse dense pa…

model-releasesjerry-liu--x

LiteParse is the best open-source, model-free document parser for AI agents. Run it over over 50+ document types, and it will parse dense pages with complex text layouts and tables, and it will extrac

LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments

arXiv:2605.10779v1 Announce Type: cross Abstract: The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content s

Tools

Live From SaaStr: Kickoff (Day 1) https://x.com/i/broadcasts/1qKVmQePgaWxB

This was a live broadcast from the SaaStr conference kickoff event on Day 1, streamed by Replit on X (formerly Twitter). The stream likely featured keynote presentations, panel discussions, or network

toolsreplit--x

LLARS: Enabling Domain Expert & Developer Collaboration for LLM Prompting, Generation and Evaluation

arXiv:2605.10593v1 Announce Type: new Abstract: We demonstrate LLARS (LLM Assisted Research System), an open-source platform that bridges the gap between domain experts and developers for building LLM

LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models

arXiv:2605.10641v1 Announce Type: cross Abstract: Large Vision-Language Models (VLMs) are successful in addressing a multitude of vision-language understanding tasks, such as Visual Question Answering

LLaVA-UHD v4: What Makes Efficient Visual Encoding in MLLMs?

arXiv:2605.08985v1 Announce Type: new Abstract: Visual encoding constitutes a major computational bottleneck in Multimodal Large Language Models (MLLMs), especially for high-resolution image inputs. T

LLiMba: Sardinian on a Single GPU -- Adapting a 3B Language Model to a Vanishing Romance Language

arXiv:2605.09015v1 Announce Type: new Abstract: Sardinian, a Romance language with roughly one million speakers, has minimal presence in modern NLP. Commercial services do not support it, and current

llm 0.32a2

model-releasessimon-willison

Release: llm 0.32a2 A bunch of useful stuff in this LLM alpha, but the most important detail is this one: Most reasoning-capable OpenAI models now use the /v1/responses endpoint instead of /v1/chat/co

LLM Advertisement based on Neuron Auctions

arXiv:2605.08326v1 Announce Type: cross Abstract: As Large Language Models (LLMs) transition into conversational agents, generative advertising emerges as a crucial monetization strategy. However, emb

LLM Agents Already Know When to Call Tools -- Even Without Reasoning

arXiv:2605.09252v1 Announce Type: new Abstract: Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latenc

LLM-Agnostic Semantic Representation Attack

arXiv:2605.08898v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent t

Tutorials

LLM-Augmented Chemical Synthesis and Design Decision Programs

arXiv:2505.07027v2 Announce Type: replace Abstract: Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of or

tutorialsarxiv-cs-ai

LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection

arXiv:2605.09518v1 Announce Type: new Abstract: Meta-learning for algorithm selection relies on a meta-dataset in which each row corresponds to a supervised learning dataset described by meta-features

safetyarxiv-cs-lg

LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

arXiv:2503.14434v3 Announce Type: replace-cross Abstract: Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automate

researcharxiv-cs-ai

Local Ai

LLM-Guided Monte Carlo Tree Search over Knowledge Graphs: Composing Mechanistic Explanations for Drug-Disease Pairs

arXiv:2605.09542v1 Announce Type: new Abstract: Extracting multi-step explanations from knowledge graphs poses a combinatorial challenge requiring both heuristic guidance (as candidates proliferate wi

local-aiarxiv-cs-ai

LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification

arXiv:2605.08448v1 Announce Type: new Abstract: Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In t

LLM Jaggedness Unlocks Scientific Creativity

arXiv:2605.10574v1 Announce Type: new Abstract: As artificial intelligence advances, models are not improving uniformly. Instead, progress unfolds in a jagged fashion, with capabilities growing uneven

LLM Translation of Compiler Intermediate Representation

arXiv:2605.08247v1 Announce Type: cross Abstract: GCC and LLVM underpin much of modern software infrastructure, relying on distinct Intermediate Representations (IRs) to drive optimizations and code g

LLM Wardens: Mitigating Adversarial Persuasion with Third-Party Conversational Oversight

arXiv:2605.08321v1 Announce Type: cross Abstract: LLMs are increasingly capable of persuasion, which raises the question of how to protect users against manipulation. In a preregistered user study (N=

LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs

arXiv:2605.10401v1 Announce Type: new Abstract: Efficient branching policies are essential for accelerating Mixed Integer Linear Programming (MILP) solvers. Their design has long relied on hand-crafte

Hardware

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges

arXiv:2605.10807v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor i

hardwarearxiv-cs-lg

LLMs with in-context learning for Algorithmic Theoretical Physics

arXiv:2605.08212v1 Announce Type: cross Abstract: There is an increasing number of algorithmic computations in theoretical physics. These, while conceptually simple, can nevertheless be time-consuming

LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems

arXiv:2605.08305v1 Announce Type: cross Abstract: Large Language Model (LLM) systems have been the frontier of AI in many application domains, leading to new challenges and opportunities for hyperpara

Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle

arXiv:2605.08850v1 Announce Type: cross Abstract: We design Local LMO - a new projection-free gradient-type method for constrained optimization. The key algorithmic idea is to replace the global linea

researcharxiv-cs-lg

Locking Pretrained Weights via Deep Low-Rank Residual Distillation

model-releasesarxiv-cs-lg

arXiv:2605.10777v1 Announce Type: new Abstract: The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling the

Log analysis is necessary for credible evaluation of AI agents

arXiv:2605.08545v1 Announce Type: new Abstract: Agent benchmarks typically report only final outcomes: pass or fail. This threatens evaluation credibility in three ways. First, scores may be inflated

LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectories

arXiv:2509.20909v2 Announce Type: replace Abstract: Large language models (LLMs) are commonly evaluated on challenging benchmarks such as AIME and Math500, where benchmark contamination can make memor

Hardware

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

arXiv:2605.10886v1 Announce Type: cross Abstract: Recent GPU generations deliver significantly higher FLOPs using lower-precision arithmetic, such as FP8. While successfully applied to large language

hardwarearxiv-cs-ai

Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities

arXiv:2605.05812v2 Announce Type: replace Abstract: Off-policy, value-based reinforcement learning methods such as Q-learning are appealing because they can learn from arbitrary experience, including

Loom: Hybrid Retrieval-Scoring Outfit Recommendation with Semantic Material Compatibility and Occasion-Aware Embedding Priors

arXiv:2605.09830v1 Announce Type: cross Abstract: We present Loom, an outfit recommendation system that combines neural embedding retrieval with structured domain scoring to generate complete, coheren

researcharxiv-cs-cv

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

arXiv:2605.09948v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action p

Lost in Translation? Exploring the Shift in Grammatical Gender from Latin to Occitan

arXiv:2605.09156v1 Announce Type: cross Abstract: The diachronic evolution from Latin to the Romance languages involved a restructuring of the grammatical gender system from a tripartite configuration

researcharxiv-cs-ai

Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models

arXiv:2605.08787v1 Announce Type: new Abstract: Recent advances in 3D medical vision-language models have enabled joint reasoning over volumetric images and text, showing strong performance in medical

Agents

love seeing my discord stay in sync with Hermes Kanban..everything here was done in plain English. I just asked my coordinator to check if w…

love seeing my discord stay in sync with Hermes Kanban..everything here was done in plain English. I just asked my coordinator to check if we have an update and the system understood the intent, route

agentsnous-research--x

Low-Cost Neural Radiance Fields

arXiv:2605.09312v1 Announce Type: new Abstract: Neural Radiance Fields (NeRF) achieve high-quality novel-view synthesis, but their long training times and reliance on dense input views limit accessibi

researcharxiv-cs-cv

Agents

Low-Cost Stereo Vision for Robust 3D Positioning of Thin Radiata Pine Branches in Autonomous Drone Pruning

arXiv:2605.08213v1 Announce Type: new Abstract: Manual pruning of radiata pine, a species of major economic importance to New Zealand forestry, is hazardous, labour-intensive, and increasingly constra

agentsarxiv-cs-cv