All entries

Lessons from the Trenches on Reproducible Evaluation of Language Models

arXiv:2405.14782v3 Announce Type: replace Abstract: Reliable evaluation of language models (LMs) remains an open challenge. Re- searchers and engineers face methodological issues such as the sensitivi

Leyline: KV Cache Directives for Agentic Inference

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DGX agent

arXiv:2606.01065v1 Announce Type: cross Abstract: Modern KV cache management assumes the chatbot workload: prompts arrive once and the cache grows append-only, so prefix caching and forward-only evict

safetyarxiv-cs-ai

2 Jun 2026

LFA: Layer Feature Attention for Run-Time Introspection of 2D Object Detectors in Automated Driving

arXiv:2606.00372v1 Announce Type: new Abstract: Reliable object detection is critical for automated driving, yet even state-of-the-art detectors inevitably make errors that can compromise safety. Intr

safetyarxiv-cs-cv

Limits of Resolution Equivariance in Fourier Neural Operators

arXiv:2606.00677v1 Announce Type: new Abstract: Fourier Neural Operators are often assumed to generalize across spatial resolutions, enabling training on a coarse grid and deployment on a finer grid.

researcharxiv-cs-lg

Limits of Spatial Imagery Reasoning in Frontier LLM Models

arXiv:2603.26779v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet they struggle with spatial tasks that require mental sim

Linear Motility Maps in Nonlinear Viscous Fluids

arXiv:2606.00063v1 Announce Type: new Abstract: Systems moving in low Reynolds number fluid regimes are known to be governed by a ``motility map'' which linearly relates their shape change rates to th

researcharxiv-cs-ro

Applications

Linear Strategic Classification with Endogenous Improvements

arXiv:2606.01198v1 Announce Type: new Abstract: Strategic classification studies settings in which agents respond to a deployed classifier by modifying observable features at a cost. Classical models

applicationsarxiv-cs-lg

Linguistics-Aware Non-Distortionary LLM Watermarking

arXiv:2606.00613v1 Announce Type: cross Abstract: Watermarking should identify language-model output without degrading quality or limiting verification to the model provider. Multilingual deployment m

LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification

arXiv:2606.00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem. For the PsyDefDetect 2026 shared task (n

safetyarxiv-cs-ai

Literary Emotions in Motion: A Soft Robotics Installation for Tactile Storytelling

arXiv:2606.00418v1 Announce Type: new Abstract: Soft robotics is increasingly explored in artistic contexts, where tactile interaction provides audiences with embodied engagement beyond visual or audi

researcharxiv-cs-ro

Hardware

LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching

arXiv:2606.00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask. As circuit features shrink below the w

hardwarearxiv-cs-lg

LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding

arXiv:2602.23881v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate t

LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models

model-releasesarxiv-cs-cv

arXiv:2606.02535v1 Announce Type: new Abstract: Large-scale generative models have demonstrated remarkable capabilities across image generation and editing tasks. However, their performance in low-lev

LLM agents patch security bugs, pass all tests, but still leave the vulnerability open [R]

researchr-machinelearning

Research demonstrates that LLM-based agents can generate functionally correct patches that pass all tests while still containing security vulnerabilities, challenging the assumption that test-passing

LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation

arXiv:2603.09403v2 Announce Type: replace Abstract: Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exist only for English

safetyarxiv-cs-cl

LLM Consortium for Software Design Refinement: A Controlled Experiment on Multi-Agent Collaboration Topologies

arXiv:2606.01490v1 Announce Type: cross Abstract: We present a controlled experiment evaluating 12 multi-agent LLM collaboration topologies for software architecture design. Using a 2imes2imes2 factor

Local Ai

LLM-Driven Co-Evolutionary Automated Heuristic Design for Bi-Component Coupled Combinatorial Optimization

arXiv:2606.00718v1 Announce Type: new Abstract: While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing methods typically generate and evolve heuri

local-aiarxiv-cs-ai

Tutorials

LLM-Evolved Pattern Generators for Optimal Classical Planning

arXiv:2606.02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning. Existing approa

tutorialsarxiv-cs-ai

LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs

arXiv:2509.20070v2 Announce Type: replace Abstract: We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of

safetyarxiv-cs-ro

LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs?

arXiv:2602.16902v4 Announce Type: replace Abstract: We introduce LLM-Wikirace, a benchmark for evaluating planning, reasoning, and world knowledge in large language models (LLMs). In LLM-Wikirace, mod

LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation

arXiv:2602.16953v3 Announce Type: replace Abstract: Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making

LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

arXiv:2606.00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support ea

Tutorials

LLMs Need Encoders for Semantic IDs Too

arXiv:2606.00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw

tutorialsarxiv-cs-ai

Applications

LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models

arXiv:2505.14752v3 Announce Type: replace Abstract: Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable

applicationsarxiv-cs-lg

lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation

arXiv:2606.00022v1 Announce Type: cross Abstract: Humor generation remains difficult not only because producing fluent, novel jokes is hard, but because 'funny' is audience-dependent and supervision i

Local Diagnostics of Continuous Normalizing Flow for Out-of-Distribution Detection

model-releasesarxiv-cs-cl

arXiv:2606.00684v1 Announce Type: cross Abstract: We address the problem of out-of-distribution (OOD) detection for target observations embedded in a subspace of the high dimensional data space. Using

Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning

arXiv:2606.01128v1 Announce Type: new Abstract: Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as

researcharxiv-cs-lg

Local Preferential Bayesian Optimization

model-releasesarxiv-cs-lg

arXiv:2606.02351v1 Announce Type: new Abstract: Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit obje

LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services

arXiv:2512.07436v3 Announce Type: replace Abstract: Recent advances in large reasoning models LRMs have enabled agentic search systems to perform complex multi-step reasoning across multiple sources.

Hardware

Lodestar: An Online-Learning LLM Inference Router

arXiv:2606.00946v1 Announce Type: cross Abstract: Efficiently serving large language model (LLM) inference tasks is crucial both for user-perceived latency such as time-to-first-token (TTFT) and for G

hardwarearxiv-cs-ai

Logit Distillation on Manifolds: Mapping by Learning

arXiv:2606.00771v1 Announce Type: cross Abstract: A simple way to improve the performance of almost any machine learning model is not to train a single but several models with diverse algorithms which

Applications

LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning

arXiv:2606.01336v1 Announce Type: new Abstract: As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a

applicationsarxiv-cs-cl

Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults

arXiv:2606.00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling

safetyarxiv-cs-lg

LongLive-RAG: A General Retrieval-Augmented Framework for Long Video Generation

arXiv:2606.02553v1 Announce Type: new Abstract: Autoregressive (AR) video diffusion enables variable-length synthesis, but long-horizon generation often suffers from accumulated errors and identity dr

researcharxiv-cs-cv

Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models

arXiv:2602.03211v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This

safetyarxiv-cs-ai

Local Ai

LookWise: Knowing When and Where to Look for Fine-Grained Visual Reasoning in Multimodal Large Language Models

arXiv:2603.00171v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) are shifting towards 'Thinking with Images' by actively exploring image details. While effective, lar

local-aiarxiv-cs-ai

Looped Transformers with Layer Normalization Provably Learn the Power Method

arXiv:2606.00605v1 Announce Type: new Abstract: Transformers have achieved remarkable success across a wide range of applications, and a growing body of work suggests that part of their strength comes

safetyarxiv-cs-lg

Lost in Delusion: Examining LLM Safety Under User Delusions and Distress

arXiv:2606.00975v1 Announce Type: new Abstract: LLM chatbots increasingly serve as a first source of support for people in psychological distress, including those whose distress is entangled with delu

safetyarxiv-cs-cl

Tools

Love that in this high-tech age of AI @swyx still uses paper 🥹

Swyx, a prominent figure in the AI community, uses paper-based tools and methods despite working in a high-tech field centered on artificial intelligence, which resonates with others who appreciate an

toolsswyx--x

Low-Pass Flow Matching

arXiv:2606.02177v1 Announce Type: new Abstract: Flow Matching typically relies on white noise sources, a choice often misaligned with the power spectra of natural data, which tend to decay with freque

safetyarxiv-cs-lg

Low-Resource Safety Failures Are Action Failures, Not Representation Failures

arXiv:2606.01196v1 Announce Type: cross Abstract: Safety alignment learned in high-resource languages transfers poorly to low-resource languages. Models refuse harmful prompts in English but fail to r

LP5X-PIM Sim: A High-Fidelity HW/SW Integrated Simulator for LPDDR5X-PIM

arXiv:2606.00636v1 Announce Type: cross Abstract: This tech note describes the architecture and execution results of the LPDDR5X-PIM simulator, developed by Samsung Electronics. Based on the latest re

Agents

LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

arXiv:2602.01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightwe

agentsarxiv-cs-lg

M^3 Scaling Law: Optimizing Multi-Epoch, Multi-Lingual, and Multi-Stage Training for Low-Resource Language Models

arXiv:2410.12325v2 Announce Type: replace Abstract: In this paper, we study a fundamental design problem in pretraining Large Language Models (LLMs) for low-resource language regimes. Existing works a

Agents

MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment

arXiv:2312.03644v3 Announce Type: replace Abstract: Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky. While independent learn

agentsarxiv-cs-lg

Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting

arXiv:2606.00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performanc

researcharxiv-cs-lg

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606.02004v1 Announce Type: new Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data. A recurring obstacle is

Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures

model-releasesarxiv-cs-lg

arXiv:2606.00938v1 Announce Type: cross Abstract: Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials. In this contribution, a supervised learning ap