Search: “llm”

Sample Complexities of Estimating Gumbel--Max Watermark Proportions with and without Reduction to Pivotal Statistics

arXiv:2607.00224v1 Announce Type: cross Abstract: Watermarking promises a statistical trace of large language model (LLM) use, but real documents, after editing or paraphrasing, rarely arrive as purel

researcharxiv-cs-lg

2 Jul 2026

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Model Releases

Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains

DGX agent

arXiv:2607.01136v1 Announce Type: cross Abstract: Agent skills package reusable operational knowledge for Large Language Model (LLM) agents, yet as they grow in scope, they become dependency-bearing a

model-releasesarxiv-cs-ai

2 Jul 2026

Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents

arXiv:2606.31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery. Recent LLM, especially AI Scientist systems, show promising potential for automated ideation. How

Hardware

AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performance

arXiv:2606.30949v1 Announce Type: new Abstract: High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challen

hardwarearxiv-cs-ai

Contrastive Reflection for Iterative Prompt Optimization

arXiv:2606.30840v1 Announce Type: new Abstract: LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR eval

DeXposure-Claw: An Agentic System for DeFi Risk Supervision

arXiv:2606.19501v2 Announce Type: replace Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read

Fork-Think with Confidence

arXiv:2606.31484v1 Announce Type: cross Abstract: Parallel thinking has enjoyed great success for boosting LLM performance on reasoning tasks without the need for any re-training. However, existing me

researcharxiv-cs-cl

Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law

arXiv:2606.31250v1 Announce Type: cross Abstract: Large language models (LLM) trained on web-scale corpora generate output that may infringe copyright, yet existing technical safeguards focus narrowly

CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models

arXiv:2501.14940v4 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety ben

CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning

arXiv:2406.03367v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planni

Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning

arXiv:2606.29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without t

Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data

arXiv:2606.29793v1 Announce Type: new Abstract: Demand for personalized financial advising is growing, but consistent advisor expertise is difficult to obtain, scale, and encode in LLM systems. Simple

safetyarxiv-cs-cl

MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Design

arXiv:2606.14202v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthe

Online Data Selection for Instruction Tuning via Gaussian Processes

arXiv:2606.30077v1 Announce Type: cross Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged a

local-aiarxiv-cs-ai

RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning

arXiv:2606.28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic mal

local-aiarxiv-cs-ai

SemJoin: Semantic Join Optimization

arXiv:2606.29532v1 Announce Type: cross Abstract: Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis. A

Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data

arXiv:2606.30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling b

'Generate' the Future of Work through AI: Empirical Evidence from Online Labor Markets

arXiv:2308.05201v4 Announce Type: replace Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functi

Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning

arXiv:2606.27483v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-hor

SidConArena: An Environment Evaluating Agents in Open-Ended,Positive-Sum Bargaining Game

arXiv:2606.27397v1 Announce Type: cross Abstract: Evaluating LLM agents requires dynamic environments that go beyond static reasoning and zero-sum games. Real-world economic interaction is often open-

Tutorials

Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation

arXiv:2606.27681v1 Announce Type: cross Abstract: World models in partially observed environments rely on latent representations that summarize interaction history, but in many modern LLM-based archit

tutorialsarxiv-cs-cl

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606.26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each cand

Joint Learning of Experiential Rules and Policies for Large Language Model Agents

arXiv:2606.27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience. Existing work has

local-aiarxiv-cs-ai

Learning User Simulators with Turing Rewards

arXiv:2606.19336v2 Announce Type: replace Abstract: Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, rese

agentsarxiv-cs-cl

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP

arXiv:2606.27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become fo

Signature filtering: a lightweight enhancement for statistical watermark detection in large language models

model-releasesarxiv-cs-lg

arXiv:2606.18430v2 Announce Type: replace Abstract: Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals

The Capability Frontier: Benchmarks Miss 82% of Model Performance

arXiv:2606.26836v1 Announce Type: new Abstract: Existing benchmarks typically report accuracy for a single model on a single run. This systematically understates real-world LLM capabilities, particula

The Geometry of Updates: Fisher Alignment at Vocabulary Scale

model-releasesarxiv-cs-cl

arXiv:2606.27242v1 Announce Type: cross Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequ

GUI agent: Guided Exploration of User-Sensitive Screens

arXiv:2606.25705v1 Announce Type: new Abstract: LLM agents are increasingly being used to automate tasks for users within an open GUI environment. They inevitably encounter screens containing user-sen

Applications

HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planning

arXiv:2606.25404v1 Announce Type: new Abstract: Large Language Models (LLMs) can reason over complex instructions but often fail to satisfy the physical and spatial constraints required for robotic ta

applicationsarxiv-cs-ro

Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty

arXiv:2606.25198v1 Announce Type: new Abstract: Autonomous AI Research promises to accelerate the scientific progress of machine learning. To realise this goal, current Large Language Model (LLM)-base

Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis

model-releasesarxiv-cs-cl

arXiv:2606.25369v1 Announce Type: cross Abstract: While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on Englis

Applications

Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI

arXiv:2606.23913v1 Announce Type: new Abstract: This article develops an architecture that creates a formally verifiable reward signal to train legal AI, adapting the LLM proposes, verifier disposes p

applicationsarxiv-cs-lg

Cycle-Consistent Neural Explanation of Formal Verification Certificates

arXiv:2606.24414v1 Announce Type: new Abstract: Formal verification produces machine-checkable certificates that attest to the satisfaction or violation of temporal properties, yet these certificates

SHERLOC: Structured Diagnostic Localization for Code Repair Agents

arXiv:2606.24820v1 Announce Type: new Abstract: LLM agents solve repository-level coding tasks through multi-turn tool use, but utilize half their budget on locating faults before editing. Dedicated l

local-aiarxiv-cs-cl

Towards Spec Learning: Inference-Time Alignment from Preference Pairs

arXiv:2606.24004v1 Announce Type: cross Abstract: Steering a large language model (LLM) toward a desired behavior typically relies on an iterative process of hand-crafting a prompt based on a careful

Tutorials

Beyond Templates: Revisiting Zero-Shot Remote Sensing through Meta-Prompting

arXiv:2606.20702v1 Announce Type: new Abstract: Vision-language models (VLMs) have sparked growing interest in zero-shot Earth Observation (EO) downstream tasks, with further gains enabled by remote-s

tutorialsarxiv-cs-cv

23 Jun 2026

Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining

model-releasesarxiv-cs-lg

arXiv:2606.22079v1 Announce Type: cross Abstract: Web data curation has been widely studied for decoder Large Language Model (LLM) pretraining. Encoders for dense-terminology domains such as medicine,

23 Jun 2026

A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth

arXiv:2601.21817v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm.

researcharxiv-cs-lg

Agreement in Representation Space for Open-Ended Self-Consistency

arXiv:2606.12003v1 Announce Type: new Abstract: Self-consistency improves LLM reasoning by sampling multiple outputs and selecting the most consistent answer, but existing formulations largely rely on

researcharxiv-cs-cl

Beyond Compaction: Structured Context Eviction for Long-Horizon Agents

model-releasesarxiv-cs-cl

arXiv:2606.11213v1 Announce Type: new Abstract: We present Context Window Lifecycle (CWL), a context-management scheme that gives long-horizon LLM agents an effectively unbounded working horizon. As a

ICA Lens: Interpreting Language Models Without Training Another Dictionary

arXiv:2606.11722v1 Announce Type: cross Abstract: Finding interpretable directions in language-model representations is critical for understanding and controlling model behavior. Sparse autoencoders (

My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents

arXiv:2606.11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes. We introduce My Chem

local-aiarxiv-cs-lg

The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales

arXiv:2606.11371v1 Announce Type: cross Abstract: Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack si

Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation

arXiv:2606.12199v1 Announce Type: cross Abstract: Spoken dialogue models typically start from text LLM backbones, yet reasoning often degrades when conditioning on speech instead of text. We attribute

safetyarxiv-cs-cl

Assessing Automated Prompt Injection Attacks in Agentic Environments

arXiv:2606.10525v1 Announce Type: cross Abstract: Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effec

10 Jun 2026

How can we assess human-agent interactions? Case studies in software agent design

arXiv:2510.09801v3 Announce Type: replace Abstract: While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of re

10 Jun 2026

IntentKV: Cross-Turn Intent-Aware KV Cache Pruning for Agent Inference

arXiv:2606.09916v1 Announce Type: cross Abstract: Multi-turn LLM agents fan short queries into long trajectories of tool calls, search results, and intermediate reasoning. Both KV memory and KV read b