Search: “llm”

AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing

arXiv:2606.26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and m

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Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare

DGX agent

arXiv:2606.26104v1 Announce Type: cross Abstract: Animal-welfare advocates produce a lot of writing, and increasingly that writing trains the language models that millions of people then ask about ani

model-releasesarxiv-cs-ai

26 Jun 2026

Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection

arXiv:2606.27314v1 Announce Type: new Abstract: To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive meanin

Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News

arXiv:2606.26489v1 Announce Type: new Abstract: News media play a central role in shaping public perceptions of climate change, and whether coverage emphasizes threats or solutions has measurable effe

Context Recycling for Long-Horizon LLM Inference

arXiv:2606.26105v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong capabilities in short-context reasoning but degrade in performance over long conversational horizons due t

Empirical Software Engineering TerraProbe: A Layered-Oracle Framework for Detecting Deceptive Fixes in LLM-Assisted Terraform

arXiv:2606.26590v1 Announce Type: new Abstract: Security misconfigurations in Terraform Infrastructure-as-Code are a growing risk in cloud deployments, and large language models are increasingly used

GEOALIGN: Geometric Rollout Curation for Robust LLM Reinforcement Learning

arXiv:2606.26917v1 Announce Type: cross Abstract: Online reinforcement learning is widely used to align large language models (LLMs) with reward signals, yet training can be unstable under noisy or mi

How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?

arXiv:2606.26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-doma

LLM-based Models for Detecting Emerging Topics in Service Feedback

arXiv:2606.26595v1 Announce Type: new Abstract: Enhancing the analysis of service feedback is essential for public sector organizations, particularly tax administrations, where trust and compliance de

Hardware

Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization

arXiv:2606.26453v1 Announce Type: new Abstract: We present KernelPro, a closed-loop multi-agent system that automatically generates, profiles, and iteratively optimizes GPU kernel code by integrating

hardwarearxiv-cs-lg

ASSCG: Just-Right Gating over Chattering for Fast-Slow LLM Planning in Autonomous Driving

arXiv:2606.25509v1 Announce Type: cross Abstract: Large language models (LLMs) can improve autonomous driving planning but are costly to query online, and existing fast-slow planners often rely on han

agentsarxiv-cs-cv

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

arXiv:2510.04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiv

LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges

arXiv:2606.25057v1 Announce Type: new Abstract: The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large languag

safetyarxiv-cs-cl

Applications

LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning

arXiv:2606.24901v1 Announce Type: new Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environ

applicationsarxiv-cs-lg

Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents

arXiv:2601.03785v3 Announce Type: replace Abstract: Long-term human-agent dialogues are organized by topic continuity: adjacent turns often develop the same goal, plan, problem, or event, while relate

agentsarxiv-cs-cl

RAS: Measuring LLM Safety Through Refusal Alignment

arXiv:2606.25750v1 Announce Type: cross Abstract: Safety evaluation of large language models (LLMs) is commonly performed by querying models with unsafe or jailbreak prompts and judging whether their

ReviewGuard: Aligning LLM-Assisted Peer Review with Long-Term Scientific Impact

arXiv:2606.24892v1 Announce Type: cross Abstract: Peer review is central to scientific quality control, yet it can undervalue papers that later achieve substantial citation impact. While frontier larg

SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation

arXiv:2606.25497v1 Announce Type: new Abstract: Object-Goal Navigation (ObjNav) requires embodied agents to autonomously locate specified targets using only egocentric visual observations. Existing mo

safetyarxiv-cs-ro

The 4/elta Bound: Designing Predictable LLM-Verifier Systems for Formal Method Guarantee

arXiv:2512.02080v3 Announce Type: replace-cross Abstract: The integration of Formal Verification tools with Large Language Models (LLMs) offers a path to scale software verification beyond manual work

Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations

arXiv:2601.22548v4 Announce Type: replace-cross Abstract: Recent research has shown that large language models (LLMs) favor their own outputs when acting as judges, undermining the integrity of automa

Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation

arXiv:2511.21056v2 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) datasets are critical to the downstream performance of large language models, yet they often contain low-quality

safetyarxiv-cs-cl

Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators

arXiv:2509.03647v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from 'self-preference bias': a tendency to favor thei

Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability

arXiv:2606.23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure. This paper presents a scalable a

FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction

arXiv:2606.24679v1 Announce Type: cross Abstract: Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning an

IPO Finance Agent: Evaluation of LLM Financial Analysts beyond Finance Agent v2, with Automated Rubric Generation -- the Case of the SpaceX (SPCX) IPO

arXiv:2606.23032v2 Announce Type: replace Abstract: Finance Agent v2 (by Vals AI) has emerged as the reference benchmark for evaluating both Anthropic Claude and OpenAI ChatGPT frontier language model

Hardware

OpenAI and Broadcom unveil LLM-optimized inference chip

OpenAI and Broadcom announced a collaboration on a specialized inference chip designed to optimize the performance and efficiency of large language model deployments. The chip, referred to as 'Jalapen

hardwareopenai

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data

arXiv:2505.24622v3 Announce Type: replace Abstract: Many high-stakes screening tasks require predicting rare outcomes from unstructured text, where errors are costly and decisions must be auditable. W

Applications

Scaling Laws for Task-Specific LLM Distillation

arXiv:2606.24747v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications w

applicationsarxiv-cs-ai

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training

arXiv:2507.01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation. However, exposing

EvoRubrics: Dynamic Rubrics as Rewards via Adversarial Co-Evolution for LLM Reinforcement Learning

arXiv:2606.23038v1 Announce Type: new Abstract: Rubric-based rewards offer interpretable and fine-grained optimization signals for reinforcement learning in open-ended tasks where verifiable answers a

safetyarxiv-cs-lg

Good-Enough LLM Obfuscation (GELO)

arXiv:2603.05035v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly served on shared accelerators where an adversary with read access to device memory can observe K

Hardware

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation

arXiv:2606.23419v1 Announce Type: new Abstract: Autoregressive decoding with LLMs is primarily bottlenecked by GPU memory bandwidth, especially in edge-computing settings. While quantization is essent

hardwarearxiv-cs-lg

Harnessing Agent Skills: Architectural Patterns and a Reference Architecture for Skill-Mediated LLM Agents

arXiv:2606.20631v1 Announce Type: cross Abstract: Agent skills externalise reusable agent-facing behavioural knowledge and guidance as persistent artefacts that can be discovered, activated, and inter

agentsarxiv-cs-lg

LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms

arXiv:2606.20729v1 Announce Type: cross Abstract: Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed app

agentsarxiv-cs-lg

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

arXiv:2606.13007v2 Announce Type: replace Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity. Howev

safetyarxiv-cs-lg

Tell Me: An LLM-powered Mental Well-being Assistant with RAG, Synthetic Dialogue Generation, and Agentic Planning

arXiv:2511.14445v2 Announce Type: replace-cross Abstract: We present Tell Me, a mental well-being system that leverages advances in large language models to provide accessible, context-aware support f

agentsarxiv-cs-lg

VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training

arXiv:2508.03058v2 Announce Type: replace Abstract: Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stabil

safetyarxiv-cs-lg

Beyond Third-Person Audits: Situated Interaction Auditing for User-Centered LLM Bias Research

arXiv:2606.12247v1 Announce Type: cross Abstract: Research on bias in large language models (LLMs) has predominantly focused on third-person audits, which study how models represent or evaluate demogr

safetyarxiv-cs-cl

Local Ai

Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment

arXiv:2606.11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes. We evaluate the efficacy of a general-purpose GenAI Large Language Model

local-aiarxiv-cs-ai

Certifiable Safe RLHF: Semantic Grounding and Fixed Penalty Constraint Optimization for Safer LLM Alignment

arXiv:2510.03520v2 Announce Type: replace-cross Abstract: Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility

Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning

arXiv:2606.11675v1 Announce Type: new Abstract: Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study

arXiv:2606.12234v1 Announce Type: new Abstract: Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved t

researcharxiv-cs-cl

Applications

'That's AI Slop, You Bot!' Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments

arXiv:2606.12073v1 Announce Type: cross Abstract: Generative AI has made fluent prose cheap to produce, breaking the old promise to readers that good writing meant real thinking. How have readers resp

applicationsarxiv-cs-ai

The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents

arXiv:2601.12164v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as analytical tools across multilingual contexts, yet their outputs may carry systemati

The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes

arXiv:2606.11470v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved strong performance across natural language processing tasks, yet reliable reasoning remains an open challenge

agentsarxiv-cs-cl

An Industrial-Scale Insurance LLM Achieving Verifiable Domain Mastery and Hallucination Control without Competence Trade-offs

arXiv:2603.14463v2 Announce Type: replace Abstract: Adapting Large Language Models (LLMs) to high-stakes vertical domains like insurance presents a significant challenge: scenarios demand strict adher

10 Jun 2026

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

arXiv:2606.10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making. However, different causal

10 Jun 2026

Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity

arXiv:2606.10198v1 Announce Type: cross Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confid