Search: “llm”

BLUEX v2: Benchmarking LLMs on Open-Ended Questions from Brazilian University Entrance Exams

model-releasesarxiv-cs-cl

arXiv:2606.22723v2 Announce Type: replace Abstract: Although Large Language Models (LLMs) excel in many tasks, their assessment in Portuguese has received less attention, particularly for open-ended,

1 Jul 2026

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Agents

Can LLMs Imagine Moral Alternatives Beyond Binary Dilemmas?

DGX agent

arXiv:2606.31213v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed as moral advisors and agents, they need to address dilemmas between two competing values. Ho

agentsarxiv-cs-ai

1 Jul 2026

Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization

arXiv:2510.06732v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small,

Local Ai

Aristotelian Virtue Profiling of LLMs through Ethical Dilemmas

arXiv:2606.28683v1 Announce Type: new Abstract: Large Language Models (LLMs) often face ethical tradeoffs in which several responses may be defensible but express different priorities, such as fairnes

local-aiarxiv-cs-ai

Poller: Are LLMs Suitable for Evaluating the Poetry Understanding Task?

arXiv:2606.30556v1 Announce Type: new Abstract: Traditional automatic evaluation methods have been shown to be unsuitable for modern Chinese poetry because of the distinct nature of this literary genr

researcharxiv-cs-cl

Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management

arXiv:2510.03310v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate human behavior in business, economics, and the social sciences, offering a low-

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs

arXiv:2606.28438v1 Announce Type: cross Abstract: Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control. We st

Application of LLMs to Threat Assessment of Foreign Peacekeeping Missions

arXiv:2606.27106v1 Announce Type: cross Abstract: We present a novel approach for applying Large Language Models (LLMs) to threat assessment in the context of foreign peacekeeping missions. Building o

26 Jun 2026

Local Ai

Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting

arXiv:2606.26487v1 Announce Type: cross Abstract: Large language models (LLMs) are attractive for context-aware time series forecasting because they can integrate heterogeneous textual signals, yet th

local-aiarxiv-cs-ai

26 Jun 2026

ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement

arXiv:2603.03742v2 Announce Type: replace Abstract: Despite the remarkable performance of large language models (LLMs) in text-to-SQL (SQL generation), correctly producing SQL queries remains challeng

researcharxiv-cs-cl

24 Jun 2026

GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs

arXiv:2606.23299v1 Announce Type: new Abstract: Configuring the hyperparameters of Mixed-integer programming (MIP) solvers is a high-dimensional, instance-dependent optimization problem where suboptim

researcharxiv-cs-lg

23 Jun 2026

Tutorials

On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

arXiv:2606.17276v3 Announce Type: replace-cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been

tutorialsarxiv-cs-lg

23 Jun 2026

Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training

arXiv:2606.11854v1 Announce Type: cross Abstract: There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs). While Low-Rank Adaptation (LoRA) introduces add

11 Jun 2026

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

model-releasesarxiv-cs-cl

arXiv:2606.11898v1 Announce Type: new Abstract: Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenari

11 Jun 2026

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

arXiv:2603.02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasin

safetyarxiv-cs-ai

10 Jun 2026

RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty

arXiv:2602.12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitatin

10 Jun 2026

Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP

arXiv:2505.11189v3 Announce Type: replace Abstract: Large language models (LLMs) can amplify misinformation, undermining societal goals such as the UN SDGs. We study three documented drivers of misinf

Applications

Can LLMs extract scientific consensus? A case study in high-temperature superconductivity

arXiv:2606.07570v1 Announce Type: cross Abstract: Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolvin

applicationsarxiv-cs-lg

Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors

arXiv:2606.08816v1 Announce Type: cross Abstract: Predicting the effect of an unseen gene knockout perturbation on transcriptomic gene expression remains a highly challenging problem for virtual cell

Agents

To Nuke or Not to Nuke: LLMs' (Missing) Ethical Reasoning and Actions in a High-Stakes Decision-Making Simulation

arXiv:2606.08310v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as long-horizon agents with decision-making capacities. While LLMs can show ethical competence on

agentsarxiv-cs-ai

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs

arXiv:2606.06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence

researcharxiv-cs-lg

8 Jun 2026

LLMCodec: Adapting Video Codecs for Efficient Weight Compression of Large Language Models

arXiv:2606.05861v1 Announce Type: cross Abstract: The rapid development of large language models(LLMs) has led to remarkable advances in natural language processing. However, the increasing scale of t

6 Jun 2026

Semantic Partial Grounding via LLMs

arXiv:2602.22067v2 Announce Type: replace Abstract: Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded action

6 Jun 2026

Willing but Unable: Separating Refusal from Capability in Code LLMs via Abliteration

arXiv:2606.05396v1 Announce Type: cross Abstract: Producing a labeled vulnerable code at scale is a recurring obstacle for learning-based vulnerability detection: mined corpora carry substantial label

safetyarxiv-cs-ai

6 Jun 2026

Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications

arXiv:2601.06056v2 Announce Type: replace-cross Abstract: During 2025 and 2026, the Energy Performance of Buildings Directive is being implemented in the European Union member states, requiring all me

safetyarxiv-cs-cv

5 Jun 2026

Computational conceptual history of scientific concepts: From early digital methods to LLMs

arXiv:2606.04118v1 Announce Type: new Abstract: This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy,

researcharxiv-cs-cl

4 Jun 2026

Tutorials

Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

arXiv:2606.03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning. Modular ap

tutorialsarxiv-cs-ai

GTBench: A Curriculum-Grounded Benchmark for Evaluating LLMs as Mathematical Research Assistants in Graph Theory

arXiv:2606.03144v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as self-study assistants in technical disciplines, yet their reliability as mathematical reasoning as

Letting Tutor Personas Speak Up for LLMs: Learning Steering Vectors from Dialogue via Preference Optimization

arXiv:2602.07639v2 Announce Type: replace Abstract: With the emergence of large language models (LLMs) as a powerful class of generative artificial intelligence (AI), their use in tutoring has become

safetyarxiv-cs-cl

Test-Time Optimization of Physical Query Plans with LLMs

arXiv:2602.10387v2 Announce Type: replace-cross Abstract: Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.g., runtime, memory, and I/O) using predefined

Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework

arXiv:2602.18008v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on s

2 Jun 2026

When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs

arXiv:2602.03554v2 Announce Type: replace-cross Abstract: Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning. However, objective evalu

2 Jun 2026

EMCEE: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context

arXiv:2503.05846v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric train

1 Jun 2026

Tutorials

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs

arXiv:2510.00419v2 Announce Type: replace Abstract: Zeroth-order optimizers have recently emerged as an attractive approach for fine-tuning large language models (LLMs), as they avoid backpropagation

tutorialsarxiv-cs-lg

1 Jun 2026

Agents

SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMs

arXiv:2605.30711v1 Announce Type: cross Abstract: Agentic LLMs must continuously decide whether newly extracted facts should be added, merged with existing memories, or ignored, yet prior work has foc

agentsarxiv-cs-ai

1 Jun 2026

Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs

arXiv:2605.29928v1 Announce Type: cross Abstract: As AI-generated and AI-assisted content floods online spaces, source labels attached to such content can distort human reasoning judgments, with downs

MusTBENCH: Benchmarking and Advancing Temporal Grounding in Music LLMs

arXiv:2605.29300v1 Announce Type: cross Abstract: Recent Large Audio-Language Models (LALMs) have demonstrated promising abilities in understanding musical content. However, whether their responses ar

OOD-GraphLLM: Graph Large Language Model for Out-of-Distribution Generalized Drug Synergy Prediction

model-releasesarxiv-cs-lg

arXiv:2605.30247v1 Announce Type: new Abstract: Drug synergy prediction (DSP) aims to identify efficacious drug combinations under various cellular contexts with different targets. However, the contin

PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers

arXiv:2605.30094v1 Announce Type: new Abstract: Poker is a landmark challenge for artificial intelligence. The dominant approach relies on equilibrium solvers built on counterfactual regret minimizati

Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox

model-releasesarxiv-cs-lg

arXiv:2605.27772v1 Announce Type: cross Abstract: Audio large language models (Audio LLMs) demonstrate strong performance on speech understanding tasks, yet their ability to understand paralinguistic

28 May 2026

Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning

arXiv:2605.28409v1 Announce Type: new Abstract: Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for

28 May 2026

Detached Skip-Links and R-Probe: Decoupling Feature Aggregation from Gradient Propagation for MLLM OCR

arXiv:2603.20020v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) excel at high-level reasoning yet fail on OCR tasks where fine-grained visual details are compromised

27 May 2026

Scaling GraphLLM with Bilevel-Optimized Sparse Querying

arXiv:2602.09038v2 Announce Type: replace-cross Abstract: LLMs have recently shown strong potential in enhancing node-level tasks on text-attributed graphs (TAGs) by providing explanation features. Ho

27 May 2026

Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation

arXiv:2602.13203v2 Announce Type: replace-cross Abstract: Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and d

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs

arXiv:2605.21602v2 Announce Type: replace Abstract: Many safety and alignment failures of large language models (LLMs) occur due to out-of-distribution (OOD) situations: unusual prompt or response pat

DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking

model-releasesarxiv-cs-lg

arXiv:2605.26087v1 Announce Type: cross Abstract: Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of establis

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

arXiv:2605.25246v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimizat

LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs

arXiv:2605.23965v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on logical reasoning benchmarks, yet their reliability remains uncertain. Existing evaluations r