Search: “llm”

LLM Evaluation on Unseen Questions: Contextual Multidimensional IRT Model

arXiv:2608.22295v1 Announce Type: cross Abstract: Evaluation of large language models (LLMs) increasingly requires predicting how a model will perform on new questions or tasks before collecting large

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LLM Pedagogical Behavior in AI Tutoring Interactions

DGX agent

arXiv:2608.22993v1 Announce Type: new Abstract: Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students us

tutorialsarxiv-cs-cl

25 Aug 2026

Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation

arXiv:2608.23484v1 Announce Type: new Abstract: We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a mul

One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing

arXiv:2511.12484v2 Announce Type: replace-cross Abstract: With the integration of massive distributed energy resources and the widespread participation of novel market entities, the operation of activ

Training Proactive and Personalized LLM Agents

arXiv:2511.02208v2 Announce Type: replace Abstract: Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents

An LLM agent for end-to-end computational materials discovery

arXiv:2608.20434v1 Announce Type: cross Abstract: The coordination of multi-scale tasks is an effective strategy for computational materials discovery, yet the repeated application of diverse algorith

Applications

Auditable by Construction: An Ontology-Driven Framework for Trustworthy LLM Analytics in Enterprise Finance

arXiv:2608.20661v1 Announce Type: new Abstract: Enterprise adoption of large language models in finance is constrained less by fluency than by trust: in Financial Planning and Analysis (FP&A) and othe

applicationsarxiv-cs-ai

Calibrating Criterion Revision in LLM Agents: Failure Modes and a Trace-Anchored Protocol

arXiv:2608.20729v1 Announce Type: new Abstract: Language-model agents can improve after failure or carry text across episodes without revising what counts as success. We study the narrower attribution

local-aiarxiv-cs-ai

ConceptTS: LLM-Guided Concept Bottlenecks for Interpretable Multivariate Time-Series Forecasting

arXiv:2608.21277v1 Announce Type: new Abstract: State-of-the-art multivariate time-series forecasters can model complex temporal and cross-variable dependencies, yet their opaque representations provi

local-aiarxiv-cs-lg

Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge

arXiv:2608.21021v1 Announce Type: cross Abstract: Real-world fault analysis in 5G and emerging 6G networks demands domain expertise to analyze free-text diagnostics, including root-cause explanations

SEISMO: Explanation-Aware, Trajectory-Conditioned LLM Agents for Sample-Efficient Molecular Optimisation

arXiv:2602.00663v3 Announce Type: replace Abstract: Optimizing molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry

CacheRoute: Planned Prefix-Affinity Routing for Large-Scale LLM Serving

model-releasesarxiv-cs-lg

arXiv:2608.19677v1 Announce Type: cross Abstract: Prefix caching avoids prefill only when a repeated request returns to a server that still holds the prefix KV. Cache-blind balancing disperses that re

Hardware

FlashPrefill V2: Block-Sparse Prefill Attention for Long-Context LLM Serving

arXiv:2608.19758v1 Announce Type: new Abstract: Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, partic

hardwarearxiv-cs-cl

PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization

arXiv:2507.06127v2 Announce Type: replace-cross Abstract: Prefix adders are fundamental arithmetic circuits, but their design space grows exponentially with bit-width, posing significant optimization

Write Once, Run Everywhere: The Axon DSL for Shape-Safe and Framework-Agnostic LLM Architectures

arXiv:2608.19889v1 Announce Type: new Abstract: The entire ecosystem of open-source language models effectively relies on a single platform. What if this platform was forced to shut down tomorrow? Imp

local-aiarxiv-cs-ai

Persona-Guided LLM Agents for Task-Oriented Dialogue

model-releasesarxiv-cs-cl

arXiv:2608.18085v1 Announce Type: new Abstract: Prior work has shown that large language models (LLMs) can express diverse personality traits in open-ended text generation. However, it remains unclear

20 Aug 2026

Same Facts, Different Updates: Inference Setup Shapes LLM Behavior in Medical Allocation

arXiv:2608.18108v1 Announce Type: cross Abstract: Large language models are being incorporated into sensitive and important decision-making processes across nearly all fields. While prior work studies

20 Aug 2026

Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training

arXiv:2608.16926v1 Announce Type: new Abstract: Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while p

local-aiarxiv-cs-lg

19 Aug 2026

GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents

arXiv:2608.16890v1 Announce Type: new Abstract: Clinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulatory submissio

19 Aug 2026

Applications

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation

arXiv:2608.15949v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendatio

applicationsarxiv-cs-ai

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv:2506.07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG)

LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning

arXiv:2607.10139v3 Announce Type: replace-cross Abstract: Selecting the correct answer from a pool of candidate reasoning chains is the engine of test-time scaling, yet the standard selectors each car

PDDLCoder: Agentic PDDL Generation for LLM-Assisted Symbolic Planning

arXiv:2608.16637v1 Announce Type: new Abstract: LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans. Recent hybrid methods instead transla

PLSQLBench: Benchmarking LLM Systems for Executable Procedural Database Programming

model-releasesarxiv-cs-cl

arXiv:2608.15931v1 Announce Type: new Abstract: We present PLSQLBench, to our knowledge the first benchmark for evaluating whether LLMs can write executable PL/SQL programs, with correctness measured

Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis

arXiv:2608.16775v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making a

When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents

arXiv:2608.16806v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, d

Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classification

arXiv:2608.13866v1 Announce Type: cross Abstract: Large language models (LLMs) can generate synthetic training data for text classification, but the quality of generated samples is heterogeneous: some

researcharxiv-cs-cl

17 Aug 2026

Not All Tokens Are Equal: Inflation-Aware Routing for Agentic LLM Systems

arXiv:2608.13571v1 Announce Type: cross Abstract: When a language model fails to answer a query on the first attempt, an agentic system retries, consuming additional tokens each time. This retry overh

local-aiarxiv-cs-ai

17 Aug 2026

When Lexical Change Misleads: Rethinking Dynamic Topic Model Evaluation with Traditional and LLM-Based Metrics

arXiv:2608.13835v1 Announce Type: new Abstract: Dynamic topic models capture evolving word distributions, but traditional coherence metrics may fail when vocabulary changes while semantic meaning pers

researcharxiv-cs-cl

17 Aug 2026

Dead text or binding clause? Measuring and restoring constraint influence in black-box LLM dialogues

arXiv:2608.12599v1 Announce Type: new Abstract: Multi-turn dialogues let users revoke constraints as easily as impose them, but revocation does not reliably take effect: models keep enacting withdrawn

Don't Want Your LLM to Recommend Nuclear Strike? Try Asking It in Japanese

arXiv:2608.12373v1 Announce Type: new Abstract: Large language models are increasingly used in strategic and advisory contexts, yet their safety alignment is typically evaluated in English only. We te

Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization

arXiv:2608.12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-

Refusing Intent, Not Form: Wrapper-Based Intent-Group Supervision for LLM Safety

model-releasesarxiv-cs-cl

arXiv:2608.13304v1 Announce Type: new Abstract: Safety tuning can improve harmful refusal, but models may learn surface-form shortcuts: wrapped harmful prompts bypass safety, while similarly wrapped b

AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection

arXiv:2608.11679v1 Announce Type: new Abstract: Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems. Even with skilled operators, interpreting anomalies

Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation

arXiv:2608.11513v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code

LODESTAR: Trustworthy Entropy Is Navigated, Not Merely Measured -- Reinforced Polarizer Keeps a Frozen LLM from Being Confidently Misled by the Wrong Evidence

arXiv:2608.11922v1 Announce Type: new Abstract: Predictive-distribution entropy makes a strong selection rule in retrieval-augmented question answering: across five QA benchmarks, keeping the candidat

researcharxiv-cs-cl

Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning

arXiv:2608.11705v1 Announce Type: new Abstract: Aligned large language models (LLMs) are expected to exhibit safety behavior based on the content of the user request: they should refuse unsafe request

Carefully Considering Culture: Analyzing LLM Alignment in Single- and Multi-Cultural Settings using Cultural Consensus Theory

arXiv:2608.09937v1 Announce Type: new Abstract: Recent work in NLP has probed large language models for their understanding of cultural norms across countries. However, this work typically considers d

safetyarxiv-cs-cl

12 Aug 2026

Closed-Loop LLM Co-Pilots for Digital Agriculture

arXiv:2608.09949v1 Announce Type: new Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided

12 Aug 2026

Withholding the Completing Chunk: Deterministic Pair-Completion Guardrails for Streaming LLM Output

arXiv:2608.10279v1 Announce Type: cross Abstract: Streaming language-model output creates a release-timing problem: complete-response moderation acts after streamed text has escaped, whereas repeated

12 Aug 2026

Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaigns

arXiv:2608.07637v1 Announce Type: new Abstract: Long-running molecular simulation campaigns require repeated continuation from saved states, provenance-aware progression, adaptive assessment, and occa

Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy

arXiv:2608.09857v1 Announce Type: cross Abstract: Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on

AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

arXiv:2604.02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification a

Business Arena: Benchmarking LLM Agents in a Realistic Marketplace

arXiv:2608.08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work. Operators must infer opportunities from partial signals, commit capital under uncertainty,

ComboShoppingBench: Evaluating LLM Agents for Budget-Constrained Basket Shopping with Coupons

arXiv:2608.09282v1 Announce Type: new Abstract: Real-world shopping often requires constructing a basket of complementary items rather than retrieving a single product. Such combo-shopping tasks arise

Effect of Abstractions and Prompting Strategies on LLM-Guided High-Performance Optimizations

arXiv:2608.08085v1 Announce Type: cross Abstract: Code performance optimization is a vital aspect of modern software development, as it enables faster response times and reduced resource usage. These

Knowing You Is Everything: LLM Agents Achieve Near-Perfect Profile-Consistent Reaction Prediction in Social Media Simulation

arXiv:2608.07498v1 Announce Type: cross Abstract: Autonomous AI agents in social media present concrete risks to democratic discourse and platform governance, while also offering tools for pre-deploym

LLM-Based Embeddings for Program Analysis and Optimization

arXiv:2608.07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs. We

researcharxiv-cs-lg