Search: “llm”

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to re

researcharxiv-cs-lg

14 Oct 2026

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Agents

CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits

DGX agent

arXiv:2610.13064v1 Announce Type: new Abstract: Large Language Model (LLM) agents have demonstrated remarkable capabilities in tackling complex tasks through agentic workflows. However, manually desig

agentsarxiv-cs-lg

12 Oct 2026

EvoBelief: Falsifiable World Models for Self-Improving LLM Agents

arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can

Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs

arXiv:2610.12762v1 Announce Type: new Abstract: Hardware datasheets contain engineering specifications essential for component selection, system integration, and hardware compatibility verification. H

Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence

arXiv:2610.13015v1 Announce Type: cross Abstract: Reinforcement learning has become the foundational paradigm for enhancing the reasoning capabilities of LLMs. Yet the dominant approach, Proximal Poli

safetyarxiv-cs-ai

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

arXiv:2610.13207v1 Announce Type: new Abstract: Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive pattern

safetyarxiv-cs-cl

Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

arXiv:2610.13810v1 Announce Type: cross Abstract: Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate. They inject non existent entities and off-topic

safetyarxiv-cs-cl

Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL

arXiv:2610.13020v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. Yet long-horizon autoregressive rollouts introduc

local-aiarxiv-cs-lg

TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.12828v1 Announce Type: new Abstract: Outcome-supervised group-relative policy optimization (GRPO) assigns the same advantage to every token in an LLM agent trajectory, leaving useful and un

Verifier-Mediated Multi-LLM Coordination for Multi-Robot Path Planning

arXiv:2610.12761v1 Announce Type: new Abstract: Foundation models are increasingly used for high-level multi-robot task planning, yet independently generated symbolic plans can become jointly infeasib

researcharxiv-cs-ro

Arbiter: Detecting Interference in LLM Agent System Prompts

arXiv:2603.08993v2 Announce Type: replace-cross Abstract: System prompts for LLM-based coding agents are software artifacts that govern agent behavior, yet lack the testing infrastructure applied to c

9 Oct 2026

Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning

arXiv:2610.10655v1 Announce Type: cross Abstract: Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning

9 Oct 2026

Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model

arXiv:2602.07878v2 Announce Type: replace-cross Abstract: LLM inference is inherently expensive, even a modest slowdown can translate into substantial operating costs and severe availability risks. Re

researcharxiv-cs-ai

9 Oct 2026

Arctic Questions, Missing Answers: A Dataset and Benchmark for LLM Abstention in Arctic Science

model-releasesarxiv-cs-cl

arXiv:2610.09446v1 Announce Type: new Abstract: Large language models (LLMs) should abstain from scientific multiple-choice questions when no option is valid, but frequent abstention alone does not de

LiveMACE: Process-Aware Evaluation of LLM Agent Capabilities in Evolving Markets

model-releasesarxiv-cs-cl

arXiv:2610.09872v1 Announce Type: cross Abstract: Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, wh

MIRROR: From Imitation to Internalization in LLM Personalization

arXiv:2610.09795v1 Announce Type: new Abstract: The demand for personalized LLMs is shifting from style imitation toward content quality. We investigate whether self-distillation can bridge this gap i

safetyarxiv-cs-cl

Applications

Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

arXiv:2602.22647v3 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit fr

applicationsarxiv-cs-cl

Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue

arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning a

researcharxiv-cs-cl

Calibration Is Not Control: Intervention Value for LLM-Agent Oversight

arXiv:2606.21399v2 Announce Type: replace Abstract: Runtime oversight often intervenes when an LLM agent's calibrated failure score crosses a threshold. Yet states with the same failure risk can diffe

safetyarxiv-cs-ai

Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?

arXiv:2610.07208v1 Announce Type: new Abstract: Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-ec

researcharxiv-cs-lg

Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints

arXiv:2610.07362v1 Announce Type: new Abstract: We evaluate large language models (LLMs) in multi-turn interactions through their time-to-event: the number of interaction steps required to produce an

DySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching

arXiv:2610.08268v1 Announce Type: cross Abstract: Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models

local-aiarxiv-cs-ai

Applications

FluidPD: In-Place Elasticity for SLO-Aware Prefill-Decode Disaggregated LLM Serving

arXiv:2610.06917v1 Announce Type: new Abstract: Prefill-decode disaggregation is becoming a common architecture for LLM serving because it separates two phases with distinct execution patterns and SLO

applicationsarxiv-cs-ai

Tutorials

Learning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis

arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. E

tutorialsarxiv-cs-ai

LOGIC: An LLM Benchmark for Intent-Grounded Change Impact in Aerospace Electrical Systems

arXiv:2610.07580v1 Announce Type: new Abstract: Aerospace electrical-design revisions can contain multiple genuine changes, although an engineering request may authorize only a subset. Propagating eve

Applications

Polar: LLM-Powered Synthesis of Real-World Cyber Evidence for Prioritization and Mitigation

arXiv:2610.07298v1 Announce Type: cross Abstract: Cyber threat analysis increasingly depends on evidence distributed across vendor advisories, vulnerability databases, and threat intelligence sources.

applicationsarxiv-cs-ai

Selective Critique for Cost-Aware LLM Agents in Long-Horizon Decision Making

arXiv:2610.07335v1 Announce Type: cross Abstract: Improving the reliability of large language model (LLM) agents in long-horizon decision-making remains a key challenge. When deployed as autonomous ag

local-aiarxiv-cs-ai

An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection

arXiv:2610.05600v1 Announce Type: new Abstract: High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the `

safetyarxiv-cs-lg

Attention Tax, Handoff Tax: A Stylised Model of When Multi-Agent LLM Systems Help

arXiv:2610.06069v1 Announce Type: cross Abstract: Recent work on multi-agent LLM systems reaches sharply different conclusions: some results show that a single agent with the same information and comp

agentsarxiv-cs-lg

MemPilot: Orchestrating On-Demand Multimodal Memory Curation for LLM Agents

arXiv:2610.06830v1 Announce Type: cross Abstract: Memory has become integral to the LLM agent ecosystem, supporting information retention and reuse across interactions. However, most existing agent me

safetyarxiv-cs-lg

ORCA: The Annealed Spectral Conditioning Optimizer for Faster, Better LLM Training

arXiv:2610.06116v1 Announce Type: new Abstract: Modern LLM optimizers such as Muon often produce weight matrices with higher effective rank than Adam, yet further spectral control has delivered only m

VERA: Verdict-Conditioned Reliability for Adaptive LLM Judges

arXiv:2610.05452v1 Announce Type: new Abstract: Accurately estimating judgment reliability is a central challenge in adapting LLM judges to newly verified feedback while preserving previously learned

Capability Scaling-Down Laws for LLM Compression

arXiv:2610.02462v1 Announce Type: cross Abstract: LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable

researcharxiv-cs-cl

Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents

arXiv:2610.02654v1 Announce Type: new Abstract: Models of social contagion usually assume how individuals adopt beliefs and derive population behavior from it. We instead empirically measure belief ad

agentsarxiv-cs-ai

D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?

arXiv:2610.03226v1 Announce Type: cross Abstract: GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal ho

LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling

arXiv:2511.06260v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as behavioral proxies for self-interested travelers in agent-based traffic models. Although

agentsarxiv-cs-ai

Why Does Adaptive Batching Help LLM Pretraining? A Perspective from Unbounded Variance

arXiv:2610.02355v1 Announce Type: new Abstract: Increasing the batch size during training is a common practice in large language model (LLM) pretraining, yet the theoretical justification behind its s

researcharxiv-cs-lg

Certainty Is Not Just Correctness: Rethinking Token-Level Certainty in LLM Reasoning

arXiv:2610.00296v1 Announce Type: cross Abstract: Token-level certainty is widely used as a proxy for correctness in LLM training and inference. However, the performance of certainty-based methods dep

local-aiarxiv-cs-ai

Chaining Skills to Hijack LLM Agents

arXiv:2610.01564v1 Announce Type: cross Abstract: LLM agents use skills to improve performance on specialized tasks. To complete a user request, an agent may invoke several skills in sequence, allowin

DeFA: Dependency-Guided Failure Attribution for LLM Agents

arXiv:2610.01256v1 Announce Type: new Abstract: Errors in LLM agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understandi

agentsarxiv-cs-ai

Hardware

FastCI: Efficient GPU-Intensive CI for LLM Training Frameworks

arXiv:2610.01967v1 Announce Type: new Abstract: As large language models (LLMs) keep growing in size and complexity, their training frameworks evolve at a rapid pace as well. Therefore, continuous int

hardwarearxiv-cs-lg

Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI

arXiv:2610.00682v1 Announce Type: new Abstract: Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering

researcharxiv-cs-ai

Quantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift

arXiv:2607.17384v3 Announce Type: replace Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM

When Does a Second Model Help? Cross-Model Review in LLM Verification

arXiv:2610.01471v1 Announce Type: cross Abstract: Large language models now generate code, documentation, and analyses, and are increasingly used to review such output. We ask when a second review by

XOR-Trellis: Ultra-Low-Complexity Dequantization and Curvature-Aware Hadamard-Free LLM Quantization

arXiv:2610.00432v1 Announce Type: cross Abstract: Trellis-coded quantization enables high-dimensional compression of large language model (LLM) weights at ultra-low bit widths without the exponentiall

researcharxiv-cs-ai

Covert Assistance: Helpful LLM Agents Evade Oversight in Multi-Agent Systems

model-releasesarxiv-cs-cl

arXiv:2609.39050v1 Announce Type: cross Abstract: As multi-agent systems enter high-stakes domains, the possibility that agents may circumvent safety boundaries is a growing concern. Prior work has ex

1 Oct 2026

Optimal Design for Active Preference Learning with Biased LLM Judges

arXiv:2609.38860v1 Announce Type: cross Abstract: Learning from human preferences is central to large language model (LLM) alignment, but human preference annotation is costly. Active preference learn

safetyarxiv-cs-ai

1 Oct 2026

Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts

arXiv:2609.38600v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used in clinical settings, it is critical to evaluate their reliability under realistic variation in cl