Search: “llm”

Test Before You Deploy: Governing Updates in the LLM Supply Chain

arXiv:2604.27789v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as core dependencies in software systems. However, the hosted LLM services evolve continuously thro

1 May 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems

DGX agent

arXiv:2604.27082v1 Announce Type: new Abstract: We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires repla

applicationsarxiv-cs-ai

1 May 2026

The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion

arXiv:2508.16131v2 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context. It can boost developer productivity while providing

researcharxiv-cs-ai

30 Apr 2026

An Investigation of Linguistic Biases in LLM-Based Recommendations

model-releasesarxiv-cs-cl

arXiv:2604.25456v1 Announce Type: new Abstract: We investigate linguistic biases in LLM-based restaurant and product recommendations given prompts varying across Southern American English (AE), Indian

29 Apr 2026

AdaRubric: Task-Adaptive Rubrics for LLM Agent Evaluation

arXiv:2603.21362v2 Announce Type: replace Abstract: LLM-as-Judge evaluation fails agent tasks because a fixed rubric cannot capture what matters for this task: code debugging demands Correctness and E

AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

arXiv:2603.21357v3 Announce Type: replace Abstract: LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBe

BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks

arXiv:2508.08127v2 Announce Type: replace Abstract: The security of LLM-based multi-agent systems (MAS) is critically threatened by propagation vulnerability, where malicious agents can distort collec

agentsarxiv-cs-ai

Bridging Reasoning and Action: Hybrid LLM-RL Framework for Efficient Cross-Domain Task-Oriented Dialogue

arXiv:2604.23345v1 Announce Type: new Abstract: Cross-domain task-oriented dialogue requires reasoning over implicit and explicit feasibility constraints while planning long-horizon, multi-turn action

CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning

arXiv:2604.23270v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs). However,

CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation

arXiv:2510.22609v2 Announce Type: replace Abstract: Accurate symptom-to-disease classification and clinically grounded treatment recommendations remain challenging, particularly in heterogeneous patie

LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support

arXiv:2604.23902v1 Announce Type: new Abstract: Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to ad

Process Supervision of Confidence Margin for Calibrated LLM Reasoning

arXiv:2604.23333v1 Announce Type: cross Abstract: Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability

RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering

arXiv:2604.22755v1 Announce Type: cross Abstract: Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remai

local-aiarxiv-cs-ai

RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment

arXiv:2604.22520v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensi

27 Apr 2026

CoFEE: Reasoning Control for LLM-Based Feature Discovery

arXiv:2604.21584v1 Announce Type: new Abstract: Feature discovery from complex unstructured data is fundamentally a reasoning problem: it requires identifying abstractions that are predictive of a tar

researcharxiv-cs-ai

24 Apr 2026

Survey on Evaluation of LLM-based Agents

arXiv:2503.16416v2 Announce Type: replace Abstract: LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environ

24 Apr 2026

Anchor-and-Resume Concession Under Dynamic Pricing for LLM-Augmented Freight Negotiation

arXiv:2604.20732v1 Announce Type: cross Abstract: Freight brokerages negotiate thousands of carrier rates daily under dynamic pricing conditions where models frequently revise targets mid-conversation

23 Apr 2026

Continuous Semantic Caching for Low-Cost LLM Serving

arXiv:2604.20021v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar queries h

applicationsarxiv-cs-cl

23 Apr 2026

WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling

arXiv:2508.16676v2 Announce Type: replace-cross Abstract: Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language mode

23 Apr 2026

Reasoning Over Space: Enabling Geographic Reasoning for LLM-Based Generative Next POI Recommendation

arXiv:2601.04562v2 Announce Type: replace Abstract: Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain l

local-aiarxiv-cs-ai

22 Apr 2026

SAW-INT4: System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving

arXiv:2604.19157v1 Announce Type: new Abstract: KV-cache memory is a major bottleneck in real-world LLM serving, where systems must simultaneously support latency-sensitive small-batch requests and hi

applicationsarxiv-cs-lg

22 Apr 2026

TurboEvolve: Towards Fast and Robust LLM-Driven Program Evolution

arXiv:2604.18607v1 Announce Type: cross Abstract: LLM-driven program evolution can discover high-quality programs, but its cost and run-to-run variance hinder reliable progress. We propose TurboEvolve

researcharxiv-cs-ai

22 Apr 2026

Hardware

Bit-Flip Vulnerability of Shared KV-Cache Blocks in LLM Serving Systems

arXiv:2604.17249v1 Announce Type: cross Abstract: Rowhammer on GPU DRAM has enabled adversarial bit flips in model weights; shared KV-cache blocks in LLM serving systems present an analogous but previ

hardwarearxiv-cs-lg

Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages

arXiv:2508.14913v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated significant capabilities in solving mathematical problems expressed in natural language. However, mul

local-aiarxiv-cs-cl

CLASP: Training-Free LLM-Assisted Source Code Watermarking via Semantic-Preserving Transformations

arXiv:2510.11251v2 Announce Type: replace-cross Abstract: The proliferation of open-source code and large language models (LLMs) for code generation has amplified the risks of unauthorized reuse and i

local-aiarxiv-cs-lg

End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning

arXiv:2506.02718v2 Announce Type: replace Abstract: Large language models (LLMs) are versatile, yet their deployment in complex real-world settings is limited by static knowledge cutoffs and the diffi

safetyarxiv-cs-lg

Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence

arXiv:2601.11886v2 Announce Type: replace Abstract: In high-stakes domains like medicine, it may be generally desirable for models to faithfully adhere to the context provided. But what happens if the

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification

arXiv:2604.17271v1 Announce Type: new Abstract: Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommenda

applicationsarxiv-cs-cl

On the Shelf Life of Fine-Tuned LLM-Judges: Future-Proofing, Backward-Compatibility, and Question Generalization

arXiv:2509.23542v2 Announce Type: replace Abstract: The LLM-as-a-judge paradigm is widely used in both evaluating free-text model responses and reward modeling for model alignment and fine-tuning. Rec

Tutorials

River-LLM: Large Language Model Seamless Exit Based on KV Share

arXiv:2604.18396v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional performance across diverse domains but are increasingly constrained by high inference latency

tutorialsarxiv-cs-cl

SQL Query Engine: A Self-Healing LLM Pipeline for Natural Language to PostgreSQL Translation

model-releasesarxiv-cs-cl

arXiv:2604.16511v1 Announce Type: cross Abstract: We present SQL Query Engine, an open-source, self-hosted service that translates natural language questions into validated PostgreSQL queries through

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution

arXiv:2604.15840v1 Announce Type: new Abstract: Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent's evolving behavior and le

agentsarxiv-cs-cl

COMPASS: Benchmarking Constrained Optimization in LLM Agents

model-releasesarxiv-cs-lg

arXiv:2510.07043v2 Announce Type: replace Abstract: Human decision-making often involves constrained optimization. As LLM agents are deployed to assist with real-world tasks like travel planning, shop

Exploring LLM-based Verilog Code Generation with Data-Efficient Fine-Tuning and Testbench Automation

arXiv:2604.15388v1 Announce Type: cross Abstract: Recent advances in large language models have improved code generation, but their use in hardware description languages is still limited. Moreover, tr

In-Context Distillation with Self-Consistency Cascades: A Simple, Training-Free Way to Reduce LLM Agent Costs

arXiv:2512.02543v2 Announce Type: replace Abstract: Deploying LLM agents at scale typically requires choosing between quality and cost. Existing cost-reduction approaches fail to preserve agility: the

agentsarxiv-cs-lg

Ragged Paged Attention: A High-Performance and Flexible LLM Inference Kernel for TPU

arXiv:2604.15464v1 Announce Type: cross Abstract: Large Language Model (LLM) deployment is increasingly shifting to cost-efficient accelerators like Google's Tensor Processing Units (TPUs), prioritizi

Adaptive Layer Selection for Layer-Wise Token Pruning in LLM Inference

arXiv:2601.07667v2 Announce Type: replace Abstract: Due to the prevalence of large language models (LLMs), key-value (KV) cache reduction for LLM inference has received remarkable attention. Among num

Building Trust in the Skies: A Knowledge-Grounded LLM-based Framework for Aviation Safety

arXiv:2604.13101v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into aviation safety decision-making represents a significant technological advancement, yet their sta

Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis

arXiv:2604.14877v1 Announce Type: new Abstract: Does reinforcement learning genuinely expand what LLM agents can do, or merely make them more reliable? For static reasoning, recent work answers the se

agentsarxiv-cs-lg

Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting

arXiv:2505.11017v2 Announce Type: replace Abstract: Time series forecasting is critical across multiple domains, where time series data exhibit both local patterns and global dependencies. While Trans

local-aiarxiv-cs-lg

The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows

arXiv:2604.14807v1 Announce Type: cross Abstract: The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing,

Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception

arXiv:2510.23853v3 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlook

FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation

model-releasesarxiv-cs-lg

arXiv:2602.23636v3 Announce Type: replace Abstract: Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed

16 Apr 2026

LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks

model-releasesarxiv-cs-cl

arXiv:2604.13072v1 Announce Type: new Abstract: LLM-based agents are increasingly expected to handle real-world assistant tasks, yet existing benchmarks typically evaluate them under isolated sources

16 Apr 2026

When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration

arXiv:2604.13349v1 Announce Type: new Abstract: Communication in Large Language Model (LLM)-based multi-agent systems is moving beyond discrete tokens to preserve richer context. Recent work such as L

agentsarxiv-cs-lg

16 Apr 2026

CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades

arXiv:2604.12262v1 Announce Type: cross Abstract: Cascaded LLM systems coordinate models of varying sizes with human experts to balance accuracy, cost, and abstention under uncertainty. However, singl

agentsarxiv-cs-ai

15 Apr 2026

CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems

arXiv:2604.12461v1 Announce Type: new Abstract: LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology whi

15 Apr 2026

Designing Reliable LLM-Assisted Rubric Scoring for Constructed Responses: Evidence from Physics Exams

arXiv:2604.12227v1 Announce Type: new Abstract: Student responses in STEM assessments are often handwritten and combine symbolic expressions, calculations, and diagrams, creating substantial variation