Search: “llm”

arXiv:2604.28129v1 Announce Type: cross Abstract: Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where indi

applicationsarxiv-cs-ai

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DGX agent

arXiv:2604.26960v1 Announce Type: cross Abstract: Transformer-based agentic AI is rapidly being deployed on major platforms to help users shop, watch, and navigate content with less effort. While thes

safetyarxiv-cs-ai

1 May 2026

PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM Reasoning

arXiv:2601.11908v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with reasoning over long contexts where relevant information is sparsely distributed. Although plan-and-execut

Local Ai

Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors

arXiv:2604.27426v1 Announce Type: cross Abstract: Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although ''local offline

local-aiarxiv-cs-ai

Stable Behavior, Limited Variation: Persona Validity in LLM Agents for Urban Sentiment Perception

arXiv:2604.28048v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as proxies for human perception in urban analysis, yet it remains unclear whether persona prompting p

Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance

arXiv:2601.08418v2 Announce Type: replace-cross Abstract: Tax code prediction is a crucial yet underexplored task in automating invoicing and compliance management for large-scale e-commerce platforms

safetyarxiv-cs-ai

The Impact of LLM Self-Consistency and Reasoning Effort on Automated Scoring Accuracy and Cost

arXiv:2604.26954v1 Announce Type: cross Abstract: Strategic model selection and reasoning settings are more effective than ensembling for optimizing automated scoring with large language models (LLMs)

To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing

arXiv:2604.27296v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bo

Agents

Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction

arXiv:2604.27221v1 Announce Type: new Abstract: Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and het

agentsarxiv-cs-ai

A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring

arXiv:2602.23163v3 Announce Type: replace Abstract: Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanis

Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control

arXiv:2604.26326v1 Announce Type: cross Abstract: Reinforcement learning (RL) has unlocked complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performa

Hardware

AHASD: Asynchronous Heterogeneous Architecture for LLM Adaptive Drafting Speculative Decoding on Mobile Devices

arXiv:2604.25326v2 Announce Type: replace-cross Abstract: Speculative decoding enhances the inference efficiency of large language models (LLMs) by generating drafts using a small draft language model

hardwarearxiv-cs-ai

Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction

arXiv:2604.26209v1 Announce Type: cross Abstract: Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context.

HealthNLP_Retrievers at ArchEHR-QA 2026: Cascaded LLM Pipeline for Grounded Clinical Question Answering

arXiv:2604.26880v1 Announce Type: new Abstract: Patient portals now give individuals direct access to their electronic health records (EHRs), yet access alone does not ensure patients understand or ac

Agents

MappingEvolve: LLM-Driven Code Evolution for Technology Mapping

arXiv:2604.26591v1 Announce Type: cross Abstract: Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimizati

agentsarxiv-cs-ai

Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation

arXiv:2604.26456v1 Announce Type: cross Abstract: The digitisation of classical Sanskrit literature is impeded by a scarcity of annotated resources, particularly for Named Entity Recognition. While re

One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety

arXiv:2604.25921v1 Announce Type: new Abstract: Large Language Models (LLMs) are trained to refuse harmful requests, yet they remain vulnerable to jailbreak attacks that exploit weaknesses in conversa

Agents

RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis

arXiv:2602.01297v3 Announce Type: replace Abstract: Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges fo

agentsarxiv-cs-ai

SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts

arXiv:2604.26506v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts -- adversarial instruc

Safety Is Not Universal: The Selective Safety Trap in LLM Alignment

arXiv:2601.04389v2 Announce Type: replace-cross Abstract: Current safety evaluations of large language models (LLMs) create a dangerous illusion of universal protection by aggregating harms under gene

Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens

arXiv:2604.26355v1 Announce Type: new Abstract: Reasoning in Large Language Models incurs significant inference-time compute, yet the token-level information structure of reasoning traces remains unde

Verified Critical Step Optimization for LLM Agents

arXiv:2602.03412v2 Announce Type: replace Abstract: As large language model agents tackle increasingly complex long-horizon tasks, effective post-training becomes critical. Prior work faces fundamenta

Doing More With Less: Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling

arXiv:2604.25098v1 Announce Type: cross Abstract: While current Large Language Models (LLMs) exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), their massive parameter

Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing

model-releasesarxiv-cs-lg

arXiv:2602.11786v2 Announce Type: replace Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety through breadth-oriented evaluation acr

Incompressible Knowledge Probes: Estimating Black-Box LLM Parameter Counts via Factual Capacity

model-releasesarxiv-cs-lg

arXiv:2604.24827v1 Announce Type: new Abstract: Closed-source frontier labs do not disclose parameter counts, and the standard alternative -- inference economics -- carries 2imes+ uncertainty from har

PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference

arXiv:2604.24971v1 Announce Type: cross Abstract: We present PolyKV, a system in which multiple concurrent inference agents share a single, asymmetrically compressed KV cache pool. Rather than allocat

Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution

arXiv:2604.03472v2 Announce Type: replace Abstract: Co-evolutionary self-play, where one language model generates problems and another solves them, promises autonomous curriculum learning without huma

AeSlides: Incentivizing Aesthetic Layout in LLM-Based Slide Generation via Verifiable Rewards

arXiv:2604.22840v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong potential in agentic tasks, particularly in slide generation. However, slide generation poses a

AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications

arXiv:2512.20164v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content mo

ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis

arXiv:2404.10141v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) models have achieved remarkable progress in high-quality image synthesis, yet most benchmarks rely on simple, self-contain

Applications

Chat-Scene++: Exploiting Context-Rich Object Identification for 3D LLM

arXiv:2603.27507v2 Announce Type: replace Abstract: Recent advancements in multi-modal large language models (MLLMs) have shown strong potential for 3D scene understanding. However, existing methods s

applicationsarxiv-cs-cv

Complete Cyclic Subtask Graphs for Tool-Using LLM Agents: Flexibility, Cost, and Bottlenecks in Multi-Agent Workflows

arXiv:2604.22820v1 Announce Type: cross Abstract: Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks for recovery and exploration, but added multi-agent workflow flexibil

Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning

arXiv:2604.23987v1 Announce Type: new Abstract: Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspect

researcharxiv-cs-lg

JudgeSense: A Benchmark for Prompt Sensitivity in LLM-as-a-Judge Systems

arXiv:2604.23478v1 Announce Type: new Abstract: Large language models are increasingly deployed as automated judges for evaluating other models, yet the stability of their verdicts under semantically

LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment

arXiv:2506.11480v4 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing LLMs' reasoning abilities, yet its data ineffic

Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows

arXiv:2604.23855v1 Announce Type: new Abstract: We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The a

LLM-Assisted Op-Amp Behavioral-Level Design via Agentic Human-Mimicking Reasoning

arXiv:2601.21321v2 Announce Type: replace Abstract: This paper proposes White-Op, an operational amplifier (op-amp) behavioral-level parameter design framework assisted by the human-mimicking reasonin

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling

arXiv:2604.24715v1 Announce Type: new Abstract: Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transforme

Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs

arXiv:2509.25414v2 Announce Type: replace-cross Abstract: Large language models are often adapted using parameter-efficient techniques such as Low-Rank Adaptation (LoRA), formulated as y = W_0x + BAx,

Hardware

RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference

arXiv:2505.02922v3 Announce Type: replace Abstract: Recent large language models (LLMs) are rapidly extending their context windows, yet inference throughput lags due to increasing GPU memory and band

hardwarearxiv-cs-lg

RouteNLP: Closed-Loop LLM Routing with Conformal Cascading and Distillation Co-Optimization

arXiv:2604.23577v1 Announce Type: new Abstract: Serving diverse NLP workloads with large language models is costly: at one enterprise partner, inference costs exceeded $200K/month despite over 70% of

Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning

arXiv:2511.01490v2 Announce Type: replace Abstract: As synthetic data becomes widely used in language model development, understanding its impact on model behavior is crucial. This paper investigates

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training

arXiv:2604.24088v1 Announce Type: cross Abstract: Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of inte

When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL~2~DL

arXiv:2604.23398v1 Announce Type: new Abstract: We report a reproducible error pattern in GPT-5.4 on OWL~2~DL compliance queries: the model frequently answers ``unknown'' when the reasoner-entailed an

Dharma, Data and Deception: An LLM-Powered Rhetorical Analysis of Cow-Urine Health Claims on YouTube

arXiv:2604.22606v1 Announce Type: new Abstract: Health misinformation remains one of the most pressing challenges on social media, particularly when cultural traditions intersect with scientific-sound

PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training

arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining creates a subtle but

safetyarxiv-cs-ai

SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking

arXiv:2604.22438v1 Announce Type: cross Abstract: Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing appr

The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology

arXiv:2505.20435v3 Announce Type: replace-cross Abstract: Existing interpretability methods for Large Language Models (LLMs) predominantly capture linear directions or isolated features. This overlook