Search: “llm”

arXiv:2510.05188v2 Announce Type: replace Abstract: Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narrative

local-aiarxiv-cs-ai

14 Apr 2026

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Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage

DGX agent

arXiv:2603.23966v3 Announce Type: replace-cross Abstract: With frequently evolving Advanced Persistent Threats (APTs) in cyberspace, traditional security solutions approaches have become inadequate fo

model-releasesarxiv-cs-ai

14 Apr 2026

The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems

arXiv:2604.11309v1 Announce Type: cross Abstract: Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constrain

14 Apr 2026

Token-Budget-Aware Pool Routing for Cost-Efficient LLM Inference

arXiv:2604.09613v1 Announce Type: cross Abstract: Production vLLM fleets provision every instance for worst-case context length, wasting 4-8x concurrency on the 80-95% of requests that are short and s

14 Apr 2026

Hardware

CSAttention: Centroid-Scoring Attention for Accelerating LLM Inference

arXiv:2604.08584v1 Announce Type: cross Abstract: Long-context LLMs increasingly rely on extended, reusable prefill prompts for agents and domain Q&A, pushing attention and KV-cache to become the domi

hardwarearxiv-cs-ai

13 Apr 2026

Local Ai

Offline-First LLM Architecture for Adaptive Learning in Low-Connectivity Environments

arXiv:2603.03339v5 Announce Type: replace-cross Abstract: Artificial intelligence (AI) and large language models (LLMs) are transforming educational technology by enabling conversational tutoring, per

local-aiarxiv-cs-cl

13 Apr 2026

Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios

arXiv:2604.09377v1 Announce Type: new Abstract: Large language models (LLMs) exhibit substantial variability in performance and computational cost across tasks and queries, motivating routing systems

13 Apr 2026

3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience

model-releasesarxiv-cs-cv

arXiv:2604.08042v1 Announce Type: new Abstract: Sketching in 3D space enables expressive reasoning about shape, structure, and spatial relationships, yet generating 3D sketches through natural languag

A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection

arXiv:2604.08381v1 Announce Type: new Abstract: Sarcasm is a rhetorical device that expresses criticism or emphasizes characteristics of certain individuals or situations through exaggeration, irony,

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM

arXiv:2604.06666v1 Announce Type: cross Abstract: Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating

AsyncTLS: Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention

arXiv:2604.07815v1 Announce Type: new Abstract: Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory. While token-level sparse att

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization

arXiv:2508.13993v2 Announce Type: replace Abstract: Long-context modeling is critical for a wide range of real-world tasks, including long-context question answering, summarization, and complex reason

Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook

arXiv:2604.06210v2 Announce Type: cross Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks fac

Agents

EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents

arXiv:2604.07549v1 Announce Type: new Abstract: Conversational diagnosis prediction requires models to track evolving evidence in streaming clinical conversations and decide when to commit to a diagno

agentsarxiv-cs-cl

LLM-Augmented Knowledge Base Construction For Root Cause Analysis

arXiv:2604.06171v1 Announce Type: cross Abstract: Communications networks now form the backbone of our digital world, with fast and reliable connectivity. However, even with appropriate redundancy and

Agents

Mina: A Multilingual LLM-Powered Legal Assistant Agent for Bangladesh for Empowering Access to Justice

arXiv:2511.08605v3 Announce Type: replace Abstract: Bangladesh's low-income population faces major barriers to affordable legal advice due to complex legal language, procedural opacity, and high costs

agentsarxiv-cs-cl

On the Step Length Confounding in LLM Reasoning Data Selection

arXiv:2604.06834v1 Announce Type: cross Abstract: Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain-of-thought reasoning, through supervised

Sell More, Play Less: Benchmarking LLM Realistic Selling Skill

arXiv:2604.07054v2 Announce Type: replace Abstract: Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language

TeamLLM: A Human-Like Team-Oriented Collaboration Framework for Multi-Step Contextualized Tasks

arXiv:2604.06765v1 Announce Type: cross Abstract: Recently, multi-Large Language Model (LLM) frameworks have been proposed to solve contextualized tasks. However, these frameworks do not explicitly em

Auditing Escalation Reliability in Healthcare LLM Agents: A Perturbation-Controlled Benchmark of Workflow-State Failures

arXiv:2610.21270v1 Announce Type: new Abstract: Trustworthy biomedical AI requires reliable action selection when evidence is missing, stale, conflicting, or late, not only accurate answers on static

Beyond Generation: Using Multimodal LLMs as Detectors of LLM-Generated Deepfake Facial Images

model-releasesarxiv-cs-cv

arXiv:2610.21760v1 Announce Type: new Abstract: As image generation models become increasingly capable of producing highly realistic content, traditional forensic techniques are struggling to keep up.

CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades

arXiv:2610.20668v1 Announce Type: cross Abstract: We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read se

CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty

arXiv:2610.22292v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming prevalent in scenarios where honesty is critical. However, despite alignment training, these models still ex

Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker

arXiv:2610.21597v1 Announce Type: cross Abstract: Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive. We evaluate this for t

Agents

Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration

arXiv:2610.21604v1 Announce Type: new Abstract: Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex

agentsarxiv-cs-ai

INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples

arXiv:2610.21038v1 Announce Type: cross Abstract: Machine learning models for tabular prediction typically assume that training data are representative of the population on which they will be deployed

Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning

arXiv:2606.22305v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-

Poison Valleys: Diagnosing Path-Dependent Failure in LLM Code Repair

arXiv:2610.21514v1 Announce Type: cross Abstract: When code-repair agents fail, final tests reveal that a patch is wrong but not which earlier commitment caused it or which software obligation failed.

TRACER: Trajectory-Aware Tool Retrieval for LLM Agents

arXiv:2610.21267v1 Announce Type: cross Abstract: Modern agents select tools from registries, but exposing entire registries incurs high token costs and can impair selection as they grow. We present T

Two Sample Sizes in Test-Time LLM Ensembling: Buffered ERM and Minimax Theory

arXiv:2610.21363v1 Announce Type: cross Abstract: Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them. Weighted ens

researcharxiv-cs-lg

Hardware

ZenFlow: Enabling Stall-Free Offloading for LLM Training

arXiv:2505.12242v4 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) often exceeds GPU memory limits, prompting systems to offload model states to CPU memory. However, ex

hardwarearxiv-cs-lg

Applications

A large-scale pipeline for LLM-assisted corpus annotation: variation and change in the English consider construction

arXiv:2510.12306v4 Announce Type: replace Abstract: As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic

applicationsarxiv-cs-cl

ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence

arXiv:2610.14381v1 Announce Type: new Abstract: Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system

Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces

arXiv:2610.18606v1 Announce Type: new Abstract: Large language models increasingly tackle tasks that require multi-step reasoning, where errors in intermediate claims can affect the final answer. Self

Claim Framing as a Driver of LLM Sycophancy

arXiv:2610.18159v1 Announce Type: cross Abstract: Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using questions

Delegating Earth science model calibration to knowledge-guided LLM agents

arXiv:2610.18378v1 Announce Type: new Abstract: Applying process-based Earth science models to new sites and questions often requires expert calibration decisions. Reliable delegation to language-mode

Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories

arXiv:2610.19614v1 Announce Type: cross Abstract: In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations. We show that succes

Applications

FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization

arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model be

applicationsarxiv-cs-ai

Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors

arXiv:2610.17164v1 Announce Type: new Abstract: The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecule

MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation

arXiv:2610.19827v1 Announce Type: cross Abstract: LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their

Local Ai

Narrative Paradoxes in LLM Inference: Shaping Reasoning Trajectories toward Misalignment

arXiv:2610.16312v1 Announce Type: new Abstract: Explicit reasoning in large language models (LLMs) is often associated with reliability, yet it can still pose risks when a model infers harmful goals u

local-aiarxiv-cs-ai

Plans Fade Fast: Why Context Management Is Load-Bearing for LLM Agents

arXiv:2606.22953v2 Announce Type: replace Abstract: Context managers summarize or evict old messages so agents can operate beyond a finite window. This creates a reliability problem: removing history

Right Answer, Wrong Reason: Accuracy, Consistency, and Consensus Are Misleading Indicators of LLM Faithfulness in Clinical Decision Support

arXiv:2609.32817v2 Announce Type: replace Abstract: Clinical Large Language Models (LLMs) achieve strong medical-exam accuracy; however, a correct answer does not guarantee that the explanation names

Applications

Semantics as a Shield: Label Disguise Defense (LDD) against Prompt Injection in LLM Sentiment Classification

arXiv:2511.21752v3 Announce Type: replace-cross Abstract: Prompt injection can cause large language models (LLMs) to deviate from their intended tasks by following instructions embedded in untrusted i

applicationsarxiv-cs-ai

Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents

arXiv:2610.19349v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) provides no reward-relative learning signal when all rollouts receive identical rewards. In long-horizon tas

Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions

arXiv:2610.14334v1 Announce Type: new Abstract: In Model Context Protocol (MCP) systems, LLMs select tools from metadata rather than from their implementations. Names and descriptions summarize what a

ATTUNE: Agentic Throughput Tuning for LLM Training

model-releasesarxiv-cs-lg

arXiv:2610.17528v1 Announce Type: cross Abstract: Training recipes are tuned by hand for one model, cluster and workload, and every new model, accelerator, framework version or dataset means tuning ag

14 Oct 2026

Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports