Search: “llm”

Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture The Flag Challenges

arXiv:2604.19354v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive settings r

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HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing

DGX agent

arXiv:2604.19071v1 Announce Type: new Abstract: Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills

model-releasesarxiv-cs-cl

22 Apr 2026

OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration

arXiv:2505.11765v3 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recentl

Local Ai

'The Order in the Horse's Heart': A Case Study in LLM-Assisted Stylometry for the Discovery of Biblical Allusion in Modern Literary Fiction

arXiv:2604.19447v1 Announce Type: new Abstract: We present a dual-track pipeline for detecting biblical allusions in literary fiction and apply it to the novels of Cormac McCarthy. A bottom-up embeddi

local-aiarxiv-cs-cl

When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification

arXiv:2602.11199v2 Announce Type: replace Abstract: Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or r

Concurrent Criterion Validation of a Validity Screen for LLM Confidence Signals via Selective Prediction

arXiv:2604.17716v1 Announce Type: new Abstract: The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid. We test whether these classifications

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference

arXiv:2601.13684v2 Announce Type: replace Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods

HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents

arXiv:2604.18349v1 Announce Type: new Abstract: Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions withou

LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search

arXiv:2604.16555v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automatically discover high-performing deep neural network (DNN) architectures. However, conventional algorit

agentsarxiv-cs-cv

LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies

arXiv:2604.16763v1 Announce Type: new Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goal

safetyarxiv-cs-lg

ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization

arXiv:2604.17512v1 Announce Type: new Abstract: Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operation

PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment

arXiv:2508.05132v2 Announce Type: replace Abstract: As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on k

safetyarxiv-cs-cl

QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks

arXiv:2604.17842v1 Announce Type: new Abstract: LLM benchmarks are increasingly dynamic: instead of containing a fixed set of questions, they define templates and parameters that can generate an effec

Screen Before You Interpret: A Portable Validity Protocol for Benchmark-Based LLM Confidence Signals

arXiv:2604.17714v1 Announce Type: new Abstract: LLM confidence signals are used for abstention, routing, and safety-critical decisions. No standard practice exists for checking whether a confidence si

Local Ai

Semantic Step Prediction: Multi-Step Latent Forecasting in LLM Reasoning Trajectories via Step Sampling

arXiv:2604.18464v1 Announce Type: new Abstract: Semantic Tube Prediction (STP) leverages representation geometric to regularize LLM hidden-state trajectories toward locally linear geodesics during fin

local-aiarxiv-cs-lg

Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict

arXiv:2506.06485v4 Announce Type: replace Abstract: Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely e

safetyarxiv-cs-cl

COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation

arXiv:2604.15001v2 Announce Type: replace Abstract: LLM-based RTL code generation methods increasingly target both functional correctness and PPA quality, yet existing approaches universally decouple

20 Apr 2026

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning

arXiv:2509.25300v4 Announce Type: replace-cross Abstract: While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement lear

20 Apr 2026

Applications

When the Loop Closes: Architectural Limits of In-Context Isolation, Metacognitive Co-option, and the Two-Target Design Problem in Human-LLM Systems

arXiv:2604.15343v1 Announce Type: cross Abstract: We report a detailed autoethnographic case study of a single-subject who deliberately constructed and operated a multi-modal prompt-engineering system

applicationsarxiv-cs-ai

20 Apr 2026

AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization

arXiv:2511.15915v2 Announce Type: replace-cross Abstract: We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerator

Calibration-Gated LLM Pseudo-Observations for Online Contextual Bandits

arXiv:2604.14961v1 Announce Type: new Abstract: Contextual bandit algorithms suffer from high regret during cold-start, when the learner has insufficient data to distinguish good arms from bad. We pro

safetyarxiv-cs-lg

CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations

arXiv:2604.14691v1 Announce Type: cross Abstract: LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often

agentsarxiv-cs-cl

Local Ai

HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization

arXiv:2604.14159v1 Announce Type: new Abstract: Mobile input method editors (IMEs) are the primary interface for text input, yet they remain constrained to manual typing and struggle to produce person

local-aiarxiv-cs-cl

IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation

arXiv:2511.01014v3 Announce Type: replace Abstract: Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imp

LLM Predictive Scoring and Validation: Inferring Experience Ratings from Unstructured Text

arXiv:2604.14321v1 Announce Type: new Abstract: We tasked GPT-4.1 to read what baseball fans wrote about their game-day experience and predict the overall experience rating each fan gave on a 0-10 sur

Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving

arXiv:2507.18454v2 Announce Type: replace-cross Abstract: CPUs are critical for LLM serving due to their availability, cost efficiency, and edge applicability. However, efficient CPU serving is hinder

An Empirical Investigation of Practical LLM-as-a-Judge Improvement Techniques on RewardBench 2

arXiv:2604.13717v1 Announce Type: new Abstract: LLM-as-a-judge, using a language model to score or rank candidate responses, is widely used as a scalable alternative to human evaluation in RLHF pipeli

Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection

arXiv:2604.13899v1 Announce Type: new Abstract: Instruction-tuned LLMs can annotate thousands of instances from a short prompt at negligible cost. This raises two questions for active learning (AL): c

Evaluating LLM-Based Translation of a Low-Resource Technical Language: The Medical and Philosophical Greek of Galen

arXiv:2602.24119v2 Announce Type: replace Abstract: Purpose: This study evaluates the quality of commercial large language model (LLM) machine translation (MT) for Ancient Greek technical prose and be

Applications

Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin

arXiv:2604.13062v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for sym

applicationsarxiv-cs-cl

AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection

arXiv:2604.11950v1 Announce Type: cross Abstract: While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation,

AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow

arXiv:2604.11945v1 Announce Type: cross Abstract: High-fidelity numerical simulation of subsurface flow is computationally intensive, especially for many-query tasks such as uncertainty quantification

Cooperative Memory Paging with Keyword Bookmarks for Long-Horizon LLM Conversations

arXiv:2604.12376v1 Announce Type: cross Abstract: When LLM conversations grow beyond the context window, old content must be evicted -- but how does the model recover it when needed? We propose cooper

EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics in a Hybrid LLM Architecture

arXiv:2604.12167v1 Announce Type: new Abstract: We present (Experience-Modulated Biologically-inspired Emergent Reasoning), a hybrid cognitive architecture that reorganises the relationship between la

From Myopic Selection to Long-Horizon Awareness: Sequential LLM Routing for Multi-Turn Dialogue

arXiv:2604.12385v1 Announce Type: new Abstract: Multi-turn dialogue is the predominant form of interaction with large language models (LLMs). While LLM routing is effective in single-turn settings, ex

safetyarxiv-cs-cl

Tutorials

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines

arXiv:2604.12223v1 Announce Type: cross Abstract: Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetl

tutorialsarxiv-cs-ai

ProbeLogits: Kernel-Level LLM Inference Primitives for AI-Native Operating Systems

model-releasesarxiv-cs-lg

arXiv:2604.11943v1 Announce Type: cross Abstract: An OS kernel that runs LLM inference internally can read logit distributions before any text is generated -- and act on them as a governance primitive

Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning

arXiv:2604.10701v1 Announce Type: cross Abstract: Credit assignment is a central challenge in reinforcement learning (RL). Classical actor-critic methods address this challenge through fine-grained ad

Decomposing and Reducing Hidden Measurement Error in LLM Evaluation Pipelines

arXiv:2604.11581v1 Announce Type: new Abstract: LLM evaluations drive which models get deployed, which safety standards get adopted, and which research conclusions get published. Yet these scores carr

From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience

arXiv:2604.11041v1 Announce Type: new Abstract: Semiconductor supply chains face unprecedented resilience challenges amidst global geopolitical turbulence. Conventional Large Language Model (LLM) plan

Improving LLM Unlearning Robustness via Random Perturbations

arXiv:2501.19202v5 Announce Type: replace Abstract: Here, we show that current LLM unlearning methods inherently reduce models' robustness, causing them to misbehave even when a single non-adversarial

Infusing Theory of Mind into Socially Intelligent LLM Agents

arXiv:2509.22887v2 Announce Type: replace Abstract: Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based socia

Toward Generalized Cross-Lingual Hateful Language Detection with Web-Scale Data and Ensemble LLM Annotations

arXiv:2604.09625v1 Announce Type: new Abstract: We study whether large-scale unlabelled web data and LLM-based synthetic annotations can improve multilingual hate speech detection. Starting from texts

When More Thinking Hurts: Overthinking in LLM Test-Time Compute Scaling

arXiv:2604.10739v1 Announce Type: new Abstract: Scaling test-time compute through extended chains of thought has become a dominant paradigm for improving large language model reasoning. However, exist

When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies

arXiv:2604.10996v1 Announce Type: cross Abstract: Can large language models (LLMs) generate continuous numerical features that improve reinforcement learning (RL) trading agents? We build a modular pi

safetyarxiv-cs-ai

AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting

arXiv:2511.08947v5 Announce Type: replace Abstract: Time series forecasting plays a crucial role in decision-making across many real-world applications. Despite substantial progress, most existing met

13 Apr 2026

BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis

arXiv:2604.07361v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have been widely used in diverse brain network analysis tasks based on preprocessed functional magnetic resonance imagi

safetyarxiv-cs-lg

13 Apr 2026

Reinforcement-aware Knowledge Distillation for LLM Reasoning