Search: “llm”

When Safety Routing Breaks: Understanding Alignment Fragility under Benign Fine-Tuning

arXiv:2609.01455v1 Announce Type: cross Abstract: Benign fine-tuning severely weakens the safety alignment of large language models (LLMs), so we study why refusal behavior is so fragile. While prior

2 Sept 2026

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Model Releases

WorldBench: Culturally Grounded Benchmark for Multilingual Agents

DGX agent

arXiv:2609.01056v1 Announce Type: new Abstract: Despite the growing use of LLM-powered agents to solve multi-step tasks in complex environments, existing benchmarks rarely test state preservation, per

model-releasesarxiv-cs-ai

2 Sept 2026

Agent-Based Model Framework for the North Carolina Modeling Infectious Diseases Program (NC MInD ABM) Overview, Design Concepts, and Details Protocol

arXiv:2202.06853v2 Announce Type: cross Abstract: To help facilitate a variety of simulations related to healthcare facilities in North Carolina, we have developed an agent-based model (ABM) to accura

Agent2UCB: Agentic System for Generative Engine Optimization

arXiv:2608.29063v1 Announce Type: new Abstract: Large language model driven search engines such as Google AI Overviews and Perplexity have created new opportunities for Generative Engine Optimization

BAITBENCH: Measuring Agent Reward Hacking with Optional Shortcuts Planted in ML Tasks

arXiv:2608.30724v1 Announce Type: cross Abstract: LLM agents are increasingly used to run autonomous ML experiments, iterating on target metrics with little human oversight. Prior work has documented

Balance of Benchmarks: Semantic Density Reweighting for Benchmark Multiplicity and Task-Conditioned Evaluation

arXiv:2608.30044v1 Announce Type: new Abstract: Language models are commonly compared by averaging scores across a benchmark list with equal weight. Such lists grow through publication outside an expl

Co-Evolving Actor-Conditioned Critics for Non-Verifiable Generation

arXiv:2608.30397v1 Announce Type: new Abstract: Natural-language critiques provide supervision beyond scalar rewards for non-verifiable generation, which lacks deterministic verifiers. In critique-gui

Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction

arXiv:2601.05107v2 Announce Type: replace Abstract: As LLM-based agents are increasingly used in long-term interactions, cumulative memory is critical for enabling personalization and maintaining styl

Disappearing Ink: Obfuscation Breaks N-gram Code Watermarks in Theory and Practice

arXiv:2507.05512v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for code generation, making reliable identification of machine-generated code important for

researcharxiv-cs-ai

Evaluating Tiny Recursive Models Across Training for Code Generation

arXiv:2608.29376v1 Announce Type: new Abstract: Code generation increasingly relies on large transformer models, whose capability advances with scale. Yet such a scale is costly, creating demand for s

GuardianAgent: Policy-Conditioned Risk-Adaptive Anonymization with Verified Adversarial Escalation

arXiv:2608.29251v1 Announce Type: new Abstract: Privacy protection for live web traffic requires more than detecting private spans. Agent-based privacy protection systems must determine whether an out

HSRM: Hidden-State Reward Models for Test-Time Verification

arXiv:2608.30841v1 Announce Type: new Abstract: Large language models can often generate plausible mathematical reasoning traces, but reliably identifying the correct solution among multiple candidate

Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems

arXiv:2505.15741v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm de

Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length

arXiv:2512.07019v4 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications a

MIND: Unified Inquiry and Diagnosis RL with Criteria Grounded Clinical Supports for Psychiatric Consultation

arXiv:2603.03677v2 Announce Type: replace-cross Abstract: Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when e

More Haste, Less Speed: Weaker Single-Layer Watermark Improves Distortion-Free Watermark Ensembles

arXiv:2602.11793v2 Announce Type: replace-cross Abstract: Watermarking has emerged as a crucial technique for detecting and attributing content generated by large language models. While recent advance

Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models

arXiv:2601.15436v3 Announce Type: replace Abstract: We propose a novel perspective for probing LLM sycophancy in a direct and neutral way, mitigating various forms of uncontrolled bias, noise, or mani

PermitGPT: A Unified Generative-AI Pipeline for Construction Hazard Forecasting, Permit Prediction, and Community Impact

arXiv:2608.28728v1 Announce Type: new Abstract: Urban construction governance requires early decisions that connect workplace safety, permitting requirements, and community impact, yet the relevant ev

PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization

arXiv:2608.30597v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) simplifies alignment through pairwise comparisons but assumes all observed preferences are reliable. Real data of

ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research

arXiv:2606.20122v2 Announce Type: replace Abstract: Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The out

local-aiarxiv-cs-ai

Tutorials

StructSynth: Dependency Graphs as Generation Plans for Low-Data Tabular Synthesis with Language Models

arXiv:2508.02601v2 Announce Type: replace-cross Abstract: Tabular data derives its value from inter-feature dependencies, yet preserving them during synthesis is fragile when samples are scarce. Exist

tutorialsarxiv-cs-ai

Tensor Methods for Language Models: From Token Representation to Training, Adaptation, Inference, Compression, and Interpretability

arXiv:2608.30505v1 Announce Type: cross Abstract: Large language models (LLMs) are built from structured high-dimensional objects such as token representations, weights, adaptation updates, caches, an

Test-Time Scaling for Scientific Equation Discovery

arXiv:2608.28660v1 Announce Type: cross Abstract: Test-time scaling (TTS) improves language model reasoning by allocating additional test-time compute, but prior work mainly studies closed-ended tasks

The reach of a verification tool decides its value: A controlled study of verification surface, artifact quality, and cost in AI coding agents

arXiv:2608.28795v1 Announce Type: cross Abstract: Modern artificial-intelligence coding agents can be equipped with tools for checking their own work e.g. a linter, a boot probe, a shell, a screenshot

The Role of Network Topology and Opponent Information in Shaping Cooperation in Multi-Agent Reinforcement Learning Systems

arXiv:2608.28977v1 Announce Type: new Abstract: Several works have investigated the influence of graph topology on cooperation among artificial agents, while the majority of the literature has focused

TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning

arXiv:2608.30924v1 Announce Type: new Abstract: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on str

local-aiarxiv-cs-cl

XQDT: eXplainable and Quantitative Data-Text Alignment Metric with Feedback Signals

arXiv:2608.29948v1 Announce Type: new Abstract: Evaluating data-text alignment remains challenging: existing metrics often provide limited explanations for the scores, while prompt-based LLM-as-Judge

local-aiarxiv-cs-cl

BEACON: Behavior-Anchored Cross-Source Knowledge Graph Construction for Cyber Threat Intelligence

arXiv:2608.28394v1 Announce Type: cross Abstract: Cyber threat intelligence (CTI) is foundational to modern cyber defense, yet much of it resides in unstructured reports whose volume and heterogeneity

PersonaEdit: Representative Sample Selection for Personalized Model Editing

arXiv:2608.27816v1 Announce Type: new Abstract: Personalization has attracted growing interest in LLM applications, yet existing retrieval-based approaches depend heavily on retrieval quality and degr

Securing Multi-Agent GIS Systems: Risk Evaluation and Prompt Hardening Optimization

arXiv:2606.17092v2 Announce Type: replace-cross Abstract: Agentic systems are increasingly integrated with geographic information systems (GIS), where multi-agent coordination enables complex conversa

agentsarxiv-cs-cl

What Makes Agent Memory Useful for Reliable Unanswerable Question Handling?

arXiv:2608.27924v1 Announce Type: new Abstract: Reliable handling of unanswerable questions (UAQs) is critical for trustworthy LLM-based agents. Although memory is widely used in agent systems, its ro

agentsarxiv-cs-cl

Agent Mesh: Reliability Primitives for Non-Idempotent Agent Delegation - Identity Adequacy and Evidence Adequacy

arXiv:2608.26225v1 Announce Type: new Abstract: Autonomous agents increasingly perform bounded software tasks under an orchestrator that retries, resumes, and budgets them. The machinery such orchestr

AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design

arXiv:2608.26747v1 Announce Type: new Abstract: Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomousl

Evaluating AI Generated Summaries for Cancer Patients

arXiv:2608.26154v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly being integrated into digital health platforms to generate summaries of complex medical data. Although t

Instruction Quality Matters: Refining Instructions for Effective Preference Learning

arXiv:2608.26779v1 Announce Type: new Abstract: Preference learning optimizes models using response pairs, yet the informativeness of these pairs is fundamentally shaped by the instructions from which

MemToC: Benchmarking Memory-Tool Conflict Resolution in Large Language Models

arXiv:2608.26295v1 Announce Type: cross Abstract: Tool-augmented LLMs must arbitrate between two fallible sources when a tool return conflicts with their parametric memory, yet existing evaluations me

Omni-Interactive Universal Embedder

arXiv:2608.27044v1 Announce Type: new Abstract: Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their

Performance Foundations of Parallel & Distributed Reasoning Language Models

arXiv:2608.27046v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) and other RL-style post-training paradigms have been used for aligning large language models (LL

Prediction of Prediction (PoP): Inter-Layer Activation Fusion for Single-Pass Hallucination Detection in Large Language Models

arXiv:2608.27165v1 Announce Type: new Abstract: Autoregressive large language models (LLMs) routinely generate factually incorrect outputs with high decoding confidence, limiting their deployment in h

Redwood: A Frontier AI Accelerator Designed, Verified, and Deployed from Scratch in 2 Weeks by AI

arXiv:2608.26418v1 Announce Type: cross Abstract: Modern AI workloads and the hardware that runs them evolve on different timescales: architectural definition precedes volume silicon by years, while t

Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverable

arXiv:2608.26958v1 Announce Type: cross Abstract: Scaling model-generated data is usually viewed as improving distillation: more examples should increase coverage, reduce noise, and produce stronger s

SkillChain: Closing the Loop on Skill Evolution for Image-Based E-Commerce AI Assistants

arXiv:2606.12984v2 Announce Type: replace Abstract: Image-based AI assistants are now deployed at production scale on e-commerce platforms, where a single uploaded image can trigger fundamentally diff

Surgical Alignment in Knowledge Graph Training for Clinical Diagnosis with Large Language Models

arXiv:2608.26587v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) offer structured medical knowledge that can ground large language model (LLM) reasoning in clinical diagnosis applicat

local-aiarxiv-cs-cl

TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack

arXiv:2608.26126v1 Announce Type: new Abstract: Telecommunications is a high-leverage domain for large language model (LLM)-based reasoning because routine engineering workflows require joint groundin

Cross-Dataset Stability of Expert-Informed Skill Prompting and Fine-Tuning for Chinese Metaphor Identification

arXiv:2608.25579v1 Announce Type: new Abstract: Metaphor-identification performance can change markedly across datasets that differ in text distribution and annotation policy. We examine whether a fix

E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search

arXiv:2510.22733v3 Announce Type: replace Abstract: Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-bas

From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset

arXiv:2601.04632v2 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance on many tasks, but their progress remains uneven across languages and cultures, often reflec

Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting

arXiv:2608.25115v1 Announce Type: new Abstract: Existing methods for improving Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or