Search: “llm”

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

arXiv:2604.03532v2 Announce Type: replace Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Repres

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Research

Leak It: A Probabilistic Approach to Training-Data Extraction from Black-Box Language Models

DGX agent

arXiv:2608.00144v1 Announce Type: cross Abstract: Membership inference (MIA) on language models is usually summarised by an aggregate ROC-AUC, but such evaluations are confounded: model-free blind bas

researcharxiv-cs-cl

4 Aug 2026

OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution

arXiv:2608.00677v1 Announce Type: new Abstract: AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model i

PredAct-Bench: Benchmarking Tool-Augmented Dialogue under Controlled Tool Noise

arXiv:2608.02372v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in task-oriented dialogue systems that support multi-step decision-making in high-stakes domains

Pruned BPE: Post-training Visibility Pruning and Token Reallocation for Byte Pair Encoding

arXiv:2608.00837v1 Announce Type: new Abstract: Byte Pair Encoding (BPE) is widely used for subword tokenization, but standard BPE exposes every learned merge token to the downstream model, including

QR-Erase: Efficient Subspace-Based Machine Unlearning with Layer Localization

arXiv:2608.01422v1 Announce Type: new Abstract: Machine unlearning seeks to remove targeted information from trained models without requiring costly retraining. Existing optimization-based methods oft

Qwen-CUA: Native Computer Use for (almost) Everything

arXiv:2608.02352v1 Announce Type: cross Abstract: Native computer use offers a general interface for agents to operate almost any software available to people, but requires long-horizon state tracking

Role Steering of Language Models for Social Simulations

arXiv:2608.00023v1 Announce Type: new Abstract: Social simulations built from language-model agents need role-conditioned behavior that can be checked before agents are placed into a simulated populat

Semantic Alignment of AI Models: Concept Collapse, Checkpoint Dynamics, and Cross-Lingual Transfer

arXiv:2608.01585v1 Announce Type: new Abstract: Language model benchmarking is a difficult task. Outcome reasoning alone does not test the model's conceptualization of language and popular open-source

safetyarxiv-cs-cl

SERL-SQL: Selective Hindsight Distillation for Text-to-SQL Reinforcement Agentic Learning

arXiv:2608.00485v1 Announce Type: new Abstract: Recent Text-to-SQL systems increasingly rely on multi-turn interaction, execution feedback, and reinforcement learning. However, most existing methods u

Sixteen models, fewer than two voices: measuring ensemble dispersion where no answer is uniquely correct

arXiv:2608.00285v1 Announce Type: new Abstract: Sixteen language models drawn from ten families produced, on average, the semantic diversity of 1.69 distinct formulations of a psychotherapeutic case,

TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation

arXiv:2603.00025v3 Announce Type: replace Abstract: Direct Preference Optimization (DPO) is effective for offline alignment but poorly matched to ontology-driven structured prediction, where preferred

The Learning Objective Governs Perceptual Narrowing: A Cross-Lingual, Layer-Wise, Ten-Seed Study of Self-Supervised Speech Encoders

arXiv:2608.00507v1 Announce Type: new Abstract: Perceptual narrowing---the developmental loss of non-native phoneme discrimination in the first year of life itep{werker1984}---is a canonical developme

Understanding Sparse Attention Selectivity in Long-Context Foundation Models via Counterfactual Evaluation

arXiv:2608.01676v1 Announce Type: new Abstract: Sparse attention is widely deployed in long-context serving stacks, yet no framework audits how discarding blocks changes the influence of specific cont

Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narratives

arXiv:2607.29064v1 Announce Type: cross Abstract: Police crash narratives contain information that may supplement structured crash databases, but manual review is labor-intensive and it remains unclea

Can AI Evaluate AI Scientists? A Benchmarking Study of Autonomous Research Generation Systems Using Automated Multi-Model Review

arXiv:2607.28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery. However, evaluating and compari

Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL

arXiv:2607.29246v1 Announce Type: new Abstract: Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a

DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat

arXiv:2607.29577v1 Announce Type: new Abstract: Games and simulators make valuable benchmarks by turning decisions into measurable outcomes, but many current suites under-test rules-rich tactical reas

EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses

arXiv:2607.28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence. Existing diagnosis-prediction benchmarks are poorly sui

ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics

arXiv:2603.11872v3 Announce Type: replace-cross Abstract: Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI sy

agentsarxiv-cs-ai

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

arXiv:2603.24917v3 Announce Type: replace Abstract: Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across seque

FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale

arXiv:2601.22146v3 Announce Type: replace Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised 'predict the next word' object

Generative AI in Action: Field Experimental Evidence from Alibaba's Customer Service Operations

arXiv:2603.29888v2 Announce Type: replace-cross Abstract: In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations. In a l

agentsarxiv-cs-ai

HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation

arXiv:2607.29600v1 Announce Type: new Abstract: Vision-and-language navigation (VLN) enables robots to follow instructions in previously unseen environments. Recently, a training-free paradigm has eme

agentsarxiv-cs-ro

Language Models Agree With Each Other, Not With Readers

arXiv:2607.29274v1 Announce Type: cross Abstract: Claims that language models homogenise are usually measured against human judgements collected for the study, which makes the human side an artifact o

Learning Latent Reasoning Traces for Scalar Reward Models End-to-End

arXiv:2607.29185v1 Announce Type: new Abstract: Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs ena

safetyarxiv-cs-cl

Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

arXiv:2607.28802v1 Announce Type: new Abstract: Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the

MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification

arXiv:2607.28681v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain. Recent methods that utilize graph neural networks (GNN

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation

arXiv:2603.17205v3 Announce Type: replace-cross Abstract: Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce

researcharxiv-cs-ai

Reproducing Human Individual Motor Signatures: A Data-Driven Approach for Repetitive Motion

arXiv:2503.15225v3 Announce Type: replace-cross Abstract: The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities---such as rehabilitation therapy, spor

agentsarxiv-cs-ai

Hardware

Studying quantization trade-offs for efficient inference deployment in machine translation

arXiv:2607.29397v1 Announce Type: new Abstract: Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latenc

hardwarearxiv-cs-cl

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

arXiv:2607.28687v1 Announce Type: cross Abstract: As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routi

researcharxiv-cs-ai

Applications

The Checking Problem: What must be true before AI ships in a regulated firm

arXiv:2607.28666v1 Announce Type: new Abstract: Enterprise AI programmes stall at a rate that is widely quoted and poorly explained. This paper measures the mechanism. Six document-heavy workflows of

applicationsarxiv-cs-cl

Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents

arXiv:2607.29254v1 Announce Type: new Abstract: AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential

ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine

model-releasesarxiv-cs-cv

arXiv:2607.28625v1 Announce Type: new Abstract: Embodied intelligence faces a fundamental data bottleneck. Models must capture how first-person perception, whole-body motion, dexterous manipulation, o

AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching

arXiv:2607.27130v1 Announce Type: new Abstract: Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching. The existing OM systems identify only

Ask don't tell: Reducing sycophancy in large language models

arXiv:2602.23971v4 Announce Type: replace-cross Abstract: Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an align

Local Ai

Auto Research for Materials: Auditable AI-Scientist Workflows with Held-Out Transfer

arXiv:2607.17100v2 Announce Type: replace-cross Abstract: Auto Research uses language-model agents to propose, implement, and evaluate machine-learning changes in a closed loop, but is usually judged

local-aiarxiv-cs-ai

Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning

model-releasesarxiv-cs-lg

arXiv:2607.27968v1 Announce Type: new Abstract: Machine unlearning seeks to selectively remove specific knowledge from trained language models without full retraining, a growing necessity under privac

Applications

Building a User Foundation Model for the Open Web

arXiv:2607.28019v1 Announce Type: new Abstract: User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments whe

applicationsarxiv-cs-lg

Tutorials

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising

arXiv:2607.28236v1 Announce Type: cross Abstract: Pre-trained language models have significantly improved sentence representation learning, yet their embedding remain sensitive to semantic preserving

tutorialsarxiv-cs-cl

Applications

Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO

arXiv:2607.27756v1 Announce Type: cross Abstract: Spoken dialog systems are typically designed for clean, dyadic interactions in which a single user and an assistant take turns speaking. Real-world so

applicationsarxiv-cs-cl

Context-Informed Ship Trajectory Prediction via Conditional Attention

arXiv:2607.27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation. While recent Transformer-based architect

safetyarxiv-cs-lg

Creative Transformation in Literary Texts: Modelling Change Across Representational Levels

arXiv:2607.28513v1 Announce Type: new Abstract: Creativity is often framed as the production of novelty, yet many cultural works emerge through transformation of earlier artifacts and not through isol

safetyarxiv-cs-cl

Cybersecurity Detection Classification with Reasoning-enabled Language Models

arXiv:2607.28460v1 Announce Type: new Abstract: A major issue in Security Operations Centers (SOCs) is alert fatigue, as the number of detections reported is more than staff can triage in a given day.

researcharxiv-cs-lg

Epistemic diversity across language models mitigates knowledge collapse

model-releasesarxiv-cs-lg

arXiv:2512.15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems. This feedback loop can degrade model quality,

Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?

arXiv:2607.26367v1 Announce Type: new Abstract: An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model. We study this skill as an AI-agent t

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

arXiv:2607.28568v1 Announce Type: new Abstract: Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a