Search: “models”

Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA

arXiv:2607.21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the nak

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Explainable quantum-compressed machine learning for complex fluid flows

DGX agent

arXiv:2607.21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear f

model-releasesarxiv-cs-lg

27 Jul 2026

fMRI2Face: A Full-HD fMRI-Video Dataset and Geometry-Guided Neural Decoding Framework for Dynamic Human Face Reconstruction

arXiv:2607.22302v1 Announce Type: new Abstract: Reconstructing dynamic human faces from brain activity provides a powerful way to study how the mind perceives identity, expression, and facial motion.

Tutorials

FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts

arXiv:2607.22075v1 Announce Type: new Abstract: Continual Learning for Named Entity Recognition (CLNER) enable models to incrementally learn new entity types without forgetting previously acquired one

tutorialsarxiv-cs-cl

IR275K: A Benchmark for Infrared Multi-Frame Super-Resolution Toward Efficient Remote Sensing

arXiv:2607.22380v1 Announce Type: new Abstract: Efficient processing is becoming increasingly important in infrared remote sensing, where satellite constellations produce large volumes of observations

Research

Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs

arXiv:2606.18681v2 Announce Type: replace Abstract: Despite their remarkable performance, Vision Language Models (VLMs) incur substantial computational overhead due to the large number of visual token

researcharxiv-cs-cv

Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Mode

arXiv:2607.22083v1 Announce Type: cross Abstract: We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-

safetyarxiv-cs-cl

NWaaS: A Non-Intrusive and Privacy-Preserving Watermarking-as-a-Service System with Adaptive Resource Scheduling

arXiv:2507.18036v2 Announce Type: replace-cross Abstract: Securing intellectual property (IP) in Machine Learning as a Service is critical yet challenging. While deep neural network watermarking serve

One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments

arXiv:2607.22119v1 Announce Type: cross Abstract: Multi-stream robot manipulation policies achieve unparalleled sample efficiency and generalization by modeling actions relative to environmental refer

Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning

arXiv:2507.01551v3 Announce Type: replace-cross Abstract: Process Reinforcement Learning~(PRL) has demonstrated considerable potential in enhancing the reasoning capabilities of Large Language Models~

safetyarxiv-cs-cl

AI Assistants Overassist

arXiv:2607.21306v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance from AI assis

Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs

arXiv:2607.20479v1 Announce Type: new Abstract: Training probes to detect deceptive outputs from large language models is still an open problem. Recent work has demonstrated that detection probes fail

Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning

arXiv:2607.21558v1 Announce Type: new Abstract: Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy

safetyarxiv-cs-ai

CAMeR: Keyword-Gated Hybrid Activation for Adaptive Memory Retention in LLM Agents

arXiv:2607.20458v1 Announce Type: cross Abstract: Large language model (LLM) agents operating over extended dialogues accumulate vast amounts of information, yet existing memory systems either retain

CRAG-MM-Diagnostics: Enabling Stage-Wise Analysis of Knowledge-Intensive VQA

arXiv:2607.21155v1 Announce Type: cross Abstract: Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants by requiri

DFAH-Bench: Benchmarking Observable Agent Instability in Financial Decision-Making

arXiv:2607.20491v1 Announce Type: new Abstract: Standard evaluation benchmarks measure what a tool-using agent decides, not whether it arrives at that decision through the same process each time. We i

Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention

arXiv:2607.20457v1 Announce Type: cross Abstract: Inference with large language models (LLMs) on long sequences is computationally expensive due to the quadratic complexity of self-attention. Distribu

Evaluating the Effectiveness of Persona Simulation in Opinion Prediction with GPT-4.1

arXiv:2607.20589v1 Announce Type: new Abstract: Persona simulation involves utilizing large language models (LLMs) to anticipate human choices or interactions based on specific characteristic informat

GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries

arXiv:2607.20757v1 Announce Type: cross Abstract: Transformers are known to have internal continuous symmetries that leave outputs invariant, while modifying quantization. GaugeQuant leverages this in

Geometric Configurations of Perturbed Jailbreak Prompts

arXiv:2607.20581v1 Announce Type: cross Abstract: Perturbation techniques that turn unsuccessful jailbreak prompts into successful ones are continuously evolving, constituting a major security threat

Gradient Concentration, Not Weight Saliency, Explains Representation-Level Class Unlearning

arXiv:2607.21353v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data while preserving model utility. Many state-of-the-art approaches pursue this g

Is MoE Routing a Huffman Code? Discovering the Frequency-Diversity Law in Chain-of-Thought

arXiv:2607.20427v1 Announce Type: cross Abstract: Mixture-of-Experts architectures have revolutionized scaling, yet the underlying logic of their routing remains a black box. In this paper, we uncover

Isolating LLM Alignment from Regex: Zero Coverage and Metric-Dependent Divergence Under Adversarial Mutation

arXiv:2607.20494v1 Announce Type: new Abstract: Production LLM applications commonly stack a regex filter in front of model-side alignment; prior work found no measurable coverage gain from adding a l

Research

Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts

arXiv:2607.20462v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output

researcharxiv-cs-ai

Hardware

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

arXiv:2607.20709v1 Announce Type: new Abstract: Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agen

hardwarearxiv-cs-ai

PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails

arXiv:2607.20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks. In practice, users frequently provide underspeci

PILD: Physics-Informed Learning via Diffusion

arXiv:2601.21284v2 Announce Type: replace-cross Abstract: Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits

safetyarxiv-cs-ai

PromptPack: Scaling LLM Annotation Agents for Online Recommendation

arXiv:2607.20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deploying a single

RE-AD: Real-Time Requirement Adherence for Data Labeling

arXiv:2607.20455v1 Announce Type: cross Abstract: Human-annotated data remains fundamental to training frontier Large Language Models (LLMs). However, crowd-sourced annotations often suffer from quali

Robust Critics: Defending LLMs Against Multi-Turn Attacks

arXiv:2607.20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is one of the c

safetyarxiv-cs-ai

Local Ai

Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating

arXiv:2607.20481v1 Announce Type: new Abstract: Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained rout

local-aiarxiv-cs-ai

Scene Parameter Saliency via Differentiable Light Transport

arXiv:2607.21562v1 Announce Type: new Abstract: Gradient-based saliency methods reveal which input features most influence a neural network's output, and are a standard tool for model interpretability

Tutorials

SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations

arXiv:2607.20445v1 Announce Type: new Abstract: In conversations, human emotions are transient; however, they tend to persist across multiple utterances. For example, we rarely switch instantly betwee

tutorialsarxiv-cs-cl

Applications

SoccerSynth Field: enhancing field detection with synthetic data from virtual soccer simulator

arXiv:2503.13969v2 Announce Type: replace Abstract: Field detection in team sports is an essential task in sports video analysis. However, collecting large-scale and diverse real-world datasets for tr

applicationsarxiv-cs-cv

StabilityBench: Benchmarking Instability in LLMs

arXiv:2607.20558v1 Announce Type: cross Abstract: AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services. Yet their real-world behavior remains poor

Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs

arXiv:2509.14257v3 Announce Type: replace-cross Abstract: Large Language Model agents achieve strong performance on multi-step reasoning and tool-use tasks, but their impressive capabilities typically

Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction

arXiv:2607.20911v1 Announce Type: new Abstract: We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring pro

The Devil is in the Spectrum: Mitigating Representation Collapse in LLMs via Topologically Regularized Side-Path

arXiv:2607.20484v1 Announce Type: new Abstract: Large Language Models (LLMs) are fundamentally limited by representation collapse, a bottleneck that severely degrades long-context performance. We iden

The Geometry of Personality: Activation Steering with Jungian Cognitive Functions

arXiv:2607.20803v1 Announce Type: cross Abstract: Activation steering enables control and interpretation of LLMs, yet existing work primarily models personality through static trait frameworks such as

Three-Pronged Spectral Control for Federated Parameter Efficient Fine Tuning

arXiv:2607.20914v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning (PEFT) enables communication-efficient adaptation of large pretrained models on decentralized edge data, but i

WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance

arXiv:2511.12997v2 Announce Type: replace Abstract: Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tas

Webly Supervised Multi-Label Recognition: Evaluation Benchmark and Dual-Branch Multi-Label Contrastive Learning

arXiv:2607.20874v1 Announce Type: new Abstract: Training deep learning models with freely available web images can reduce their dependence on costly manual annotations. Although webly supervised learn

What is Good? Extracting and Testing Implicit Theories of Literary Quality from LLM Reasoning Traces

arXiv:2607.20425v1 Announce Type: new Abstract: What makes writing 'good' remains a persistent question in literary studies and computational linguistics. We present a two-study investigation of how r

When Are Reasoning-Based Guardrails Not Efficient? ResponseGuard: A Fast Vision-Language Guard for Real-Time Moderation

arXiv:2607.21401v1 Announce Type: cross Abstract: A vision-language AI assistant returns its answer as a stream of generated tokens. Therefore, a safety guard that watches that answer has to keep up w

Windowed-MTP: Removing the Full-Context Draft-KV Tax at Million-Token Context

arXiv:2607.21535v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive generation by having a cheap draft propose tokens that a target verifies in parallel. Frontier models

Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured Agent Skills

arXiv:2607.20999v1 Announce Type: new Abstract: Agent Skills package reusable procedural knowledge as external artifacts for frozen language-model agents, yet existing optimizers do not jointly resolv

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering

arXiv:2507.07046v3 Announce Type: replace-cross Abstract: Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial

23 Jul 2026

Agents

Agent-Centric Animal Pose Forecasting