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FiRE: Enhancing MLLMs with Fine-Grained Context Learning for Complex Image Retrieval

arXiv:2607.27959v1 Announce Type: new Abstract: Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significa

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First-order Constrained Trilevel Optimization Over Distributed Networks for Robust Coreset Selection

DGX agent

arXiv:2607.27632v1 Announce Type: new Abstract: With the rapid advancement of the Internet of Things (IoT), massive amounts of data are generated across distributed edge networks. Training models on f

local-aiarxiv-cs-lg

31 Jul 2026

Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents

arXiv:2607.27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless. We test this claim for multi-turn, tool-calling agents, where it n

FlexiGrad: Adaptive Gradient Modulation for Hierarchical Fine-Grained Classification

model-releasesarxiv-cs-cv

arXiv:2607.17563v2 Announce Type: replace Abstract: Many fine-grained recognition tasks contain hierarchical labels such as order, family and species. Although this supervision should be beneficial, j

Flux-OPD: On-Policy Distillation with Evolving Contexts

arXiv:2607.28022v1 Announce Type: new Abstract: Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. C

Tutorials

FootprintNet: State-Transition-Guided Dynamic Footprint Learning for Multi-temporal Remote Sensing Change Detection

arXiv:2607.27969v1 Announce Type: new Abstract: Despite substantial progress in remote sensing multi-temporal change detection (MTCD), most existing MTCD methods still represent the dynamic process at

tutorialsarxiv-cs-cv

Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States

arXiv:2607.27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring rema

FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing

model-releasesarxiv-cs-ai

arXiv:2508.02092v3 Announce Type: replace-cross Abstract: Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable

From Expert Reduction to Behavioral Divergence: Tracing Numerical State through Sparse MoE Inference

arXiv:2607.28097v1 Announce Type: new Abstract: Mathematically equivalent expert-reduction orders can produce observably different sparse-MoE executions. We isolate this effect in native DeepSeek-V4-F

From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

arXiv:2607.27654v1 Announce Type: new Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of do

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

Applications

Fully Inductive Cardinality Estimation

arXiv:2607.28311v1 Announce Type: cross Abstract: Query optimization of Basic Graph Patterns (BGP) SPARQL queries over Knowledge Graphs (KG) requires accurate cardinality estimation. Recently publishe

applicationsarxiv-cs-lg

Functional Cache Grafting: Robust and Rapid Code-Policy Synthesis for Embodied Agents

arXiv:2606.13097v2 Announce Type: replace-cross Abstract: Code-writing large language models (CodeLLMs) generate executable code policies for embodied agents by translating natural language goals and

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FunL2O: LLM-Guided Feature Function Design for Learning to Optimize

arXiv:2607.27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other

researcharxiv-cs-lg

Generalization and Trade-off in Adversarial Training: An RKHS Perspective via Kernel Integral Operators

arXiv:2607.27995v1 Announce Type: cross Abstract: Adversarial training has emerged as a powerful approach for protecting models against adversarial attacks in a broad range of real-world applications.

Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

arXiv:2607.27975v1 Announce Type: new Abstract: We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-value

researcharxiv-cs-lg

Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes

arXiv:2607.28505v1 Announce Type: new Abstract: The rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the le

GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation

arXiv:2607.28082v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in structured query generation, making them a natural choice for Text-to-SPARQL, whic

GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation

arXiv:2607.28397v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) over knowledge graphs requires retrievers that can effectively capture both graph structure and semantic informat

Goal-Oriented Semantic Communication for ISAC-Enabled Robotic Obstacle Avoidance

arXiv:2603.02291v2 Announce Type: replace Abstract: We investigate an integrated sensing and communication (ISAC)-enabled BS for the unmanned aerial vehicle (UAV) obstacle avoidance task, and propose

researcharxiv-cs-ro

Hardware

GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure

arXiv:2607.26181v1 Announce Type: new Abstract: Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a co

hardwarearxiv-cs-ai

Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search

arXiv:2607.27422v1 Announce Type: new Abstract: Good action rankings do not make a contrastive critic safe to maximize. These critics increasingly act as value-like objectives for best-of-K selection,

Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning

arXiv:2607.27766v1 Announce Type: new Abstract: On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference. Thi

GradMAP: Faster Layer Pruning with Gradient Metric and Projection Compensation

arXiv:2602.14649v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning abilities, but their high computational costs limit their practical deployment. Recent studies

Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

arXiv:2607.26656v1 Announce Type: cross Abstract: Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real C

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Graph Neural Multilevel Preconditioners for Iterative Solvers

arXiv:2607.28456v1 Announce Type: cross Abstract: Solving large, sparse linear systems is a core task in scientific computing, and efficient iterative solvers rely critically on effective and robust p

Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets

arXiv:2607.28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically re

Group-Reflective Self-Distillation for Agentic Reinforcement Learning

arXiv:2607.28076v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language model agents. However, terminal rewards provide only co

Tutorials

Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method

arXiv:2607.26634v1 Announce Type: cross Abstract: Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). A

tutorialsarxiv-cs-ai

GuidedRAG: Semantic Steering of Retrieval-Augmented Generation

arXiv:2607.26071v1 Announce Type: cross Abstract: In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage

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GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning

model-releasesarxiv-cs-ai

arXiv:2607.26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather t

Agents

GVR-Coder: A Visual-Feedback Framework for Structured SVG Generation in Complex Document and Meeting Scenarios

arXiv:2607.28073v1 Announce Type: cross Abstract: In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load. To facilitate efficient informa

agentsarxiv-cs-cv

GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference

arXiv:2607.27694v1 Announce Type: cross Abstract: Low-bit quantization is essential for efficient LLM inference, and both rotation and fine-grained group quantization have shown individual promise. Ho

Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA

arXiv:2502.10497v2 Announce Type: replace Abstract: Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, parti

Hallucinations Leave a Grounding Signature:Verifier-Guided Decoding for Selective Object Correction

arXiv:2607.27823v1 Announce Type: new Abstract: Large vision-language models (LVLMs) often hallucinate objects that are absent from an image. Despite recent progress, existing mitigation methods still

researcharxiv-cs-cv

HALO: Heterogeneous Admission through Localized Obligations for Safe Agentic Execution

arXiv:2607.27636v1 Announce Type: cross Abstract: Recent agentic AI systems may return a heterogeneous response containing notices, requests, handoffs, and actions. Conditions can change before extern

safetyarxiv-cs-ro

Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer

arXiv:2607.28394v1 Announce Type: new Abstract: Hand-object interaction (HOI) modeling remains challenging because it requires joint reasoning about hand articulation, object geometry, contact, semant

researcharxiv-cs-cv

HARGO: Heterogeneity-Aware Reward-Guided Optimization for RL Post-Training of LLMs on HPC Tasks

arXiv:2607.28301v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) can equip large language models (LLMs) with domain knowledge for high-performance computing (HPC) tasks such as data race d

Harness-G: A Graph-Structured Harness for Search Agents

arXiv:2607.27652v1 Announce Type: new Abstract: Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions u

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Harnessing the Potential of Optimizing Data Mixtures via Bayesian Domain Reweighting

arXiv:2607.27928v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is fundamentally influenced by the distributional composition of multi-domain pre-training data. While m

Applications

HealthCAT: An Interpretable Encoder-only Transformer Framework for Health Indicator Prediction and Temporal Interpretation of Wearable Sensor Data

arXiv:2607.27635v1 Announce Type: cross Abstract: Wearable sensors continuously capture fine-grained multivariate time-series data, providing opportunities to model behavioural patterns associated wit

applicationsarxiv-cs-lg

Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study

arXiv:2607.27577v1 Announce Type: cross Abstract: Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.g., music-only or v

Hierarchical Multilevel Monte Carlo for Order-Optimal Neural Actor-Critic in Average-Reward CMDPs

arXiv:2607.28390v1 Announce Type: new Abstract: Constrained Markov Decision Processes (CMDPs) provide a natural framework for reinforcement learning in safety-critical applications, where agents maxim

HOMER: Huber-of-Means for Efficient and Robust Estimation in Hilbert Spaces

arXiv:2607.27532v1 Announce Type: cross Abstract: Heavy tails weaken high-confidence control for the empirical mean. Geometric median-of-means (MOM) also lacks a threshold that moves toward mean effic

researcharxiv-cs-lg

How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series

arXiv:2601.10191v2 Announce Type: replace Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet th

researcharxiv-cs-ai

HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs

arXiv:2607.27379v1 Announce Type: new Abstract: High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds

safetyarxiv-cs-cl

Human diversity fuels collective creativity that large language models cannot simulate or sustain

arXiv:2607.26899v1 Announce Type: cross Abstract: Diverse human groups produce diverse ideas, the raw material of innovation. Generative AI challenges this engine twice over: everyday AI assistance ma

researcharxiv-cs-ai

Human Mesh Modeling for Anny Body

arXiv:2511.03589v3 Announce Type: replace Abstract: Parametric body models provide the structural basis for many human-centric tasks, yet existing models often rely on costly 3D scans and learned shap

researcharxiv-cs-cv