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arXiv:2605.27944v1 Announce Type: new Abstract: With rapid advances in audio-visual generative models, reliable forgery detection becomes increasingly critical. Existing methods for audio-visual deepf

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FundaPod: A Multi-Persona Agent Pod Platform with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research

DGX agent

arXiv:2605.27864v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered on p

agentsarxiv-cs-ai

28 May 2026

GenSBI: Generative Methods for Simulation-Based Inference in JAX

arXiv:2605.27499v1 Announce Type: new Abstract: Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending na

researcharxiv-cs-lg

Global Policy-Space Response Oracles for Two-Player Zero-Sum Games

arXiv:2605.28273v1 Announce Type: new Abstract: The Policy-Space Response Oracles (PSRO) framework scales equilibrium computation to large zero-sum games by iteratively expanding a restricted strategy

Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems

arXiv:2605.27766v1 Announce Type: new Abstract: LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongsi

safetyarxiv-cs-ai

GRADE: Generalizable Reasoning-Aware Dialogue Evaluation for AI Tutors

arXiv:2605.27866v1 Announce Type: new Abstract: Evaluating AI tutor responses requires more than factual correctness: tutors must identify mistakes, locate errors, provide guidance, and offer actionab

GradientStabilizer:Fix the Norm, Not the Gradient

arXiv:2502.17055v4 Announce Type: replace-cross Abstract: Training instability in modern deep learning systems is frequently triggered by rare but extreme gradient-norm spikes, which can induce oversi

Graph Neural Networks for Source Detection: A Review and Benchmark Study

arXiv:2512.20657v2 Announce Type: replace-cross Abstract: The source detection problem arises when an epidemic process unfolds over a contact network, and the objective is to identify its point of ori

Local Ai

HarmoVid: Relightful Video Portrait Harmonization

arXiv:2605.28811v1 Announce Type: new Abstract: We present a method for harmonizing the lighting of a foreground video to match a target background scene, adjusting shadows, color tone, and illuminati

local-aiarxiv-cs-cv

HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation

arXiv:2603.14773v2 Announce Type: replace-cross Abstract: Fine-tuning large models on edge devices is severely hindered by the memory-intensive backpropagation (BP) in standard frameworks like federat

Local Ai

Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting

arXiv:2603.16985v2 Announce Type: replace Abstract: Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibil

local-aiarxiv-cs-lg

Internally Referenced Low-Light Enhancement

arXiv:2605.28605v1 Announce Type: new Abstract: Self-supervised low-light image enhancement (LLIE) is highly appealing as it eliminates the reliance on external paired data. However, the lack of exter

IRPO: Boosting Image Restoration via Post-training GRPO

arXiv:2512.00814v3 Announce Type: replace Abstract: Post-training has become effective for high-level generation, but its role in low-level vision remains underexplored. Existing image restoration met

researcharxiv-cs-cv

Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning

arXiv:2605.28495v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning. It independently updates its low-rank factors (A and B), creating

Knowledge Dependency Estimation for Reliable Question Answering

arXiv:2605.28047v1 Announce Type: new Abstract: Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In

Learning Compositional Latent Structure with Vector Networks

arXiv:2605.28007v1 Announce Type: cross Abstract: Deep networks are powerful function approximators, but they typically store many different computations in shared weight matrices, making it difficult

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs

arXiv:2507.06999v2 Announce Type: replace-cross Abstract: Reasoning is essential for large language models (LLMs), especially in complex tasks such as mathematical problem solving. However, multimodal

Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

arXiv:2605.27413v1 Announce Type: cross Abstract: Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design

LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts

arXiv:2602.11564v2 Announce Type: replace Abstract: Recent advances in video diffusion models have significantly improved visual quality, yet ultra-high-resolution (UHR) video generation remains a for

researcharxiv-cs-cv

LV-OSD: Language-Vision-Complementary Open-Set Object Detection

arXiv:2605.28271v1 Announce Type: new Abstract: Object detection is an important task in computer vision, which aims to detect the objects of interest. through the given category list or query images.

MaskClaw: Edge-Side Personalized Privacy Arbitration for GUI Agents with Behavior-Driven Skill Evolution

model-releasesarxiv-cs-cl

arXiv:2605.28646v1 Announce Type: cross Abstract: GUI agents rely on screenshots to infer intent and operate across applications, but these screenshots often contain private messages, medical records,

MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation

arXiv:2502.12468v2 Announce Type: replace-cross Abstract: The LLM-as-a-Judge paradigm shows promise for evaluating generative content but lacks reliability in reasoning-intensive scenarios, such as pr

MemCog: From Memory-as-Tool to Memory-as-Cognition in Conversational Agents

arXiv:2605.28046v1 Announce Type: new Abstract: Existing agent memory systems universally follow what we term a Memory-as-Tool paradigm where a single query triggers one-shot retrieval of flat passage

MeniOmni: A Structured Multimodal Benchmark for Holistic Meniscus Injury Assessment

arXiv:2605.28161v1 Announce Type: new Abstract: Clinical diagnosis of meniscus injuries requires radiologists to integrate volumetric MRI evidence with patient context (e.g., sex, age, BMI) and to pro

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation

arXiv:2605.12515v2 Announce Type: replace Abstract: Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's langua

Narrative Flattening: How Post-Training Compresses Thematic, Affective, and Stylistic Variation in LLM Fiction

arXiv:2605.27878v1 Announce Type: new Abstract: Large language models produce fluent fiction, yet their creative output is widely seen as flat. We ask where this quality originates in the training and

On the Intrinsic Limits of Transformer Image Embeddings in Non-Solvable Spatial Reasoning

arXiv:2601.03048v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) excel in semantic recognition but exhibit systematic failures in spatial reasoning tasks such as mental rotation. W

On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note

arXiv:2605.27563v1 Announce Type: cross Abstract: This short note presents a dimension-independent subgaussian concentration bound for Gaussian vectors under coordinate-wise nonlinear mappings. Discov

Optimal LTLf Synthesis

arXiv:2605.11544v2 Announce Type: replace Abstract: Strategy synthesis typically follows an all-or-nothing paradigm, returning unrealisable whenever a specification cannot be guaranteed in an uncertai

Optimal ridge regularization revisited

arXiv:2605.28679v1 Announce Type: new Abstract: We consider L^2-regularized linear (ridge) regression over a finite data sample X with bounded covariance and linear prediction targets y with additive

PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft

arXiv:2605.27762v1 Announce Type: new Abstract: We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-res

Personal Visual Memory from Explicit and Implicit Evidence

model-releasesarxiv-cs-cl

arXiv:2605.28806v1 Announce Type: cross Abstract: Long-term memory is increasingly important for personalized AI agents, yet existing benchmarks and methods remain largely text-centric. Even when imag

Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity

arXiv:2605.27385v1 Announce Type: cross Abstract: Federated reinforcement learning (FedRL) enables multiple agents to collaboratively train a global policy without sharing raw data, making it ideal fo

POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation

arXiv:2605.28237v1 Announce Type: cross Abstract: Real-world navigation is fundamentally driven by Points of Interest (POIs), yet reaching a precise POI remains a critical 'final-meters' challenge. Ex

Privacy Protection Against Personalized Text-to-Image Synthesis via Cross-image Consistency Constraints

arXiv:2504.12747v2 Announce Type: replace Abstract: The rapid advancement of diffusion models and personalization techniques has made it possible to recreate individual portraits from just a few publi

researcharxiv-cs-cv

Agents

RAG-Coding: Enhancing LLM Medical Coding with Structured External Knowledge

arXiv:2605.27377v1 Announce Type: cross Abstract: We present RAG-Coding, an agentic method for automated ICD-10-CM coding. RAG-Coding orchestrates four large language model (LLM) agents and grounds th

agentsarxiv-cs-ai

RAGe: A Retrieval-Augmented Generation Evaluation Framework

arXiv:2605.27445v1 Announce Type: cross Abstract: Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high

RASR: Retrieval-Augmented Super Resolution for Practical Reference-based Image Restoration

arXiv:2508.09449v2 Announce Type: replace Abstract: Reference-based Super Resolution (RefSR) improves upon Single Image Super Resolution (SISR) by leveraging high-quality reference images to enhance t

REED: Post-Training Representation Editing for Cross-Domain Linguistic Steganalysis

arXiv:2605.28298v1 Announce Type: new Abstract: In real-world scenarios of linguistic steganalysis, tested texts usually come from unseen domains with different vocabularies, topics, writing styles, a

Reevaluating Policy Gradient Methods for Imperfect-Information Games

arXiv:2502.08938v4 Announce Type: replace Abstract: In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information game

Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations

arXiv:2605.28553v1 Announce Type: new Abstract: In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained on

safetyarxiv-cs-ai

Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?

arXiv:2605.27881v1 Announce Type: new Abstract: Search agents powered by large language models can autonomously decompose queries, retrieve information, and synthesize answers through multi-step reaso

ReverseMath: Answer Inversion for Scalable and Verifiable Mathematical Problem Generation

arXiv:2605.27709v1 Announce Type: new Abstract: Mathematical reasoning benchmarks are vital for evaluating large language models (LLMs), but many are static and repeatedly exposed through public evalu

RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs

arXiv:2509.21128v2 Announce Type: replace Abstract: Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR) and supervised fine-tuning (SFT) on

Robust Moment-Based Estimation via Spectral Gradient Reweighting

arXiv:2605.27718v1 Announce Type: cross Abstract: Moment-based estimation is a theoretically attractive approach to parametric inference, especially when likelihood-based estimation is unavailable, mi

RW-TTT: Batched Serving for Request-Owned Test-Time Training State

arXiv:2605.28053v1 Announce Type: new Abstract: Test-time training (TTT) adapts an LLM during generation by reading and updating request-owned state, such as fast weights, low-rank deltas, or streamin

Safe In-Context Reinforcement Learning

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w

Self-Consistency via Marginal Sharpening

arXiv:2605.28142v1 Announce Type: cross Abstract: Inference-time sampling can elicit strong reasoning abilities from language models without additional training. Existing power-sampling methods do so