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ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards

model-releasesarxiv-cs-cl

arXiv:2510.00568v3 Announce Type: replace Abstract: Search agents powered by Large Language Models (LLMs) have demonstrated significant potential in tackling knowledge-intensive tasks. Reinforcement l

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Human DGX agent

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Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning

DGX agent

arXiv:2605.07263v1 Announce Type: cross Abstract: Over-the-air federated learning (OTA-FL) reduces uplink latency by exploiting waveform superposition, but conventional analog aggregation schemes typi

safetyarxiv-cs-ai

11 May 2026

Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding

arXiv:2605.07575v1 Announce Type: cross Abstract: Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall sho

Response Time Enhances Alignment with Heterogeneous Preferences

arXiv:2605.06987v1 Announce Type: new Abstract: Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this sta

Rethinking Dense Optical Flow without Test-Time Scaling

arXiv:2605.08000v1 Announce Type: new Abstract: Recent progress in dense optical flow has been driven by increasingly complex architectures and multi-step refinement for test-time scaling. While these

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts

arXiv:2605.07307v1 Announce Type: new Abstract: Modern reasoning language models generate dense, sequential chain-of-thought traces implicitly assuming that every token contributes and that steps must

Rethinking Experience Utilization in Self-Evolving Language Model Agents

arXiv:2605.07164v1 Announce Type: new Abstract: Self-evolving agents improve by accumulating and reusing experience from past interactions. Existing work has largely focused on how experience is const

Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective

arXiv:2605.07331v1 Announce Type: cross Abstract: Reinforcement learning, including reinforcement learning with verifiable rewards (RLVR), has emerged as a powerful approach for LLM post-training. Cen

Tutorials

Rethinking State Tracking in Recurrent Models Through Error Control Dynamics

arXiv:2605.07755v1 Announce Type: cross Abstract: The theory of state tracking in recurrent architectures has predominantly focused on expressive capacity: whether a fixed architecture can theoretical

tutorialsarxiv-cs-cl

Rethinking Weight Tying: Pseudo-Inverse Tying for LM Stable Training and Updates

model-releasesarxiv-cs-cl

arXiv:2602.04556v2 Announce Type: replace Abstract: Weight tying is widely used in compact language models to reduce parameters by sharing the token table between the input embedding and the output pr

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

arXiv:2605.06173v2 Announce Type: replace-cross Abstract: Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems

Retrieval from Within: An Intrinsic Capability of Attention-Based Models

arXiv:2605.05806v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decode

Retrieval Heads are Dynamic

arXiv:2602.11162v2 Announce Type: replace Abstract: Recent studies have identified 'retrieval heads' in Large Language Models (LLMs) responsible for extracting information from input contexts. However

Retrieve, Integrate, and Synthesize: Spatial-Semantic Grounded Latent Visual Reasoning

arXiv:2605.07106v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made remarkable progress on vision-language reasoning, yet most methods still compress visual evidence int

Revisiting Adam for Streaming Reinforcement Learning

arXiv:2605.06764v1 Announce Type: cross Abstract: Learning from a sequence of interactions, as soon as observations are perceived and acted upon, without explicitly storing them, holds the promise of

Revisiting Transformer Layer Parameterization Through Causal Energy Minimization

arXiv:2605.07588v1 Announce Type: cross Abstract: Transformer blocks typically combine multi-head attention (MHA) for token mixing with gated MLPs for token-wise feature transformation, yet many choic

RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion

arXiv:2602.16548v2 Announce Type: replace Abstract: The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While

Risk-Consistent Multiclass Learning from Random Label-Subset Membership Queries

arXiv:2605.07413v1 Announce Type: new Abstract: Obtaining accurate class labels is often costly or unreliable, and may also be limited by privacy or other practical conditions. Compared with asking an

RL-RIG: A Generative Spatial Reasoner via Intrinsic Reflection

arXiv:2602.19974v2 Announce Type: replace Abstract: Recent advancements in image generation have achieved impressive results in producing high-quality images. However, existing image generation models

researcharxiv-cs-cv

Local Ai

RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics

arXiv:2510.24736v3 Announce Type: replace-cross Abstract: Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains ch

local-aiarxiv-cs-lg

Applications

Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification

arXiv:2605.07752v1 Announce Type: new Abstract: Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradati

applicationsarxiv-cs-lg

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling

arXiv:2605.07634v1 Announce Type: cross Abstract: We consider a first order stochastic optimization framework where, at each iteration, K independent identically distributed (i.i.d.) data point sample

Robust Sublinear Convergence Rates for Iterative Bregman Projections

model-releasesarxiv-cs-lg

arXiv:2602.01372v2 Announce Type: replace-cross Abstract: Entropic regularization provides a simple way to approximate linear programs whose constraints split into two or more tractable blocks. The re

Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices

arXiv:2605.06686v1 Announce Type: new Abstract: Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper

Rollback-Free Stable Brick Structures Generation

arXiv:2605.06947v1 Announce Type: new Abstract: While autoregressive models have advanced 3D generation, creating physically stable brick structures remains a challenge due to the strict requirements

RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation

arXiv:2605.07129v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and

Rubric-based On-policy Distillation

arXiv:2605.07396v1 Announce Type: cross Abstract: On-policy distillation (OPD) is a powerful paradigm for model alignment, yet its reliance on teacher logits restricts its application to white-box sce

Rubric-Grounded RL: Structured Judge Rewards for Generalizable Reasoning

arXiv:2605.08061v1 Announce Type: new Abstract: We argue that decomposing reward into weighted, verifiable criteria and using an LLM judge to score them provides a partial-credit optimization signal:

RuleSafe-VL: Evaluating Rule-Conditioned Decision Reasoning in Vision-Language Content Moderation

arXiv:2605.07760v1 Announce Type: new Abstract: Platform content moderation applies explicit policy rules and context-dependent conditions to decide whether user content is allowed, restricted, or rem

S2M-Net: Spectral-Spatial Mixing for Medical Image Segmentation with Morphology-Aware Adaptive Loss

arXiv:2601.01285v2 Announce Type: replace Abstract: Medical image segmentation requires balancing local precision for boundary-critical clinical applications, global context for anatomical coherence,

S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models

model-releasesarxiv-cs-cl

arXiv:2503.05085v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have fundamentally reshaped speech-to-speech (S2S) systems, enabling increasingly natural spoken int

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence

arXiv:2605.06230v2 Announce Type: replace Abstract: As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool

Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents

arXiv:2605.07630v1 Announce Type: cross Abstract: When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may b

Safety Anchor: Defending Harmful Fine-tuning via Geometric Bottlenecks

arXiv:2605.05995v2 Announce Type: replace-cross Abstract: The safety alignment of Large Language Models (LLMs) remains vulnerable to Harmful Fine-tuning (HFT). While existing defenses impose constrain

SAGE: Hierarchical LLM-Based Literary Evaluation through Ontology-Grounded Interpretive Dimensions

arXiv:2605.07102v1 Announce Type: new Abstract: Evaluating literary quality requires assessing interpretive dimensions such as cultural representation, emotional depth, and philosophical sophisticatio

safetyarxiv-cs-cl

Saliency-Aware Regularized Quantization Calibration for Large Language Models

arXiv:2605.05693v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is an effective approach for deploying large language models (LLMs) under memory and latency constraints. Most exis

SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild

arXiv:2605.07604v1 Announce Type: cross Abstract: 3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scene

Tutorials

Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability

arXiv:2605.07212v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained a

tutorialsarxiv-cs-ai

Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents

arXiv:2605.06908v1 Announce Type: cross Abstract: Adaptive test-time compute for LLM agents aims to invoke extra computation only when it improves performance. Existing methods typically use confidenc

Sample Complexity of Stochastic Optimization with Integer Variables

arXiv:2605.07239v1 Announce Type: new Abstract: We establish sample complexity results for stochastic optimization over the integers, especially with a view to understand the complexity with respect t

SARA: Semantically Adaptive Relational Alignment for Video Diffusion Models

arXiv:2605.07800v1 Announce Type: new Abstract: Recent video diffusion models (VDMs) synthesize visually convincing clips, yet still drop entities, mis-bind attributes, and weaken the interactions spe

safetyarxiv-cs-cv

Sat3R: Satellite DSM Reconstruction via RPC-Aware Depth Fine-tuning

arXiv:2605.07264v1 Announce Type: new Abstract: Accurate Digital Surface Model (DSM) reconstruction from satellite imagery is critical for applications such as disaster response, urban planning, and l

SatSurfGS: Generalizable 2D Gaussian Splatting for Sparse-View Satellite Surface Reconstruction

arXiv:2605.07181v1 Announce Type: new Abstract: Sparse-view satellite image surface reconstruction remains highly challenging, fundamentally because the reliability of multi-view matching under satell

Saving Foundation Flow-Matching Priors for Inverse Problems

arXiv:2511.16520v2 Announce Type: replace-cross Abstract: Foundation flow-matching (FM) models promise a universal prior for solving inverse problems (IPs), yet today they trail behind domain-specific

researcharxiv-cs-cv

SB-TRPO: Towards Safe Reinforcement Learning with Hard Constraints

arXiv:2512.23770v3 Announce Type: replace-cross Abstract: In safety-critical domains, reinforcement learning (RL) agents must often satisfy strict, zero-cost safety constraints while accomplishing tas

Scalable Equilibrium Propagation via Intermediate Error Signals for Deep Convolutional CRNNs

arXiv:2508.15989v2 Announce Type: replace Abstract: Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in whic

Scalable Option Learning in High-Throughput Environments

arXiv:2509.00338v3 Announce Type: replace-cross Abstract: Hierarchical reinforcement learning (RL) has the potential to enable effective decision-making over long timescales. Existing approaches, whil

Scaling Categorical Flow Maps

model-releasesarxiv-cs-lg

arXiv:2605.07820v1 Announce Type: new Abstract: Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they u