All entries

Regulating Branch Parallelism in LLM Serving

arXiv:2605.06914v1 Announce Type: cross Abstract: Recent methods expose intra-request parallelism in LLM outputs, allowing independent branches to decode concurrently. Existing serving systems execute

researcharxiv-cs-ai

Reinforcement Learning for Exponential Utility: Algorithms and Convergence in Discounted MDPs

DGX agent

arXiv:2605.08053v1 Announce Type: new Abstract: Reinforcement learning (RL) for exponential-utility optimization in discounted Markov decision processes (MDPs) lacks principled value-based algorithms.

safetyarxiv-cs-lg

11 May 2026

Agents

RelAgent: LLM Agents as Data Scientists for Relational Learning

arXiv:2605.07840v1 Announce Type: new Abstract: Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, g

agentsarxiv-cs-lg

Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend

arXiv:2605.06055v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) inference requires large-scale token exchange across devices, making dispatch and combine major bottlenecks in both p

researcharxiv-cs-lg

Reliable Chain-of-Thought via Prefix Consistency

arXiv:2605.07654v1 Announce Type: cross Abstract: Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority

RELO: Reinforcement Learning to Localize for Visual Object Tracking

arXiv:2605.07379v1 Announce Type: cross Abstract: Conventional visual object trackers localize targets using handcrafted spatial priors, often in the form of heatmaps. Such priors provide only surroga

Hardware

Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement

arXiv:2605.06298v2 Announce Type: replace-cross Abstract: Training world models on vast quantities of unlabelled videos is a critical step toward fully autonomous intelligence. However, the prevailing

hardwarearxiv-cs-ai

Rep2Text: Decoding Full Text from a Single LLM Token Representation

arXiv:2511.06571v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In t

Repeated Deceptive Path Planning against Learnable Observer

arXiv:2605.07174v1 Announce Type: new Abstract: We study the problem of deceptive path planning (DPP), where an agent aims to conceal its true destination from external observers. While existing work

Replicating Human Motivated Reasoning Studies with LLMs

arXiv:2601.16130v2 Announce Type: replace-cross Abstract: Motivated reasoning - the idea that individuals processing information may be motivated to either arrive at accurate beliefs or arrive at desi

researcharxiv-cs-ai

reply to Hinton’s reply to me, for additional context:

reply to Hinton’s reply to me, for additional context: Dear @geoffreyhinton, I literally never said that AI systems “JUST regurgitate”; that’s plainly false. I don’t believe it, and I didn’t say it. (

safetygary-marcus--x

Industry

Report: AI chipmaker Cerebras to increase IPO price target amid surging investor demand

Artificial intelligence chipmaker Cerebras Systems Inc. is expected to increase the size and price of its initial public offering later today as investor demand for access to its shares continues to r

industrysiliconangle

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

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

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

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

Industry

Retail markdown optimization: from reactive markdowns to proactive

This Databricks resource examines retail markdown optimization strategies, contrasting reactive markdown approaches (responding to inventory issues after they occur) with proactive markdown strategies

industrydatabricks

Rethinking Dense Optical Flow without Test-Time Scaling

model-releasesarxiv-cs-cv

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

researcharxiv-cs-lg

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

researcharxiv-cs-ai

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

researcharxiv-cs-lg

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

model-releasesarxiv-cs-cv

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