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The Kalman Evolve: Closing the Gap in Kalman Filtering via Interpretable Algorithm Discovery

arXiv:2605.26830v1 Announce Type: cross Abstract: State estimation is a fundamental problem in control and signal processing, for which the Kalman Filter provides an optimal solution under linear dyna

applicationsarxiv-cs-ai

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Human DGX agent

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The Labyrinth and the Thread: Rethinking Regularizations in Sequential Knowledge Editing for Large Language Models

DGX agent

arXiv:2605.26670v1 Announce Type: cross Abstract: Sequential editing of structured knowledge in large language models allows targeted factual updates without retraining, yet existing methods often rel

safetyarxiv-cs-ai

27 May 2026

The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

arXiv:2605.26494v1 Announce Type: new Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum

The Necessity of a Unified Framework for LLM-Based Agent Evaluation

arXiv:2602.03238v2 Announce Type: replace Abstract: With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents present

The Need for an External Observer Formalizing the Sufficiency Gap: A Mathematical Extension of Mixture Identifiability and Contextual Grounding in Sequence Models

arXiv:2605.26711v1 Announce Type: new Abstract: We construct a binary mixed-regime process with one deterministic textual regime and one random regime governed by an unobserved latent state. Even an i

agentsarxiv-cs-cl

The Rescue Effect: Spatio-Semantic Early Exit Bypasses Quantization Collapse in CLIP

arXiv:2605.26415v1 Announce Type: cross Abstract: Deploying Vision-Language Models on resource-constrained hardware typically requires INT8 quantization, but in joint-embedding architectures such as C

The Role of Causal Features in Strategic Classification for Robustness and Alignment

arXiv:2605.27163v1 Announce Type: new Abstract: In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility in a classifi

safetyarxiv-cs-lg

The Sensation Modulating Network:Haltability as the architectural ground for object-directed phenomenology

arXiv:2605.26856v1 Announce Type: cross Abstract: Cognitive science remains split between cognitivism - which accounts for recursion and language but cannot ground formal symbols in meaning - and 4E a

The Stability of Singular Distribution: A Spectral Perspective on the Two-Phase Dynamics of Language Model Pre-training

model-releasesarxiv-cs-lg

arXiv:2605.26489v1 Announce Type: new Abstract: Large language model pre-training typically exhibits a two-phase trajectory: a fast initial loss drop followed by a prolonged slow improvement. We ident

The Strongest Teacher Is Not Always the Best Teacher: Student-Centric Answer Selection

arXiv:2605.26872v1 Announce Type: cross Abstract: LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current p

Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction

arXiv:2505.11063v3 Announce Type: replace Abstract: LLM-based agents solve complex tasks through iterative reasoning, tool use, and environment interaction, where each intermediate thought directly sh

Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining

arXiv:2605.26759v1 Announce Type: new Abstract: Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typica

applicationsarxiv-cs-lg

Timestep-Aware SVDQuant-GPTQ for W4A4 Quantization of Wan2.2-I2V

arXiv:2605.27003v1 Announce Type: cross Abstract: W4A4 quantization of large video diffusion Transformers offers substantial memory savings but is hindered by two main challenges: sparse large-magnitu

To model human linguistic prediction, make LLMs less superhuman

arXiv:2510.05141v2 Announce Type: replace Abstract: When we read, we make predictions about upcoming words; these predictions influence our reading behavior. The success of large language models (LLMs

safetyarxiv-cs-cl

Tool-Schema Compression Enables Agentic RAG Under Constrained Context Budgets

arXiv:2605.26165v1 Announce Type: cross Abstract: Agentic RAG systems that equip language models with dozens to hundreds of tool definitions face a critical resource conflict: tool schemas consume the

Touch-R1: Reinforcing Touch Reasoning in MLLMs

arXiv:2605.27154v1 Announce Type: new Abstract: While rule-based reinforcement learning has recently catalyzed explicit reasoning in multimodal models, tactile reasoning remains largely underexplored.

Tournament-GRPO: Group-Wise Tournament Rewards for Reinforcement Learning in Open-Ended Long-Form Generation

arXiv:2605.26958v1 Announce Type: cross Abstract: Reinforcement learning in open-ended long-form generation is challenging because reliable reference answers and automatic metrics are often unavailabl

Towards Controllable Image Generation through Representation-Conditioned Diffusion Models

arXiv:2605.27343v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for high-quality image generation and editing, but guiding these models to produce specific outputs rema

Towards Drone-based Mapping of Volcanic Gases using Gas Tomography

arXiv:2605.27180v1 Announce Type: new Abstract: Volcanoes emit large amounts of CO2, directly influencing human lives. Mapping volcanic gas emissions helps to forecast eruptions and understand the imp

researcharxiv-cs-ro

Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records

arXiv:2605.26463v1 Announce Type: cross Abstract: Data consistency between unstructured clinical notes and structured tables in Electronic Health Records (EHRs) is essential for patient safety and cli

Hardware

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

arXiv:2605.26720v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong empirical gains as self-evolving agents for CUDA kernel generation, driven by feedback-conditioned planni

hardwarearxiv-cs-ai

Towards Generalization-Oriented Models for Vehicle Routing Problems with Mixture-of-Experts

arXiv:2605.26776v1 Announce Type: cross Abstract: In recent years, Deep Reinforcement Learning (DRL) has achieved substantial progress on Vehicle Routing Problems (VRPs). However, existing DRL-based m

Local Ai

Towards Interpretable Federated Learning

arXiv:2302.13473v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In

local-aiarxiv-cs-lg

Tutorials

Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM

arXiv:2605.26405v1 Announce Type: new Abstract: Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback

tutorialsarxiv-cs-cl

Towards Real-World Identification of Fatigued Muscle Groups via Musculoskeletal Simulation

arXiv:2605.26151v1 Announce Type: cross Abstract: Contactless diagnosis of musculoskeletal disorders can potentially improve population health as well as robot behaviours in collaborative settings. Ho

applicationsarxiv-cs-ro

Towards Shared Embodied Intelligence in Humanoid Robots through Optimization Development and Testing of the Human Aware ergoCub Robot

arXiv:2605.26991v1 Announce Type: new Abstract: Collaboration is central to human behavior, enabling tasks beyond individual capability. This ability arises from coordinating actions through internal

researcharxiv-cs-ro

TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents

arXiv:2601.05899v2 Announce Type: replace Abstract: Recent breakthroughs in Large Language Models (LLMs) have positioned them as a promising paradigm for agents, with long-term planning and decision-m

TPS-Drive: Task-Guided Representation Purification for VLM-based Autonomous Driving

arXiv:2605.27038v1 Announce Type: new Abstract: Vision-Language Models (VLMs) provide a promising foundation for autonomous driving planning, yet bridging semantic reasoning and precise 3D spatial for

safetyarxiv-cs-ro

Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry

arXiv:2605.27071v1 Announce Type: new Abstract: Key knowledge for steel-industry volatile organic compounds (VOCs) governance is scattered across unstructured scientific literature, making it difficul

Tracing Computation Density in LLMs

arXiv:2605.27033v1 Announce Type: cross Abstract: Transformer-based large language models (LLMs) are comprised of billions of parameters arranged in deep and wide computational graphs, but it is not c

TrackRef3D: Multi-View Consistent Track-then-Label for Open-World Referring Segmentation in 3D Gaussian Splatting

arXiv:2605.26576v1 Announce Type: new Abstract: Referring 3D Gaussian Splatting (R3DGS), which utilizes natural language for 3D object segmentation, has emerged as a crucial capability for embodied AI

Training-Free Vector Quantization via Gaussian VAEs

arXiv:2512.06609v3 Announce Type: replace-cross Abstract: Vector-quantized variational autoencoders (VQ-VAEs) are discrete autoencoders that compress images into discrete tokens. However, they are dif

Transfer Learning using 66 Diseases for Disease Forecasting Applications

arXiv:2605.27269v1 Announce Type: new Abstract: Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models

researcharxiv-cs-lg

Tutorials

Transformers Can Learn Posterior Predictive Distributions In-Context

arXiv:2605.26713v1 Announce Type: cross Abstract: Prior-data fitted networks (PFNs) have recently emerged as a powerful approach for Bayesian prediction tasks, approximating the posterior predictive d

tutorialsarxiv-cs-lg

Tutorials

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently

arXiv:2511.17852v2 Announce Type: replace Abstract: Transformers can acquire Chain-of-Thought (CoT) capabilities to solve complex reasoning tasks through fine-tuning. Reinforcement learning (RL) and s

tutorialsarxiv-cs-lg

Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules

arXiv:2605.26470v1 Announce Type: new Abstract: Generative posterior sampling using diffusion models has emerged as a dominant paradigm for solving inverse problems in imaging, which usually consists

safetyarxiv-cs-cv

Trust, Geometry, and Rules: A Credibility-Aware Reinforcement Learning Framework for Safe USV Navigation under Uncertainty

arXiv:2605.26974v1 Announce Type: new Abstract: Autonomous navigation of Unmanned Surface Vehicles (USVs) that is safe and compliant with the International Regulations for Preventing Collisions at Sea

safetyarxiv-cs-ro

Trust Region Q Adjoint Matching

arXiv:2605.27079v1 Announce Type: cross Abstract: Off-policy reinforcement learning of pretrained flow policies remains challenging due to the instability of optimization arising from the multi-step s

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models

arXiv:2605.26161v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are increasingly pretrained on large corpora, raising concerns that evaluation datasets may have been exposed du

Turning Bias into Bugs: Bandit-Guided Style Manipulation Attacks on LLM Judges

arXiv:2605.26156v1 Announce Type: cross Abstract: The known stylistic biases in LLM judges, such as a preference for verbosity or specific sentence structures, present an underexplored security vulner

TWIST: Closed-Loop token Synchronization for Application-Aware Wireless Digital Twins

arXiv:2605.27205v1 Announce Type: cross Abstract: Wireless digital twins require repeated synchronization between a time-evolving physical scene and its digital counterpart under limited and time-vary

Two-Parameter Flows for Learning Population Dynamics of Physical Systems

model-releasesarxiv-cs-lg

arXiv:2605.26285v1 Announce Type: new Abstract: This work addresses the problem of learning the dynamics of high-dimensional probability densities over time using unlabeled samples, without assuming a

Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent

arXiv:2605.27078v1 Announce Type: cross Abstract: Training loss and accuracy are the standard signals used to monitor generalization during deep neural network training. Two well-documented phenomena

UCPO: Uncertainty-Aware Policy Optimization

arXiv:2601.22648v2 Announce Type: replace Abstract: The key to building trustworthy large language models (LLMs) lies in endowing them with inherent uncertainty expression capabilities, thereby mitiga

UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action

arXiv:2510.17790v3 Announce Type: replace-cross Abstract: Computer-use agents face a fundamental limitation. They rely exclusively on primitive GUI actions (click, type, scroll), creating brittle exec

applicationsarxiv-cs-cl

Uncertainty-Aware Budget Allocation for Adaptive Test-Time Reasoning

arXiv:2605.26849v1 Announce Type: new Abstract: Sampling multiple responses improves language model reasoning, but uniform compute allocation is inefficient: easy questions are over-sampled while hard

researcharxiv-cs-cl

Uncertainty-Aware Gaussian Map for Vision-Language Navigation

arXiv:2605.26503v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agen

agentsarxiv-cs-cv

Local Ai

Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty

arXiv:2603.15500v2 Announce Type: replace Abstract: LLMs often exhibit Aha moments such as self-correction after tokens like 'Wait,' yet the underlying mechanism remains unclear. Standard LLMs collaps

local-aiarxiv-cs-ai