Search: “routing”

How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformers

arXiv:2604.17935v1 Announce Type: new Abstract: The key-value (KV) cache is the dominant memory bottleneck during Transformer inference, yet little is known theoretically about how aggressively it can

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Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance

DGX agent

arXiv:2604.17650v1 Announce Type: new Abstract: LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, p

applicationsarxiv-cs-cl

21 Apr 2026

Tutorials

MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation

arXiv:2604.17245v1 Announce Type: new Abstract: High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand

tutorialsarxiv-cs-ro

MoCo: A One-Stop Shop for Model Collaboration Research

arXiv:2601.21257v2 Announce Type: replace Abstract: Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multi

safetyarxiv-cs-cl

Tools

QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard

QIMMA (قِمّة) is a quality-focused leaderboard for evaluating Arabic Large Language Models, developed by TIIUAE. The leaderboard benchmarks Arabic LLMs across various performance metrics and tasks to

toolshugging-face

Sparse Feature Coactivation Reveals Causal Semantic Modules in Large Language Models

arXiv:2506.18141v3 Announce Type: replace Abstract: We identify semantically coherent, context-consistent network components in large language models (LLMs) using coactivation of sparse autoencoder (S

TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics

arXiv:2604.18210v1 Announce Type: cross Abstract: Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep le

When Background Matters: Breaking Medical Vision Language Models by Transferable Attack

arXiv:2604.17318v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in clinical diagnostics, yet their robustness to adversarial attacks remains largely unexplored, pos

researcharxiv-cs-cv

x1: Learning to Think Adaptively Across Languages and Cultures

arXiv:2604.16917v1 Announce Type: new Abstract: Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single domi

Attending #AIDev26 by @DeepLearningAI? Join @AI21Labs, @trychroma + @Baseten for a panel on optimizing modern AI systems. Drinks. Nikkei foo…

model-releasesai21-labs--x

Attending #AIDev26 by @DeepLearningAI? Join @AI21Labs, @trychroma + @Baseten for a panel on optimizing modern AI systems. Drinks. Nikkei food. No fluff. April 28 | 5PM | Kaiyō SF Register → http://lum

Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks

arXiv:2603.11487v5 Announce Type: replace Abstract: Transformers often display an attention sink: probability mass concentrates on a fixed, content-agnostic position. Are sinks a byproduct of the opti

DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling

arXiv:2604.15594v1 Announce Type: cross Abstract: Modern datacenters schedule heterogeneous workloads across geo-distributed sites with diverse compute capacities, electricity prices, and thermal cond

DB-FGA-Net: Dual Backbone Frequency Gated Attention Network for Multi-Class Brain Tumor Classification with Grad-CAM Interpretability

arXiv:2510.20299v3 Announce Type: replace-cross Abstract: Brain tumors are a challenging problem in neuro-oncology, where early and precise diagnosis is important for successful treatment. Deep learni

Deep Learning-Enabled Modality Transfer Between Independent Microscopes for High-Throughput Imaging

arXiv:2403.18026v2 Announce Type: replace-cross Abstract: High-throughput biological imaging is often constrained by a trade-off between acquisition speed and image quality. Fast imaging modalities, s

local-aiarxiv-cs-lg

Disentangling Mathematical Reasoning in LLMs: A Methodological Investigation of Internal Mechanisms

arXiv:2604.15842v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities, yet their internal mechanisms for handling reasoning-intensive tasks remain unde

Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration

arXiv:2604.16104v1 Announce Type: cross Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential fo

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use

arXiv:2604.15646v1 Announce Type: new Abstract: Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet wr

Flexible Empowerment at Reasoning with Extended Best-of-N Sampling

arXiv:2604.15614v1 Announce Type: new Abstract: This paper proposes a novel method that incorporates empowerment when reasoning actions in reinforcement learning (RL), thereby achieving the flexibilit

safetyarxiv-cs-lg

Tutorials

Fusing Cellular Network Data and Tollbooth Counts for Urban Traffic Flow Estimation

arXiv:2604.15782v1 Announce Type: new Abstract: Traffic simulations, essential for planning urban transit infrastructure interventions, require vehicle-category-specific origin-destination (OD) data.

tutorialsarxiv-cs-lg

Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)

arXiv:2604.15772v1 Announce Type: new Abstract: Reinforcement learning (RL) often struggles in real-world tasks with high-dimensional state spaces and long horizons, where sparse or fixed rewards seve

agentsarxiv-cs-ro

Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation

arXiv:2604.15482v1 Announce Type: cross Abstract: Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning dema

researcharxiv-cs-ai

Import AI 454: Automating alignment research; safety study of a Chinese model; HiFloat4

This newsletter covers three main topics: advances in automating alignment research to improve AI safety processes, a safety evaluation study of a Chinese AI model, and technical details about HiFloat

safetyimport-ai

MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis

arXiv:2604.15729v1 Announce Type: cross Abstract: Whole Slide Image (WSI) analysis is pivotal in computational pathology, enabling cancer diagnosis by integrating morphological and architectural cues

{pi}_{0.7}: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

arXiv:2604.15483v1 Announce Type: new Abstract: We present a new robotic foundation model, called {pi}_{0.7}, that can enable strong out-of-the-box performance in a wide range of scenarios. {pi}_{0.7}

Power to the Clients: Federated Learning in a Dictatorship Setting

arXiv:2510.22149v3 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively lea

Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability

arXiv:2604.16106v1 Announce Type: cross Abstract: The Project of AI is a world-building endeavor, wherein those who fund and develop AI systems both operate through and seek to sustain networks of pow

safetyarxiv-cs-ai

Safe and Energy-Aware Multi-Robot Density Control via PDE-Constrained Optimization for Long-Duration Autonomy

arXiv:2604.15524v1 Announce Type: cross Abstract: This paper presents a novel density control framework for multi-robot systems with spatial safety and energy sustainability guarantees. Stochastic rob

safetyarxiv-cs-ro

Target-Oriented Pretraining Data Selection via Neuron-Activated Graph

arXiv:2604.15706v1 Announce Type: new Abstract: Everyday tasks come with a target, and pretraining models around this target is what turns them into experts. In this paper, we study target-oriented la

An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management

arXiv:2604.14882v1 Announce Type: cross Abstract: Rapid urbanization and continuous population growth have made municipal solid waste management increasingly challenging. These challenges highlight th

Beyond Translation: Evaluating Mathematical Reasoning Capabilities of LLMs in Sinhala and Tamil

arXiv:2602.14517v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support

CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation

arXiv:2604.14630v1 Announce Type: new Abstract: Recent advances in unsupervised video object segmentation have highlighted the potential of two-stream architectures that integrate appearance and motio

researcharxiv-cs-cv

DeepPrune: Parallel Scaling without Inter-trace Redundancy

arXiv:2510.08483v2 Announce Type: replace Abstract: Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-o

Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach

arXiv:2411.00361v4 Announce Type: replace Abstract: Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However

safetyarxiv-cs-lg

Doubly Outlier-Robust Online Infinite Hidden Markov Model

arXiv:2604.14322v1 Announce Type: cross Abstract: We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is missp

Grading the Unspoken: Evaluating Tacit Reasoning in Quantum Field Theory and String Theory with LLMs

arXiv:2604.14188v1 Announce Type: cross Abstract: Large language models have demonstrated impressive performance across many domains of mathematics and physics. One natural question is whether such mo

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution

arXiv:2602.07529v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequ

MemGround: Long-Term Memory Evaluation Kit for Large Language Models in Gamified Scenarios

model-releasesarxiv-cs-cl

arXiv:2604.14158v1 Announce Type: new Abstract: Current evaluations of long-term memory in LLMs are fundamentally static. By fixating on simple retrieval and short-context inference, they neglect the

One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators

arXiv:2604.15181v1 Announce Type: new Abstract: Extrapolative prediction of complex nonlinear dynamics remains a central challenge in engineering. This study proposes a one-shot learning method to ide

Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models

arXiv:2604.14888v1 Announce Type: new Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains

safetyarxiv-cs-cl

ReasonScaffold: A Scaffolded Reasoning-based Annotation Protocol for Human-AI Co-Annotation

arXiv:2603.21094v3 Announce Type: replace Abstract: Human annotation is central to NLP evaluation, yet subjective tasks often exhibit substantial variability across annotators. While large language mo

Similarity-Distance-Magnitude Activations

arXiv:2509.12760v4 Announce Type: replace-cross Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax

Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models

arXiv:2512.23578v3 Announce Type: replace Abstract: In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn

What Is the Minimum Architecture for Prolepsis? Early Irrevocable Commitment Across Tasks in Small Transformers

model-releasesarxiv-cs-cl

arXiv:2604.15010v1 Announce Type: cross Abstract: When do transformers commit to a decision, and what prevents them from correcting it? We introduce extbf{prolepsis}: a transformer commits early, task

An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning

model-releasesarxiv-cs-cv

arXiv:2211.16780v3 Announce Type: replace-cross Abstract: In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because pre

ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding

arXiv:2603.27064v2 Announce Type: replace-cross Abstract: Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a ca

safetyarxiv-cs-cl

Composite Silhouette: A Subsampling-based Aggregation Strategy

arXiv:2604.13816v1 Announce Type: new Abstract: Determining the number of clusters is a central challenge in unsupervised learning, where ground-truth labels are unavailable. The Silhouette coefficien

safetyarxiv-cs-lg

Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation

arXiv:2604.13761v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost an

researcharxiv-cs-cv

Empirical Evidence of Complexity-Induced Limits in Large Language Models on Finite Discrete State-Space Problems with Explicit Validity Constraints

arXiv:2604.13371v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly described as possessing strong reasoning capabilities, supported by high performance on mathematical, logi