Search: “routing”

Up until yesterday, our entire MTS team has operated under the philosophy of tokenmaxxing as much as possible on Claude Max plans. With Fabl…

model-releasesjerry-liu--x

Up until yesterday, our entire MTS team has operated under the philosophy of tokenmaxxing as much as possible on Claude Max plans. With Fable, this may no longer be possible: - One of our team members

11 Jun 2026

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Research

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models

DGX agent

arXiv:2606.10277v1 Announce Type: new Abstract: Though wireless foundation models (WFMs) have shown strong potential in learning universal channel representations, their adaptation to various downstre

researcharxiv-cs-lg

10 Jun 2026

Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It

arXiv:2606.11052v1 Announce Type: new Abstract: Chain-of-thought (CoT) supervised fine-tuning (SFT) is widely adopted to improve reasoning ability, yet we find that it systematically degrades long-con

Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents

arXiv:2606.10315v1 Announce Type: cross Abstract: LLM-as-judge is the default instrument for evaluating conversational agents, yet its reliability is almost always reported as agreement with human rat

Continual LLM Upcycling: A Predictor-Gated Bank-Wise Sparsity Training Recipe for Dense-to-Sparse LLMs

arXiv:2606.10722v1 Announce Type: new Abstract: We study dense-to-sparse continual training as a way to construct channel-sparse large language models from dense checkpoints. Starting from a Qwen2.5-8

Continuous Neural Reparameterization as a Deep Geometric Prior for Robust Fixed-Chart UV Repair

model-releasesarxiv-cs-cv

arXiv:2606.10050v1 Announce Type: cross Abstract: Traditional UV unwrapping relies on direct optimization of geometric distortion energies and can fail through invalid initialization, local minima, or

Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport

model-releasesarxiv-cs-lg

arXiv:2606.11017v1 Announce Type: new Abstract: Airport surface operations increasingly constrain performance at high-throughput hubs. This study examines arrival taxi-in decisions at Hartsfield-Jacks

From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs

arXiv:2606.10298v1 Announce Type: new Abstract: When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reli

researcharxiv-cs-ai

Applications

From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs

arXiv:2606.10147v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer?

applicationsarxiv-cs-ai

Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters

arXiv:2606.09924v1 Announce Type: cross Abstract: Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computati

TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts

model-releasesarxiv-cs-lg

arXiv:2606.09885v1 Announce Type: new Abstract: Mixture-of-Experts large language models (LLMs) scale efficiently through sparse activation, yet their deployment is fundamentally constrained by the la

Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins

arXiv:2606.08214v1 Announce Type: new Abstract: Flexible robotic automation requires systems that interpret operator intent, verify physical feasibility, and recover from execution failures across bot

agentsarxiv-cs-ro

AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models

arXiv:2606.08952v1 Announce Type: new Abstract: Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world. A key bottleneck li

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2606.08684v1 Announce Type: new Abstract: We present BLUE, a minimal method for better language use in vision-language-action (VLA) models for autonomous driving (AD). Through extensive analysis

CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control

arXiv:2606.09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather tha

local-aiarxiv-cs-ai

Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction

arXiv:2606.09480v1 Announce Type: new Abstract: Molecular systems involve interactions across multiple spatial scales, from local coordination and short-range perturbations to long-range electrostatic

researcharxiv-cs-lg

Training-Inference Kernel Contracts: Bounding Divergence in Post-Training and Deployment

arXiv:2606.07581v1 Announce Type: cross Abstract: A modern post-training pipeline often writes one symbol for its policy, pi_theta, while evaluating it through two different programs: a training kerne

FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

arXiv:2606.07235v1 Announce Type: cross Abstract: Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken acros

researcharxiv-cs-lg

8 Jun 2026

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

arXiv:2606.07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to ca

8 Jun 2026

Super-powerful AI models will launch in the coming weeks. We are looking at a potential step change in model capabilities. The biggest mista…

Super-powerful AI models will launch in the coming weeks. We are looking at a potential step change in model capabilities. The biggest mistake right now is to lock into one vendor. I say this not only

tutorialsdair-ai--x

7 Jun 2026

BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection

arXiv:2606.05586v1 Announce Type: new Abstract: In remote sensing object detection, Convolutional Neural Networks (CNNs) excel at capturing local details while Vision Transformers (ViTs) are better at

local-aiarxiv-cs-cv

5 Jun 2026

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

arXiv:2606.05415v1 Announce Type: new Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inco

agentsarxiv-cs-cl

5 Jun 2026

Anycast Performance in Context

arXiv:2606.04298v1 Announce Type: cross Abstract: IP anycast lets a service advertise one address from many physical sites, leaving BGP to map each client to a site. It is central to the DNS root serv

Consensus is Strategically Insufficient: Reasoning-Trace Disagreement as a Knowledge-Representation Signal

arXiv:2606.04223v1 Announce Type: new Abstract: Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation. We argue th

LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling

arXiv:2606.04552v1 Announce Type: new Abstract: Genomic foundation models increasingly adopt large language model architectures, yet almost universally rely on fixed tokenization schemes such as k-mer

LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models

model-releasesarxiv-cs-lg

arXiv:2606.04485v1 Announce Type: new Abstract: Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar toke

Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling

arXiv:2606.04284v1 Announce Type: cross Abstract: Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with hum

tutorialsarxiv-cs-ai

Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching

arXiv:2606.03911v1 Announce Type: new Abstract: Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of pair

researcharxiv-cs-cv

Decomposing how prompting steers behavior

arXiv:2606.03093v1 Announce Type: new Abstract: Prompting steers large language models (LLMs) and vision-language models (VLMs) without weight updates, but it remains unclear how instruction changes r

researcharxiv-cs-ai

Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety Monitoring

arXiv:2512.23234v3 Announce Type: replace-cross Abstract: Infrared gas leak detection is important for industrial safety and environmental monitoring, but automatic detection remains challenging becau

local-aiarxiv-cs-ai

From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds

arXiv:2606.03557v1 Announce Type: new Abstract: As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge. Users interact through in-world interfaces in mul

NeuroArmor: Safe-Variant-Guided Representation Consistency for Selective Re-Anchoring in Jailbreak Defense

arXiv:2606.03486v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks that hide harmful intent behind seemingly ordinary requests such as role-play, translatio

A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL

arXiv:2606.02398v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation,

Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams

arXiv:2606.01770v1 Announce Type: cross Abstract: Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infras

AXIOM: A Trust-First Neuro-Symbolic Execution Architecture for Verifiable Mathematical Reasoning

arXiv:2606.00671v1 Announce Type: new Abstract: We present AXIOM, a trust-first neuro-symbolic execution architecture for natural-language mathematical reasoning. In AXIOM, the language model function

Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference

arXiv:2606.01007v1 Announce Type: cross Abstract: Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert

local-aiarxiv-cs-ai

Dynamic Coordination Strategy Selection for Enterprise Multi-Agent Systems

arXiv:2606.00804v1 Announce Type: cross Abstract: Enterprise multi-agent systems increasingly expose multiple coordination patterns, but deployments often lack evidence for when to use consensus, deba

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs

arXiv:2606.01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop

tutorialsarxiv-cs-lg

Hierarchical Online Prompt Mutation with Dual-Loop Feedback for Guardrailed Evidence Document Generation: A Production-Evaluation Case Study

arXiv:2606.01472v1 Announce Type: cross Abstract: High-stakes production document-generation systems require language models to be adaptive, evidence-grounded, and auditable. We present HOPM, a hierar

Leyline: KV Cache Directives for Agentic Inference

arXiv:2606.01065v1 Announce Type: cross Abstract: Modern KV cache management assumes the chatbot workload: prompts arrive once and the cache grows append-only, so prefix caching and forward-only evict

MESA: Improving MoE Safety Alignment via Decentralized Expertise

arXiv:2606.00651v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale Large Language Models (LLMs) efficiently, enabling greater capacity with reduced computational cost by dy

Monitoring Agentic Systems Before They're Reliable

arXiv:2606.02494v1 Announce Type: cross Abstract: Agentic systems entering production typically operate as partially integrated assemblies where structural defects, not task-level errors, dominate the

Optimizing Diversity and Quality through Base-Aligned Model Collaboration

arXiv:2511.05650v2 Announce Type: replace-cross Abstract: Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across g

Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context

arXiv:2606.01101v1 Announce Type: cross Abstract: The quadratic complexity of self-attention remains a bottleneck for Large Language Models (LLMs) processing ultra-long contexts. The Naive Bayes Cogni

researcharxiv-cs-ai

DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics

arXiv:2605.30538v1 Announce Type: new Abstract: Disasters are inevitable and increasingly costly, and effective response depends on querying structured tabular data: precise, information-dense records

researcharxiv-cs-lg

1 Jun 2026

dMoE: dLLMs with Learnable Block Experts

arXiv:2605.30876v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance whil

tutorialsarxiv-cs-cl

1 Jun 2026

The Latin Substrate: How Language Models Represent and Mediate Script Choice

arXiv:2605.31363v1 Announce Type: new Abstract: Many languages are written in multiple scripts, requiring large language models (LLMs) to generate equivalent linguistic content in distinct orthographi

researcharxiv-cs-cl

1 Jun 2026

Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference

arXiv:2605.29467v1 Announce Type: cross Abstract: Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be pre