Search: “routing”

It really just needs some encouragement (Qwen3.8-27B-UD-IQ4_XS)

model-releasesr-localllama

I know I am probably missing the point of this benchmark, but I wanted to see if the model is capable of generating something better or is that the best it can do. So I gave it some encouragement. I f

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Lean Graph Search for Formal Theorem Proving

DGX agent

arXiv:2610.22339v1 Announce Type: new Abstract: Retrieving relevant lemmas is crucial for formal theorem proving, enabling provers to build machine-checkable proofs from established mathematical resul

researcharxiv-cs-lg

16 Oct 2026

Applications

Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs

arXiv:2610.21223v1 Announce Type: new Abstract: Accurate medium- to long-range epidemic forecasting requires extrapolating high-dimensional, heterogeneous and nonstationary dynamics from partial obser

applicationsarxiv-cs-lg

Location-Based Safety Management with Wearable Seamless Indoor-Outdoor Positioning

arXiv:2610.21503v1 Announce Type: new Abstract: Reliable worker localization is essential for real-time industrial safety management, but seamless outdoor-indoor positioning remains challenging. GNSS

safetyarxiv-cs-ro

Hardware

Making every stage of medical device document assessment with large language models deterministic

arXiv:2610.21300v1 Announce Type: new Abstract: To show that a medical device complies with regulation, experts judge whether documents and records meet each requirement and record the judgment for ot

hardwarearxiv-cs-cl

OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents

arXiv:2610.21496v1 Announce Type: new Abstract: Reliable repository-level coding requires agents not only to generate correct code but also to preserve constraints and respond to tool feedback across

OS-Helix: Unifying GUI Interaction and Code Execution in Native Computer-Using Agents

arXiv:2610.22234v1 Announce Type: cross Abstract: Real desktop automation is neither purely visual nor purely programmatic. Agents must understand user-facing interfaces while efficiently manipulating

safetyarxiv-cs-ai

Pathology foundation models for training-free case retrieval in salivary gland tumors

arXiv:2610.22087v1 Announce Type: new Abstract: In pathology, pattern recognition relies on a frame of reference built through extensive training and experience, which may still fall short when encoun

researcharxiv-cs-cv

Phase Kernel Lifts Capacity of Dense Associative Memory

arXiv:2610.21493v1 Announce Type: new Abstract: Dense associative memory underlies modern attention mechanisms; however, Modern Hopfield Networks (MHNs) with linear kernels yield overlapping attractor

researcharxiv-cs-lg

PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning

arXiv:2610.21308v1 Announce Type: cross Abstract: Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text ra

PriorityFEC: Deadline-Aware Streaming FEC for KV-Cache Handoff in Edge Multi-Agent Systems

arXiv:2610.21390v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) systems are increasingly deployed at the edge to support complex tasks that require collaboration and informati

RAC: A Relational Agent Contract for Constraint-Governed Engineering Tasks

arXiv:2610.20935v1 Announce Type: new Abstract: Engineering agents must produce artifacts that satisfy exact, coupled constraints. Existing solutions often require agents to make semantic decisions an

Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization

arXiv:2606.09449v2 Announce Type: replace Abstract: Autoformalization (AF) requires both prover acceptance and fidelity to an informal source, but prover acceptance alone cannot establish fidelity, an

researcharxiv-cs-cl

StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning

arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and inter

safetyarxiv-cs-ai

Tutorials

The Density of Thought: Predicting and Steering Reasoning in Large Language Models

arXiv:2610.21286v1 Announce Type: new Abstract: We investigate whether variation in information density across chain-of-thought (CoT) reasoning steps predicts answer correctness and can serve as a con

tutorialsarxiv-cs-ai

TRACE: Topology-Aware Reconnection via Prediction-Adaptive Connectivity Enforcement for Thin-Structure Segmentation

arXiv:2610.22210v1 Announce Type: cross Abstract: Thin, elongated structures are particularly sensitive to connectivity errors, as even a few local segmentation mistakes can disrupt otherwise continuo

Velox: Spatial Memory Makes World Models Efficient

arXiv:2610.22311v1 Announce Type: new Abstract: Video world models turn actions and camera motion into long visual rollouts, making video a natural interface for interactive simulation. Such interacti

safetyarxiv-cs-cv

What Does History Add? Calendar Controls and Information-Timing Audits for University Trajectory Forecasting

arXiv:2610.21419v1 Announce Type: new Abstract: We audit the predictive value of historical summaries in a rule-based university trajectory simulator. Seventeen ridge specifications and persistence se

agentsarxiv-cs-lg

BANGLAPLAN: A Benchmark for Constraint-Based Planning and Decision-Making in Bangla

arXiv:2610.19704v1 Announce Type: new Abstract: Large language models (LLMs) increasingly help users arrange appointments, plan journeys, and allocate limited resources. These tasks require models to

Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling

arXiv:2610.18583v1 Announce Type: new Abstract: Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequenc

researcharxiv-cs-ai

Confidently Wrong: Detecting Hallucinations in Financial Question Answering from LLM Internal States

arXiv:2607.11414v2 Announce Type: replace Abstract: Large language models (LLMs) in financial applications fail most consequentially when they are confidently wrong. Hedged, uncertain answers invite s

Decomposability-Guided Cooperative Coevolution for Large-Scale Itinerary Planning

arXiv:2506.06121v3 Announce Type: replace Abstract: Large-scale itinerary planning is a variant of the traveling salesman problem, aiming to determine an optimal path that maximizes the collected poin

Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents

arXiv:2606.18947v2 Announce Type: replace Abstract: Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injec

safetyarxiv-cs-ai

Local Ai

Efficient contextual partial monitoring with general function approximation

arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round t

local-aiarxiv-cs-lg

Local Ai

Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking

arXiv:2610.17767v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overt

local-aiarxiv-cs-ai

Fascinating paper from Google DeepMind. They studied a market where AI agents bid to answer each user prompt. Each agent bids its expected r…

Fascinating paper from Google DeepMind. They studied a market where AI agents bid to answer each user prompt. Each agent bids its expected reward minus its inference cost. A first-price auction is sim

agentsdair-ai--x

Applications

FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization

arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model be

applicationsarxiv-cs-ai

FON: A Draft-Revise Dual-Organ Language Model without Sequence-Level Quadratic Attention

arXiv:2610.16301v1 Announce Type: new Abstract: Self-attention assigns two functions to one mechanism-where to read and how to mix-at the price of O(L^2) training, an O(T) decode state, and position s

INTACT: Inducing Latent Reasoning on a Frozen Backbone

arXiv:2610.19568v1 Announce Type: new Abstract: Latent reasoning replaces natural-language chains of thought with compact continuous 'soft-token' representations, reducing the number of intermediate t

Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities

arXiv:2610.20203v1 Announce Type: cross Abstract: This paper investigates the use of Behavior Cloning (BC) for autonomous driving from a bird's-eye view (BEV) perspective in simulated urban environmen

safetyarxiv-cs-lg

JevGPT: Thinking Fast and Slow in Autonomous Research

arXiv:2610.18272v1 Announce Type: cross Abstract: Autonomous research agents must acquire and reason over evidence within a limited budget. Yet relevant sources can remain uninspected or unused, leavi

Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers

arXiv:2610.15501v1 Announce Type: new Abstract: Large language models make decisions through parallel prefill and sequential decoding. Although effective, this approach incurs decoding costs and does

Tutorials

Learning How to Remember: Memory Modality Selection for GUI Agents

arXiv:2610.17928v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) have substantially improved the capabilities of GUI agents, while memory augmentation has emerged as a

tutorialsarxiv-cs-ai

MADDIE: Multi-Agent Drug Discovery Engine

arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to m

MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents

arXiv:2610.14956v1 Announce Type: new Abstract: The Model Context Protocol provides a shared interaction substrate for the agentic web, allowing heterogeneous services to expose capabilities through a

MHWA: Multi-timescale Hierarchical World-Action Model

arXiv:2610.14727v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for sample efficient decision making, yet their open loop rollouts often exhibit increas

researcharxiv-cs-ai

Applications

ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction

arXiv:2610.20223v1 Announce Type: new Abstract: Vision-language-action (VLA) models have emerged as a powerful paradigm for robotic manipulation, enabling robots to generate actions from visual observ

applicationsarxiv-cs-ro

OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning

model-releasesarxiv-cs-lg

arXiv:2610.20418v1 Announce Type: new Abstract: Tabular foundation models enable in-context learning by predicting unseen observations from a labeled support table without task-specific parameter upda

Tutorials

Overcoming a Learning Bottleneck in Impartial Games: A Circuit Complexity Approach

arXiv:2411.06403v4 Announce Type: replace Abstract: Why can neural networks struggle to learn optimal play in a game with a simple, efficient solution algorithm? Motivated by empirical difficulties wi

tutorialsarxiv-cs-ai

PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation

arXiv:2603.23678v2 Announce Type: replace Abstract: Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy const

Price of Anarchy of First-Price Auctions in Agentic Markets

arXiv:2610.20587v1 Announce Type: cross Abstract: An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value

agentsarxiv-cs-lg

Refine Actions If Needed: Adaptive Action Chunk Refinement for Robot Manipulation

arXiv:2610.19765v1 Announce Type: new Abstract: Computationally lightweight manipulation policies predict executable actions with low inference latency, but prediction errors can limit task success. I

safetyarxiv-cs-ro

Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials

arXiv:2610.19630v1 Announce Type: cross Abstract: Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput

researcharxiv-cs-lg

Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers

arXiv:2610.17820v1 Announce Type: cross Abstract: TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns

Scheduling Parallel Optical Circuit Switches for AI Training

arXiv:2603.07373v2 Announce Type: replace-cross Abstract: The rapid growth of AI training has dramatically increased datacenter traffic demand and energy consumption, which has motivated renewed inter

SkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents

arXiv:2610.15481v1 Announce Type: new Abstract: Independently authored skills can impose incompatible requirements, competing preferences, and redundant instructions when jointly loaded by an LLM agen

Hardware

Speculative Sampling in Interactive Video Generation

arXiv:2610.20556v1 Announce Type: new Abstract: Interactive video generation commonly relies on small bidirectional chunks to reduce response latency. Yet our preliminary experiments show that larger

hardwarearxiv-cs-cv

State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study

arXiv:2610.15407v1 Announce Type: new Abstract: An end-to-end success rate alone cannot distinguish failure to learn an action from failure to apply it as task state changes. We study this distinction