Search: “inference”

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

arXiv:2508.04903v4 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However

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Read the Room Before Your Model Generates

DGX agent

arXiv:2610.18820v2 Announce Type: new Abstract: Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for select

model-releasesarxiv-cs-ai

15 Oct 2026

RECAP: Calibrating Low-Bit Reasoning Models on the History They Read

arXiv:2610.15259v1 Announce Type: new Abstract: Low-bit quantization reduces the cost of serving reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and

Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding

arXiv:2610.19723v1 Announce Type: new Abstract: Interpreting an ambiguous utterance in real-world interactions often requires context beyond a single utterance. When the utterance refers to objects in

ReGRA: Residual-Gradient Adaptation for Complementary Low-Rank Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2610.19700v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning by learning low-rank weight updates. Increasing rank expands adaptation capacity, but

Representation Conditioning for Generation

arXiv:2610.20608v1 Announce Type: new Abstract: Representation-alignment methods effectively accelerate the diffusion transformer training. REPA, for example, uses a pretrained vision encoder to super

safetyarxiv-cs-cv

Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection

arXiv:2610.18566v1 Announce Type: new Abstract: Large language model (LLM) routers reduce serving cost by choosing which model to call and how many output tokens to grant it for each query. Existing r

Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution

model-releasesarxiv-cs-cv

arXiv:2610.19495v1 Announce Type: cross Abstract: Retrieving useful context for a robot requires matching its task, execution stage, and the continuation that should follow. We introduce Robota-Embed,

Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning

arXiv:2610.19378v1 Announce Type: cross Abstract: We seek a hierarchical resolution of residual error under a composite learning objective as a principled mechanism for identifying and exploiting addi

researcharxiv-cs-cv

RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can

arXiv:2610.14299v1 Announce Type: cross Abstract: Recent Speech Full-Duplex (FDX) models are capable of engaging in naturalistic conversation by listening and speaking at the same time. Building on th

researcharxiv-cs-ai

Same Claim, Different Interpretations: How LLM Agents Resolve Ambiguity in Scientific Claims

arXiv:2610.20457v1 Announce Type: new Abstract: Co-scientists and agentic scientific claim verifiers are increasingly used to assess claims across the sciences. Natural-language scientific claims, wri

Tutorials

Score-Based Training for Energy-Based TTS Models

arXiv:2505.13771v2 Announce Type: replace-cross Abstract: Noise contrastive estimation (NCE) is a popular method for training energy-based models (EBM) with intractable normalisation terms. The key id

tutorialsarxiv-cs-lg

ScreenASR-Bench: A Tiered Benchmark for Speech Recognition with Visual Context

arXiv:2610.16260v1 Announce Type: new Abstract: Current ASR models perform well on general benchmarks but still struggle with acoustically ambiguous words. Visual context can aid recognition in screen

Self-Evolving Visual Agent via Margin Reward

arXiv:2610.15084v1 Announce Type: new Abstract: Visual agents that interleave reasoning with tool invocation have posed a promising direction for multimodal tasks. However, learning effective tool inv

Local Ai

Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs

arXiv:2610.14209v1 Announce Type: new Abstract: The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the use

local-aiarxiv-cs-ai

SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models

arXiv:2610.19327v1 Announce Type: new Abstract: Robotic ultrasound can standardize image acquisition and reduce operator workload and variability, but policy learning remains limited by the lack of re

safetyarxiv-cs-cv

Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility

arXiv:2610.20139v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have emerged as a promising approach to mechanistic interpretability by decomposing language-model activations into sparse, p

researcharxiv-cs-cl

StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions

arXiv:2610.17309v1 Announce Type: cross Abstract: We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-gu

Hardware

Temporal Serverless Workers on Cloud Run: Scale workers ahead of demand

The open-source Temporal execution platform is rapidly becoming a foundational layer of modern software infrastructure — especially for orchestration in distributed systems and AI agent workloads. How

hardwaregoogle-cloud-ai

Tutorials

Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction

arXiv:2610.19552v1 Announce Type: new Abstract: Foundation models for dynamical systems can forecast unseen partial differential equations (PDEs), ordinary differential equations (ODEs), and time seri

tutorialsarxiv-cs-lg

The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation

arXiv:2610.20456v1 Announce Type: new Abstract: The scaling relationship of compute and performance for language models (LMs) has been extensively studied; more recently, models are being designed to

The Imperishable Record, Forgiveness, and the Future of Criminal Justice

arXiv:2610.15499v1 Announce Type: cross Abstract: Digital technologies can preserve records of wrongdoing indefinitely, and AI can give those records continuing influence over a person's future. We de

researcharxiv-cs-ai

The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness

arXiv:2603.09200v2 Announce Type: replace-cross Abstract: Situational awareness, the capacity of an AI system to recognize its own nature, understand its training and deployment context, and reason st

TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

arXiv:2610.19725v1 Announce Type: new Abstract: Time-series foundation models provide strong zero-shot forecast, but deployment for real- world applications still requires task-specific designs for mo

agentsarxiv-cs-lg

To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models

arXiv:2610.19934v1 Announce Type: new Abstract: Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much

researcharxiv-cs-cl

TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch

model-releasesarxiv-cs-lg

arXiv:2610.18924v1 Announce Type: cross Abstract: Interpreting electron paramagnetic resonance (EPR) spectra commonly requires repeated forward simulation and optimization of spin Hamiltonian paramete

Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy

arXiv:2610.19492v1 Announce Type: new Abstract: Streaming Inverse Text Normalization (ITN) is vital for converting spoken-form outputs from streaming Automatic Speech Recognition into formatted writte

safetyarxiv-cs-cl

Tracing Morphology, Semantics, and Divergence in Language Acquisition

arXiv:2610.19041v1 Announce Type: cross Abstract: Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve in

researcharxiv-cs-cl

Training Language Models to Fail Gracefully

arXiv:2610.20401v1 Announce Type: new Abstract: Reinforcement learning makes language models better at solving tasks, but the same mechanism also rewards guessing. When success is the only way to earn

Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation

arXiv:2610.15227v1 Announce Type: new Abstract: Unified vision-language models (VLMs) perform both text-to-image (T2I) and image-to-text (I2T) generation with one shared Transformer backbone, yet how

researcharxiv-cs-ai

Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring

arXiv:2610.15231v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks su

safetyarxiv-cs-ai

Applications

Vela: User Behavior as Supervision Sparks LLM Intelligence

arXiv:2610.14709v1 Announce Type: cross Abstract: Large language models are increasingly deployed in applications that act on users' long-term behavior histories, from personal assistants to recommend

applicationsarxiv-cs-ai

Verifier-Grounded Agentic RL for Provably Correct Code

arXiv:2610.19298v1 Announce Type: new Abstract: Reinforcement learning for code usually rewards passing tests, which a model can do while remaining wrong on the inputs the tests do not cover. A progra

Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

arXiv:2501.12375v4 Announce Type: replace Abstract: Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal i

researcharxiv-cs-cv

VIGOR-MPC: Vision-Guided Objective Regulation MPC

arXiv:2610.20333v1 Announce Type: new Abstract: Controlling an agent in an environment solely perceived through onboard vision is challenging: perception yields noisy, variable-length, and partially o

agentsarxiv-cs-ro

Tutorials

What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books

arXiv:2610.20104v1 Announce Type: new Abstract: Autoregressive transformers trained on limit order book (LOB) message streams are increasingly proposed as foundation models for market microstructure.

tutorialsarxiv-cs-lg

What the Gemini agent means for startups

model-releasesgoogle-cloud-ai

Small teams, short runway, and constant pressure to ship faster — it’s the reality we see every day among the founders we work with at Google for Startups. When you are building a company from the gro

When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems

arXiv:2610.16039v1 Announce Type: cross Abstract: Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, since information disclosed to o

When Goals Leak Across Relationships: How Cached Goal Representations Shape Cross-Client Decisions

arXiv:2610.17365v1 Announce Type: new Abstract: As LLM agents take on more autonomous roles, they may interact with multiple clients and counterparties whose goals and incentives conflict. If an agent

3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for Cross-View Object Re-Identification

model-releasesarxiv-cs-cv

arXiv:2610.16692v1 Announce Type: new Abstract: We address the Cross-View Object Re-Identification (ReID) task, aiming to match identities across drastically different viewpoints. We focus on Single-V

A 35B multimodal model running on a phone with ~3GB memory? MiniCPM-V 4.7 demo is pretty wild

model-releasesr-localllama

Just saw this demo from OpenBMB for MiniCPM-V 4.7, and the on-device capability is pretty impressive. A 35B-A3B MoE vision-language model running locally on mobile devices is already interesting, but

A Survey of Flow Matching in Reinforcement Learning

arXiv:2610.16275v1 Announce Type: new Abstract: Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling expressive

Adversarial Gradient Forcing for Robust Few-Step Autoregressive Video Generation

arXiv:2610.14553v1 Announce Type: new Abstract: Autoregressive video generation models generate videos sequentially, with each new block conditioned on previously generated blocks. By training on the

safetyarxiv-cs-cv

AlphaPilot: Multi-Turn Tool-Using LLM Agents for Adaptive Alpha Mining with Reinforcement Learning

arXiv:2610.15488v1 Announce Type: cross Abstract: Formulaic alpha mining seeks interpretable expressions that rank assets by future returns. The task is challenging because it demands simultaneous pro

agentsarxiv-cs-lg

ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs

arXiv:2610.15368v1 Announce Type: new Abstract: Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks. Within a containerized environme

Local Ai

ARMED: Adaptive Retrieval-Augmented Medication Recommendation

arXiv:2610.16440v1 Announce Type: new Abstract: Medication recommendation requires jointly considering diagnoses, comorbidities, longitudinal patient history, and treatment interactions. Although rece

local-aiarxiv-cs-lg

Applications

Articulated 3D Scene Graphs for Open-World Mobile Manipulation

arXiv:2602.16356v2 Announce Type: replace-cross Abstract: Semantic scene understanding has enabled robots to reason and perform affordance-driven object interaction. Despite this, real-world manipulat

applicationsarxiv-cs-cv

Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence

model-releasesarxiv-cs-lg

arXiv:2610.14005v1 Announce Type: cross Abstract: Under the accelerated pace of AI development, any benchmark whose difficulty is calibrated with humans or current AI systems becomes circular, or satu