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SPECTRA-VLA: Base-Fine Hyperspectral Token Transfer at Frozen Vision-Language-Action Interfaces

arXiv:2610.19651v1 Announce Type: new Abstract: Hyperspectral imaging distinguishes RGB-similar objects, but a pretrained vision-language-action model needs a state its frozen decoder can use. SPECTRA

researcharxiv-cs-cv

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Spectral-MoE: Heterogeneous Per-Layer Expert Utilization in Dense-to-MoE Conversion

DGX agent

arXiv:2610.19232v1 Announce Type: new Abstract: Large language models impose substantial memory demands and arithmetic costs during inference. Dense-to-mixture-of-experts (MoE) conversion is a model o

model-releasesarxiv-cs-lg

15 Oct 2026

Tutorials

Spectral structure learning for clinical time series

arXiv:2502.11680v2 Announce Type: replace Abstract: We develop and evaluate a structure learning algorithm for clinical time series. Clinical time series are multivariate time series observed in multi

tutorialsarxiv-cs-lg

Agents

Spectral World Renderer: Unified 3D Hyperspectral Unmixing

arXiv:2610.19520v1 Announce Type: new Abstract: Cameras and the foundation models built on them recognize what a 3D scene contains, but not what it is made of: surfaces of the same colour can be diffe

agentsarxiv-cs-cv

SpectrumQuant: Spectrum Alignment for LLM Quantization

model-releasesarxiv-cs-lg

arXiv:2610.20303v1 Announce Type: new Abstract: Orthogonal transforms have emerged as an effective tool for low-bit quantization of large language models (LLMs), as they reshape activation distributio

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

Hardware

Stable Token-level Scheduling for Serverless LLM Inference

arXiv:2610.17689v1 Announce Type: new Abstract: Serverless inference is emerging as an efficient deployment model for Large Language Models (LLMs) due to its ability to elastically scale resources, im

hardwarearxiv-cs-ai

StableJudge: A Lightweight Adversarial Defense Framework for LLM-as-a-Judge

arXiv:2610.16814v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges to assign scalar scores that drive downstream decision-making, including tasks such as pa

STAMP as a topology-aware molecular language for fragment-level learning and editing

arXiv:2610.18438v1 Announce Type: cross Abstract: Molecular fragments provide chemically meaningful units, but existing molecular languages rarely make their locations and connections explicit for edi

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

State-Matched Sequence Conformal Prediction

arXiv:2610.19484v1 Announce Type: cross Abstract: Sequential conformal prediction needs to decide which past errors should calibrate uncertainty for the current target. Recency and online error feedba

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

State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions

arXiv:2507.13638v4 Announce Type: replace-cross Abstract: A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive k

StateCraft: Augmenting Video World Models with Abstract-State Reasoning

model-releasesarxiv-cs-cv

arXiv:2610.19795v1 Announce Type: new Abstract: Video world models aim to simulate complex environments with realistic appearance and dynamics. Maintaining consistency over long interactions requires

Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering

arXiv:2610.19359v1 Announce Type: cross Abstract: Activation steering adds a vector to a model's hidden states to control a named behaviour. A gain on that behaviour, however, does not show that the v

Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition

arXiv:2601.07239v2 Announce Type: replace Abstract: Deterministic inference is a comforting ideal in classical software: the same program on the same input should always produce the same output. As la

Local Ai

STP-GUI: Spatially-Aware Token Pruning for Efficient Web-GUI Agents

arXiv:2610.20390v1 Announce Type: new Abstract: With the rapid development of vision-language models (VLMs), Web-GUI agents have achieved promising performance in interacting with real-world interface

local-aiarxiv-cs-cv

Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

arXiv:2602.22583v2 Announce Type: replace Abstract: Example-based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems

StreamErase: Towards Ultra-Fast Streaming Video Erasing

model-releasesarxiv-cs-cv

arXiv:2610.20137v1 Announce Type: new Abstract: Current video object removal methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction.

Structural Baselines Matter for Agent Failure Detection and Attribution

arXiv:2610.16661v1 Announce Type: new Abstract: An LLM agent using tools can fail silently. It stops before the task is done, raises no error, and answers fluently. Two kinds of monitor address this,

Structural Chain-of-Thought: Reasoning with Executable Computational Graphs

arXiv:2610.15107v1 Announce Type: new Abstract: Chain-of-thought prompting enables reasoning in large language models but produces explanations that are difficult to verify, expensive to generate, and

researcharxiv-cs-ai

Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent

arXiv:2610.19187v1 Announce Type: cross Abstract: Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, e

Structure-Preserving Document-Level Data Augmentation via Discourse Parsing

arXiv:2610.19497v1 Announce Type: new Abstract: Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Proces

Student Copula Markov Networks

arXiv:2610.19135v1 Announce Type: cross Abstract: This paper extends the applicability of probabilistic graphical models to non Gaussian data. It establishes a characterization of the pairwise Markov

Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning

arXiv:2407.12703v5 Announce Type: cross Abstract: Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based met

Suggested Is Not Superseded: A Pre-Registered Evaluation of a System-One Model as a Memory-Retirement Gate

arXiv:2610.15710v1 Announce Type: new Abstract: Long-term memory for assistants needs a gate that decides when a new utterance retires an old stored claim. Benchmark labels for this step mark a pair a

SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba

arXiv:2610.16636v1 Announce Type: cross Abstract: State space models provide efficient linear-complexity processing for long sequences, but their performance frequently degrades as context length grow

Superintelligence and the Triple Risk of an AI Race

arXiv:2610.17085v1 Announce Type: cross Abstract: On many estimates, artificial general intelligence (AGI) is likely to emerge within the next few decades or even sooner. Once it does, it may quickly

Agents

SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm

arXiv:2610.20431v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scien

agentsarxiv-cs-cl

SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning

model-releasesarxiv-cs-cl

arXiv:2601.21649v2 Announce Type: replace-cross Abstract: The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Lan

SWEPatchTest: Evaluating Software Tests Beyond Reproduction

arXiv:2610.18004v1 Announce Type: cross Abstract: Coding agents use generated tests to decide whether a proposed patch satisfies an issue's requirements. A suite that fails on the buggy program and pa

Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction

arXiv:2610.19640v1 Announce Type: new Abstract: The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that gene

TacClaw-Infinity: A Large-Scale Vision-Tactile-Language-Action Dataset for Dexterous Manipulation

arXiv:2610.20320v1 Announce Type: new Abstract: Large-scale robot learning increasingly relies on diverse human demonstrations, yet tactile manipulation data remain difficult to scale, and existing mu

researcharxiv-cs-ro

Tactile-Guided Robot Learning: From Representation Learning to Foundation Models

arXiv:2610.20134v1 Announce Type: new Abstract: Tactile sensing is becoming central to robust, adaptive, and dexterous robotic manipulation. Beyond its traditional role in contact detection and reacti

safetyarxiv-cs-ro

Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems

arXiv:2610.19362v1 Announce Type: new Abstract: Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly

Applications

Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference

arXiv:2610.20292v1 Announce Type: new Abstract: Reliable medical reasoning requires LLMs to dynamically access external biomedical evidence to improve factual trustworthiness and reasoning accuracy. H

applicationsarxiv-cs-cl

TANGO: RNA Topology and Geometry Co-Design

arXiv:2610.20277v1 Announce Type: new Abstract: Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthet

safetyarxiv-cs-lg

Target-Token Asymmetry Can Masquerade As Depth Specialization

arXiv:2610.19344v1 Announce Type: new Abstract: Layer-ablation studies often perturb early or late layers of a language model, compare the damage to prediction on two corpora, and read a larger early-

Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults

arXiv:2610.18494v1 Announce Type: cross Abstract: Objective digital measures of depression severity could complement conventional symptom assessments and facilitate more timely clinical evaluation. He

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

Tutorials

Test-Time Scaling via Budgeted Multi-Attribute Verification

arXiv:2609.34322v2 Announce Type: replace Abstract: Verifying LLM-generated answers under a shared computational budget requires jointly deciding which candidates to inspect and which verification att

tutorialsarxiv-cs-ai

TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series

model-releasesarxiv-cs-lg

arXiv:2610.19557v1 Announce Type: new Abstract: Contrastive learning for time series depends on deciding which pairs of inputs should be treated as similar and dissimilar. Because few augmentations re

Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance

arXiv:2610.19860v1 Announce Type: cross Abstract: In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by

TextualRL: Textual Reinforcement Learning for Context Management

arXiv:2610.18216v1 Announce Type: cross Abstract: LLM agents can adapt to task feedback by revising the textual context that guides their behavior, without changing their model parameters. However, tu

TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering

model-releasesarxiv-cs-cv

arXiv:2610.19551v1 Announce Type: new Abstract: Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missi

The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization

arXiv:2610.20439v1 Announce Type: new Abstract: We study classification dynamics under small initialization and identify a pronounced non-monotone behavior of accuracy that we call the accuracy well:

The Agent Knew: Internal States Forecast Unauthorized Actions and Stop Them, and the Lies That Follow

arXiv:2610.17301v1 Announce Type: new Abstract: Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact de

Agents

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery

arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, an

agentsarxiv-cs-cl