Search: “reasoning”

Principled Under Pressure: Post-Training Decides Whether LLMs Act on Their Own Moral Judgment

arXiv:2610.08670v1 Announce Type: cross Abstract: Language models increasingly act as agents. An agent that says an action is wrong and then takes it anyway is a different failure from one that does n

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Local Ai

ProactiveVLA: Augmenting Embodied Memory through Proactive Environment Exploration

DGX agent

arXiv:2610.06999v1 Announce Type: new Abstract: Rapid adaptation to a new environment requires a robot to acquire useful knowledge about local objects, states, and interactions from limited experience

local-aiarxiv-cs-ro

7 Oct 2026

Pseudowords as probes: Large Language Models show little of the sublexical sensitivity that governs human pseudoword processing

arXiv:2610.07936v1 Announce Type: new Abstract: Systematicity, the probabilistic mapping of form to meaning, permeates language at all levels, and sublexical cues have been shown to govern human pseud

safetyarxiv-cs-cl

RAM-Net: Linear-Time Sequence Modeling with Sparsely Addressable State

arXiv:2602.11958v2 Announce Type: replace-cross Abstract: Linear attention offers an efficient alternative to full attention with a fixed-size recurrent state. However, this state is shared by all tok

researcharxiv-cs-cl

Rethinking Adapter Placement: A Dominant Adaptation Module Perspective

arXiv:2605.06183v2 Announce Type: replace Abstract: Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method that places trainable low-rank adapters into frozen pre-trained m

Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability

arXiv:2610.08448v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different tokenizers, comparing teacher and student

Safeguarding LLMs via Model-Agnostic Latent Safety Signals from Dark Knowledge

arXiv:2610.07532v1 Announce Type: cross Abstract: LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks incl

SanSi: A Looped Typed Decision Model for System 1.5 Thinking

arXiv:2610.07730v1 Announce Type: cross Abstract: Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a

Self-Retrospection Distillation: Turning Post-hoc Experiences into Prior Foresight

arXiv:2610.08077v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after intera

SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation

arXiv:2602.16863v3 Announce Type: replace-cross Abstract: The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging cl

Small Language Models for Smart Data Model Classification at the Edge: A Cost-Aware Hybrid Approach

arXiv:2610.07093v1 Announce Type: new Abstract: The rapid proliferation of heterogeneous data sources within the Internet of Things (IoT) across domains such as smart cities, energy management, and en

researcharxiv-cs-ai

Smart Content Ingestion for Generative AI Workloads

arXiv:2610.07091v1 Announce Type: new Abstract: The evolution of machine learning has progressively changed where intelligence resides in an AI system. In conventional machine learning the task, data

agentsarxiv-cs-ai

SoloQ: Calibration-Free Quantization for Diffusion Language Models

arXiv:2610.07121v1 Announce Type: new Abstract: Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based t

SpeedrunBench: Challenging LLM Agents with Video Game Speedrunning

arXiv:2610.08076v1 Announce Type: new Abstract: Frontier LLM agents have been shown to be capable of solving increasingly complex tasks for which humans have measurable solutions. This begs the pertin

Structuring MoE Expert Selection for Agentic Reinforcement Learning

arXiv:2610.07332v1 Announce Type: cross Abstract: Long-horizon LLM agents are frequently implemented using sparse mixture-of-experts (MoE) models, yet the co-design of agentic behavior and MoE structu

agentsarxiv-cs-cl

Local Ai

Topology-Consistent Task Planning over Cellular Workflow Complexes for LLM-based Agents

arXiv:2610.07004v1 Announce Type: new Abstract: Task planning for LLM agents requires workflows that satisfy both user intent and complex sub-task dependencies. While existing planners work well for s

local-aiarxiv-cs-ai

TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models

arXiv:2610.07767v1 Announce Type: cross Abstract: Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generatio

safetyarxiv-cs-cl

Tutorials

Verdicts Without Annotated Evidence: Rejection Sampling or Label-Only Post-Training for Evidence Recovery?

arXiv:2610.06962v1 Announce Type: cross Abstract: In many review workflows the verdict is the only thing retained. The passages behind it are not marked, because that annotation costs far more than re

tutorialsarxiv-cs-ai

Tutorials

Verified, not generated: expert-verified AI study materials and the distribution of learning gains in a university course

arXiv:2610.07097v1 Announce Type: new Abstract: Experimental studies of generative AI in education mostly report average effects, yet field evidence shows that AI can narrow attainment gaps or widen t

tutorialsarxiv-cs-ai

Verifying Coordination in Parallel Coding Agents: NP-Bench and a Scheduling Planner

arXiv:2610.07261v1 Announce Type: new Abstract: A team of coding agents can look fine agent by agent yet fail as a team: each passes its own tests while the merged result is broken, and single-agent e

Visual Abstention in Unified Multimodal Models

arXiv:2610.07887v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand abou

Hardware

WavePrune: One period is often enough for RoPE

arXiv:2610.06963v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) encodes token positions by rotating each two-dimensional channel of the query and key vectors at a channel-specific fre

hardwarearxiv-cs-cl

Weight Oracles: Reading Neural Network Weights with Language Models

arXiv:2610.07334v1 Announce Type: new Abstract: Interpretability methods for neural networks are predominantly reactive: they analyse activations produced during specific forward passes, requiring kno

When Tools Lie: Reliability of Mathematical Agents Under Corrupted Tool Feedback

arXiv:2610.08097v1 Announce Type: cross Abstract: Mathematical problem solving often requires deterministic computational steps that agents delegate to tools and implicitly trust. Yet tools can fail s

WorkflowOps: Learning Agent Collaboration Priors for Multi-Agent Workflow Orchestration

arXiv:2610.07860v1 Announce Type: new Abstract: Multi-agent systems are increasingly deployed for complex knowledge work, yet their orchestration layers remain largely memoryless: each new task is dec

agentsarxiv-cs-ai

WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?

arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical

Local Ai

A KKL Observer Perspective on Reservoir Computing

arXiv:2610.04343v1 Announce Type: cross Abstract: Reservoir computing (RC) is a machine learning technique for data-driven modeling of dynamics for forecasting and control, primarily studied in comput

local-aiarxiv-cs-lg

A Multimodal Sequence-to-Sequence Model for Cross-Subject Prediction of Brain Responses to Naturalistic Stimuli

arXiv:2507.18104v3 Announce Type: replace Abstract: Brain encoding models predict time-resolved neural activity from computational representations of ongoing experience, providing a principled framewo

researcharxiv-cs-cv

Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement

arXiv:2610.05782v1 Announce Type: cross Abstract: Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces signific

safetyarxiv-cs-lg

AIProver: Agentic Auto-Formalization of Mathematical Research via Certificate-Driven Evolving Harness

arXiv:2610.05367v1 Announce Type: cross Abstract: Proof auto-formalization translates natural-language (NL) theorems and proofs into a formal language (FL) such as Lean, enabling mechanical verificati

Aligning the Query Space: Greedy Information Projection for Language Model Data Selection

arXiv:2603.13790v2 Announce Type: replace Abstract: Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundame

researcharxiv-cs-lg

Are We Measuring Anticipation? Auditing Privileged Information in Procedural Video Evaluation

arXiv:2610.03826v1 Announce Type: new Abstract: Benchmark scores license claims about the capabilities being evaluated. We audit the inference licensed by an evaluation protocol, rather than the predi

ARO: Aligned Representation learning for multi-Omics data

arXiv:2610.06443v1 Announce Type: new Abstract: The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant ba

researcharxiv-cs-lg

Applications

ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image

arXiv:2610.05249v1 Announce Type: new Abstract: Embodied agents benefit from 3D environments that combine visual fidelity to real-world observations with physical interactivity. Existing single-image

applicationsarxiv-cs-cv

Attention Tax, Handoff Tax: A Stylised Model of When Multi-Agent LLM Systems Help

arXiv:2610.06069v1 Announce Type: cross Abstract: Recent work on multi-agent LLM systems reaches sharply different conclusions: some results show that a single agent with the same information and comp

agentsarxiv-cs-lg

Tutorials

Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models

arXiv:2610.05125v1 Announce Type: new Abstract: Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the d

tutorialsarxiv-cs-lg

Certification of Real Images through Calibrated Content Authentication

arXiv:2610.05870v1 Announce Type: new Abstract: Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detecto

COMIC: Agentic Sketch Comedy Generation

arXiv:2603.11048v2 Announce Type: replace Abstract: We propose a fully automated AI system that produces short comedic videos similar to sketch shows such as Saturday Night Live. Starting from charact

agentsarxiv-cs-cv

ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution

arXiv:2610.04605v1 Announce Type: new Abstract: Many visual explanation methods in computer vision highlight pixel importance but struggle to link these low-level cues to semantically meaningful conce

safetyarxiv-cs-cv

Deep Learning Denoising of Real SWOT Sea Surface Height Observations

arXiv:2610.03739v1 Announce Type: cross Abstract: The SWOT (Surface Water Ocean Topography) mission is currently providing unpreceded high-resolution measurements of Sea Surface Height (SSH), revealin

Applications

Dual-Scale Relational Graph Transformers for Ecosystem-Aware Fraud Detection

arXiv:2610.04138v1 Announce Type: new Abstract: Account takeover (ATO) fraud is a growing threat to digital banking, requiring effective detection while minimizing friction for legitimate customers. P

applicationsarxiv-cs-lg

Evolving LLM-Generated Features for Interpretable Classification

arXiv:2610.03951v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as classifiers, yet they operate as opaque systems whose decisions are difficult to interpret, which

safetyarxiv-cs-lg

FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models

arXiv:2610.05790v1 Announce Type: new Abstract: Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecolog

From Transformers to Weighted Automata: Towards the Verification of Large Language Models

arXiv:2610.04569v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical settings, yet their black-box nature makes it difficult to provide formal gu

safetyarxiv-cs-lg

HeuFouFT: Task-Guided Metaheuristic Coordinate Search for Fourier Fine-Tuning

arXiv:2610.06437v1 Announce Type: new Abstract: We introduce Heuristic-Guided Fourier Fine-Tuning (HeuFouFT), a task-guided framework for selecting trainable frequency coordinates in Fourier fine-tuni

agentsarxiv-cs-lg

InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models

arXiv:2312.01886v4 Announce Type: replace Abstract: Large vision-language models (LVLMs) have demonstrated their incredible capability in visual question answering. However, this rich visual interacti

Learning Discriminative Geometry for Drifting Models

arXiv:2610.04703v1 Announce Type: cross Abstract: Recently proposed Drifting Models shift iterative distribution refinement from inference to training, enabling effective one-step generation. However,

researcharxiv-cs-cv

Learning Interpretable Differentiable Logic Networks for Tabular Regression

arXiv:2505.23615v2 Announce Type: replace Abstract: Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be

researcharxiv-cs-lg