Search: “models”

arXiv:2610.02488v1 Announce Type: new Abstract: Language-model agents increasingly rely on harnesses that manage bounded context, persistent memory, tools, verification, and repeated execution, yet ex

local-aiarxiv-cs-lg

Hesitation Has a Geometry: Entropy-Trained Hyperbolic Probes for Sparse Activation Steering

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DGX agent

arXiv:2610.02391v1 Announce Type: cross Abstract: When a large language model solves a mathematical problem, its reasoning is largely hierarchical, and the solution often branches at a few tokens wher

model-releasesarxiv-cs-cl

5 Oct 2026

IntentCoding: Amplifying User Intent in Code Generation

arXiv:2602.00066v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong capabilities in code generation, but their adherence to fine-grained user intent with multiple constrai

Kinematics-Induced Multimodal 3D Human Pose Estimation with Subject-Level Privacy

model-releasesarxiv-cs-cv

arXiv:2610.02943v1 Announce Type: new Abstract: Multimodal 3D Human Pose Estimation (3D HPE) combines complementary information from RGB, LiDAR, and mmWave radar, but models trained on correlated obse

On Unlearning for Time-series Forecasting

arXiv:2610.02865v1 Announce Type: new Abstract: Time-series forecasting is widely used in sensitive domains. Models in these settings are often trained on longitudinal user- or entity-level records, w

Prompted to Discriminate: Generalizing Malicious-Input Probes in the Wild

arXiv:2610.02413v1 Announce Type: new Abstract: LLM agents increasingly rely on activation probes as runtime monitors for prompt injection, jailbreaks, and unsafe requests, reading the model's own hid

RxnOptBench: Benchmarking LLMs for Reaction-Condition Optimization in Organic Methodology

arXiv:2610.02242v1 Announce Type: cross Abstract: Chemical reaction-condition optimization -- choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize y

Safeguarding Mutual Correction in Source-Free Domain Adaptation via Cut Statistics

arXiv:2610.02981v1 Announce Type: new Abstract: Source-Free Domain Adaptation (SFDA) aims to adapt a source-pretrained model to an unlabeled target domain without access to the original source domain.

safetyarxiv-cs-ai

Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite

arXiv:2610.02826v1 Announce Type: new Abstract: Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that ma

Slow-Fast Multi-Teacher On-Policy Distillation for Capability Preservation

arXiv:2610.02324v1 Announce Type: cross Abstract: Foundation multimodal large language models are designed to support a broad spectrum of capabilities across diverse domains. Multi-teacher on-policy d

safetyarxiv-cs-ai

TasteBench: Multimodal Benchmark for Sensory Prediction, from Molecules to Sustainable Foods

arXiv:2610.02599v1 Announce Type: new Abstract: Sustainable protein discovery lacks the fast computational proxies, analogous to molecular docking or density functional theory, that accelerate drug an

TerraVis: Towards Evaluation of World-Grounded Visual Consistency in Text-to-Image Generation via MLLM Workflows

model-releasesarxiv-cs-cv

arXiv:2610.02959v1 Announce Type: new Abstract: Recent text-to-image models have made substantial progress in photorealism, aesthetics, and text-image alignment. Yet visually appealing images can stil

Trained Agentic Context Management

model-releasesarxiv-cs-cl

arXiv:2610.02404v1 Announce Type: cross Abstract: We study long context language models. Instead of training long context natively, or designing a long context harness, we train a model over the simpl

A Matched-Budget Audit Framework for Recaptioned Image-Text Supervision Distributions

arXiv:2610.00952v1 Announce Type: cross Abstract: Recaptioned image-text corpora are now standard for text-to-image (T2I) training, with vision--language model (VLM) captioners replacing sparse alt-te

AnyJev Technical Report

arXiv:2610.00831v1 Announce Type: new Abstract: A typed decision is a choice among a fixed set of options, returned as a probability rather than as text. Systems that need typed decisions today use mo

Capabilities Ain't All You Need: Measuring Propensities in AI

arXiv:2602.18182v5 Announce Type: replace-cross Abstract: AI evaluation has primarily focused on measuring capabilities, with formal approaches inspired from Item Response Theory (IRT) being increasin

Hardware

CAST: Cost-Aware Speculative Trees from One-Pass Block Drafters

arXiv:2610.00321v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by drafting future tokens cheaply and verifying them with the target model in parallel

hardwarearxiv-cs-lg

Every Ablation Is a Dose: Counterweights and the Semblance of Self-Repair

arXiv:2610.02173v1 Announce Type: new Abstract: Ablate a component of a language model, and other components often appear to adjust and compensate. This phenomenon, termed self-repair, has been observ

FedLore: Communication and Memory Efficient Federated Learning via Shared Gradient Low-Rank Projection

arXiv:2610.01620v1 Announce Type: new Abstract: Federated training of foundation models is constrained by client memory and communication costs. LoRA-based methods reduce these costs through low-rank

Local Ai

From Task Mixtures to Specialized Experts

arXiv:2610.00580v1 Announce Type: new Abstract: In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data d

local-aiarxiv-cs-lg

GPEC: Efficient Pre-LLM Gaussian Process Embedding Correction for Cardiac Video Caption Generation

model-releasesarxiv-cs-cv

arXiv:2610.00196v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for video understanding and caption generation, but their performance may decline i

GUI-HARVEST: Self-Improving GUI Agents through Evidence-Driven Harness Evolution

arXiv:2610.00948v1 Announce Type: cross Abstract: The executable harness surrounding a GUI model determines how observations are assembled, actions are executed, and verification, recovery, and termin

In Dialogue with Intelligence: Toward Insightful Co-Augmentation

arXiv:2505.22767v4 Announce Type: replace-cross Abstract: Dialogue with a large language model can lead a person to insight: a sudden change in how they understand a problem. This perspective asks how

researcharxiv-cs-ai

IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs

arXiv:2610.00426v1 Announce Type: new Abstract: We introduce IrekoGPT, a post-hoc method for converting pretrained LLMs into slimmable models whose width can be adjusted at inference time. Building on

JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

arXiv:2610.00437v1 Announce Type: new Abstract: LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models

Learning Rate Transfer for Hybrid Transformer-SSM Architectures

arXiv:2610.01172v1 Announce Type: new Abstract: We study learning rate (LR) scaling for hybrid architectures combining Transformer and State-Space Model (SSM) blocks, a class adopted by several recent

M^2Weather: A Benchmark for Joint Multi-Station and Multi-Variable Weather Forecasting

arXiv:2610.00370v1 Announce Type: new Abstract: Station weather forecasting is fundamentally shaped by both complex spatial dependencies across stations and strong physical coupling among weather vari

Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning

arXiv:2610.01936v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter bound knowledge and th

Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents

arXiv:2610.02002v1 Announce Type: cross Abstract: Large Language Model (LLM) agents now take part in organizational work, where many authors record decisions across documents over months. Because a re

MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation

arXiv:2505.17613v2 Announce Type: replace Abstract: Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation,

MoRA: MoE Pruning via Router Bias Learning and Expert Approximation

arXiv:2610.00367v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable parameter scaling with limited per-token computation by activating only a small subset of experts for each token,

Multi-Reference Path Tracking Control for an Agricultural Tractor with Nonlinear Model Predictive Control

arXiv:2610.00057v1 Announce Type: new Abstract: Guiding a tractor along a predefined reference path is a key component of precision agriculture. This study develops a path tracking controller based on

researcharxiv-cs-ro

Not Too Hard, Not Too Easy: Learning from Intermediate States for LLM Structured Reasoning

arXiv:2609.33149v2 Announce Type: replace Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how t

Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning

arXiv:2606.13607v3 Announce Type: replace Abstract: When large language models (LLMs) fail to generalize or make content-sensitive errors in reasoning, it is often taken as evidence that LLMs are not

researcharxiv-cs-ai

Target-Dependent Limits of Causal Repair: A Leading-Log Frontier in a Gaussian Model

arXiv:2610.00424v1 Announce Type: cross Abstract: Knowing how much a causal predictor could improve need not reveal the gain of the repair actually learned. We quantify this gap in a scalar Gaussian c

researcharxiv-cs-ai

The Delegation Danger Band: Why Mid-Capability Sub-Agents Over-Trust Inherited Stale State

arXiv:2610.00041v1 Announce Type: cross Abstract: Agent frameworks increasingly delegate work by forking sub-agents; a common default makes the child inherit the parent's full working context. We meas

Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining

arXiv:2610.00438v1 Announce Type: new Abstract: Humanoid whole-body manipulation has advanced rapidly, enabling policies to coordinate locomotion, posture, bimanual interaction, and dexterous hand mov

safetyarxiv-cs-ro

Two Clocks in Diffusion MLLMs: When Answers Stabilize Before Rationales Unfold

arXiv:2610.00953v1 Announce Type: cross Abstract: An answer candidate in a masked diffusion MLLM can stabilize while its rationale is still unfolding. We distinguish retrospective stabilization of the

A helps B while B hurts A: directed transfer in instruction-tuning mixture

arXiv:2609.39702v1 Announce Type: new Abstract: Adapting a language model to a specialized corpus means choosing which instruction-tuning tasks to train on under a fixed budget, and testing one choice

Aligning Thoughts with Answers: Probability Rewards to Tame Thinking Drift

arXiv:2609.39183v1 Announce Type: new Abstract: This paper studies extbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target

researcharxiv-cs-cv

Certification-Based Differentially Private Learning

arXiv:2609.39629v1 Announce Type: new Abstract: Differential privacy (DP) in machine learning is typically achieved by adding noise to model parameters (private learning) or to model outputs (private

Code to Control: Synthesizing Parameterized Reactive Controllers

arXiv:2609.38733v1 Announce Type: new Abstract: Recent LLM-based approaches to control either invoke a language model to select actions or synthesize world models that require planning at every decisi

safetyarxiv-cs-ai

Tutorials

Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis

arXiv:2609.39955v1 Announce Type: new Abstract: Single-step retrosynthesis models are commonly evaluated by their ability to recover recorded reactions. In practice, chemists may need to choose among

tutorialsarxiv-cs-ai

EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery

model-releasesarxiv-cs-cl

arXiv:2609.40340v1 Announce Type: new Abstract: Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks. Supplying relevant documents

EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making

arXiv:2609.38334v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model

safetyarxiv-cs-cl

Faithful Dual-constrained Erasure for Robust LLM Safety Alignment

arXiv:2609.39279v1 Announce Type: cross Abstract: Machine unlearning has emerged as a crucial mechanism for removing hazardous knowledge and enforcing safety alignment in Large Language Models (LLMs).

GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed

arXiv:2609.39600v1 Announce Type: cross Abstract: Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We vi

Tutorials

Hermes: Learning Contextual Reasoning Unlocks Test-Time Scaling

arXiv:2609.38332v1 Announce Type: cross Abstract: Test-time scaling improves model performance by allocating additional compute during inference. Using this compute effectively across multiple context

tutorialsarxiv-cs-ai