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arXiv:2610.21736v1 Announce Type: new Abstract: Multi-objective optimization involves diverse preferences over conflicting objectives. Rather than returning a finite set of trade-off solutions, prefer

applicationsarxiv-cs-ai

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In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases

DGX agent

arXiv:2610.21789v1 Announce Type: cross Abstract: Existing safety benchmarks rely largely on synthetic prompts with loosely grounded taxonomies and offer little focused analysis of severe crime catego

model-releasesarxiv-cs-ai

16 Oct 2026

Incentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care Prediction

arXiv:2610.21449v1 Announce Type: new Abstract: Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (E

agentsarxiv-cs-ai

Inertially Significant Humanoid Teleoperation via Whole Body Model Predictive Control and Online Parameter Estimation

model-releasesarxiv-cs-ro

arXiv:2610.22176v1 Announce Type: new Abstract: The effective deployment of humanoid robots in practical tasks of interest will require a capability of sensing and adapting to variations in inertial p

Interpreting Language Model Parameters

arXiv:2610.20748v1 Announce Type: cross Abstract: Mechanistic interpretability aims to uncover how neural networks use their parameters to implement their impressive neural algorithms. Although previo

Iterative Neural Variational Solver of Committor Function in Under-damped Langevin Dynamics

model-releasesarxiv-cs-lg

arXiv:2610.20617v1 Announce Type: cross Abstract: The committor function describes the probability that a stochastic dynamical system, while transitioning between two metastable states, will first rea

KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents

arXiv:2610.22331v1 Announce Type: new Abstract: Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions

KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context

arXiv:2412.07251v1 Announce Type: cross Abstract: Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increas

L2G-SR: Learning to Guide LLM-based Symbolic Regression

arXiv:2610.20685v1 Announce Type: new Abstract: Symbolic regression searches for an equation that explains observed data. Numerical error is commonly used to guide the search, but it alone can favor a

tutorialsarxiv-cs-ai

Local Ai

L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data

arXiv:2610.21356v1 Announce Type: new Abstract: Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts. We pres

local-aiarxiv-cs-cv

LAMP: Layerwise Adaptive Mixing of Predictors for Training-Free Few-Shot Vision-Language Adaptation

arXiv:2610.22182v1 Announce Type: new Abstract: Few-shot adaptation often relies on an encoder's final global representation, leaving complementary information in intermediate layers underused. Combin

researcharxiv-cs-cv

LCT: Losslessly Compressed Tensors with GPU friendly execution for Training

model-releasesarxiv-cs-lg

arXiv:2610.22179v1 Announce Type: new Abstract: Lossless tensor compression could reduce memory usage during training, but training requires tensors to be repeatedly re-encoded. We introduce Losslessl

Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning

arXiv:2606.22305v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-

safetyarxiv-cs-cl

Learning Not To Interfere: Adapting Experts For Model Merging

arXiv:2603.13467v2 Announce Type: replace-cross Abstract: Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialize

researcharxiv-cs-ai

Learning Rewards in Strategic Games

arXiv:2610.21912v1 Announce Type: cross Abstract: Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another

Local Ai

Learning Self-Orchestration in Decentralized Agent Swarms

arXiv:2610.22322v1 Announce Type: cross Abstract: The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents

local-aiarxiv-cs-ai

Learning Speech Attribute Representations via Multi-Teacher Distillation

arXiv:2610.21941v1 Announce Type: new Abstract: Recent post-training approaches have shown that self-supervised speech encoders can be adapted to learn a single utterance-level speech attribute throug

tutorialsarxiv-cs-cl

Learning to Search for Executable Patternmaking from Certified Programs

arXiv:2610.21579v1 Announce Type: cross Abstract: Recent models generate sewing patterns from images or text, and program-based representations make these patterns executable. These systems produce a

researcharxiv-cs-ai

Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials

arXiv:2610.20684v1 Announce Type: cross Abstract: Learning informative representations of mechanical Triply Periodic Minimal Surface (TPMS) metamaterials is important for mechanics analysis and struct

safetyarxiv-cs-cv

Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval

model-releasesarxiv-cs-cl

arXiv:2610.21030v1 Announce Type: cross Abstract: Reasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints. However, additional reasoning may introduce ove

LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents

arXiv:2610.22280v1 Announce Type: new Abstract: Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alon

Let's Ask Gauss: Improved One-Run Privacy Auditing

arXiv:2606.12733v3 Announce Type: replace Abstract: Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guar

researcharxiv-cs-lg

Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries

arXiv:2610.22273v1 Announce Type: cross Abstract: Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottle

LOOP-MAC: Learning-Based Multi-Agent Coordination with Guaranteed Local Feasibility in a Virtual Power Plant

arXiv:2310.17882v3 Announce Type: replace Abstract: By aggregating distributed energy resources (DERs) into a single dispatchable resource, virtual power plants (VPPs) enable the grid to use the flexi

agentsarxiv-cs-lg

LoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models

arXiv:2610.21594v1 Announce Type: new Abstract: Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers. However, generating each token still involves seque

MAGMA: Multiplier-Augmented Geometric Motion Algorithm

arXiv:2610.21852v1 Announce Type: new Abstract: Solving optimal control problems for high-dimensional mechanical systems subject to constraints remains challenging due to nonlinear dynamics and non-co

researcharxiv-cs-ro

MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments

arXiv:2610.20939v1 Announce Type: new Abstract: Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to G

agentsarxiv-cs-ai

MAIA: Anticipating Interaction Anomalies in Contact-Rich Manipulation via Multimodal World Models

arXiv:2610.21686v1 Announce Type: new Abstract: Reliable contact-rich manipulation requires detecting execution deviations early enough for corrective action. Yet most monitoring methods are reactive,

Manifold Generative Annealing for Contact Constraint-aware Impedance Control with Reinforcement Learning Prior

arXiv:2610.21140v1 Announce Type: new Abstract: Contact-rich robotic tasks require coordinated motion and impedance under uncertain interaction dynamics and physical constraints. While recent methods

ManiFusion: Unlocking the High-Throughput Generation via Superposition in Manifold Space

arXiv:2610.21433v1 Announce Type: new Abstract: Diffusion models achieve high-quality image generation but remain computationally expensive due to iterative denoising across many timesteps. Existing a

tutorialsarxiv-cs-cv

MedPAO-VL: Interpretable and Protocol-Compliant Agent for Structured Radiology Report Generation

arXiv:2610.21794v1 Announce Type: new Abstract: Despite the remarkable generalization capabilities of Large Language Models, generating clinically accurate and structured radiology reports from medica

safetyarxiv-cs-cv

Mem4Act: Learning Effective Latent Memory for Long-Horizon Robotic Manipulation

arXiv:2610.22323v1 Announce Type: new Abstract: Long-horizon robotic manipulation fundamentally requires memory: agents must retain task-relevant information from past observations and interactions an

MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning

arXiv:2610.21091v1 Announce Type: new Abstract: Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone d

Memory as Weights: Internalizing Long-Term History for Streaming Videos

arXiv:2610.22070v1 Announce Type: cross Abstract: Streaming vision-language models (VLMs) must continuously integrate information from the past while remaining responsive to current observations and u

MemScope: Evolving Agent Memory Architectures through Localized Code Repair

arXiv:2610.21170v1 Announce Type: new Abstract: Agent memory architectures define how LLM agents store and reuse experience across tasks, but they are typically fixed after deployment, limiting their

MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling

arXiv:2610.21492v1 Announce Type: new Abstract: Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing met

researcharxiv-cs-ai

MIDI-REPA: Improving Text-to-Music Generation with Symbolic Representation Alignment

model-releasesarxiv-cs-lg

arXiv:2610.20967v1 Announce Type: cross Abstract: State-of-the-art text-to-music models increasingly rely on scaling model capacity, data, and compute, while still learning musical structure directly

Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models

arXiv:2603.14313v2 Announce Type: replace Abstract: Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move

safetyarxiv-cs-cl

MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support

arXiv:2610.22314v1 Announce Type: new Abstract: With people increasingly turning to AI chatbots for mental health support, the need to evaluate these tools is becoming more pressing. Recent benchmarks

researcharxiv-cs-cl

Mitigating Memorization Where It Happens

arXiv:2602.08901v2 Announce Type: replace Abstract: Large language models (LLMs) can memorize and reproduce training sequences verbatim, raising privacy and copyright concerns. Existing mitigation met

researcharxiv-cs-lg

Hardware

MJev-Spatial: System One Spatial Intelligence via Native Attention Matching

arXiv:2610.22276v1 Announce Type: new Abstract: Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial re

hardwarearxiv-cs-cv

MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos

arXiv:2610.20998v1 Announce Type: new Abstract: Passive human videos of everyday activity offer robots access to a massive amount of human experience without requiring a demonstration for each task. H

Motion- and Gravity-Aware Feedforward 3D Reconstruction

arXiv:2610.22177v1 Announce Type: cross Abstract: Feed-forward transformer models have recently achieved strong performance in multi-view 3D reconstruction, but they still rely primarily on appearance

tutorialsarxiv-cs-ai

MR-MPCC: Model Predictive Contouring Control for Robotic Manipulator Tracking under Moving Reference Frames

arXiv:2610.21292v1 Announce Type: new Abstract: Maintaining small path-tracking errors under moving reference frames is essential for robotic manipulation tasks with tight execution tolerances, such a

MSP: Modality Self-Play for Training Multimodal LLMs Without Paired Cross-Modal Data

arXiv:2610.20965v1 Announce Type: new Abstract: Large multimodal models have achieved remarkable progress by scaling model capacity and training on massive paired multimodal data. However, this paradi

Multi-Agent Transactive Memory

arXiv:2606.19911v2 Announce Type: replace Abstract: Decentralized deployment of LLM agents with diverse capabilities motivates infrastructure for knowledge sharing across heterogeneous agent populatio

agentsarxiv-cs-ai

Multi-Teacher On-Policy Distillation for Flow-Based Vision-Language-Action Models

arXiv:2610.21522v1 Announce Type: new Abstract: Specialized vision-language-action (VLA) policies acquire strong domain-specific skills, yet consolidating these skills into one shared policy remains c

MYOPIA: Understanding the Limits of Tree-Based Reasoning in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.21573v1 Announce Type: new Abstract: Decision trees can make vision-language model (VLM) classification interpretable by replacing a single prediction with a sequence of root-to-leaf questi