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LLM-based Conversational AI Knowledge Assistant for MyBuddy Humanoid Robot

arXiv:2609.24742v1 Announce Type: new Abstract: Humanoid robots are increasingly being popular and developed for human-centered applications, yet their ability to provide intelligent conversations and

researcharxiv-cs-ro

Tutorials

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Human DGX agent

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LLM-Based FORM Code Generation with Verification-Driven Fine-Tuning

DGX agent

arXiv:2609.23367v1 Announce Type: cross Abstract: FORM is a domain-specific symbolic manipulation language widely used in particle physics for processing the very large algebraic expressions arising f

tutorialsarxiv-cs-cl

22 Sept 2026

LLMs Anchor on Chief Complaint and Fail to Integrate Evidence in Sequential Clinical Triage

arXiv:2609.22904v1 Announce Type: new Abstract: Triage in the emergency department (ED) is a sequential decision process that unfolds turn by turn. Existing evaluations of large language models (LLMs)

LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

arXiv:2609.23193v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromisi

Locally Private Inference for Riemannian Stochastic Optimization

arXiv:2609.22642v1 Announce Type: cross Abstract: We develop inference for manifold-valued population minimizers when each observation belongs to a different participant and only locally private messa

Logits are All We Need to Adapt Closed Models

arXiv:2502.06806v5 Announce Type: replace-cross Abstract: Many commercial Large Language Models (LLMs) are often closed-source, limiting developers to prompt tuning for aligning content generation wit

Long-Tail Rebalancing for Non-Verbal Vocalization-Aware ASR: A Track~1 System for the NVVSpeech Challenge

arXiv:2609.23462v1 Announce Type: cross Abstract: Non-verbal vocalizations (NVVs) carry important paralinguistic information but are often omitted by conventional automatic speech recognition (ASR) sy

Look Before You Steer: Geometry Predicts SAE Feature Steerability

arXiv:2609.22782v1 Announce Type: new Abstract: Steering with SAE features requires per-feature coefficient tuning, which currently demands intervention sweeps. We ask whether properties of the SAE it

Look Where It Counts: A Free, Label-Free Visual Evidence Signal for Fine-Grained Vision-Language Reasoning

arXiv:2609.24244v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) fail at fine-grained visual questions less because they cannot reason than because they never see the evidence:

local-aiarxiv-cs-cv

LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models

arXiv:2609.24196v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) perform 'latent reasoning' by recursively refining internal latent representations with shared weights, offering a more

local-aiarxiv-cs-cl

LoRA-generating hypernetworks for efficient on-device LLM generative personalization

arXiv:2609.24979v1 Announce Type: new Abstract: On-device large language models (`LLMs'), e.g. running on mobile phones, are ripe for improvement via personalization. The limited compute resources of

Low-Rank Frequency Convolution and Noise-Range Augmentation for Real-Time Pitch Estimation on Edge Devices

arXiv:2609.23340v1 Announce Type: cross Abstract: Pitch estimation on an edge device is constrained in three ways at once. The model must be small, it must stay accurate when the input is noisy, and o

Low resource cross-modal alignment using HGNN to enhance speech representation

arXiv:2609.23191v1 Announce Type: new Abstract: Speech-text space alignment is a multimodal representation learning method consisting to map different speech and text into a shared representation spac

safetyarxiv-cs-cl

LPINNs: First-Layer Gated Localization for Physics-Informed Neural Networks

arXiv:2609.22984v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) use one shared representation over the computational domain, which can become difficult to optimize on long dom

Agents

Luck Is Not Skill: When Do Paired Rollouts Help Group-Relative RL of LLM Agents?

arXiv:2609.24144v1 Announce Type: new Abstract: Group-relative reinforcement learning compares rollouts of the same prompt, but independent environment noise can obscure these comparisons. We study pa

agentsarxiv-cs-lg

M-CIF: Multi-Scale Alignment For CIF-Based Non-Autoregressive ASR

arXiv:2510.22172v2 Announce Type: replace-cross Abstract: The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanism

safetyarxiv-cs-cl

M3GA-Wild: A Large-Scale Dataset and Benchmark for Multi-Modal Multi-session Ground-to-Aerial Place Recognition in Forests

model-releasesarxiv-cs-cv

arXiv:2609.23003v1 Announce Type: new Abstract: We present M3GA-Wild, the first benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests. M3GA-Wild unifies and extends ex

Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States

model-releasesarxiv-cs-cl

arXiv:2609.23371v1 Announce Type: new Abstract: Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persistent i

Machine Learning-Based Prediction of Childhood Stunting in Bangladesh: Fairness and Temporal Robustness Assessment

arXiv:2609.24386v1 Announce Type: new Abstract: Childhood stunting remains a major public health concern in Bangladesh and reflects long-term growth failure influenced by child, maternal, household, s

safetyarxiv-cs-lg

Applications

Machine Learning for Invisible Dark Boson Searches at the Electron-Ion Collider

arXiv:2609.22484v1 Announce Type: cross Abstract: We investigate whether adding t, the positive magnitude of the squared nuclear four-momentum transfer, enables boosted decision trees (BDTs) to improv

applicationsarxiv-cs-lg

Machine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey

arXiv:2609.22294v1 Announce Type: cross Abstract: Underwater Optical Wireless Communication (UOWC) has emerged as a promising technology for high-speed underwater data transmission, offering significa

safetyarxiv-cs-lg

Applications

Macro-Scale Electrostatic Origami Motor

arXiv:2601.21976v2 Announce Type: replace Abstract: Origami structures have been an active area of research due to their high volume-to-mass ratio, packability, and shape adaptability. Previously-deve

applicationsarxiv-cs-ro

MambaX: Image Super-Resolution with State Predictive Control

arXiv:2511.18028v2 Announce Type: replace Abstract: Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches mainl

researcharxiv-cs-cv

Manipulation Feasible Navigation Among Movable Obstacles with Discrete Contact Pushing

arXiv:2609.23312v1 Announce Type: new Abstract: In environments with large movable obstacles, detour-only navigation can be inefficient or even infeasible, while obstacle interaction requires reasonin

safetyarxiv-cs-ro

Marginal Calibration Does Not Compose: Hidden Dependence in Modular Robot Navigation

arXiv:2609.23731v1 Announce Type: cross Abstract: Robotic systems are typically composed of multiple independently developed modules that work together to perceive, predict, and act in the environment

safetyarxiv-cs-lg

Agents

MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniques

arXiv:2411.00527v5 Announce Type: replace-cross Abstract: Utilizing the complementary strengths of wavelength-specific range or depth sensors is crucial for robust computer-assisted tasks such as auto

agentsarxiv-cs-cv

MarsRecon: Self-Supervised and Multimodal Surface Representations for Mars

arXiv:2609.22379v1 Announce Type: new Abstract: High-resolution orbital imagery offers a rich record of the Martian surface, but sparse geological labels limit supervised representation learning. We p

local-aiarxiv-cs-cv

Hardware

Mask-Aware Execution for Efficient JEPA Training

arXiv:2609.22674v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) are becoming a core representation-learning primitive and a building block for latent world models ac

hardwarearxiv-cs-lg

Tutorials

MaskVLA: Visual Masking Against Trajectory Overfitting of Vision-Language-Action Model

arXiv:2609.23565v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models integrate vision-language understanding with executable robot actions, enabling end-to-end learning for robot contro

tutorialsarxiv-cs-cv

Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery

arXiv:2609.23924v1 Announce Type: new Abstract: Pretrained EEG foundation models are increasingly proposed as general-purpose encoders for brain-computer interfaces, yet recent benchmarks disagree abo

Math2Visual-X: A Modular Framework for Pedagogically Aligned Lower-Primary Math Visuals Generation

arXiv:2609.22647v1 Announce Type: new Abstract: Visual representations can help lower-primary learners understand Math Word Problems, but generating classroom-usable visuals remains difficult. Existin

researcharxiv-cs-cv

MCP-GRANITE Benchmark: GRANularity Interface TEsting for MCP-Based LLM Agents

arXiv:2609.24161v1 Announce Type: cross Abstract: As LLM agents increasingly interact with external tools through standardized protocols such as MCP, tool-interface design becomes a critical yet under

Agents

MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback

arXiv:2606.12916v2 Announce Type: replace-cross Abstract: Molecular dynamics (MD) is the canonical in-silico method for atomistic molecular science, simulating molecular behavior from first-principle

agentsarxiv-cs-cl

ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence

arXiv:2609.24271v1 Announce Type: new Abstract: Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical inter

local-aiarxiv-cs-ro

ME-VLM:A Unified VLM for Embodied Cognition and Agent Coordination

arXiv:2609.24526v1 Announce Type: new Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and

local-aiarxiv-cs-cv

Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving

arXiv:2609.23085v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajectori

Measuring Behavioural Signatures of Large Language Models through Psychometric Profiling

arXiv:2609.22934v1 Announce Type: new Abstract: Large language models (LLMs) increasingly mediate human decisions and communication, yet their behavioural regularities remain difficult to characterize

safetyarxiv-cs-cl

Measuring the Assistant's Harmlessness Preferences on the User Turn

arXiv:2609.23935v1 Announce Type: new Abstract: Post-training turns a general next-token predictor into a chat model with a persistent assistant persona. If that persona is a character the model plays

local-aiarxiv-cs-cl

Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles

arXiv:2609.22220v1 Announce Type: new Abstract: Benchmarks for LLM-generated GPU kernels decide correctness with a few random inputs and a loose floating-point tolerance, and their verdicts now feed l

MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts

model-releasesarxiv-cs-cv

arXiv:2609.24455v1 Announce Type: new Abstract: Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However,

Applications

MechaTerp-TRACE: A Novel Approach for Component Ablation Analysis in Language Models

arXiv:2609.22163v1 Announce Type: new Abstract: Interpretability research on large language models has produced accounts of factual recall in feed-forward layers and of token relationships in self-att

applicationsarxiv-cs-cl

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

model-releasesarxiv-cs-cl

arXiv:2603.00842v2 Announce Type: replace Abstract: Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remain

MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents

arXiv:2609.24259v1 Announce Type: new Abstract: The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over

Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization

arXiv:2502.01562v3 Announce Type: replace Abstract: As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks throu

Memory That Looks Forward: A Zero-Inference Prospective Term for Personal Memory Retrieval

model-releasesarxiv-cs-cl

arXiv:2609.22091v1 Announce Type: new Abstract: Retrieval over a personal memory store is retrospective: it surfaces what resembles the query, and it is blind to what the user has committed to do. We

Memory vs. Context? Influential Factors of Factual Recall in Language Models

arXiv:2609.24238v1 Announce Type: new Abstract: We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contradi

Merge++: Universal Merge Refinement Through Data-Free Checkpoint Inversion

arXiv:2609.22886v1 Announce Type: new Abstract: Model merging consolidates fine-tuned experts into one multi-task model without retraining. All existing data-free methods approach this problem entirel

Tutorials

Metadata Supervised Imaging Representations for Modelling and Controlling Acquisition Variability

arXiv:2607.11295v2 Announce Type: replace Abstract: Biomedical imaging data exhibit substantial acquisition variability, where identical biological structures can appear markedly different due to diff

tutorialsarxiv-cs-cv