Search: “benchmark”

JIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling

arXiv:2609.33906v1 Announce Type: cross Abstract: Generative models often suffer from mode collapse and limited sample diversity. While prior works attempt to mitigate this by jointly generating a bat

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Just-In-Time Agent Memory with Runtime Agentic Research

DGX agent

arXiv:2609.34385v1 Announce Type: cross Abstract: Memory is critical for AI agents. Many existing agent-memory systems follow an Ahead-of-Time (AOT) design, constructing memory before a specific reque

safetyarxiv-cs-lg

29 Sept 2026

Just MLPs: Efficient Visual State Reconstruction for Multimodal Language Models

arXiv:2609.34972v1 Announce Type: new Abstract: Long visual token sequences often account for a substantial fraction of the computational overhead in multimodal large language models~(MLLMs). Existing

Tutorials

Kernel-Based Steering of CLIP with Vision-Language Model Preferences

arXiv:2609.32203v1 Announce Type: new Abstract: Large vision-language models (VLMs) can judge visual similarity, but their judgments are not directly available as compact image embeddings for efficien

tutorialsarxiv-cs-cv

KeyRec: Bounded Visual Memory for Streaming and Long-Video Understanding

arXiv:2609.32182v1 Announce Type: new Abstract: Vision-language models are increasingly used to understand long videos and continuous streams. However, dense visual tokens accumulate with video durati

lambda-JEPA Spectral Anti-Collapse Regularization for Self-Supervised Learning

arXiv:2609.35288v1 Announce Type: cross Abstract: Joint-embedding self-supervised learning typically combines an invariance objective across augmented views with additional mechanisms to prevent repre

lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers

arXiv:2609.33630v1 Announce Type: cross Abstract: Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end

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Applications

Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning

arXiv:2606.15685v2 Announce Type: replace-cross Abstract: Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behav

applicationsarxiv-cs-cv

Learning Propagation Geometry from Message-Passing Feedback

arXiv:2609.34711v1 Announce Type: new Abstract: Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geome

Learning the Graph and the Embedding Together: Classifier-Independent Rewiring for Heterophilic Node Classification

arXiv:2609.32613v1 Announce Type: new Abstract: Graph neural networks lose much of their advantage on heterophilic graphs, where connected nodes often carry different labels. Graph rewiring is a popul

researcharxiv-cs-lg

Learning to Reason with Persistent Object States for Video Instance Segmentation

arXiv:2609.35539v1 Announce Type: new Abstract: Video segmentation models maintain object identities by carrying instance information across frames. Under prolonged occlusion, reappearance, or interac

Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning

arXiv:2609.32785v1 Announce Type: cross Abstract: Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic exper

Tutorials

Learning Where and What to Restore for Composite Image Restoration

arXiv:2503.17915v2 Announce Type: replace-cross Abstract: Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different f

tutorialsarxiv-cs-cv

LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation

arXiv:2609.35046v1 Announce Type: new Abstract: Category-level 6D pose estimation from a single RGB-D observation is inherently under-constrained, since partial visible geometry must be interpreted to

Length-Independent State Tracking Under a Parallel Scan

arXiv:2609.32447v1 Announce Type: new Abstract: Learning robust and scalable finite-state tracking is fundamental to sequence processing. While linear recurrent neural networks (RNNs), linear attentio

researcharxiv-cs-lg

Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning

arXiv:2601.06641v3 Announce Type: replace Abstract: Membership inference attacks (MIAs) pose a serious privacy threat in federated learning (FL). While MIAs have been extensively studied in standard F

LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models

arXiv:2603.01563v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requ

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LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing

arXiv:2605.06332v2 Announce Type: replace Abstract: Constructive neural routing solvers usually score the next action by matching a decoder context to candidate embeddings, leaving deterministic one-s

researcharxiv-cs-lg

Livin' on a Prior: Likelihood Score Approximation for Inverse Problems

arXiv:2609.34446v1 Announce Type: new Abstract: Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrai

LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation

arXiv:2609.33521v1 Announce Type: new Abstract: Trust evaluation plays a critical role in cybersecurity by supporting risk mitigation and decision-making. A variety of trust evaluation methods have be

LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization

arXiv:2609.34427v1 Announce Type: new Abstract: Scaling LLM-based optimization from textbook-scale instances to real-world, industrial tasks remains a critical open challenge. Existing approaches are

LocalProp: Neuro-Localized Memory-Efficient Backpropagation

arXiv:2609.32517v1 Announce Type: cross Abstract: The current deep learning training paradigm employs end-to-end backpropagation, regardless of the training stage, i.e. pre-training or fine-tuning. Ho

LogicEnvGen: Task-Logic Driven Generation of Diverse Simulated Environments for Embodied AI

model-releasesarxiv-cs-ro

arXiv:2601.13556v2 Announce Type: replace Abstract: Simulated environments play an essential role in embodied AI, functionally analogous to test cases in software engineering. However, existing enviro

Long-Horizon Scaling: How Model Capabilities Shape the Returns to Computation

arXiv:2609.35236v1 Announce Type: new Abstract: Long-horizon agents improve solutions through sustained interaction, execution, and task feedback. Scaling studies relate performance to resources and c

safetyarxiv-cs-lg

LoopLUT: 3D Lookup Tables with Progressive Region Refinement for Real-Time 4K Image Enhancement

arXiv:2609.33593v1 Announce Type: new Abstract: Color enhancement of 4K images must meet a quality target under a tight compute budget. Three-dimensional lookup tables (3D LUTs) dominate real-time enh

Tutorials

LVMT: Video Mask Transformer for Long-term Video Segmentation

arXiv:2609.34895v1 Announce Type: new Abstract: Existing online video segmentation methods struggle to track objects in long, complex videos with long-term occlusions. We hypothesize that this limitat

tutorialsarxiv-cs-cv

Agents

M3OS: A Monte Carlo Graph Search-Orchestrated Multi-Agent LLM System for Evidence-Traced Molecular Optimization

arXiv:2609.34491v1 Announce Type: new Abstract: Small-molecule optimization integrates medicinal-chemistry reasoning and computational evidence through iterative, multi-objective decisions. When large

agentsarxiv-cs-lg

MAD-Guard: Controlled Study of Autoregressive Generation versus Direct Decision Interfaces for Closed Multimodal Forensic Tasks

arXiv:2609.33683v1 Announce Type: new Abstract: When should multimodal foundation models generate tokens, and when should they directly output a decision? We present MAD-Guard, a controlled study of o

MAESTRO: a Multimodal Auditory-attention Egocentric Speech-TRacking Open corpus

arXiv:2609.31898v1 Announce Type: cross Abstract: Humans rely on gaze, head movements, and visual cues to attend to speakers in noisy environments, yet auditory attention decoding (AAD) has been studi

Agents

Marathoner: Ultra-Long-Horizon Autonomous Intelligence

arXiv:2609.34378v1 Announce Type: new Abstract: Humans naturally possess the ability to work persistently toward long-term goals. Given a challenging task, humans can continuously work for months or e

agentsarxiv-cs-cv

MaskCoFT: Masked Co-Adaptive Fine-Tuning for Memory-Efficient MoE Inference

arXiv:2609.34077v1 Announce Type: new Abstract: Mixture-of-experts (MoE) language models often exceed the memory of a single GPU. Expert offloading keeps most experts in host memory and loads them on

Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation

model-releasesarxiv-cs-ro

arXiv:2609.35431v1 Announce Type: new Abstract: This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server

MetaSampling: Making Frame Samplers Efficient for Long-Video Question Answering

arXiv:2609.33998v1 Announce Type: new Abstract: Frame selection is an important component of long-video question answering (VQA) with Multimodal Large Language Models (MLLMs). Existing frame-selection

MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference

arXiv:2609.34330v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visual to

MM-OPD: Towards One More Bottleneck Between Perception and Reasoning

arXiv:2609.32690v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) advance visual reasoning by strengthening both perception and reasoning, implicitly assuming a process t

safetyarxiv-cs-cv

MM-VeriRec: Failure-Guided Fusion for Verifiable Agentic Multimodal Recommendation

arXiv:2609.31718v1 Announce Type: cross Abstract: Images often carry recommendation constraints that text metadata only hints at. A movie may need to look dark, a product may need a minimal style, and

Models Designed to Forget: Machine Unlearning via Key Deletion

arXiv:2603.15033v4 Announce Type: replace Abstract: Machine unlearning for vision models is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove

MORPH: Self-Organising Multi-Robot Task Allocation via Neuroplasticity-Inspired Adaptive Topology

model-releasesarxiv-cs-ro

arXiv:2609.32745v1 Announce Type: new Abstract: Multi-robot task allocation (MRTA) in dynamic environments faces a fundamental tension: effective coordination requires learned structure, but that stru

Agents

Motion Forcing: A Decoupled Framework for Robust Video Generation in Motion Dynamics

arXiv:2603.10408v2 Announce Type: replace Abstract: The ultimate goal of video generation is to satisfy a fundamental trilemma: achieving high visual quality, maintaining rigorous physical consistency

agentsarxiv-cs-cv

Multimodal LLMs Outperform Pathology Foundation Models in Cross-Domain Histological Similarity

arXiv:2609.32876v1 Announce Type: new Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons cross slid

Natural Image Autoencoder-Based fMRI Representations for Trait and State Prediction

arXiv:2609.34167v1 Announce Type: new Abstract: Foundation models pre-trained on large-scale fMRI datasets have shown strong downstream performance, but at substantial data and computation cost. To in

Neural Harmonic Measure Operator

arXiv:2609.35752v1 Announce Type: new Abstract: We introduce Neural Harmonic Measure Operator (NHMO), a neural solver for elliptic PDE problems on variable-shape domains. The harmonic measure of a dom

Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling

arXiv:2602.16954v3 Announce Type: replace Abstract: While deep generative models excel at capturing graph data distributions, they struggle to satisfy complex, hard constraints. In unconstrained setti

NeuronSifter: Intervention Planning in CNS Microenvironments

arXiv:2609.35445v1 Announce Type: new Abstract: Prioritizing central nervous system (CNS) interventions requires predicting how a dose, route, and schedule act on a partially observed microenvironment

No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation

arXiv:2609.34745v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) combines independently developed domain teachers into a single student by distilling their predictions on st

Not Every Term Adds New Structure: Sobolev Novelty for Symbolic Regression

arXiv:2609.32597v1 Announce Type: new Abstract: Symbolic regression (SR) aims to discover compact and meaningful mathematical equations from data, but searching the vast combinatorial space of symboli

OmniMoE-VL: A Sparse Vision-Language Model with Coupled Visual-Depth Routing

arXiv:2609.32780v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly use sparse mixture-of-experts (MoE) to scale language-side computation, yet visual information is typically r

OmniTide: Co-Designing Algorithms and Systems for Efficient On-Device Omni-LLM Streaming

arXiv:2609.34653v1 Announce Type: cross Abstract: On-device streaming omni-modal inference safeguards user privacy and eliminates prohibitive per-token API costs, but faces a critical bottleneck: the

local-aiarxiv-cs-lg