Search: “benchmark”

Streaming Video Instruction Tuning

arXiv:2512.21334v2 Announce Type: replace Abstract: We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that

StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding

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DGX agent

arXiv:2604.09000v1 Announce Type: new Abstract: Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memo

model-releasesarxiv-cs-cv

13 Apr 2026

Structured Uncertainty guided Clarification for LLM Agents

arXiv:2511.08798v2 Announce Type: replace-cross Abstract: LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and

SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

arXiv:2604.08477v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathem

researcharxiv-cs-cl

SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models

arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and eve

applicationsarxiv-cs-cl

TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction

arXiv:2604.08921v1 Announce Type: new Abstract: Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventio

local-aiarxiv-cs-cv

Tango: Taming Visual Signals for Efficient Video Large Language Models

arXiv:2604.09547v1 Announce Type: new Abstract: Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances th

local-aiarxiv-cs-cv

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2505.12318v2 Announce Type: replace Abstract: Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by up

Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios

arXiv:2604.09377v1 Announce Type: new Abstract: Large language models (LLMs) exhibit substantial variability in performance and computational cost across tasks and queries, motivating routing systems

researcharxiv-cs-cl

Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning

arXiv:2604.09150v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks by leveraging long Chain-of-Thought (CoT), but often suffer from overthinking,

safetyarxiv-cs-cl

Thinking Deeper, Not Longer: Depth-Recurrent Transformers for Compositional Generalization [R]

researchr-machinelearning

This research paper explores a depth-recurrent transformer architecture designed to improve compositional generalization in language models by increasing computational depth rather than sequence lengt

TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference

arXiv:2604.09220v1 Announce Type: new Abstract: Implicit neural video representations encode entire video sequences within the parameters of a neural network and enable constant time frame reconstruct

Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models

arXiv:2603.01400v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) demonstrate strong video understanding but suffer from inefficiency due to redundant visual tokens. Existing pru

Training event-based neural networks with exact gradients via Differentiable ODE Solving in JAX

arXiv:2603.08146v3 Announce Type: replace Abstract: Existing frameworks for gradient-based training of spiking neural networks face a trade-off: discrete-time methods using surrogate gradients support

safetyarxiv-cs-lg

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory

arXiv:2604.09320v1 Announce Type: cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond indi

researcharxiv-cs-lg

Uncertainty Estimation for the Open-Set Text Classification systems

arXiv:2604.08560v1 Announce Type: cross Abstract: Accurate uncertainty estimation is essential for building robust and trustworthy recognition systems. In this paper, we consider the open-set text cla

Unified Multimodal Uncertain Inference

arXiv:2604.08701v1 Announce Type: new Abstract: We introduce Unified Multimodal Uncertain Inference (UMUI), a multimodal inference task spanning text, audio, and video, where models must produce calib

VAGNet: Vision-based accident anticipation with global features

arXiv:2604.09305v1 Announce Type: new Abstract: Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance

VisionFoundry: Teaching VLMs Visual Perception with Synthetic Images

arXiv:2604.09531v1 Announce Type: cross Abstract: Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contr

researcharxiv-cs-ai

VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning

arXiv:2604.08639v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep learning models in safety critical applications, yet no consensus exists on which UQ m

We conducted cyber evaluations of Claude Mythos Preview and found that it is the first model to complete an AISI cyber range end-to-end. 🧵

model-releasesboris-cherny--x

Boris Cherny and colleagues conducted cybersecurity evaluations of Claude Mythos Preview, finding it to be the first AI model to successfully complete an AISI (AI Safety Institute) cyber range end-to-

Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels

arXiv:2510.06499v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a tra

researcharxiv-cs-ai

When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning

arXiv:2510.07517v5 Announce Type: replace Abstract: Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their o

WildDet3D: Scaling Promptable 3D Detection in the Wild

arXiv:2604.08626v1 Announce Type: new Abstract: Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--

applicationsarxiv-cs-cv

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning

arXiv:2604.08728v1 Announce Type: new Abstract: Cooperation in multi-agent reinforcement learning (MARL) benefits from inter-agent communication, yet most approaches assume idealized channels and exis

safetyarxiv-cs-lg

Tutorials

WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

arXiv:2604.08958v1 Announce Type: cross Abstract: Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to

tutorialsarxiv-cs-ai

XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifiers

arXiv:2604.09489v1 Announce Type: cross Abstract: Model poisoning attacks pose a significant security threat to Federated Learning (FL). Most existing model poisoning attacks rely on collusion, requir

According to Waymo's published data, their technology is preventing injuries & deaths. My view is that if this is true, and I have yet to se…

According to Waymo's published data, their technology is preventing injuries & deaths. My view is that if this is true, and I have yet to see a debunking of their data, then we safety advocates should

safetyethan-mollick--x

AceStep 1.5 XL Turbo + LTX 2.3 on an 8GB RTX 5060 Laptop

local-air-stablediffusion

This r/StableDiffusion post demonstrates running AceStep 1.5 XL Turbo alongside LTX Video 2.3 on a laptop equipped with an 8GB NVIDIA RTX 5060 GPU — a notable feat given that the XL (4B) model require

Agents

Little quality of life update: Skills should be called more proactively now! ~20% more likely to load the right skill for the job! And, of c…

Little quality of life update: Skills should be called more proactively now! ~20% more likely to load the right skill for the job! And, of course, in Hermes Agent fashion - it did the prompt improveme

agentsnous-research--x

Tools

MiniMax M2.7 is now on Together AI. Trained by letting it run its own RL loop, resulting in the highest open-source score on MLE Bench Lite.

MiniMax M2.7 is now available on Together AI, offering a powerful open-source model trained using a self-directed reinforcement learning loop. This training methodology enabled the model to achieve th

toolstogether-ai--x

Okay, I have to admit LTX is better after all. I have fully dropped MagiHuman after your critiques.

local-air-stablediffusion

This r/StableDiffusion post reflects a community member reconsidering their preference between LTX (Lightricks' open-source AI video model) and daVinci-MagiHuman after peer feedback, ultimately favori

'There's a new generation of empirical deep learning researchers, hacking away at whatever seems trendy, blowing with the wind' [D]

researchr-machinelearning

This is a discussion thread on r/MachineLearning critiquing a perceived cultural shift in AI research, where a newer wave of empirical deep learning researchers is characterized as prioritizing trendy

A lot of our education on writing well focuses on logic, clarity, and argument. AI will force us to think more about style. The boredom that…

model-releasesethan-mollick--x

A lot of our education on writing well focuses on logic, clarity, and argument. AI will force us to think more about style. The boredom that comes from everything on the internet reading Claude-y now,

11 Apr 2026

I am not just talking about the em-dashes and phrasing ('doing the heavy lifting'). Those are more fixable than style, which will always def…

applicationsethan-mollick--x

I am not just talking about the em-dashes and phrasing ('doing the heavy lifting'). Those are more fixable than style, which will always default to an LLM-normal even if that normal changes with new L

11 Apr 2026

struggling choosing one edit model from klein 9b or qwen 2511.

model-releasesr-stablediffusion

This r/StableDiffusion thread discusses the community debate around choosing between FLUX.2 [klein] 9B and Qwen Image Edit 2511 as an image editing model, two strong open-source contenders in the spac

11 Apr 2026

A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction

arXiv:2604.06207v1 Announce Type: cross Abstract: This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), ai

applicationsarxiv-cs-ai

A Decomposition Perspective to Long-context Reasoning for LLMs

arXiv:2604.07981v1 Announce Type: new Abstract: Long-context reasoning is essential for complex real-world applications, yet remains a significant challenge for Large Language Models (LLMs). Despite t

applicationsarxiv-cs-cl

A Spatial-Spectral-Frequency Interactive Network for Multimodal Remote Sensing Classification

arXiv:2510.04628v2 Announce Type: replace Abstract: Deep learning-based methods have achieved significant success in remote sensing Earth observation data analysis. Numerous feature fusion techniques

A Study of LLMs' Preferences for Libraries and Programming Languages

arXiv:2503.17181v3 Announce Type: replace-cross Abstract: Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntact

researcharxiv-cs-ai

ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning

arXiv:2604.08050v1 Announce Type: new Abstract: In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging

Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation

arXiv:2604.07665v1 Announce Type: new Abstract: Self-supervised monocular depth estimation (MDE) has received increasing interests in the last few years. The objects in the scene, including the object

Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs

arXiv:2604.06699v1 Announce Type: cross Abstract: Automated prompt optimization is crucial for eliciting reliable reasoning from large language models (LLMs), yet most API-only prompt optimizers itera

researcharxiv-cs-lg

Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning

arXiv:2512.10510v2 Announce Type: replace-cross Abstract: Offline-to-Online Reinforcement Learning (O2O RL) faces a critical dilemma in balancing the use of a fixed offline dataset with newly collecte

safetyarxiv-cs-ai

AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding

arXiv:2604.08077v1 Announce Type: new Abstract: Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fi

ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines

arXiv:2512.24933v2 Announce Type: replace-cross Abstract: Multi-step LLM pipelines can solve complex tasks, but jointly optimizing prompts across steps remains challenging due to missing step-level su

applicationsarxiv-cs-lg

Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment

arXiv:2604.06289v1 Announce Type: cross Abstract: In this paper, we analyze and improve the adversarial robustness of a convolutional neural network (CNN) that assists crystal-collimator alignment at

local-aiarxiv-cs-lg

AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization

model-releasesarxiv-cs-lg

arXiv:2503.03088v4 Announce Type: replace-cross Abstract: The Segment Anything Model (SAM) has revolutionized image and video segmentation with its powerful zero-shot capabilities. However, its massiv