All entries

Native Long Video Understanding Models locally?

I've been building a personal project and wanted to check with the community on multi-modal inputs since I can't find a lot of material around this online. Ultimately I'm trying to build something tha

Natural Language Processing Psychometrics

arXiv:2608.07316v1 Announce Type: cross Abstract: Natural Language Processing (NLP) models predicting mental health outcomes rarely specify what they measure: contextual knowledge, emotional content,

researcharxiv-cs-ai

10 Aug 2026

Need real world ML problems to evaluate my educational ML tools

I'm a retired platform engineer, coding mainly in Rust, and involved with a ML study group. I developed a ML programming language (alternative to Python, Colab) to help me learn (and teach) ML concept

Needle 2: 14MB agentic LLM for phones, wearables, smart home and robots.

Hey LocalLlaMa, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots a

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs

model-releasesarxiv-cs-lg

arXiv:2608.06554v1 Announce Type: new Abstract: Hidden Markov models (HMMs) are widely used probabilistic models for discrete sequential data but can be limited when hidden dynamics are complex. Hidde

NiyamAI - An Intent-Bound AI Agent with Cryptographically Verifiable Guardrails using Zero-Knowledge Proofs

arXiv:2608.07167v1 Announce Type: new Abstract: Giving an AI agent the ability to send emails, query databases, or execute commands is useful--until the agent is tricked into doing something it should

Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling

arXiv:2608.07040v1 Announce Type: new Abstract: Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations. While recent works ha

NTDH: Complex Reasoning for Comprehensive Affective Analysis

arXiv:2608.06425v1 Announce Type: new Abstract: Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outpu

safetyarxiv-cs-cl

Hardware

Nvidia partners with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR on a $500B funding package for AI infrastructure development (Financial Times)

Financial Times: Nvidia partners with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR on a $500B funding package for AI infrastructure development — Apollo, Blackstone and Goldman Sa

hardwaretechmeme

NxN E-valuation: Hypothesis Certification via a Conformal CRT Null

arXiv:2608.06621v1 Announce Type: new Abstract: We propose NxN E-valuation, a handy, e-value-based hypothesis-certification algorithm that lets a hypothesis be verified without building any case-speci

Oh no, we aren’t going to go back to this sort of prompting again, are we? I would love Anthropic to test if it actually works robustly, bec…

model-releasesethan-mollick--x

Oh no, we aren’t going to go back to this sort of prompting again, are we? I would love Anthropic to test if it actually works robustly, because our experiments (with slightly older models) found it d

oldsymbol{lambda}-Orthogonality Regularization for Compatible Representation Learning

arXiv:2509.16664v2 Announce Type: cross Abstract: Retrieval systems rely on representations learned by increasingly powerful models. However, due to the high training cost and inconsistencies in learn

researcharxiv-cs-cv

Local Ai

omlab/VLX-Seek-1.5-10B · Hugging Face

VLX-Seek-1.5-10B VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical setting

local-air-localllama

Tutorials

Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

arXiv:2608.07385v1 Announce Type: cross Abstract: Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable c

tutorialsarxiv-cs-ai

On theCUBE Pod: Black Hat exposes agentic threat, theCUBE remembers David Floyer

Artificial intelligence systems are developing faster than cybersecurity experts — and the energy grid — can keep up with. At the recent Black Hat USA event, security analysts viewed the rise of AI-dr

agentssiliconangle

Online Conformal Prediction Beyond Feedback

arXiv:2608.07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications. Online conformal prediction (OCP) provid

safetyarxiv-cs-lg

Local Ai

Online Monitoring and Corrective Steering of Programming Agents

arXiv:2608.06701v1 Announce Type: cross Abstract: Fixing GitHub issues in large-scale projects is a long-horizon task, especially when a fix requires changes across multiple locations or the issue des

local-aiarxiv-cs-ai

Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks

arXiv:2608.06520v1 Announce Type: new Abstract: We study online cooperative control of a multi-agent system under Byzantine attacks. Namely, an unknown, fixed subset of agents are Byzantine comprised

agentsarxiv-cs-lg

OpenAI releases GPT-5.6-Cyber, a more cyber-permissive version of GPT-5.6 Sol, to some partners and expands its Daybreak cybersecurity initiative (Sam Sabin/Axios)

Sam Sabin / Axios: OpenAI releases GPT-5.6-Cyber, a more cyber-permissive version of GPT-5.6 Sol, to some partners and expands its Daybreak cybersecurity initiative — OpenAI is introducing a more cybe

model-releasestechmeme

OpenAI unveils two Daybreak tiers: Daybreak Blue, which provides access to frontier models, and Daybreak Red, which offers purpose-trained cybersecurity models (OpenAI)

OpenAI: OpenAI unveils two Daybreak tiers: Daybreak Blue, which provides access to frontier models, and Daybreak Red, which offers purpose-trained cybersecurity models — Introducing new ways to unlock

model-releasestechmeme

Optimal Neural Network Approximation via Empirical Least Squares with Deterministic Samples

model-releasesarxiv-cs-lg

arXiv:2608.06687v1 Announce Type: cross Abstract: We develop a rigorous theory of discrete residual least-squares approximation for elliptic spectral equations mathfrak L_eta u=f using linearized ReLU

Tutorials

Optimization as a Dynamical System: Generative Schedules from Latent ODEs

arXiv:2509.23052v2 Announce Type: replace Abstract: We present a new meta-learning method to determine the optimal learning rate schedule for gradient descent. It leverages training runs from a hyperp

tutorialsarxiv-cs-lg

Applications

Optimized Certainty Equivalent Risk Minimization Using Samples: Algorithms, Convergence Rates, and Applications

arXiv:2608.07113v1 Announce Type: cross Abstract: We consider the optimization of the Optimized Certainty Equivalent (OCE) risk, with applications including portfolio optimization in finance, and unce

applicationsarxiv-cs-lg

Optimizing Spectral Prediction in MXene-Based Metasurfaces Through Multi-Channel Spectral Refinement and Savitzky-Golay Smoothing

arXiv:2602.08406v2 Announce Type: replace-cross Abstract: The prediction of electromagnetic spectra for MXene-based solar absorbers, where MXenes are a family of two-dimensional transition metal carbi

PACE: Primitive-Aware Code Evolution for Automated Algorithm Design

arXiv:2608.07395v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs. While this whole-program p

Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots

model-releasesarxiv-cs-cv

arXiv:2603.13108v2 Announce Type: replace-cross Abstract: Panoramic imagery provides holistic 360{eg} visual coverage for environmental perception in quadruped robots. However, existing occupancy pred

PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model

arXiv:2608.06794v1 Announce Type: new Abstract: While diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objecti

safetyarxiv-cs-cv

Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings

arXiv:2511.23204v2 Announce Type: replace Abstract: Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exce

researcharxiv-cs-cv

Local Ai

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers

arXiv:2507.07814v2 Announce Type: replace Abstract: We introduce a novel upper bound on the local Lipschitz constant of the dot-product self-attention block showing its dependence on the attention map

local-aiarxiv-cs-lg

PCAE: Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder

arXiv:2601.19179v2 Announce Type: replace Abstract: Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA). Prior studies have demonstrated that linear auto

researcharxiv-cs-lg

People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe

arXiv:2608.07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of

safetyarxiv-cs-ai

PHASE-Tree: Modeling Character-State Evolution in Long-Horizon Role-Playing Dialogue

arXiv:2608.06975v1 Announce Type: cross Abstract: Long-horizon role-playing demands that characters remain recognizable as they evolve with the narrative. Yet existing work falls short on two fronts:

PHOENIX: Fine-Tuned SLM-Powered Autonomous Satellite Lifetime Extension via Predictive Self-Healing and Multi-Agent AI Recovery

arXiv:2608.07126v1 Announce Type: cross Abstract: Most CubeSats, small and low-cost satellites roughly the size of a shoebox, do not survive as long as they were designed to: a study of 178 missions f

Plan-and-Avoid: Real-Time Aircraft Trajectory Coordination in a Multi-Agent Environment

arXiv:2608.06648v1 Announce Type: new Abstract: This paper presents a real-time Plan-and-Avoid (PAA framework for coordinating cooperative multi-agent airspace operations around a declared priority tr

agentsarxiv-cs-ro

Playing with physics is so cool in Minimax H3

model-releasesr-stablediffusion

Prompt: 'integrated_multimodal_description: [Shot 1] Live-action, ultra-realistic first-person footage at night on a rainy city street, filmed with authentic handheld smartphone qualities. The phone i

Please Share Your Experience About Muse Glimmer

I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and sa

Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models

arXiv:2608.06690v1 Announce Type: cross Abstract: Most language-model access controls regulate behavior while leaving the same computation available to every request. We study a different systems ques

Post-Grokking Collapse at the Representation-Readout Interface in Muon-Trained Transformers

arXiv:2608.07436v1 Announce Type: new Abstract: Under the standard split, Muon gets hidden matrices and AdamW embeddings/output head. Muon groks modular addition faster, but its solutions do not hold.

Pre-Inference Routing for Cost-Efficient Document Field Extraction

arXiv:2608.06607v1 Announce Type: new Abstract: Most document-extraction systems use a single model for all documents. This is simple but can be costly for easy cases and less effective for difficult

researcharxiv-cs-cl

PRISM: Principled Reference Identification for Schrodinger Bridge Model

arXiv:2608.06893v1 Announce Type: new Abstract: Schrodinger bridge models restore a clean signal from a degraded observation by following the conditional bridges of a reference process, yet this refer

researcharxiv-cs-lg

Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving

arXiv:2603.06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their r

agentsarxiv-cs-ai

Progress-Certified Reversible Simplex Supervision of Goal-Reaching Reinforcement Learning

arXiv:2601.19499v2 Announce Type: replace Abstract: Task completion is difficult to certify when state aggregation, model mismatch, and disturbances invalidate nominal RL transitions. We present a rev

safetyarxiv-cs-ro

Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training

arXiv:2608.06792v1 Announce Type: cross Abstract: Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior. In practice, a single pretrained foun

safetyarxiv-cs-ai

Progressive Content Refinement with Decaying Reward Joint LinUCB

arXiv:2608.06750v1 Announce Type: cross Abstract: Iterative refinement has significantly enhanced Large Language Model (LLM) performance; however, existing methods ranging from feedback-based Self-Ref

Provable Training Data Identification for Large Language Models

arXiv:2510.09717v3 Announce Type: replace-cross Abstract: Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However,

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

arXiv:2608.06901v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidl

researcharxiv-cs-cv

PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents

arXiv:2608.07438v1 Announce Type: new Abstract: Human-like cognition does not select past experience by topical similarity alone: affective significance and unresolved conflict also shape what becomes

PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks

arXiv:2608.07066v1 Announce Type: new Abstract: Spiking neural networks (SNNs) enable sparse and event-driven computation, but their low-bit deployment remains incomplete because recurrent membrane st