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here’s a good application of harness permissions Programmatically Enforced Auto-Research: - auto-research loops usually expose a set of file…

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here’s a good application of harness permissions Programmatically Enforced Auto-Research: - auto-research loops usually expose a set of files that the agent is allowed to edit to hill climb a metric/e

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HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement

arXiv:2604.10675v1 Announce Type: new Abstract: We propose a method to learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling the implicit placement know

tutorialsarxiv-cs-cv

HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation

arXiv:2510.07794v2 Announce Type: replace-cross Abstract: Agentic RAG is a powerful technique for incorporating external information that LLMs lack, enabling better problem solving and question answer

Human vs. Machine Deception: Distinguishing AI-Generated and Human-Written Fake News Using Ensemble Learning

arXiv:2604.09960v1 Announce Type: new Abstract: The rapid adoption of large language models has introduced a new class of AI-generated fake news that coexists with traditional human-written misinforma

Intent-aligned Formal Specification Synthesis via Traceable Refinement

arXiv:2604.10392v1 Announce Type: cross Abstract: Large language models are increasingly used to generate code from natural language, but ensuring correctness remains challenging. Formal verification

Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs

arXiv:2604.10403v1 Announce Type: new Abstract: We address jailbreaks, backdoors, and unlearning for large language models (LLMs). Unlike prior work, which trains LLMs based on their actions when give

safetyarxiv-cs-lg

Learning and Enforcing Context-Sensitive Control for LLMs

arXiv:2604.10667v1 Announce Type: cross Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limit

tutorialsarxiv-cs-ai

Learning to Adapt: In-Context Learning Beyond Stationarity

model-releasesarxiv-cs-lg

arXiv:2604.10946v1 Announce Type: new Abstract: Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key c

LLMs Should Incorporate Explicit Mechanisms for Human Empathy

arXiv:2604.10557v1 Announce Type: cross Abstract: This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy. As LLMs become increasingly deployed in

LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval

arXiv:2601.14706v3 Announce Type: replace Abstract: In this paper, we present LookBench (We use the term 'look' to reflect retrieval that mirrors how people shop -- finding the exact item, a close sub

LRD-Net: A Lightweight Real-Centered Detection Network for Cross-Domain Face Forgery Detection

arXiv:2604.10862v1 Announce Type: new Abstract: The rapid advancement of diffusion-based generative models has made face forgery detection a critical challenge in digital forensics. Current detection

Local Ai

MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification

arXiv:2601.15498v2 Announce Type: replace Abstract: Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent me

local-aiarxiv-cs-lg

MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment

arXiv:2601.22361v2 Announce Type: replace-cross Abstract: Assessing the veracity of online content has become increasingly critical. Large language models (LLMs) have recently enabled substantial prog

Min-k Sampling: Decoupling Truncation from Temperature Scaling via Relative Logit Dynamics

arXiv:2604.11012v1 Announce Type: new Abstract: The quality of text generated by large language models depends critically on the decoding sampling strategy. While mainstream methods such as Top-k, Top

MMRareBench: A Rare-Disease Multimodal and Multi-Image Medical Benchmark

arXiv:2604.10755v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced clinical tasks for common conditions, but their performance on rare diseases remains largely unte

Hardware

MoBo, CPU, RAM suggestion | I'm terrible at guessing hardware | no gpu

This Reddit thread from r/ollama features a user seeking community recommendations for motherboard, CPU, and RAM components to build a system optimized for running Ollama locally without a dedicated G

hardwarer-ollama

Multi-modal, multi-scale representation learning for satellite imagery analysis just needs a good ALiBi

arXiv:2604.10347v1 Announce Type: new Abstract: Vision foundation models have been shown to be effective at processing satellite imagery into representations fit for downstream tasks, however, creatin

Multimodal Diffusion Forcing for Forceful Manipulation

arXiv:2511.04812v2 Announce Type: replace-cross Abstract: Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB i

tutorialsarxiv-cs-ai

Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score

arXiv:2405.16091v2 Announce Type: replace Abstract: Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explor

researcharxiv-cs-cv

Applications

Non-stationary Diffusion For Probabilistic Time Series Forecasting

arXiv:2505.04278v3 Announce Type: replace-cross Abstract: Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing De

applicationsarxiv-cs-ai

On The Application of Linear Attention in Multimodal Transformers

arXiv:2604.10064v1 Announce Type: new Abstract: Multimodal Transformers serve as the backbone for state-of-the-art vision-language models, yet their quadratic attention complexity remains a critical b

researcharxiv-cs-cv

Local Ai

Pair2Scene: Learning Local Object Relations for Procedural Scene Generation

arXiv:2604.11808v1 Announce Type: new Abstract: Generating high-fidelity 3D indoor scenes remains a significant challenge due to data scarcity and the complexity of modeling intricate spatial relation

local-aiarxiv-cs-cv

PEMANT: Persona-Enriched Multi-Agent Negotiation for Travel

arXiv:2604.10475v1 Announce Type: new Abstract: Modeling household-level trip generation is fundamental to accurate demand forecasting, traffic flow estimation, and urban system planning. Existing stu

safetyarxiv-cs-ai

Persona Non Grata: Single-Method Safety Evaluation Is Incomplete for Persona-Imbued LLMs

arXiv:2604.11120v1 Announce Type: new Abstract: Personality imbuing customizes LLM behavior, but safety evaluations almost always study prompt-based personas alone. We show this is incomplete: prompti

Query Lower Bounds for Diffusion Sampling

arXiv:2604.10857v1 Announce Type: cross Abstract: Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by mi

researcharxiv-cs-ai

Applications

Resisting Humanization: Ethical Front-End Design Choices in AI for Sensitive Contexts

arXiv:2603.24853v2 Announce Type: replace Abstract: Ethical debates in AI have primarily focused on back-end issues such as data governance, model training, and algorithmic decision-making. Less atten

applicationsarxiv-cs-ai

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

arXiv:2604.09860v1 Announce Type: cross Abstract: The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid

S4M: 4-points to Segment Anything

arXiv:2503.05534v3 Announce Type: replace Abstract: Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred bo

researcharxiv-cs-cv

SafeConstellations: Mitigating Over-Refusals in LLMs Through Task-Aware Representation Steering

arXiv:2508.11290v3 Announce Type: replace Abstract: LLMs increasingly exhibit over-refusal behavior, where safety mechanisms cause models to reject benign instructions that seemingly resemble harmful

safetyarxiv-cs-cl

Hardware

SatReg: Regression-based Neural Architecture Search for Lightweight Satellite Image Segmentation

arXiv:2604.10306v1 Announce Type: new Abstract: As Earth-observation workloads move toward onboard and edge processing, remote-sensing segmentation models must operate under tight latency and energy c

hardwarearxiv-cs-cv

SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?

arXiv:2604.10718v1 Announce Type: new Abstract: Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly p

Script-a-Video: Deep Structured Audio-visual Captions via Factorized Streams and Relational Grounding

arXiv:2604.11244v1 Announce Type: new Abstract: Advances in Multimodal Large Language Models (MLLMs) are transforming video captioning from a descriptive endpoint into a semantic interface for both vi

SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding

arXiv:2604.10152v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs)

SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding

arXiv:2604.09557v1 Announce Type: cross Abstract: Speculative Decoding (SD) has emerged as a critical technique for accelerating Large Language Model (LLM) inference. Unlike deterministic system optim

StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs

arXiv:2509.22220v2 Announce Type: replace Abstract: Prevalent semantic speech tokenizers, designed to capture linguistic content, are surprisingly fragile. We find they are not robust to meaning-irrel

STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction

arXiv:2604.09737v1 Announce Type: cross Abstract: Structured prediction requires models to generate ontology-constrained labels, grounded evidence, and valid structure under ambiguity, label skew, and

SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning

arXiv:2604.10228v1 Announce Type: new Abstract: Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this

tutorialsarxiv-cs-ai

The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise

arXiv:2604.09780v1 Announce Type: new Abstract: Mixture of Experts (MoEs) are now ubiquitous in large language models, yet the mechanisms behind their 'expert specialization' remain poorly understood.

researcharxiv-cs-ai

Applications

(This is a big part of what was called emergence in earlier academic work on unexpected LLM ability gains)

applicationsethan-mollick--x

Ethan Mollick discusses the concept of 'emergence' in large language models (LLMs), referring to the phenomenon where AI systems appear to suddenly develop unexpected capabilities as they scale. The p

Toward Generalized Cross-Lingual Hateful Language Detection with Web-Scale Data and Ensemble LLM Annotations

model-releasesarxiv-cs-cl

arXiv:2604.09625v1 Announce Type: new Abstract: We study whether large-scale unlabelled web data and LLM-based synthetic annotations can improve multilingual hate speech detection. Starting from texts

Trajectory-based actuator identification via differentiable simulation

arXiv:2604.10351v1 Announce Type: new Abstract: Accurate actuation models are critical for bridging the gap between simulation and real robot behavior, yet obtaining high-fidelity actuator dynamics ty

safetyarxiv-cs-ro

Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards

arXiv:2604.10110v1 Announce Type: new Abstract: Large Language Models (LLMs) have become a key foundation for enabling personalized smart home experiences. While existing studies have explored how sma

Tuning Qwen2.5-VL to Improve Its Web Interaction Skills

arXiv:2604.09571v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have sparked growing interest in using them to automate web tasks, yet their feasibility as independe

Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization

arXiv:2604.10721v1 Announce Type: cross Abstract: Natural-language Guided Cross-view Geo-localization (NGCG) aims to retrieve geo-tagged satellite imagery using textual descriptions of ground scenes.

Variable Selection Using Relative Importance Rankings

model-releasesarxiv-cs-lg

arXiv:2509.10853v2 Announce Type: replace-cross Abstract: Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature.

Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks

arXiv:2604.10166v1 Announce Type: cross Abstract: Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven contr

VisText-Mosquito: A Unified Multimodal Dataset for Visual Detection, Segmentation, and Textual Explanation on Mosquito Breeding Sites

arXiv:2506.14629v3 Announce Type: replace-cross Abstract: Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreak

Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval

arXiv:2604.10167v1 Announce Type: cross Abstract: Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational