All entries

Dissecting model behavior through agent trajectories

arXiv:2606.17454v3 Announce Type: replace Abstract: AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through

DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction

Content type

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DGX agent

arXiv:2610.02574v1 Announce Type: new Abstract: High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple mol

model-releasesarxiv-cs-lg

5 Oct 2026

Distilling Stockfish on a Billion Positions, Full 3.9B Dataset Available [P]

researchr-machinelearning

In this project, I distilled the Stockfish value function into a ResNet/ViT model using 1 billion positions from the Gigafish dataset. The 3.9 billion position dataset is available on huggingface: htt

Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis

arXiv:2610.02659v1 Announce Type: cross Abstract: Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with rec

researcharxiv-cs-ai

Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination

arXiv:2610.02868v1 Announce Type: cross Abstract: Learning robust survival models under distribution shift is an important but challenging problem in many applications. In heterogeneous populations, a

researcharxiv-cs-ai

Divergence controls entropy in distillation

arXiv:2610.03529v1 Announce Type: cross Abstract: Distillation has become a core primitive of large language model training, but its properties are not yet well understood. We take an entropic perspec

safetyarxiv-cs-cl

DIVINE: Simple Cross-Market Stock Pretraining via Diverse Indicator Reconstruction

model-releasesarxiv-cs-lg

arXiv:2610.02866v1 Announce Type: new Abstract: Financial time-series pretraining typically learns from masked observations, contrastive relations, or future outcomes---yet existing objectives struggl

DNAlign: Dynamic Null-Space Safe Alignment for LLMs

arXiv:2610.02844v1 Announce Type: new Abstract: Ensuring the safe and reliable deployment of large language models (LLMs) remains a fundamental challenge. Existing safety alignment approaches either i

Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents

arXiv:2606.18537v2 Announce Type: replace Abstract: Humans often acquire new skills by observing others, since observed behaviors implicitly reveal how to act reasonably in an environment. However, ob

safetyarxiv-cs-lg

Do Large Language Models Know Colombian Law? A Reliability Benchmark for the Colombian Legal System

arXiv:2610.03639v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support legal practice, education, and research, yet their reliability in national legal systems o

Do ResNets Route? Sparse Interaction Experts in Residual Networks

arXiv:2610.02907v1 Announce Type: new Abstract: Residual networks execute every block for every input, yet their functional contributions need not be input independent. We formulate a trained ResNet a

Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation

arXiv:2610.02353v1 Announce Type: cross Abstract: Personalized large language models often require a complete adaptation state for each user. However, this paradigm scales poorly as the user populatio

researcharxiv-cs-cl

Local Ai

Does Physics Live in the Activations? Localizing Physical Quantities in Video Diffusion Models

arXiv:2610.03154v1 Announce Type: new Abstract: Video generation models produce strikingly realistic sequences and are increasingly proposed as world models, yet recent benchmarks reveal pronounced de

local-aiarxiv-cs-cv

Agents

Does telling your agent to plan ahead actually help? It's standard practice to add a 'plan ahead' or 'think about the other players' instruc…

Does telling your agent to plan ahead actually help? It's standard practice to add a 'plan ahead' or 'think about the other players' instruction to an agent's prompt. In auctions and matching markets,

agentsdair-ai--x

Industry

Downgrading user roles in Amazon Quick

Amazon Quick doesn't offer a direct console path to downgrade a user from Admin or Author to Reader. This post walks through two reliable methods: a manual delete-and-recreate approach and an AWS CLI

industryaws-ml-blog

DR-IPC: Disturbance-Resilient Integrated Planning and Control for LiDAR-Based Quadrotor Navigation

model-releasesarxiv-cs-ro

arXiv:2610.03530v1 Announce Type: new Abstract: LiDAR-based quadrotor navigation in cluttered environments remains challenging under external disturbances, particularly when obstacle-aware motion gene

DriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift

arXiv:2610.03390v1 Announce Type: cross Abstract: Few-step neural text-to-speech models often rely on short- ened diffusion or flow-matching schedules, or on distillation from pretrained multi-step te

Drive vs. Decay: On the Training Dynamics of Joint-Embedding Predictive Architectures

arXiv:2610.02344v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) are prone to representation collapse, typically mitigated through empirical heuristics. We develop an e

Applications

Dual-Context Analog Retrieval for Time Series Forecasting

arXiv:2610.03491v1 Announce Type: new Abstract: Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does

applicationsarxiv-cs-lg

DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation

arXiv:2610.03543v1 Announce Type: new Abstract: Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video

safetyarxiv-cs-cv

DyadMem: A Long-Term Memory Benchmark of How Agents Work with Users

arXiv:2610.03020v1 Announce Type: new Abstract: Long-term agents must remember not only what is true about a user, but also how a particular agent should work with that user as their shared history ev

Agents

Dynamic Expert Pruning for Multi-Agent Systems

arXiv:2610.02951v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale language models efficiently by activating only a few experts per token, but the saving is confined to com

agentsarxiv-cs-ai

Dynamic LLM Routers are Often Misguided

arXiv:2610.02762v1 Announce Type: new Abstract: Dynamic LLM routers promise to cut inference costs by sending each query to the cheapest model that can answer it correctly. We analyze six commercial r

Hardware

DyRA: Dynamic Residual Approximation for Efficient Matrix Multiplication in DNNs

arXiv:2610.02882v1 Announce Type: cross Abstract: Large-scale foundation models achieve strong performance across diverse tasks, but their size makes inference costly, largely due to dense matrix mult

hardwarearxiv-cs-ai

EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling

arXiv:2610.02298v1 Announce Type: cross Abstract: 3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the r

Effects of interpulse-interval variation on deep-learning classification of bat vocalizations

arXiv:2610.02284v1 Announce Type: new Abstract: Temporal context may aid automated bat-species classification, but the contribution of specific features remains unclear. We investigated whether variat

Hardware

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

arXiv:2610.02795v1 Announce Type: new Abstract: Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution d

hardwarearxiv-cs-lg

Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling

arXiv:2610.02410v1 Announce Type: cross Abstract: Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often ineff

Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability

arXiv:2610.03509v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning allows humans to inspect how large language models reach their answers, and oversee model behaviour. This reasoning com

researcharxiv-cs-ai

Local Ai

Electronic Density versus Geometry for Machine-Learned Molecular Absorption Spectra

arXiv:2610.03444v1 Announce Type: cross Abstract: Molecular optical absorption spectroscopy provides a direct probe of electronic structure and is widely used for molecular identification, interpretat

local-aiarxiv-cs-lg

EmbPASS: Towards Cross-Embodiment Open Panoramic Segmentation

model-releasesarxiv-cs-cv

arXiv:2610.03248v1 Announce Type: new Abstract: Panoramic images provide a complete 360-degree field of view, enabling comprehensive scene understanding for embodied perception. However, heterogeneous

Emergent Structure in the Marginal Attention Space of Language Models

arXiv:2610.03109v1 Announce Type: new Abstract: While representation similarity across independently trained language models is well-documented, how internal mechanics such as attention behave across

researcharxiv-cs-cl

ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generation

arXiv:2610.02538v1 Announce Type: cross Abstract: Inference-time control steers a pretrained generative model towards a target distribution without retraining. We study tilted targets pi_0propto G_0,p

Energy Saving in 5G and Beyond Networks: A Quantum Reinforcement Learning Approach

arXiv:2610.02403v1 Announce Type: cross Abstract: Energy saving has become a critical challenge in 5G and beyond networks. The rapid growth of connected devices has increased the overall network energ

Enhancing Biomedical Named Entity Recognition via Multiple Programming Languages Instruction Tuning and Ensemble Method

arXiv:2610.02949v1 Announce Type: new Abstract: Instruction tuning has become a common paradigm for applying large language models (LLMs) to biomedical named entity recognition (BioNER). However, exis

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching

arXiv:2509.20810v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual er

EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models

arXiv:2610.02744v1 Announce Type: new Abstract: Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large la

safetyarxiv-cs-cl

Equivariant Flow Matching for Electron Density Prediction

arXiv:2610.02651v1 Announce Type: cross Abstract: Machine learning surrogates for density functional theory (DFT) have been increasingly used to reduce the cost of first-principles calculations. In th

Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation

arXiv:2610.03333v1 Announce Type: cross Abstract: Imitation learning for contact-rich manipulation requires high-quality expert data that is expensive to obtain. This makes learning a sample-efficient

Equs launches a personal AI platform with private storage to give users greater control over data

model-releasessiliconangle

Equs Inc., a startup developing a trust layer for artificial intelligence data, today announced the launch of Equs X, a personal AI that lets customers retain control of their information. Equs X pair

Error Propagation in Dynamic Programming: From Stochastic Control to American Option Pricing

arXiv:2509.20239v2 Announce Type: replace-cross Abstract: This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating

Estimating prevalence with precision and accuracy

arXiv:2507.06061v2 Announce Type: replace-cross Abstract: Unlike classification, whose goal is to estimate the class of each data point, quantification (or prevalence estimation) aims to estimate the

Evaluating and Improving the Robustness of Large Language Models to Input Sequence Variations

arXiv:2610.02432v1 Announce Type: cross Abstract: Large language models (LLMs) in production systems face prompt injections, trojans (backdoors), and manipulation of automatic quality metrics. This th

Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty

arXiv:2610.02877v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as automatic judges, with validity typically assessed via alignment with human scores. However, aggregate a

safetyarxiv-cs-cl

Agents

Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore

Multi-agent systems need deeper guarantees than fluent responses: they must select the right tools, respect constraints, and explain their decisions. Learn how to build a Strands-based multi-agent sup

agentsaws-ml-blog

Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks

arXiv:2610.02549v1 Announce Type: new Abstract: Time and event expression extraction are fundamental temporal reasoning tasks, but the problem remains difficult due to annotation ambiguity, domain sen

researcharxiv-cs-cl

Evaluating the Retrieval Robustness of Large Language Models

arXiv:2505.21870v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG coul

Evaluating VQA in Vision Language Models using Cooperative Principles

arXiv:2610.02878v1 Announce Type: new Abstract: We evaluate the performance of Vision Language Models in Visual Question Answering (VQA) when questions violate Grice's maxims. To do this, we use VLMs