Search: “persistence”

Framework for an AI-Ready ARPES Community Database

arXiv:2610.15236v1 Announce Type: cross Abstract: Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization i

Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2610.18126v1 Announce Type: new Abstract: Freshness shapes an LLM decision at the moment it is made; the action it commits to is latent, carried out later, in a changing world. We therefore intr

researcharxiv-cs-ai

15 Oct 2026

From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents

model-releasesarxiv-cs-cv

arXiv:2610.19420v1 Announce Type: cross Abstract: Simulated environments enable scalable training data generation for embodied agents, but more data does not necessarily provide what a particular agen

From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship

arXiv:2506.23101v2 Announce Type: replace-cross Abstract: Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially

From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence

model-releasesarxiv-cs-cl

arXiv:2610.20417v1 Announce Type: new Abstract: Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures ac

Function-Space Temporal Self-Distillation for Policy Optimization

arXiv:2610.19687v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over respo

safetyarxiv-cs-cl

How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark

arXiv:2610.18201v1 Announce Type: cross Abstract: Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs) r

Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering

arXiv:2610.14960v1 Announce Type: cross Abstract: Can an autonomous AI agent reverse engineer a binary as well as a skilled human analyst? Do they adopt the same strategy, and can agents scale to real

Large Language Models Extend Known Research But Do Not Create New Research Directions

arXiv:2610.17439v1 Announce Type: new Abstract: The success of autonomous research systems relies on the quality of the scientific hypotheses they generate. Recent studies report that human reviewers

LED-BENCH: A Benchmark for Large-format Engineering Drawing Understanding

model-releasesarxiv-cs-cv

arXiv:2610.19571v1 Announce Type: new Abstract: Real-world engineering drawings are often large-format and information-dense. Answering practical engineering questions therefore requires both fine-gra

LintEval: Benchmarking LLMs on Linter-Guided File Refactoring

model-releasesarxiv-cs-cl

arXiv:2610.20027v1 Announce Type: cross Abstract: Linters are widely used in code maintenance and real-time IDE editing to surface lint alerts related to performance, style, error-prone, and other pat

Applications

Mechanistic Knobs in LLMs: Retrieving and Steering High-Order Semantic Features via Sparse Autoencoders

arXiv:2601.02978v3 Announce Type: replace-cross Abstract: Recent work in Mechanistic Interpretability (MI) has enabled the identification and intervention of internal features in Large Language Models

applicationsarxiv-cs-ai

Applications

ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction

arXiv:2610.20223v1 Announce Type: new Abstract: Vision-language-action (VLA) models have emerged as a powerful paradigm for robotic manipulation, enabling robots to generate actions from visual observ

applicationsarxiv-cs-ro

Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making

arXiv:2610.18298v1 Announce Type: new Abstract: What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the publi

Tutorials

Overcoming a Learning Bottleneck in Impartial Games: A Circuit Complexity Approach

arXiv:2411.06403v4 Announce Type: replace Abstract: Why can neural networks struggle to learn optimal play in a game with a simple, efficient solution algorithm? Motivated by empirical difficulties wi

tutorialsarxiv-cs-ai

PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces

arXiv:2610.15208v1 Announce Type: cross Abstract: Deep research requires connecting multiple steps of search, reasoning, and computation, with intermediate findings guiding what to investigate next. A

PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information?

model-releasesarxiv-cs-cl

arXiv:2610.19314v1 Announce Type: new Abstract: Large language model (LLM) evaluators are increasingly used to assess personalized assistants, yet whether they can reliably judge personalization has r

Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents

arXiv:2610.19439v1 Announce Type: new Abstract: While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantia

agentsarxiv-cs-lg

Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems

arXiv:2505.13546v2 Announce Type: replace-cross Abstract: Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Existi

agentsarxiv-cs-cl

RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control

arXiv:2610.17584v1 Announce Type: new Abstract: Operational mobile networks are rich in observations but poor in interventions: they continuously generate large volumes of passive telemetry, while con

RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution

arXiv:2610.14659v1 Announce Type: new Abstract: Streaming speech assistants can launch tools before users finish speaking, overlapping execution with incoming speech to reduce user waiting time. Howev

Reset Early, Reset Often, Eliminate Models

arXiv:2610.18204v1 Announce Type: cross Abstract: Connection-tableau provers typically construct closed tableaux by backtracking search. This gives lightweight proof procedures, but their low-memory s

Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion

arXiv:2610.19494v1 Announce Type: new Abstract: Real-time monocular human pose estimation is an important sensing component for human-centered intelligent systems. While recent camera-based methods ha

researcharxiv-cs-cv

RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment

arXiv:2610.17665v2 Announce Type: new Abstract: Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipatin

SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems

arXiv:2610.18308v1 Announce Type: cross Abstract: Conversational Recommender Systems (CRSs) require reliable user simulators for cost-effective evaluation. LLM-based simulators have made realistic sim

SonicWorld: Source-Centric Latent Dynamics for Spatial Audio Reasoning

arXiv:2610.17131v2 Announce Type: cross Abstract: Dynamic spatial audio reasoning requires a model to associate changing acoustic observations with the correct source and recover its spatial state at

StateCraft: Augmenting Video World Models with Abstract-State Reasoning

model-releasesarxiv-cs-cv

arXiv:2610.19795v1 Announce Type: new Abstract: Video world models aim to simulate complex environments with realistic appearance and dynamics. Maintaining consistency over long interactions requires

The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much

arXiv:2610.15938v1 Announce Type: new Abstract: Evaluation of text generated by large language models (LLMs) is increasingly delegated to LLMs acting as judges, yet what such a judge must be shown rem

Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems

arXiv:2610.14736v1 Announce Type: new Abstract: Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting. How

Training a Compact, Strong, and Realistic Text-to-Speech Model

arXiv:2610.14776v1 Announce Type: cross Abstract: Text-to-speech (TTS) systems are conventionally optimized for intelligibility and speaker similarity. However, neither objective captures the prosodic

TRAP: Tool-result Replay Attack for Probing Misbinding in LLM Agents

arXiv:2610.16333v1 Announce Type: new Abstract: As AI agents increasingly act through external tools, tool results are no longer simple outputs but execution artifacts that must remain correctly assoc

Uncovering Robustness Weaknesses in LLM Agents via Persona Evolution with Task-Integrity Constraints

arXiv:2610.16155v1 Announce Type: new Abstract: Interactive-agent evaluations commonly rely on standardized LLM-based user simulators that exhibit broadly cooperative behavior, potentially overlooking

Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks

arXiv:2610.17609v1 Announce Type: new Abstract: Training-free self-improvement lets an agent use feedback to revise the response it is about to return and to update the harness state that shapes how i

What Does Disagreement Mean? A Semantic Identifiability Frontier for Pairwise Preference Learning

arXiv:2610.20450v1 Announce Type: cross Abstract: Pairwise comparison data are a standard supervision signal in preference learning, but disagreement in such data can reflect stable latent heterogenei

researcharxiv-cs-lg

Tutorials

What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities

arXiv:2610.19476v1 Announce Type: cross Abstract: Post-training often improves the performance of large audio-language models on audio understanding and reasoning benchmarks. However, these improvemen

tutorialsarxiv-cs-cl

When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems

arXiv:2610.16039v1 Announce Type: cross Abstract: Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, since information disclosed to o

Local Ai

When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models

arXiv:2610.19814v1 Announce Type: new Abstract: Transformer-based tabular foundation models (TFMs) achieve state-of-the-art performance on small to medium-sized tabular prediction tasks while processi

local-aiarxiv-cs-lg

When Does the Dependency Graph Help Code-to-Architecture Mapping?

arXiv:2610.19769v1 Announce Type: cross Abstract: Architectural technical debt arises when a system's implementation drifts from its inten ded modular decomposition, while the mapping between source f

researcharxiv-cs-lg

When Pairwise Preferences Cycle: A Reliability-Anchored Preference Walk for Video GRPO

arXiv:2610.20089v1 Announce Type: new Abstract: Pairwise preference feedback has become a widely adopted supervision signal for reinforcement learning in video generation models, as pairwise compariso

safetyarxiv-cs-cv

Tutorials

Why 3D Reconstruction Breaks Down on Under-Ice AUV Video, and How to Predict It

arXiv:2610.19993v1 Announce Type: new Abstract: Reconstruction of 3D structures using under-ice AUV video footage has been inaccurate, although it is not certain whether this is due to shortcomings in

tutorialsarxiv-cs-cv

Why Software Engineering Is Indispensable in the Age of Coding Agents

arXiv:2610.10226v2 Announce Type: replace-cross Abstract: Can AI make Software Engineering (SE) -- the discipline -- obsolete? And can it make software engineers -- the professionals -- redundant? Thi

A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes

arXiv:2610.14493v1 Announce Type: new Abstract: Recent advances in machine learning have improved rainfall-runoff prediction, yet most data-driven models remain difficult to interpret and do not expli

agentsarxiv-cs-lg

ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models

arXiv:2610.18596v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves robot foundation models (RFMs) beyond their demonstrations, but real-world deployment demands more:

safetyarxiv-cs-lg

Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models

arXiv:2610.15343v1 Announce Type: new Abstract: World models and video understanding models are increasingly deployed in dynamic visual environments, yet their robustness to physical adversarial threa

safetyarxiv-cs-cv

Industry

Anthropic warns small AI models could self-replicate through IoT devices

Anthropic cautions that lightweight AI models—requiring far less compute than frontier models—could soon gain the ability to self‑replicate across Internet‑of‑Things devices, creating new autonomous w

industryars-technica

Beyond Adjacent Layers: Graph-Guided Layer Fusion for Compressing Large Language Models

model-releasesarxiv-cs-lg

arXiv:2610.15159v1 Announce Type: new Abstract: Existing depth-compression methods for large language models (LLMs) score layers individually or group only adjacent or contiguous layers, implicitly as

Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning

arXiv:2610.15543v1 Announce Type: cross Abstract: We study federated learning from continuously arriving data: each client samples from its own distribution, these distributions drift over time, and t

researcharxiv-cs-lg

Building Trustworthy Household Robots through Open-World Safety

arXiv:2610.14034v1 Announce Type: new Abstract: Foundation models are expanding robot capabilities, enabling robots to handle more diverse objects, follow sophisticated instructions, and operate in co

safetyarxiv-cs-ro