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Trust Hinders Bidirectional Human-AI Alignment

arXiv:2610.15136v1 Announce Type: new Abstract: Trust calibration remains the dominant framing of AI-assisted decision making: the goal is to close the gap between perceived and actual trustworthiness

safetyarxiv-cs-ai

Trust in Depth: Giving agents more autonomy without losing control

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DGX agent

Enterprise leaders want AI agents to take on more work but don't fully trust them to act correctly every time. Trust in Depth is AWS's layered approach-scope, know, bound, observe, and prove-that lets

applicationsaws-ml-blog

15 Oct 2026

Trustworthy AI Must Account for Interactions

arXiv:2504.07170v3 Announce Type: replace Abstract: Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainabil

safetyarxiv-cs-lg

Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows

model-releasesarxiv-cs-cl

arXiv:2610.19316v1 Announce Type: cross Abstract: Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switching providers may require processing acc

TSMC posts strong third-quarter results, but shares drop amid AI selloff

Taiwan Semiconductor Manufacturing Co. today posted better-than-expected financial results for the third quarter, but its shares nevertheless dropped 2%. The dip is tied to a broader selloff in artifi

applicationssiliconangle

Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers

model-releasesarxiv-cs-cl

arXiv:2610.19608v1 Announce Type: cross Abstract: Counts of which brands a large language model names are useful only if they hold long enough to act on. We test this with a pre-registered replication

Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference

arXiv:2610.18617v1 Announce Type: new Abstract: An embodied agent must prioritise among competing bodily needs. In active inference, it can respond to an urgent need by increasing the precision of inf

Local Ai

Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems

arXiv:2610.19539v1 Announce Type: new Abstract: Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation. Thi

local-aiarxiv-cs-lg

Typed Decision Models on Tabular Data

arXiv:2610.19536v1 Announce Type: new Abstract: Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead

researcharxiv-cs-cl

UA-Bench: Benchmarking Large Audio-Language Models on Underwater Acoustic Recognition

arXiv:2610.16942v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are fast becoming a general interface to sound, yet how far their broad pretraining carries into specialized domai

UDHRBench: Benchmarking Human Rights in Large Language Models

arXiv:2610.15179v1 Announce Type: cross Abstract: Large language models are increasingly deployed in contexts requiring adherence to ethical and legal norms, yet their alignment with internationally r

UltraBERT: Beyond the Limit of BERT Pre-Training

arXiv:2610.15028v1 Announce Type: cross Abstract: Recent bidirectional encoders have been modernized with innovations from causal large language models (LLMs). However, the pretraining recipe itself r

researcharxiv-cs-ai

Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies

arXiv:2610.19714v1 Announce Type: new Abstract: Learning robust driving policies that generalize beyond their training domain remains a central challenge in motion planning. We propose an uncertainty-

safetyarxiv-cs-ro

Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling

arXiv:2502.12713v2 Announce Type: replace Abstract: Echocardiography plays a fundamental role in the extraction of clinical parameters (e.g. left ventricular volume and ejection fraction) required to

Unconstrained CTI-REALM with Open-Weights

arXiv:2610.14180v1 Announce Type: new Abstract: Organisations that want language-model agents for security work face a choice between open-weight models, which they can run and audit themselves, and f

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

Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation

arXiv:2610.15227v1 Announce Type: new Abstract: Unified vision-language models (VLMs) perform both text-to-image (T2I) and image-to-text (I2T) generation with one shared Transformer backbone, yet how

researcharxiv-cs-ai

Unequal Voices: How LLMs Construct Constrained Queer Narratives

arXiv:2507.15585v2 Announce Type: replace-cross Abstract: One way social groups are marginalized in discourse is that the narratives told about them often default to a narrow, stereotyped range of top

researcharxiv-cs-ai

Universal Sample-Based Embeddings of Higher-Order Objects

arXiv:2610.19899v1 Announce Type: new Abstract: Neural networks operate naturally on finite-dimensional vectors, yet many problems require reasoning about higher-order objects such as distributions, f

researcharxiv-cs-lg

Hardware

Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming

arXiv:2610.19121v1 Announce Type: cross Abstract: Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reaso

hardwarearxiv-cs-cl

Unlocking Token Rewards via Training-Free Reward Attribution

arXiv:2610.18704v1 Announce Type: new Abstract: In this paper, we propose an extremely efficient, training-free method to extract token-level reward signals directly from an existing deep reward model

Upstream Influence, Downstream Re-Anchoring in LLM Agent Chains

arXiv:2610.17723v1 Announce Type: new Abstract: People act on evaluative judgments produced by LLM agents, and in multi-agent systems those judgments pass through several agents before reaching a pers

Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring

arXiv:2610.15231v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks su

safetyarxiv-cs-ai

v0.40.5

model-releasesollama-releases

NVIDIA Nemotron 3.5 Super NVIDIA Nemotron 3.5 Super is a vision-language model for coding, reasoning, tool use, and document understanding. It pairs a 120B-parameter hybrid Mamba-2/Transformer mixture

v0.40.5-rc1: New model... (#19030)

model-releasesollama-releases

ci: shorten Windows Vulkan build path Keep the nested shader-generator compiler probes below Windows path limits in checkouts with longer repository names. mlx: fix Nemotron bugs and support global sc

v0.7.0

model-releasesllama-cpp-releases

Overview llama.cpp v0.7.0 adds the LiliCorr speculative decoding technique and MTP for GLM5-Next, a new GPU cache for MoE experts kept in host memory (--moe-cache-mib, up to ~2.2x decode speedup when

VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting

arXiv:2610.19813v1 Announce Type: new Abstract: Spatio-temporal forecasting is fundamental to many real-world systems, yet deployment environments are inherently varying: temporal distributions may ev

applicationsarxiv-cs-lg

Tutorials

VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution

arXiv:2610.20509v1 Announce Type: new Abstract: Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing hig

tutorialsarxiv-cs-cv

VASPO: Self-Distillation with Privileged Value Estimation

arXiv:2610.20516v1 Announce Type: new Abstract: On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local toke

safetyarxiv-cs-cl

VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning

arXiv:2610.14939v1 Announce Type: new Abstract: GRPO-style reinforcement learning has advanced vision-language reasoning, but its sequence-level rewards provide limited credit assignment for individua

safetyarxiv-cs-ai

VEDA: Generation of 3D Molecules via Variance-Exploding Diffusion with Annealing

arXiv:2511.09568v3 Announce Type: replace-cross Abstract: Diffusion models show promise for 3D molecular generation, but face a fundamental trade-off between sampling efficiency and conformational acc

Vela: User Behavior as Supervision Sparks LLM Intelligence

arXiv:2610.14709v1 Announce Type: cross Abstract: Large language models are increasingly deployed in applications that act on users' long-term behavior histories, from personal assistants to recommend

applicationsarxiv-cs-ai

Verification and Forward Invariance of Control Barrier Functions for Differential-Algebraic Systems

arXiv:2603.13509v2 Announce Type: replace-cross Abstract: Differential-algebraic equations (DAEs) arise in power networks, chemical processes, and multibody systems, where algebraic constraints encode

safetyarxiv-cs-ro

Verification as an Architectural Layer for LLM Agents: A V-Model Design, and a Pilot Study of Its Deterministic Core

arXiv:2609.31937v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents built on the ReAct pattern concentrate four responsibilities in one model: selecting a strategy, choosing ea

Verifier-Grounded Agentic RL for Provably Correct Code

arXiv:2610.19298v1 Announce Type: new Abstract: Reinforcement learning for code usually rewards passing tests, which a model can do while remaining wrong on the inputs the tests do not cover. A progra

safetyarxiv-cs-lg

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

Tutorials

VeriPIE: Checkable Query Plans and Evidence-Guided Selection for Text-to-SQL

arXiv:2610.17493v1 Announce Type: cross Abstract: Financial Artificial Intelligence (AI) governance and model risk management (MRM) emphasize independent validation, transparency, and effective challe

tutorialsarxiv-cs-ai

Very impressed with LFM2.5-24-A2B in Pi Agent

model-releasesr-localllama

Just downloaded the new LFm2.5-24B-A2B LiquidAI model for testing. They claimed some pretty good performance, and I've had good experiences with their prior releases. Some context - I use Ornith 1.5-3

VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation

arXiv:2610.14149v1 Announce Type: new Abstract: VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and ve

VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?

arXiv:2605.06068v2 Announce Type: replace Abstract: For years, we have built LLM serving systems like any other critical infrastructure, with a single general-purpose stack hand-tuned over many engine

Video-Browser: Towards Open-web Agentic Video Browsing

model-releasesarxiv-cs-cv

arXiv:2512.23044v3 Announce Type: replace Abstract: Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypas

Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

arXiv:2501.12375v4 Announce Type: replace Abstract: Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal i

VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness

arXiv:2610.19424v1 Announce Type: new Abstract: LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reason

agentsarxiv-cs-cv

VIGOR-MPC: Vision-Guided Objective Regulation MPC

arXiv:2610.20333v1 Announce Type: new Abstract: Controlling an agent in an environment solely perceived through onboard vision is challenging: perception yields noisy, variable-length, and partially o

agentsarxiv-cs-ro

Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells

model-releasesarxiv-cs-lg

arXiv:2610.18949v1 Announce Type: cross Abstract: Existing machine-learning models for perovskite solar cells struggle to predict devices made from new materials. Most treat device layers as arbitrary

Virtual Head Attention

arXiv:2610.17959v1 Announce Type: cross Abstract: The design of attention mechanisms in long-context large language models faces an ``impossible triangle'' among KV cache size, training and prefill FL

VisibilitySplat: Ray-Traced Occlusion for Relighting with 3D Gaussian Splatting

arXiv:2610.19847v1 Announce Type: new Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light

Visible but Not Avoided: State-Dependent Re-Proposal of Rejected Candidates in Catalog-Based LLM Optimization Loops

arXiv:2610.15738v1 Announce Type: new Abstract: LLM optimization loops often show the proposer the candidates it has already tried and rejected, but leave it to the proposer to avoid them. We ask whet