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Task Vector Geometry Underlies Dual Modes of Task Inference in Transformers

arXiv:2605.03780v1 Announce Type: cross Abstract: Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to

Applications

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TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations

DGX agent

arXiv:2605.03045v1 Announce Type: new Abstract: Causal Discovery (CD) is a powerful framework for scientific inquiry. Yet, its practical adoption is hindered by a reliance on strong, often unverifiabl

applicationsarxiv-cs-lg

6 May 2026

TeamUp: Semantic Project Matching and Team Formation for Learning at Scale

arXiv:2605.03237v1 Announce Type: cross Abstract: Project-based learning improves student engagement and learning outcomes, yet allocating students to appropriately challenging projects while forming

safetyarxiv-cs-cl

Tempered Guided Diffusion

arXiv:2605.03712v1 Announce Type: cross Abstract: Training-free conditional diffusion provides a flexible alternative to task-specific conditional model training, but existing samplers often allocate

researcharxiv-cs-lg

Tenability and Weak Semantics: Modeling Non-uniform Defense -- Extended Version

arXiv:2605.02024v1 Announce Type: new Abstract: In Dung-style abstract argumentation, various semantics capture notions of acceptability of arguments. The admissibility semantics capture the notion th

Hardware

Test-Time Training with KV Binding Is Secretly Linear Attention

arXiv:2602.21204v3 Announce Type: replace-cross Abstract: Test-time training (TTT) with KV binding as sequence modeling layer is commonly interpreted as a form of online meta-learning that memorizes a

hardwarearxiv-cs-cv

Tutorials

Text-Conditional JEPA for Learning Semantically Rich Visual Representations

arXiv:2605.03245v1 Announce Type: cross Abstract: Image-based Joint-Embedding Predictive Architecture (I-JEPA) offers a promising approach to visual self-supervised learning through masked feature pre

tutorialsarxiv-cs-cv

The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

arXiv:2408.12622v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet resea

safetyarxiv-cs-lg

The Coding Limits of Robust Watermarking for Generative Models

arXiv:2509.10577v3 Announce Type: replace-cross Abstract: We study a basic question about cryptographic watermarking for generative models: how reliable can a watermark remain when an adversary is all

Agents

The Conversations Beneath the Code: Triadic Data for Long-Horizon Software Engineering Agents

arXiv:2605.02244v1 Announce Type: cross Abstract: Frontier software engineering agents have saturated short-horizon benchmarks while regressing on the work that constitutes senior engineering: long-ho

agentsarxiv-cs-ai

The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate

arXiv:2605.00914v1 Announce Type: cross Abstract: Multi-agent debate, where teams of LLMs iteratively exchange rationales and vote on answers, is widely deployed under the assumption that peer review

The Counterexample Game: Iterated Conceptual Analysis and Repair in Language Models

arXiv:2605.03936v1 Announce Type: new Abstract: Conceptual analysis -- proposing definitions and refining them through counterexamples -- is central to philosophical methodology. We study whether lang

The Design and Composition of Structural Causal Decision Processes

arXiv:2605.02681v1 Announce Type: cross Abstract: We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing s

Local Ai

The Detector Teaches Itself: Lightweight Self-Supervised Adaptation for Open-Vocabulary Object Detection

arXiv:2605.03642v1 Announce Type: new Abstract: Open-vocabulary object detection aims to recognize objects from an open set of categories, which leverages vision-language models (VLMs) pre-trained on

local-aiarxiv-cs-cv

The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence

arXiv:2605.02106v1 Announce Type: new Abstract: Contemporary artificial intelligence systems achieve strong performance through large-scale parameterization, retrieval augmentation, and training on ex

The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech

arXiv:2508.15524v3 Announce Type: replace Abstract: We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative val

The Garden of Forking Paths: Narrative Arc-Conditioned Gameplay Planning

arXiv:2605.01245v1 Announce Type: cross Abstract: Narrative archetypes (e.g., Hero's Journey, Three-act structure) provide universal story structures that resonate across cultures and media and are im

Applications

The Manokhin Probability Matrix: A Diagnostic Framework for Classifier Probability Quality

arXiv:2605.03816v1 Announce Type: cross Abstract: The Brier score conflates two distinct properties of probabilistic predictions: reliability (calibration error) and resolution (discriminatory power).

applicationsarxiv-cs-lg

The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission

arXiv:2605.00844v1 Announce Type: cross Abstract: When large language models (LLMs) are consulted as forecasting tools, the independence of individual errors -- the foundation of collective intelligen

Hardware

The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm

arXiv:2505.16932v5 Announce Type: replace-cross Abstract: Computing the polar decomposition and the related matrix sign function has been a well-studied problem in numerical analysis for decades. Rece

hardwarearxiv-cs-cl

The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development

arXiv:2605.01160v1 Announce Type: cross Abstract: Since 2022, AI-powered coding assistants have produced contradictory evidence: controlled studies report 20-56% productivity gains on well-scoped task

The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It

model-releasesarxiv-cs-cl

arXiv:2605.03258v1 Announce Type: cross Abstract: Large language models often fail at simple counting tasks, even when the items to count are explicitly present in the prompt. We investigate whether t

The TTS-STT Flywheel: Synthetic Entity-Dense Audio Closes the Indic ASR Gap Where Commercial and Open-Source Systems Fail

model-releasesarxiv-cs-cl

arXiv:2605.03073v1 Announce Type: new Abstract: Niche-domain Indic ASR -- digit strings, currency amounts, addresses, brand names, English/Indic codemix -- is under-served by both open-source SOTA and

TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization

arXiv:2605.01418v1 Announce Type: new Abstract: Time-series generative models often lack control over temporal granularity, forcing users to accept whatever granularity the model produces. To enable t

TMD-Bench: A Multi-Level Evaluation Paradigm for Music-Dance Co-Generation

arXiv:2605.01809v1 Announce Type: cross Abstract: Unified audio-visual generation is rapidly gaining industrial and creative relevance, enabling applications in virtual production and interactive medi

To Use AI as Dice of Possibilities with Timing Computation

arXiv:2605.01134v1 Announce Type: new Abstract: The dominant noun-based modeling paradigm has fundamentally constrained AI development, precluding any adequate representation of the future as an open

To Vibe Research or Not to Vibe Research? Generative AI in Qualitative Research

arXiv:2605.00922v1 Announce Type: cross Abstract: There has been intense debate among qualitative researchers about whether generative AI is suitable for qualitative research. In this paper, we summar

To Write or to Automate Linguistic Prompts, That Is the Question

arXiv:2603.25169v2 Announce Type: replace Abstract: LLM performance is highly sensitive to prompt design, yet whether automatic prompt optimization can replace expert prompt engineering in linguistic

Toggling stiffness via multistability

arXiv:2510.09511v2 Announce Type: replace-cross Abstract: Variable stiffness is a key capability in biological and robotic systems, enabling adaptive interaction across tasks and environments. Mechani

researcharxiv-cs-ro

Toward a Science of Intent: Closure Gaps and Delegation Envelopes for Open-World AI Agents

arXiv:2604.25000v2 Announce Type: replace Abstract: Recent work has framed intelligence in verifiable tasks as reducing time-to-solution through learned structure and test-time search, while systems w

Toward Generative Quantum Utility via Correlation-Complexity Map

model-releasesarxiv-cs-lg

arXiv:2603.06440v2 Announce Type: replace Abstract: We study a practical question in generative quantum machine learning: given a classical dataset, can we determine, before training, whether it is we

Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts

arXiv:2605.03348v1 Announce Type: new Abstract: We propose S3 (Specialization, Selection, Sparsification), a framework that rethinks multimodal learning through a structural perspective. Instead of en

researcharxiv-cs-lg

Towards accurate extreme event likelihoods from diffusion model climate emulators

arXiv:2605.03802v1 Announce Type: cross Abstract: ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model

researcharxiv-cs-lg

Towards Agentic Runtime Healing

arXiv:2408.01055v2 Announce Type: replace-cross Abstract: Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human int

Towards an End-to-End System for 3D Tracking of Physical Objects in Virtual Immersive Environments

arXiv:2605.02901v1 Announce Type: cross Abstract: This work aims to establish an end-to-end system for tracking of physical 3D objects for virtual reality (VR) applications. We focus on training appli

researcharxiv-cs-cv

Towards Multi-Agent Autonomous Reasoning in Hydrodynamics

arXiv:2605.01102v1 Announce Type: new Abstract: Single-agent systems (SAS) have become the default pattern for LLM-driven scientific workflows, but routing planning, tool use, and synthesis through a

Towards Safer Large Reasoning Models by Promoting Safety Decision-Making before Chain-of-Thought Generation

arXiv:2603.17368v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieved remarkable performance via chain-of-thought (CoT), but recent studies showed that such enhanced reasoning cap

Towards Understanding Specification Gaming in Reasoning Models

arXiv:2605.02269v1 Announce Type: new Abstract: Specification gaming is a critical failure mode of LLM agents. Despite this, there has been little systematic research into when it arises and what driv

TRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domains

arXiv:2605.03838v1 Announce Type: new Abstract: We introduce TRACE, a cross-domain engineering framework for trustworthy agentic AI in operationally critical domains. TRACE combines a four-layer refer

safetyarxiv-cs-cl

Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection

arXiv:2605.03358v1 Announce Type: new Abstract: When orthodontists trace cephalometric radiographs, they follow a structured workflow: identify the soft tissue profile, partition the skull into anatom

safetyarxiv-cs-cv

Local Ai

Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection

arXiv:2605.02958v1 Announce Type: cross Abstract: Representation Engineering typically relies on static refusal vectors derived from terminal representations. We move beyond this paradigm, demonstrati

local-aiarxiv-cs-cl

Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools

model-releasesarxiv-cs-lg

arXiv:2605.03789v1 Announce Type: cross Abstract: We propose Conformal Seasonal Pools (CSP), a training-free probabilistic time-series forecaster that mixes same-season empirical draws with signed res

Agents

Transformer-Guided Deep Reinforcement Learning for Optimal Takeoff Trajectory Design of an eVTOL Drone

arXiv:2511.14887v2 Announce Type: replace Abstract: The rapid advancement of electric vertical takeoff and landing (eVTOL) aircraft offers a promising opportunity to alleviate urban traffic congestion

agentsarxiv-cs-lg

Transformers with Selective Access to Early Representations

arXiv:2605.03953v1 Announce Type: cross Abstract: Several recent Transformer architectures expose later layers to representations computed in the earliest layers, motivated by the observation that low

TriBench-Ko: Evaluating LLM Risks in Judicial Workflows

model-releasesarxiv-cs-cl

arXiv:2605.03792v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into legal workflows. However, existing benchmarks primarily address proxy tasks, such as bar e

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration

arXiv:2605.01970v2 Announce Type: cross Abstract: Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characte

Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

arXiv:2605.02640v1 Announce Type: new Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), is increasingly deployed in high-stakes domains, en

Agents

Truth or Tribe: How In-group Favoritism Prioritize Facts in Persona Agents

arXiv:2605.01329v1 Announce Type: new Abstract: In-group favoritism refers to the phenomena of favoring members of one's in-group over out-group members and is widely observed in numerous social coope

agentsarxiv-cs-ai