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Teradata Corp. is expanding its Tera artificial intelligence assistant with a context engine and execution system designed to help agents carry out data tasks across enterprise systems. Tera is an age

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Testing the Construct Validity of a Functional Valence Axis in LLM Agents

DGX agent

arXiv:2609.22850v1 Announce Type: new Abstract: Contrastive activation directions are often interpreted from what they decode or how strongly they steer behavior. But what evidence is sufficient to id

safetyarxiv-cs-lg

22 Sept 2026

The Answer-Basin Representation Hypothesis: We Are Not Probing or Steering Concepts

arXiv:2609.24821v1 Announce Type: new Abstract: The Linear Representation Hypothesis associates high-level concepts with directions in language models, but it remains unclear how these concept-related

safetyarxiv-cs-cl

The Bairong System for MLC-SLM 2026: Dynamic Question-Aware Evidence Routing for Multilingual Conversational Speech Understanding

arXiv:2609.22214v1 Announce Type: new Abstract: Long multilingual conversational spoken question answering requires systems to balance long-range transcript semantics with sparse acoustic and speaker-

safetyarxiv-cs-cl

The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage

arXiv:2606.16560v2 Announce Type: replace Abstract: Automatic semantic change detection aims to identify how word meanings shift over time, offering insights into both linguistic and societal change.

The Copy Ceiling: An Input-Exposure Control for Ontology-Grounded Generation over Curated Corpora

arXiv:2609.24885v1 Announce Type: new Abstract: When a language model answers from a curated corpus via graph-based retrieval, a large grounding uplift does not establish reasoning over the retrieved

safetyarxiv-cs-cl

Applications

The Corroboration Illusion: When More News Makes LLM Forecasts Less True

arXiv:2609.22246v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to forecast real-world events by retrieving and reasoning over news. We show that this dependence on

applicationsarxiv-cs-cl

The Effect of Quantization on Clinical Benchmarks: Accuracy and Safety Across Model Families

arXiv:2609.22216v1 Announce Type: cross Abstract: Quantization enables deployment of large language models on resource-constrained clinical edge devices, but its effect on clinical accuracy and safety

The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventions

arXiv:2609.23374v1 Announce Type: new Abstract: Reinforcement learning (RL) offers a natural language for healthcare decisions whose conse- quences unfold over time, yet most reported progress remains

safetyarxiv-cs-lg

The Exponential Price of Determinism in Nonsmooth Nonconvex Optimization

arXiv:2609.23837v1 Announce Type: cross Abstract: We study the complexity of finding (elta,epsilon)-Goldstein stationary points of nonsmooth nonconvex Lipschitz functions. By now, it is known that ran

The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic docu…

model-releasesjerry-liu--x

The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic documents, LiteParse processed pages at 2.8ms/page and 1.5× fast

Industry

The first model of Laya was created with a single over night training and fine-tuning. It only took 15 hours from training to push the model…

First model of Laya was produced with a single overnight training and subsequent fine‑tuning run, taking only 15 hours from the start of training to publishing the model. It was released on Hugging Fa

industryclem-delangue--x

The Limits of Speculation: Bounding Speculative Decoding in Mixture-of-Experts

arXiv:2609.22156v1 Announce Type: new Abstract: Speculative decoding in Mixture-of-Experts (MoE) models faces the problem of unstable verification cost caused by input-dependent expert loading. To stu

The longer the AI psychodrama goes on, the more it’s clear we are dealing with a conventional business bubble. What’s different this time is…

The longer the AI psychodrama goes on, the more it’s clear we are dealing with a conventional business bubble. What’s different this time is the lengths to which the companies will go to put off the r

safetygary-marcus--x

The Neural Forcing for Three-Dimensional Incompressible Navier-Stokes finite time blowup

arXiv:2609.23934v1 Announce Type: new Abstract: We present a two-part neural framework for forced three-dimensional incompressible Navier--Stokes flow. Part~I develops the computational forcing system

The Operational Value of Spatial Dependence in Renewable Forecast Scenarios for Single-Period Economic Dispatch: A Controlled Ablation Study

arXiv:2609.23988v1 Announce Type: cross Abstract: Renewable forecasts are evaluated by statistical skill (e.g., CRPS), but grid operators pay for realized dispatch cost. We diagnose what drives dispat

The Price of Self-Calibration: Exact Evidence Budgets and Manufactured Blind Sets in Adaptive Monitoring

model-releasesarxiv-cs-lg

arXiv:2609.23254v1 Announce Type: new Abstract: Self-calibrating monitors adapt their threshold online to guarantee a prescribed long-run false-alarm rate under arbitrary drift. We compute the price o

The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models

arXiv:2609.23369v1 Announce Type: new Abstract: Generation-free world action models (WAMs) retain future-video prediction during training but act from internal video features at inference, leaving unc

researcharxiv-cs-cv

The Role of AI in Online Reviews

arXiv:2609.22198v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) creates new opportunities for strategic content generation on online platforms, including potentially

The Role of Coordinates in Pareto Regret for Adversarial Multi-Objective Bandits

arXiv:2609.23092v1 Announce Type: new Abstract: Adversarial multi-objective bandits hold the potential to help us optimize choices (arms) whose reward is a multidimensional vector chosen by an adversa

The Role of Dataset Linguistic Structure in the Cultural Awareness of Large Language Models

arXiv:2602.01161v2 Announce Type: replace Abstract: The global deployment of large language models (LLMs) has raised concerns about cultural misalignment, yet the linguistic properties of fine-tuning

The shape of quark flavors

arXiv:2609.22812v1 Announce Type: cross Abstract: We construct the Yukawa couplings of the quark sector as overlap integrals of Gaussian wave functions in extra spatial dimensions. Assuming that the w

The Situated Identity Test: Distinguishing Persistent Cognitive Identity from Persona Imitation

arXiv:2609.22195v1 Announce Type: new Abstract: Large language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals

Tutorials

The Undetected Damage of Quantization on Retrieval and How to Fix It

arXiv:2609.24322v1 Announce Type: new Abstract: We show that a quantized model that keeps its classification accuracy still changes 14 to 46% of its top-1 retrieval results, and that aggregate ranking

tutorialsarxiv-cs-lg

The Ups and Downs of Backprop Weights

model-releasesarxiv-cs-lg

arXiv:2609.22554v1 Announce Type: new Abstract: Backpropagation (BP) has driven the remarkable success of modern deep learning by enabling large hierarchical networks to learn complex functions end-to

The Visual Target Matters: Learning across the Visual Hierarchy for Brain-to-Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2609.24136v1 Announce Type: new Abstract: Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented

Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies

arXiv:2609.24682v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models map observations to actions with no objective that accounts for how the world responds, so their robustness is bou

safetyarxiv-cs-cv

This is why people who call themselves decent vote for indecent policies decade after decade after decade. Because it never hurts them. Thei…

model-releasesanthropic--x

This is why people who call themselves decent vote for indecent policies decade after decade after decade. Because it never hurts them. Theirs is a monstrous indifference, because empathy is for the l

this-that-model-1.0: A typed decision model that decides in 30 ms, for a millionth of a cent

arXiv:2609.23886v1 Announce Type: new Abstract: Software delegates more of its branches to models every year: which queue a ticket enters, whether a command is safe to run, whether a claim clears with

Time-Incremental Continued Pretraining of LLMs: Knowledge Updates Without Catastrophic Forgetting

arXiv:2609.23916v1 Announce Type: new Abstract: Large language models (LLMs) drift out of date the moment their pretraining ends, yet retraining from scratch is prohibitively expensive. Continued pret

Time Series Foundation Models for Process Model Forecasting

arXiv:2512.07624v2 Announce Type: replace Abstract: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of d

Hardware

Tip Manipulation in Soft Everting Robots via Wall Retraction and Deployable Fingers

arXiv:2609.23132v1 Announce Type: new Abstract: Soft everting robots can traverse long, confined paths by continuously growing, yet active interaction remains limited to a single tool fixed at or near

hardwarearxiv-cs-ro

To Consolidate or not to Consolidate? Evaluating the Impact of Consolidation in Multi-Reference Training using Peer Reviews

arXiv:2609.22805v1 Announce Type: new Abstract: Natural language generation (NLG) tasks span the spectrum of conditional entropy, ranging from highly constrained machine translation to open-ended dial

Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models

arXiv:2609.22097v1 Announce Type: new Abstract: The evaluation of large language models (LLMs) on coding tasks has primarily focused on performance metrics such as pass@k. As LLMs continue to advance,

researcharxiv-cs-cl

Token Utility Is Selection-Conditioned: Coupled Selection of Prompt Context and Response Supervision for Efficient Instruction Tuning

arXiv:2609.22943v1 Announce Type: new Abstract: Efficient large language model (LLM) instruction tuning requires selecting response supervision with supporting prompt context. Existing methods typical

ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification

arXiv:2609.24903v1 Announce Type: new Abstract: Tone languages constitute over 50-70% of the world's languages, but the vast majority are low-resource, lacking the large transcribed corpora needed for

researcharxiv-cs-cl

Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks

arXiv:2609.23435v1 Announce Type: cross Abstract: Multi-omics sequences contain complex biological patterns, yet deciphering their mechanisms for automated scientific discovery remains challenging. As

Toollery: Scaling LLM Agents to Thousands of Skills and Tools

arXiv:2609.22218v1 Announce Type: cross Abstract: As LLM agents are exposed to hundreds to tens of thousands of skills, tools, and API functions, full-library prompting becomes costly, slow, and less

Topographic Training Concentrates Causal Circuits Without Improving Neuron Monosemanticity

arXiv:2609.24379v1 Announce Type: new Abstract: Mechanistic interpretability of vision transformers seeks to decompose model computation into human-readable units, but learned representations entangle

researcharxiv-cs-cv

Hardware

Topology-Aware Workload Scheduling with NVIDIA Topograph

**Summary:** NVIDIA Topograph discovers and normalizes cluster topology from cloud APIs or on‑prem fabric systems into a common model that is published as Kubernetes node labels, Slurm configuration,

hardwarenvidia-developer

Tutorials

Topology-Informed Visual Prompting For Vision Language Action Policies

arXiv:2609.23944v1 Announce Type: new Abstract: Vision-language-action (VLA) policies can struggle with manipulation tasks with complex obstacle geometries due to partial observability. These complex

tutorialsarxiv-cs-ro

Toscani-Fourier Distance on Probability Measures: Wasserstein Control, Topological Equivalence on Model Classes, and Duality

arXiv:2609.23163v1 Announce Type: cross Abstract: Comparing probability measures in machine learning trades transport geometry against computational cost: Wasserstein distances encode the geometry of

Touch2Robot: Robot Touch in the Human Demonstration Loop

arXiv:2609.24660v1 Announce Type: new Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot han

safetyarxiv-cs-ro

Tutorials

Toward a foundation model for forest point clouds

arXiv:2609.24787v1 Announce Type: new Abstract: Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current models

tutorialsarxiv-cs-cv

Applications

Toward a Unified Mathematics of Concepts

arXiv:2609.24554v1 Announce Type: new Abstract: Concepts are commonly defined as abstract, compact representations of knowledge and treated as basic units of intelligent behavior. Yet, cognition, psyc

applicationsarxiv-cs-cl

Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder

arXiv:2609.22113v1 Announce Type: new Abstract: Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predi

safetyarxiv-cs-lg

Toward Human-in-the-Loop Robot Failure Recovery: Bridging Communication Gaps in Human-Robot Collaboration

model-releasesarxiv-cs-ro

arXiv:2609.24055v1 Announce Type: new Abstract: Robots can recover from failures by asking bystanders for help, but effective human-in-the-loop recovery requires communication that accounts for differ

Agents

Toward Mission-Critical ISAC: Reliable Energy-Aware Coordination in UAV Swarms

arXiv:2609.22292v1 Announce Type: cross Abstract: Unmanned aerial vehicle (UAV) swarms deployed in mission-critical applications must simultaneously track a mobile aerial target and maintain reliable

agentsarxiv-cs-ro