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TabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins

model-releasesarxiv-cs-ai

arXiv:2603.15481v3 Announce Type: replace-cross Abstract: Data-free knowledge distillation enables model compression without original training data, critical for privacy-sensitive tabular domains. How

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TAEC: Trajectory-Aware Evidence Coordination for Multi-Step Visual RAG

DGX agent

arXiv:2609.37349v1 Announce Type: cross Abstract: Multi-step visual retrieval-augmented generation (RAG) answers complex questions by repeatedly retrieving visual evidence, updating an intermediate st

tutorialsarxiv-cs-ai

30 Sept 2026

TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2509.23140v2 Announce Type: replace Abstract: Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform

Tail-Influence Sampling for CVaR Policy Evaluation

arXiv:2609.38096v1 Announce Type: new Abstract: Policies with similar mean returns can differ sharply in rare failures, yet estimating lower-tail conditional value-at-risk (CVaR) accurately can requir

safetyarxiv-cs-lg

Taking the data released by @flightradar24 and applying some physical modeling - yes it's very possible that airspeed exceeded Mach 1, but i…

model-releasesanthropic--x

Taking the data released by @flightradar24 and applying some physical modeling - yes it's very possible that airspeed exceeded Mach 1, but it's hard to be certain because of inconsistent reporting in

Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing

model-releasesarxiv-cs-ro

arXiv:2609.37334v1 Announce Type: new Abstract: Vision-language-action (VLA) policies often fail when a robot's executed motion deviates from their commanded action. Such execution errors arise from t

TaoFlowForge: Progressive Native Mesh Generation via Cascaded Flow Matching

arXiv:2609.37139v1 Announce Type: new Abstract: 3D content generation technology has significantly advanced the work of designers, as well as the 3D printing and gaming industries. However, it remains

applicationsarxiv-cs-cv

Targeted Visual Counterfactual Explanations for Contrastive Vision-Language Model

arXiv:2609.37638v1 Announce Type: new Abstract: Current explanation methods for contrastive vision--language models such as CLIP mainly identify important regions without showing how to change the inp

tutorialsarxiv-cs-cv

Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning

arXiv:2609.36178v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment of

local-aiarxiv-cs-ai

TaRL: Learning General and Physical Rewards from Tactile Demonstrations

arXiv:2609.36785v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to sequence precise contacts, maintain stable grasps, and apply directed forces. Reinforcement learning (RL) c

local-aiarxiv-cs-ro

Task-Oriented Visual Feature Compression via Residual Vector Quantization for Device-Edge Multimodal Inference

arXiv:2609.37090v1 Announce Type: new Abstract: Large multimodal models (LMMs) support diverse visual understanding and reasoning tasks but are often impractical to run entirely on resource-constraine

local-aiarxiv-cs-cv

Task-Relevant Null-Space Residuals for Non-Injective Neural Mappings

arXiv:2609.37272v1 Announce Type: new Abstract: Non-injective mappings in neural networks map distinct inputs to the same representation, thereby implicitly inducing equivalence relations in the input

tutorialsarxiv-cs-ai

TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks

arXiv:2609.36968v1 Announce Type: new Abstract: Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples. While conventional methods r

applicationsarxiv-cs-lg

Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback

model-releasesarxiv-cs-ai

arXiv:2609.37356v1 Announce Type: new Abstract: Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-based

Technical note on: Zero-Training Feature-Space Alignment via Information Geometry

arXiv:2609.37302v1 Announce Type: new Abstract: Deep vision models often degrade under distribution shift. Test-time adaptation can improve robustness but typically requires iterative optimization, hy

safetyarxiv-cs-cv

Temporal-Aware Fusion for Robust Outdoor LiDAR Localization

arXiv:2609.36432v1 Announce Type: new Abstract: LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches often encounte

researcharxiv-cs-cv

Tensor-Train Compressed Separable PINNs: A Curvature-Aware Optimization Framework for Parametric PDEs in High Dimensions

model-releasesarxiv-cs-lg

arXiv:2609.36165v1 Announce Type: cross Abstract: In this work, we develop a second-order optimization framework for physics-informed neural networks (PINNs) applied to high-dimensional parametric par

Test-Time Adaptation of Manipulation Policies Under Actuator Degradation

arXiv:2609.36182v1 Announce Type: new Abstract: Robot manipulation policies are usually trained under the assumption that a commanded action produces the same motion as it did during training even aft

safetyarxiv-cs-ro

Agents

Text2Sim: Agentic Physics-Based Simulation Generation with Distilled Expertise

arXiv:2609.36593v1 Announce Type: cross Abstract: Creating diverse physical simulations remains labor-intensive because assets, layout, physical parameters, motion, control, and rendering must be desi

agentsarxiv-cs-ai

Texture Space Material Diffusion

arXiv:2609.37654v1 Announce Type: new Abstract: We present a method for generating high quality materials for 3D objects entirely in texture space. We finetune a video diffusion transformer for text-g

researcharxiv-cs-cv

thank god for embed5. somehow Cohere is really good at not looking at your data while being the best at indexing it

thank god for embed5. somehow Cohere is really good at not looking at your data while being the best at indexing it The Cohere Embeddings team has been cooking 🍳 Several key innovations in the model:

model-releasescohere--x

Industry

The AI Tamagotchis are coming

While AI has made plenty of inroads on people's phones and computers, it's largely failed in dedicated devices. But over the next year, two major AI companies, Meta and OpenAI, will attempt to change

industrythe-verge-ai

The Camera Inside the Editor: Reading the Implicit Camera of Image Editors with Painted Calibration Patterns

arXiv:2609.37732v1 Announce Type: new Abstract: Instruction-based image editors insert objects, restyle scenes and render new viewpoints, but it is unknown which camera they assume when they paint int

The Canonical Order Problem: When Large Language Models Are Unreliable Knowledge Bases for Multi-Valued Relations

arXiv:2609.36209v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as knowledge bases (KBs) due to the vast amount of knowledge they acquire during pre-training. While

applicationsarxiv-cs-cl

The Commit-Abstain Circuit: Why Language Models Hallucinate Instead of Abstaining

arXiv:2609.32964v2 Announce Type: replace Abstract: Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to

local-aiarxiv-cs-ai

The Decision Value of Perception Compute

arXiv:2609.35910v1 Announce Type: cross Abstract: Adaptive perception spends extra computation on inputs where perception is expected to improve. When perception feeds a downstream decision system, a

The Default Trap: Rethinking Plan Evaluation in Tool-Using LLM Agents

arXiv:2609.36829v1 Announce Type: new Abstract: An executor can respond strongly to a change in a supplied plan's priority while showing a small change in the same information-selection probability wh

researcharxiv-cs-ai

The Detectability Gap: Hidden Heterogeneity in Hallucination Detection Across Language Models

arXiv:2609.35860v1 Announce Type: cross Abstract: Sampling based consistency is widely used for hallucination detection, yet aggregate performance can conceal systematic differences in which errors ar

researcharxiv-cs-ai

The Domain Is a Residue: Adapting Self-Supervised Features, Not Generators

arXiv:2609.37330v1 Announce Type: new Abstract: Clearing fog, rain or snow from footage, or turning renders into photographs, must remove the source domain and keep the scene. Unpaired translators car

The Download: OpenAI’s chief research officer explains its hacking response

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. “We’re not going to shoot ourselves in the foot” over hack fal

researchmit-tech-review

The finite-horizon five-expert prediction problem

arXiv:2609.38035v1 Announce Type: cross Abstract: We give an explicit solution to the five expert prediction with expert advice partial differential equation (PDE) in the finite-time horizon setting.

The Geometry of Inference in Transformer Residual Streams

arXiv:2609.37824v1 Announce Type: new Abstract: Transformer language models build predictions through successive residual updates, but how their representations become specific to an eventual outcome

safetyarxiv-cs-cl

The Layer Mystery of VLA: An Information-Theoretical Analysis of VLA Latent Interface

arXiv:2609.36118v1 Announce Type: new Abstract: Vision-language-action (VLA) policies connect a pretrained vision-language backbone to an action head through a latent interface, but which backbone lay

safetyarxiv-cs-ai

The Price of Token Boundaries: Compression Certificates and Prediction

arXiv:2609.35869v1 Announce Type: new Abstract: Pre-tokenisation restricts which text fragments can become prediction units, but its compression cost is obscured when tokenisers are compared only unde

researcharxiv-cs-ai

The Rashomon Wikipedia: A Data-Perspectivist Analysis of Divergent Historical Narratives

arXiv:2609.37498v1 Announce Type: new Abstract: Wikipedia aims to provide a unified, neutral record of history, yet its independent language editions often function as distinct epistemic communities,

safetyarxiv-cs-cl

The Role of Feed-Forward Layers in Transformer Dynamics

arXiv:2609.36230v1 Announce Type: new Abstract: We study the dynamical behavior of tokens in transformers from a control-theoretic perspective. Our model includes the feed-forward layer present after

applicationsarxiv-cs-lg

The Safety Operator: Modulating the Expression of Safety Instructions via Spectral Optimization

arXiv:2609.36434v1 Announce Type: new Abstract: Context tokens in a transformer-based language model can be absorbed into the model's weights as a multiplicative operator. We study this operator in th

safetyarxiv-cs-ai

The Signed Geometry of One-Shot Recourse: On-Path Validity and the Signed-Curvature Criterion

arXiv:2609.36252v1 Announce Type: new Abstract: Closed-form recourse moves a rejected user along the unit gradient hat g of the classifier score f by the promised distance d_p=|f(x)|/|nabla f(x)|, at

The Surge of Anti-Semitism in German Social Media following the October 7 Attacks

arXiv:2609.36290v1 Announce Type: cross Abstract: We investigate the extent to which the Hamas attacks on Israel of October 7, 2023, have affected German social media debates about Judaism and Israel.

researcharxiv-cs-cl

The Teacher Is a Direction, Not a Destination: Extrapolating RL-Induced Representation Residuals in On-Policy Distillation

arXiv:2609.36484v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student to match the teacher's next-token distributions on the student's own trajectories and has yielded substant

safetyarxiv-cs-lg

The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment

model-releasesarxiv-cs-ai

arXiv:2609.37914v1 Announce Type: cross Abstract: Fine-tuning large language models on narrow, misaligned tasks can undo their post-training alignment and induce novel misaligned behaviors -- a phenom

The Uneven Decline of Collective Knowledge Production: Evidence from Stack Overflow After Generative AI

arXiv:2609.36069v1 Announce Type: cross Abstract: Generative AI (Gen AI) is reshaping how individuals learn and work, but its consequences for collective knowledge, the shared body of knowledge that o

tutorialsarxiv-cs-ai

The Universal Classifier for Graph Learning

arXiv:2609.36302v1 Announce Type: new Abstract: While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learning

The Vote Hides the Failure: Aggregation Choice and Noise Robustness in Heart Murmur Detection

arXiv:2609.37161v1 Announce Type: new Abstract: Noise robustness in automated phonocardiogram (PCG) murmur detection, and how it is measured, remains underexamined despite growing interest in low-reso

Theory on Attention Dynamics for Out-of-Distribution In-Context Learning

arXiv:2609.36448v1 Announce Type: new Abstract: Transformers have demonstrated remarkable in-context learning (ICL) capabilities, enabling them to perform new tasks without additional fine-tuning. How

Think Before You Restore: Risk-Aware Manchu Manuscript Restoration with Stroke-Guided Attention

arXiv:2609.36243v1 Announce Type: cross Abstract: Full-page blind restoration of historical Manchu manuscripts is challenging due to scarce annotations, unknown degradation regions, and fragile connec

tutorialsarxiv-cs-ai

Think Before You Score: Thinking Reward Model for Visual Generation

arXiv:2609.37372v1 Announce Type: new Abstract: Visual reward models are essential for evaluating and improving visual generation models, yet existing approaches typically map task conditions and cand

safetyarxiv-cs-cv

Think, Then Look: Active Spatial Reasoning for House-Scale 3D Scene Understanding

arXiv:2512.03284v2 Announce Type: replace Abstract: Spatial reasoning in large-scale 3D environments remains challenging for current vision--language models, which are typically constrained to room-sc