Search: “routing”

Select, Label, Evaluate: Active Testing in NLP

arXiv:2603.21840v2 Announce Type: replace Abstract: Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly exp

StreamEMS: Streaming Video Understanding with Self-Evolving Memory Scheme for Vision-Language Models

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DGX agent

arXiv:2608.27881v1 Announce Type: new Abstract: Recently, many streaming video understanding methods have been proposed by constructing an external memory to store historical data for computational re

researcharxiv-cs-cv

31 Aug 2026

Local Ai

TACIT-Switch: Cost-Aware Model Escalation for LLM Agents from Censored Supervision

arXiv:2608.27911v1 Announce Type: new Abstract: Agents with smaller language-model backbones are less expensive but can drift into persistent failure modes, whereas those with larger backbones are gen

local-aiarxiv-cs-lg

Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs

arXiv:2608.27948v1 Announce Type: new Abstract: Test-time adaptation (TTA) on graphs aims to adapt a graph neural network (GNN) that is well-trained on the training graph to the test graph, which invo

safetyarxiv-cs-lg

Today, Transluce is releasing the most expansive independent evaluation to date of how AI systems respond to users experiencing mental healt…

model-releasesyohei-nakajima--x

Today, Transluce is releasing the most expansive independent evaluation to date of how AI systems respond to users experiencing mental health crises. We evaluated 77 model variants from OpenAI, Anthro

b10687

model-releasesllama-cpp-releases

opencl: use a better matmul path on two Adreno GPU generations (#27640) opencl: default the Adreno xmem F16xF32 GEMM on for X2E kernel_mul_mm_f16_f32_l4_lm is the slowest matmul this backend has on Ad

29 Aug 2026

CG4AI: A Column Generation Framework for Training AI Models Under Constraints

arXiv:2608.26375v1 Announce Type: cross Abstract: Standard machine-learning training minimizes a loss function over a dataset, but does not guarantee that the resulting model will satisfy predefined r

CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes

arXiv:2608.27455v1 Announce Type: new Abstract: Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods

Do Coding Agents Need Executable World Models, Simplification, and Verification to Solve ARC-AGI-3?

arXiv:2607.15439v2 Announce Type: replace Abstract: Our previous ARC-AGI-3 agent bundled executable world modeling, prompted simplification, and exact replay verification, leaving their individual con

Evaluator-Dependent Patient-Adaptive ECG Lead-Channel Allocation

arXiv:2608.26827v1 Announce Type: new Abstract: Patient-conditioned acquisition policies for ECG lead-channel selection can outperform population-wide fixed protocols by tailoring the channel budget t

safetyarxiv-cs-cv

FAN-LoRA: A Fourier-Adaptive Nonlinear Low-Rank Adaptor for Medical Foundation Model Domain Adaptation

model-releasesarxiv-cs-cv

arXiv:2608.26531v1 Announce Type: new Abstract: The advent of vision foundation models, notably the Segment Anything Model (SAM), has catalyzed significant advancements in natural image segmentation.

Fixed-Haven Reservation for Online Multi-Agent Pickup and Delivery in Dense Warehouses

model-releasesarxiv-cs-ro

arXiv:2608.26759v1 Announce Type: cross Abstract: Dense warehouses often contain single-lane aisles, dead ends, and tree-like guidepaths that leave little room for idle agents to wait without blocking

J-Zero: Unified Challenger--Solver--Judge Co-Evolution from Zero Data

arXiv:2608.26582v1 Announce Type: cross Abstract: Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human sup

researcharxiv-cs-ai

Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact

arXiv:2608.27271v1 Announce Type: cross Abstract: The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understan

agentsarxiv-cs-ro

On Scope Classification and Current Knowledge-Editing Benchmarks: A Negative Result, with INLAY as a Gradient-Free Case Study

arXiv:2608.26292v1 Announce Type: cross Abstract: Every memory-based knowledge editor in the SERAC lineage depends on a scope decision: given a query, does a stored edit apply? We report that current

Parameter-Efficient pretrained-CT-to-MRI Transfer for Rectal Cancer Segmentation: Performance-Calibration Trade-offs

model-releasesarxiv-cs-cv

arXiv:2608.27178v1 Announce Type: new Abstract: Accurate rectal cancer segmentation from magnetic resonance imaging (MRI) is essential for adaptive radiotherapy and tumor response assessment, but depl

RECAP-Forcing: Retaining Content Appearances for Long Video Generation

arXiv:2608.26671v1 Announce Type: new Abstract: Long autoregressive video generation faces a fundamental memory challenge: with a finite attention window, a model must decide which information from an

Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion

arXiv:2608.26794v1 Announce Type: new Abstract: Scaling video generation to long durations reveals a critical bottleneck: current models lack robust long-term memory. This deficiency can be studied al

SkillChain: Closing the Loop on Skill Evolution for Image-Based E-Commerce AI Assistants

arXiv:2606.12984v2 Announce Type: replace Abstract: Image-based AI assistants are now deployed at production scale on e-commerce platforms, where a single uploaded image can trigger fundamentally diff

safetyarxiv-cs-cl

Using Poly-Encoders for Computationally Efficient Automated Creativity Assessment

arXiv:2608.26165v1 Announce Type: cross Abstract: Automated creativity assessment has been a long standing challenge, with traditional methods often being resource intensive or lacking practical accur

researcharxiv-cs-ai

Align Then Adapt: Label-Efficient Adapter Learning for Asymmetric Dense Retrieval

arXiv:2604.03403v2 Announce Type: replace-cross Abstract: Dense retrieval systems increasingly face an asymmetry between complex instruction-like queries and relatively simple, static document collect

AWM: Answerable Working Memory for Long-Document VQA Agents

arXiv:2608.25618v1 Announce Type: new Abstract: Long-document visual question answering increasingly relies on VLM agents that retrieve candidate pages, inspect page images, write findings to working

agentsarxiv-cs-cl

Conditional Total Correlation and the Serial Depth of Adaptive Parallel Sampling

arXiv:2608.25505v1 Announce Type: cross Abstract: Motivated by parallel decoding in masked diffusion models, we study adaptive parallel sampling of discrete vectors: in each round, a deterministic pol

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DualOPSD: Adaptive Privileged Teachers for On-Policy Self-Distillation

arXiv:2608.26019v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a privileged copy of the student model to provide dense supervision without an external teacher. OPSD keeps this

safetyarxiv-cs-lg

Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs

arXiv:2608.25888v1 Announce Type: new Abstract: Timely detection of crop stress is critical for sustaining yields under increasing drought frequency, yet conventional vegetation index thresholds or im

Hardware

ExFold: Unified Expert Folding for Training-Free MoE Prefill-Decode Acceleration

arXiv:2608.24938v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale capacity for strong quality while keeping per-token compute bounded through sparse expert activation. Yet low-late

hardwarearxiv-cs-lg

From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations

model-releasesarxiv-cs-cl

arXiv:2608.25894v1 Announce Type: new Abstract: Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon w

Groundhog Bit-Flip Attack: Seeding Infinite Generation Loops in Mixture-of-Experts LLMs through Bit Flips

arXiv:2608.25276v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable scalable and efficient large language models (LLMs) by selectively activating expert sub-networks through

agentsarxiv-cs-cl

How Much Rank Does LoRA Need? Rank-Error Bounds for Transformer Attention

arXiv:2608.26052v1 Announce Type: cross Abstract: Choosing the rank of a low-rank adaptation (LoRA) update is usually an empirical task. In this paper, we provide a task-dependent theory of the approx

Improving Cross-Site Whole-Heart Segmentation

arXiv:2608.25109v1 Announce Type: cross Abstract: Whole-heart segmentation from CT and MRI is essential for quantitative cardiac image analysis, but remains challenging under multi-center and multi-mo

safetyarxiv-cs-cv

Local Ai

Resilient Decentralized Wireless Federated Learning via Gradient Tracking with AdamW

arXiv:2608.25535v1 Announce Type: new Abstract: Wireless Internet-of-Things (IoT) edge networks require decentralized learning (DecL) methods that can operate reliably under both heterogeneous local d

local-aiarxiv-cs-lg

SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs

model-releasesarxiv-cs-cv

arXiv:2608.25068v1 Announce Type: new Abstract: Depth pruning removes entire Transformer blocks to reduce the inference cost of large language models, but disrupts the hidden-state distributions expec

Tutorials

Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon

arXiv:2608.25990v1 Announce Type: new Abstract: Orthogonal optimisers such as Muon can substantially accelerate large language model pretraining relative to Adam, yet the mechanism remains incompletel

tutorialsarxiv-cs-lg

What Should a Large Language Model See? Physical Invariants as a Data Representation for PDE Discovery

model-releasesarxiv-cs-lg

arXiv:2608.25189v1 Announce Type: new Abstract: Understanding how molecular interactions govern macroscopic behaviour is a central challenge in molecular sciences. However, conventional theory buildin

Evaluating Language Models on Cross-Language Code Functional Equivalence

arXiv:2608.23961v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) have demonstrated strong performance across a variety of code-understanding tasks, leading many to believe th

Joint Optimization of Tool Creation and Use for Large Language Model Agents

arXiv:2608.24571v1 Announce Type: new Abstract: Tool-augmented language models are bounded by the APIs humans bothered to write; existing tool-creation systems patch this by prompting a frozen LLM at

safetyarxiv-cs-ai

Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents

arXiv:2608.24087v1 Announce Type: cross Abstract: Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and

safetyarxiv-cs-ai

Luce: Relightable Gaussians for 3D Asset Generation

researchapple-ml-research

High-fidelity image-to-3D generation requires a 3D representation that captures both geometry and appearance. To support relighting and integration into standard rendering pipelines, the representatio

[Megathread] GLM-5.3-Flash - former ox-alpha

model-releasesr-localllama

Megathread for discussing the release of GLM-5.3-Flash. Quants Fine-Tunes & Abliterations Chat Templates Inference Server Support & Configuration Experiences, Benchmarks & Model Comparisons We'll try

On-policy Distillation with Verifiable Reward

arXiv:2608.24696v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training larg

safetyarxiv-cs-ai

Orchestration is the new challenge for CX in the age of AI agents

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of t

safetyventurebeat-ai

PinSieve: Production Selective VLM Serving and a Governed Memory Flywheel for Enterprise Content-Quality Triage

arXiv:2608.24040v1 Announce Type: new Abstract: Enterprise AI agents in production often need to be bounded, stateful, observable, and governable rather than fully autonomous. We present PinSieve, a p

agentsarxiv-cs-lg

Restoring Without Forgetting: Continual Learning Across Image Degradations

arXiv:2608.23799v1 Announce Type: cross Abstract: Recent progress in image restoration has converged on all-in-one architectures that jointly handle multiple degradations within a single network. Thes

RetrievalFormer: A Dual-Encoder Transformer for Efficient Approximate Nearest Neighbor Retrieval and Cold-Item Recommendation

arXiv:2608.24079v1 Announce Type: cross Abstract: A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering bot

researcharxiv-cs-lg

ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection

arXiv:2608.23923v1 Announce Type: new Abstract: Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles.

safetyarxiv-cs-cv

Unlocking the power of partnership: How humans and machines can work together to improve face recognition

arXiv:2510.02570v2 Announce Type: replace Abstract: Human review of consequential decisions by face recognition algorithms creates a collaborative human-machine system. We establish the circumstances

Velocity-coupled Representation Refinement for Satellite Orbit Prediction

arXiv:2608.23728v1 Announce Type: new Abstract: Satellite orbit prediction, which aims to forecast future orbital trajectories from historical observations, is important for collision warning and safe

ClawProBench: Trace-Aware Evaluation of AI Agents with Runtime Coverage and Frozen Workplace-Style Holdouts

arXiv:2608.22510v1 Announce Type: new Abstract: Agent benchmarks often evaluate only final answers even when agents run on stateful runtimes. We argue this under-specifies what is being evaluated: the