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Rethinking Demonstration Unlearning in Imitation Learning for Robotics

arXiv:2608.20784v1 Announce Type: cross Abstract: Imitation learning for robotics depends on human demonstrations, some of which people may later ask to remove. Retraining without them is the natural

safetyarxiv-cs-lg

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Rethinking Expressivity and Efficiency in Test-Time Training

DGX agent

arXiv:2608.21308v1 Announce Type: new Abstract: Test-Time Training (TTT) enables long-context processing via continuous weight updates during inference, but current methods struggle to balance the exp

researcharxiv-cs-lg

24 Aug 2026

Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource Utility

arXiv:2608.20418v1 Announce Type: cross Abstract: We introduce Malaria-Instruct, a curated instruction-following dataset derived from the ChEMBL Legacy Malaria corpus for Malaria virtual screening, an

RISE: Adaptive Imagination for World Action Models

arXiv:2608.20430v1 Announce Type: new Abstract: World Action Models (WAMs) improve planning by incorporating future world evolution into action generation, yet existing methods allocate a fixed imagin

RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Prediction

arXiv:2608.20656v1 Announce Type: cross Abstract: Traffic sensors commonly record flow, speed, and occupancy, but standard traffic flow forecasting benchmarks and models rarely exploit all three raw m

Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning

model-releasesarxiv-cs-ro

arXiv:2608.21032v1 Announce Type: new Abstract: Vehicle-to-Everything (V2X) cooperation enables beyond-line-of-sight perception, mitigating occlusions in single-vehicle sensing. However, existing V2X

Robust Discovery of Coarse-Grained Continuum Equations from Microscopic Dynamics

arXiv:2608.20404v1 Announce Type: cross Abstract: The discovery of governing partial differential equations (PDEs) directly from spatiotemporal data has emerged as a powerful tool for understanding th

Robust Validation to Geometric Perturbations for Autonomous Pose Estimation

arXiv:2608.21066v1 Announce Type: new Abstract: Deploying autonomous systems in safety-critical domains demands guaranteed robustness against physically plausible geometric perturbations rather than a

RODE: A Radial-Orthogonal Decoupled Engine for Optimization

model-releasesarxiv-cs-lg

arXiv:2608.21024v1 Announce Type: new Abstract: Modern neural network training increasingly uses matrix-aware optimizers, yet their conditioned matrix step is typically added directly to the weight, j

Applications

Root cause analysis via difference graph discovery from linear time-series data

arXiv:2608.21117v1 Announce Type: new Abstract: Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis

applicationsarxiv-cs-ai

Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding

arXiv:2608.20805v1 Announce Type: new Abstract: Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Althou

SAC-Copula: Quality-Preserving Watermarking for Diffusion Language Models via Smooth Correlated Gumbel Fields

arXiv:2608.20839v1 Announce Type: new Abstract: Watermarking diffusion language models (DLMs) requires mechanisms compatible with iterative parallel unmasking rather than autoregressive decoding. Exis

researcharxiv-cs-cl

SAGE: A Unified Algebra and Self-Adaptive Execution for AI Functions in SQL

arXiv:2608.20630v1 Announce Type: new Abstract: SQL systems increasingly expose AI functions for tasks such as classification, extraction, filtering, ranking, retrieval, joining, and summarization. De

Scalable Distributed Simulation-Based Testing for Automated Driving Systems

arXiv:2608.20904v1 Announce Type: new Abstract: Virtual scenario-based testing is a key enabler for validating automated driving systems (ADS) and intelligent transport systems (ITS). However, executi

safetyarxiv-cs-ro

Scaling Muon for Diffusion Transformers

arXiv:2608.20818v1 Announce Type: cross Abstract: The matrix-aware optimizer Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end

Scaling Unsupervised Word Alignment to Documents via Structural Constraints

arXiv:2608.21023v1 Announce Type: new Abstract: Word alignment has traditionally been studied between sentences, but many cross-lingual tasks increasingly require correspondences across full documents

safetyarxiv-cs-cl

SCOPE: A Generative Approach for LLM Prompt Compression

arXiv:2508.15813v2 Announce Type: replace-cross Abstract: A big issue in modern LLM applications is they tend to feed long context to LLM, which results in high inference cost and latency, and may exc

Agents

SDAD: Spec-Driven Agentic Development for the AI-Native SDLC

arXiv:2608.20341v1 Announce Type: new Abstract: Frontier coding agents backed by large language models with context windows from hundreds of thousands to millions of tokens are restructuring the Softw

agentsarxiv-cs-ai

Agents

SEISMO: Explanation-Aware, Trajectory-Conditioned LLM Agents for Sample-Efficient Molecular Optimisation

arXiv:2602.00663v3 Announce Type: replace Abstract: Optimizing molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry

agentsarxiv-cs-ai

Self-Speculation for Faster Reasoning Models

model-releasesarxiv-cs-cl

arXiv:2608.20359v1 Announce Type: new Abstract: Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performance

Self-Supervised Speech Representations Track Spoken Language Convergence to Adult Models in Infants and Children Who Are Deaf/Hard-of-Hearing

arXiv:2608.20396v1 Announce Type: new Abstract: Language development is characterized by a gradual convergence of children's speech toward adult patterns. Measuring this process has traditionally requ

researcharxiv-cs-cl

Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models

arXiv:2608.20916v1 Announce Type: new Abstract: Vision foundation models (VFMs) offer strong generalization capabilities for domain-adaptive object detection (DAOD). However, existing VFM-based method

Applications

SENTRY: Deterministic, Intelligent Risk Assessment for IT Change Management

arXiv:2608.21203v1 Announce Type: new Abstract: Technology change management in large financial institutions depends on risk assessments that are accurate, consistent, and auditable. In practice, many

applicationsarxiv-cs-ai

Agents

Share the Judge, Learn the Deferral: Where Specialization Helps LLM Evaluation

arXiv:2607.27984v2 Announce Type: replace Abstract: Agentic systems generate outputs faster than human review. We contrast two LLM evaluator specialization strategies: specialized judge weights, or ru

agentsarxiv-cs-ai

Shared Physics Responses Recover Hidden Rankings in Neural Operator Libraries

arXiv:2608.20441v1 Announce Type: new Abstract: Selecting the optimal neural-operator prediction during deployment is challenging when high-fidelity reference solutions are unavailable. We demonstrate

Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networks

arXiv:2608.20858v1 Announce Type: cross Abstract: Transportation networks, in particular multi-class transportation networks (i.e., networks with mixed vehicle types), are complex systems that are cha

Shortcut Learning in a Public Grape Disease Dataset: Annotation Granularity as a Modulator, Not a Cause

arXiv:2608.20663v1 Announce Type: new Abstract: Public datasets for agricultural disease detection are usually judged fit for use from reported metrics, which say nothing about whether the annotation

researcharxiv-cs-cv

Significant Other AI: Identity, Memory, and Emotional Regulation as Long-Term Relational Intelligence

arXiv:2512.00418v3 Announce Type: replace-cross Abstract: Significant Others (SOs) stabilize identity, regulate emotion, and support narrative meaning-making, yet many people today lack access to such

Six misconceptions about large language models: A minimal model and diagnostic taxonomy

arXiv:2608.20421v1 Announce Type: cross Abstract: Large language models (LLMs) are now embedded in scientific, educational, and governance workflows, with debates centering on their capabilities, mech

safetyarxiv-cs-ai

SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation

arXiv:2509.20377v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has significantly improved the performance of large language models (LLMs) on knowledge-intensive tasks i

SlidesGen-Bench: Evaluating Slides Generation via Computational and Quantitative Metrics

model-releasesarxiv-cs-cl

arXiv:2601.09487v2 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts

Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models

model-releasesarxiv-cs-lg

arXiv:2504.05978v4 Announce Type: replace Abstract: Reinforcement learning (RL) is a powerful framework for decision-making in uncertain environments, but it often requires large amounts of data to le

Socialized Division and Collaboration: Rethinking Class-Incremental Learning under Optimization Conflicts

arXiv:2608.21044v1 Announce Type: new Abstract: Class-incremental learning is commonly instantiated as a single-model paradigm, where a unified model sequentially adapts to an unbounded stream of sess

Source-Free MT Evaluation Is Not MT Evaluation

arXiv:2608.20925v1 Announce Type: cross Abstract: Reference-based metrics remain the standard choice in machine translation evaluation, partly because quality estimation methods often correlate less w

safetyarxiv-cs-ai

SPARC: Single-Pass Scaling for Motion Forecasting with Conformal Bayesian Last Layers

arXiv:2608.20802v1 Announce Type: new Abstract: Human motion forecasters are increasingly accurate and fast, but reliable deployment requires uncertainty estimates that are structured, calibrated, and

SPARCL: Spectral Partitioned Analytic Continual Learning

arXiv:2608.21307v1 Announce Type: new Abstract: Analytic continual learning has emerged as a strong exemplar-free alternative to gradient-based class-incremental learning because it replaces iterative

SPARK-SAM: Self-Prompt Adaptation with Response Knowledge for SAM in Infrared Small Target Segmentation

model-releasesarxiv-cs-cv

arXiv:2608.20754v1 Announce Type: new Abstract: Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a misma

Sparse Light Field Sampling Improves Casual 3D and 4D Reconstruction

arXiv:2608.20602v1 Announce Type: cross Abstract: Many consumer smartphones, stereo cameras, and light field cameras record multiple synchronized viewpoints in a single exposure event. However, novel

researcharxiv-cs-cv

Sparse Token Routing in Efficient Transformers

model-releasesarxiv-cs-cl

arXiv:2608.20632v1 Announce Type: new Abstract: Efficient-transformer research often motivates token pruning and adaptive computation with the claim that not all tokens require equal computational eff

Spatially Aware Dictionary-Free Koopman Eigenfunction Identification for Modeling and Control

arXiv:2511.22648v2 Announce Type: replace Abstract: A spatially aware dictionary-free eigenfunction discovery (SADFED) framework is proposed for identification of low-rank Koopman models from data wit

SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges

arXiv:2504.18882v3 Announce Type: replace-cross Abstract: Neuroimaging provides essential tools for characterizing brain activity, structure, and connectivity through modalities that capture complemen

Specification Portability Across LLM Development Agents: Cross-Agent Compatibility in Specification-Driven Software Migration

arXiv:2608.21208v1 Announce Type: cross Abstract: This paper investigates cross-agent specification portability using Oracle-to-PostgreSQL migration as a controlled software transformation task. The s

SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control

arXiv:2608.21175v1 Announce Type: cross Abstract: Safe and efficient shape-aware navigation in heterogeneous crowds and robot fleets remains challenging. Traditional approaches often assume homogeneou

STAR-OPD: Structured Aspect-Cascade-Aware On-Policy Reward Distillation for ABSA Quadruple Extraction

arXiv:2608.20831v1 Announce Type: cross Abstract: Aspect-based sentiment analysis (ABSA) quadruple extraction requires jointly predicting target, aspect, opinion, and sentiment over reviews that often

safetyarxiv-cs-ai

StateSight: Benchmarking Latent Spatial-State Reconstruction in Vision-Language Models

arXiv:2608.20414v1 Announce Type: new Abstract: Vision-language models are increasingly used for multimodal question answering, yet their ability to reconstruct latent spatial structure from a single

STCO: Conditional Neural Operators for Time-Dependent PDEs

arXiv:2608.20477v1 Announce Type: new Abstract: Neural operators have emerged as efficient surrogates for time-dependent physical systems governed by partial differential equations (PDEs), but their f

Stored in Optimizer State, Valued by Later Training: A Causal Account of Subliminal Trait Transfer

model-releasesarxiv-cs-lg

arXiv:2608.20442v1 Announce Type: new Abstract: Subliminal trait transfer allows a student model to acquire behavioral dispositions from teacher-generated data in which the trait is not semantically e

Local Ai

Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding

arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive super

local-aiarxiv-cs-cv